Automated detection of cavernous malformations in brain MRI images

Cavernous malformation or cavernoma is a kind of brain vessel abnormality that can cause serious symptoms such as seizures, intracerebral hemorrhage and various neurological deficits. It is one of the most common epileptogenic lesions that can be identified by physicians based on magnetic resonance imaging (MRI) of the brain. However, visual detection of cavernomas in a large set of brain MRI slices is a time-consuming task. This paper proposes a computer aided cavernomas detection method based on T2-weighted MRI analysis. The proposed method includes the following steps: template matching to find suspected cavernoma regions and classification based on support vector machines (SVMs) to remove most of the false positives. The performance of the proposed technique is evaluated and a sensitivity of 0.96 is obtained after testing.