A new detection algorithm for microcalcification clusters in mammographic screening

Weiying Xie; Yide Ma; Yunsong Li

doi:10.1117/12.2180765

21 May 2015 A new detection algorithm for microcalcification clusters in mammographic screening

Weiying Xie, Yide Ma, Yunsong Li

Proceedings Volume 9501, Satellite Data Compression, Communications, and Processing XI; 95010S (2015) https://doi.org/10.1117/12.2180765
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

A novel approach for microcalcification clusters detection is proposed. At the first time, we make a short analysis of mammographic images with microcalcification lesions to confirm these lesions have much greater gray values than normal regions. After summarizing the specific feature of microcalcification clusters in mammographic screening, we make more focus on preprocessing step including eliminating the background, image enhancement and eliminating the pectoral muscle. In detail, Chan-Vese Model is used for eliminating background. Then, we do the application of combining morphology method and edge detection method. After the AND operation and Sobel filter, we use Hough Transform, it can be seen that the result have outperformed for eliminating the pectoral muscle which is approximately the gray of microcalcification. Additionally, the enhancement step is achieved by morphology. We make effort on mammographic image preprocessing to achieve lower computational complexity. As well known, it is difficult to robustly achieve mammograms analysis due to low contrast between normal and lesion tissues, there are also much noise in such images. After a serious preprocessing algorithm, a method based on blob detection is performed to microcalcification clusters according their specific features. The proposed algorithm has employed Laplace operator to improve Difference of Gaussians (DoG) function in terms of low contrast images. A preliminary evaluation of the proposed method performs on a known public database namely MIAS, rather than synthetic images. The comparison experiments and Cohen’s kappa coefficients all demonstrate that our proposed approach can potentially obtain better microcalcification clusters detection results in terms of accuracy, sensitivity and specificity.

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Weiying Xie, Yide Ma, and Yunsong Li "A new detection algorithm for microcalcification clusters in mammographic screening", Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010S (21 May 2015); https://doi.org/10.1117/12.2180765

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KEYWORDS

Image segmentation

Image processing

Hough transforms

Detection and tracking algorithms

Image analysis

Mammography

Blob detection

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