Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Histometric features for the objective grading of prostatic adenocarcinoma in histologic specimens were analyzed in five cases each of well, moderately and poorly differentiated lesions. Tissue sections from the selected cases were stained by the Feulgen method and digitized by a video-based microphotometer. Twenty total fields were recorded for each grade: ten at high resolution (an image sampling of 0.5 micron per pixel) and ten at low resolution (0.8 micron per pixel), with two fields per case recorded at each resolution. The images were segmented by an automated expert system-guided scene segmentation procedure. The performance of that procedure was measured by comparing the automated counts of nuclei in the segmented fields to the visual counts made by a pathologist in the same fields. For well, moderately and poorly differentiated cases, respectively, the nuclear counts made by the expert system at high resolution were 2.7%, 4.2% and 4.7% higher than the visual counts (as estimated from a total of 6,628 nuclei), but 1.2%, 2.5% and 1.1% lower at low resolution (10,329 nuclei). High-resolution features and tissue textural features were computed for each case. The high-resolution features showed good separation between the three groups of cases. The tissue textural features showed consistent separation between well and moderately differentiated cases. The relaxation of the spatial resolution (to 0.8 micron/pixel spacing) did not affect the selection of features, but led to less separation between the data from different grades. In conclusion, the automated system performed satisfactorily in distinguishing sections of prostatic tumors of varying degrees of differentiation. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Abstract The wide-ranging biologic malignancy of prostate cancer is strongly correlated with its extensive and diverse morphologic appearances. Histologic grading is a valuable research tool that could and should be used more extensively and systematically in patient care. It can improve clinical staging, as outlined by Oesterling et al (J Urol 138: 92–98, 1987), during selection of patients for possible prostatectomy by helping to identify the optimal treatment. Some of the recurrent practical problems with grading (reproducibility, "undergrading" of biopsies, and "lumping" of grades) are discussed and recommendations are made. The newer technologically sophisticated but single-parameter tumor measurements are compared with one important advantage of histologic grading: the ability to encompass the entire low to high range of malignancy. The predictive success of grading suggests that prostate cancers have more or less fixed degrees of malignancy and growth rates (a hypothesis of "biologic determinism") rather than a steady increase in malignancy with time. Most of the observed facts can be interpreted on that basis, including the interrelations of tumor size, grade, and malignancy. The increasing age-adjusted incidence of diagnosed prostate cancer is attributed to new diagnostic tools and increased diagnostic zeal. <s> BIB002 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Part I. The Theory of Moving Interfaces: 1. Theory of front evolution 2. Level set formulation Part II. Algorithms, Analysis, and Implementations of Level Set Methods: 3. Traditional techniques for tracking interfaces 4. Hyperbolic conservation laws 5. Approximating the level set equation 6. Viscosity solutions and Hamilton-Jacobi equations 7. A hierarchy of fast level set methods 8. A fast marching level set method 9. Extensions to the basic method Part III. Applications: 10. Geometry: curve/surface shrinkage and self-similar surfaces 11. Grid generation 12. Image enhancement and noise removal 13. Minimal surfaces and surfaces of prescribed curvature 14. Combustion, crystal growth, and two-fluid flow 15. Computer vision: shape detection and recognition 16. Applications of the fast marching level set method 17. Etching and deposition in microchip fabrication 18. New areas. <s> BIB003 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Counting of mitotic cells has been shown to be of prognostic value in breast cancer in different retrospective studies. Up to now the number of mitoses is assessed mainly manually according to a standardized but strict protocol. Although such a manual procedure is reasonably reproducible, automatic counting of mitotic cells offers the potential for greater objectivity and reproducibility. This paper describes the influence of resolution on automatic recognition by image processing of mitotic cells in Feulgen stained breast cancer sections. Using the image recording, correction and segmentation procedure described in a previous study, five specimens were analyzed: one was used to serve as a training set and four were put aside for later use as independent test set. For each slide, objects from a pre-selected area were recorded at increasing resolution. For each object, contour features and optical density measurements were computed and stored in a data file for statistical analysis. The results showed that increased resolution using a 40× objective lowered the number of misclassified mitoses compared with a 20× objective (overall mean percentage of misclassified mitoses over training and all test specimens: 20x, 24.57; 40x, 7.96). The number of misclassifications of non-mitoses was almost stable per specimen but varied between specimens (19–42%) due to differences among tissues. ::: ::: ::: ::: Given the improvement in classifying mitoses and the possibility to evaluate interactively the measurement result, the described semi-automated mitoses pre-screener of histological sections may be suitable for further testing in a clinical setting. Cytometry 28:135–140, 1997. © 1997 Wiley-Liss, Inc. <s> BIB004 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> To achieve the extreme accuracy rates demanded by applications in unsupervised automated cytology, it is frequently necessary to supplement the primary segmentation algorithm with a segmentation quality control system. The more robust the segmentation strategy, the less severe the data pruning need be at the segmentation validation stage. These issues are addressed as we describe our cell nucleus segmentation strategy which is able to achieve 100% accurate segmentation from a data set of 19946 cell nucleus images by automatically discarding the most difficult cell images. The automatic quality checking is applied to enhance-the performance of a robust energy minimisation based segmentation scheme which already achieved a 99.47% accurate segmentation rate. <s> BIB005 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Automated image analysis of bronchial tissue offers the opportunity to quantify stained area and staining intensity in a standardized way to obtain robust estimates of inflammatory cell counts and cytokine expression from multiple large areas of histopathologic sections. We compared fully automated digital image analysis with interactive digital cell counting and semiquantitative scoring of cytokine expression in terms of repeatability and agreement in bronchial biopsies in 52 patients with mild to moderate atopic asthma. Immunohistochemistry with antibodies against CD3, interleukin (IL)-4, IL-5, and interferon-gamma protein was performed on frozen tissue sections, using 3-amino-9-ethylcarbazole as chromogen and hematoxylin as counterstaining. IL-4 and IL-5 messenger RNAs were localized by in situ hybridization without hematoxylin staining. Separation of 3-amino-9-ethylcarbazole and hematoxylin-stained pixels was achieved by linear combination of red- and blue-filtered gray-scale images. Using baseline biopsy specimens, fully automated CD3+ cell counts showed perfect repeatability (r = 1.0) and a strong linear relationship with the interactive procedure (r = 0.98). Automated densitometry showed perfect repeatability (1.0) and a moderate to strong relationship with semiquantitative scoring of protein and messenger RNA expression (r = 0.43-0.89). Relationships between automated and semiquantitative assessments of changes in cytokine expression during 2 years of follow-up were moderate to strong (r = 0.40-0.84). We conclude that fully automated cell counts and automated densitometric analyses in bronchial tissue of patients with asthma are unbiased and help to reduce variability in inflammatory outcomes. <s> BIB006 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Comparison of Pathologist-Detected and Automated Computer-Assisted Image Analysis Detected Sentinel Lymph Node Micrometastases in Breast Cancer <s> BIB007 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> To develop an optimal sampling strategy for tissue microarrays using automated digital analysis for androgen receptor (heterogeneous expression) and the cellular proliferation marker Ki-67 (homogeneous expression and evaluated by others using nonautomated methods).Tissue microarrays were constructed from 23 radical prostatectomy specimens and immunostained for androgen receptor expression and cellular proliferation. Automated digital image analysis was used, and the minimum number of cores necessary to capture variance change <3% was determined. Androgen receptor immunostaining was described by percent positive nuclei (PPN) and mean optical density (MOD).Androgen receptor PPN variance measurements showed that 5 cores should be obtained when a single block of a radical prostatectomy specimen contained cancer. If all of 15 blocks contained cancer, 2 cores should be obtained from each of 6 blocks. An optimal sampling strategy was developed for androgen receptor PPN, androgen receptor MOD and Ki-67 PPN.The selection of the number of cores to sample is a tradeoff between the number of cores available that contain cancer and the amount of work involved in the analysis. Sampling no fewer than 5 but no more than 12 cores per radical prostatectomy specimen can capture tissue heterogeneity. <s> BIB008 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> OBJECTIVE ::: To investigate and develop an automated technique for astrocytoma malignancy grading compatible with the clinical routine. ::: ::: ::: STUDY DESIGN ::: One hundred forty biopsies of astrocytomas were collected from 2 hospitals. The degree of tumor malignancy was defined as low or high according to the World Health Organization grading system. From each biopsy, images were digitized and segmented to isolate nuclei from background tissue. Morphologic and textural nuclear features were quantified to encode tumor malignancy. Each case was represented by a 40-dimensional feature vector. An exhaustive search procedure in feature space was utilized to determine the best feature combination that resulted in the smallest classification error. Low and high grade tumors were discriminated using support vector machines (SVMs). To evaluate the system performance, all available data were split randomly into training and test sets. ::: ::: ::: RESULTS ::: The best vector combination consisted of 3 textural and 2 morphologic features. Low and high grade cases were discriminated with an accuracy of 90.7% and 88.9%, respectively, using an SVM classifier with polynomial kernel of degree 2. ::: ::: ::: CONCLUSION ::: The proposed methodology was based on standards that are common in daily clinical practice and might be used in parallel with conventional grading as a second-opinion tool to reduce subjectivity in the classification of astrocytomas. <s> BIB009 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> We present a region-based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over-segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two- as well as three-dimensional images. <s> BIB010 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> The process of histopathology, comprising tissue staining and morphological pattern recognition, has remained largely unchanged for over 140 years1. Although it is integral to clinical and research activities, histopathologic recognition remains a time-consuming, subjective process to which only limited statistical confidence can be assigned because of inherent operator variability2,3. Although immunohistochemical approaches allow limited molecular detection, significant challenges remain in using them for quantitative, automated pathology. Vibrational spectroscopic approaches, by contrast, directly provide nonperturbing molecular descriptors4, but a practical spectroscopic protocol for histopathology is lacking. Here we couple high-throughput Fourier transform infrared (FTIR) spectroscopic imaging5 of tissue microarrays6 with statistical pattern recognition of spectra indicative of endogenous molecular composition and demonstrate histopathologic characterization of prostatic tissue. This automated histologic segmentation is applied to routine archival tissue samples, incorporates well-defined tests of statistical significance7 and eliminates any requirement for dyes or molecular probes. Finally, we differentiate benign from malignant prostatic epithelium by spectroscopic analyses. <s> BIB011 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> OBJECTIVE ::: To design an automatic system for recognition and count of two different cell families on histologic slides. ::: ::: ::: STUDY DESIGN ::: The segmentation strategy uses color information on the image. The morphologic operations and Support Vector Machine approaches are used for each color to obtain precise segmentation of the image into separate cells for recognition. ::: ::: ::: RESULTS ::: A large set of histologic slides of bone marrow was assessed byour system and the results compared to the score of a human expert. The results are in good agreement. The difference is within acceptable limits (below 10%). ::: ::: ::: CONCLUSION ::: The automatic system of cell recognition and extraction is accurate and provides a useful tool for cell recognition and count on histologic slides. <s> BIB012 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Objective: Assessing the Ki-67 labeling index (LI) is laborious and time consuming. Therefore, an automated computer-based method was developed, which is able to identify and analyze immunolabeled and hematoxylin-stained nuclei in digital images of routine immunohistochemical slides. Material and methods: The method is based on a plugin for the public domain image analysis software ImageJ, which runs on every operating system (free download at http://rsb.info. nih.gov/ij/). Percentage of Ki-67 immunostained nuclei were determined in 5 high power fields (×40) of immunostained slides (DAB detection technique, hematoxylin counterstain) of 20 Grade I, 20 Grade II, and 10 Grade III meningiomas conventionally by two independent investigators and automatically, respectively. The time effort was measured for each counting procedure. Results: Enumerating conventionally or automatically did not reveal any significant differences in the mean labeling indices. Ki-67 LIs discriminated sufficiently between meningiomas of Grade I (median 1.7% Investigator 1 and 1.5% Investigator 2 vs. 1.5% automatically), Grade II (7.6%, 8% vs. 7.3%), and Grade III meningiomas (22%, 21% vs. 22%). The computer-based results correlated very closely with those obtained by manual counting (correlation coefficient = 0.98). The mean time effort for counting procedure per image was 374s (130 s-435 s) for the conventional and 11 s (7 s - 12 s) for the automated method. Conclusions: The described method can reliably assess the Ki-67 LI much faster than conventional enumerating. The computerized method has the advantages of objectivity, accuracy, repeatability, and ease of use. There is no request for special stains nor special image acquiring systems. The plugin can be downloaded at the "Morphometrie" section ofhttp:// www.uniklinikum-saarland.de/neuropathologie. <s> BIB013 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> BackgroundTumor classification is inexact and largely dependent on the qualitative pathological examination of the images of the tumor tissue slides. In this study, our aim was to develop an automated computational method to classify Hematoxylin and Eosin (H&E) stained tissue sections based on cancer tissue texture features.MethodsImage processing of histology slide images was used to detect and identify adipose tissue, extracellular matrix, morphologically distinct cell nuclei types, and the tubular architecture. The texture parameters derived from image analysis were then applied to classify images in a supervised classification scheme using histologic grade of a testing set as guidance.ResultsThe histologic grade assigned by pathologists to invasive breast carcinoma images strongly correlated with both the presence and extent of cell nuclei with dispersed chromatin and the architecture, specifically the extent of presence of tubular cross sections. The two parameters that differentiated tumor grade found in this study were (1) the number density of cell nuclei with dispersed chromatin and (2) the number density of tubular cross sections identified through image processing as white blobs that were surrounded by a continuous string of cell nuclei. Classification based on subdivisions of a whole slide image containing a high concentration of cancer cell nuclei consistently agreed with the grade classification of the entire slide.ConclusionThe automated image analysis and classification presented in this study demonstrate the feasibility of developing clinically relevant classification of histology images based on micro- texture. This method provides pathologists an invaluable quantitative tool for evaluation of the components of the Nottingham system for breast tumor grading and avoid intra-observer variability thus increasing the consistency of the decision-making process. <s> BIB014 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> M-FISH (Multicolor Fluorescence In Situ Hybridization) is a recently developed cytogenetic technique for cancer diagnosis and research on genetic disorders which uses 5 fluors to label uniquely each chromosome and a fluorescent DNA stain. In this paper, an automated method for chromosome classification in M-FISH images is presented. The chromosome image is initially decomposed into a set of primitive homogeneous regions through the morphological watershed transform applied to the image intensity gradient magnitude. Each segmented area is then classified using a Bayes classifier. We have evaluated our methodology on a commercial available M-FISH database. The classifier was trained and tested on non-overlapping chromosome images and an overall accuracy of 89% is achieved. By introducing feature averaging on watershed basins, the proposed technique achieves substantially better results than previous methods at a lower computational cost. <s> BIB015 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Neuroblastoma is a childhood cancer of the nervous system. Current prognostic classification of this disease partly relies on morphological characteristics of the cells from H&E-stained images. In this work, an automated cell nuclei segmentation method is developed. This method employs morphological top-hat by reconstruction algorithm coupled with hysteresis thresholding to both detect and segment the cell nuclei. Accuracy of the automated cell nuclei segmentation algorithm is measured by comparing its outputs to manual segmentation. The average segmentation accuracy is 90.24+/-5.14% <s> BIB016 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> The aim of the presented system is simplification of the daily pathological routine of prostatic cancer diagnostics. The system combines telepathology with computer-aided diagnostics algorithms. To the best of our knowledge, this is the first approach proposing such a comprehensive method. Our system is designed to accumulate knowledge in learning process during diagnostics. Our system targets image acquisition and interpretation stages. The image acquisition subsystem solves various problems related to microscopical slide digitization like biomedical image registration, data representation, and processing. The interpretation subsystem bases on Gabor filter texture features as well as on color features. A support vector machine classifier together with feature selection is used for computer-aided diagnostics. The experimental validation of the system bases on a database of more than three thousand samples.During the experimental evaluation, the system exhibited successful interaction with a pathologist. <s> BIB017 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Gene expression analysis has identified biologically relevant subclasses of breast cancer. However, most classification schemes do not robustly cluster all HER2+ breast cancers, in part due to limitations and bias of clustering techniques used. In this article, we propose an alternative approach that first separates the HER2+ tumors using a gene amplification signal for Her2/neu amplicon genes and then applies consensus ensemble clustering separately to the HER2+ and HER2- clusters to look for further substructure. We applied this procedure to a microarray data set of 286 early-stage breast cancers treated only with surgery and radiation and identified two basal and four luminal subtypes in the HER2- tumors, as well as two novel and robust HER2+ subtypes. HER2+ subtypes had median distant metastasis-free survival of 99 months [95% confidence interval (95% CI), 83-118 months] and 33 months (95% CI, 11-54 months), respectively, and recurrence rates of 11% and 58%, respectively. The low recurrence subtype had a strong relative overexpression of lymphocyte-associated genes and was also associated with a prominent lymphocytic infiltration on histologic analysis. These data suggest that early-stage HER2+ cancers associated with lymphocytic infiltration are a biologically distinct subtype with an improved natural history. <s> BIB018 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> BackgroundWe present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420–700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis.ResultsFor classification using all available image bands, we find the best performance (equal tradeoff between detection rate and false alarm rate) is obtained from either the multispectral or our "ccd" RGB imagery, with an overall increase in performance of 0.79% compared to the next best performing image type. For classification using single image bands, the single best multispectral band (in the red portion of the spectrum) gave a performance increase of 0.57%, compared to performance of the single best RGB band (red). Additionally, red bands had the highest coefficients/preference in our classifiers. Principal components analysis of the multispectral imagery indicates only two significant image bands, which is not surprising given the presence of two stains.ConclusionOur results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery. <s> BIB019 </s> Histopathological Image Analysis: A Review <s> IV. AUTOMATED DETECTION AND SEGMENTATION OF HISTOPATHOLOGY IMAGES <s> Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a methodology for automated detection and segmentation of structures of interest in digitized histopathology images. The scheme integrates image information from across three different scales: (1) low- level information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and (3) domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian classifier to generate a likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape models are used to identify glands and nuclei from the low-level likelihood scenes. Structural constraints are imposed via domain- specific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. In this paper we demonstrate the utility of our glandular and nuclear segmentation algorithm in accurate extraction of various morphological and nuclear features for automated grading of (a) prostate cancer, (b) breast cancer, and (c) distinguishing between cancerous and benign breast histology specimens. The efficacy of our segmentation algorithm is evaluated by comparing breast and prostate cancer grading and benign vs. cancer discrimination accuracies with corresponding accuracies obtained via manual detection and segmentation of glands and nuclei. <s> BIB020
One of the prerequisites to grading or diagnosis of disease in histopathology images is often the identification of certain histological structures such as lymphocytes, cancer nuclei, and glands. The presence, extent, size, shape, and other morphological appearance of these structures are important indicators for presence or severity of disease. For instance, the size of the glands in prostate cancer tend to reduce with higher Gleason patterns BIB002 . Similarly, the presence of a large number of lymphocytes in breast cancer histopathology is strongly suggestive of poor disease outcome and survival BIB018 . Consequently, a prerequisite to identification and classification of disease is the ability to automatically identify these structures. These approaches can either be global, in which they attempt to simultaneously segment all the structures in the image scene or local approaches which target specific structures. Another motivation for detecting and segmenting histological structures has to do with the need for counting of objects, generally cells or cell nuclei. Cell counts can have diagnostic significance for some cancerous conditions. Bibbo et al. BIB001 reported 1.1%-4.7% error in cell counts compared to manual counts for Feulgen-stained prostate specimens. Belien et al. BIB004 found 19%-42% error in counting mitoses in Feulgen-stained breast tissue sections. In immunohistochemically stained bone marrow biopsies, Markiewicz et al. BIB012 reported 2.8%-10.0% difference in counts between manual and automatic methods, while Kim et al. BIB013 found a correlation of 0.98 between manual and automatic counts of immunostained slides of meningiomas. Sont et al. BIB006 found a correlation of 0.98 between automated and semi-automated methods for inflammatory cell counts in immunostained bronchial tissue. A. Local, Structural Segmentation 1) Nuclear Segmentation: Numerous works have been conducted  - BIB014 on segmentation of various structures in breast histopathology images using methodologies such as thresholding, fuzzy c-means clustering, and adaptive thresholding BIB014 . Thresholding tends to work only on uniform images and does not produce consistent results if there is considerable variability within image sets. Watershed algorithms tend to pose the same problem BIB015 due to variability in image sets. Active contours are widely used in image segmentation; however, contours enclosing multiple overlapping objects pose a major limitation. In addition, inclusion of other irrelevant objects from the background further complicates the possibility of obtaining a viable segmentation. The pixel-level analysis of unstained prostate slides by Fourier transform infrared spectroscopy resulted in 94%-100% accuracy in the pixel-level classification of ten histologic classes as reported by Fernandez et al. in BIB011 . The pixel-level classification of nuclear material by Boucheron et al. BIB019 resulted in performances (equal tradeoff between detection and false alarm rates) of 88%-90% for H&E stained breast tissue. The use of automated methods for pixel-level analysis is perhaps more common for immunostained or fluorescently stained specimens. Singh et al. BIB008 reported 98% accuracy in the detection of positive and negative prostate nuclei immunostained for androgen receptor protein expression. Analysis of cytokeratin-stained lymph node sections yielded 95% detection of stained cells as reported by Weaver et al. in BIB007 . However, these studies focus only on finding nuclei on a pixel level. In H&E stained imagery of astrocytomas and bladder tissue, Glotsos et al. BIB009 reported that 94% of nuclei were correctly delineated. Latson et al. found 25% poorly segmented nuclei, 4.5%-16.7% clumped nuclei, and 0.4%-1.5% missed nuclei in H&E stained breast biopsies. Fluorescently stained imagery of cervical and prostate carcinomas allowed for 91%-96% accuracy in cell segmentation by Wahlby et al. BIB010 , where the accuracy here is calculated based on manual cell counts (i.e., not taking into account the accuracy of the actual nuclear delineation). Korde et al. used image intensity thresholding to segment nuclei in the bladder and in skin tissue  . Gurcan et al. leveraged gray level morphology followed by hysteresis thresholding to achieve cell nuclei segmentation in digitized H&E stained slides  , BIB016 . Other algorithms have been proposed using more complex techniques, such an active contour scheme for pap-stained cervical cell images by Bamford and Lovell BIB005 and a fuzzy logic engine proposed by Begelman et al. BIB017 for prostate tissue that uses both color-and shape-based constraints. In  and BIB020 , nuclear segmentation from breast and prostate cancer histopathology was achieved by integrating a Bayesian classifier driven by image color and image texture and a shape-based template matching algorithm (Fig. 4) . Fig. 4 (a) shows a Ductal carcinoma in situ (DCIS) study with a number of nuclei closely packed together. The likelihood image representing the probability of each pixel corresponding to a nuclear region is shown in Fig. 4(b) . Note that several nuclei lie adjacent to each other and hence template matching is used to extricate the individual nuclei. Fig. 4(c) shows the result of thresholding the Bayesian likelihood scene (95% confidence level). Template matching is then done at every location in Fig. 4(c) . Only those image locations where correspondence between the binary segmentation [ Fig. 4(c) ] and the template was found are shown as bright. The final nuclear boundary detection (green dots) is displayed in Fig. 4(d) . 2) Gland Segmentation: In a recently presented scheme for extracting glandular boundaries from histopathology scenes  , the algorithm consists of three distinct components: In the first stage, a Bayesian classifier is trained based on color and textural information to automatically identify nuclei, cytoplasm, and lumen regions in the scene. Following low-level Bayesian classification, structural constraints are incorporated to constrain the segmentation by using image information regarding the specific order of arrangement of glandular structures (central lumen, surrounding cytoplasm, and nuclear periphery) in order to reduce the number of false positive gland regions. Finally, a shape-based segmentation method in the form of level sets BIB003 is initialized within candidate lumen regions as determined from the Bayesian classifier. Hence, the level set surface evolution is controlled by the Bayesian probability scene derived via use of the low-level image information. The level set evolution is stopped at the interface between lumen and cytoplasm and, thus, a segmentation of the inner gland boundary is obtained. A second level set is then initialized within the cytoplasm area and used to capture the outer gland margin. Once the possible gland lumens are found, boundary segmentation is performed using level-sets. A boundary evolving in time and in the 2-D space defined by the grid of pixels is represented by the zero level set of a level set function , where and are 2-D Cartesian coordinates of . The evolution of is then described by a level-set formulation adopted from BIB003 ( 4.1) where the function defines the speed of the evolution. The curve evolution is driven by the nuclei likelihood image. The initial contour is initialized automatically using the detected lumen area from the candidate gland regions. The curve is evolved outward from the detected lumen regions in the combined nuclei likelihood image to avoid noise and allow smoother evolution relative to the original image. The intensities of the nuclei likelihood image form the stopping gradient. The algorithm is run until the difference in the contours in two consecutive iterations is below an empirically determined threshold. During training, size distributions similar to those used to calculate object likelihood are created using the final contours. These nuclear boundary based distributions are used to remove regions that are too large to be true glands. Finally, the lumen and nuclear boundaries extracted from true gland regions are passed on to the next step for feature extraction. Sample results from the automated gland segmentation algorithm are shown in Fig. 5 . The lumen boundaries are displayed in a solid blue contour and the interior nuclear boundaries are displayed as dashed black lines. Results of our gland segmentation algorithm are shown for sample images from benign epithelium, intermediate, and high-grade prostate cancer (from left to right).