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

Abstract

This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH) morphology and context) and graph-analysis algorithms (e.g., Dijkstra path). The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels.

A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends

“…Robust to healthy and pathological retina images Al Shehhi [50] 2016 Black top hat (BTH), Graph cut and segmentation, Dijkstra shortest path and segmentation Tolerant to noisy images, detect small and tinny vessels Khan [51] 2016 Hessian matrix and eigenvalues approach, Otsu thresholding…”

“…Al Shehhi et al [50] suggested a graph-based method for extraction of retinal vasculature. Preprocessing steps are used to enhance contrast and to create essential features to enhance vessel structure due to the sensitivity of vessel patterns to multiscale/multiorientation structure.…”