Development of a cerebral aneurysm segmentation method to prevent sentinel hemorrhage
Yousra Regaya, Abbes Amira, Sarada Prasad Dakua
Abstract
Abstract Image segmentation being the first step is always crucial for brain aneurysm treatment planning; it is also crucial during the procedure. A robust brain aneurysm segmentation has the potential to prevent the blood leakage, also known as sentinel hemorrhage. Here, we present a method combining a multiresolution and a statistical approach in two dimensional domain to segment cerebral aneurysm in which the Contourlet transform (CT) extracts the image features, while the Hidden Markov Random Field with Expectation Maximization (HMRF-EM) segments the image, based on the spatial contextual constraints. The proposed algorithm is tested on Three-Dimensional Rotational Angiography (3DRA) datasets; the average values of segmentation accuracy, DSC, FPR, FNR, specificity, and sensitivity, are found to be 99.72%, 93.52%, 0.07%, 5.23%, 94.77%, and 99.96%, respectively.