Breast cancer diagnosis based on a suitable combination of deformable models and artificial neural networks techniques

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

Volumn 4756 LNCS, Issue , 2007, Pages 803-811

  López, Yosvany ^a   Novoa, Andra ^a   Guevara, Miguel A ^a   Silva, Augusto ^b  

^a Universidad de Ciego de Avila   (Cuba)

^b UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

Artificial neural networks; Breast cancer; Deformable models; Mammography images

Indexed keywords

COMPUTER AIDED DIAGNOSIS; DATABASE SYSTEMS; IMAGE ANALYSIS; MAMMOGRAPHY; MATHEMATICAL MODELS; MEDICAL IMAGING; NEURAL NETWORKS;

ARCHITECTURAL DISTORTIONS; BREAST CANCER; DEFORMABLE MODELS;

TUMORS;

EID: 38549181889     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (16)

1. Nishikawa, S.R., Papaioannou, J., Onsis, R., Haldemann Heusler, R.A., Giger, M.L., et al.: Performance of a prototype clinical intelligent mammography workstation. In: Digital mammography, Elsevier, Amsterdam (1996)
2. Roehrig, J.D.T., Hasegawa, A., Hunt, B., Marshall, J., Romsdahl, H., et al.: Clinical results with R2 ImageChecker system. In: Digital mammography, Kluwer academic Publishers, Dordrecht (1998)
3. Shih-Chung, B.L.H.L., Wang, Y., Kinnard, L., Freedman, M.T.: Multiple Circular Path Convolution Neural Network System for Detection of Mammographic Masses. IEEE Transaction on Medical Imaging 150-158 (2002)
4. Sepehr M.H.Jamarani, B.G.R.-r., Behnam, C.H.: A Novel Method for Breast Cancer Prognosis Using Wavelet Packet Based Neural Network. In: IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China (2005)
5. Segyeong Joo, Y.S.Y., Moon, W.K., Kim, H.C.: Computer-Aided Diagnosis of Solid Breast Nodules: Use of an Artificial Neural Network Based on Multiple Sonographic Features. IEEE Transaction on Medical Imaging 1292-1300 (2004)
6. Paul Sajda, C.S., Pearson, J.: Learning Contextual Relationships in Mammograms Using a Hierarchical Pyramid Neural Network. IEEE Transaction on Medical Imaging 239-250 (2002)
7. Ping Zhang, B.V., Kumar, K.: Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recognition Letters 1-11 (2004)
8. Lúcio, F.A., Campos, A.C.S., Barros, A.K.: Diagnosis of Breast Cancer in Digital Mammograms Using Independent Component Analysis and Neural Networks. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, Springer, Heidelberg (2005)
9. Artur Chodorowski, U.M., Langille, M., Hamarneh, G.: Color Lesion Boundary Detection Using Live Wire. Medical Imaging (2005)
10. Jianming Liang, T.M., Terzopoulos, D.: United Snakes. Medical Image Analysis p. 215-233 (2006)
11. Ghassan Hamarneh, J.H.: DTMRI: Segmentation using DT-Snakes and DT-Livewire. In: IEEE International Symposium on Signal Processing and Information Technology (2006)
12. Kelvin Poon, G.H.: Rafeef Abugharbieh, Segmentation of Complex Objects with Non-Spherical Topologies from Volumetric Medical Images using 3D Livewire (2006)
13. Karsten Rodenacker, E.B.: A feature set for cytometry on digitized microscopic images. Analytical Cellular Pathology 1-36 (2003)
14. Hinton, G.E., William, D.E.R.R.J.: Learning internal representation by back-propagation errors. Nature 323, 533-536 (1986)
15. Widrow, B.: M.E.H. Adaptive switching circuits. in IRE WESCON Convention Record, New York IRE (1960)
16. Kohonen, T. (ed.): Self-Organization and Associative Memory, 2nd edn. Springer, Berlin (1987)