Investigation of Wave Atom Sub-Bands via Breast Cancer Classification
Authors: Nebi Gedik, Ayten Atasoy
Abstract:
This paper investigates successful sub-bands of wave atom transform via classification of mammograms, when the coefficients of sub-bands are used as features. A computer-aided diagnosis system is constructed by using wave atom transform, support vector machine and k-nearest neighbor classifiers. Two-class classification is studied in detail using two data sets, separately. The successful sub-bands are determined according to the accuracy rates, coefficient numbers, and sensitivity rates.
Keywords: Breast cancer, wave atom transform, SVM, k-NN.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315493
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1070References:
[1] Y. Li, H. Chen, L. Cao, J. Ma, “A survey of computer-aided detection of breast cancer with mammography,” J Health Med Informat, vol. 7, no. 4, pp. 238, 2016.
[2] Cheng H.D., Shan J., Ju W., Guo Y., Zhang L. “Automated breast cancer detection and classification using ultrasound images: a survey”, Pattern Recognit., vol. 43, pp. 299–317, 2010.
[3] A. Jotwani, J. Gralow, “Early detection of breast cancer,” Mol. Diagn. Ther., vol. 13, no. 6, pp. 349–357, 2009.
[4] J.L. Jasmine, S. Baskaran, A. Govardhan, “An automated mass classification system in digital mammograms using contourlet transform and support vector machine,” Int. J. Comput. Appl., vol. 31, no. 9, pp. 54–61, 2011.
[5] J. Dinnes, S. Moss, J. Melia, R. Blanks, F. Song, J. Kleijnen, “Effectiveness and cost-effectiveness of double reading of mammograms in breast cancer screening: findings of a systematic review,” The Breast, vol. 10, no. 6, pp. 455–463, 2001.
[6] G. Zhang, P. Yan, H. Zhao, X. Zhang, “A computer-aided diagnosis system in mammography using artificial neural networks,” In BioMedical Engineering and Informatics 2008. International Conference on IEE; China, 2008.
[7] S.D. Tzikopoulos, M.E. Mavroforakis, H.V. Georgiou, “A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry,” Computer methods and programs in biomedicine, vol. 102, n. 1, pp. 47-63, 2011.
[8] T.M. Deserno, M Soiron., J.E.E. de Oliveira, A. de Araujo, “Towards computer-aided diagnostics of screening mammography using content-based image retrieval,” Proceedings of the Conference on Graphics, Patterns and Images (SIBGRAPI), Alagoas – Brazil, 2011.
[9] Z. Wang, G. Yu, Y. Kang, Y. Zhao, Q. Qu, “Breast tumor detection in digital mammography based on extreme learning machine,” Neurocomputing, vol. 128, pp. 175-184, 2014.
[10] D. Moura, M. Guevara López, “An evaluation of image descriptors combined with clinical data for breast cancer diagnosis,” Int. J. Comp. Ass. Rad. Surg., vol. 8, no. 4, pp. 561–574, 2013.
[11] S. Liu, C.F. Babbs, E.J. Delp, “Multiresolution detection of speculated lesions in digital mammograms,” Image Processing, IEEE Transactions on, vol. 10, no. 6, pp. 874-884, 2001.
[12] C.B.R. Ferreira, D.L. Borges, “Analysis of mammogram classification using a wavelet transform decomposition,” Pattern Recognition Letters, vol. 24, no. 7, pp. 973–982, 2003.
[13] S. Ergin, O. Kilinc, “A new feature extraction framework based on wavelets for breast cancer diagnosis,” Computers in Biology and Medicine, vol. 51, pp. 171–182, 2014.
[14] F. Moayedi, Z. Azimifar, R. Boostani, S. Katebi, “Contourlet-based mammography mass classification using the SVM family,” Computers in Biology and Medicine, vol. 40, no. 4, pp. 373-383, 2010.
[15] J.S. Leena Jasmine, S. Baskaran, A. Govardhan, “An automated mass classification system in digital mammograms using contourlet transform and support vector machine,” International Journal of Computer Applications, vol. 31, no. 9, pp. 0975–8887, 2011.
[16] F. Pak, H.R Kanan., A. Alikhassi, “Breast cancer detection and classification in digital mammography based on non-subsampled contourlet transform (NSCT) and super-resolution,” Computer Methods and Programs in Biomedicine, vol. 122, no. 2, pp. 89–107, 2015.
[17] M.M. Eltoukhy, I. Faye, B.B. Samir, “Breast cancer diagnosis in digital mammogram using multi-scale curvelet transform,” Computerized Medical Imaging and Graphics, vol. 34, pp. 269–276, 2010.
[18] S.V. Francis, M. Sasikala, S. Saranya, “Detection of breast abnormality from thermograms using curvelet transform based feature extraction,” Journal of Medical Systems, vol. 38, pp. 1-23, 2014.
[19] J. Ma, “Characterization of textural surfaces using wave atoms,” Applied Physics Letters, vol. 90, pp. 264101, 2007.
[20] Z. Haddad, A. Beghdadi, A. Serir, A. Mokraou, “Image quality assessment based on wave atoms transform,” Proceedings of 2010 IEEE 17th International Conference on Image Processing; Hong Kong, 2010.
[21] J. Rajeesh, R.S. Moni, S.S. Kumar, “Performance analysis of wave atom transform in texture classification,” Signal, Image and Video Processing, vol. 8, pp. 923–930, 2014.
[22] A. Rajesh, M. Ellappan, “Classification of micro-calcification based on wave atom transform,” Journal of Computer Science, vol. 10, no. 8, pp. 1543-1547, 2014.
[23] M. Elangeeran, S. Ramasamy, K. Arumugam, “A novel method for benign and malignant characterization of mammographic microcalcifications employing wave atom features and circular complex valued–extreme learning machine,” IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) Symposium on Cognitive Computing in Information Processing; Singapore, 2014.
[24] N. Gedik, A. Atasoy, “Performance evaluation of the wave atom algorithm to classify mammographic images,” Turk. J. Elec. Eng. & Comp. Sci., vol. 22, pp. 957–969, 2014.
[25] L. Demanet, L. Ying, “Wave atoms and sparsity of oscillatory patterns,” Appl. Comput. Harmon Anal., vol. 23, pp. 368–387, 2007.
[26] MIAS database, http://peipa.essex.ac.uk/info/mias.html. (10.09.2017).
[27] DDSM database, http://marathon.csee.usf.edu/Mammography/Database.html. (10.09.2017).