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A Method to Differentiate Mild Cognitive Impairment and Alzheimer in MR Images using Eigen Value Descriptors

  • Systems-Level Quality Improvement
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Abstract

Automated analysis and differentiation of mild cognitive impairment and Alzheimer’s condition using MR images is clinically significant in dementic disorder. Alzheimer’s Disease (AD) is a fatal and common form of dementia that progressively affects the patients. Shape descriptors could better differentiate the morphological alterations of brain structures and aid in the development of prospective disease modifying therapies. Ventricle enlargement is considered as a significant biomarker in the AD diagnosis. In this work, a method has been proposed to differentiate MCI from the healthy normal and AD subjects using Laplace-Beltrami (LB) eigen value shape descriptors. Prior to this, Reaction Diffusion (RD) level set is used to segment the ventricles in MR images and the results are validated against the Ground Truth (GT). LB eigen values are infinite series of spectrum that describes the intrinsic geometry of objects. Most significant LB shape descriptors are identified and their performance is analysed using linear Support Vector Machine (SVM) classifier. Results show that, the RD level set is able to segment the ventricles. The segmented ventricles are found to have high correlation with GT. The eigen values in the LB spectrum could show distinction in the feature space better than the geometric features. High accuracy is observed in the classification results of linear SVM. The proposed automated system is able to distinctly separate the MCI from normal and AD subjects. Thus this pipeline of work seems to be clinically significant in the automated analysis of dementic subjects.

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References

  1. Kidd, P.M., Alzheimer's disease, amnestic mild cognitive impairment, and age-associated memory impairment: current understanding and progress toward integrative prevention. Altern. Med. Rev. 13:85–115, 2008.

    PubMed  Google Scholar 

  2. Alzheimer's Association, 2013 Alzheimer's disease facts and figures. Alzheimers Dement. 9:208–245, 2013.

    Article  Google Scholar 

  3. Tolea, M.I., and Galvin, J.E., Current guidelines for dementia screening: shortcomings and recommended changes. Neurodegener. Dis. Manag. 3:565–573, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Da, S.L., Helder, F., Jair, M.A., and Renato, A., Application of paraconsistent artificial neural networks as a method of aid in the diagnosis of Alzheimer disease. J. Med. Syst. 34:1073–1081, 2010.

    Article  Google Scholar 

  5. Dubois, B., and Albert, M.L., Amnestic MCI or prodromal Alzheimer's disease? Lancet. Neurol.. 3:246–248, 2004.

    Article  PubMed  Google Scholar 

  6. Fan Y, Batmanghelich N, Clark CM, Davatzikos C, Alzheimer’s Disease Neuroimaging Initiative (2008) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. NeuroImage 39:1731–1743.

  7. Zhang, Y., Wang, S., Phillips, P., et al., Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomed. Signal. Process. Control. 21:58–73, 2015.

    Article  Google Scholar 

  8. Zhang, Y., Dong, Z., Phillips, P., et al., Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front. Comput. Neurosci. 9:66, 2015.

    PubMed  PubMed Central  Google Scholar 

  9. Zhang, Y., Wang, S., and Dong, Z., Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog. Electromagn. Res. 144:171–184, 2014.

    Article  Google Scholar 

  10. Zhang, D., Wang, Y., Zhou, L., et al., Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage. 55:856–867, 2011.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zhang, Y., Dong, Z., Wang, S., et al., Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM). Entropy. 17:1795–1813, 2015.

    Article  Google Scholar 

  12. Colliot, O., Chételat, G., Chupin, M., et al., Discrimination between Alzheimer disease, Mild Cognitive Impairment, and Normal Aging by Using Automated Segmentation of the Hippocampus 1. Radiol. 248:194–201, 2008.

    Article  Google Scholar 

  13. Vemuri, P., Gunter, J.L., Senjem, M.L., et al., Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. NeuroImage. 39:1186–1197, 2008.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Misra, C., Fan, Y., and Davatzikos, C., Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. NeuroImage. 44:1415–1422, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Daliri, M.R., Automated diagnosis of Alzheimer disease using the scale-invariant feature transforms in magnetic resonance images. J. Med. Syst. 36:995–1000, 2012.

    Article  PubMed  Google Scholar 

  16. Wagholikar, K., Vijayraghavan, S., and Ashok, W.D., Modeling paradigms for medical diagnostic decision support: a survey and future directions. J. Med. Syst. 36:3029–3049, 2012.

    Article  PubMed  Google Scholar 

  17. Davatzikos, C., Fan, Y., Wu, X., et al., Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol. Aging. 29:514–523, 2008.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Magnin, B., Mesrob, L., Kinkingnéhun, S., et al., Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiol. 51:73–83, 2009.

    Article  Google Scholar 

  19. Lao, Z., Shen, D., Xue, Z., et al., Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage. 21:46–57, 2004.

    Article  PubMed  Google Scholar 

  20. Ye J, Chen K, Wu et al (2008) Heterogeneous data fusion for Alzheimer's disease study. 14th ACM SIGKDD International conference on knowledge discovery and data mining 1025–1033.

  21. Desikan RS, Cabral HJ, Hess CP et al Alzheimer's Disease Neuroimaging Initiative (2009) Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease. Brain 132:2048–2057.

  22. Gerardin, 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. 47:1476–1486, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Cuingnet R, Gerardin E, Tessieras J et al Alzheimer's Disease Neuroimaging Initiative (2011) Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 56:766–781.

  24. Bay, O.F., and Ali, B.U., Survey of fuzzy logic applications in brain-related researches. J. Med. Syst. 27:215–223, 2003.

    Article  PubMed  Google Scholar 

  25. Rajendran, P., and Madheswaran, M., An improved brain image classification technique with mining and shape prior segmentation procedure. J. Med. Syst. 36:747–764, 2012.

    Article  PubMed  CAS  Google Scholar 

  26. Ferrarini, L., Palm, W.M., Olofsen, H., et al., MMSE scores correlate with local ventricular enlargement in the spectrum from cognitively normal to Alzheimer disease. NeuroImage. 39:1832–1838, 2008.

    Article  PubMed  Google Scholar 

  27. Nestor SM, Rupsingh R, Borrie M et al Alzheimer's Disease Neuroimaging Initiative. (2008) Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database. Brain 131:2443–2454.

  28. Jack, C.R., Shiung, M.M., Weigand, S.D., et al., Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology. 65:1227–1231, 2005.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Schott, J.M., Price, S.L., Frost, C., et al., Measuring atrophy in Alzheimer disease A serial MRI study over 6 and 12 months. Neurology. 65:119–124, 2005.

    Article  PubMed  CAS  Google Scholar 

  30. Silbert, L.C., Quinn, J.F., Moore, M.M., et al., Changes in premorbid brain volume predict Alzheimer’s disease pathology. Neurology. 61:487–492, 2003.

    Article  PubMed  CAS  Google Scholar 

  31. Kempton, M.J., Tracy, S.A.U., Simon, B., et al., A comprehensive testing protocol for MRI neuroanatomical segmentation techniques: evaluation of a novel lateral ventricle segmentation method. NeuroImage. 58:1051–1059, 2011.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kayalvizhi, M., Kavitha, G., Sujatha, C.M., et al., Minkowski functionals based brain to ventricle index for analysis of AD progression in MR images. Measurement, 2015. doi:10.1016/j.measurement. 2015.06.021.

    Google Scholar 

  33. Ferrarini, L., Palm, W.M., Olofsen, H., et al., Shape differences of the brain ventricles in Alzheimer's disease. NeuroImage. 32:1060–1069, 2006.

    Article  PubMed  Google Scholar 

  34. Konukoglu, E., Glocker, B., Criminisi, A., et al., WESD–Weighted Spectral Distance for Measuring Shape Dissimilarity. IEEE. Trans. Pattern. Anal. Mach. Intell. 35:2284–2297, 2013.

    Article  PubMed  Google Scholar 

  35. Reuter, M., Wolter, F.E., Shenton, M., et al., Laplace–Beltrami eigenvalues and topological features of eigenfunctions for statistical shape analysis. Comput. Aided. D. 41:739–755, 2009.

    Article  Google Scholar 

  36. Reuter, M., Wolter, F.E., and Peinecke, N., Laplace–Beltrami spectra as ‘Shape-DNA’ of surfaces and solids. Comput. Aided. D. 38:342–366, 2006.

    Article  Google Scholar 

  37. McKean, H.P., and Singer, I.M., Curvature and the eigenvalues of the Laplacian. J. Differ. Geom.. 1:43–69, 1967.

    Google Scholar 

  38. Smith, L., The asymptotics of the heat equation for a boundary value problem. Invent. Math. 63:467–493, 1981.

    Article  Google Scholar 

  39. Seo, S., and Chung, M.K., Laplace-beltrami eigenfunction expansion of cortical manifolds. IEEE Int Symp Biomed Imaging: Nano Macro:372–375, 2011.

  40. Shishegar R, Soltanian-Zadeh H, Moghadasi SR (2011) Hippocampal shape analysis in epilepsy using Laplace-Beltrami spectrum. IEEE 19th Iranian Conference on Electrical Engineering (ICEE) 1–5.

  41. Marcus, D.S., Wang, T.H., Parker, J., et al., Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19:1498–1507, 2007.

    Article  PubMed  Google Scholar 

  42. Wang J, Ekin A, De Haan G (2008). Shape analysis of brain ventricles for improved classification of Alzheimer’s patients. 15th IEEE International Conference on Image Processing (ICIP) 2252–2255.

  43. Zhang, K., Zhang, L., Song, H., et al., Reinitialization-free level set evolution via reaction diffusion. IEEE Trans. Image Process. 22:258–271, 2013.

    Article  PubMed  Google Scholar 

  44. Kayalvizhi, M., Anandh, K.R., Kavitha, G., et al., Analysis of Anatomical Regions in Alzheimer’s Brain MR Images Using Level Sets and Minkowski Functionals. J Mech Med Biol. 15:1–7, 2015.

    Article  Google Scholar 

  45. Cárdenes, R., de Luis-García, R., and Bach-Cuadra, M., A multidimensional segmentation evaluation for medical image data. Comput Meth Prog Bio. 96:108–124, 2009.

    Article  Google Scholar 

  46. Courant, R., and Hilbert, D., Methods of mathematical physics. CUP Archive, 1966.

  47. Wu, X., Kumar, V., Quinlan, J.R., et al., Top 10 algorithms in data mining. Knowl. Inf. Syst. 14:1–37, 2008.

    Article  Google Scholar 

  48. Shang C, Barnes D (2010) Combining support vector machines and information gain ranking for classification of mars mcmurdo panorama images. 17th IEEE International Conference on Image Processing (ICIP) 1061–1064.

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Correspondence to K. R. Anandh.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Anandh, K.R., Sujatha, C.M. & Ramakrishnan, S. A Method to Differentiate Mild Cognitive Impairment and Alzheimer in MR Images using Eigen Value Descriptors. J Med Syst 40, 25 (2016). https://doi.org/10.1007/s10916-015-0396-y

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