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Mammogram Retrieval: Image Selection Strategy of Relevance Feedback for Locating Similar Lesions
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Author(s): Chee-Chiang Chen (Tungs’ Taichung MetroHarbor Hospital, Taiwan), Pai-Jung Huang (Tungs’ Taichung MetroHarbor Hospital, Chi Mei Foundation Hospital, Taipei Medical University Hospital, & Taipei Medical University, Taiwan), Chih-Ying Gwo (Ching Yun University, Taiwan), Yue Li (Nankai University, China)and Chia-Hung Wei (Ching Yun University, Taiwan)
Copyright: 2013
Pages: 9
Source title:
Modern Library Technologies for Data Storage, Retrieval, and Use
Source Author(s)/Editor(s): Chia-Hung Wei (Ching Yun University, Taiwan)
DOI: 10.4018/978-1-4666-2928-8.ch004
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Abstract
Content-based image retrieval (CBIR) has been proposed by the medical community for inclusion in picture archiving and communication systems (PACS). In CBIR, relevance feedback is developed for bridging the semantic gap and improving the effectiveness of image retrieval systems. With relevance feedback, CBIR systems can return refined search results using a learning algorithm and selection strategy. In this study, as the retrieving process proceeds further, the proposed learning algorithm can reduce the influence of the original query point and increase the significance of the centroid of the clusters comprising the features of those relevant images identified in the most recent round of search. The proposed selection strategy is used to find a good starting point and select a set of images at each round to show that search result and ask for the user’s feedback. In addition, a benchmark is proposed to measure the learning ability to explain the retrieval performance as relevance feedback is incorporated in CBIR systems. The performance evaluation shows that the average precision rate of the proposed scheme was 0.98 and the learning ability reach to 7.17 through the five rounds of relevance feedback.
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