Automated Detection of Cavernous Malformations in Brain MRI Images

Huiquan Wang1, Hongming Xu, Nizam Ahmed1, Mrinal Mandal

  • 1University of Alberta

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Contributed Papers

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11:30 - 13:30 | Fri 26 May | Emerald III, Rose, Narcissus & Jasmine | FrPS1T1

Poster I

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Abstract

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.

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