CFP last date
20 June 2024
Reseach Article

MRI Brain Image segmentation using Adaptive Thresholding and K-means Algorithm

by I. M. Kazi, S. S. Chowhan, U. V. Kulkarni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 8
Year of Publication: 2017
Authors: I. M. Kazi, S. S. Chowhan, U. V. Kulkarni

I. M. Kazi, S. S. Chowhan, U. V. Kulkarni . MRI Brain Image segmentation using Adaptive Thresholding and K-means Algorithm. International Journal of Computer Applications. 167, 8 ( Jun 2017), 11-15. DOI=10.5120/ijca2017914330

@article{ 10.5120/ijca2017914330,
author = { I. M. Kazi, S. S. Chowhan, U. V. Kulkarni },
title = { MRI Brain Image segmentation using Adaptive Thresholding and K-means Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 8 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 11-15 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017914330 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:14:17.253407+05:30
%A I. M. Kazi
%A S. S. Chowhan
%A U. V. Kulkarni
%T MRI Brain Image segmentation using Adaptive Thresholding and K-means Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 8
%P 11-15
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Segmentation of human brain from MRI without human interference is a major challenge in the field of medical image processing. Brain segmentation is used to extract different features of the image for analyzing, interpretation and understanding of images. The objective of brain MRI segmentation is to precisely identify the major tissue structures in these image volumes. There are a number of methods exist to segment the brain. In this paper, we have implemented a new approach based on adaptive thresholding and K-means clustering algorithm, which is used to get cerebrospinal fluid (CSF), Gray Matter (GM), White Matter (WM) and others. In order to segment an image thresholding method is adopted but a fixed threshold is not appropriate for segmentation, if the background is rough, hence adaptive thresholding method is more suitable for segmentation and K-means clustering algorithm is also used for segmenting MR brain image into K different tissue types, which include gray matter, white matter, and CSF. The efficiency and accuracy of the algorithm are proven by the experiments on the MR brain images.

  1. M. K. Beyer, C. C. Janvin, J. P. Larsen, and D. Aarsland, “An MRI study of patients with Parkinson’s disease with mild cognitive impairment and dementia using voxel based morphometry,” J. Neurol. Neurosurg Psychiatry, vol. 78, no. 3, pp. 254–259, March 2007.
  2. D.Selvaraj, R.Dhanasekaran,” Novel approach for segmentation of brain magnetic resonance imaging using intensity based thresholding”, ICCCCT, 978-1-4244-7770-8/10/ IEEE pp. 502-507, 2010.
  3. P. E. Grant, “StructuralMR imaging,” Epilepsia, vol. 45, no. s4, pp.4–16, 2004.
  4. J. J. Wisco, G. Kuperberg, D. Manoach, B. T. Quinn, E. Busa, B. Fischl, S. Heckers, and A. G. Sorensen, “Abnormal cortical folding patterns within Broca’s area in schizophrenia: Evidence from structural MRI,” chizophrenia Res., vol. 94, no. 1–3, pp. 317–327, Aug. 2007.
  5. D. H. Miller, “Biomarkers and surrogate outcomes in neurodegenerative disease: Lesions from multiple sclerosis,” NeuroRx, vol. 1, pp. 284–294, 2004.
  6. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2002).
  7. Dr.Vipul Singh,,Digital Image Processing with MATLAB and Lab VIEW; ELSEVIER 2013.
  8. M.-R. Nazem-Zadeh, E. Davoodi-Bojd, and H. Soltanian- Zadeh, “Atlas based fibre bundle segmentation using principal diffusion directions and spherical harmonic coefficients”, NeuroImage, vol. 54, pp. S146-S164, (2011)
  9. Nailah Afshan, Shaima Qureshi and Syed Mujtiba Hussain, “Comparative study of tumer detection algorithms”, International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), 978-1-4799-5097-3/14/ IEEE, pp.251-256,(2014).
  10. G. Evelin Sujji, Y.V.S. Lakshmi, G. Wiselin Jiji, “MRI Brain Image Segmentation based on Thresholding”, International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-3 Number-1 Issue-8 pp. 97-101, March-2013
  11. Rajeshwar Dass, Priyanka, Swapna Devi, “Image Segmentation Techniques”, International Journal of Electronics & Communication Technology (IJECT), vol. 3, no. 1, pp. 2230-7109, 2012.
  12. Harsimranjot Kaur, Dr. Reecha Sharma, “A Survey on Techniques for Brain Tumor Segmentation from Mri”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 11, Issue 5, Ver. I (Sep.-Oct .2016), PP 01-05
  13. Jin Liu, Min Li, Jianxin Wang, Fangxiang Wu, Tianming Liu, and Yi Pan, “A Survey of MRI-Based Brain Tumor Segmentation Methods”, TSINGHUA SCIENCE AND TECHNOLOGY ISSNll1007-0214ll04/10llpp578-595 Volume 19, Number 6, December 2014
  14. Tara.saikumar, P. Yugander, B.Smitha, K.L.N. Srinivas gupta, “An Adaptive Threshold Algorthim for MRI Brain Image segmentation on Level set method”, International Journal of Advances in Computer Networks and its Security, pp. 367-370
  15. D.Selvaraj, R.Dhanasekaran, “MRI brain image segmentation techniques – A review”, Indian Journal of Computer Science and Engineering (IJCSE), ISSN: 0976-5166, Vol. 4 No.5, pp.364-381, Oct-Nov 2013
  16. S. Jansi, P. Subashini, “Optimized Adaptive Thresholding based Edge Detection Method for MRI Brain Images”, International Journal of Computer Applications (0975 – 8887), Volume 51– No.20, August 2012.
  17. P.Subashini, M.Krishnaveni, Suresh Kumar Thakur, “Quantitative Performance Evaluation of Segmentation Methods for SAR ship images”, Proceedings of the Third Annual ACM Bangalore Conference, 2010.
  18. Pierre D. Wellner, “Adaptive Thresholding for the DigitalDesk”, Technical Report EPC-1993-110.
  19. Shen, Shan, William Sandham, Malcolm Granat, and Annette Sterr, “MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization”, IEEE transactions on information technology in biomedicine, vol. 9, no. 3, pp. 459- 467, 2005.
  20. P. Dhanalakshmi & T. Kanimozhi, “Automatic Segmentation of Brain Tumor using K-Means Clustering and its Area Calculation”, International Journal of Advanced Electrical and Electronics Engineering (IJAEEE), ISSN (Print): 2278-8948, Volume-2, Issue-2, pp. 130-134, 2013
  21. Jasdeep Kaur and Preetinder Kaur, "Fuzzy Logic based Adaptive Noise Filter for Real Time Image Processing Applications", International Journal of Computer Applications 58(9): 1-5, November 2012.
Index Terms

Computer Science
Information Sciences


Adaptive thresholding K-means clustering algorithm cerebrospinal fluid (CSF) Gray Matter (GM) White Matter (WM).