A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Registration. <s> Image registration is a real challenge because physicians handle many images. Temporal registration is necessary in order to follow the various steps of a disease, whereas multimodal registration allows us to improve the identification of some lesions or to compare pieces of information gathered from different sources. This paper presents an algorithm for temporal and/or multimodal registration of retinal images based on point correspondence. As an example, the algorithm has been applied to the registration of fluorescein images (obtained after a fluorescein dye injection) with green images (green filter of a color image). The vascular tree is first detected in each type of images and bifurcation points are labeled with surrounding vessel orientations. An angle-based invariant is then computed in order to give a probability for two points to match. Then a Bayesian Hough transform is used to sort the transformations with their respective likelihoods. A precise affine estimate is finally computed for most likely transformations. The best transformation is chosen for registration. <s> BIB001 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Registration. <s> 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing. 10. Image Segmentation. 11. Representation and Description. 12. Object Recognition. <s> BIB002 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Registration. <s> Purpose: In this paper, the aim is to demonstrate enhanced processing of sequences of fundus images obtained using a commercial AO flood illumination system. The purpose of the work is to (1) correct for uneven illumination at the retina (2) automatically select the best quality images and (3) precisely register the best images. Methods: Adaptive optics corrected retinal images are pre-processed to correct uneven illumination using different methods; subtracting or dividing by the average filtered image, homomorphic filtering and a wavelet based approach. These images are evaluated to measure the image quality using various parameters, including sharpness, variance, power spectrum kurtosis and contrast. We have carried out the registration in two stages; a coarse stage using cross-correlation followed by fine registration using two approaches; parabolic interpolation on the peak of the cross-correlation and maximum-likelihood estimation. The angle of rotation of the images is measured using a combination of peak tracking and Procrustes transformation. Results: We have found that a wavelet approach (Daubechies 4 wavelet at 6th level decomposition) provides good illumination correction with clear improvement in image sharpness and contrast. The assessment of image quality using a ‘Designer metric’ works well when compared to visual evaluation, although it is highly correlated with other metrics. In image registration, sub-pixel translation measured using parabolic interpolation on the peak of the cross-correlation function and maximum-likelihood estimation are found to give very similar results (RMS difference 0.047 pixels). We have confirmed that correcting rotation of the images provides a significant improvement, especially at the edges of the image. We observed that selecting the better quality frames (e.g. best 75% images) for image registration gives improved resolution, at the expense of poorer signal-tonoise. The sharpness map of the registered and de-rotated images shows increased sharpness over most of the field of view. Conclusion: Adaptive optics assisted images of the cone photoreceptors can be better pre-processed using a wavelet approach. These images can be assessed for image quality using a ‘Designer Metric’. Two-stage image registration including correcting for rotation significantly improves the final image contrast and sharpness. <s> BIB003 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Registration. <s> Being able to image the human retina in high resolution opens a new era in many important fields, such as pharmacological research for retinal diseases, researches in human cognition, nervous system, metabolism and blood stream, to name a few. In this paper, we propose to share the knowledge acquired in the fields of optics and imaging in solar astrophysics in order to improve the retinal imaging in the perspective to perform a medical diagnosis. The main purpose would be to assist health care practitioners by enhancing the spatial resolution of the retinal images and increase the level of confidence of the abnormal feature detection. We apply a nonlinear registration method using local correlation tracking to increase the field of view and follow structure evolutions using correlation techniques borrowed from solar astronomy technique expertise. Another purpose is to define the tracer of movements after analyzing local correlations to follow the proper motions of an image from one moment to another, such as changes in optical flows that would be of high interest in a medical diagnosis. <s> BIB004
Image registration is an image processing technique designed for aligning multiple images of the same configuration. Generally, image registration is the procedure to transform multiple datasets, photographs, and times depth into one coordinate system to accomplish several tasks like image rotating, scaling, and skewing. The image configuration of the same object can vary due to the acquisition at different times, different acquisition devices or acquisition inefficiency caused by the variation of camera angle, movement of the objects, orientation, sensor resolution, distance, including other crucial facts and the image registration process; organized to perform the alignment of the geometrical aberrated images, based on a standard image BIB002 . Image registration plays a supreme act in analyzing the fundus retinal image because it is the way to protect and provide the accurate information that is critically important for diagnosing the related diseases. The study of BIB004 proposed an algorithm applying a nonlinear registration method based on correlation tracking to enhance the retinal imaging with a high spatial resolution for clear and precise detection of retinal abnormalities. The local correlation was also analyzed in BIB004 experiment to observe the actual movement of an image in a different time period like the variation in optical flows that can be the feature of interest of diseases diagnosing. A hybrid retinal image registration technique for Early Treatment Diabetic Retinopathy Studies (ETDRS) was designed in  . This technique was able to extract retinal microvascular structure applying local entropy-based thresholding and maximized the mutual information of binary image pair to estimate the zeroth order translation. Image quality was assessed regarding the definition of ETDS based on the translation model and then finally affine/quadratic model estimation had been applied after image pair was accepted  . The necessity of temporal image registration is to observe the different steps of disease and the detection of lesions in fundus retinal image can be improved by multimodal image registration. The study of BIB001 presented a temporal and multimodal retinal image registration technique based on point correspondence. This method first detects the vascular tree and labeled the bifurcation point. After that, it matches the probability for matching two points computing an angle-based invariant and uses a Bayesian Hough transform to distinguish the respective similarities and finally it computes a fine estimation to choose the best similar transformation for registration BIB001 . Figure 12 shows (a) original fluorescein image; (b) fluorescein image two years later of temporal registration; (c) final result of the registration BIB001 . An advanced sequential processing method for the retinal image was proposed by authors of BIB003 where they used cross-correlation followed by a fine registration employing parabolic interpolation on the peak of the cross-correlation, maximum-likelihood estimation for precise registration, and a combination of peak tracking and Procrustes transformation to measure angle rotation of the fundus retinal image.