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Reseach Article

A Masked-Watershed algorithm for segmentation of Brain tumour MR Images

by C. Latha, K. Perumal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 1
Year of Publication: 2016
Authors: C. Latha, K. Perumal

C. Latha, K. Perumal . A Masked-Watershed algorithm for segmentation of Brain tumour MR Images. International Journal of Computer Applications. 146, 1 ( Jul 2016), 16-22. DOI=10.5120/ijca2016910601

@article{ 10.5120/ijca2016910601,
author = { C. Latha, K. Perumal },
title = { A Masked-Watershed algorithm for segmentation of Brain tumour MR Images },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 1 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 16-22 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016910601 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:49:06.559322+05:30
%A C. Latha
%A K. Perumal
%T A Masked-Watershed algorithm for segmentation of Brain tumour MR Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 1
%P 16-22
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Medical imaging creates use of the technology to non-invasively make known the interior structure of the human body. By way of medical imaging modalities patient’s life be able to be improved throughout an accurate and rapid remedial without any side effects. The most important reason of this paper is to increased methodology that may accurately classify a tumor growth from abnormal tissues. This examination work has distinguished region growing segmentation Technique and implement and reflect. From our validation test comes about, this examination work uncovered that this model neglects to create useful groups order force effect poor tumor characterization precision. The images used for tumor segmentation are obtained from MRI modality. During this research, we’ve developed an image segmentation technique based on catchments basins and ridge lines to segment the brain image accurately. This technique relies on interest techniques for extracting tumor portion of the images in the watershed segmentation. Direct application of watershed leads to over-segmentation because of the presence of noise and different irregularities accepted in digital images. Thus to avoid this, we’ve carried out some preprocessing to eliminate noise present within the MRI images through acquisition stage. Then mask the watershed segmentation. After preprocessing step, we have a tendency to calculate the morphological operation of the input images. This exists one of the real issues to predict the tumor design and to address this issue; the masked watershed Segmentation Technique is proposed and resolute overall Sensitivity, Specificity, and Accuracy.

  1. Changmin Zhang, Shuaiqi Zhang, Junxia Wu and Shaoxiong Han, “An improved wa- tershed algorithm for color image segmentation,” 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE),Year 2012, pp.69-72.
  2. Md. Sharifur Rahman and Md. Rafiqual Islam, “Counting objects in an image by marker controlled watershed segmentation and thresholding,” 3rd IEEE International on Advance Computing Conference (IACC),Year 2013, pp.1251-1256. |
  3. Xianwei Han, Yili Fu and Haifeng Zhang,“A fast two-step marker-controlled watershed image segmentation method, “International Conference on Mechatronics and Automa- tion (ICMA), Year 2012, pp.1375-1380. | |
  4. Boren Li, Mao Pan, and Zixing Wu, “An improved segmentation of high spatial res- olution remote sensing image using Marker-based Watershed Algorithm,” 20th IEEE International Conference onGeoinformatics , Year 2012, pp.1-5. | |
  5. Farheen K. Siddiqui and Vineet Richhariya,“An Efficient Image Segmentation Approach through Enhanced Watershed Algorithm,” Computer Engineering and Intel- ligent Systems, Vol.4,No.6, pp 1-7,2013. | |
  6. P.P. Acharjya, A. Sinha, S. Sarkar, S. Dey and S. Ghosh,“A New Approach Of Watershed Algorithm Using Distance Transform Applied To Image Segmenta- tion,”International Journal of Innovative Research in Computer and Communication Engineering, Vol.1,No.2, pp 185-189, April 2013. | |
  7. Dibyendu Ghoshal and Pinaki Pratim Acharjya,“Effect of Various Spatial Sharpening Filters on the Performance of the Segmented Images using Watershed Approach based on Image Gradient Magnitude and Direction,” International Journal of Computer Applications, Vol. 82, No.6, pp 19-25, November 2013. | |
  8. Md.Habibur Rahman and Md.Rafiqual Islam, “Segmentation of color image using adaptive thresholding and masking with watershed algorithm,” IEEE International Conference on Informatics, Electronics & Vision (ICIEV), Year 2013, pp.1-6. | |
  9. Rabul H Laskar, Kalyan Banerjee and Debajit Basak,“Removal of High Density Salt and Pepper Noise from Color Images through Variable Window Size,” IEEE International Conference on Circuits, Power and Computing Technologies, Year 2013, pp. 1132-1136. | |
  10. M.Sonka, V.Hlavac, and R.Boyle, Image Processing, Analysis, and Machine Vision, PWS Publishing, 1999. | shed transform method provides better segmentation result without preprocessing. Zhang et al. (2012)
  11. P. Perona, J. Malik. “Scale space and edge detection using anisotropic diffusion.” IEEE Transaction Pattern Analysis Machine Intell.12 629–639 (1990).
  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). W. Chen, M. Ding, Y. Miao, L. Luo, “
Index Terms

Computer Science
Information Sciences


MRI Brain tumor morphological operation Watershed segmentation and Masked-Watershed Segmentation.