{"title":"Investigation of Wave Atom Sub-Bands via Breast Cancer Classification","authors":"Nebi Gedik, Ayten Atasoy","volume":129,"journal":"International Journal of Biomedical and Biological Engineering","pagesStart":552,"pagesEnd":558,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10008417","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.<\/p>\r\n","references":"[1]\tY. Li, H. Chen, L. Cao, J. Ma, \u201cA survey of computer-aided detection of breast cancer with mammography,\u201d J Health Med Informat, vol. 7, no. 4, pp. 238, 2016.\r\n[2]\tCheng H.D., Shan J., Ju W., Guo Y., Zhang L. \u201cAutomated breast cancer detection and classification using ultrasound images: a survey\u201d, Pattern Recognit., vol. 43, pp. 299\u2013317, 2010.\r\n[3]\tA. Jotwani, J. Gralow, \u201cEarly detection of breast cancer,\u201d Mol. Diagn. Ther., vol. 13, no. 6, pp. 349\u2013357, 2009.\r\n[4]\tJ.L. Jasmine, S. Baskaran, A. Govardhan, \u201cAn automated mass classification system in digital mammograms using contourlet transform and support vector machine,\u201d Int. J. Comput. Appl., vol. 31, no. 9, pp. 54\u201361, 2011.\r\n[5]\tJ. Dinnes, S. Moss, J. Melia, R. Blanks, F. Song, J. Kleijnen, \u201cEffectiveness and cost-effectiveness of double reading of mammograms in breast cancer screening: findings of a systematic review,\u201d The Breast, vol. 10, no. 6, pp. 455\u2013463, 2001.\r\n[6]\tG. Zhang, P. Yan, H. Zhao, X. Zhang, \u201cA computer-aided diagnosis system in mammography using artificial neural networks,\u201d In BioMedical Engineering and Informatics 2008. International Conference on IEE; China, 2008.\r\n[7]\tS.D. Tzikopoulos, M.E. Mavroforakis, H.V. Georgiou, \u201cA fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry,\u201d Computer methods and programs in biomedicine, vol. 102, n. 1, pp. 47-63, 2011.\r\n[8]\tT.M. Deserno, M Soiron., J.E.E. de Oliveira, A. de Araujo, \u201cTowards computer-aided diagnostics of screening mammography using content-based image retrieval,\u201d Proceedings of the Conference on Graphics, Patterns and Images (SIBGRAPI), Alagoas \u2013 Brazil, 2011.\r\n[9]\tZ. Wang, G. Yu, Y. Kang, Y. Zhao, Q. Qu, \u201cBreast tumor detection in digital mammography based on extreme learning machine,\u201d Neurocomputing, vol. 128, pp. 175-184, 2014.\r\n[10]\tD. Moura, M. Guevara L\u00f3pez, \u201cAn evaluation of image descriptors combined with clinical data for breast cancer diagnosis,\u201d Int. J. Comp. Ass. Rad. Surg., vol. 8, no. 4, pp. 561\u2013574, 2013.\r\n[11]\tS. Liu, C.F. Babbs, E.J. Delp, \u201cMultiresolution detection of speculated lesions in digital mammograms,\u201d Image Processing, IEEE Transactions on, vol. 10, no. 6, pp. 874-884, 2001.\r\n[12]\tC.B.R. Ferreira, D.L. Borges, \u201cAnalysis of mammogram classification using a wavelet transform decomposition,\u201d Pattern Recognition Letters, vol. 24, no. 7, pp. 973\u2013982, 2003.\r\n[13]\tS. Ergin, O. Kilinc, \u201cA new feature extraction framework based on wavelets for breast cancer diagnosis,\u201d Computers in Biology and Medicine, vol. 51, pp. 171\u2013182, 2014.\r\n[14]\tF. Moayedi, Z. Azimifar, R. Boostani, S. Katebi, \u201cContourlet-based mammography mass classification using the SVM family,\u201d Computers in Biology and Medicine, vol. 40, no. 4, pp. 373-383, 2010.\r\n[15]\tJ.S. Leena Jasmine, S. Baskaran, A. Govardhan, \u201cAn automated mass classification system in digital mammograms using contourlet transform and support vector machine,\u201d International Journal of Computer Applications, vol. 31, no. 9, pp. 0975\u20138887, 2011.\r\n[16]\tF. Pak, H.R Kanan., A. Alikhassi, \u201cBreast cancer detection and classification in digital mammography based on non-subsampled contourlet transform (NSCT) and super-resolution,\u201d Computer Methods and Programs in Biomedicine, vol. 122, no. 2, pp. 89\u2013107, 2015.\r\n[17]\tM.M. Eltoukhy, I. Faye, B.B. Samir, \u201cBreast cancer diagnosis in digital mammogram using multi-scale curvelet transform,\u201d Computerized Medical Imaging and Graphics, vol. 34, pp. 269\u2013276, 2010.\r\n[18]\tS.V. Francis, M. Sasikala, S. Saranya, \u201cDetection of breast abnormality from thermograms using curvelet transform based feature extraction,\u201d Journal of Medical Systems, vol. 38, pp. 1-23, 2014.\r\n[19]\tJ. Ma, \u201cCharacterization of textural surfaces using wave atoms,\u201d Applied Physics Letters, vol. 90, pp. 264101, 2007.\r\n[20]\tZ. Haddad, A. Beghdadi, A. Serir, A. Mokraou, \u201cImage quality assessment based on wave atoms transform,\u201d Proceedings of 2010 IEEE 17th International Conference on Image Processing; Hong Kong, 2010.\r\n[21]\tJ. Rajeesh, R.S. Moni, S.S. Kumar, \u201cPerformance analysis of wave atom transform in texture classification,\u201d Signal, Image and Video Processing, vol. 8, pp. 923\u2013930, 2014.\r\n[22]\tA. Rajesh, M. Ellappan, \u201cClassification of micro-calcification based on wave atom transform,\u201d Journal of Computer Science, vol. 10, no. 8, pp. 1543-1547, 2014.\r\n[23]\tM. Elangeeran, S. Ramasamy, K. Arumugam, \u201cA novel method for benign and malignant characterization of mammographic microcalcifications employing wave atom features and circular complex valued\u2013extreme learning machine,\u201d IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) Symposium on Cognitive Computing in Information Processing; Singapore, 2014.\r\n[24]\tN. Gedik, A. Atasoy, \u201cPerformance evaluation of the wave atom algorithm to classify mammographic images,\u201d Turk. J. Elec. Eng. & Comp. Sci., vol. 22, pp. 957\u2013969, 2014.\r\n[25]\tL. Demanet, L. Ying, \u201cWave atoms and sparsity of oscillatory patterns,\u201d Appl. Comput. Harmon Anal., vol. 23, pp. 368\u2013387, 2007.\r\n[26]\tMIAS database, http:\/\/peipa.essex.ac.uk\/info\/mias.html. (10.09.2017).\r\n[27]\tDDSM database, http:\/\/marathon.csee.usf.edu\/Mammography\/Database.html. (10.09.2017).","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 129, 2017"}