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Proceedings Paper

A semantic image retrieval approach between visual features and medical concepts
Author(s): Jin Li; Hong Liang
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Paper Abstract

In the medical domain, digital images are produced in ever-increasing quantities, which offer great opportunities for diagnostics, therapy and training. So how to manage these data and utilize them effectively and efficiently possess significant technical challenges. Thus, the technique of Content-based Medical Image Retrieval (CBMIR) emerges as the times require. However, current CBMIR is not sufficient to capture the semantic content of images. Accordingly, in this paper, an innovative approach for medical image knowledge representation and retrieval is proposed by focusing on the mapping modeling between visual feature and semantic concept. Firstly, the low-level fusion visual features are extracted based on statistical features. Secondly, a set of disjoint semantic tokens with appearance in medical images is selected to define a Visual and Medical Vocabulary. Thirdly, to narrow down the semantic gap and increase the retrieval efficiency, we investigate support vector machine (SVM) to associate low-level visual image features with their highlevel semantic. Experiments are conducted with a medical image DB consisting of 300 diverse medical images obtained from the Hei Longjiang Province Hospital. And the comparison of the retrieval results shows that the approach proposed in this paper is effective.

Paper Details

Date Published: 11 July 2009
PDF: 8 pages
Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890G (11 July 2009); doi: 10.1117/12.836399
Show Author Affiliations
Jin Li, Harbin Engineering Univ. (China)
Hong Liang, Harbin Engineering Univ. (China)


Published in SPIE Proceedings Vol. 7489:
PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering

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