Automated Detection of Architectural Detection in Mammograms Using Template Matching
International Journal of Biomedical Science and Engineering
Volume 2, Issue 1, February 2014, Pages: 1-6
Accepted: Apr. 15, 2014;
Published: Apr. 30, 2014
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O'tega A. Ejofodomi, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Edikan Nse Gideon, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Gbenga Olalekan Oladipo, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Etse Rosemary Oshomah, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Breast Cancer is one of the leading causes of death, and its early detection increases the survival rate and treatment options available to patients. Computer-Aided-Detection (CAD) systems have been developed to assist radiologists with the task of locating cancer in mammograms. Unfortunately, these CAD systems have demonstrated less than 50% efficiency in detecting Architectural Distortions (AD), which is a sign of breast cancer. This paper presents a method of detecting AD with better sensitivity results. Forty mammograms containing AD were obtained from the Digital Database for Screening Mammography (DDSM). Each mammogram was preprocessed using breast segmentation techniques to extract the breast region from the mammogram. The mammograms were enhanced using contrast-limited adaptive histogram equalization (CLAHE). Next, the enhanced mammograms were filtered with a bank of 180 Gabor filters to extract the texture orientation from the images. Based on the fact that ADs contain spicules radiating in all direction, AD templates were designed in MATLAB. These templates were cross-correlated with the Gabor filtered mammograms to obtain ROIs that were most likely to contain ADs. The developed algorithm is intended to assist the radiologist by flagging regions likely to contain AD, prompting the radiologist to take a closer look at those regions. The current algorithm developed in MATLAB automatically flags seven suspicious blocks of 25 by 25 pixels per image, and demonstrated a sensitivity of 87.5% with a False Positive per Image (FPI) of 5.2. Future work will focus on the reduction of FPI.
O'tega A. Ejofodomi,
Edikan Nse Gideon,
Gbenga Olalekan Oladipo,
Etse Rosemary Oshomah,
Automated Detection of Architectural Detection in Mammograms Using Template Matching, International Journal of Biomedical Science and Engineering.
Vol. 2, No. 1,
2014, pp. 1-6.
American Cancer Society: Cancer Facts and Figures (2013). American Cancer Society, Inc., 2013. www.cancer.org. Accessed February 28, 2013.
Cancer Research, UK. http://www.cancerresearchuk.org. Accessed February 28, 2013.
American College of Radiology ACR BI-RADS - Mammography, Ultrasound & Magnetic Resonance Imaging, 4th edn. Reston, VA (2003).
K. Kerlikowske, P. A. Carney, B. Geller, M. T. Mandelson, S. H. Taplin, K. Malvin, V. Ernster, N. Urban, G. Cutter, R. Rosenberg, R. Ballard-Barbash. “Performance of screening mammography among women with and without a first-degree relative with breast cancer,” Annals of Internal Medicine 133: 855-863, 2009.
T. M. Kolb, J. Lichy, J. H. Newhouse. “Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations,” Radiology 225: 165-175, 2000.
R. E. Bird, T. W. Wallace, B. C. Yankaskas. “Analysis of cancers missed at screening mammography,” Radiology 184: 613-617, 1992.
The CADx Second Look system: http:/www.icadmed.com Accessed January 30, 2014.
The R2 Technology’s Image Checker: http://www.r2tech.com Accessed January 30, 2014.
H. Bornefalk. “Estimation and comparison of CAD system performance in clinical settings,” Academic Radiology. Vol 12, Issue 6 pp. 687-694, 2005.
M. P. Sampat, M. K. Markey, A. C. Bovik. “Computer-aided detection and diagnosis in mammography,” IEEE EMBS pp. 4973-4978, 2004.
A. Kamra, S. Singh, V. K. Jain. “Towards the Detection of Architecture Distortion in Mammograms: A Review. International Journal of Computer Applications,” Volume 46 No.7 pp. 44-49, 2012.
Q. Guo, J. Shao, V. Ruiz. “Investigation of support vector machine for the detection of architectural distortion in mammographic images,” J. Phys. Conf. Ser., 15: 88–94, 2005.
G. D. Tourassi, D. M. Delong, and C. E. Floyd Jr. “A study on the computerized fractal analysis of architectural distortion in screening mammograms,” Physics in Medicine and Biology, 51(5):1299–1312, 2006.
T. Matsubara, D. Fukuoka, N. Yagi, T. Hara, H. Fujita, Y. Inenaga, S. Kasai, A. Kano, T. Endo, and T. Iwase. “Detection method for architectural distortion based on analysis of structure of mammary gland on mammograms,” In Proceedings of the 19th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2005), pages 1036–1040, Berlin, Germany, 2005. Elsevier.
T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai, T. Endo, and T. Iwase, “Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms,” In H. U. Lemke, M. W. Vannier, K. Inamura, A. G. Farman, K. Doi, and J. H. C. Reiber, editors, International Congress Series: Proceedings of the 17th International Congress and Exhibition on Computer Assisted Radiology and Surgery, pages 950–955, London, UK, June 2003. Elsevier.
T. Matsubara, D. Yamazaki, T. Hara, H. Fujita, S. Kasai, T. Endo, and T. Iwase. “Automated detection of architectural distortion on mammograms,” In H.-O. Peitgen, editor, Digital Mammography IWDM 2002: 6th International Workshop on Digital Mammography, pages 350–352, Bremen, Germany, June 2002. Springer-Verlag
N. Eltonsy, G. Tourasi., A. Elmaghraby. “Investigating performance of a morphological based CAD scheme in detecting architectural distortion in screening mammograms,” Proceedings of the 20th International Congress and Exhibition on Computer assisted radiology and Surgery, pp 336-338, 2006.
R. Zwiggelaar, T. C. Parr, J. E. Schumm, I. W. Hutt, C. J. Taylor, S. M. Astley and C. R. M. Boggis. “Model-based detection of spiculated lesions in mammograms,” Medical Image Analysis, 3(1), 39-62, 1999.
S. Banik, R. M. Rangayyan, and J. E. Desautels. “Detection of architectural distortion in prior mammograms of interval-cancer cases with neural networks,” Proc. IEEE EMBS, 6667-6670, 2009.
R. M. Rangayyan, F. J. Ayres. “Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis,” Int JCARS Vol. 2:347–361, 2008.
M. P. Sampat, G. J. Whitman. “Evidence Based Detection of Spiculated Masses and Architectural Distortions,” SPIE. Vol. 5747, pp. 26-37, 2005
X. Zheng, Z. Lao. “Multiscale quantification of tissue spiculation and distortion for detection of architectural distortion and spiculated mass in mammography,” Medical Imaging: Computer-Aided Diagnosis, Proc. of SPIE The International Society of Optical Engineering, Vol. 7963, 2011.
R. Yoshikawa, A. Teramoto, T. Matsubara, H. Fujita. “Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter,” Medical Imaging: Computer-Aided Diagnosis, SPIE, Vol. 8670, 2013.
O. Ejofodomi, M. Olawuyi, D. U. Onyishi, G. Ofualagba. “Detecting Architectural Distortion in Mammograms using a Gabor Filtered Probability Map Algorithm,” IFIP Advances in Information and Communication Technology, Vol. 412, pp.328-335, 2013.