Discrimination between Alzheimer's Disease and Amyotrophic Lateral Sclerosis via Affine Invariant Spherical Harmonics Analysis of SPECT Images

Kateřina Horaisová, Julie Dudášová, Jaromír Kukal, Robert Rusina, Radoslav Matěj, Marie Buncová


Alzheimer's Disease (AD) is the most frequent form of degenerative dementia and its early diagnosis is essential for effective treatment. Functional imaging modalities including Single Photon Emission Computed Tomography (SPECT) are often used with such an aim. However, conventional evaluation of SPECT images relies on manual reorientation and visual evaluation of tomographic slices which is time consuming, subjective and therefore prone to error. Our aim is to show an automatic Computer-Aided Diagnosis (CAD) system for  improving the early detection of the AD. For this purpose, affine invariant descriptors of 3D SPECT image can be useful. The method consists of four steps: evaluation of invariant descriptors obtained using spherical harmonic analysis, statistical testing of their significance, application of regularized binary index models, and model verification via leave-one-out cross-validation scheme. The second approach is based on Support Vector Machine classifier and visualization with use of self-organizing maps. Our approaches were tested on SPECT data from 11 adult patients with definite Alzheimer's disease and 10 adult patients with Amyotrophic Lateral Sclerosis (ALS) who were used as controls. A significant difference between SPECT spherical cuts of AD group and ALS group was both visually and numerically evaluated.


Alzheimer's disease; amyotrophic lateral sclerosis; image processing; SPECT; affine transformation; spherical harmonics; classification; statistical analysis; logit model; probit model; Cauchy model; support vector machine; self-organizing neural network


AMERICAN PSYCHIATRIC ASSOCIATION. Diagnostic and statistical manual of mental disorders (DSM-IV-TR), 4th ed. Washington, DC. 2000.

BERKSON J. Application of the logistic function to bio-assay. Journal of the American Statistical Association. 1944, 39 (227), pp. 357-365.

BLISS C.I. The method of probits. Science. 1934, 79 (2037), pp. 38-39.

ČECH P., KUKAL J., ČERNÝ J., SCHNEIDER B., SVOZIL D. Automatic workflow for the classification of local DNA conformations. BMC Bioinformatics. 2013, 14:205.

CHANG C.-C., LIN C.-J. LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology. 2011, 2(3), pp. 27:1-27:27.

CLAUS J.J., HARSKAMP F. VAN, BRETELER M.M.B., KRENNING E.P., KONING I. DE, CAMMEN J.M. VAN DER, HOFMAN A., HASAN D. The diagnostic value of SPECT with Tc 99m HMPAO in Alzheimer's disease: a population-based study. Neurology. 1994, 44(3), pp. 454-461.

CORTES C., VAPNIK V. Support-Vector Networks, Machine Learning. 1995, 20(3), pp. 273-297.

DEVOUS M.D. Functional brain imaging in the dementias: role in early detection, differential diagnosis, and longitudinal studies. European Journal of Nuclear Medicine and Molecular Imaging. 2002, 29 (12), pp. 1685-96.

DOUGALL N., NOBILI F., EBMEIER K.P. Predicting the accuracy of a diagnosis of Alzheimer's disease with (99m)Tc HMPAO single photon emission computed tomography. Psychiatry Research: Neuroimaging. 2004, 131 (2), pp. 157-168.

DOUGALL N.J., BRUGGINK S., EBMEIER K.P. Systematic review of the diagnostic accuracy of (99m)Tc-HMPAO-SPECT in dementia. American Journal of Geriatric Psychiatry. 2004, 12 (6), pp. 554-570.

EREMENKO A., JAKOBSON D., NADIRASHVILI N. On nodal sets and nodal domains on S-2 and R-2. Annales de l'Institut Fourier. 2007, 57 (7), pp. 2345-2360.

GERARDIN E., CHÉTELAT G., CHUPIN M., CUINGNET R., DESGRANGES B., KIM H.-S., NIETHAMMER M., DUBOIS B., LEHÉERICY S., GARNERO L., EUSTACHE F., COLLIOT O. Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging. NeuroImage. 2009, 47 (4), pp. 1476-1486.

GUTMAN B., WANG Y., MORRA J., TOGA A.W., THOMPSON. P.M. Disease Classification With Hippocampal Shape Invariants. Hippocampus. 2009, 19 (6), pp. 572-578.

HILBE J. M. Logistic Regression Models. Chapman & Hall/CRC Press, 2009.

HORAISOVÁ K., KUKAL J. Leaf Classification from Binary Image via Artificial Intelligence. Biosystems Engineering. 2016, 142, pp. 83-100.

KOHONEN T. Self-Organizing Maps. Springer Series in Information Sciences, Springer Berlin Heidelberg. 2001, 30.

KOHONEN T., HYNNINEN J., KANGAS J., LAAKSONEN J. SOM PAK: The self-organizing map program package. Tech. Rep. A31, Helsinki University of Technology, Laboratory of Computer and Information Science. 1996.

LÖTJÖNEN J., WOLZ R., KOIKKALAINEN J., JULKUNEN V., THURFJELL L., LUNDQVIST R., WALDEMAR G., SOININEN H., RUECKERT D. Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease. NeuroImage. 2011, 56 (1), pp. 185-196.

MCKHANN G., DRACHMAN D.A., FOLSTEIN M., KATZMAN R., PRICE D.L., STADLAN E.M. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer's disease. Neurology, 1984, 34 (7), pp. 939-44.

MCNEILL R., SARE G.M., MANOHARAN M., TESTA H.J., MANN D.M.A., NEARY D., SNOWDEN J.S., VARMA A.R. Accuracy of single-photon emission computed tomography in differentiating frontotemporal dementia from Alzheimer's disease. Journal of Neurology, Neurosurgery & Psychiatry. 2007, 78 (4), pp. 350-355.

MÜLLER C. Spherical Harmonics. Lecture Notes in Mathematics, Springer. 1966, 17.

RILEY K.F., HOBSON M.P., BENCE S.J. Mathematical Methods for Physics and Engineering. Cambridge University Press, Cambridge. 2006.

SAMMON J.W. A nonlinear mapping for structure analysis. IEEE Transactions and Computers. 1969, C-18(5), pp. 401-409

SAYEED A., PETROU M., SPYROU N., KADYROV A., SPINKS T. Diagnostic features of Alzheimer's disease extracted from PET sinograms. Physics in Medicine and Biology. 2001, 47 (1), pp. 137-148.

SHI Y., THOMPSON P.M., ZUBICARAY G.I. DE, ROSE S.E., TU Z., DINOV I., TOGA A.W. Direct mapping of hippocampal surfaces with intrinsic shape context. NeuroImage. 2007, 37 (3), pp. 792-807.

TALBOT P.R., LLOYD J.J., SNOWDEN J.S., NEARY D., TESTA H.J. A clinical role for (99m)Tc-HMPAO SPECT in the investigation of dementia?. Journal of Neurology, Neurosurgery & Psychiatry. 1998, 64 (3), pp. 306-313.

WOOLDRIDGE J.M. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press. 2002.

ZHOU L., LIEBY P., BARNES N., RÉGLADE-MESLIN CH., WALKER J., CHERBUIN N., HARTLEY R. Hippocampal Shape Analysis for Alzheimer's Disease Using an Efficient Hypothesis Test and Regularized Discriminative Deformation. Hippocampus. 2009, 19 (6), pp. 533-540.

DOI: http://dx.doi.org/10.14311/NNW.2018.%25x


  • There are currently no refbacks.

Should you encounter an error (non-functional link, missing or misleading information, application crash), please let us know at nnw.ojs@fd.cvut.cz.
Please, do not use the above address for non-OJS-related queries (manuscript status, etc.).
For your convenience we maintain a list of frequently asked questions here. General queries to items not covered by this FAQ shall be directed to the journal editoral office at nnw@fd.cvut.cz.