Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach

Abstract

A hybrid segmentation algorithm is proposed is this paper to extract the blood vesselsfrom the fundus image of retina. Fundus camera captures the posterior surface of the eye and thecaptured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinalhaemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysisof vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the fieldof ophthalmology. It is derived from the literature review that no unique segmentation algorithm issuitable for images of different eye-related diseases and the degradation of the vessels differ frompatient to patient. If the blood vessels are extracted from the fundus images, it will make thediagnosis process easier. Hence, this paper aims to frame a hybrid segmentation algorithmexclusively for the extraction of blood vessels from the fundus image. The proposed algorithm ishybridized with morphological operations, bottom hat transform, multi-scale vessel enhancement(MSVE) algorithm, and image fusion. After execution of the proposed segmentation algorithm, thearea-based morphological operator is applied to highlight the blood vessels. To validate theproposed algorithm, the results are compared with the ground truth of the High-Resolution Fundus(HRF) images dataset. Upon comparison, it is inferred that the proposed algorithm segments theblood vessels with more accuracy than the existing algorithms.

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

“…It can also be observed in the same table that the sensitivity and AUC of the developed framework are the best among the unsupervised approaches respectively. The sensitivity of [44] and [45] are the second-best while that of the [16] thirdbest among the unsupervised approaches. It is pertinent to note here that [44] and [16] achieved such high sensitivity at the cost of specificity and accuracy.…”

“…The sensitivity of [44] and [45] are the second-best while that of the [16] thirdbest among the unsupervised approaches. It is pertinent to note here that [44] and [16] achieved such high sensitivity at the cost of specificity and accuracy. Our obtained specificity is slightly lower than the specificity of [46], [47], and [48] which are the best, the second best and third best among unsupervised methods respectively.…”

Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

“…Image enhancement schemes, such as Gaussian smoothing, morphological top-hat filtering, and contrast enhancement, are first used to increase the contrast and reduce the noise, and then the segmentation task is carried out via adaptive local thresholding [28]. Sundaram et al proposed a hybrid segmentation approach that uses techniques such as morphology, multi-scale vessel enhancement, and image fusion i.e., area-based morphology and thresholding are used to highlight blood vessels [29]. Zhao et al proposed an infinite active contour model to automatically segment retinal blood vessels, where hybrid region information of the image is used for small vasculature structure [30].…”

A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation

“…Morphology-based decision rules and fractional derivatives helped this method achieve competitive performance parameters on the DRIVE database. Sundaram et al [38] used a hybrid vessel segmentation algorithm based on morphological operators coupled with multi-scale vascular refinement. They used bottom-hat transform alongside fusion of resultant multi-scale images to tackle discontinuities at the boundaries of vessels.…”