Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM
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 d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/12/7/135 |
| _version_ | 1828475949637697536 |
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| author | Sheng Cai Pei-Zhong Liu Yan-Min Luo Yong-Zhao Du Jia-Neng Tang |
| author_facet | Sheng Cai Pei-Zhong Liu Yan-Min Luo Yong-Zhao Du Jia-Neng Tang |
| author_sort | Sheng Cai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T06:21:26Z |
| format | Article |
| id | doaj.art-bdb21a945c854c0c9d2036a17c052bd8 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-11T06:21:26Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-bdb21a945c854c0c9d2036a17c052bd82022-12-22T01:17:49ZengMDPI AGAlgorithms1999-48932019-06-0112713510.3390/a12070135a12070135Breast Microcalcification Detection Algorithm Based on Contourlet and ASVMSheng Cai0Pei-Zhong Liu1Yan-Min Luo2Yong-Zhao Du3Jia-Neng Tang4College of Engineering, Huaqiao University, Quanzhou 362021, ChinaCollege of Engineering, Huaqiao University, Quanzhou 362021, ChinaCollege of Computer Science and Technology, Huaqiao University, Xiamen 361021, ChinaCollege of Engineering, Huaqiao University, Quanzhou 362021, ChinaCollege of Engineering, Huaqiao University, Quanzhou 362021, ChinaMicrocalcification 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.https://www.mdpi.com/1999-4893/12/7/135computer-aided diagnosismammographyContourletadaptive support vector machineclassifier |
| spellingShingle | Sheng Cai Pei-Zhong Liu Yan-Min Luo Yong-Zhao Du Jia-Neng Tang Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM Algorithms computer-aided diagnosis mammography Contourlet adaptive support vector machine classifier |
| title | Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM |
| title_full | Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM |
| title_fullStr | Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM |
| title_full_unstemmed | Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM |
| title_short | Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM |
| title_sort | breast microcalcification detection algorithm based on contourlet and asvm |
| topic | computer-aided diagnosis mammography Contourlet adaptive support vector machine classifier |
| url | https://www.mdpi.com/1999-4893/12/7/135 |
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