Effective extraction of Gabor features for adaptive mammogram retrieval

Chia Hung Wei, Yue Li, Chang Tsun Li

    Research output: Book chapter/Published conference paperConference paperpeer-review

    24 Citations (Scopus)


    Breast cancer is one of the most common diseases among women. Content-based mammogram retrieval has been proposed to aid various medical procedures. To develop a content-based mammogram retrieval system, textural feature extraction is one of the crucial requirements. This study proposes a Gabor filtering method for the extraction of textural features, which firstly performs Gabor filtering on the underlying image, applies the physical properties of a probability wave to probability transformation and then computes features to describe the textural pattern of the mammogram. This study also proposes an adaptive strategy for feature selection, filter selection and feature weighting, which utilizes a user's relevance feedback to reduce the redundancy in the representation and incorporates the user's information needs in image retrieval. Experimental results show that hypothesis tests can effectively find discriminated features and this retrieval system can improve its performance through just a few rounds of relevance feedback.

    Original languageEnglish
    Title of host publicationProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
    Number of pages4
    Publication statusPublished - 2007
    EventIEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China
    Duration: 02 Jul 200705 Jul 2007


    ConferenceIEEE International Conference onMultimedia and Expo, ICME 2007


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