Tissue segmentation in ultrasound images by using genetic algorithms

Expert Systems with Applications

Volumn 34, Issue 4, 2008, Pages 2739-2746

  Dokur, Zümray ^a   Ölmez, Tamer ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Genetic algorithms; Incremental neural network; Segmentation of ultrasound images; Wavelet transform

Indexed keywords

FEATURE EXTRACTION; GENETIC ALGORITHMS; NEURAL NETWORKS; PIXELS; TISSUE; ULTRASONICS; WAVELET TRANSFORMS;

INCREMENTAL NEURAL NETWORKS; TISSUE SEGMENTATION; ULTRASOUND IMAGES;

IMAGE ANALYSIS;

EID: 38649106091     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.05.002     Document Type: Article

References (26)

1. Arrowsmith, M.J., Varley, M.R., Picton, P.D., & Heys, J.D. (1999). Hybrid neural network system for texture analysis. In Seventh international conference on image processing and its applications, Vol. 1, pp. 339-343.
2. Dasarathy B., and Holter E. Image characterization based on joint gray level run length distributions. Pattern Recognition Letters 12 (1991) 497-502
3. Dokur, Z., & Ölmez, T. (2000). Classification of magnetic resonance images using a novel neural network. In IEEE-EMBS Asia Pacific conference on biomedical engineering, Hangzhou, China.
4. Dokur Z., and Ölmez T. ECG beat classification by a novel hybrid neural network. Computer Methods and Programs in Biomedicine 66 (2001) 167-181
5. Dokur Z., and Ölmez T. Segmentation of ultrasound images by using a hybrid neural network. Pattern Recognition Letters 23 14 (2002) 1825-1836
6. http://drgdiaz.com.
7. http://www.fantom.suite.dk.
8. Feleppa E.J., Kalisz A., Melgar J.B.S., Lizzi F.L., Liu T., Rosado A.L., et al. Typing of prostate tissue by ultrasonic spectrum analysis. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control 43 4 (1996) 609-619
9. Goldberg D.E. Genetic algorithms in search optimization and machine learning (1989), Addison Wesley Pub. Comp. Inc
10. Haering N., and Lobo N.D. Features and classification methods to locate deciduous trees in images. Computer Vision and Image Understanding 75 1-2 (1999) 133-149
11. Haralick R.M., Shanmugan K., and Dinstein I. Texture feature for image classification. IEEE Transactions on Systems Man and Cybernetics 3 (1973) 610-621
12. Kim N.D., Amin V., Wilson D., Rouse G., and Udpa S. Ultrasound image texture analysis for characterizing intramuscular fat-content of live beef-cattle. Ultrasound Imaging 20 3 (1998) 191-205
13. Kohonen T. Self-organization and associative memory (1988), Springer-Verlag, New York
14. Kurnaz, M.N., Dokur, Z., & Ölmez, T. (2001). Segmentation of ultrasound images by using an incremental self-organized map. In: 23rd Annual international conference of the IEEE-EMBS, Vol. 3 (pp. 2638-2640).
15. Lefebvre F., Berger G., and Laugier P. Automatic detection of the boundary of the calcaneus from ultrasound parametric images using an active contour model-clinical assessment. IEEE Transactions on Medical Imaging 17 1 (1998) 45-52
16. Mallat S. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on PAMI 11 (1989) 674-693
17. Ogawa K., Fukushima M., Kubota K., and Hisa N. Computer-aided diagnostic system for diffuse liver-diseases with ultrasonography by neural networks. IEEE Transactions on Nuclear Science 45 6 (1998) 3069-3074
18. Ölmez, T., & Yazgan, E. (1992). Abnormal tissue detection in computer tomography images using artificial neural networks. In Proceedings of the Annual International Conference of the IEEE-EMBS, Vol. 14. pp. 998-999.
19. Ölmez T., and Dokur Z. Classification of heart sounds using an artificial neural network. Pattern Recognition Letters 24 1-3 (2003) 617-629
20. Parkkinen J., Selkainaho K., and Oja E. Detecting texture periodicity from co-occurrence matrix. Pattern Recognition Letters 11 (1990) 43-50
21. Polikar, R. (1996). Fundamental Concepts &An Overview of the Wavelet Theory, Tutorial, http://users.rowan.edu/~polikar/WAVELETS/WTpart1.html.
22. Qian W., Li L.H., and Clarke L.P. Image feature extraction for mass detection in digital mammography-Influence of wavelet analysis. Medical Physics 26 3 (1999) 402-408
23. Reza, N., & Ying, S. (1991). Classification of digital angiograms using artificial neural networks, In 13th Annual international conference of the IEEE-EMBS 13(3) (pp. 1440-1441).
24. Tian B., Shaikh M.A., Azimisadjadi M.R., Vonderhaar T.H., and Reinke D.L. A study of cloud classification with neural networks using spectral and textural features. IEEE Transactions on Neural Networks 10 1 (1999) 138-151
25. Wu C.M., Chen Y.C., and Hsieh K.S. Texture feature for classification of ultrasound liver images. IEEE Transactions on Medical Imaging 11 2 (1992) 141-152
26. Zhang W., Yoshida H., Nishikawa R.M., and Doi K. Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms. Medical Physics 25 6 (1998) 949-956