Dual-Space pyramid matching for medical image classification

Authors:
Yang Hu;Mingjing Li;Zhiwei Li;Wei-ying Ma
Affiliations:
University of Science and Technology of China, Hefei, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China
Venue:
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Year:
2007

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Abstract

With the increasing of medical images that are routinely acquired in clinical practice, automatic medical image classification has become an important research topic recently. In this paper, we propose an efficient medical image classification algorithm, which works by mapping local image patches to multi-resolution histograms built both in feature space and image space and then matching sets of features though weighted histogram intersection. The matching produces a kernel function that satisfies Mercer's condition, and a multi-class SVM classifier is then applied to classify the images. The dual-space pyramid matching scheme explores not only the distribution of local features in feature space but also their spatial layout in the images. Therefore, more accurate implicit correspondence is built between feature sets. We evaluate the proposed algorithm on the dataset for the automatic medical image annotation task of ImageCLEFmed 2005. It outperforms the best result of the campaign as well as the pyramid matchings that only perform in single space.