Medical image classification by supervised machine learning

Authors:
Pei-Cheng Cheng;Been-Chian Chien;Wei-Pang Yang
Affiliations:
Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, R.O.C. and Department of Information Management, Ching Yun University, Taiwan, R.O.C;Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan, R.O.C.;Department of Information Management, National Dong Hwa University, Hualien, Taiwan, R.O.C.
Venue:
AIC'06 Proceedings of the 6th WSEAS International Conference on Applied Informatics and Communications
Year:
2006

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Abstract

In this paper, Support Vector Machine (SVM) was used to learn image feature characteristics for image classification. Several image visual features describe the shape, edge, and texture of image (including histogram, spatial layout, coherence moment and gabor features) have been employed in this paper to categorize the 500 test images into 46 classes. The result shows that the spatial relationship of pixels is a very important feature in medical image data, because medical image data always have similar anatomic regions (lung, liver, head, and so on).