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An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms

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Abstract

The present study introduces an efficient algorithm for automatic segmentation and detection of mass present in the mammograms. The problem of over and under-segmentation of low-contrast mammographic images has been solved by applying preprocessing on original mammograms. Subtraction operation performed between enhanced and enhanced inverted mammogram significantly highlights the suspicious mass region in mammograms. The segmentation accuracy of suspicious region has been improved by combining wavelet transform and fast fuzzy c-means clustering algorithm. The accuracy of mass segmentation has been quantified by means of Jaccard coefficients. Better sensitivity, specificity, accuracy, and area under the curve (AUC) are observed with support vector machine using radial basis kernel function. The proposed algorithm is validated on Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets. Highest 91.76% sensitivity, 96.26% specificity, 95.46% accuracy, and 96.29% AUC on DDSM dataset and 94.63% sensitivity, 92.74% specificity, 92.02% accuracy, and 95.33% AUC on MIAS dataset are observed. Also, shape analysis of mass is performed by using moment invariant and Radon transform based features. The best results are obtained with Radon based features and achieved accuracies for round, oval, lobulated, and irregular shape of mass are 100%, 70%, 64%, and 96%, respectively.

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  1. Computer Science and Engineering, Indian Institute of Information Technology, Design & Manufacturing, Jabalpur, 482005, India

    Kanchan Lata Kashyap, Manish Kumar Bajpai & Pritee Khanna

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  1. Kanchan Lata Kashyap
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Correspondence to Kanchan Lata Kashyap.

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Kashyap, K.L., Bajpai, M.K. & Khanna, P. An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms. Multimed Tools Appl 77, 9249–9269 (2018). https://doi.org/10.1007/s11042-017-4751-5

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