@inproceedings{4b50433291d84bd0af6d8a929009efdd,
title = "Cluster-based differential features to improve detection accuracy of focal cortical dysplasia",
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.",
keywords = "Computer-aided diagnosis, Epilepsy, Focal cortical dysplasia, Histogram, MRI",
author = "Yang, {Chin Ann} and Mostafa Kaveh and Bradley Erickson",
year = "2012",
doi = "10.1117/12.905313",
language = "English (US)",
isbn = "9780819489647",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
booktitle = "Medical Imaging 2012",
note = "Medical Imaging 2012: Computer-Aided Diagnosis ; Conference date: 07-02-2012 Through 09-02-2012",
}