Extraction of Brain Tumour in MRI Images using Marker Controlled Watershed Transform Technique in MATLAB
DOI:
https://doi.org/10.14738/jbemi.24.1260Keywords:
Medical Image Processing, MRI, Brain, Tumor, Extraction, Watershed Transformation, Marker Controlled Watershed Transform, MATLABAbstract
In recent years, substantial research has been carried out in the field of image processing to evaluate different structures and information from images. Image processing techniques have played a pivotal role in a wide range of medical image applications. They have been widely used to design different computational algorithms for extracting clinical information from medical images in different modalities including MRI, CT and Ultrasound. This paper aims to propose the use of image processing techniques in the medical field. The objective of this paper is to develop a MATLAB based algorithm that can be used to extract a brain tumor from a MRI Image. In this research, we have performed some noise removal functions, segmentation techniques and morphological operations for detection and extraction which are the basic concepts of image processing. We have developed a Watershed Transform technique based on internal and external markers. The detection and extraction of tumor from MRI image of the brain is done by using MATLAB software.
References
(1) J. Mehena and M. C. Adhikary, “Brain Tumor Segmentation and Extraction of MR Images Based on Improved Watershed Transform,” vol. 17, no. 1, pp. 1–5, 2015.
(2) S. Z. Oo and A. S. Khaing, “Brain Tumor Detection and Segmentation Using Watershed Segmentation and Morphological Operation,” IJRET Int. J. Res. Eng. Technol., vol. 3, no. 3, pp. 367–374, 2014.
(3) S. Sivaperumal, M. Sundhararajan, and T. Nadu, “Advance Feature Extraction of Mri Brain Image and Detection Using Local Segmentation Method With Watershed,” vol. 3, no. 4, pp. 87–94, 2013.
(4) Najman, L., Schmitt, M. Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(12), 1163–1173 (1996).
(5) Shafarenko, L., Petrou, M., Kittler, J.: Automatic watershed segmentation of randomly textured color images. IEEE Transactions on Image Processing 6(11), 1530–1544 (1997)
(6) Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall (2002)
(7) v.Grau, A. Mewes et aI., "Improved Watershed Transform for Medical Image Segmentation Using Prior Information", IEEE Transactions on Medical Imaging, 23(4),p p. 447-458 (2004).
(8) D. Jayadevappa, S. Kumar and D. Murty, "A Hybrid Segmentation Model Based on Watershed and Gradient Vector Flow for the Detection of Brain Tumour", International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 2, No. 3, pp. 29 -42 (2009)
(9) B. Marcotegui and F. Meyer, "Bottom up Segmentation of Image Sequences for Coding", Annals of Telecommunications, 52 (7-8),p p. 397 – 407 (1997)
(10) P. Soille, Morphological Image Analysis: Principles and Applications, Second Edition, Springer,2004
(11) L. Vincent and P. Soille, "Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations", IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), pp. 583- 598 (1991)
(12) H. Digabel and C. Lantuejoul, "Iterative Algorithms", In Actes du Second Symposium Europeend'Analyse Quantitative des Microstructures en Sciences des Materiaux, Biologie et Medecine, Caen, 4-7 October 1977 J.-L. Chermant, Ed., RiedererVerlag, Stuttgart,p p. 85 – 99 (1978).
(13) S. Beucher and C. Lantuejoul, "Use of Watersheds in Contour Detection", Proceedings of International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, Rennes, (1979).
(14) J. Roerdink and A. Meijster, "The Watershed Transfonn: Definitions, Algorithms and Parallelization Strategies", FundamentaInjormaticae, 41, 2000, pp. 187-228.
(15) P. Soille, Morphological Image Analysis: Principles and Applications, Second Edition, Springer, (2004).
(16) L. Vincent and P. Soille, "Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations", IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), pp. 583- 598 (1991).
(17) Beucher, S., Meyer, F., 1993. The morphological approach to segmentation: the watershed transformation. In: Dougherty, E. (Ed.), Mathematical Morphology in Image Processing. Marcel Dekker, New
York.
(18) Meyer, F. and Beucher, S., “Morphological Segmentation,” Journal of Visual Communication and Image Representation, v.11, p. 21–46 (1990).
(19) Erlend Hodneland1, Xue-Cheng Tai2, Joachim Weickert3, Nickolay V. Bukoreshtliev1, Arvid Lundervold1, and Hans-Hermann Gerdes1, ”
Level set methods for watershed image Segmentation” Scale Space and Variational Methods in Computer Vision, v.4485, p. 178–190 (2007).
(20) Vincent, L., Dougherty, E.R.: Morphological Segmentation for Textures and Parti- cles. In: Digital Image Processing Methods. E. Dougherty, Editor, Marcel-Dekker, New York 43–102(1994).
(21) Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6) 583–598(1991).
(22) Felkel, P., Bruckschwaiger, M., Wegenkittl, R.: Implementation and complexity of the watershed-from-markers algorithm computed as a minimal cost forest. Com- puter Graphics Forum 20 (2001-2002)
(23) Ma, Z.; Tavares, J. M. R. S.; Jorge, R. N.; Mascarenhas, T. A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Computer Methods in Biomechanics and Biomedical Engineering, vol. 13, no. 2, pp. 235–246
(2010).
(24) Gonalves, P. C.; Tavares, J. M. R.; Jorge, R. N.: Segmentation and simulation of objects represented in images using physical principles. Computer Modeling in Engineering - Sciences, vol. 32, no. 2, pp. 45–55(2008).
(25) Vasconcelos, M. J. M.; Tavares, J. M. R. S.: Methods to automatically built point distribution models for objects like hand palms and faces represented in images. Computer Modeling in Engineering - Sciences, vol. 36, no. 3, pp. 213– 241(2008).
(26) Derraz, F.; Beladgham, M.; Khelif, M.: Application of active contour models in medical image segmentation. In ITCC ´04: Proceedings of the Interna- tional Conference on Information Technology: Coding and Computing (ITCC’04) Volume 2, pp. 675–681, Washington, DC, USA. IEEE Computer Society (2004).
(27) M. M. Ahmed and D. Bin Mohamad, “Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model,” Int. J. Image Process., vol. 2, no. 1, pp. 27–34, 2008
