A PCA-based approach for brain aneurysm segmentation

Abstract

Segmentation of brain aneurysm is of paramount importance in aneurysm treatment planning. However, the segmentation of intensity varying and low-contrast cerebral blood vessels is an extremely challenging task. In this paper, we present an approach to segmenting the brain vasculature in low contrast computed tomography angiography and magnetic resonance angiography. The main contributions are: (1) a stochastic resonance based methodology in discrete Hartley transform domain is developed to enhance the contrast of a selected angiographic image for patch placement, and (2) a multi-scale adaptive principal component analysis based method is proposed that estimates the phase map of input images. Level-set method is applied to the phase-map data in order to segment the vasculature. We have tested the algorithm on real datasets obtained from two sources, CIBC institute and Hamad Medical corporation. The average Dice coefficient (in %) is found to be \(94.1\pm 1.2\) (value indicates the mean and standard deviation) whereas average false positive ratio, false negative ratio, and specificity are found to be 0.019, \(7.55\times 10^{-3}\), and 0.75, respectively. Average Hausdorff distance between segmented contour and ground truth is determined to be 2.97 mm. The qualitative and quantitative results show promising segmentation accuracy reflecting the potential of the proposed method.