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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Content-based medical image retrieval using dynamically optimized regional features


    Contributors:
    Wei Xiong, (author) / Bo Qiu, (author) / Qi Tian, (author) / Changsheng Xu, (author) / Sim Heng Ong, (author) / Foong, K. (author)


    Publication date :

    2005-01-01


    Size :

    202230 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Content-Based Medical Image Retrieval using Dynamically Optimized Regional Features

    Xiong, W. / Qiu, B. / Tian, Q. et al. | British Library Conference Proceedings | 2005


    Statistical Shape Features for Content-Based Image Retrieval

    Brandt, S. / Laaksonen, J. / Oja, E. | British Library Online Contents | 2002


    Content-Based Image Retrieval from Large Medical Databases

    Kak, A. / Pavlopoulou, C. | British Library Conference Proceedings | 2002


    Retrieval based on image content using DC-image

    Wang, Qinghai / Mo, Yu L. | SPIE | 2001


    Using Human Perceptual Categories for Content-Based Retrieval from a Medical Image Database

    Shyu, C.-R. / Pavlopoulou, C. / Kak, A. C. et al. | British Library Online Contents | 2002