Balkan Journal of Electrical and Computer Engineering

2147-284X 2147-284X

MUSA YILMAZ

10.17694/bajece.557693

Electrical Engineering

Elektrik Mühendisliği

A New Feature Extraction Approach Using Contourlet Transform and T-Test Statistics for Mammogram Classification

https://orcid.org/0000-0002-1560-1058

Gedik

Nebi

01 31 2020

8 1 16 20 04 24 2019 01 25 2020

2013

Balkan Journal of Electrical and Computer Engineering

In this study, a CADsystem is recommended for the classification of mammography images asnormal-abnormal and benign malignant. The proposed system consists of thefeature extraction, determination of the distinguishing capabilities of thefeatures and selection of the features using by dynamic thresholding accordingto the determined distinguishing capabilities. It uses the contourlet transformto extract features. The distinguishing capabilities of the features aredetermined by using t-test statistics, and the thresholds are applied to thosevalues to select effective ones. Classification is performed using supportvector machine algorithm for every iteration with each thresholding step. Amongthe results of the iteration performed, the optimum data that have the bestperformance, which is they have maximum accuracy result with the minimum numberof features, is selected as the optimum value. To evaluate the optimal featureset, classification carries out using the feature set applying 5-foldcross-validation. According to the results, the proposed method can be acceptedas a successful CAD system.

Classification CAD Mammogram Contourlet transform

[1] M.D. Chin, K.K. Evans, J.M. Wolfe, J. Bowen, J.W. Tanaka, “Inversion effects in the expert classification of mammograms and faces”, Cognitive Research: Principles and Implications, vol. 3, 2018, pp. 31.

[2] Y. Wang, H. Shi, S. M, “A new approach to the detection of lesions in mammography using fuzzy clustering”, J. Int. Med. Res. vol. 39, no. 6, 2011, pp. 2256–2263.

[3] N.J. Massat, A. Dibden, D. Parmar, J. Cuzick, P.D. Sasieni, S.W. Duffy, “Impact of screening on breast cancer mortality: the UK program 20 years on”, Cancer Epidemiology and Prevention Biomarkers, vol. 25, no. 3, 2016, pp. 455-62.

[4] T. Onega, L.E. Goldman, R.L. Walker, D.L. Miglioretti, D.S. Buist, S. Taplin, B.M. Geller, D.A. Hill, R. Smith-Bindman, “Facility mammography volume in relation to breast cancer screening outcomes”, J. Med. Screen, vol. 23, 2016, pp. 31.

[5] M.M. Pawar, S.N. Talbar, “Genetic fuzzy system (GFS) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis” Perspectives in Science, vol.8, 2016, pp. 247–250.

[6] L. Berlin, “Radiologic errors, past, present and future”, Diagnosis, vol. 1, no. 1, 2014, pp. 79–84.

[7] Y. Li, H. Chen, Y. Yang, L. Cheng, L. Cao, “A bilateral analysis scheme for false positive reduction in mammogram mass detection”, Computers in Biology and Medicine, vol. 57, 2015, pp. 84–95.

[8] N. Gedik, A. Atasoy, “Performance evaluation of the wave atom algorithm to classify mammographic images”, Turk. J. Elec. Eng. & Comp. Sci., vol.22, 2014, pp. 957–969.

[9] V. Chaurasia, S. Pal, “A novel approach for breast cancer detection using data mining techniques”, International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 1, 2014, pp. 1-17.

[10] L. Dora, S. Agrawal, R. Panda, A. Abraham, “Optimal breast cancer classification using Gauss–Newton representation based algorithm”, Expert Systems with Applications, vol. 85, 2017, pp. 134-145.

[11] N. Gedik, “Breast cancer diagnosis system via contourlet transform with sharp frequency localization and LS-SVM”, Journal of medical imaging and health informatics, vol. 5, 2015, pp. 1–9.

[12] W. Yang, L. Tianhui, “A Robust Feature Vector Based on Waveatom Transform for Mammographic Mass Detection,” ICVR 2018 Proceedings of the 4th International Conference on Virtual Reality, Hong Kong, pp.133-139, 24-26 February 2018.

[13] N. Gedik, “A new feature extraction method based on multi-resolution representations of mammograms”, Applied Soft Computing, vol. 44, no. 1, 2016, pp. 128-133.

[14] M.M. Jadoon, Q. Zhang, I.U. Haq, A. Jadoon, A. Basit, S. Butt, “Classification of mammograms for breast cancer detection based on curvelet transform and multi-layer perceptron”, Biomedical Research, vol. 28, no. 10, 2017, pp. 4311-4315.

[15] Y. Chen, Y. Zhang, H.M. Lu, X.Q. Chen, J.W. Li, S.H. Wang, “Wavelet energy entropy and linear regression classifier for detecting abnormal breasts”, Multimed Tools Appl., vol. 77, 2018, pp. 3813–3832.

[16] M.M. Eltoukhy, I. Faye, B.B. Samir, “A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation”, Computers in biology and medicine, vol. 42, no. 1, 2012, pp. 123–128.

[17] M.M. Eltoukhy, I. Faye, “An optimized feature selection method for breast cancer diagnosis in digital mammogram using multiresolution representation”, Applied Mathematics and Information Sciences, vol. 8, no. 6, 2014, pp. 2921-2928.

[18] D. Sehrawat, A. Sehrawat, D. Jaiswal, A. Sen, “Detection and classification of tumour in mammograms using discrete wavelet transform and support vector machine”, International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 5, 2017, pp. 1328-1334.

[19] Y. Lu, M.N. Do, “A new contourlet transform wıth sharp frequency localızatıon”, IEEE 2006 International Conference on Image Processing, Atlanta, Georgıa, U.S.A., pp.1629-1632, 8-11 October 2006.

[20] H. Liu, J. Li, L. Wong, “A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns”, Genome Inf., vol. 13, no. 1, 2002, pp. 51–60.

[21] http://peipa.essex.ac.uk/info/mias.html (20.11.2018)