Semantic feature extraction for brain CT image clustering using nonnegative matrix factorization

Authors:
Weixiang Liu;Fei Peng;Shu Feng;Jiangsheng You;Ziqiang Chen;Jian Wu;Kehong Yuan;Datian Ye
Affiliations:
Research Center of Biomedical Engineering, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;Research Center of Biomedical Engineering, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;Research Center of Biomedical Engineering, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;mTools Ltd., Guangzhou, China;mTools Ltd., Guangzhou, China;Research Center of Biomedical Engineering, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;Research Center of Biomedical Engineering, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;Research Center of Biomedical Engineering, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China
Venue:
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Year:
2008

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Abstract

Brain computed tomography (CT) image based computeraided diagnosis (CAD) system is helpful for clinical diagnosis and treatment. However it is challenging to extract significant features for analysis because CT images come from different people and CT operator. In this study, we apply nonnegative matrix factorization to extract both appearance and histogram based semantic features of images for clustering analysis as test. Our experimental results on normal and tumor CT images demonstrate that NMF can discover local features for both visual content and histogram based semantics, and the clustering results show that the semantic image features are superior to low level visual features.