Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction technique

Abstract

Reliable, fast and efficient optic disc localization and blood-vessel detection are the primary tasks in computer analyses of retinal image. Most of the existing algorithms suffer due to inconsistent image contrast, varying individual condition, noises and computational complexity. This paper presents an algorithm to automatically detect landmark features of retinal image, such as optic disc and blood vessel. First, optic disc and blood vessel pixels are detected from blue plane of the image. Then, using OD location the vessel pixels are connected. The detection scheme utilizes basic operations like edge detection, binary thresholding and morphological operation. This method was evaluated on standard retinal image databases, such as STARE and DRIVE. Experimental results demonstrate that the high accuracy achieved by the proposed method is comparable to that reported by the most accurate methods in literature, alongside a substantial reduction of execution time. Thus the method may provide a reliable solution in automatic mass screening and diagnosis of the retinal diseases.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

“…In [ 8 ], the authors presented a curvelet-based algorithm for the detection of the optic disc and exudates on low contrast images. In [ 9 ], the authors utilized basic operations like edge detection, binary thresholding, and morphological operation for detecting the optic disc and blood vessel pixels. In [ 10 ], the authors described a new filtering approach which includes Sobel edge detection, texture analysis, intensity, and template matching methods for the detection of the optic disc.…”

An Efficient Vessel Segmentation Based on Hierarchical Swarm Optimization Scheme and Mean Shift Clustering with Vessel Connectivities for Retinal Images

“…Earlier, various schemes are focused for segmentation and it classified into pattern recognition using machine learning or model-based (Imani et al, 2015;Niemeijer et al, 2010;Marin et al, 2011) mathematical morphology (Imani et al, 2015;Fig. 1: A sample image for vessel selection from the DRIVE dataset Mithun et al, 2014), kernel-based analysis (Bankhead et al, 2012;Wang et al, 2013;Fraz et al, 2012a, b) and tracking-based or path-based Artificial Intelligent (AI) methods (Zhao et al, 2015;Estrada et al, 2012;Yin et al, 2012). Here, kernel based analysis with threshold based segmentation has been discussed because they are more related to presented work.…”

An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

“…The studies of retinal images can be classified into pattern recognition (machine learning/model-based) [6,16,17], mathematical morphology [6,18], kernel-based analysis [10,11,17] and tracking-based/path-based (Artificial Intelligent, AI) methods [5,19,20]. Here, morphological and AI methods are further discussed because they are more related to presented work in this paper.…”

“…Morphological methods examine the geometric vessellike structure of retinal image by probing it with small patterns called structuring elements (SE) of predefined size and shape. Due to sensitivity of vessel-like patterns to different scales and orientations, most methods use multiscale or/and multiorientation structuring elements [18,21,22], such as multistructure morphological operators [8,12], and multiscale white top-hat with linear structuring elements [9]. One of the challenges is that there are several structures in retinal images such as optical disk, exudates, microaneurysms, and hemorrhages, which degrade the performance 2 Mathematical Problems in Engineering of vessel detection methods.…”