A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> A technique called optical coherence tomography (OCT) has been developed for noninvasive cross-sectional imaging in biological systems. OCT uses low-coherence interferometry to produce a two-dimensional image of optical scattering from internal tissue microstructures in a way that is analogous to ultrasonic pulse-echo imaging. OCT has longitudinal and lateral spatial resolutions of a few micrometers and can detect reflected signals as small as approximately 10(-10) of the incident optical power. Tomographic imaging is demonstrated in vitro in the peripapillary area of the retina and in the coronary artery, two clinically relevant examples that are representative of transparent and turbid media, respectively. <s> BIB001 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Objective: To demonstrate optical coherence tomography for high-resolution, noninvasive imaging of the human retina. Optical coherence tomography is a new imaging technique analogous to ultrasound B scan that can provide cross-sectional images of the retina with micrometer-scale resolution. Design: Survey optical coherence tomographic examination of the retina, including the macula and optic nerve head in normal human subjects. Settings Research laboratory. Participants: Convenience sample of normal human subjects. Main Outcome Measures: Correlation of optical coherence retinal tomographs with known normal retinal anatomy. Results: Optical coherence tomographs can discriminate the cross-sectional morphologic features of the fovea and optic disc, the layered structure of the retina, and normal anatomic variations in retinal and retinal nerve fiber layer thicknesses with 10- μm depth resolution. Conclusion: Optical coherence tomography is a potentially useful technique for high depth resolution, cross-sectional examination of the fundus. <s> BIB002 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> We present a highly accurate, robust system for measuring retinal thickness in optical coherence tomography (OCT) images. OCT is a relatively new imaging modality giving cross sectional images that are qualitatively similar to ultrasound but with 10 /spl mu/m resolution. We begin with a 1-dimensional edge detection kernel to yield edge primitives, which are then selected, corrected, and grouped to form a coherent boundary by use of a Markov model of retinal structure. We have tested the system extensively, and only one of 650 evaluation images caused the algorithm to fail. The anticipated clinical application for this work is the automatic determination of retinal thickness. A careful quantitative evaluation of the system performance over a 1 mm region near the fovea reveals that in more than 99% of the cases, the automatic and manual measurements differed by less than 25 /spl mu/m (below clinical significance), and in 89% of the tests the difference was less than 10 /spl mu/m (near the resolution limit). Current clinical practice involves only a qualitative, visual assessment of retinal thickness. Therefore, a robust, quantitatively accurate system should significantly improve patient care. <s> BIB003 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Purpose To report an image analysis algorithm that was developed to provide quantitative thickness measurement of retinal layers on optical coherence tomography (OCT) images. Design Prospective cross-sectional study. Methods Imaging was performed with an OCT3 commercial instrument in 10 visually normal healthy subjects. A dedicated software algorithm was developed to process the raw OCT images and detect the depth location of peaks from intensity profiles. Quantitative thickness measurements of three retinal layers, in addition to total retinal thickness, were derived. Total retinal thickness measurements obtained by the algorithm were compared with measurements provided by the standard OCT3 software. Results The total retinal thickness profile demonstrated foveal depression, corresponding to normal anatomy, with a thickness range of 160 to 291 μm. Retinal thickness measured by the algorithm and by the standard OCT3 software were highly correlated ( R = 0.98). The inner retinal thickness profile predictably demonstrated a minimum thickness at the fovea, ranging between 58 to 217 μm along the 6-mm scan. The outer retinal thickness profile displayed a maximum thickness at the fovea, ranging between 66 to 107 μm along the 6-mm scan. The photoreceptor outer segment thickness profile was relatively constant along the 6-mm scan through the fovea, ranging between 42 to 50 μm. The intrasubject variabilities of the inner retina, outer retina, and photoreceptor outer segment thickness was 14, 10, and 6 μm, respectively. Conclusions Thickness measurements of retinal layers derived from OCT images have potential value for objectively documenting disease-related retinal thickness abnormalities and monitoring progressive changes over time. <s> BIB004 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> PURPOSE ::: To develop a software algorithm to perform automated segmentation of retinal layer structures on linear macular optical coherence tomography (StratusOCT; Carl Zeiss Meditec, Inc., Dublin, CA) scan images and to test its performance in discriminating normal from glaucomatous eyes in comparison with conventional circumpapillary nerve fiber layer (cpNFL) thickness measurement. ::: ::: ::: METHODS ::: Four layer structures within the retina were defined: the macular nerve fiber layer (mNFL), the inner retinal complex (IRC; retinal ganglion cell [RGC] layer + inner plexiform and nuclear layers), outer plexiform layer (OPL), and outer retinal complex (ORC; outer nuclear layer + photoreceptor layer). Normal and glaucomatous eyes underwent fast macular map and fast NFL OCT scans. Linear macular images were analyzed using the developed algorithm, and the results were compared with the cpNFL thickness measurement. ::: ::: ::: RESULTS ::: Forty-seven subjects (23 normal and 24 with glaucoma) were analyzed. mNFL, cpNFL, IRC, and the total retinal thicknesses were significantly greater in normal than in glaucomatous eyes (P < or = 0.0002; Wilcoxon), whereas OPL thickness did not show a significant difference (P = 0.46). ORC thickness was significantly greater in glaucomatous than normal eyes (P = 0.035). Areas under the receiver operator characteristic curve (AROCs) for discriminating normal from glaucomatous eyes were highest with mNFL + IRC (0.97) and lowest with OPL (0.56). AROCs for OPL and ORC were significantly smaller than those for mNFL, IRC, mNFL+IRC, and cpNFL (P < or = 0.01). AROCs for IRC, mNFL + IRC, and cpNFL were significantly larger than for retinal thickness (P < or = 0.049). Among the best-performing parameters (mNFL, IRC, mNFL + IRC, and cpNFL) there was no significant difference in AROCs (P > or = 0.15). ::: ::: ::: CONCLUSIONS ::: The newly developed macular segmentation algorithm described herein demonstrated its ability to quantify objectively the glaucomatous damage to RGCs and NFL and to discriminate between glaucomatous and normal eyes. Further algorithm refinement and improvements in resolution and image quality may yield a more powerful methodology for clinical glaucoma evaluation. <s> BIB005 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Optical coherence tomography (OCT) uses retroreflected light to provide micrometer-resolution, cross-sectional scans of biological tissues. OCT's first application was in ophthalmic imaging where it has proven particularly useful in diagnosing, monitoring, and studying glaucoma. Diagnosing glaucoma is difficult and it often goes undetected until significant damage to the subject's visual field has occurred. As glaucoma progresses, neural tissue dies, the nerve fiber layer thins, and the cup-to-disk ratio increases. Unfortunately, most current measurement techniques are subjective and inherently unreliable, making it difficult to monitor small changes in the nervehead geometry. To our knowledge, this paper presents the first published results on optic nervehead segmentation and geometric characterization from OCT data. We develop complete, autonomous algorithms based on a parabolic model of cup geometry and an extension of the Markov model introduced by Koozekanani, et al. to segment the retinal-nervehead surface, identify the choroid-nervehead boundary, and identify the extent of the optic cup. We present thorough experimental results from both normal and pathological eyes, and compare our results against those of an experienced, expert ophthalmologist, reporting a correlation coefficient for cup diameter above 0.8 and above 0.9 for the disk diameter. <s> BIB006 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> The purpose of this paper was to propose a new computer method for quantitative evaluation of representative features of the retina using optical coherence tomography (OCT). A multi-step approach was devised and positively tested for segmentation of the three main retinal layers: the vitreo-retinal interface and the inner and outer retina. Following a preprocessing step, three regions of interest were delimited. Significant peaks corresponding to high and low intensity strips were located along the OCT A-scan lines and accurate boundaries between different layers were obtained by maximizing an edge likelihood function. For a quantitative description, thickness measurement, densitometry, texture and curvature analyses were performed. As a first application, the effect of intravitreal injection of triamcinolone acetonide (IVTA) for the treatment of vitreo-retinal interface syndrome was evaluated. Almost all the parameters, measured on a set of 16 pathologic OCT images, were statistically different before and after IVTA injection (p < 0.05). Shape analysis of the internal limiting membrane confirmed the reduction of the pathological traction state. Other significant parameters, such as reflectivity and texture contrast, exhibited relevant changes both at the vitreo-retinal interface and in the inner retinal layers. Texture parameters in the inner and outer retinal layers significantly correlated with the visual acuity restoration. According to these findings an IVTA injection might be considered a possible alternative to surgery for selected patients. In conclusion, the proposed approach appeared to be a promising tool for the investigation of tissue changes produced by pathology and/or therapy. <s> BIB007 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> PURPOSE ::: To visualize, quantitatively assess, and interpret outer retinal morphology by using high-speed, ultrahigh-resolution (UHR) OCT. ::: ::: ::: METHODS ::: Retinal imaging was performed in the ophthalmic clinic in a cross-section of 43 normal subjects with a 3.5-microm, axial-resolution, high-speed, UHR OCT prototype instrument, using a radial scan pattern (24 images, 1500 axial scans). Outer retinal layers were automatically segmented and measured. High-definition imaging was performed with a 2.8-microm axial-resolution, high-speed, UHR OCT research prototype instrument, to visualize the finer features in the outer retina. ::: ::: ::: RESULTS ::: Quantitative maps of outer retinal layers showed clear differences between the cone-dominated fovea and the rod-dominated parafovea and perifovea, indicating that photoreceptor morphology can explain the appearance of the outer retina in high-speed, UHR OCT images. Finer, scattering bands were visualized in the outer retina using high-definition imaging and were interpreted by comparison to known anatomy. ::: ::: ::: CONCLUSIONS ::: High-speed UHR OCT enables quantification of scattering layers in the outer retina. An interpretation of these features is presented and supported by quantitative measurements in normal subjects and comparison with known anatomy. The thick scattering region of the outer retina previously attributed to the retinal pigment epithelium (RPE) is shown to consist of distinct scattering bands corresponding to the photoreceptor outer segment tips, RPE, and Bruch's membrane. These results may advance understanding of the outer retinal appearance in OCT images. The normative measurements may also aid in future investigations of outer retinal changes in age-related macular degeneration and other diseases. <s> BIB008 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> PURPOSE ::: To understand better the influence of retinal blood vessels (BVs) on the interindividual variation in the retinal nerve fiber layer (RNFL) thickness measured with optical coherence tomography (OCT). ::: ::: ::: SUBJECTS AND METHODS ::: RNFL thickness profiles were measured by OCT in 16 control individuals and 16 patients. The patients had advanced glaucoma defined by abnormal disc appearance, abnormal visual fields, and a mean visual field deviation worse than -10 dB. ::: ::: ::: RESULTS ::: In general, the OCT RNFL thickness profiles showed 4 local maxima, with the peak amplitudes in the superior and inferior regions occurring in the temporal (peripapillary) disc region. There was considerable variability among individuals in the location of these maxima. However, the 4 maxima typically fell on, or near, a major BV with the temporal and inferior peaks nearly always associated with the main temporal branches of the superior and inferior veins and arteries. In the patients' hemifields with severe loss (mean visual field deviation worse than -20 dB), the signals associated with the major BVs were in the order of 100 to 150 microm. ::: ::: ::: CONCLUSIONS ::: The variation in the local peaks of the RNFL profiles of controls correlates well with the location of the main temporal branches of the superior and inferior veins and arteries. This correspondence is, in part, due to a direct BV contribution to the shape of the OCT RNFL and, in part, due to the fact that BVs develop along the densest regions of axons. Although the overall BV contribution was estimated to be relatively modest, roughly 13% of the total peripapillary RNFL thickness in controls, their contribution represents a substantial portion locally and increases in importance with disease progression. <s> BIB009 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> PURPOSE ::: To use optical coherence tomography (OCT) to identify the specific retinal layers and macular regions damaged in glaucoma. ::: ::: ::: DESIGN ::: Observational cross-sectional study. ::: ::: ::: PARTICIPANTS ::: One hundred forty-nine participants in the Advanced Imaging for Glaucoma Study, divided into 3 groups: normal (N) perimetric glaucoma (PG), and glaucoma suspect and preperimetric glaucoma (GSPPG) with 44, 73, and 29 persons, respectively. ::: ::: ::: METHODS ::: The Zeiss Stratus OCT system (Carl Zeiss Meditec, Inc., Dublin, CA) was used to map the macula over a 6-mm diameter and to scan the circumpapillary nerve fiber layer (cpNFL). The macular OCT images were exported for automatic segmentation using software developed by the authors. The thickness of the macular nerve fiber layer (mNFL), ganglion cell layer (mGCL), inner plexiform layer (mIPL), inner nuclear layer (mINL), outer retinal layer (mORL), and total retinal thickness were measured. Thickness measurements of GSPPG and PG eyes were compared with those of N eyes. The ability to differentiate between GSPPG and PG eyes against N eyes was assessed by fractional loss, standardized deviation, and the area under the receiver operating characteristic curve. ::: ::: ::: MAIN OUTCOME MEASURES ::: Area-weighted average thicknesses of retinal sublayers in the macula. ::: ::: ::: RESULTS ::: The mNFL, mGCL, mIPL, and mINL were significantly (P<0.001) thinner in both the GSPPG and PG eyes than in the N eyes. In PG eyes, mNFL, mGCL, and mIPL thinning was most severe (approximately 20%), mINL thinning was intermediate (7%), and mORL thinning was minimal (3%). The repeatability (coefficient of variation and intraclass correlation) of thickness measurements was improved by combining the mNFL, mGCL, and mIPL measurements as the inner retinal layer (mIRL). The mIRL was the best macular parameter for glaucoma diagnosis and had discriminant power comparable with that of the cpNFL. The fractional loss of mIRL thickness was most severe in the inferior perifoveal region for both the PG and GSPPG groups. ::: ::: ::: CONCLUSIONS ::: Glaucoma leads to thinning of the mNFL, mGCL, mIPL, and mINL, even before detectable visual field changes occur. A combination of the 3 innermost layers seems to provide optimal glaucoma detection. Increasing the sampling of peripheral macula with a new OCT scan pattern may improve glaucoma diagnosis further. <s> BIB010 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Optical coherence tomography (OCT) is a non-invasive, depth resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present an automatic segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapt Chan---Vese's energy-minimizing active contours without edges for OCT images, which suffer from low contrast and are highly corrupted by noise. We adopt a multi-phase framework with a circular shape prior in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on 20 OCT images from four rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy and average Dice similarity coefficient of 0.85, specifically 0.94 for the the ganglion cell layer, which is the relevant layer for glaucoma diagnosis. <s> BIB011 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> PURPOSE ::: To evaluate the performance of an automated algorithm for determination of the cup and rim from close-to-isotropic spectral domain (SD) OCT images of the optic nerve head (ONH) and compare to the cup and rim as determined by glaucoma experts from stereo color photographs of the same eye. ::: ::: ::: METHODS ::: Thirty-four consecutive patients with glaucoma were included in the study, and the ONH in the left eye was imaged with SD-OCT and stereo color photography on the same day. The cup and rim were segmented in all ONH OCT volumes by a novel voxel column classification algorithm, and linear cup-to-disc (c/d) ratio was determined. Three fellowship-trained glaucoma specialists performed planimetry on the stereo color photographs, and c/d was also determined. The primary outcome measure was the correlation between algorithm-determined c/d and planimetry-derived c/d. ::: ::: ::: RESULTS ::: The correlation of algorithm c/d to experts 1, 2, and 3 was 0.90, 0.87, and 0.93, respectively. The c/d correlation of expert 1 to 2, 1 to 3, and 2 to 3, were 0.89, 0.93, and 0.88, respectively. ::: ::: ::: CONCLUSIONS ::: In this preliminary study, we have developed a novel algorithm to determine the cup and rim in close-to-isotropic SD-OCT images of the ONH and have shown that its performance for determination of the cup and rim from SD-OCT images is similar to that of planimetry by glaucoma experts. Validation on a larger glaucoma sample as well as normal controls is warranted. <s> BIB012 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images. <s> BIB013 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> This paper presents optical coherence tomography (OCT) signal intensity variation based segmentation algorithms for retinal layer identification. Its main ambition is to reduce the calculation time required by layer identification algorithms. Two algorithms, one for the identification of the internal limiting membrane (ILM) and the other for retinal pigment epithelium (RPE) identification are implemented to evaluate structural features of the retina. Using a 830 nm spectral domain OCT device, this paper demonstrates a segmentation method for the study of healthy and diseased eyes. <s> BIB014 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> The paper presents an algorithm designed to detect layers of eye’s retina using an area analysis. This analysis has been implemented and tested in images obtained by means of Copernicus OCT (Optical Coherence Tomography). The algorithm created is an original approach to detect layers, contours and psuedoparallels. The approach presented is an expansion of approaches described in [1] and [2],[3] and enables identification and recognition of external limiting membranes, retina and others. The algorithm has been implemented in Matlab and C environment. <s> BIB015 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> A novel automated boundary segmentation algorithm is proposed for fast and reliable quantification of nine intra-retinal boundaries in optical coherence tomography (OCT) images. The algorithm employs a two-step segmentation schema based on gradient information in dual scales, utilizing local and complementary global gradient information simultaneously. A shortest path search is applied to optimize the edge selection. The segmentation algorithm was validated with independent manual segmentation and a reproducibility study. It demonstrates high accuracy and reproducibility in segmenting normal 3D OCT volumes. The execution time is about 16 seconds per volume (480x512x128 voxels). The algorithm shows potential for quantifying images from diseased retinas as well. <s> BIB016 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Segmentation of retinal blood vessels can provide important information for detecting and tracking retinal vascular diseases including diabetic retinopathy, arterial hypertension, arteriosclerosis and retinopathy of prematurity (ROP). Many studies on 2-D segmentation of retinal blood vessels from a variety of medical images have been performed. However, 3-D segmentation of retinal blood vessels from spectral-domain optical coherence tomography (OCT) volumes, which is capable of providing geometrically accurate vessel models, to the best of our knowledge, has not been previously studied. The purpose of this study is to develop and evaluate a method that can automatically detect 3-D retinal blood vessels from spectral-domain OCT scans centered on the optic nerve head (ONH). The proposed method utilized a fast multiscale 3-D graph search to segment retinal surfaces as well as a triangular mesh-based 3-D graph search to detect retinal blood vessels. An experiment on 30 ONH-centered OCT scans (15 right eye scans and 15 left eye scans) from 15 subjects was performed, and the mean unsigned error in 3-D of the computer segmentations compared with the independent standard obtained from a retinal specialist was 3.4 ± 2.5 voxels (0.10 ± 0.07 mm). <s> BIB017 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Optical coherence tomography (OCT) is becoming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, a method for automated characterization of the normal macular appearance in spectral domain OCT (SD-OCT) volumes is reported together with a general approach for local retinal abnormality detection. Ten intraretinal layers are first automatically segmented and the 3-D image dataset flattened to remove motion-based artifacts. From the flattened OCT data, 23 features are extracted in each layer locally to characterize texture and thickness properties across the macula. The normal ranges of layer-specific feature variations have been derived from 13 SD-OCT volumes depicting normal retinas. Abnormalities are then detected by classifying the local differences between the normal appearance and the retinal measures in question. This approach was applied to determine footprints of fluid-filled regions-SEADs (Symptomatic Exudate-Associated Derangements)-in 78 SD-OCT volumes from 23 repeatedly imaged patients with choroidal neovascularization (CNV), intra-, and sub-retinal fluid and pigment epithelial detachment. The automated SEAD footprint detection method was validated against an independent standard obtained using an interactive 3-D SEAD segmentation approach. An area under the receiver-operating characteristic curve of 0.961 ? 0.012 was obtained for the classification of vertical, cross-layer, macular columns. A study performed on 12 pairs of OCT volumes obtained from the same eye on the same day shows that the repeatability of the automated method is comparable to that of the human experts. This work demonstrates that useful 3-D textural information can be extracted from SD-OCT scans and-together with an anatomical atlas of normal retinas-can be used for clinically important applications. <s> BIB018 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> By measuring the thickness of the retinal nerve fiber layer, retinal optical coherence tomography (OCT) images are now increasingly used for the diagnosis of glaucoma. This paper reports an automatic OCT layer segmentation technique that can be used for computer-aided glaucoma diagnosis. In the proposed technique, blood vessels are first detected through an iterative polynomial smoothing procedure. OCT images are then filtered by a bilateral filter and a median filter sequentially. In particular, both filters suppress the local image noise but the bilateral filter has a special characteristic that keeps the global trend of the image value variation. After the image filtering, edges are detected and the edge segments corresponding to the layer boundary are further identified and clustered to form the layer boundary. Experiments over OCT images of four subjects show that the proposed technique segments layers of OCT images efficiently. <s> BIB019 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Purpose To report normal baseline thickness maps for 6 retinal layers generated by segmentation of spectral-domain optical coherence tomography (SD-OCT) images in normal subjects. Intersubject thickness variability and thickness variations in 9 macular sectors were established. Design Prospective cross-sectional study. Materials and Methods SD-OCT imaging was performed in 15 normal subjects. Nineteen SD-OCT images were acquired, encompassing a 6 × 5-mm retinal area, centered on the fovea. Each image was analyzed using an automated segmentation algorithm to derive thickness profiles of 6 retinal layers. Thickness data obtained from all scans were combined to generate thickness maps of 6 retinal layers: nerve fiber layer, ganglion cell layer + inner plexiform layer, inner nuclear layer, outer plexiform layer, outer nuclear layer + photoreceptor inner segments, and photoreceptor outer segments. Mean and standard deviation of thickness measurements were calculated in 9 macular sectors and 6 retinal layers. Intersubject and intrasector thickness variations were established based on standard deviation of measurements. Results Minimum and maximum thickness of the nerve fiber layer were observed in the foveal and nasal perifoveal areas, respectively. The largest thickness variation among subjects and intrasector variability were observed in perifoveal areas. Thickness of the ganglion cell layer + inner plexiform layer and intersubject thickness variability were largest in parafoveal areas. The inner nuclear layer thickness was relatively constant in parafoveal and perifoveal areas and intrasector thickness variations were largest in the foveal area. The outer plexiform layer thickness was relatively constant in foveal and parafoveal areas and higher than in perifoveal areas. Intersubject thickness variability in inner nuclear layer and outer plexiform layer was relatively uniform in all macular sectors. The outer nuclear layer + photoreceptor inner segments thickness map displayed maximum thickness in the foveal area and intersubject thickness variability was largest superior to the fovea. Thickness of the photoreceptor outer segments layer, thickness variations among subjects, and intrasector thickness variability were relatively constant. There was a significant correlation between total retinal thickness derived by thickness mapping and SD-OCT commercial software. Conclusion Normal thickness maps for 6 retinal layers were generated and thickness variations among subjects and macular areas were assessed. This technique is promising for investigating thickness changes attributable to disease in specific retinal layers and macular areas. <s> BIB020 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> A novel statistical model based on texture and shape for fully automatic intraretinal layer segmentation of normal retinal tomograms obtained by a commercial 800nm optical coherence tomography (OCT) system is developed. While existing algorithms often fail dramatically due to strong speckle noise, non-optimal imaging conditions, shadows and other artefacts, the novel algorithm’s accuracy only slowly deteriorates when progressively increasing segmentation task difficulty. Evaluation against a large set of manual segmentations shows unprecedented robustness, even in the presence of additional strong speckle noise, with dynamic range tested down to 12dB, enabling segmentation of almost all intraretinal layers in cases previously inaccessible to the existing algorithms. For the first time, an error measure is computed from a large, representative manually segmented data set (466 B-scans from 17 eyes, segmented twice by different operators) and compared to the automatic segmentation with a difference of only 2.6% against the inter-observer variability. <s> BIB021 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Automated measurements of the retinal nerve fiber layer thickness on circular OCT B-Scans provide physicians additional parameters for glaucoma diagnosis. We propose a novel retinal nerve fiber layer segmentation algorithm for frequency domain data that can be applied on scans from both normal healthy subjects, as well as glaucoma patients, using the same set of parameters. In addition, the algorithm remains almost unaffected by image quality. The main part of the segmentation process is based on the minimization of an energy function consisting of gradient and local smoothing terms. A quantitative evaluation comparing the automated segmentation results to manually corrected segmentations from three reviewers is performed. A total of 72 scans from glaucoma patients and 132 scans from normal subjects, all from different persons, composed the database for the evaluation of the segmentation algorithm. A mean absolute error per A-Scan of 2.9 µm was achieved on glaucomatous eyes, and 3.6 µm on healthy eyes. The mean absolute segmentation error over all A-Scans lies below 10 µm on 95.1% of the images. Thus our approach provides a reliable tool for extracting diagnostic relevant parameters from OCT B-Scans for glaucoma diagnosis. <s> BIB022 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader. <s> BIB023 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> In the context of biomedical imaging analysis and computer-assisted diagnosis, segmentation analysis is an intense field of research and development. The most difficult part of medical image analysis is the automated localization and delineation of structures of interest. Automated data evaluation is one way of enhancing the clinical utility of measurements. In particular, medical image segmentation extracts meaningful information and facilitate the display of this information in a clinically relevant way. A crucial role for automated information extraction in medical imaging usually involves the segmentation of regions of the image in order to quantify volumes and areas of interest of biological tissues for further diagnosis and localization of pathologies. Optical coherence tomography (OCT) is a powerful imaging modality used to image various aspects of biological tissues, such as structural information, blood flow, elastic parameters, change of polarization states and molecular content (Huang et al., 1991). OCT uses the principle of low coherence interferometry to generate two or three dimensional imaging of biological samples by obtaining high-resolution cross-sectional backscattering profiles. A variety of successful algorithms for computer-aided diagnosis by means of OCT image analysis are presented in the literature, but robust use in clinical practice is still a major challenge for ongoing research in OCT image analysis. There are, therefore, efforts being made to improve clinical decision making based on automated analysis of OCT data. Particularly, in ophthalmology, efforts have been made to characterize clinically important features, such as damage to the fovea and optic nerve, automatically. The transfer of image analysis models from algorithmic development into clinical application is currently the major bottleneck due to the complexity of the overall process. For example, the process to establish an application for OCT medical image analysis requires difficult and complex tasks that should considers the following actions: 1) to define the OCT image data structures representing relevant biomedical features and the algorithms determining a valid example for given image values, 2) to select meaningful values for all technical parameters of the image data structures and algorithms and, as a result, to configure such a method to operate on specific OCT clinical data, 3) to run the algorithm with the selected parameters to find the individual model instance that best explains the input image and 4) to validate the procedure to ensure a trustworthy result from an automated segmentation algorithm even if a gold standard is unavailable. <s> BIB024 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Current OCT devices provide three-dimensional (3D) in-vivo images of the human retina. The resulting very large data sets are difficult to manually assess. Automated segmentation is required to automatically process the data and produce images that are clinically useful and easy to interpret. In this paper, we present a method to segment the retinal layers in these images. Instead of using complex heuristics to define each layer, simple features are defined and machine learning classifiers are trained based on manually labeled examples. When applied to new data, these classifiers produce labels for every pixel. After regularization of the 3D labeled volume to produce a surface, this results in consistent, three-dimensionally segmented layers that match known retinal morphology. Six labels were defined, corresponding to the following layers: Vitreous, retinal nerve fiber layer (RNFL), ganglion cell layer & inner plexiform layer, inner nuclear layer & outer plexiform layer, photoreceptors & retinal pigment epithelium and choroid. For both normal and glaucomatous eyes that were imaged with a Spectralis (Heidelberg Engineering) OCT system, the five resulting interfaces were compared between automatic and manual segmentation. RMS errors for the top and bottom of the retina were between 4 and 6 μm, while the errors for intra-retinal interfaces were between 6 and 15 μm. The resulting total retinal thickness maps corresponded with known retinal morphology. RNFL thickness maps were compared to GDx (Carl Zeiss Meditec) thickness maps. Both maps were mostly consistent but local defects were better visualized in OCT-derived thickness maps. <s> BIB025 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> revieW of algoriThms for ocT image preprocessing <s> Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping is applied to 2D and 3D OCT datasets composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers.In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node.The weights of a graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity.The first diffusion map clusters the data into three parts, the second of which is the area of interest. The other two sections are eliminated from the remaining calculations. In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities.The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals). The mean unsigned border positioning errors(mean - SD) was 8.52 - 3.13 and 7.56 - 2.95 micrometer for the 2D and 3D methods, respectively. <s> BIB026
Preprocessing is the first and one of the most important parts of an image processing system. OCT images suffer from the intrinsic speckle noise, which decreases the image quality and complicates the image analysis. This particular noise is the foundation of existing problems in the precise identification of the boundaries of the various cellular layers of the retina and other specific retinal features present in the OCT tomograms. BIB024 Usually all OCT image analysis methods proposed in the literature consist of a preprocessing step before performing any main processing steps. Table 1 shows a relatively complete classification of denoising algorithms employed in OCT segmentation. As it can be seen in this table, median filter and non-linear anisotropic filter are the most popular methods in OCT image denoising. The basic problem associated with most of the denoising algorithms is their intrinsic consequencein decreasing the image resolution. The popularity of methods like non-linear anisotropic filters and wavelet diffusion can be justified through their ability in preserving the edge information. It is also important to know that a great number of newly developed algorithms which utilize graph based algorithms are literally independent from noise and do not use any particular denoising algorithm. BIB011 BIB012 BIB016 BIB026 revieW of algoriThms for ocT image segmenTaTion OCT image segmentation is expected to help the Low-pass filtering Hee M.R. BIB002 2D lineal smoothing Huang Y. BIB001 Median filter George A.,  Koozekanani D., BIB003 Herzog A.,  Shahidi M., BIB004 Shrinivasan VJ., BIB008 Lee K., BIB017 and Boyer K. BIB006 Mean filter Ishikawa H., BIB005 Mayer M.  Two 1D filters: 1) median filtering along the A-scans; 2) Gaussian kernel in the longitudinally direction Baroni M. BIB007 Directional filtering Bagci A.M.  Adaptive vector-valued kernel function Mishra A. BIB013 SVM approach Fuller A.R.  Wavelet shrinkage Quellec G. BIB018 Non-linear anisotropic filter Gregori G.,  Garvin M.,  Cabrera Fernández D.  Circular Symmetric Laplacian mixture model in wavelet diffusion Kafieh  None Yazdanpanah A., BIB011 Abramoff M.D., BIB012 Yang Q., BIB016 Kafieh BIB026 OCT -Optical coherence tomography ; SVM --support vector machine ophthalmologists in diagnosis and treatment of retinal pathologies; however, it should be mentioned that structural changes due to diseases usually changes the OCT appearance, considerably. Therefore, a low portion of available researches have focused on structurally elaboratedillnesses and most of papers deal with pathologies that despite making changes in thickness or placement of layers, would retain the logical organization of retinal layers. The other common problem in image segmentation is unavoidable speckle noise consideration which was thoroughly reviewed in previous section. The only point to be discussed in noise management is that new versions of OCT imaging like Spectralis Heidelberg are equipped with high speed of imaging which makes the capable of obtaining many pictures in each instant and averaging them to reduce the unwanted speckle noise. The intensity fluctuation is the third tip to be considered in OCT layer separation. Different researchers have diverse ideas about this problem; some investigations show that this fluctuation is only due to noise of the imaging system, but a great deal of papers assume it as a result of absorption or scattering in retinal layers, where the intensity diminishes with increasing the depth of imaging in retina. The problem of blood vessels should also be considered in OCT images, which makes discontinuities in boundaries of different layers; The robustness of the algorithm in presence of blood vessel artifacts is discussed in many papers, BIB016 BIB026 BIB009 BIB019 and some researchers tried to propose an algorithm in preprocessing step to compensate for the effect of these vessels. BIB019 Furthermore, motion artifacts can decrease the quality of images and mutilate the operation of segmentation method; such problems are recently solved in new versions of OCT imaging systems like Spectralis Heidelberg which has an eye tracking systems to automatically compensate the eye movements. In this section we focus on different segmentation methods, introduced in OCT image segmentation researches. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. We define five separate families of segmentation approaches: Methods applicable to A-scan, B-scan, active contour approaches (frequently in 2-D), analysis methods utilizing artificial intelligence, and segmentation methods using 3D graphs constructed from the 3D OCT volumetric images. A-Scan methods were firstly introduced by Hee BIB002 and were popular until 2005.  BIB003 The method introduced in Hee BIB002 was on the basis of variations in intensity and measured retinal and RNFL thickness. Instead of using simple threes holding, this method was based on one-dimensional edge detection in each A-Scan and was looking for the two most effective edges using peak detection. Huang BIB001 used a similar method in hereditary retinal degenerations in experimental animals and humans. In 2000, George  used the similar dual threshold to segment and choriocapillaries from OCT images without taking into account any spatial characteristics of OCT images. In designing a retinal boundary detector, Koozekanani BIB003 used retinal anatomy and the principles of OCT operation to make various assumptions about the image boundary characteristics. For instance, the normal retina has smooth boundaries without discontinuities or gaps and the inner boundary is always above the outer boundary. Because the OCT acquires each A-scan separately, they applied one-dimensional edge detection to each image column (A-scan) individually, to overcome the multiplicative speckle noise in 2-D images which could make edge detectors problematic. Each A-scan penetrates both retinal boundaries and so they assumed that every image column intersects exactly two boundaries. They marked the positively sloped, leading edges of peaks, as they are more consistent and easier to detect. They modeled the boundary displacement between adjacent scans as a Mth order Markov sequence. Boundary deviations from one A-scan to the next were due to the actual slope of the retinal surface, patient motion relative to the OCT machine and image noise. They represented the boundary of interest (inner or outer) as a weighted sum of the neighboring A-scans, making the boundary model Mth order. Thus, they used an autoregressive mathematical model for this Markov process. Retinal thickness was calculated with an error comparable to the 10μm resolution of the OCT system used, representing a substantial improvement over clinical measurements provided by the Humphrey 2000 OCT built-in algorithm. Since this model relied on simply connecting 1D points, it was sensitive to noise. Thus, it demanded to apply special rules to correct the errors in the extracted layer borders. Herzog  proposed a method for retinal layer segmentation from axial TDOCTs through the optic nerve head (ONH). The basis of the method was looking for curves with maximum number of located points on boundaries and with minimum rate of change. An example of retinal choroid boundary identification in Herzog's  method is shown in Figure 7a and the best fit model used for extracting the cup limits is demonstrated in Figure 7b . Given the retinal-vitreal boundary in Herzog,  the retina and optic cup can be modeled through the parametric set of equations. The left and right retinal-optic disk regions were modeled using straight line segments while the optic cup was modeled by a parabolic segment. The goal was to find where the breakpoints (the extents of the optic cup) were located. If two points were picked arbitrarily for the breakpoints, the model parameters in Equation could be found via least-squares fit. Shahidi BIB004 and Ishikawa BIB005 proposed similar algorithms based on intensity variations. It is worthy of mentioning that the work of Ishikawa BIB005 was the first report demonstrating that the thickness of the innermost layers in the macula had diagnostic power comparable with that of circum papillary nerve fiber layer (cpNFL) in glaucoma studies BIB024 and the number of segmented retinal layers in this study were four. A new approach in preprocessing was also introduced by Ishikawa: BIB005 1. Aligned z-offset (starting location of the meaningful signal on each sampling line) by cross-correlation (shifting sampling lines so that the sum of the products of adjacent pixels is maximized; Figure 8a ) Figure 8b ]. Bagci  in 2007 used correlation of axial A-scans to find six retinal layers and many papers  BIB020 used the same method for retinal thickness analysis in OCT datasets. In 2008 Shrinivasan BIB008 modified the algorithm proposed by Koozekanani BIB003 to work on UHR OCT. In this study the thick scattering region of the outer retina previously attributed to the RPE was shown to consist of distinct scattering bands corresponding to the photoreceptor outer segment tips, RPE, and Bruch's membrane.Six retinal layers were totally segmented in this algorithm. A-Scan methods lacked the contribution from 3D image context and suffered from excessive computation time and lack of layer detection accuracy; however, additional A-scan approaches have been introduced recently. BIB014 BIB015  Fabritius BIB014 incorporated 3D intensity information to improve the intensity based segmentation and segmented the ILM and RPE directly from the OCT data without massive pre-processing in about 17-21 seconds with error smaller than 5 pixels in 99.7% of scans. B-Scan methods allowed dealing with 2D noise by incorporating better denoising algorithms during the preprocessing step. However, the dependency of these algorithms on noise reduction required very complicated and time-consuming denoising methods which made these algorithms too weak from the speed point of view. BIB005 BIB006 BIB007 Additionally, the underlying intensity based methods and the relevant threshold selection was a difficult problem that made the methods case-dependent. In 2006 Boyer BIB006 introduced a parabolic model of the cup geometry and used a modified version of the Markov model corporate by Koozekanani. BIB003 In this work, they extracted the parameters needed for clinical evaluations like the cup-to-disk ratio for the first time in TDOCT images. One year later, in 2007, Baroni BIB007 used edge likelihood function maximizing for boundary detection. Along the A-scans, median filtering was applied (as 1D filter of 5 pixels); this eliminated isolated noisy pixels without losing small details. In the second step, the reflectivity values were summed row by row, to obtain a longitudinal cumulative grey level histogram [ Figure 9a -d]. Detection of these peaks allowed an approximate delimitation of retina. Boundary detection was performed in the third step. The image was considered as overlapping cross-sectional stripes, 10 pixels wide. Each stripe was averaged to obtain a cross sectional grey level profile, which was filtered using the Gaussian gradient. The maxima and minima of the filtered profiles were detected by a simple peak detector and considered significant if their value was greater than the average value. The most important drawback of this method was that it assumed to have completely aligned images whose HRC layer was completely horizontal. In this study, for the first time, they reported the potential of thickness, densitometry, texture and curvature in TDOCT images for identifying retinal diseases. In 2008, Tan BIB010 utilized dynamic programming and 2D gradient information to extract boundaries and showed the relation between the thickness of retinal intra layers like RNFL, GCL and IPL and probability of suffering from glaucoma. Recently in 2010, Kajic BIB021 used a Dual-tree complex wavelet (DTCW) denoising algorithm in preprocessing and utilized a statistical model based on texture and shape that captured the variance of the training data to segment eight layers on unseen data. Active contours approaches for OCT image segmentation were first proposed by Cabrera Fernández  and modified by Yazdanpanah. BIB011 Unfortunately the required processing time and exact error reports are not available for any of the mentioned papers, which make such methods difficult to compare with other published methods. Regardless, active contour algorithms surpass the performance of intensity based B-scan approaches, both in resistance to 2D noise and in accuracy. Cabrera Fernández  in 2004 applied active contours (a gradient vector flow (GVF) snake Model) to extract fluid-filled regions in the retinal structure of AMD patients. One year later, Mujat  used deformable splines to calculate the thickness of RNFL in Two volumetric SDOCT data sets of the same eye from a single subject. The algorithm performed well in 350 frames with only few and isolated boundary detection errors. The advantage of this automated snake methodology was that it was able to provide larger area maps of the RNFL thickness facilitating the correct registration of ROIs with visual field defects which could allow better longitudinal evaluation of RNFL thinning in glaucoma studies. In 2009, Yazdanpanah BIB011 introduced a modified Chan-Vese's energy-minimizing active contour algorithm in a multi-phase framework to segments SDOCT data from rodent models. Using the anatomical knowledge of expert, a circular shape prior was defined to avoid initial training. The approach had three main features. First, it could segment all intra-retinal layers simultaneously due to the multi-phase property of the algorithm. Second, they incorporated a shape prior term that enabled the algorithm to accurately segment retinal layers, even where the region-based information was missing, such as in inhomogeneous regions. Finally, the algorithm was robust and avoided the re-initialization problem that was associated with the level set approach. In the same year, Mishra BIB013 used OCT data from healthy and diseased rodent retina and speckle noise and other typical artifacts in OCT images were handled by using an adaptive vector-valued kernel function in the precise layer boundary optimization step. For segmentation purpose, a modified active contour algorithm was utilized by using sparse dynamic programming method and two-step kernel based optimization scheme. Recently in 2010, Mayer BIB022 used the minimization of an energy function consisting of gradient and local smoothing terms on 72 scans from glaucoma patients and 132 scans from normal subjects. A mean absolute error per A-Scan of 2.9 μm was achieved on glaucomatous eyes, and 3.6 μm on healthy eyes which proved that the approach provided a reliable tool for extracting diagnostic relevant parameters from OCT B-Scans for glaucoma diagnosis. In 2011, Ghorbel  proposed a method for the segmentation of eight retinal layers in Heidelberg spectralis SDOCT images. The approach was based on global segmentation algorithms, such as active contours and Markov random fields. Moreover, a Kalman filter was designed to model the approximate parallelism between the photoreceptor segments and to detect them. The performance of the algorithm was tested on a set of 700 retinal images acquired in-vivo from healthy subjects. Artificial intelligence based approaches were presented in   and relied on a multiresolution hierarchical support vector machine (SVM) or on fuzzy C-means clustering techniques. The first one reported to have low ability in detection (six pixels of line difference and 8% of thickness difference) and a high time complexity (two minutes). But, the latter reported to have better results by only two pixels of linear difference and 45 seconds of time complexity. Overall, these methods cannot be categorized as established standard approaches since later-introduced methods like graph-based approaches can surpass them both in accuracy and time complexity. In 2007 Fuller,  developed a semiautomatic segmentation system in which the morphology of retinal structures could be discovered and refined by a clinician. The clinician interactively specified the location of a retinal layer on a few select slices of the volume. This selection was then extrapolated throughout the entire volume using a SVM classifier in order to create segmentation. Once segmented, they provided visualizations and measurements of the resulting segmentation to aid in disease diagnosis. The main visualization interface was an interactive 3D volume rendering of the segmented portions of the volume. Furthermore, SVM considered a voxel's mean value and variance across multiple resolutions [ Figure 10 ] in order to gracefully handle the speckle noise and to give the SVM a global perspective over feature shapes. They used a radial basis function kernel since they assumed it can represent the feature space well and also that speckle noise is normally distributed across the data. Additionally, it allows non-linear separation of the space. They reported that 68% of the thickness differences between the SVM segmentation and the manual segmentation fell below six voxel units. One year later in 2008, Mayer  calculated the RNFL thickness in circular SDOCT B-scans from 5 normal and 7 glaucoma eyes using Fuzzy C-means clustering technique without the need of parameter adaptation for pathological data. They reported that 97% of the upper and 74% of the lower RNFL layer boundary points lied within a two pixel range from the manual segmentation of the evaluation data set. Vermeer BIB025 in 2011 presented a method for three-dimensional retinal layer segmentation in OCT images by a flexible method that learned from provided examples. Parts of representative OCT scans were manually segmented and used by the algorithms to learn from. Learning and classification of pixels was done by a support vector machine. Smoothness of the detected interfaces was guaranteed by the level set regularization that was applied after the pixel classification. The procedure was the same for all layers, except for the manually segmented data used to train the classifier. RMS errors for the top and bottom of the retina were between 4 and 6 μm, while the errors for intra-retinal interfaces were between 6 and 15 μm. 3D graph-based methods seem so far to be best suited for the task in comparison to the above-mentioned approaches. Their time requirements can be reduced to about 45 seconds per 3D volume (480 × 512 × 128 voxels) and they routinely achieve high accuracy with about 2.8 μm of layer-surface segmentation error. Such methods take advantage of newly developed 3D imaging systems, which provide better visualization and 3D rendering of the segmented boundaries. BIB017 BIB018 BIB012 BIB026 By design benefitting from contextual information represented in the analysis graph, these methods are robust to noise and do not require advanced noise reduction techniques in the preprocessing steps. While there is no theoretical limit on the number of layers that can be simultaneously segmented by these approaches, up to 11 layers are routinely identified in retinal OCT images, performance that is unavailable to the other above-referenced algorithms. In 2008, Garvin  developed an automated 3-D segmentation approach for the division of the retina on macular optical coherence tomography (OCT) scans into five layers. She compared its performance on OCT scans of patients with unilateral anterior ischemic optic neuropathy to that of human experts. Figure 11b and c shows an example of the six surfaces (labeled 1-6) they desired to find on each 3-D composite image [ Figure 11a ]. The 3-D composite image associated with each eye in Garvin's  work was created in two major steps. In the first step [ Figure 12 ], raw scans for a particular angular location (e.g., all the vertical scans) were individually aligned so that boundary six (the retinal pigment epithelium) appeared approximately straight in the aligned image. Each scan was aligned by first finding boundaries 1, 5, and 6 simultaneously using an optimal graph search approach performed in 2-D. To ensure smoothness, a least-squares spline was fit to boundary six. The columns were then translated so that this spline would be a straight line in the aligned image. In the second step of this stage, each aligned image was registered to the first image in its location set by exhaustively searching for the best whole-pixel translation. After the determination of  constructed a mipmap-like hierarchy in order to compute varying levels of data distributions to sample as input to a SVM algorithm. Topleft image shows a slice through a level having a resolution of 475×150×48. Remaining images show levels having a resolution half the one before it. (The change in the "bump" is due to the influence of neighboring slices in front and behind the shown slice.) Figure 12 : Individual scan alignment (top and bottom of images have been cropped to aid in visualization)  Figure 11: Example composite image (from Garvin  ) with labeled intralayer segmentation and 3-D visualization of three surfaces (top and bottom of images have been cropped to aid in visualization) (a) Composite image (b) Six surfaces (labeled 1-6) and five corresponding intralayers (labeled A-E). The anatomical correspondence is our current presumption based on histology and example images from higher-resolution research OCT scanners: (a) nerve fiber layer (NFL), (b) ganglion cell layer and inner plexiform layer (GCL + IPL), (c) inner nuclear layer and outer plexiform layer (INL+OPL), (d) outer nuclear layer and photoreceptor inner segments (ONL + IS), (e) photoreceptor outer segments (OS). (c) Example 3-D visualization of surfaces 1, 3, and 4  c b a surfaces 1, 5, and 6, the remaining surfaces were found sequentially (allowing the utilization of other surface locations in the cost functions) in the following order: surface 4, surface 3, and finally, surface 2. The graph search approach guaranteed that the optimal feasible (satisfied smoothness and interaction constraints) surfaces would be found with respect to the designed cost functions. Figure 13 demonstrates three example results reflecting the best, median, and worst performances according to the overall unsigned border positioning error in garvin.  They reported overall mean unsigned border positioning error to be 6.1 ± 2.9 μm, a result comparable to the interobserver variability (6.9 ± 3.3 μm). In 2009, Abràmoff BIB012 combined a multiscale 3D graph search algorithm and a voxel column classification algorithm using a k-NN classifier to segment the ONH cup and rim in SDOCT data from 34 glaucoma patients. This preliminary study showed for the first time a high correlation between segmentation results of the ONH cup and rim from SDOCT images and planimetry results obtained by glaucoma experts on the same eye. They reported the correlation of algorithm to three independent experts was 0.90, 0.87, and 0.93, respectively. In 2010, Lee BIB017 presented an improved and fully automatic method based on a similar methodology using graph search combined with a k-NN classifier that employed contextual information combined with a convex hullbased fitting procedure to segment the ONH cup and rim on 27 SDOCT scans from 14 glaucoma patients. They reported unsigned error for the optic disc cup was 2.52 ± 0.87 pixels (0.076 ± 0.026 mm) and for the neuroretinal rim was 2.04 ± 0.86 pixels (0.061 ± 0.026 mm). In the same year, Yang BIB016 proposed a method in which detection of 9 boundaries followed a two-step segmentation algorithm Figure 14 . First, a customized canny edge [ BIB016 Panel a is the original OCT image acquired using Topcon 3D OCT-1000 equipment. The image was first aligned as shown in b and the ILM and IS/OS were detected as in c; d, e and f illustrate the BM/Choroid, OS/RPE, IPL/INL, NFL/GCL, GCL/ IPL, INL/OPL and ELM were detected in order; in the end, all nine boundaries were converted back to the original OCT image coordinates as shown in g BIB016 detector was used to create a map showing local main edges. A complementary gradient map in the axial direction was also acquired with a larger kernel. Second, a graph was built based on a combination of the axial intensity gradient and the canny edge maps. The layer boundary was then extracted bythe shortest path search applied to the graph using a dynamic programming algorithm. Overall the ICC of each boundary was above 0.94, the mean coefficient of variation was less than 7.4%, and the mean standard deviation was less than 2.8 μm. Algorithm was able to segment low intensity and low contrast OCT images in a very short time without degrading the accuracy. In addition, pre extraction of vessel locations, which is not a trivial operation, was unnecessary in this method, as shown in Figure 15 . Again in 2010, Quellec BIB018 introduced a novel ten-layer automated segmentation of OCT data from 13 normal eyes and from 23 eyes with CNV, intra-, and subretinal fluid and pigment epithelial detachment and twenty-one textural features were implemented in 3-D and measured locally for each layer. The variations of texture and thickness across the macula in these ten layers, averaged over thirteen normal eyes, defined the normal appearance of maculae in SD-OCT scans. A machine learning approach that classifies the retinal pathology based on feature-and layer-specific properties in comparison with the normal appearance of maculae was reported. In this study, they reported an extended method for automated segmentation of 10 intraretinal layers identified in Figure 16 from 3-D macular OCT scans using multiscale 3-D graph search technique. The basic concept of this approach was to detect the retinal surfaces in a subvolume constrained by the retinal surface segmented in a low-resolution image volume. The cost functions for the graph searches, capable of detecting the retinal surfaces having the minimum costs, were inverted gradient magnitudes of the dark-to-bright transition from top to bottom of the OCT volume for surfaces 1, 3, 5, 7, 9, 10 and those of the bright-to-dark transition for surfaces 2, 4, 6, 8, 11 [ Figure 16 ]. They reported that mean unsigned surface positioning errors were less than 6 μm and confirmed that useful 3-D textural information can be also extracted from SD-OCT scans to aid local retinal abnormality detection. Furthermore in 2010, chiu BIB023 reported a skillful approach based on graph-based theory and dynamic programming thatsignificantly reduced the processing time required for image segmentation and featureextraction. This methodology was able to address sources of instability such asthe merging of layers at the fovea, uneven tissue reflectivity, vessel hypo-reflectivity and thepresence of pathology. Interestingly, the approach incorporates an automatic initialization thatbypasses the need for manual endpoint selection. In 2012, Kafieh BIB026 proposed a segmentation method capable of detecting 12 retinal boundaries using diffusion map based segmentation algorithm. In contrast to recent methods of graph based OCT image segmentation, this approach did not require edge-based image information and rather relied on regional image texture. Consequently, the method demonstrated robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion map was applied on 2D and 3D OCT datasets and in each application; the procedure was composed of two steps (one for partitioning the data to important and less important sections, and another one for localization of internal layers). In the first step, the data pixels/voxels were grouped in rectangular/cubic sets to form a graph node. The weights of graph were also calculated based on geometric distances of pixels/voxels and differences of their mean intensity. The first Figure 17 : Construction of graph nodes from a 3D OCT BIB026 Figure 18: (Up-left) Surface 1, (Up-right) First and 7 th surfaces, (Down-left) Surfaces 9 to12, (Down-right) Surfaces 3 to7 BIB026 diffusion map clustered the data into three parts, second of which was the area of interest and the two other sections were eliminated from the next calculations. In the second step, the remaining area went through another diffusion map algorithm and the internal layers were localized based on their similarity of texture [ Figure 17 ]. Figure 18 shows an example of extracted OCT layers using the mentioned diffusion-map based method. BIB026 The method was tested on 23 datasets from two patient groups (10 datasets, 3D-OCT data from patients diagnosed with glaucoma and 13 datasets, 3D-OCT obtained from normal eyes without pathologies). The mean unsigned border positioning errors (mean ± SD) was 8.52 ± 3.13 and 7.56 ± 2.95 micrometers for the 2D and3D approaches, respectively. In addition, pre extraction of vessel locations was unnecessary in this method, as shown in Figure 19 . Table 2 shows a brief look at the mentioned approaches and compares the OCT systems, preprocessing method, error range, and computation time. There is no doubt that algorithms and research projects work on a limited number of images with some determinate abnormalities (or even on normal subjects) and such limitations make them more appropriate for bench and not for the bedside. The automatic segmentation will never replace the physicians but the improved segmentation BIB002 Huang, BIB001 George,  Koozekanani, BIB003 Gregori,  Herzog,  Shahidi, BIB004 Ishikawa, BIB005 Shrinivasan, BIB008 Bagci,  Fabritius, BIB014 Koprowski, BIB015 Lu  TD BIB014 it is About 17-21 seconds B-scan Boyer, BIB006 Baroni, BIB007 Tan, BIB010 Kajic BIB021 TD OCT (OCT 3000 Zees-Humphrey, OCT 2Carl-Zeiss Meditec, Stratus)/ SDOCT (RTVue100°CT, Optovue, Freemont, CA) 2D median filter, Gaussian smoothing filtering, bilateral filter 4.2-5 μm 9.42 seconds on a Pentium, 1.8 GHz processor with1 GB of RAM Active contours Cabrera Fernández,  Mishra, BIB013 Yazdanpanah, BIB011 Mujat,  Mayer, BIB022 Ghorbel  TD Fuller,  Mayer,  Vermeer BIB025 experimental 3D OCT, SD OCT (Spectralis) SVM approach, 2D mean filter, directional filtering Around 6 voxels and in recent studies like BIB025 between 6 and 15 μm 45-120 seconds on a 2GHz Pentium IV on a computer with 3GB of RAM (dual processor3GHz IntelXeon) 3D graphs Garvin,  Abràmoff, BIB012 Lee, BIB017 Yang, BIB016 Quellec, BIB018 Chiu, BIB023 Kafieh BIB026 TD OCT (Stratus OCT)/ SD OCT (Cirrus-OCT Topcon 3D OCT-1000) Figure19: Robustness of the proposed algorithm to blood vessel artifacts BIB026 methods and the ability of these methods in providing more information in less complicated data will offer valuable help to ophthalmologists in better diagnosis and treatment of retinal pathologies.