Detection of Intracranial Aneurysm in Angiographic Images Using Fuzzy Approaches

The detection of cerebral aneurysms is of a paramount importance in the prevention of intracranial sub-arachnoid hemorrhage. We propose in this paper, a complete detection scheme, consisting of two phases, to detect blobs and aneurysms in cerebral 2D-DSA images. The first classification phase extracts cerebral vasculature by means of the fusion of multiple classifiers. The second detection phase involves detecting the aneurysms in the vascular tree from the segmented images using fuzzy Mathematical Morphology. The idea is to model the imprecision on the size and the shape of aneurysms using fuzzy logic. The experimental results on our test images, demonstrate the usefulness of the proposed method, which induces to a high sensitivity with a low number of false positives, and compares favorably to existing detection approaches.