Calcification descriptor and relevance feedback learning algorithms for content-based mammogram retrieval

Chia Hung Wei, Chang Tsun Li

    Research output: Book chapter/Published conference paperChapter

    9 Citations (Scopus)

    Abstract

    In recent years a large number of digital mammograms have been generated in hospitals and breast screening centers. To assist diagnosis through indexing those mammogram databases, we proposed a content-based image retrieval framework along with a novel feature extraction technique for describing the degree of calcification phenomenon revealed in the mammograms and six relevance feedback learning algorithms, which fall in the category of query point movement, for improving system performance. The results show that the proposed system can reach a precision rate of 0.716 after five rounds of relevance feedback have been performed.

    Original languageEnglish
    Title of host publicationDigital Mammography - 8th International Workshop, IWDM 2006, Proceedings
    PublisherSpringer-Verlag London Ltd.
    Pages307-314
    Number of pages8
    Volume4046 LNCS
    ISBN (Print)3540356258, 9783540356257
    Publication statusPublished - 2006
    Event8th International Workshop on Digital Mammography, IWDM 2006 - Manchester, United Kingdom
    Duration: 18 Jun 200621 Jun 2006

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume4046 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference8th International Workshop on Digital Mammography, IWDM 2006
    Country/TerritoryUnited Kingdom
    CityManchester
    Period18/06/0621/06/06

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