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

Chin Ann Yang, Mostafa Kaveh, Bradley Erickson

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2012
Subtitle of host publicationComputer-Aided Diagnosis
StatePublished - Dec 1 2012
EventMedical Imaging 2012: Computer-Aided Diagnosis - San Diego, CA, United States
Duration: Feb 7 2012Feb 9 2012

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2012: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego, CA


  • Computer-aided diagnosis
  • Epilepsy
  • Focal cortical dysplasia
  • Histogram
  • MRI

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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