An efficient neural network based method for medical image segmentation

Computers in Biology and Medicine

Volumn 44, Issue 1, 2014, Pages 76-87

  Torbati, Nima ^a   Ayatollahi, Ahmad ^a   Kermani, Ali ^a  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

Author keywords

Artificial neural network (ANN); Computer aided diagnosis (CAD) systems; Medical image segmentation; Pattern recognition

Indexed keywords

BREAST ULTRASOUND IMAGES; COMPUTER AIDED DIAGNOSIS(CAD); INITIAL SEGMENTATION; MOVING AVERAGES; SEGMENTATION RESULTS; SELF-ORGANIZING MAP NETWORKS; SUPERVISED NEURAL NETWORKS; X RAY COMPUTERIZED TOMOGRAPHY;

COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; CONFORMAL MAPPING; DISCRETE WAVELET TRANSFORMS; IMAGE SEGMENTATION; MAGNETIC RESONANCE; MEDICAL IMAGE PROCESSING; MEDICAL IMAGING; PATTERN RECOGNITION; TUMORS;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; COMPUTER AIDED DESIGN; COMPUTER ASSISTED TOMOGRAPHY; DISCRETE WAVELET TRANSFORM; ECHOMAMMOGRAPHY; GRAY MATTER; HUMAN; IMAGE ANALYSIS; IMAGING SYSTEM; INCREMENTAL SUPERVISED NEURAL NETWORK; INTERMETHOD COMPARISON; MEDICAL IMAGE SEGMENTATION; NERVE CELL; NEUROIMAGING; NOISE; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSICIAN; PRIORITY JOURNAL; SELF ORGANIZING MAP; WAVELET ANALYSIS; WHITE MATTER;

ARTIFICIAL NEURAL NETWORK (ANN); COMPUTER AIDED DIAGNOSIS (CAD) SYSTEMS; MEDICAL IMAGE SEGMENTATION; PATTERN RECOGNITION;

ALGORITHMS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); TOMOGRAPHY, X-RAY COMPUTED;

EID: 84888588407     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2013.10.029     Document Type: Article

References (30)

1. Gonzalez.R.C., Woods R.E. Digital Image Processing 1992, Addison-Wesley, Reading, MA.
2. Canny J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986, 8:679-698.
3. Parker J.R. Algorithms for Image Processing and Computer Vision 1997, Wiley, New York.
4. Lemieux L., Hagemann G., Krakow K., Woermann F.G. Fast, accurate, and reproducible automatic segmentation of the brain in T1 weighted volume MRI data. Magn. Reson. Med. 1999, 42:127-135.
5. Pohle R., Toennies K.D. Segmentation of medical images using adaptive region growing. Proc. SPIE-Med. Imaging 2001, 4322:1337-1346.
6. Law T.Y., Heng P.A. Automated extraction of bronchus from 3D CT images of lung based on genetic algorithm and 3D region growing. Proc. SPIE-Med. Imaging 2000, 3979:906-916.
7. Kannan S.R. A new segmentation system for brain MR images based on fuzzy techniques. J. Appl. Soft Comput. 2008, 8:1599-1606.
8. Chuang K.S., et al. Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 2006, 30:9-15.
9. Al-Sultan K.S., Fedjki C.A. A Tabu search-based algorithm for the fuzzy clustering problem. Pattern Recognition 1997, 30:2023-2030.
10. Ahmed M.N., et al. Modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 2002, 21:193-199.
11. Noble J., Boukerroui D. Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 2006, 25:987-1010.
12. Jiang J., Trundle P., Ren J. Medical image analysis with artificial neural networks. Comput. Med. Imaging Graph. 2010, 34:617-631.
13. Powell S., Magnotta V.A., Johnson H., Jammalamadaka V.K., Pierson R., Andreasen N.C. Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures. NeuroImage 2008, 39:238-247.
14. P.L. Chang, W.G. Teng, Exploiting the self-organizing map for medical image segmentation, in: 20th IEEE International Symposium on Computer-Based Medical Systems, vol. 7, 2007, pp. 281-288.
15. Iscan Z., Kurnaz M.N., Dokur Z., Olmez T. Ultrasound image segmentation by using wavelet transform and self-organizing neural network. Neural Inform. Process.-Lett. Rev. 2006, 10(8-9):183-191.
16. Chang C.Y., Chung P.C. Medical image segmentation using a contextual constraint-based Hopfield neural cube. Image Vision Comput. 2001, 19:669-678.
17. Cheng K.S., Lin J.S., Mao C.W. Application of competitive Hopfield neural network to medical image segmentation. IEEE Trans. Med. Imaging 1996, 15:560-561.
18. Z. Xiao, J. Shi, Q. Chang, Automatic image segmentation algorithm based on PCNN and fuzzy mutual information, in: IEEE International Conference on Computer and Information Technology, 2009, pp. 241-245.
19. Kohonen T. Self-organization, and Associative Memory 1989, Springer-Verlag, Berlin.
20. Iscan Z., Yuksel A., Dokur Z., Korurek M., Olmez T. Medical image segmentation with transform and moment based features and incremental supervised neural network. Digital Signal Process. 2009, 19:890-901.
21. Tucci M., Raugi M. Adaptive FIR neural model for centroid learning in self-organizing maps. IEEE Trans. Neural Networks 2010, 21.
22. Haykin S. Neural Networks: A Comprehensive Foundation 1999, Prentice-Hall. 2nd ed.
23. Madabhushi A., Metaxas D.N. Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans. Med. Imaging 2003, 22(2):155-169.
24. http://www.drgdiaz.com/ascitis_bladder.sthml.
25. http://www.bic.mni.mcgill.ca/brainweb/.
26. Udupa J.K., LeBlanc V.R., Ying Z.G., Imielinska C., Schmidt H., Currie L.M., Hirsch B.E., Woodburn J. A framework for evaluating image segmentation algorithms. Comput. Med. Imaging Graph. 2006, 30(2):75-87.
27. Crum W.R., Camara O., Hill D.L.G. Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imaging 2006, 25(11):1451-1461.
28. J. Shan, H.D. Cheng, Y. Wang, A novel automatic seed point selection algorithm for breast ultrasound images, in: 19th International Conference on Pattern Recognition, 2008, pp. 1-4.
29. Hassanien A., Al-Qaheri H., El-Dahshan E.A. prostate boundary detection in ultrasound images using biologically-inspired spiking neural network. Appl. Soft Comput. 2011, 11(2):2035-2041.
30. Hassanien A., Kim T. Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural network. J. Appl. Logic 2012, 10(4):277-284.