Breast Mass Detection in Mammography Based on Image Template Matching and CNN

In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.

[1]  Lianghai Jin,et al.  Using PSO to improve dynamic programming based algorithm for breast mass segmentation , 2010, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA).

[2]  S. Amutha,et al.  Mammographic image enhancement using modified mathematical morphology and Bi-orthogonal wavelet , 2011, 2011 IEEE International Symposium on IT in Medicine and Education.

[3]  Zheng Zhang,et al.  Generalized Incomplete Multiview Clustering With Flexible Locality Structure Diffusion , 2020, IEEE Transactions on Cybernetics.

[4]  Georgia D. Tourassi,et al.  A Concentric Morphology Model for the Detection of Masses in Mammography , 2007, IEEE Transactions on Medical Imaging.

[5]  Farrukh Nagi,et al.  Automated breast profile segmentation for ROI detection using digital mammograms , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[6]  Anselmo Cardoso de Paiva,et al.  Breast cancer detection in mammography using spatial diversity, geostatistics, and concave geometry , 2018, Multimedia Tools and Applications.

[7]  Thomas M. Cover,et al.  Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing) , 2006 .

[8]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[10]  Edwin Valarezo,et al.  Simultaneous Detection and Classification of Breast Masses in Digital Mammograms via a Deep Learning YOLO-based CAD System , 2018, Comput. Methods Programs Biomed..

[11]  Nan Wu,et al.  Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening , 2019, IEEE Transactions on Medical Imaging.

[12]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

[13]  G. Hemantha Kumar,et al.  Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector , 2021, SN Computer Science.

[14]  Yizhou Yu,et al.  Cross-View Correspondence Reasoning Based on Bipartite Graph Convolutional Network for Mammogram Mass Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Andrea Boni,et al.  A SVM-based approach to microwave breast cancer detection , 2006, Eng. Appl. Artif. Intell..

[16]  Robertas Damasevicius,et al.  A neuro-heuristic approach for recognition of lung diseases from X-ray images , 2019, Expert Syst. Appl..

[17]  Yifan Wu,et al.  Automatic Mass Detection from Mammograms with Region-Based Convolutional Neural Network , 2018, IGTA.

[18]  Yuning Jiang,et al.  FoveaBox: Beyound Anchor-Based Object Detection , 2019, IEEE Transactions on Image Processing.

[19]  Ying-Che Kuo,et al.  Mass Detection in Digital Mammograms System Based on PSO Algorithm , 2014, 2014 International Symposium on Computer, Consumer and Control.

[20]  A. Jemal,et al.  Cancer Statistics, 2021 , 2021, CA: a cancer journal for clinicians.

[21]  K. Katanoda,et al.  Five-year relative survival rate of breast cancer in the USA, Europe and Japan. , 2014, Japanese journal of clinical oncology.

[22]  B. Reljin,et al.  Local contrast enhancement in digital mammography by using mathematical morphology , 2005, International Symposium on Signals, Circuits and Systems, 2005. ISSCS 2005..

[23]  Jing Xiao,et al.  MommiNet: Mammographic Multi-view Mass Identification Networks , 2020, MICCAI.

[24]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[25]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[26]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[27]  Keith E. Muller,et al.  Contrast-limited adaptive histogram equalization: speed and effectiveness , 1990, [1990] Proceedings of the First Conference on Visualization in Biomedical Computing.

[28]  Marcin Ciecholewski,et al.  Microcalcification Segmentation from Mammograms: A Morphological Approach , 2016, Journal of Digital Imaging.

[29]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[31]  Imane Daoudi,et al.  Automatic computer-aided diagnosis system for mass detection and classification in mammography , 2020, Multimedia Tools and Applications.

[32]  Martin Kom,et al.  Automated detection of masses in mammograms by local adaptive thresholding , 2007, Comput. Biol. Medicine.

[33]  Marios Savvides,et al.  Feature Selective Anchor-Free Module for Single-Shot Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[35]  Zheng Zhang,et al.  CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network , 2020, IJCAI.

[36]  Shanmugam Veeramani,et al.  Machine Learning Classification Techniques for Breast Cancer Diagnosis , 2019, IOP Conference Series: Materials Science and Engineering.

[37]  Abdulhamit Subasi,et al.  Breast cancer diagnosis using GA feature selection and Rotation Forest , 2015, Neural Computing and Applications.

[38]  Haichao Cao,et al.  Breast mass detection in digital mammography based on anchor-free architecture , 2020, Comput. Methods Programs Biomed..

[39]  Rene Vargas-Voracek,et al.  Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information. , 2003, Medical physics.

[40]  Feng Liu,et al.  A fully automated scheme for mass detection and segmentation in mammograms , 2012, 2012 5th International Conference on BioMedical Engineering and Informatics.

[41]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Marcin Woźniak,et al.  Deep neural network correlation learning mechanism for CT brain tumor detection , 2021, Neural Computing and Applications.

[43]  Mia K Markey,et al.  A model-based framework for the detection of spiculated masses on mammography. , 2008, Medical physics.

[44]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .