THE HISTOGRAM EQUALIZATION-OBLIGATED LINEAR FUNCTION-IMAGE CLASSIFICATION BASED ON SELF-INFORMATION THEORY AND FUZZY C-MEANS CLUSTERING ALGORITHM IN INFORMATION TRANSMISSION

Authors

  • Bongkarn Homnan Research Center, Dhurakij Pundit University
  • Watit Benjapolakul Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University

Keywords:

Classification, histogram, equalization, fuzzy, image, information

Abstract

The development of the multimedia information is continually growing especially in an e-health technology. The specific Magnetic Resonance (MR) brain image classification using fuzzy c-means clustering algorithm and the self-information in this paper is proposed. Retained and with the optimum number of pixels, the classified MR has been histogram equalized by the obligated linear function. As a result, the benefit of fuzzy c-means clustering algorithm and the constrained self-information on the corresponding histogram equalization-obligated linear function-structural pixel position scheme image shows that the proposed method can give more delicate image information. In addition, the information entropy of the corresponding histogram-based image shows that the proposed method can also give the lower information entropy-based data compression ratio.

References

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Vasuda, P., & Satheesh, S. (2010). Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation. International Journal on Computer Science and Engineering, 2(5), 1713–1715.

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Published

2020-07-17

How to Cite

Homnan, B., & Benjapolakul, W. (2020). THE HISTOGRAM EQUALIZATION-OBLIGATED LINEAR FUNCTION-IMAGE CLASSIFICATION BASED ON SELF-INFORMATION THEORY AND FUZZY C-MEANS CLUSTERING ALGORITHM IN INFORMATION TRANSMISSION. SUTHIPARITHAT JOURNAL, 27(84), 105–116. retrieved from https://so05.tci-thaijo.org/index.php/DPUSuthiparithatJournal/article/view/245036

Issue

Vol. 27 No. 84 (2013): October-December

Section

Research Articles

License

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