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Title: Liver tumor detection and classification using content-based image retrieval
Authors: Chi, Y.
Liu, J.
Venkatesh, S.K. 
Zhou, J.
Tian, Q.
Nowinski, W.L.
Keywords: computer-aided diagnosis
content-based image retrieval
focal liver mass detection and classification
hyper-cube structure indexing
multiphase CT scans
multiple-phase encoded texture feature
Issue Date: 2011
Citation: Chi, Y., Liu, J., Venkatesh, S.K., Zhou, J., Tian, Q., Nowinski, W.L. (2011). Liver tumor detection and classification using content-based image retrieval. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 7963 : -. ScholarBank@NUS Repository.
Abstract: Computer aided liver tumor detection and diagnosis can assist radiologists to interpret abnormal features in liver CT scans. In this paper, a general frame work is proposed to automatically detect liver focal mass lesions, conduct differential diagnosis of liver focal mass lesions based on multiphase CT scans, and provide visually similar case samples for comparisons. The proposed method first detects liver abnormalities by eliminating the normal tissue/organ from the liver region, and in the second step it ranks these abnormalities with respect to spherical symmetry, compactness and size using a tumoroid measure to facilitate fast location of liver focal mass lesions. To differentiate liver focal mass lesions, content-based image retrieval technique is used to query a CT model database with known diagnosis. Multiple-phase encoded texture features are proposed to represent the focal mass lesions. A hypercube indexing structure based method is adopted as the retrieval strategy and the similarity score is calculated to rank the retrieval results. Good performances are obtained from eight clinical CT scans. With the proposed method, the clinician is expected to improve the accuracy of differential diagnosis. © 2011 SPIE.
Source Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
ISBN: 9780819485052
ISSN: 16057422
DOI: 10.1117/12.877919
Appears in Collections:Staff Publications

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