Positive unanimous voting algorithm for focal cortical dysplasia detection on magnetic resonance image

Focal cortical dysplasia (FCD) is the main cause of epilepsy and can be automatically detected via magnetic resonance (MR) images. However, visual detection of lesions is time consuming and highly dependent on the doctor's personal knowledge and experience. In this paper, we propose a new framework for positive unanimous voting (PUV) to detect FCD lesions. Maps of gray matter thickness, gradient, relative intensity, and gray/white matter width are computed in the proposed framework to enhance the differences between lesional and non-lesional regions. Feature maps are further compared with the feature distributions of healthy controls to obtain feature difference maps. PUV driven by feature and feature difference maps is then applied to classify image voxels into lesion and non-lesion. The connected region analysis then refines the classification results by removing the tiny fragment regions consisting of falsely classified positive voxels. The proposed method correctly identified 8/10 patients with FCD lesions and 30/31 healthy people. Experimental results on the small FCD samples demonstrated that the proposed method can effectively reduce the number of false positives and guarantee correct detection of lesion regions compared with four single classifiers and two recent methods.