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 casImage of about 9000 images.
['Wei Xiong', 'Bo Qiu', 'Qi Tian', 'Changsheng Xu', 'Sim Heng Ong', 'Kelvin Weng Chiong Foong']
Content-based medical image retrieval using dynamically optimized regional features