Autosoft Journal

Online Manuscript Access


Detection of architectural distortion in mammograms using geometrical properties of thinned edge structures


Authors



Abstract

The proposed method detects the most commonly missed breast cancer symptom, Architectural Distortion. The basis of the method lies in the analysis of geometrical properties of abnormal patterns that correspond to Architectural Distortion in mammograms. Pre-processing methods are employed for the elimination of Pectoral Muscle (PM) region from the mammogram and to localize possible centers of Architectural Distortion. Regions that are candidates to contain centroids of Architectural Distortion are identified using a modification of the isotropic SUSAN filter. Edge features are computed in these regions using Phase Congruency, which are thinned using Gradient Magnitude Maximization. From these thinned edges, relevant edge structures are retained based on three geometric properties namely eccentricity to retain near linear structures, perpendicular distance from each such structure to the centroid of the edges and quadrant support membership of these edge structures. Features for classification are generated from these three properties; a feed-forward neural network, trained using a combination of backpropagation and a metaheuristic algorithm based on Cuckoo search, is employed for classifying the suspicious regions identified by the modified filter for Architectural Distortion, as normal or malignant. Experimental analyses were carried out on mammograms obtained from the standard databases MIAS and DDSM as well as on images obtained from Lakeshore Hospital in Kochi, India. The classification step yielded a sensitivity of 89%, 89.8.7% and 97.6% and specificity of 90.9, 85 and 96.7% on 60 images from MIAS, 100 images from DDSM database and 100 images from Lakeshore Hospital respectively


Keywords


Pages

Total Pages: 15
Pages: 183-197

DOI
10.1080/10798587.2017.1257544


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways


Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Published

Volume: 23
Issue: 1
Year: 2017

Cite this document


References

American College of Radiology (ACR) American College of Radiology

Amit Kamra ACEEE International Journal on Information Technology 2.2 (2012)

Phadke Anuradha, C. International Journal of Application or Innovation in Engineering & Management (IJAIEM) 2.2 (2013)

Labani, Satyanarayana, Smita Asthana, and Sonia Chauhan. "Breast and Cervical Cancer Risk in India: An Update." Indian Journal of Public Health 58.1 (2014): 5. Crossref. Web. https://doi.org/10.4103/0019-557X.128150

Ayres, F.J., and R.M. Rangayvan. "Characterization of Architectural Distortion in Mammograms." IEEE Engineering in Medicine and Biology Magazine 24.1 (2005): 59-67. Crossref. Web. https://doi.org/10.1109/MEMB.2005.1384102

Ayres, Fábio J., and Rangaraj M. Rangayyan. "Reduction of False Positives in the Detection of Architectural Distortion in Mammograms by Using a Geometrically Constrained Phase Portrait Model." International Journal of Computer Assisted Radiology and Surgery 1.6 (2007): 361-369. Crossref. Web. https://doi.org/10.1007/s11548-007-0072-x

Baeg S. Electronic Letters on Computer Vision and Image Analysis 1.1 (2002)

Banik, S, R M Rangayyan, and J E L Desautels. "Detection of Architectural Distortion in Prior Mammograms." IEEE Transactions on Medical Imaging 30.2 (2011): 279-294. Crossref. Web. https://doi.org/10.1109/TMI.2010.2076828

Banik, Shantanu, Rangaraj M. Rangayyan, and J. E. Leo Desautels. "Measures of Angular Spread and Entropy for the Detection of Architectural Distortion in Prior Mammograms." International Journal of Computer Assisted Radiology and Surgery 8.1 (2012): 121-134. Crossref. Web. https://doi.org/10.1007/s11548-012-0681-x

Burt, P., and E. Adelson. "The Laplacian Pyramid as a Compact Image Code." IEEE Transactions on Communications 31.4 (1983): 532-540. Crossref. Web. https://doi.org/10.1109/TCOM.1983.1095851

Camilus, K. Santle, V. K. Govindan, and P.S. Sathidevi. "Pectoral Muscle Identification in Mammograms." Journal of Applied Clinical Medical Physics 12.3 (2011): 215-230. Crossref. Web. https://doi.org/10.1120/jacmp.v12i3.3285

Canny, John. "A Computational Approach to Edge Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8.6 (1986): 679-698. Crossref. Web. https://doi.org/10.1109/TPAMI.1986.4767851

Casti, Paola et al. "Automatic Detection of the Nipple in Screen-Film and Full-Field Digital Mammograms Using a Novel Hessian-Based Method." Journal of Digital Imaging 26.5 (2013): 948-957. Crossref. Web. https://doi.org/10.1007/s10278-013-9587-6

Chakraborty, Jayasree et al. "Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature." Journal of Digital Imaging 25.3 (2011): 387-399. Crossref. Web. https://doi.org/10.1007/s10278-011-9421-y

Chandrashekar, Girish, and Ferat Sahin. "A Survey on Feature Selection Methods." Computers & Electrical Engineering 40.1 (2014): 16-28. Crossref. Web. https://doi.org/10.1016/j.compeleceng.2013.11.024

Chen K. Volume 35 of Studies in Computational Intelligence

ZhiYu Chen et al. "Gray-Level Grouping (GLG): An Automatic Method for Optimized Image Contrast Enhancement-Part I: The Basic Method." IEEE Transactions on Image Processing 15.8 (2006): 2290-2302. Crossref. Web. https://doi.org/10.1109/TIP.2006.875204

Edward. J. Deneck Barron”s Regents Exams and Answers: Earth science-- the physical setting

Ferrari, R.J. et al. "Automatic Identification of the Pectoral Muscle in Mammograms." IEEE Transactions on Medical Imaging 23.2 (2004): 232-245. Crossref. Web. https://doi.org/10.1109/TMI.2003.823062

Field, David J. "Relations Between the Statistics of Natural Images and the Response Properties of Cortical Cells." Journal of the Optical Society of America A 4.12 (1987): 2379. Crossref. Web. https://doi.org/10.1364/JOSAA.4.002379

Fukunaga K. Introduction to statistical pattern recognition

Sheela, K. Gnana, and S. N. Deepa. "Review on Methods to Fix Number of Hidden Neurons in Neural Networks." Mathematical Problems in Engineering 2013 (2013): 1-11. Crossref. Web. https://doi.org/10.1155/2013/425740

Guo, Q, J Shao, and V Ruiz. "Investigation of Support Vector Machine for the Detection of Architectural Distortion in Mammographic Images." Journal of Physics: Conference Series 15 (2005): 88-94. Crossref. Web. https://doi.org/10.1088/1742-6596/15/1/015

Hajian-Tilaki K. Caspian Journal of Internal Medicine

Hashimoto Beverly Practical Digital Mammography

Huttenlocher, D.P., G.A. Klanderman, and W.J. Rucklidge. "Comparing Images Using the Hausdorff Distance." IEEE Transactions on Pattern Analysis and Machine Intelligence 15.9 (1993): 850-863. Crossref. Web. https://doi.org/10.1109/34.232073

Ichikawa T. Proceedings of SPIE Medical Imaging, Image Processing

ICMR Three year report of population based cancer registries 2009—2011

Bolc, Leonard et al., eds. "Computer Vision and Graphics." Lecture Notes in Computer Science (2010): n. pag. Crossref. Web. https://doi.org/10.1007/978-3-642-15910-7

Jiao-hong Y. The Scientific World Journal

Karsoliya Saurabh International Journal of Engineering Trends and Technology 3.6 (2012)

Kovesi P. Videre: Journal of Computer Vision Research, Massachusetts Institute of Technology 1.3 (1999)

Lao, Zhiqiang, and Xin Zheng. "Multiscale Quantification of Tissue Spiculation and Distortion for Detection of Architectural Distortion and Spiculated Mass in Mammography." Ed. Ronald M. Summers and Bram van Ginneken. Medical Imaging 2011: Computer-Aided Diagnosis (2011): n. pag. Crossref. Web. https://doi.org/10.1117/12.877330

Ma, Fei et al. "Two Graph Theory Based Methods for Identifying the Pectoral Muscle in Mammograms." Pattern Recognition 40.9 (2007): 2592-2602. Crossref. Web. https://doi.org/10.1016/j.patcog.2006.12.011

Masek, M. (2004). Hierarchical segmentation of mammograms based on pixel intensity . (Ph.D. dissertation, Centre for Intelligent Information Processing Systems, School of Electrical, Electronic and Computer Engineering, The University of Western Australia).

Matsubara, T et al. "Novel Method for Detecting Mammographic Architectural Distortion Based on Concentration of Mammary Gland." International Congress Series 1268 (2004): 867-871. Crossref. Web. https://doi.org/10.1016/j.ics.2004.03.103

Matsubara T. Proceedings of the 22nd International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS2008) 3.1 (2008)

Ejofodomi, O”tega et al. "Detecting Architectural Distortion in Mammograms Using a Gabor Filtered Probability Map Algorithm." Artificial Intelligence Applications and Innovations (2013): 328-335. Crossref. Web. https://doi.org/10.1007/978-3-642-41142-7_34

Shetty, Priya. "India Faces Growing Breast Cancer Epidemic." The Lancet 379.9820 (2012): 992-993. Crossref. Web. https://doi.org/10.1016/S0140-6736(12)60415-2

Rangayyan, Rangaraj M., and Fábio J. Ayres. "Gabor Filters and Phase Portraits for the Detection of Architectural Distortion in Mammograms." Medical & Biological Engineering & Computing 44.10 (2006): 883-894. Crossref. Web. https://doi.org/10.1007/s11517-006-0088-3

Rangayyan, Rangaraj M. et al. "Measures of Divergence of Oriented Patterns for the Detection of Architectural Distortion in Prior Mammograms." International Journal of Computer Assisted Radiology and Surgery 8.4 (2012): 527-545. Crossref. Web. https://doi.org/10.1007/s11548-012-0793-3

Rekha L. Journal of Image and Graphics 2.2 (2014)

Smith, Stephen M., and J. Michael Brady. International Journal of Computer Vision 23.1 (1997): 45-78. Crossref. Web. https://doi.org/10.1023/A:1007963824710

Stamatia D. Applied Radiology

Suckling J. International Congress Series

Yang X-S. Nature-Inspired metaheuristic algorithms

Yoshikawa, Ruriha et al. "Automated Detection of Architectural Distortion Using Improved Adaptive Gabor Filter." Lecture Notes in Computer Science (2014): 606-611. Crossref. Web. https://doi.org/10.1007/978-3-319-07887-8_84

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




CONTACT INFORMATION


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048
EMAIL: tsiepress@gmail.com
WEB: http://www.wacong.org/tsi/