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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 9    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 9, September 2011
Optimal Thinning Algorithm for detection of FCD in MRI Images
Full Text(PDF, 3000)  PP.  
Dr.P.Subashini, S.Jansi
FCD, Parallel thinning algorithm, Skeleton, Performance Metrics, and MRI Images
Thinning is essentially a "pre-processing" step in many applications of digital image processing, computer vision, and pattern recognition. In many computer vision applications, the images interested in a scene can be characterized by structures composed of line or curve or arc patterns for shape analysis. It is used to compress the input data and expedite the extraction of image features. In this paper three different thinning algorithms are applied for MRI Brain Images to estimate performance evaluation metrics of thinned images. Image thinning reduces a large amount of memory usage for structural information storage. Experimental result shows the performance of the proposed algorithm.
[1] O. Colliot, T. Mansi, N. Bernasconi, V. Naessens, D. Klironomos, and A. Bernasconi. “Segmentation of focal cortical Dysplasia lesions on MRI using level set evolution”, Neuro Image Vol 32, Issue 4, 1 October 2006, Pages 1621-1630.

[2] Jeny Rajan, K.Kannan, C. Kesavadas, Bejoy Thomas, A.K. Gupta, “Focal Cortical Dysplasia (FCD) Lesion Analysis with Complex Diffusion Approach”, Vol 33, Issue7, Pages:553-558.

[3] Louisa Lam, Seong-Whan Lee, “A Survey of Thinning Methodologies”, IEEE transactions on pattern analysis and machine intelligence, Vol.14, No.9, September 1992.

[4] P. C. K. Kwok, ”Customizing Thinning Algorithms”, Processings of IEEE International conference of Image Processing Applications, 1989,pp. 633-637.

[5] Alberto Martin and Sabri Tosunoglu, “Image Processing techniques for Machine Vision”, 1986.

[6] T. Y. Zhang and C. Y. Suen, ""A fast parallel algorithm for thinning digital patterns,' Comm.ACM, Vo1.27, No.3.

[7] Petrosino, A.; Salvi, G., “A Two-Subcycle Thinning Algorithm and Its Parallel Implementation on SIMD Machines, IEEE Transactions on Image Processing, Feb 2000, pp. 277 – 283

[8] P. S. P. Wang and Y. Y. Zhang, “A Fast and Flexible Thinning Algorithm”, IEEE Transactions on Computers, Vol 38.Issue5,1989,pp. 741 – 745.

[9] Khalid Saeed, Marek Tabe, et al[], “K3M: A universal algorithm for image skeletonization and a review of thinning techniques”, International Journal of Applied Mathematics and Computer Science, 2010, Vol. 20, No. 2, pp. 317–335.

[10] Marisa R. De Giusti, “Manuscript Document Digitization and Recognition: A first approach”. JCS&T Vol. 5 No. 3, 2005.

[11] P. Subashini and N.Valliammal, “An efficient Thinning Algorithms for Tamil Handwritten Characters”, IETECH Journal of Advanced Computations, 2007, Vol 1, pp.056 – 062.

[12] R. T. Chin, Hong-Khoon Wan, D. L. Stover, and R. D. Iverson, ""A one-pass thinning algorithm and its parallel implementation,"" Computer Vision, Graphics, and Image Processing,1987, pp. 30- 40.

[13] Wenxing Liu,Zhaojin Wang,Guoguang Mu. Ridge Track and The Application of Thinning Fingerprint Post-Processing [J]. Photoelectron laser, 2002, Vol 13(2), pp.184-187.

[14] Qiulin zhang, Xijun zhu, “Study of the Thinning Algorithm for Thenar Palmprint”, IEEE, 2010 First ACIS International Symposium on CDEE, October 2010,pp. 179-182.

[15] Nikolaos G Bourbakis, Won Jang,”An efficient, parallelsymmetric thinning algorithm and its hardware implementation“, Microprocessor and Microprogramming, 1988, Vol 23, pp.115-121.

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