Tumor detection by using Zernike moments on segmented magnetic resonance brain images

Expert Systems with Applications

Volumn 37, Issue 3, 2010, Pages 2540-2549

  Iscan, Zafer ^a   Dokur, Zümray ^a   Ölmez, Tamer ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Brain tumor detection; Continuous wavelet transform; Incremental neural networks; MR image segmentation; Symmetry axis determination; Zernike moments

Indexed keywords

BRAIN TUMOR DETECTION; CONTINUOUS WAVELET TRANSFORM; INCREMENTAL NEURAL NETWORKS; MR IMAGE SEGMENTATION; SYMMETRY AXIS DETERMINATION; ZERNIKE MOMENTS;

DIGITAL IMAGE STORAGE; HISTOLOGY; IMAGE SEGMENTATION; MAGNETIC RESONANCE; NEURAL NETWORKS; WAVELET TRANSFORMS;

TUMORS;

EID: 70449523073     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.08.003     Document Type: Article

References (31)

1. Cabral J.E., White K.S., Kim Y., and Effmann L. Interactive segmentation of brain tumors in MR images using 3D region growing. Medical Imaging: Image Processing 1898 (1993) 171-181
2. Clark M.C., Hall L.O., Goldgof D.B., Velthuizen R., Murtagh F.R., and Silbiger M.S. Automatic tumor segmentation using knowledge-based techniques. IEEE Transactions on Medical Imaging 17 (1998) 187-201
3. Csernansky J.G., Joshi S.C., Wang L., Miller J.P., and Miller M.I. Hippocampal morphometry in schizophrenia by high dimensional brain mapping. Proceedings of the National Academy of Sciences 95 (1998) 11406-11411
4. Csernansky J.G., Wang L., Joshi S., Miller J., Gado M., et al. Early date is distinguished from aging by high-dimensional mapping of the hippocampus. Neurology 55 (2000) 1636-1643
5. 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.
6. Dokur Z., Iscan Z., and Ölmez T. Segmentation of medical images by using wavelet transform and incremental self-organizing map. Lecture Notes in Artificial Intelligence, LNAI 4293 (2006) 800-809
7. Feleppa E.J., Kalisz A., Melgar J.B.S., Lizzi F.L., Liu T., Rosado A.L., Shao M.C., Fair W.R., Wang Y., Cookson M.S., Reuter V.E., and Heston W.D.W. Typing of prostate tissue by ultrasonic spectrum analysis. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control 3 4 (1996) 609-619
8. Gibbs P., Buckley D., Blackband S., and Horsman A. Tumour volume determination from MR images by morphological segmentation. Physics in Medicine and Biology 13 (1996) 2437-2446
9. 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
10. Hsiang W.C., and Sun N.Y. Segmentation of kidney from ultrasound B-mode images with texture-based classification. Computer Method & Programs in Biomedicine 84 2-3 (2006) 114-123
11. Ho, S, Bullitt, E, & Gerig, G. (2002). Level set evolution with region competition: automatic 3-D segmentation of brain tumors. In International conference on pattern recognition (Vol. 1, pp. 532-535).
12. Iscan Z., Kurnaz M.N., Dokur Z., and Ölmez T. Ultrasound image segmentation by using wavelet transform and self-organizing neural network. Neural Information Processing - Letters and Reviews 10 8-9 (2006) 183-191
13. Jain A.K. Fundamentals of digital image processing (1989), Prentice Hall Inc.
14. Joshi S., Lorenzen P., Gerig G., and Bullitt E. Structural and radiometric in brain images. Medical Image Analysis 7 (2003) 155-170
15. Junck L., Moen J.G., Hutchins G.D., Brown M.B., and Kuhl D.E. Correlation methods for the centering, rotation, and alignment of functional brain images. Journal of Nuclear Medicine 31 (1990) 1220-1226
16. 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
17. Llobet R., Perez-Cortes J.C., Toselli A.H., and Juan A. Computer-aided detection of prostate cancer. International Journal of Medical Informatics 76 (2007) 547-556
18. Mancas, M., & Gosselin, B. (2003). Iterative watersheds and fuzzy tumor visualization. In Proceeding of the IEEE visualization conference.
19. Kaus M., Warfield S.K., Nabavi A., Black P.M., Jolesz F.A., and Kikinis R. Automated segmentation of MRI of brain tumors. Radiology 218 (2001) 586-591
20. Prima S., Ourselin S., and Ayache N. Computation of the midsagittal plane in 3D brain images. IEEE Transactions on Medical Imaging 21 (2002) 122-138
21. Retico A., Delogu P., Fantacci M.E., and Kasae P. An automatic system to discriminate malignant from benign massive lesions on mammograms. Nuclear Instruments and Methods in Physics Research, A (2006) 596-600
22. Saha P.K., and Udupa J.K. Fuzzy connected object delineation: Axiomatic path strength definition and the case of multiple seeds. Computer Vision and Image Understanding 83 (2001) 275-295
23. Smith, S, & Jenkinson, M. (1999). Accurate robust symmetry estimation. In Proceedings of medical image computing and computer-assisted intervention.
24. Sun C., and Sherrah J. 3D symmetry detection using the extended Gaussian image. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 2 (1997) 164-168
25. Styner M., and Gerig G. Medial models incorporating object variability for 3d shape analysis. Information Processing in Medical Imaging (2001), Springer 502-516
26. Thompson P.M., Mega M.S., Vidal C., and Toga A.W. Detecting disease-specific patterns of brain structure using cortical pattern matching and a population-based probabilistic brain atlas. Information Processing in Medical Imaging (2001), Springer 488-501
27. Udupa J.K., and Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graphical Models and Image Processing 58 3 (1996) 246-261
28. Udupa J.K., Saha P.K., and Lotufo R.A. Relative fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 11 (2002) 1485-1500
29. Udupa J.K., Wei L., Samarasekera S., Miki Y., Buchem M.A., and Grossman R.I. Multiple sclerosis lesion quantification using fuzzy-connectedness principles. IEEE Transactions on Medical Imaging 16 (1997) 598-609
30. Warfield S.K., Kaus M., Jolesz F.A., and Kikinis R. Adaptive, template moderated, spatially varying statistical classification. Medical Image Analysis 4 1 (2000) 43-55
31. 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