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10 July 2009 A semantic image retrieval approach between visual features and medical concepts
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Proceedings Volume 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering; 74890G (2009) https://doi.org/10.1117/12.836399
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jin Li and Hong Liang "A semantic image retrieval approach between visual features and medical concepts", Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890G (10 July 2009); https://doi.org/10.1117/12.836399
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