Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

Efficient Pattern Matching Algorithm for Classified Brain Image

Print
PDF
International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 57 - Number 4
Year of Publication: 2012
Authors:
Ragavachari Harini
C. Chandrasekar
10.5120/9100-3233

Ragavachari Harini and C Chandrasekar. Article: Efficient Pattern Matching Algorithm for Classified Brain Image. International Journal of Computer Applications 57(4):5-10, November 2012. Full text available. BibTeX

@article{key:article,
	author = {Ragavachari Harini and C. Chandrasekar},
	title = {Article: Efficient Pattern Matching Algorithm for Classified Brain Image},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {57},
	number = {4},
	pages = {5-10},
	month = {November},
	note = {Full text available}
}

Abstract

The primary notion relying in image processing is image segmentation and classification. The intention behind the processing is to originate the image into regions. Variation formulations that effect in valuable algorithms comprise the essential attributes of its region and boundaries. Works have been carried out both in continuous and discrete formulations, though discrete version of image segmentation does not approximate continuous formulation. An existing work presented unsupervised graph cut method for image processing which leads to segmentation inaccuracy and less flexibility. To enhance the process, our first work describes the process of formation of kernel for the medical images by performing the deviation of mapped image data within the scope of each region. But the segmentation of image is not so effective based on the regions present in the given medical image. To overcome the issue, we implement a Bayesian classifier as our second work to classify the image effectively. The segmented image classification is done based on its classes and processes using Bayesian classifiers. With the classified image, it is necessary to identify the objects present in the image. For that, in this work, we exploit the use of pattern matching algorithm to identify the feature space of the objects in the classified image. An experimental evaluation is carried out to estimate the performance of the proposed efficient pattern matching algorithm for classified brain image system [EPMACB] in terms of estimation of object position, efficiency and compared the results with an existing multi-region classifier method.

References

  • M. Ben Salah, A. Mitiche et. Al. , "Image partitioning with kernel mapping and graph cuts", Proceedings of 2010 IEEE 17th International Conference on Image Processing September 26-29, 2010, Hong Kong
  • Mikiyas Teshome1 et. Al. , "A simple binary image similarity matching method based on exact pixel matching", 2009 International Conference on Computer Engineering and ApplicationsIPCSIT vol. 2 (2011)
  • Raju Bhukya et. Al. , "Exact Multiple Pattern Matching Algorithm using DNA Sequence and Pattern Pair, International Journal of Computer Applications (0975 – 8887)Volume 17– No. 8, March 2011
  • Yong ki Lee et. Al. , "Tag-based object similarity computation using term space dimension reduction", Proceeding SIGIR '09 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, Pages 790-791
  • Ziad A. A Alqadi, et. Al. , 'Multiple Skip Multiple Pattern Matching algorithms'. IAENG International. Vol 34(2),2007.
  • Devaki-Paul, "Novel Devaki-Paul Algorithm for Multiple Pattern Matching" International Journal of Computer Applications (0975 – 8887) Vol 13– No. 3, January 2011.
  • D. Cremers, M. Rousson, and R. Deriche, "A review of statistical approches to level set segmentation: integrating color, texture, motion and shape," IJCV, 72(2), 2007.
  • M. Ben Salah, A. Mitiche, and I. Ben Ayed, "A continuous labeling for multiphase graph cut image partitioning," In Adv. In Visu. Comp. , LNCS, G. Bebis et al. (Eds. ), vol. 5358, pp. 268- 277, Springer-Verlag, 2008.
  • I. Ben Ayed, A. Mitiche, and Z. Belhadj, "Multiregion Level-Set Partitioning of Synthetic Aperture Radar Images," IEEE TPAMI, vol. 27, no. 5, pp. 793-800, 2005.
  • G. LAVANYA,et. Al. , "BREAST TUMOUR DETECTION AND CLASSIFICATION USING NAÏVE BAYES CLASSIFIER ALGORITHM", International Journal of Emerging trends in Engineering and Development ISSN 2249-6149 Issue 2, Vol. 3 (April-2012)
  • Mohamed Ben Salah, Amar Mitiche, and Ismail Ben Ayed, " Multiregion Image Segmentation by Parametric Kernel Graph Cuts", IEEE Transactions On Image Processing, vol. 20, no. 2, Feb 2011.
  • I. B. Ayed, A. Mitiche, and Z. Belhadj, "Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 28, no. 9, pp. 1493–1500, Sep. 2006.