References
- 1. Ferri CP, Prince M, Brayne C, et al. Global prevalence of dementia: a Delphi consensus study. Lancet. 2005; 366:2112-7.
- 2. Ritchie CW, Ames D, Clayton T, et al. Metaanalysis of randomized trials of the efficacy and safety of donepezil, galantamine, and rivastigmine for the treatment of Alzheimer disease. Am. J. Geriatr. Psychiatry. 2004; 12: 358-69.10.1097/00019442-200407000-00003
- 3. Whitehead A, Perdomo C, Pratt RD, et al. Donepezil for the symptomatic treatment of patients with mild to moderate Alzheimer’s disease: a meta-analysis of individual patient data from randomized controlled trials. Int. J. Geriatr. Psychiatry. 2004; 19:624-3310.1002/gps.1133
- 4. Devanand DP, Pradhaban G, Liu X, et al. Hippocampal and entorhinal atrophy in mild cognitive impairment. Neurology. 2007; 68:828-36.10.1212/01.wnl.0000256697.20968.d7
- 5. Gerardinn E, Chételat G, Chupin M, et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. NeuroImage. 2009; 47:1476-86.10.1016/j.neuroimage.2009.05.036
- 6. Whitwell JL, Josephs KA, Murray ME, et al. MRI correlates of neurofibrillary tangle pathology at autopsy. Neurology. 2008; 71:743-9.10.1212/01.wnl.0000324924.91351.7d
- 7. Scahill RI, Schott JM, Stevens JM, et al. Mapping the evolution of regional atrophy in Alzheimer’s disease: Unbiased analysis of fluid-registered serial MRI. PNAS. 2002; 99:4703-7.10.1073/pnas.052587399
- 8. Desikan RS, Cabral HJ, Hess CP, et al. Automated MRI measures identify individuals with mild cognitiveimpairment and Alzheimer’s disease. Brain. 2009; 132:1-10.
- 9. Freeborough PA, Fox NC. MR image texture analysis applied to the diagnosis and tracking of Alzheimer’s disease. IEEE transactions on medical imaging. 1998; 17:475-9.
- 10. Rajeesh J, Moni RS, Palanikumar S, et al. A versatile algorithm for the automatic segmentation of hippocampus based on level set. International Journal of Biomedical Engineering and Technology. 2011; 7:213-24.
- 11. Xia Y, Hu Q, Aziz A, et al. A knowledge-driven algorithm for a rapid and automatic extraction of the human cerebral ventricular system from MR neuroimages, Neuro Image. 2004; 21:269-82.
- 12. Rajeesh J, Moni RS, Palanikumar S. A comparative analysis of filtes for histogram smoothing in automatic segmentation of CN from brain MR image. National Journal of Technology. 2009; 5:84-7.
- 13. Sethian JA. Level set methods and fast marching methods. In: Cambridge, UK: Cambridge University Press.1996.
- 14. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using matlab. Englewood Cliffs, NJ: Prentice-Hall, 2004.
- 15. Haralick RM, Shanmugam K, Dinstein I. Textural features for.image classification. IEEE Trans. Syst., Man, Cybern. 1973; 6:610-21.
- 16. Selvan S, Ramakrishnan S. SVD-based modeling for image texture classification using wavelet transformation. IEEE Transactions on Image Processing. 2007; 11:2688-96.
- 17. Lao Z, Shen D, Xue Z, et al. Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage. 2004; 21:46-57.10.1016/j.neuroimage.2003.09.027
- 18. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 1998; 2:121-67.10.1023/A:1009715923555
- 19. Sonka M, Fitzpatrick JM. Handbook of Medical Imaging: Medical Image Processing and Analysis. Bellingham, WA: SPIE; 2004.