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

Shelda Sajeev, Mariusz Bajger, Gobert Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467367950
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
EventInternational Conference on Digital Image Computing: Techniques and Applications, DICTA 2015 - Adelaide, Australia
Duration: 23 Nov 201525 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
CountryAustralia
CityAdelaide
Period23/11/1525/11/15

Fingerprint Dive into the research topics of 'Segmentation of Breast Masses in Local Dense Background Using Adaptive Clip Limit-CLAHE'. Together they form a unique fingerprint.

  • Cite this

    Sajeev, S., Bajger, M., & Lee, G. (2015). Segmentation of Breast Masses in Local Dense Background Using Adaptive Clip Limit-CLAHE. In 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015 [7371305] (2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DICTA.2015.7371305