An investigation of volumetric and corpus callosum dimension to detect brain disorders
Affiliation(s)
Department of Biomedical Engineering, Karunya University, Coimbatore, India. Dr.N.G.P. Institute of Technology, Coimbatore, India.
Department of Biomedical Engineering, Karunya University, Coimbatore, India. Dr.N.G.P. Institute of Technology, Coimbatore, India.
ABSTRACT
Alzheimer’s disease (AD), Mental retardation, Cerebral Palsy, and other Dementias are the neurogenerative brain disorders which are statistically proven that 2% - 3% of people affected in the world today. The proposed method considered the symptoms which stands distinct for Alzheimer’s disease. Many structural neuroimaging studies have found the atrophy of the Corpus Callosum (CC) and the decrease in brain volume in AD. The measurement, area has been extracted from the gradient mask of the image to characterize the local atrophy of the CC. The result showed decreased area of the CC in AD when compared to the control groups. The volume has also been calculated by volume rendering and voxel size measurement for the same set of control groups and was found to be reduced in the AD patients. These findings confirmed the pathology characteristics in AD of brain disorders. The system’s validity with respect to results obtained with conventional diagnosis has been examined and proved to offer better results.
Alzheimer’s disease (AD), Mental retardation, Cerebral Palsy, and other Dementias are the neurogenerative brain disorders which are statistically proven that 2% - 3% of people affected in the world today. The proposed method considered the symptoms which stands distinct for Alzheimer’s disease. Many structural neuroimaging studies have found the atrophy of the Corpus Callosum (CC) and the decrease in brain volume in AD. The measurement, area has been extracted from the gradient mask of the image to characterize the local atrophy of the CC. The result showed decreased area of the CC in AD when compared to the control groups. The volume has also been calculated by volume rendering and voxel size measurement for the same set of control groups and was found to be reduced in the AD patients. These findings confirmed the pathology characteristics in AD of brain disorders. The system’s validity with respect to results obtained with conventional diagnosis has been examined and proved to offer better results.
Cite this paper
Prabakar, S. and Porkumaran, K. (2012) An investigation of volumetric and corpus callosum dimension to detect brain disorders. Journal of Biomedical Science and Engineering, 5, 369-377. doi: 10.4236/jbise.2012.57047.
Prabakar, S. and Porkumaran, K. (2012) An investigation of volumetric and corpus callosum dimension to detect brain disorders. Journal of Biomedical Science and Engineering, 5, 369-377. doi: 10.4236/jbise.2012.57047.
References
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[1] Chandra V., Ganguli M., Pandav R., Johnston J., Belle S., DeKosky S.T.,(1998), “Prevalence of Alzheimer's disease and other dementias in rural India: The Indo-US study,” Neurology, 51(4), 1000-1008. [2] Dias, A. and Patel, V.(2009), “Closing the treatment gap for dementia in India,” Indian Journal of Psychiatry, 51 (5), 93-97. [3] Fitzpatrick AL, Kuller LH, Lopez OL, Kawas CH, Jagust W (2005), “Survival following dementia onset: Alzheimer's disease and vascular dementia,” J Neurol Sci 15;229-230. [4] Lyoo I.K, Satlin. A, Lee C.K,(1997), “Regional atrophy of the corpus callosum in subjects with Alzheimer’s disease and multi-infarct dementia”, Psychiatry Research, 63-72. [5] Sundaram SK, Sivaswamy L, Makki MI, Behen ME, Chugani HT. (2008), "Absence of arcuate fasciculus in children with global developmental delay of unknown etiology: a diffusion tensor imaging study". J Pediatr 152 (2): 250–5. [6] Cooney G, Jahoda A, Gumley A, Knott F(2006), "Young people with intellectual disabilities attending mainstream and segregated schooling: perceived stigma, social comparison and future aspirations". J Intellect Disabil Res 50 (Pt 6): 432–44. [7] Alireza Gharabaghi, Frank Kunath, Michael Erb, Ralf Saur,Stefan Heckl, Marcos Tatagiba, Wolfgang Grodd and Hans-Otto Karnath, (2009), “Perisylvian white matter connectivity in the human right Hemisphere,” BMC Neu-roscience, pp. 10-15. [8] Good C.D, Scahill R I, Fox N.C, (2002), “Automatic differentiation of anatomical patterns in the human brain: validation with studies of degenerative dementias”, Journal of NeuroImage, pp. 29-46. [9] Hampel .H, Teipel S.J, Alexander G.E, (1998), “Corpus callosum atrophy is a possible indicator of region and cell type-specific neuronal degeneration in Alzheimer disease: a magnetic resonance imaging analysis”, Journal of Neurology, pp.193-198. [10] Tomimoto .H, Lin J.X, Matsuo.A, (2004), “Different mechanism of corpus callosum atrophy in Alzheimer’s disease and vascular dementia”, Journal of Neurology, pp. 398-406. [11] Thomann P.A, Wustenberg.T, Pantel.J,(2006), “Structural changes of the corpus callosum in mild cognitive impairment and Alzheimer’s disease”, Dementia and Geriatric Cognitive Disorders, pp. 215-220. [12] Teipel SJ, Bayer.W, Alexander G.E,(2003), “Regional pattern of hippocampus and corpus callosum atrophy in Alzheimer’s disease in relation to dementia severity: evidence for early neocortical degeneration”, Neurobiology of Aging, pp. 85-94. [13] Ashburner J., Friston K.J,(2000), “Voxel-based morphometry- the methods”, Journal of NeuroIimage, pp. 805-821. [14] Bozzali.M, Falini .A, Franceschi. M(2002), “White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging”, Journal of Neurology, Neurosurgery and Psychiatry, pp. 742-746G. [15] Baron .V, Chetelat .G, Desgranges .B(2001), “In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer’s disease”, Journal of NeuroImage, pp. 298-309. [16] Busatto G.F, G.E.J. Garrido, O.P. Almeida,(2003), “A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease”, Neurobiology of Aging, 2003, pp. 221-231. [17] Silbert L C, MD, Quinn J.F, MD, et al(2003), “Changes in premorbid brain volume predict Alzheimer’s disease pathology,” American Academy of Neurology,Vol-61 pp: 487-492. [18] Sateesh Kumar H C , Raja K B, Venugopal K R and Patnaik L M(2009), “Automatic Image Segmentation using Wavelets,” IJCSNS International Journal of Computer Science and Network Security, vol. 9 , No. 2,pp. 305-313. [19] Xiao-Ping Zhang and M.D. Desai(1997), “Wavelet based automatic thresholding for image segmentation,” Proc. of International Conference on Image Processing, Santa Barbara,vol. 1, pp.224.