DIAGNOSIS AND CLASSIFICATION OF DEMENTIA USING MRI IMAGES
DOI:
https://doi.org/10.29121/granthaalayah.v5.i4RACSIT.2017.3345Keywords:
MRI Images, Dementia, Level Set Segmentation, Region of Interest, K-Nearest NeighborAbstract [English]
The proposed work is to present an effective approach to diagnoseof dementia using MRI images and classify into different stages. There are many manual segmentation algorithms on detection and classification or very simple and specific segmentation algorithms to segment each region of interest exclusively. Thus, the proposed system shall use one of the most effective automatic segmentation techniques on MRI images at once. The regions of interest to segment are CSF (Cerebralspinal fluid), gray matter, and white matter and ventricles using the effective segmentation method called level set segmentation. The features are extracted from these four regions of interest and classification of the dementia is performed using K-nearest neighbor.
Downloads
References
V. Miller, S. Erlien, and J. Piersol, “Identifyingdementia in MRI scans using machine learning,” pp. 18–22.
M. Y. Bhanumurthy and K. Anne, “An Automate Segmentation of Brain MRI for detection of Normal Tissues using Improved Machine Learning Approach,” pp. 3–8, 2015. DOI: https://doi.org/10.1109/ICACCS.2015.7324087
J. Alirezaie, M. E. Jernigan, and C. Nahmias, “Automatic Segmentation of Cerebral MR Images using Artificial Neural Networks,” vol. 45, no. 4, pp. 2174–2182, 1998.
F. Prados, M. J. Cardoso, M. C. Yiannakas, L. R. Hoy, E. Tebaldi, H. Kearney, M. D. Liechti, D. H. Miller, O. Ciccarelli, C. A. M. G. Wheeler-kingshott, and S. Ourselin, “Fully automated grey and white matter spinal cord segmentation,” Nat. Publ. Gr., no. October, pp. 1–10, 2016. DOI: https://doi.org/10.1038/srep36151
B. Clangphukhieo, “Automated Segmentation of a Ventricle Boundary from CT Brain Image Based on Naïve Bayes Classifier,” pp. 1168–1173.
Y. Ge, R. I. Grossman, J. S. Babb, M. L. Rabin, L. J. Mannon, and D. L. Kolson, “Age-Related Total Gray Matter and White Matter Changes in Normal Adult Brain . Part I : Volumetric MR Imaging Analysis,” no. September, pp. 1327–1333, 2002.
M. Ito, K. Sato, M. Fukumi, and I. Namura, “Brain Tissues Segmentation for Diagnosis of Alzheimer-Type Dementia,” pp. 3847–3849, 2011.
R. S. Kumari, T. Varghese, P. S. Mathuranath, and C. Kesavdas, “Segmentation of MR Brain Images Using FCM Technique in Frontotemporal Dementia,” pp. 27–30, 2012. DOI: https://doi.org/10.1049/cp.2012.2191
T. F. Chen, “Medical Image Segmentation using Level Sets,” pp. 1–8, 2008.
M. M. Dessouky and T. E. Taha, “Selecting and Extracting Effective Features for Automated Diagnosis of Alzheimer ’ s Disease,” vol. 81, no. 4, pp. 17–28, 2013. DOI: https://doi.org/10.5120/14000-2039
E. E. Bron, M. Smits, W. J. Niessen, S. Klein, and D. Neuroimaging, “Feature Selection Based on the SVM Weight Vector for Classification of Dementia,” vol. 19, no. 5, pp. 1617–1626, 2015.
Downloads
Published
How to Cite
Issue
Section
License
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.