Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 27, No. 1 (January 2014) 51-56   

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  DETECTION OF ALZHEIMER\\\\\\\'S DISEASE USING MULTITRACER POSITRON EMISSION TOMOGRAPHY IMAGING
 
H. Dehghan, A. A. Pouyan and H. Hassanpour
 
( Received: March 17, 2013 – Accepted: June 20, 2013 )
 
 

Abstract    Alzheimer\'s disease is characterized by impaired glucose metabolism and demonstration of amyloid plaques. Individual positron emission tomography tracers may reveal specific signs of pathology that is not readily apparent on inspection of another one. Combination of multitracer positron emission tomography imaging yields promising results. In this paper, 57 Alzheimer\'s disease neuroimaging initiative subjects that had FDG and PiB-positron emission tomography neuroimaging scans at the same time were used for development of proposed multitracer classification method. The subject’s brain image was automatically parcellated into 48 pre-defined regions of interest. Then 96 features are extracted for each subject. The principal features are extracted using principal component analysis, then they are combined based on intersection strategy. Finally, a support vector machine is adopted to evaluate the classification accuracy. Combination of two tracers with positron emission tomography scan yielded a higher diagnostic accuracy in Alzheimer\'s disease compared to individual tracer and other combination methods.

 

Keywords    Alzheimer’s disease; Multitracer PET; neuroimaging; Principal Component

 

چکیده    بیماری آلزایمر بااختلال متابولیسم گلوکز ونمایش پلاک آمیلوئیدمشخص می­شود. ردیاب­های توموگرافی گسیل پوزیترون تکی ممکن است نشانه­های خاص از پاتولوژی را نشان دهند که در بازرسی از نوع دیگر به راحتی آشکار نمی­باشد.ترکیب پوزیترون تصویربرداری توموگرافی گسیل چند ردیاب، نتایج امیدوار کننده­ای را به دست آورده است. در این مقاله از57بیماری آلزایمرتصویربرداری عصبی از افرادی که FDGوPIB-پوزیترون تصویربرداری عصبی توموگرافی اسکن شده در یک زمان دارند برای توسعه روش طبقه بندی چند ردیاب پیشنهادی استفاده شده است. تصویر مغ زفردبه طور خودکار به 48ناحیه از پیش تعریف شده مورد توجه تقسیم می­شود. سپس 96 ویژگی برای هر فرد استخراج می­گردد. ویژگی­های اصلی با استفاده ازتجزیه مولفه­های اصلی(PCA) استخراج شده، سپس آنها براساس استراتژی تقاطع با هم ترکیب می­شوند. درنهایت یک ماشین بردار پشتیبان (SVM)برای ارزیابی دقت کلاس­بندی استفاده شده است. ترکیبی از دو ردیاب با اسکن توموگرافی گسیل پوزیترون منجر به دقت تشخیص بالاتری دربیماری آلزایمردر مقایسه باتک ردیاب وروش­های ترکیبی دیگرمی­شود.

References   

1.     SR., G., "Is it alzheimer's disease?", Postgrad Med,  Vol. l0l, (1997), 42-43.

2.     Folstein, M. F., "Differential diagnosis of dementia: The clinical process", Psychiatric Clinics of North America,  Vol. 20, No. 1, (1997), 45-57.

3.     Morris, J. C., "Differential diagnosis of alzheimer's disease", Clinics in Geriatric Medicine,  Vol. 10, No. 2, (1994), 257.

4.     Petrella, J. R., Coleman, R. E. and Doraiswamy, P. M., "Neuroimaging and early diagnosis of alzheimer disease: A look to the future1", Radiology,  Vol. 226, No. 2, (2003), 315-336.

5.     López, M., Ramírez, J., Górriz, J. M., Álvarez, I., Salas-Gonzalez, D., Segovia, F., Chaves, R., Padilla, P., and Gómez-Río, M., "Principal component analysis-based techniques and supervised classification schemes for the early detection of alzheimer's disease", Neurocomputing,  Vol. 74, No. 8, (2011), 1260-1271.

6.     Mebane-Sims, I., "2009 alzheimer's disease facts and figures", Alzheimer's & Dementia,  (2009).

7.     Prince, M. and Jackson, J., "World alzheimer report 2009", Alzheimer's Disease International,  (2009).

8.     Nordberg, A., "Pet imaging of amyloid in alzheimer's disease", The Lancet Neurology,  Vol. 3, No. 9, (2004), 519-527.

9.     Apostolova, L. G. and Thompson, P. M., "Mapping progressive brain structural changes in early alzheimer's disease and mild cognitive impairment", Neuropsychologia,  Vol. 46, No. 6, (2008), 1597-1612.

10.   Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W., Trojanowski, J. Q., Toga, A. W., and Beckett, L., "Ways toward an early diagnosis in alzheimer’s disease: The alzheimer’s disease neuroimaging initiative (adni)", Alzheimer's and Dementia,  Vol. 1, No. 1, (2005), 55-66.

11.   Braak, H. and Braak, E., "Staging of alzheimer's disease-related neurofibrillary changes", Neurobiology of Aging,  Vol. 16, No. 3, (1995), 271-278.

12.   Delacourte, A., David, J., Sergeant, N., Buee, L., Wattez, A., Vermersch, P., Ghozali, F., Fallet-Bianco, C., Pasquier, F., and Lebert, F., "The biochemical pathway of neurofibrillary degeneration in aging and alzheimer’s disease", Neurology,  Vol. 52, No. 6, (1999), 1158-1158.

13.   Nordberg, A., "Amyloid imaging in alzheimer's disease", Neuropsychologia,  Vol. 46, No. 6, (2008), 1636-1641.

14.   Damoulas, T. and Girolami, M. A., "Combining feature spaces for classification", Pattern Recognition,  Vol. 42, No. 11, (2009), 2671-2683.

15.   Berger, J. O., "Statistical decision theory and bayesian analysis", Springer,  (1985).

16.   Lee, W.-J., Verzakov, S. and Duin, R. P., Kernel combination versus classifier combination, in Multiple classifier systems., Springer. (2007). 22-31.

17.   Sonnenburg, S., Rätsch, G. and Schäfer, C., "A general and efficient multiple kernel learning algorithm",  (2006).

18.   Zhang, D., Wang, Y., Zhou, L., Yuan, H. and Shen, D., "Multimodal classification of alzheimer's disease and mild cognitive impairment", Neuroimage,  Vol. 55, No. 3, (2011), 856-867.

19.   Friston, K. J., Ashburner, J. T., Kiebel, S. J., Nichols, T. E. and Penny, W. D., "Statistical parametric mapping: The analysis of functional brain images: The analysis of functional brain images", Academic Press,  (2011).

20.   Ramírez, J., Górriz, J., Segovia, F., Chaves, R., Salas-Gonzalez, D., López, M., Álvarez, I., and Padilla, P., "Computer aided diagnosis system for the alzheimer's disease based on partial least squares and random forest spect image classification", Neuroscience Letters,  Vol. 472, No. 2, (2010), 99-103.

21.   Woods, R. P., Grafton, S. T., Holmes, C. J., Cherry, S. R. and Mazziotta, J. C., "Automated image registration: I. General methods and intrasubject, intramodality validation", Journal of Computer Assisted Tomography,  Vol. 22, No. 1, (1998), 139-152.

22.   Kabani, N. J., "3d anatomical atlas of the human brain", Neuroimage,  Vol. 7, No., (1998), P-0717.

23.   In Cowan, J., Tesauro, G. and Alspector, J., "Fast non-linear dimension reduction",  Vol., No.

24.   Rizk-Jackson, A., Stoffers, D., Sheldon, S., Kuperman, J., Dale, A., Goldstein, J., Corey-Bloom, J., Poldrack, R. A., and Aron, A. R., "Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic huntington's disease using machine learning techniques", Neuroimage,  Vol. 56, No. 2, (2011), 788-796.

25.   Luts, J., Ojeda, F., Van de Plas, R., De Moor, B., Van Huffel, S., and Suykens, J. A., "A tutorial on support vector machine-based methods for classification problems in chemometrics", Analytica Chimica Acta,  Vol. 665, No. 2, (2010), 129-145.

26.   Sanchez, A. and David, V., "Advanced support vector machines and kernel methods", Neurocomputing,  Vol. 55, No. 1, (2003), 5-20.

27.   Schölkopf, B. and Smola, A., Learning with kernels., MIT Press, Cambridge, MA. (2002)

28.   Vapnik, V., "The nature of statistical learning theory", Springer,  (2000).

29.   Magnin, B., Mesrob, L., Kinkingnéhun, S., Pélégrini-Issac, M., Colliot, O., Sarazin, M., Dubois, B., Lehéricy, S., and Benali, H., "Support vector machine-based classification of alzheimer’s disease from whole-brain anatomical mri", Neuroradiology,  Vol. 51, No. 2, (2009), 73-83.

30.   Górriz, J., Ramírez, J., Lassl, A., Salas-Gonzalez, D., Lang, E., Puntonet, C., Álvarez, I., López, M., and Gómez-Río, M., "Automatic computer aided diagnosis tool using component-based svm", in Nuclear Science Symposium Conference Record, 2008. NSS'08. IEEE, (2008), 4392-4395.

31.   Illán, I., Górriz, J., Ramírez, J., Salas-Gonzalez, D., López, M., Segovia, F., Chaves, R., Gómez-Rio, M., and Puntonet, C. G., " 18 F-FDG pet imaging analysis for computer aided alzheimer’s diagnosis", Information Sciences,  Vol. 181, No. 4, (2011), 903-916.

32.   Dukart, J., Mueller, K., Horstmann, A., Barthel, H., Möller, H. E., Villringer, A., Sabri, O., and Schroeter, M. L., "Combined evaluation of fdg-pet and mri improves detection and differentiation of dementia", PLoS One,  Vol. 6, No. 3, (2011).

33.   Hinrichs, C., Singh, V., Xu, G. and Johnson, S. C., "Predictive markers for ad in a multi-modality framework: An analysis of mci progression in the adni population", Neuroimage,  Vol. 55, No. 2, (2011), 574-589.

34.   Hinrichs, C., Singh, V., Xu, G. and Johnson, S., Mkl for robust multi-modality ad classification, in Medical image computing and computer-assisted intervention–miccai, Springer, (2009). 786-794.

35.           M.M. López, J. Ramírez, J. M. Górriz, I. Álvarez, D. Salas-Gonzalez, F. Segovia, R. Chaves, “SVM-based CAD system for early detection of the Alzheimer’s disease using kernel PCA and LDA”, Neuroscience Letters, Vol. 464, (2009) 233-238 .





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