Abstract
Brain computed tomography (CT) image based computer-aided 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.
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Liu, W. et al. (2007). Semantic Feature Extraction for Brain CT Image Clustering Using Nonnegative Matrix Factorization . In: Zhang, D. (eds) Medical Biometrics. ICMB 2008. Lecture Notes in Computer Science, vol 4901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77413-6_6
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DOI: https://doi.org/10.1007/978-3-540-77413-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77410-5
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