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Asymmetry analysis of breast thermograms using automated segmentation and texture features

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Abstract

In this article, we present a new approach for breast thermogram image analysis by developing a fully automatic segmentation of right and left breast for asymmetry analysis, using shape features of the breast and Polynomial curve fitting. Segmentation results are validated with their respective Ground Truths. Histogram and grey level co-occurrence matrix-based texture features are extracted from the segmented images. Statistical test shows that features are highly significant in detection of breast cancer. We have obtained an accuracy of 90%, sensitivity of 87.5% and specificity of 92.5% for a set of eighty images with forty normal and forty abnormal using SVM RBF classifier.

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Authors and Affiliations

  1. ICE Department, MIT Manipal, Manipal University, Manipal, Karnataka, 576104, India

    Dayakshini Sathish & Surekha Kamath

  2. SOIS Manipal, Manipal University, Manipal, Karnataka, 576104, India

    Keerthana Prasad

  3. KMC Manipal, Manipal University, Manipal, Karnataka, 576104, India

    Rajagopal Kadavigere

  4. VCET Puttur, Puttur, Karnataka, 574203, India

    Roshan J. Martis

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  1. Dayakshini Sathish
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  2. Surekha Kamath
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  4. Rajagopal Kadavigere
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  5. Roshan J. Martis
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Correspondence to Dayakshini Sathish.

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Sathish, D., Kamath, S., Prasad, K. et al. Asymmetry analysis of breast thermograms using automated segmentation and texture features. SIViP 11, 745–752 (2017). https://doi.org/10.1007/s11760-016-1018-y

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  • DOI: https://doi.org/10.1007/s11760-016-1018-y

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