Cluster-based differential features to improve detection accuracy of focal cortical dysplasia

Chin-Ann Yang; Mostafa Kaveh; Bradley Erickson

doi:10.1117/12.905313

23 February 2012 Cluster-based differential features to improve detection accuracy of focal cortical dysplasia

Chin-Ann Yang, Mostafa Kaveh, Bradley Erickson

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83151G (2012) https://doi.org/10.1117/12.905313
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

In this paper, a computer aided diagnosis (CAD) system for automatic detection of focal cortical dysplasia (FCD) on T1-weighted MRI is proposed. We introduce a new set of differential cluster-wise features comparing local differences of the candidate lesional area with its surroundings and other GM/WM boundaries. The local differences are measured in a distributional sense using χ² distances. Finally, a Support Vector Machine (SVM) classifier is used to classify the clusters. Experimental results show an 88% lesion detection rate with only 1.67 false positive clusters per subject. Also, the results show that using additional differential features clearly outperforms the result using only absolute features.

Citation Download Citation

Chin-Ann Yang, Mostafa Kaveh, and Bradley Erickson "Cluster-based differential features to improve detection accuracy of focal cortical dysplasia", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83151G (23 February 2012); https://doi.org/10.1117/12.905313

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