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Pectoral Muscle and Breast Density Segmentation Using Modified Region Growing and K-Means Clustering Algorithm

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Data Communication and Networks

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1049))

Abstract

Breast Cancer is the most habitually detected neoplasm amid women in India and it is one of the principal reasons for cancer decreases in females. In order to visualize the breast cancer, radiologists prefer to use mammogram. It consists of many artifacts, which negatively influences the detection of breast cancer. Presence of pectoral muscles makes abnormality detection a cumbersome task. The recognition of glandular tissue in mammograms is imperative in evaluating asymmetry between left and right breasts and in guesstimating the radiation risk connected with screening. Thus, the proposed technique focuses on breast part extraction, muscle part removal, enhancement of mammogram, and segmentation of mammogram images into regions conforming to different densities. The anticipated method has been verified on Mini-MIAS database mammogram images with ground truth offered by expert radiologists. The results show that the proposed technique is efficient in removing pectoral muscles and segmenting different mammographic densities.

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Authors and Affiliations

  1. EECE Department, The Northcap University, Gurugram, India

    Jyoti Dabass

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  1. Jyoti Dabass
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Correspondence to Jyoti Dabass .

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Editors and Affiliations

  1. Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW, Australia

    Lakhmi C. Jain

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  3. Department of Automation and Applied Informatics, “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina E. Balas

  4. Department of Computer Engineering and Applications, Institute of Engineering and Technology, GLA University, Mathura, India

    Dilip Kumar Sharma

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Dabass, J. (2020). Pectoral Muscle and Breast Density Segmentation Using Modified Region Growing and K-Means Clustering Algorithm. In: Jain, L., Tsihrintzis, G., Balas, V., Sharma, D. (eds) Data Communication and Networks. Advances in Intelligent Systems and Computing, vol 1049. Springer, Singapore. https://doi.org/10.1007/978-981-15-0132-6_24

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  • DOI: https://doi.org/10.1007/978-981-15-0132-6_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0131-9

  • Online ISBN: 978-981-15-0132-6

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