An Overview of Pectoral Muscle Extraction Algorithms Applied to Digital Mammograms

Substantial numbers of patients are reaching to a progressive breast cancer stage due to increase in the false negatives coming out of cumbersome and tedious job of continuously observing the mammograms in fatigue. Hence, the early detection of cancer with more accuracy is highly expected to reduce the death rate. Computer Aided Detection (CADe) can help radiologists in providing a second opinion increasing the overall accuracy of detection. Pectoral muscle is a predominant density area in most mammograms and may bias the results. Its extraction can increase accuracy and efficiency of cancer detection. This work is intended to provide the researchers a systematic and comprehensive overview of different techniques of pectoral muscle extraction which are categorized into groups based on intensity, region, gradient, transform, probability and polynomial, active contour, graph theory, and soft computing approaches. The performance of all these methods is summarized in tabular form for comparison purpose. The accuracy, efficiency and computational complexities of some selected methods are discussed in view of deciding a best approach in each of the categories.

[1]  Chen-Chung Liu,et al.  A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis , 2012, Comput. Math. Appl..

[2]  Jaime S. Cardoso,et al.  Pectoral muscle detection in mammograms based on polar coordinates and the shortest path , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[3]  Byung-Woo Hong,et al.  Multipass Active Contours for an Adaptive Contour Map , 2013, Sensors.

[4]  K. Thangavel,et al.  Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications , 2007, Comput. Methods Programs Biomed..

[5]  Claudio Marrocco,et al.  Automatic segmentation of the pectoral muscle in mediolateral oblique mammograms , 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

[6]  Rodica Strungaru,et al.  Detection of pectoral muscle in mammograms using a mean-shift segmentation approach , 2010, 2010 8th International Conference on Communications.

[7]  K. Thangavel,et al.  Computer Aided Diagnosis in Digital Mammograms: Detection of Microcalcifications by Meta Heuristic Algorithms , 2005 .

[8]  Li Liu,et al.  Breast and Pectoral Muscle Contours Detection Based on Goodness of Fit Measure , 2011, 2011 5th International Conference on Bioinformatics and Biomedical Engineering.

[9]  Arnau Oliver,et al.  Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms , 2005, IbPRIA.

[10]  J. Douglas,et al.  Computerized image analysis: texture-field orientation method for pectoral muscle identification on MLO-view mammograms. , 2010, Medical physics.

[11]  A. Hassanien,et al.  Big DNA Methylation Data Analysis and Visualizing in a Common Form of Breast Cancer , 2015 .

[12]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[13]  V. K. Govindan,et al.  Computer-Aided Identification of the Pectoral Muscle in Digitized Mammograms , 2010, Journal of Digital Imaging.

[14]  M. Alamgir Hossain,et al.  An efficient pixel value based mapping scheme to delineate pectoral muscle from mammograms , 2010, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA).

[15]  U. Rajendra Acharya,et al.  Pectoral muscle segmentation: A review , 2013, Comput. Methods Programs Biomed..

[16]  Xavier Lladó,et al.  One-shot segmentation of breast, pectoral muscle, and background in digitised mammograms , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[17]  Y. P. R. D. Yapa,et al.  Automatic Breast Boundary Segmentation of Mammograms , 2015 .

[18]  Aboul Ella Hassanien,et al.  MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier , 2014, Appl. Soft Comput..

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

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

[21]  Yianni Attikiouzel,et al.  Automatic assessment of mammographic positioning on the mediolateral oblique view , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[22]  Kamila Czaplicka,et al.  Automatic Breast-Line and Pectoral Muscle Segmentation , 2011 .

[23]  Vikrant Bhateja,et al.  A Robust Polynomial Filtering Framework for Mammographic Image Enhancement From Biomedical Sensors , 2013, IEEE Sensors Journal.

[24]  Mislav Grgic,et al.  Robust automatic breast and pectoral muscle segmentation from scanned mammograms , 2013, Signal Process..

[25]  Mariusz Bajger,et al.  Two graph theory based methods for identifying the pectoral muscle in mammograms , 2007, Pattern Recognit..

[26]  Lihua Li,et al.  A Novel Pectoral Muscle Segmentation Algorithm Based on Polyline Fitting and Elastic Thread Approaching , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[27]  Vasif V. Nabiyev,et al.  A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding , 2014, Comput. Methods Programs Biomed..

[28]  Niranjan Khandelwal,et al.  Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature , 2012, Journal of Digital Imaging.

[29]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  James C. Bezdek,et al.  A geometric approach to edge detection , 1998, IEEE Trans. Fuzzy Syst..

[31]  Rangaraj M. Rangayyan,et al.  Radon-Domain Detection of the Nipple and the Pectoral Muscle in Mammograms , 2007, Journal of Digital Imaging.

[32]  Aboul Ella Hassanien,et al.  Adaptive k-means clustering algorithm for MR breast image segmentation , 2013, Neural Computing and Applications.

[33]  Xin Yuan,et al.  Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model , 2009, Journal of Zhejiang University SCIENCE C.

[34]  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).

[35]  Y. Attikiouzel,et al.  Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection , 2001, The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001.

[36]  P. S. Sathidevi,et al.  Pectoral muscle identification in mammograms , 2011, Journal of applied clinical medical physics.

[37]  R. J. Ferrari,et al.  Identification of the breast boundary in mammograms using active contour models , 2004, Medical and Biological Engineering and Computing.

[38]  K. Thangavel,et al.  Pectoral Muscles Suppression in Digital Mammograms using Hybridization of Soft Computing Methods , 2014, ArXiv.

[39]  Rangaraj M. Rangayyan,et al.  Automatic identification of the pectoral muscle in mammograms , 2004, IEEE Transactions on Medical Imaging.

[40]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[41]  Yongyi Yang,et al.  Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation , 2013, Pattern Recognit..

[42]  Kwang Nam Choi,et al.  Applied Medical Informatics , 2013 .

[43]  Ramachandran Chandrasekhar,et al.  Segmentation of the pectoral muscle edge on mammograms by tunable parametric edge detection , 2001 .

[44]  Yianni Attikiouzel,et al.  Automatic pectoral muscle segmentation on mediolateral oblique view mammograms , 2004, IEEE Transactions on Medical Imaging.

[45]  Mohamed Abid,et al.  An Automatic-Pre-processing Method For Mammographic Images , 2010, J. Digit. Content Technol. its Appl..

[46]  Jaime S. Cardoso,et al.  Pectoral muscle detection in mammograms based on the shortest path with endpoints learnt by SVMs , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[47]  Michael A. Wirth,et al.  Segmentation of the breast region in mammograms using active contours , 2003, Visual Communications and Image Processing.

[48]  Mislav Grgic,et al.  Breast border extraction and pectoral muscle detection using wavelet decomposition , 2009, IEEE EUROCON 2009.