Contourlet-based mammography mass classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4633 LNCS, Issue , 2007, Pages 923-944

  Moayedi, Fatemeh ^a   Azirnifar, Zohreh ^a   Boostani, Reza ^a   Katebi, Serajodin ^a  

^a SHIRAZ UNIVERSITY   (Iran)

Author keywords

Contourlet; Mammography; Mass detection; Support vector machine

Indexed keywords

FEATURE EXTRACTION; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; SUPPORT VECTOR MACHINES;

BREAST MASS DETECTION; MASS DETECTION;

MAMMOGRAPHY;

EID: 37849017515     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74260-9_82     Document Type: Conference Paper

References (16)

1. Mu, T., Nandi, A.K.: Detection of breast cancer using v-SVM and RBF networks with self organization selection of centers. In: Third IEEE International Seminar on Medical Applications of Signal Processing (2005)
2. Raba, D., Oliver, A., Marti, J., Peracaula, M., Espunya, J.: Breast segmentation with pectoral muscle suppression on digital mammograms. In: Marques, J.S., Pérez de la Blanca, N.: Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 471-478. Springer, Heidelberg (2005)
3. Malagelada, A.O.: Automatic mass segmentation in mammographic images. PhD thesis, Universität de Giroria (2004)
4. Roffilli, M.: Advanced machine learning techniques for digital mammography. Technical Report, Department of Computer Science University of Bologna, UBLCS2006-12 (2006)
5. Semler, L., Dettori, L., Furst, J.: Wavelet-based texture classification of tissues in computed tomography. In: IEEE International Symposium on Computer-Based Medical Systems (2005)
6. Semler, L., Dettori, L.: A comparison of wavelet-based and ridgelet-based texture classification of tissues in computed tomography. In: International Conference on Computer Vision Theory and Applications (2006)
7. Mousa, R., Munib, Q., Mousa, A.: Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural network. IEEE Trans. on Image Processing 28(4), 713-723 (2005)
8. Semler, L., Dettori, L.: Curvelet-based texture classification of tissues in computed tomography. In: IEEE International Conference on Image Processing (2006)
9. Do, M.N., Vetterli, M.: The contourlet transform: An efficient directional multi-resolution image representation. IEEE Trans, on Image Processing 14(12), 2091-2106 (2005)
10. Candes, E.J., Donoho, D.L.: Curvelets: A surprisingly effective non adaptive representation for objects with edges. In: Saint-Malo Proceedings, pp. 1-10. Vanderbilt Univ., Nashville, TN (2000)
11. Fischer, E.A., Lo, J.Y., Markey, M.K.: Bayesian networks of BI-RADS descriptors for breast lesion classification. IEEE EMBS San Francisco 4, 3031-3034 (2004)
12. Bovis, K., Singh, S., Fieldsend, J., Pinder, C.: Identification of masses in digital mammograms with MLP and RBF nets. IEEE Trans, on Image Processing 1, 342-347 (2005)
13. Freixenet, O.J., Bosch, A., Raba, D., Zwiggelaar, R.: Automatic classification of breast tissue. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 431-438. Springer, Heidelberg (2005)
14. Lin, C., Yeh, C., Liang, S., Chung, J., Kumar, N.: Support vector based fuzzy neural network for pattern classification. IEEE Trans, on Fuzzy Systems 14(1), 31-41 (2006)
15. Yu, Z., Bajaj, C.: A fast and adaptive for image contrast enhancement. In: IEEE International Conference on Image Processing (2004)
16. El-Naqa, I., Yang, Y., Wernick, M., Galatsanos, N., Nishikawa, R.: A Support Vector Machine approach for detection of microcalcifications. IEEE Trans. on Medical Imaging 21(12), 1552-1563 (2002)