Improving breast mass segmentation in local dense background: An entropy based optimization of statistical region merging method

Shelda Sajeev, Mariusz Bajger, Gobert Lee

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

2 Citations (Scopus)

Abstract

In this paper, an optimization algorithm, utilizing a component measure of entropy, is developed for automatically tuning segmentation of mammograms by the Statistical Region Merging technique. The aim of this paper is to improve the mass segmentation in dense backgrounds. The proposed algorithm is tested on a database of 89 mammograms of which 41 have masses localized in dense background and 48 have masses in non-dense background. The algorithm performance is evaluated in conjunction with six standard enhancement techniques: Adjustable Histogram Equalization, Unsharp Masking, Neutrosophy based enhancement, standard CLAHE, Adaptive Clip Limit CLAHE based on standard deviation and Adaptive Clip Limit CLAHE based on standard entropy measure. For a comparison study, same experiments are performed using Fuzzy C-means Clustering technique. The experimental results show that the automatic tuning of SRM segmentation has the potential to produce an accurate segmentation of masses located in dense background while not compromising the performance on masses located in non-dense background.

Original languageEnglish
Title of host publicationBreast Imaging - 13th International Workshop, IWDM 2016, Proceedings
EditorsKristina Lang, Anders Tingberg, Pontus Timberg
PublisherSpringer Verlag
Pages635-642
Number of pages8
ISBN (Print)9783319415451
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event13th International Workshop on Breast Imaging, IWDM 2016 - Malmo, Sweden
Duration: 19 Jun 201622 Jun 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9699
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Breast Imaging, IWDM 2016
CountrySweden
CityMalmo
Period19/06/1622/06/16

Keywords

  • Dense background
  • Enhancement
  • Entropy
  • Mammography
  • Segmentation
  • Statistical region merging

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  • Cite this

    Sajeev, S., Bajger, M., & Lee, G. (2016). Improving breast mass segmentation in local dense background: An entropy based optimization of statistical region merging method. In K. Lang, A. Tingberg, & P. Timberg (Eds.), Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings (pp. 635-642). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9699). Springer Verlag. https://doi.org/10.1007/978-3-319-41546-8_79