Efficient Segmentation and Registration of Retinal Image Using Gumble Probability Distribution and BRISK Feature Nagendra Pratap Singh 1* , Vibhav Prakash Singh 2 1 Department of Computer Science and Engineering, National Institute of Technology, Hamirpur 177005, H.P., India 2 Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad 211004, Prayagraj, India Corresponding Author Email: nps@nith.ac.in https://doi.org/10.18280/ts.370519 ABSTRACT Received: 30 July 2020 Accepted: 15 October 2020 The registration of segmented retinal images is mainly used for the diagnosis of various diseases such as glaucoma, diabetes, and hypertension, etc. These retinal diseases depend on the retinal vessel structure. The fast and accurate registration of segmented retinal images helps to identify the changes in vessels and the diagnosis of the diseases. This paper presents a novel binary robust invariant scalable key point (BRISK) feature-based segmented retinal image registration approach. The BRISK framework is an efficient keypoint detection, description, and matching approach. The proposed approach contains three steps, namely, pre-processing, segmentation using matched filter based Gumbel pdf, and BRISK framework for registration of segmented source and target retinal images. The effectiveness of the proposed approach is demonstrated by evaluating the normalized cross-correlation of image pairs. Based on the experimental analysis, it has been observed that the performance of the proposed approach is better in both aspect, registration performance as well as computation time with respect to SURF and Harris partial intensity invariant feature descriptor based registration. Keywords: retinal image, feature descriptor, segmentation, registration, probability distribution functions 1. INTRODUCTION The objective of segmented retinal image registration is to identify the disease progression by aligning two retinal images namely the source and target image. These images are taken at different time interval or different view points of the same retina. The various Retinal diseases such as diabetes [1, 2], glaucoma [3], and hypertension [4] are easily detected and diagnosed through the Retinal image registration. The variation of intensities with respect to time and poor quality of retinal images are the major challenges of retinal image registration. For example, registration of retinal image pair is difficult because the selected image pair is captured in between some years apart may be acquired from different camera with different sensitivity or may be in different modality. Nowadays, researchers are working in the area of retinal image for the disease detection and retrieval using low-level features [5, 6] and machine learning approaches [7-11]. But they are not using the relevancy of retinal image segmentation and registration. According to Brown [12], Maintz and Viergever [13], and Lester and Arridge [14] registration techniques are classified into two categories, namely area-based and feature- based technique. The area-based techniques [15] compare the intensity differences of retinal image pairs by using mutual information [15] and normalize cross correlation [16] as similarity metric and an optimization technique [17] is used to achieve the optimum similarity metric which indicates the better registration. According to the author Chanwimaluang et al. [18], area-based techniques are not suitable in case of low overlapping area of registration. To overcome this problem, generally a region of interest within image pair is selected for evaluating the similarity metric [17]. The changes in illumination and initial-misalignment also affect the performances of area-based techniques [19]. Therefore area-based techniques are susceptible to background changes due to their pathologies and changes to the viewpoints of camera [19]. On the basis of exhaustive literature survey it is found that the feature-based techniques [20, 21] are more suitable for the segmented retinal image registration in comparison to area-based techniques. The main characteristics of feature-based techniques are their robustness against illumination changes. Feature-based techniques generally extract the salient and distinct features for searching the appropriate transformation such as scaling, rotation, and translation [22] between the image pair which optimizes the correspondence between selected features. However, it is tedious task to extract the features from the poor quality images. To overcome this problem for retinal image registration, generally blood vessel structure of respective segmented retinal images is used to identify the matched feature points [23]. For extracting distinct matched feature point, a scale invariant feature transform (SIFT) has been used by various authors [24, 25]. The SIFT features are scale invariant as well as rotation invariant and provide robust matching across the change in viewpoint, changes in illumination and a substantial range of affine distortion [25, 26]. The SIFT features are highly efficient to sense a single feature that can be exactly matched with the large set of features of other images and specially designed for mono-modal image registration [24]. The main disadvantage of SIFT features are its scale invariance strategy, which is not able to provide sufficient control points in case of high order Traitement du Signal Vol. 37, No. 5, October, 2020, pp. 855-864 Journal homepage: http://iieta.org/journals/ts 855