اخبار و اعلانات

 آمار

تعداد نشریات31
تعداد شماره‌ها834
تعداد مقالات8,015
تعداد مشاهده مقاله14,852,487
تعداد دریافت فایل اصل مقاله9,586,510
Moayedi, Fatemeh, Dashti, Ebrahim. (1388). SUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS. نشریه علمی دانشگاه سیستان و بلوچستان, 7(1), 15-31. doi: 10.22111/ijfs.2010.158
Fatemeh Moayedi; Ebrahim Dashti. "SUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS". نشریه علمی دانشگاه سیستان و بلوچستان, 7, 1, 1388, 15-31. doi: 10.22111/ijfs.2010.158
Moayedi, Fatemeh, Dashti, Ebrahim. (1388). 'SUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS', نشریه علمی دانشگاه سیستان و بلوچستان, 7(1), pp. 15-31. doi: 10.22111/ijfs.2010.158
Moayedi, Fatemeh, Dashti, Ebrahim. SUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS. نشریه علمی دانشگاه سیستان و بلوچستان, 1388; 7(1): 15-31. doi: 10.22111/ijfs.2010.158

SUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS

Iranian Journal of Fuzzy Systems
مقاله 3، دوره 7، شماره 1، اردیبهشت 2010، صفحه 15-31    اصل مقاله (480.64 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2010.158
نویسندگان
Fatemeh Moayedi* 1؛ Ebrahim Dashti2
1Reza Boostani, Ali Reza Kazemi and Serajodin Katebi, Vision and Image Processing Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
2Board of Science, Azad Universitiy Branch of Jahrom, Iran
چکیده
This paper is concerned with the development of a novel classifier
for automatic mass detection of mammograms, based on contourlet feature
extraction in conjunction with statistical and fuzzy classifiers. In this method,
mammograms are segmented into regions of interest (ROI) in order to extract
features including geometrical and contourlet coefficients. The extracted features
benefit from the superiority of the contourlet method to the state of the
art multi-scale techniques. A genetic algorithm is applied for feature weighting
with the objective of increasing classification accuracy. Although fuzzy classifiers
are interpretable, the majority are order sensitive and suffer from the
lack of generalization. In this study, a kernel SVM is integrated with a nerofuzzy
rule-based classifier to form a support vector based fuzzy neural network
( SVFNN). This classifier benefits from the superior classification power of
SVM in high dimensional data spaces and also from the efficient human-like
reasoning of fuzzy and neural networks in handling uncertainty information.
We use the Mammographic Image Analysis Society (MIAS) standard data
set and the features extracted of the digital mammograms are applied to the
fuzzy-SVM classifiers to assess the performance. Our experiments resulted in
95.6%,91.52%,89.02%, 85.31% classification accuracy for the subclass FSVM,
SVFNN, fuzzy rule based and kernel SVM classifiers respectively and we conclude
that the subclass fuzzy-SVM is superior to the other classifiers.
کلیدواژه‌ها
Mammography؛ Support vector based fuzzy neural network؛ Fuzzy support vector machine؛ Contourlet
مراجع

[1] R. A. Aliev, B. G. Guirimov and R. R. Aliev,A neuro-fuzzy object classifier with modified

distance measure estimator, Iranian Journal of Fuzzy Systems, 1(1) (2004), 5-15.

[2] K. Bovis, S. Singh, J. Fieldsend and C. Pinder,Identification of masses in digital mammograms

with MLP and RBF nets, IEEE Trans. on Image Processing, 1 (2005), 342-347.

[3] E. J. Candes and D. L. Donoho,Curvelets: a surprisingly effective non adaptive representation

for objects with edges, Saint-Malo Proceedings, Nashville, TN: Vanderbilt Univ, 2000.

[4] O. Cordon and M. J. del Jesus and F. Herrera,Genetic learning of fuzzy rule based classification

systems cooperating with fuzzy reasoning methods, Technical Report, DECSAI-970130,1997.

[5] M. N. Do and M. Vetterli,The contourlet transform: an efficient directional multi-resolution

image representation, IEEE Trans. on Image Processing, 14(12) (2005), 2091-2106.

[6] I. El-Naqa, Y. Yang, M. Wernick, N. Galatsanos and R. Nishikawa,A support vector machine

approach for detection of microcalcifications, IEEE Trans. on Medical Imaging, 21(12)(2002), 1552-1563.

[7] E. A. Fischer, J. Y. Lo and M. K. Markey,Bayesian networks of BI-RADS descriptors for

breast lesion classification, IEEE EMBS, San Francisco, 4 (2004), 3031-3034.

[8] O. J. Freixenet, A. Bosch, D. Raba and R. Zwiggelaar,Automatic classification of breast

tissue, Lecture Notes in Computer Science, Pattern Recognition and Image Analysis, (2000),431-438.

[9] W. H. Land, J. L. Wong Daniel, W. McKee, T. Masters and F. R Anderson,Breast cancer

computer aided diagnosis (CAD) using a recently developed SVM/GRNN oracle hybrid, IEEE

International Conference on Systems, Man and Cybernetics, 2003.

[10] C. T. Lin, C. M. Yeh, S. F. Liang, J. F. Chung and N. Kumar, Support-vector-based fuzzy

neural network for pattern classification, IEEE Trans. on Fuzzy Systems, 14(1) (2006), 31-41.

[11] A. O. Malagelada, Automatic mass segmentation in mammographic images, PhD Thesis,

Universitat de Girona, Spain, 2004.

[12] E. G. Mansoori, M. J. Zolghadri and S. D. Katebi,Using distribution of data to enhance

prformance of fuzzy classification systems, Iranian Journal of Fuzzy Systems, 4(1) (2007),21-36.

[13] E. G. Mansoori, M. J. Zolghadri, S. D. Katebi, H. Mohabatkar, R. Boostani and M. H.Sadreddini,

Generating fuzzy for protein classification, Iranian Journal of Fuzzy Systems,5(2)(2008), 21-33.

[14] F. Moayedi, Z. Azimifar, R. Boostani and S. Katebi,Contourlet based mammography mass

classification, Lecture Notes in Computer Science, Image Analysis and Recognition, 4633(2007), 923-934.

[15] F. Moayedi, R. Boostani, Z. Azimifar and S. Katebi,A support vector based fuzzy neural network

approach for mass classification in mammography, International Conference on Digital

Signal Processing, Britain, 2007.

[16] R. Mousa, Q. Munib and A. Mousa,Breast cancer diagnosis system based on wavelet analysis

and fuzzy-neural netwrok, IEEE Trans. on Image Processing, 28(4) (2005), 713-723.

[17] D. Y. Po and N. Do, Directional multiscale modeling of images using the contourlet transform,

IEEE Trans. on Image Processing, (2006), 1-11.

[18] D. Raba, A. Oliver, J. Marti, M. Peracaula and J. Espunya, Breast segmentation with pectoral

muscle suppression on digital mammograms, Springer-Verlag: Medical Imaging: Pattern

Recognition and Image Analysis, 3523 (2005), 471-478.

[19] M. Roffilli, Advanced machine learning techniques for digital mammography, Technical Report,

Department of Computer Science University of Bologna, Italy, 2006.

[20] M. S. B. Sehgal, I. Gondal and L. Dooley, Support vector machine and generalized regression

neural network based classification fusion models for cancer diagnosis, proceedings in Fourth

IEEE International Conference on Hybrid Intelligent System, Computer Society, 2004.

[21] L. Semler and L. Dettori, A comparison of wavelet-based and ridgelet-based texture classification

of tissues in computed tomography, International Conference on Computer Vision

Theory and Applications, 2006.

[22] L. Semler, L. Dettori and J. Furst, Wavelet-based texture classification of tissues in computed

tomography, IEEE International Symposium on Computer-Based Medical Systems, 2005.

[23] J. L. Starck, E. J. Candes and D. L. Donoho, The curvelet transform for image denoising,

IEEE Trans. on Image Processing, 11(6) (2002), 670-684.

[24] C. Varelaa, P. G. Tahocesb, A. J. Mndezc, M. Soutoa and J. J. Vidala, Computerized detection

of breast masses in digitized mammograms, Computers in Biology and Medicine, 37(2)(2007), 214-226.

[25] W. Xiaodan and W. Chongming, Using membership function to improve multi-class SVM

classification, ICSP Proceeding, China, 2004.

[26] Z. Yu and C. Bajaj, A fast and adaptive for image contrast enhancement, IEEE International

Conference on Image Processing, 2004.

[27] M. Zhu and A. M. Martinez, Subclass discriminant analysis, IEEE Trans. on Pattern Analysis

and Machine Intelligence, 28(8) (2006), 1247-1286.

آمار
تعداد مشاهده مقاله: 3,112
تعداد دریافت فایل اصل مقاله: 2,203


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب