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| Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model
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Full
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Source |
International Journal of Image Processing (IJIP) |
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Table of Contents |
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Volume: 2 Issue: 1 |
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Pages: 1-34 |
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Publication
Date: February 2008 |
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ISSN
(Online): 1985-2304 |
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Pages |
27 - 34 |
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Author(s) |
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Published
Date |
30-02-2008 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: White Matter (WM), Gray Matter (GM), Cerebrospinal Fluid (CSF) |
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| Segmentation of images holds an important position in the area of image
processing. It becomes more important while typically dealing with medical
images where pre-surgery and post surgery decisions are required for the
purpose of initiating and speeding up the recovery process [5] Computer aided
detection of abnormal growth of tissues is primarily motivated by the necessity of
achieving maximum possible accuracy. Manual segmentation of these abnormal
tissues cannot be compared with modern day’s high speed computing machines
which enable us to visually observe the volume and location of unwanted tissues.
A well known segmentation problem within MRI is the task of labeling voxels
according to their tissue type which include White Matter (WM), Grey Matter
(GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor
etc. This paper describes an efficient method for automatic brain tumor
segmentation for the extraction of tumor tissues from MR images. It combines
Perona and Malik anisotropic diffusion model for image enhancement and
Kmeans clustering technique for grouping tissues belonging to a specific group.
The proposed method uses T1, T2 and PD weighted gray level intensity images.
The proposed technique produced appreciative results |
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Dong-yong Dai; Condon, B.; Hadley, D.; Rampling, R.; Teasdale, G.; "Intracranial deformation caused by brain tumors: assessment of 3-D surface by magnetic resonance imaging"IEEE Transactions on Medical Imaging Volume 12, Issue 4, Dec. 1993 Page(s):693 – 702
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http://noodle.med.yale.edu
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Matthew C. Clark “Segmenting MRI Volumes of the Brain With Knowledge- Based Clustering” MS Thesis, Department of Computer Science and Engineering, University of South Florida, 1994
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Dzung L. Pham, Chenyang Xu, Jerry L. Prince;"A Survey of Current Methods in Medical Medical Image Segmentation" Technical Report JHU / ECE 99-01, Department of Electrical and Computer Engineering. The Johns Hopkins University, Baltimore MD 21218, 1998.
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Zhou, J.; Chan, K.L.; Chong, V.F.H.; Krishnan, S.M “Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine” 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005, Page(s):6411 – 6414
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Velthuizen RP, Clarke LP, Phuphanich S, Hall LO, Bensaid AM, Arrington JA, Greenberg HM and Silbiger ML. "Unsupervised Tumor Volume Measurement Using Magnetic Resonance Brain Images," Journal of Magnetic Resonance Imaging , Vol. 5, No. 5, pp. 594-605, 1995.
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J. C. Bezdek, L. O. Hall, L. P. Clarke "Review of MR image segmentation techniques using pattern recognition. " Medical Physics vol. 20, no. 4, pp. 1033 (1993).
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Izquierdo, E.; Li-Qun Xu;Image segmentation using data-modulated nonlinear diffusion Electronics Letters Volume 36, Issue 21, 12 Oct. 2000 Page(s):1767 – 1769
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Guillermo N. Abras and Virginia L. Ballarin,; "A Weighted K-means Algorithm applied to Brain Tissue Classification", JCS&T Vol. 5 No. 3, October 2005.
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S. Wareld, J. Dengler, J. Zaers, C. Guttmann, W. Gil, J. Ettinger, J. Hiller, and R. Kikinis. “Automatic identication of grey matter structures from mri to improve the segmentation of white matter lesions”. J. of Image Guided Surgery, 1(6):326{338, 1995.
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Perona, P.; Malik, J.; “Scale-space and edge detection using anisotropic diffusion” Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume 12, Issue 7, July 1990 Page(s):629 – 639
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Dmitriy Fradkin, Ilya Muchnik (2004)"A Study of K-Means Clustering for Improving Classification Accuracy of Multi-Class SVM". Technical Report. Rutgers University, New Brunswick, New Jersey 08854, April, 2004.
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| M. Masroor Ahmed : Colleagues
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| Dzulkifli Bin Mohammad : Colleagues
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