Segmentation of Breast Masses in Local Dense Background using Adaptive Clip Limit-CLAHE

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    6 Citations (Scopus)

    Abstract

    Mass segmentation in mammograms is a challenging task if the mass is located in a local dense background. It can be due to the similarity of intensities between the overlapped normal dense breast tissue and mass. In this paper, a self-adjusted mammogram contrast enhancement solution called Adaptive Clip Limit CLAHE (ACL-CLAHE) is developed, aiming to improve mass segmentation in dense regions of mammograms. An optimization algorithm based on entropy is used to optimize the clip limit and window size of standard CLAHE. The proposed method is tested on 89 mammogram images with 41 masses localized in dense background and 48 masses in non-dense background. The results are compared with other standard enhancement techniques such as Adjustable Histogram Equalization, Unsharp Masking, Neutrosophy based enhancement, standard CLAHE and an Adaptive Clip Limit CLAHE based on standard deviation. The experimental results show that our method significantly improves the mass segmentation in local dense background without compromising the performance in local non-dense background.

    Original languageEnglish
    Title of host publication2015 International Conference on Digital Image Computing
    Subtitle of host publicationTechniques and Applications (DICTA)
    Place of PublicationPiscataway, NJ
    Pages15698859
    Number of pages8
    ISBN (Electronic)978-1-4673-6794-3
    DOIs
    Publication statusPublished - 2015
    EventInternational Conference on Digital Image Computing: Techniques and Applications, DICTA 2015 -
    Duration: 23 Nov 2015 → …

    Publication series

    Name2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015

    Conference

    ConferenceInternational Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
    Period23/11/15 → …

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