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Hierarchical Correlation of Multi-Scale Spatial Pyramid for Similar Mammogram Retrieval
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Author(s): Jinn-Ming Chang (Chi Mei Foundation Hospital, Tainan, 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: 10
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.ch003
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Abstract
In hospitals and medical institutes, a large number of mammograms are produced in ever increasing quantities and used for diagnostics and therapy. The need for effective methods to manage and retrieve those image resources has been actively pursued in the medical community. This paper proposes a hierarchical correlation calculation approach to content-based mammogram retrieval. In this approach, images are represented as a Gaussian pyramid with several reduced-resolution levels. A global search is first conducted to identify the optimal matching position, where the correlation between the query image and the target images in the database is maximal. Local search is performed in the region comprising the four child pixels at a higher resolution level to locate the position with maximal correlation at greater resolution. Finally, this position with the maximal correlation found at the finest resolution level is used as the image similarity measure for retrieving images. Experimental results have shown that this approach achieves 59% in precision and 54% in recall when the threshold of correlation is0.5.
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