Accurate vessel segmentation with progressive contrast enhancement and canny refinement

Abstract

Vessel segmentation is a key step for various medical applications, such as diagnosis assistance, quantification of vascular pathology, and treatment planning. This paper describes an automatic vesselsegmentation framework which can achieve highly accurate segmentationeven in regions of low contrast and signal-to-noise-ratios (SNRs) and atvessel boundaries with disturbance induced by adjacent non-vessel pixels.There are two key contributions of our framework. The first is a progressive contrast enhancement method which adaptively improves contrast ofchallenging pixels that were otherwise indistinguishable, and suppressesnoises by weighting pixels according to their likelihood to be vessel pixels. The second contribution is a method called canny refinement whichis based on a canny edge detection algorithm to effectively re-move falsepositives around boundaries of vessels. Experimental results on a publicretinal dataset and our clinical cerebral data demonstrate that our approach outperforms state-of-the-art methods including the vesselness basedmethod [1] and the optimally oriented flux (OOF) based method [2].