GGM classifier with multi-scale line detectors for retinal vessel segmentation

Abstract

Persistent changes in the diameter of retinal blood vessels may indicate some chronic eye diseases. Computerassisted change observation attempts may become challenging due to the emergence of interfering pathologies around blood vessels in retinal fundus images. The end result is lower sensitivity to thin vessels for certain computerized detection methods. Quite recently, multi-scale line detection method proved to be worthy for improved sensitivity towards lower-caliber vessels detection. This happens largely due to its adaptive property that responds more to the longevity patterns than width of a given vessel. However, the method suffers from the lack of a better aggregation process for individual line detectors. This paper investigates a scenario that introduces a supervised Generalized Gaussian mixture (GGM) classifier as a robust solution for the aggregate process. The classifier is built with class-conditional probability density functions as a logistic function of linear mixtures. To boost the classifier's performance, the weighted scale images are modeled as Gaussian mixtures. The classifier is trained with weighted images modeled on a Gaussian Mixture. The net effect is increased sensitivity for small vessels. The classifier's performance has been tested with three commonly available datasets: DRIVE, SATRE, and CHASE DB1. The results of the proposed method (with an accuracy of 96%,96.1% and 95% on DRIVE, STARE, and CHASE DB1, respectively

DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

“…Consequently, Computer-aided diagnostic (CAD) systems have taken a deep root in eye diagnosis owing to their fast processing and ability to scan through large datasets of fundus images [5][6][7]. These computerized techniques start by employing segmentation strategies to extract patterns of blood vessels [8,9]. That is followed by the use of automated classifiers to evaluate and analyze the extracted vessels for detection of variations in the characteristics of blood vessels [10].…”