We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Abnormality Segmentation of MRI Brain Images using Fuzzy Nearest Neighbour Approach

by T. Akhila Thankam, K. S. Angel Viji
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 19
Year of Publication: 2013
Authors: T. Akhila Thankam, K. S. Angel Viji
10.5120/11501-7215

T. Akhila Thankam, K. S. Angel Viji . Abnormality Segmentation of MRI Brain Images using Fuzzy Nearest Neighbour Approach. International Journal of Computer Applications. 67, 19 ( April 2013), 6-12. DOI=10.5120/11501-7215

@article{ 10.5120/11501-7215,
author = { T. Akhila Thankam, K. S. Angel Viji },
title = { Abnormality Segmentation of MRI Brain Images using Fuzzy Nearest Neighbour Approach },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 19 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number19/11501-7215/ },
doi = { 10.5120/11501-7215 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:25:51.124074+05:30
%A T. Akhila Thankam
%A K. S. Angel Viji
%T Abnormality Segmentation of MRI Brain Images using Fuzzy Nearest Neighbour Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 19
%P 6-12
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Many medical imaging techniques help in learning human brain. Magnetic resonance imaging, is a technique which is very efficient in differentiating between soft tissues. There are some techniques that accomplish the goal of tissues detection and extraction. The main objective is to provide a reliable tool to evaluate brain, by improving tissue contrast and visualization, thus reducing workload of specialist in the area. This allows a further systematic follow-up of the evolution of tumors or its treatment. A solution to this problem is offered by semi supervised anomaly detection after spatial normalization. It makes use of normal data modeling and then a distance measure and thresholding to determine abnormality in MRI. The estimation of the probability density function is usually used to treat every image as a network of locally coherent image partitions (overlapping blocks). So a strictly concave likelihood function for estimating abnormality onto each partition have been formulated and the local estimates are fused into a globally optimal estimate that satisfies the consistency constraints. Fuzzy based approach can be defined to enhance the performance of the system by providing active learning based approach. Fuzzy clustering algorithms proposed an energy-minimization approach to the coherent local intensity clustering (CLIC), with the aim of achieving tissue clustering of abnormalities properly. This approach provides efficient system for the easy detection of abnormalities.

References
  1. Evangelia,I. "Abnormality Segmentation in Brain Images Via Distributed Estimation". IEEE Transactions On Information Technology In Biomedical. Vol. 16 ,May 2012,no. 3.
  2. W. L. Cai, S. C. Chen, and D. Q. Zhang. "Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation", Pattern Recognit. vol. 40, Mar 2007,no. 3, pp. 825–838.
  3. S. Dambreville, Y. Rathi, and A. Tannenbaum, "A framework for image segmentation using shape models and kernel space shape priors,"IEEE Trans. Pattern Anal. Mach. Intell. , vol. 30, Aug 2008, no. 8, pp. 1385–1399.
  4. K. Sikka, N. Sinha, P. K. Singh, and A. K. Mishra, "A fully automated algorithm undermodified FCM framework for improved brain MR image segmentation," Magn. Reson. Imaging, vol. 27, Sep 2009, no. 7, pp. 994–1004.
  5. M. S. Yang and H. S. Tsai, "A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction," Pattern Recognit. Lett. , vol. 29, Sep. 1, 2008, no. 12, pp. 1713–1725.
  6. R. J. He, S. Datta, B. R. Sajja, and P. A. Narayana, "Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images," Comput. Med. Imaging Graph. , vol. 32, Jul 2008, no. 5, pp. 353–366.
  7. Y. A. Tolias and S. M. Panas, "Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions,"IEEE Trans. Syst. , Man, Cybern. A, Syst. , Humans, vol. 28, May 1998, no. 3, pp. 359–369.
  8. Y. Xia, D. G. Feng, T. J. Wang, R. C. Zhao, and Y. N. Zhang, "Image segmentation by clustering of spatial patterns," Pattern Recognit. Lett. ,vol. 28, Sep. 1,2007,no. 12, pp. 1548–1555.
  9. J. Z. Wang, J. Kong, Y. H. Lu, M. Qi, and B. X. Zhang, "A modified FCM algorithm for MRI brain image segmentation using both local and nonlocal spatial constraints," Comput. Med. Imaging Graph. , vol. 32, Dec. 2008,no. 8, pp. 685–698.
  10. L. L. He and I. R. Greenshields, "AnMRF spatial fuzzy clustering method for fMRI SPMs," Biomed. Signal Process. Control, vol. 3,Oct 2008, no. 4, pp. 327–333.
  11. D. Q. Zhang and S. C. Chen, "A novel kernelized fuzzy C-means algorithm with application in medical image segmentation," Artif. Intell. Med. , vol. 32, Sep 2004, no. 1, pp. 37–50.
  12. S. C. Chen and D. Q. Zhang, "Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,"IEEE Trans. Syst. , Man, Cybern. B, Cybern. , vol. 34, Aug 2004 no. 4, pp. 1907–1916.
  13. B. Yuan, G. J. Klir, and J. F. Swan-Stone, "Evolutionary fuzzy c-means clustering algorithm," in Proc. 1995 IEEE Int. Conf. Fuzzy Syst. Int. Joint Conf. 4th IEEE Int. Conf. Fuzzy Syst. 2nd Int. Fuzzy Eng. Symp. , pp. 663–670.
  14. S. Datta, B. R. Sajja, R. He, J. S. Wolinsky, R. K. Gupta, and P. A. Narayana, "Segmentation and quantification of black holes in multiple sclerosis," NeuroImage, vol. 29,Sep 2006, pp. 467–474.
  15. K. VanLeemput,F. Maes,D. Vandermeulen,A. Colchester,andP. Suetens,"Automated segmentation of multiple sclerosis lesions by model outlier detection," IEEE Trans. Med. Imag. , vol. 20, Aug. 2001, no. 8, pp. 677–688.
  16. F. B. Mohamed, S. Vinitski, C. F. Gonzalez, S. H. Faro, F. A. Lublin,R. Knobler, and J. E. Gutierrez, "Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: Preliminary results," Magn. Reson. Imag. , vol. 19,Sep 2001, pp. 207–218.
  17. F. Yang, T. Jiang, W. Zhu, and F. Kruggel, "White matter lesion segmentation from volumetric MR images," Lect. Notes Comput. Sci. , vol. 3150,Dec 2004, pp. 113–120.
  18. M. B. Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J. -G. Villemure, and J. -P. Thiran, "Atlas-based segmentation of pathological MR brain images using a model of lesion growth," IEEE Trans. Med. Imag. , vol. 23, no. 10,pp. 1301–1314,.
  19. E. A. Stamatakis and L. K. Tyler, "Identifying lesions on structural brain images—Validation of the method and application to neuropsychological patients," Brain Lang. , vol. 94, Oct. 2004, pp. 167–177.
  20. S. Srivastava, F. Maes, D. Vandermeulen, W. V. Paesschen, P. Dupont,and P. Suetens, "Feature-based statistical analysis of structural MR data for automatic detection of focal cortical dysplastic lesions," NeuroImage,vol. 27, July 2005, pp. 253–266.
  21. S. Shen, A. J. Szameitat, and A. Sterr, "VBM lesion detection depends on the normalization template: A study using simulated atrophy," Magn. Reson. Imag. , vol. 25, pp. 1385–1396, 2007. 540 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 12, JULY 2008, NO. 4.
  22. X. Wei, S. K. Warfield, K. H. Zou, Y. Wu, X. Li, A. Guimond, J. P. Mugler, III, R. R. Benson, L. Wolfson, H. L. Weiner, and C. R. G. Guttmann, "Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy," J. Magn. Reson. Imag. , vol. 15,2002, pp. 203–209.
  23. M. Kamber, R. Shinghal, D. L. Collins, G. S. Francis, and A. C. Evans,"Model-based 3D segmentation of multiple sclerosis lesions in magnetic resonance brainimages,"IEEETrans. Med. Imag. , vol. 14, Sep. 1995,no. 3, pp. 442–453.
  24. T. Ogawa, A. Inugami, H. Fujita, J. Hatazawa, E. Shimosegawa,K. Noguchi, T. Okudera, I. Kanno, K. Uemura, and A. Suzuki, "MR diagnosis of subacute and chronic subarachnoid hemorrhage: Comparison with CT," Amer. J. Roentgenol. , vol. 165,1995, pp. 1257–1262.
  25. R. Adolphs, H. Damasio, D. Tranel, G. Cooper, and A. R. Damasio, "Arole for somatosensory cortices in the visual recognition of emotion as revealed by three-dimensional lesion mapping," J. Neurosci. , vol. 20,2000,pp. 2683–2690.
  26. E. Bates, S. M. Wilson, A. P. Saygin, F. Dick, M. I. Sereno, R. T. Knight,and N. F. Dronkers, "Voxel-based lesion-symptom mapping," NatureNeurosci. , vol. 6, 2003,pp. 448–450.
  27. G. H. L. Lemieux, K. Krakow, and F. G. Woermann, "Fast, accurate,and reproducible automatic segmentation of the brain in weighted volume MRI data," Magn. Reson. Med. , vol. 42,1999,pp. 127–135.
  28. H. Tang, E. X. Wu, Q. Y. Ma, D. Gallagher, G. M. Perera, and T. Zhuang,"MRI brain image segmentation by multi-resolution edge detection and region selection," Comput. Med. Imag. Graph. , vol. 24, 2000,pp. 349–357.
  29. A. W. C. Liew and H. Yan, "An adaptive spatial fuzzy clustering algorithm for 3-D MRimage segmentation," IEEE Trans. Med. Imag. , vol. 22, Sep. 2003, no. 9, pp. 1063–1075.
  30. J. Ashburner and K. J. Friston, "Unified segmentation," NeuroImage,vol. 26,2005, pp. 83851.
  31. P. Anbeek, K. L. Vincken, M. J. P. van Osch, R. H. C. Bisschops, and J. Van Der Grond, "Automatic segmentation of different-sized white matter lesions by voxel probability estimation," Med. Image Anal. , vol. 8,2004,pp. 205–215.
  32. P. Anbeek, K. L. Vincken, G. S. van Bochove,M. J. P. van Osch, and J. Van Der Grond, "Probabilistic segmentation of brain tissue in MR imaging,"NeuroImage, vol. 27,2005, pp. 795–804.
  33. B. R. Sajja, S. Datta, R. He, M. Mehta, R. K. Gupta, J. S. Wolinsky, and P. A. Narayana, "Unified approach for multiple sclerosis lesion segmentation on brain MRI," Ann. Biomed. Eng. , vol. 34,Oct 2006, pp. 142–151.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy c-mean(FCM) MRI Image segmentation

Powered by PhDFocusTM