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Proceedings Paper

Cluster-based differential features to improve detection accuracy of focal cortical dysplasia
Author(s): Chin-Ann Yang; Mostafa Kaveh; Bradley Erickson
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Paper 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.

Paper Details

Date Published: 23 February 2012
PDF: 6 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83151G (23 February 2012); doi: 10.1117/12.905313
Show Author Affiliations
Chin-Ann Yang, Univ. of Minnesota, Twin Cities (United States)
Mostafa Kaveh, Univ. of Minnesota, Twin Cities (United States)
Bradley Erickson, Mayo Clinic (United States)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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