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Application of K- and Fuzzy c-Means for Color Segmentation of Thermal Infrared Breast Images

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Abstract

Color segmentation of infrared thermal images is an important factor in detecting the tumor region. The cancerous tissue with angiogenesis and inflammation emits temperature pattern different from the healthy one. In this paper, two color segmentation techniques, K-means and fuzzy c-means for color segmentation of infrared (IR) breast images are modeled and compared. Using the K-means algorithm in Matlab, some empty clusters may appear in the results. Fuzzy c-means is preferred because the fuzzy nature of IR breast images helps the fuzzy c-means segmentation to provide more accurate results with no empty cluster. Since breasts with malignant tumors have higher temperature than healthy breasts and even breasts with benign tumors, in this study, we look for detecting the hottest regions of abnormal breasts which are the suspected regions. The effect of IR camera sensitivity on the number of clusters in segmentation is also investigated. When the camera is ultra sensitive the number of clusters being considered may be increased.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Isfahan University of Technology, Isfahan, Iran

    M. EtehadTavakol & S. Sadri

  2. School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 50, Nanyang Avenue, Singapore, 639798, Singapore

    E. Y. K. Ng

  3. Adjunct NUH Scientist, Office of Biomedical Research, National University Hospital of Singapore, Singapore, 119074, Singapore

    E. Y. K. Ng

Authors
  1. M. EtehadTavakol
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  2. S. Sadri
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  3. E. Y. K. Ng
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Correspondence to E. Y. K. Ng.

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EtehadTavakol, M., Sadri, S. & Ng, E.Y.K. Application of K- and Fuzzy c-Means for Color Segmentation of Thermal Infrared Breast Images. J Med Syst 34, 35–42 (2010). https://doi.org/10.1007/s10916-008-9213-1

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  • DOI: https://doi.org/10.1007/s10916-008-9213-1

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