Research Article
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Year 2020, , 16 - 20, 31.01.2020
https://doi.org/10.17694/bajece.557693

Abstract

References

  • [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)

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

Year 2020, , 16 - 20, 31.01.2020
https://doi.org/10.17694/bajece.557693

Abstract

In this study, a CAD
system is recommended for the classification of mammography images as
normal-abnormal and benign malignant. The proposed system consists of the
feature extraction, determination of the distinguishing capabilities of the
features and selection of the features using by dynamic thresholding according
to the determined distinguishing capabilities. It uses the contourlet transform
to extract features. The distinguishing capabilities of the features are
determined by using t-test statistics, and the thresholds are applied to those
values to select effective ones. Classification is performed using support
vector machine algorithm for every iteration with each thresholding step. Among
the results of the iteration performed, the optimum data that have the best
performance, which is they have maximum accuracy result with the minimum number
of features, is selected as the optimum value. To evaluate the optimal feature
set, classification carries out using the feature set applying 5-fold
cross-validation. According to the results, the proposed method can be accepted
as a successful CAD system. 

References

  • [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)
There are 21 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Araştırma Articlessi
Authors

Nebi Gedik 0000-0002-1560-1058

Publication Date January 31, 2020
Published in Issue Year 2020

Cite

APA Gedik, N. (2020). A New Feature Extraction Approach Using Contourlet Transform and T-Test Statistics for Mammogram Classification. Balkan Journal of Electrical and Computer Engineering, 8(1), 16-20. https://doi.org/10.17694/bajece.557693

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