Breast Density Classification Based on Wavelet Transform

Cai, Sheng; Tang, Jianeng; Liu, Peizhong; Cai, Yali; Luo, Yanmin; Du, Yongzhao; Yan, Peng; Li, Ping

doi:10.1166/jmihi.2018.2430

Cited by 1 publication

(2 citation statements)

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“…Algorithms 2 shows that, LBP feature algorithm steps. Formulas (6) and (7) are complementary to Algorithm 2, which is more intuitive to understand.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Computer diagnostics can save doctors' diagnostic time and increase the accuracy of diagnostic results. Statistics show that with the advent of computer-aided diagnosis systems, the detection rate of breast cancer increased by 20% [6]. Between anatomic pathology and imaging diagnosis, R Bonfiglio [7] offers a significant new way to improve the analysis of microcalcification for breast cancer.…”

Section: Introductionmentioning

confidence: 99%

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Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

et al. 2019

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Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.

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“…Algorithms 2 shows that, LBP feature algorithm steps. Formulas (6) and (7) are complementary to Algorithm 2, which is more intuitive to understand.…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%