Detection of microcalcification clusters regions in mammograms combining discriminative deep belief networks_Journal of Biomedical Engineering

Authors：

 SONG Lixin ¹ , WEI Xueqin ¹ , WANG Qian ² , WANG Yujing ¹

1. School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, P.R.China;
2. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, P.R.China;

Corresponding author：

SONG Lixin, Email: lixinsong@hrbust.edu.cn

Keywords：

microcalcification clusters; discriminative deep belief networks; feature extraction; deep neural networks

DOI：

10.7507/1001-5515.202001034

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

In order to overcome the shortcomings of high false positive rate and poor generalization in the detection of microcalcification clusters regions, this paper proposes a method combining discriminative deep belief networks (DDBNs) to automatically and quickly locate the regions of microcalcification clusters in mammograms. Firstly, the breast region was extracted and enhanced, and the enhanced breast region was segmented to overlapped sub-blocks. Then the sub-block was subjected to wavelet filtering. After that, DDBNs model for breast sub-block feature extraction and classification was constructed, and the pre-trained DDBNs was converted to deep neural networks (DNN) using a softmax classifier, and the network is fine-tuned by back propagation. Finally, the undetected mammogram was inputted to complete the location of suspicious lesions. By experimentally verifying 105 mammograms with microcalcifications from the Digital Database for Screening Mammography (DDSM), the method obtained a true positive rate of 99.45% and a false positive rate of 1.89%, and it only took about 16 s to detect a 2 888 × 4 680 image. The experimental results showed that the algorithm of this paper effectively reduced the false positive rate while ensuring a high positive rate. The detection of calcification clusters was highly consistent with expert marks, which provides a new research idea for the automatic detection of microcalcification clusters area in mammograms.

Citation： SONG Lixin, WEI Xueqin, WANG Qian, WANG Yujing. Detection of microcalcification clusters regions in mammograms combining discriminative deep belief networks. Journal of Biomedical Engineering, 2021, 38(2): 268-275. doi: 10.7507/1001-5515.202001034 Copy

1.	Tsu V D, Jeronimo J, Anderson B O. Why the time is right to tackle breast and cervical cancer in low-resource settings. Bulletin of the World Health Organization, 2013, 91(9): 683-690.
2.	Saad G, Khadour A, Kanafani Q. ANN and Adaboost application for automatic detection of microcalcifications in breast cancer. Egypt J Radiol Nucl Med, 2016, 47(4): 1803-1814.
3.	Mabrouk M S, Afify H M, Marzouk S Y. Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques. Ain Shams Eng J, 2019, 10: 517-527.
4.	陈珊. 基于钼靶图像的乳腺肿瘤诊断若干关键性技术研究. 哈尔滨: 哈尔滨工业大学, 2018.
5.	王瑞平, 万柏坤, 高上凯. 独立分量分析在乳腺钼靶X片感兴趣区域提取中的应用. 中国生物医学工程学报, 2007, 26(4): 532-536.
6.	Zhang Y D, Wang S H, Liu G, et al. Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform. Adv Mech Eng, 2016, 8(2): 629-643.
7.	彭庆涛, 吴水才, 高宏建, 等. 基于小波分析和灰度纹理特征的乳腺X线图像微钙化点区域的提取. 北京生物医学工程, 2015, 34(5): 462-467.
8.	Wang K, Dong M, Yong Z, et al. Regions of micro-calcifications clusters detection based on new features from imbalance data in mammograms// Proceedings of the 8th International Conference on Graphic and Image Processing. Berlin: Springer, 2016, 272-282.
9.	Liu X, Mei M, Liu J, et al. Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method. EURASIP J Adv Signal Process, 2015(1): 73.
10.	Mordang J J, Janssen T, Bria A, et al. Automatic microcalcification detection in multi-vendor mammography using convolutional neural networks// International Workshop on Breast Imaging. Cham: Springer, 2016: 35-42.
11.	Schlegl T, Seeböck P, Waldstein S M, et al. Fast unsupervised anomaly detection with generative adversarial networks. Med Image Anal, 2019, 54: 30-44.
12.	Chen H, Dou Q, Wang X, et al. Mitosis detection in breast cancer histology images via deep cascaded networks// Thirtieth Aaai Conference on Artificial Intelligence. Phoenix: AAAI Press, 2016.
13.	Wang D, Khosla A, Gargeya R, et al. Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv: 2016, 1606.05718.
14.	Wang J, Yang Y. A context-sensitive deep learning approach for microcalcification detection in mammograms. Pattern Recognit, 2018, 78: 12-22.
15.	张新生, 王哲. 结合深度学习与特征多尺度融合的微钙化簇检测. 模式识别与人工智能, 2018, 31(11): 66-77.
16.	Kooi T, Litjens G, Van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal, 2017, 35: 303-312.
17.	Nagarajan V, Britto C E, Veeraputhiran M S. Feature extraction based on empirical mode decomposition for automatic mass classification of mammogram images. Medicine in Novel Technology and Devices, 2019, 1: 100004.
18.	Raghavendra U, Acharya U R, Fujita H, et al. Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images. Appl Soft Comput, 2016, 46: 151-161.
19.	朱丽丽. 基于非下采样剪切波变换和PCNN模型的乳腺钙化点检测. 兰州: 兰州大学, 2018.
20.	Saif D, El-Gokhy S M, Sallam E. Deep Belief Networks-based framework for malware detection in Android systems. Alex Eng J, 2018, 57: 4049-4057.
21.	Keyvanrad M A, Homayounpour M M. Deep belief network training improvement using elite samples minimizing free energy. Pattern Recognit Artif Intell, 2015, 29(5): 1-18.
22.	阮松, 陈松灿, 王敏. 采用多尺度多级组合分类器快速定位乳腺X片中的感兴趣区域. 中国生物医学工程学报, 2009, 28(5): 674-679.
23.	程运福, 张光玉, 崔栋, 等. 基于乳腺X线图像的微钙化点区域自动检测算法研究. 中国医学物理学杂志, 2013, 30(2): 3992-3996.
24.	Muthuvel M, Thangaraju B, Chinnasamy G. Microcalcification cluster detection using multiscale products based Hessian matrix via the Tsallis thresholding scheme. Pattern Recognit Lett, 2017, 94: 127-133.

1. Tsu V D, Jeronimo J, Anderson B O. Why the time is right to tackle breast and cervical cancer in low-resource settings. Bulletin of the World Health Organization, 2013, 91(9): 683-690.
2. Saad G, Khadour A, Kanafani Q. ANN and Adaboost application for automatic detection of microcalcifications in breast cancer. Egypt J Radiol Nucl Med, 2016, 47(4): 1803-1814.
3. Mabrouk M S, Afify H M, Marzouk S Y. Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques. Ain Shams Eng J, 2019, 10: 517-527.
4. 陈珊. 基于钼靶图像的乳腺肿瘤诊断若干关键性技术研究. 哈尔滨: 哈尔滨工业大学, 2018.
5. 王瑞平, 万柏坤, 高上凯. 独立分量分析在乳腺钼靶X片感兴趣区域提取中的应用. 中国生物医学工程学报, 2007, 26(4): 532-536.
6. Zhang Y D, Wang S H, Liu G, et al. Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform. Adv Mech Eng, 2016, 8(2): 629-643.
7. 彭庆涛, 吴水才, 高宏建, 等. 基于小波分析和灰度纹理特征的乳腺X线图像微钙化点区域的提取. 北京生物医学工程, 2015, 34(5): 462-467.
8. Wang K, Dong M, Yong Z, et al. Regions of micro-calcifications clusters detection based on new features from imbalance data in mammograms// Proceedings of the 8th International Conference on Graphic and Image Processing. Berlin: Springer, 2016, 272-282.
9. Liu X, Mei M, Liu J, et al. Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method. EURASIP J Adv Signal Process, 2015(1): 73.
10. Mordang J J, Janssen T, Bria A, et al. Automatic microcalcification detection in multi-vendor mammography using convolutional neural networks// International Workshop on Breast Imaging. Cham: Springer, 2016: 35-42.
11. Schlegl T, Seeböck P, Waldstein S M, et al. Fast unsupervised anomaly detection with generative adversarial networks. Med Image Anal, 2019, 54: 30-44.
12. Chen H, Dou Q, Wang X, et al. Mitosis detection in breast cancer histology images via deep cascaded networks// Thirtieth Aaai Conference on Artificial Intelligence. Phoenix: AAAI Press, 2016.
13. Wang D, Khosla A, Gargeya R, et al. Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv: 2016, 1606.05718.
14. Wang J, Yang Y. A context-sensitive deep learning approach for microcalcification detection in mammograms. Pattern Recognit, 2018, 78: 12-22.
15. 张新生, 王哲. 结合深度学习与特征多尺度融合的微钙化簇检测. 模式识别与人工智能, 2018, 31(11): 66-77.
16. Kooi T, Litjens G, Van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal, 2017, 35: 303-312.
17. Nagarajan V, Britto C E, Veeraputhiran M S. Feature extraction based on empirical mode decomposition for automatic mass classification of mammogram images. Medicine in Novel Technology and Devices, 2019, 1: 100004.
18. Raghavendra U, Acharya U R, Fujita H, et al. Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images. Appl Soft Comput, 2016, 46: 151-161.
19. 朱丽丽. 基于非下采样剪切波变换和PCNN模型的乳腺钙化点检测. 兰州: 兰州大学, 2018.
20. Saif D, El-Gokhy S M, Sallam E. Deep Belief Networks-based framework for malware detection in Android systems. Alex Eng J, 2018, 57: 4049-4057.
21. Keyvanrad M A, Homayounpour M M. Deep belief network training improvement using elite samples minimizing free energy. Pattern Recognit Artif Intell, 2015, 29(5): 1-18.
22. 阮松, 陈松灿, 王敏. 采用多尺度多级组合分类器快速定位乳腺X片中的感兴趣区域. 中国生物医学工程学报, 2009, 28(5): 674-679.
23. 程运福, 张光玉, 崔栋, 等. 基于乳腺X线图像的微钙化点区域自动检测算法研究. 中国医学物理学杂志, 2013, 30(2): 3992-3996.
24. Muthuvel M, Thangaraju B, Chinnasamy G. Microcalcification cluster detection using multiscale products based Hessian matrix via the Tsallis thresholding scheme. Pattern Recognit Lett, 2017, 94: 127-133.

Journal of Biomedical Engineering

Detection of microcalcification clusters regions in mammograms combining discriminative deep belief networks

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content