Computer Aided Detection of Cavernous Malformation in T2-weighted Brain MR Images

Cavernous malformation or cavernomas is abnormal development of brain blood vessels and affect an estimated 0.5% of the world population. These could cause seizures, intracerebral hemorrhage and various neurological deficits based on the location of the lesion. Radiologists usually analysis brain magnetic resonance (MR) images to detect cavernomas. However, automatic detection of cavernomas by computer has not been investigated enough. This paper proposes a computer aided cavernomas detection method based on MR images analysis. The proposed method includes three steps: brain extraction based on deformable contour (to remove the non-brain tissues from image), template matching (to find suspected cavernomas regions) and post-processing (to get rid of false positives based on size, shape and brightness information). The performance of the proposed technique is evaluated and a sensitivity of 0.92 is obtained after testing.