A method towards cerebral aneurysm detection in clinical settings

Abstract

Cerebral aneurysms are among most prevalent and devastating cerebrovascular diseases of adult population worldwide. The resulting sequelae of untimely/inadequate therapeutic intervention include sub-arachnoid hemorrhage. Geometric modeling of aneurysm being the first step in the treatment planning, the scientists therefore focus more on segmentation of aneurysm rather than its detection. A successful aneurysm detection among the bunch of vessels would certainly facilitate and ease the segmentation process. In this work, we present a novel method for aneurysm detection; the key contributions are: contrast enhancement of input image using stochastic resonance concept in wavelet domain, adaptive thresholding, and modified Hough Circle Transform. Experimental results show that the proposed method is efficient in detecting the location and type of aneurysm.