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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Liu, W., Peng, F., Feng, S., You, J., Chen, Z., Wu, J., … Ye, D. (2008). Semantic feature extraction for brain CT image clustering using nonnegative matrix factorization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4901 LNCS, pp. 41–48). https://doi.org/10.1007/978-3-540-77413-6_6
Mendeley helps you to discover research relevant for your work.