Abstract
A new approach for chest CT image retrieval is presented. The proposed algorithm is based on a combination of low-level visual features and high-level semantic information. According to the new algorithm, wavelet coefficients of the image are computed first using a wavelet transform as texture feature vectors. The zernike moment is then used as an effective descriptor of global shape of chest CT images in database, and the semantic information is extracted to improve the accuracy of retrieval. Finally, index vectors are constructed by the combination of texture, shape and semantic information, and the technique of relevance feedback is used in the algorithm to enhance the effectiveness of retrieval. The retrieval results obtained by application of our new method demonstrate an improvement in effectiveness compared to other kinds of retrieval techniques.
Sponsored by the Science and Technology Foundation of Hangzhou Normal University (2009XJ065).
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Wang, Ld., Shou, Zx. (2009). A New Approach for Chest CT Image Retrieval. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_59
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DOI: https://doi.org/10.1007/978-3-642-05253-8_59
Publisher Name: Springer, Berlin, Heidelberg
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