Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation

TL;DR

This paper introduces a scaled U-net architecture with morphological preprocessing for improved retinal vessel segmentation, achieving high accuracy and robustness against common image artifacts.

Contribution

It presents a novel scaled U-net model combined with morphological preprocessing, requiring fewer parameters but delivering superior segmentation performance.

Findings

Achieved area under ROC curve >0.9762

Classification accuracy >95.47%

Resistant to central vessel reflex

Abstract

Extracting blood vessels from retinal fundus images plays a decisive role in diagnosing the progression in pertinent diseases. In medical image analysis, vessel extraction is a semantic binary segmentation problem, where blood vasculature needs to be extracted from the background. Here, we present an image enhancement technique based on the morphological preprocessing coupled with a scaled U-net architecture. Despite a relatively less number of trainable network parameters, the scaled version of U-net architecture provides better performance compare to other methods in the domain. We validated the proposed method on retinal fundus images from the DRIVE database. A significant improvement as compared to the other algorithms in the domain, in terms of the area under ROC curve (>0.9762) and classification accuracy (>95.47%) are evident from the results. Furthermore, the proposed method is resistant to the central vessel reflex while sensitive to detect blood vessels in the presence of background items viz. exudates, optic disc, and fovea.