Comparison between Otsu’s Image Thresholding Technique and Iterative Triclass

International Journal of Computer Trends and Technology (IJCTT)          
© 2016 by IJCTT Journal
Volume-33 Number-2
Year of Publication : 2016
Authors : Prof.Sushilkumar N. Holambe, Priyanka G. Kumbhar
DOI :  10.14445/22312803/IJCTT-V33P117


Prof.Sushilkumar N. Holambe, Priyanka G. Kumbhar "Comparison between Otsu’s Image Thresholding Technique and Iterative Triclass". International Journal of Computer Trends and Technology (IJCTT) V33(2):80-82, March 2016. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Medical image segmentation is related to the segmentation of known anatomic structures from medical images. Structures consists of organs or parts such as cardiac ventricles or kidneys, abnormalities such as tumors and cysts, as well as other structures such as vessels, brain structures etc. The complete objective of this segmentation is known as computer-aided diagnosis which is used by doctors in evaluating medical images or in recognizing abnormalities in a medical image. Segmentation means the process of partitioning a digital image into multiple regions (sets of pixels). The methods of segmentation is used to simplify and change the representation of an image into something that is more meaningful and easy to understand. The result of image segmentation is a set of regions that combine the whole image, or a set of contours extracted from the image. Each of the pixels in a region is same with respect to some characteristic or computed things, such as color, concentration, or texture. Adjacent regions are not similar with each other they differs in some characteristics. A rugged segmentation procedure brings the process a long way towards successful solution of an image difficulty. Outcome of the segmentation stage is raw pixel data, consisting of both the boundary of a region and all the points in the region. In this paper, we compared two methods of image segmentation OTSU’s method and new iterative triclass thresholding technique of image segmentation.

[1] T.Romen Singh1 , Sudipta Roy2, O.Imocha Singh3, Tejmani Sinam4 , Kh.Manglem Singh, “A New Local Adaptive Thresholding Technique in Binarization,” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 2, November 2011.
[2] Ping-Sung Liao, Tse-Sheng Chen And Pau-Choo Chung, “A Fast Algorithm For Multilevel Thresholding,” Journal Of Information Science And Engineering 17, 713-727 (2001).
[3] Yan Solihin,C.G. Leedham, “The Multi-stage Approach to Grey-Scale Image Thresholding for Specific Applications,”
[4] Salem Saleh Al-amri, N.V. Kalyankar and Khamitkar S.D, “Image Segmentation by Using Threshold Techniques,” Journal Of Computing, Volume 2, Issue 5, May 2010, ISSN 2151-9617
[5] Hongmin Cai, Zhong Yang, Xinhua Cao, “A New Iterative Triclass Thresholding Technique in Image Segmentation,” IEEE Transactions On Image Processing, Vol. 23, No. 3, March 2014.
[6] DongjuLiu, JianYu, “Otsu method and K-means,” 2009 Ninth International Conference on Hybrid Intelligent Systems.
[7] Mehmet Sezgin, Bu¨ lent Sankur, “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic Imaging 13(1), 146–165 (January 2004).
[8] Ch. Hima Bindu and K. Satya Prasad, “An efficient medical image segmentation using conventional OTSU’s method”, International Journal of Advanced Science and Technology Vol. 38, January, 2012.
[9] Miss Hetal J. Vala, Prof. Astha Baxi, “A Review on Otsu Image Segmentation Algorithm”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 2, February 2013.

Segmentation, binary, thresholding.