Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIP.2005.1530621
Title: Content-based medical image retrieval using dynamically optimized regional features
Authors: Xiong, W.
Qiu, B.
Tian, Q.
Xu, C.
Ong, S.H. 
Foong, K. 
Issue Date: 2005
Source: Xiong, W.,Qiu, B.,Tian, Q.,Xu, C.,Ong, S.H.,Foong, K. (2005). Content-based medical image retrieval using dynamically optimized regional features. Proceedings - International Conference on Image Processing, ICIP 3 : 1232-1235. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2005.1530621
Abstract: This paper proposes a content-based medical image retrieval (CBMIR) framework using dynamically optimized features from multiple regions of medical images. These regional features, including structural and statistical properties of color, texture and geometry, are extracted from multiple dominant regions segmented by applying Gaussian Mixture Modeling (GMM) and the Expectation Maximization (EM) algorithm to medical images. Over them, Principal Component Analysis (PCA) is utilized to construct query templates and to reduce feature dimensions for representative feature optimization. Applying this method to the tasks of the medical imageCLEF 2004 we achieve better retrieval performance (MAP 0.4535) over the existing work on caslmage of about 9000 images. © 2005 IEEE.
Source Title: Proceedings - International Conference on Image Processing, ICIP
URI: http://scholarbank.nus.edu.sg/handle/10635/69715
ISBN: 0780391349
ISSN: 15224880
DOI: 10.1109/ICIP.2005.1530621
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