DIAGNOSIS OF ALZHEIMER’S DISEASE USING WATERSHED TECHNIQUE IN MATLAB
Keywords:Alzheimer disease, image processing, watershed technique, MRI image, gradient magnitude, MATLAB
In this research paper, we are proposing an easy and accurate method for effective diagnosis of Alzheimer’s disease by using Watershed’s marker technique in MATLAB. Ultimately, Watershed method leads us to image processing methodology.
As per facts and readings, on average, the diagnosis takes at least 7 years and sometimes, it may also take 14 years. Moreover, much research shows that during the diagnosis of Alzheimer disease, 97 percent of patients die and only 3 percent of patients survive after 14 years of diagnosis. This is mostly because of the late diagnosis and expensive methodology like single photon emission computed tomography used for detection of Alzheimer’s disease. Hence, it is well known that Alzheimer disease is classified as a neurodegenerative disorder. This is the cause and progression of the disease and it is still not well understood; however, it is associated with plaques and tangles caused in the brain.
This research that we are proposing is focused on the method for the diagnosis of Alzheimer’s disease, which is entirely based on image processing using MATLAB and it enables us to acquire fast and robust results. Not only this, it is much cheaper so that the poor can also be able to afford it and it is way better than the old methods which were being implemented. It only needs an MRI image to process and analyzes the Alzheimer’s symptoms based on the color range difference than normal brain image. Selected color schemes can be observed on MATLAB software and functions like; gradient magnitude, grayscale, foreground objects, watershed transform and background markers would be used.
Our research work is based on experimental methodology. The results that we have gathered after our research illustrate much better detection of Alzheimer’s disease and is also able to analyze other diseases like a brain tumor and much more.
Keywords: Alzheimer disease, image processing, watershed technique, MRI image, gradient magnitude, MATLAB
(1) R.Sampath, A.Saradha, “Analysis of Brain Images to Detect the Alzheimer’s Disease Using Segmentation Approach”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-10, March 2014 .
(2) “7 Stages of Alzheimer’s & Symptoms.” [Online]. Available: http://www.alz.org/alzheimers_disease_stages_of_alzheimers.asp. [Accessed: 18-Jul-2014].
(3) “Tests for Alzheimer’s & Dementia.” [Online]. Available: http://www.alz.org/alzheimers_disease_steps_to_diagnosis.asp. [Accessed: 18-Jul-2014].
(4) “Alzheimer’s Disease & Dementia.” [Online]. Available: http://www.alz.org/alzheimers_disease_what_is_alzheimers.asp. [Accessed: 18-Jul-2014].
(5) Erlend Hodneland1, Xue-Cheng Tai2, Joachim Weickert3, Nickolay V. Bukoreshtliev1, Arvid Lundervold1, and Hans-Hermann Gerdes1, 2007, ” Level set methods for watershed image Segmentation” Scale Space and Variational Methods in Computer Vision, v.4485, p. 178–190.
(6) Erlend Hodneland1, Xue-Cheng Tai2, Joachim Weickert3, Nickolay V. Bukoreshtliev1, Arvid Lundervold1, and Hans-Hermann Gerdes1, 2007, ” Level set methods for watershed image Segmentation” Scale Space and Variational Methods in Computer Vision, v.4485, p. 178–190.
(7) in-cites - The 100 Most-Cited Scientists in Neuroscience [Internet]: Thomson ISI; c2007 [cited 2008 9/16/2008].Available from: http://www.in-cites.com/nobel/2007-neu-top100.html.
(8) J. Sastre-Garriga, et al., “Grey and White Matter Volume Changes in Early Primary Progressive Multiple Sclerosis: a Longitudinal Study”, Brain, vol. 128, (2005), pp. 1454–1460.
(9) Neuroscience Journal Citation Report [Internet]: Thomson ISI; c2003. Available from: http://admin-apps.isiknowledge.com/JCR/JCR?RQ=LIST SUMMARY JOURNAL&cursor=1.
(10) P. Moreels and S. E. Smrekar, "Watershed Identification of Polygonal Patterns in Noisy SAR Images", IEEE Trans. On Image Processing, vol. 12, no. 7, (2003) July.
(11) Joan Lindsay, Danielle Laurin, René Verreault, Réjean Hébert, Barbara Helliwell, Gerry B. Hill, and Ian McDowell, “Risk Factors for Alzheimer’s Disease: A Prospective Analysis from the Canadian Study of Health and Aging” , American Journal of Epidemiology 2002.
(12) 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 (2002) 2001
(13) M. Vardavoulia, I. Andreadis and P. H. Tsalides, “A New Vector Median Filter for Colour Image Processing”, Pattern Recognition Letters, vol. 22, (2001), pp. 675–689.
(14) J. Roerdink and A. Meijster, "The Watershed Transfonn: Definitions, Algorithms and Parallelization Strategies", Fundamenta Injormaticae, 41,2000,p p. 187-228.
(15) P. Soille, “Morphological Image Analysis: Principles and Applications”, Springer-Verlag, (1999), pp. 170-171
(16) M. S. Atkins and B. T. Mackiewich, “Fully Automatic Segmentation of the Brain in MRI”, IEEE Trans. on Medical Imaging, vol. 17, no. 1, (1998) February.
(17) L. P. Clarke and Heine, et al., “MRI Segmentation: Methods and Applications”, Elsevier Publishers, Magnetic Resonance Imaging, vol. 13, no. 3, (1995).
(18) L. Najman and M Schmitt, “Geodesic Saliency of Watershed Contours and Hierarchical Segmentation”, IEEE Trans. on Pattern Anal. and Mach. Intellig., vol. 18, Issue 12, (1996), pp. 1163-1173.
(19) “Alzheimer’s Disease.” National Library of Medicine.
(20) 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), 1991, pp. 583- 598.
(21) eucher, 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.
(22) Meyer, F. and Beucher, S., 1990, “Morphological Segmentation,” Journal of Visual Communication and Image Representation, v.11, p. 21–46.
(23) Vincent, L., Dougherty, E.R.: Morphological Segmentation for Textures and Parti- cles. In: Digital Image Processing Methods. E. Dougherty, Editor, Marcel-Dekker, New York (1994) 43–102
(24) Vincent, L., Soille, P.: Watersheds in digital spaces: An eﬃcient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6) (1991) 583–59