K-MEANS, WATERSHED AND TEXTURE-BASED IMAGE SEGMENTATION

International Journal of Computer Science (IJCS Journal) Published by SK Research Group of Companies (SKRGC) Scholarly Peer Reviewed Research Journals

Format: Volume 3, Issue 2, No 2, 2015.

Copyright: All Rights Reserved ©2015

Year of Publication: 2015

Author: Dr.K.PERUMAL,C.LATHA

Reference:IJCS-103

View PDF Format

Abstract

Nowadays image processing is a highly challenging field in detecting the brain tumor from MRI Brain Scan.It has becomea popular in our research area. In this paper, MRI brain image is used to detect the brain tumor. This system includes conversion to gray, test the brain, whether it is benign or malignant, Threshold Segmentation is the simplest segmentation method, canny operator for edge detection and morphological Based de-noising used for removing the noise, clustering method combination of MRF and CRF, Watershed segmentation is the best method for grouping pixels of an image on the basis of intensity, by combining k-means with watershed Segmentation overcome the Over Segmentation and use texture Segmentation. The detailed procedure is implemented in MATLAB. By this, the brain tumor is detected accurately and also it is determined whether the patients can be cured by medicine or not.

References

[1]www.brain tumor.org ,survey of brain tumor. [2] www.cancer.org › … › Brain/CNS Tumors in Adults › Detailed Guide. [3] Yuqian Zhao, Jianxin Liu, Huifen Li, and GuiyuanLi,―Improved watershed algorithm for dowels image segmentation,‖ 7th IEEE World Congress on Intelligent Control and Automation, Year 2008. pp. 7644-7648. [4]AnjuBala, ―An Improved Watershed Image Segmentation Technique using MATLAB,” International Journal of Scientific & Engineering Research Vol.3, No.6, pp.1-4, June 2012. [5]Md.HabiburRahmanand Md.RafiqualIslam,―Segmentation of color image using adaptive thresholds and masking with watershed algorithm,‖ IEEE International Conference on Informatics, Electronics & Vision (ICIEV), Year 2013, pp.1-6. | [6] XiaoyanZhang,YongShan,Wei Wei, and ZijianZhu,‖An image segmentation method based on improved watershed algorithm.‖IEEE international Conference on Computational and Information Science(ICCIS),year 2010,pp.258-261. [7] P.P.Acharjya,A.sinha,S.Sarkar,S.Dey and S.Ghosh.‖A New Approach of Watershed Algorithm Using Distance Transform Applied To Image Segmentation.‖International Journal of Innovative Research in Computer and Communication Engineering,Vol.1,No.2,pp185-189 April 2013.. [8] F. C. Monteiro and A. Campilho, “Watershed framework to region-based image segmentation,” in Proc. International Conference on Pattern Recognition, ICPR 19th, pp. 1-4, 2008. [9] M. Hameed, M. Sharif, M. Raza, S. W. Haider, and M. Iqbal, “Framework for the comparison of classifiers for medical image segmentation with transform and moment based features,” Research Journal of Recent Sciences, vol. 2277, p. 2502, 2012. [10] P. Perona, J. Malik. ―Scale space and edge detection using anisotropic diffusion.‖ IEEE Transaction Pattern Analysis Machine Intell.12 629–639 (1990). [11] W. Chen, M. Ding, Y. Miao, L. Luo, ―Ultrasound image denoising with multi-shape patches aggregation based non-local means,‖ IEEE ICBMI, pp. 14–17, Dec. (2011). [12] W. Chen, M. Ding, Y. Miao, L. Luo, ―Ultrasound image denoising with multi-shape patches aggregation based non-local means,‖ IEEE ICBMI, pp. 14–17, Dec. (2011). [13] T.N.Tran,R.Wehrens,andL.Buydens,Clustering multispectral images: A tutorial,Chemometrics and Intelligent Laboratory Systems,vol.77,no 1,pp.3-17,2005. [14] Jichuan Shi , ―Adaptive local threshold with shape information and its application to object segmentationǁ, Page(s)1123 – 1128, Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference,1923 Dec. 2009. Roopali [15] Hossam M. Moftah, Aboul Ella Hassanien, MohamoudShoman, ―3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithmsǁ, Page(s): 320 – 324 , Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference , Nov. 29 2010-Dec. 1 2010. [16] Gang Li , ―Improved watershed segmentation with optimal scale based on ordered dither halftone and mutual informationPage(s) 296 – 300, Computer Science and InformatiOTechnology (ICCSIT), 2010 3rd IEEE International Conference ,9-11 July 2010.


Keywords

K-means, watershed and Texture Segmentation, CannyOperator, MorphologicalBased denoising, MRF AND CRF.

This work is licensed under a Creative Commons Attribution 3.0 Unported License.   

TOP
Facebook IconYouTube IconTwitter IconVisit Our Blog