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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

Breast Cancer Diagnosis in Digital Mammography Images Using Automatic Detection for the Region of Interest

Author(s): Saleem Z. Ramadan* and Mahmoud El-Banna

Volume 16, Issue 7, 2020

Page: [902 - 912] Pages: 11

DOI: 10.2174/1573405615666190717112820

Price: $65

Abstract

Background: One of the early screening methods of breast cancer that is still used today is mammogram due to its low cost. Unfortunately, this low cost accompanied with low performance rate also.

Methods: The low performance rate in mammograms is associated with low capability in determining the best region from which the features are extracted. Therefore, we offer an automatic method to detect the Region of Interest in the mammograms based on maximizing the area under receiver operating characteristic curve utilizing Genetic Algorithms.

The proposed method had been applied to the MIAS mammographic database, which is widely used in literature. Its performance had been evaluated using four different classifiers; Support Vector Machine, Naïve Bayes, K-Nearest Neighbor and Logistic Regression classifiers.

Results & Conclusion: The results showed good classification performances for all the classifiers used due to the rich information contained in the features extracted from the automatically selected Region of Interest.

Keywords: Breast cancer, mammography, genetic algorithms, automatic region of interest, classification, tumor.

Graphical Abstract

[1]
Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics. CA Cancer J Clin 2015; 65(2): 87-108.
[PMID: 25651787]
[2]
http://global-disease-burden.healthgrove.com/l/33096/Breast-Cancer-in-Jordan
[3]
Wittenberg Th, Wagner F, Gryanik A. Towards a computer assisted diagnosis system for digital breast tomosynthesis. Biomedizinische Technik 2012; 57(Suppl. 1): 223-6.
[http://dx.doi.org/10.1515/bmt-2012-4455]
[4]
American Cancer Society Breast Cancer Facts & Figures 2017-2018, Atlanta 2017. Available from: https://www.cancer.org/research/cancer-facts-statistics/breast-cancer-facts-figures.html
[5]
Wishart GC, Campisi M, Boswell M, et al. The accuracy of digital infrared imaging for breast cancer detection in women undergoing breast biopsy. Eur J Surg Oncol 2010; 36(6): 535-40.
[http://dx.doi.org/10.1016/j.ejso.2010.04.003] [PMID: 20452740]
[6]
Milosevic M, Jankovic D, Peulic A. Comparative analysis of breast cancer detection in mammograms and thermograms. Biomed Tech (Berl) 2015; 60(1): 49-56.
[http://dx.doi.org/10.1515/bmt-2014-0047] [PMID: 25720034]
[7]
Giri P, Saravanakumar K. Breast cancer detection using image processing Techniques. Orient J Comp Sci 2017; 10(2): 391-9.
[8]
Antonie M, Zaiane O, Coman A. Application of data mining techniques for medical image classification. Proceedings of the Second International Conference on Multimedia Data Mining. San Francisco, USA. New Jersey: IEEE 2001; pp. 94-101.
[9]
Varsha J. Detection of breast cancer in mammogram using support vector machine. Int J Sci Engineer Res 2015; 3(2): 26-30.
[10]
Khuzi A. Mohd, Besar R, Wan Zaki WMD, Ahmad NN. Identification of masses in digital mammogram using gray level cooccurrence matrices Biomed Imaging Interv J 2009; 5(3): 1-13.
[11]
Kinoshita SK, Marques PMA, Slaets AFF, Marana HRC, Ferrari RJ, Villela RL. Detection and characterization of mammographic masses by artificial neural network. In:Karssemeijer N, Thijssen M, Hendriks J, van Erning L. Eds. Digital mammography computational imaging and vision. Berlin: Springer 1998.
[http://dx.doi.org/10.1007/978-94-011-5318-8_85]
[12]
Mudigonda NR, Rangayyan RM, Desautels JEL. Gradient and texture analysis for the classification of mammographic masses. IEEE Trans Med Imaging 2000; 19(10): 1032-43.
[http://dx.doi.org/10.1109/42.887618] [PMID: 11131493]
[13]
Sheshadri HS, Kandaswamy A. Detection of breast cancer by mammogram image segmentation. J Cancer Res Ther 2005; 1(4): 232-4.
[http://dx.doi.org/10.4103/0973-1482.19599]]
[14]
Petrosian A, Chan HP, Helvie MA, Goodsitt MM, Adler DD. Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis. Phys Med Biol 1994; 39(12): 2273-88.
[http://dx.doi.org/10.1088/0031-9155/39/12/010] [PMID: 15551553]
[15]
Hadjiiski L, Sahiner B, Chan HP, Petrick N, Helvie M. Classification of malignant and benign masses based on hybrid ART2LDA approach. IEEE Trans Med Imaging 1999; 18(12): 1178-87.
[http://dx.doi.org/10.1109/42.819327] [PMID: 10695530]
[16]
Kim JK, Park HW. Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Trans Med Imaging 1999; 18(3): 231-8.
[http://dx.doi.org/10.1109/42.764896] [PMID: 10363701]
[17]
Suckling J, Parker J, Dance D, et al. Mammographic Image Analysis Society (MIAS) database v121, 2015. Available from: https://www.repository.cam.ac.uk/handle/1810/250394
[18]
Cahoon TC, Sutton MA, Bezdek JC. Breast cancer detection using image processing techniques. IEEE Trans Instrum Meas 2017.
[19]
Mencattini A, Salmeri M, Lojacono R. Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans Instrum Meas 2008; 57(7): 1422-30.
[20]
Ramadan S. Reducing premature convergence problem in genetic algorithm: application on travel salesman problem. Comput Inform Sci 2013; 6(1): 47-57.
[21]
Rangayyan M. Analysis of bilateral asymmetry in mammograms using directional, morphological, and density features. J Electron Imaging 2007; 16(1): 013003.
[22]
Chaudhuri D, Samal A. A simple method for fitting of bounding rectangle to closed regions. Pattern Recognit 2007; 40(7): 1981-9.
[http://dx.doi.org/10.1016/j.patcog.2006.08.003]

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