Analysis of Brain SPECT Images for the Diagnosis of Alzheimer Disease Using First and Second Order Moments

This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the selection of the voxels which present greater overall difference between both modalities (normal and Alzheimer) and also lower dispersion. We measure the dispersion of the intensity values for normals and Alzheimer images by mean of the standard deviation images. The mean value of the intensities of selected voxels is used as feature for different classifiers, including support vector machines with linear kernels, fitting a multivariate normal density to each group and the k-nearest neighbors algorithm. The proposed methodology reaches an accuracy of 92 % in the classification task.

[1]  Wojtek J. Krzanowski,et al.  Principles of multivariate analysis : a user's perspective. oxford , 1988 .

[2]  A. Lassl,et al.  Automatic computer aided diagnosis tool using component-based SVM , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[3]  H J Testa,et al.  Accuracy of single-photon emission computed tomography in differentiating frontotemporal dementia from Alzheimer’s disease , 2006, Journal of Neurology, Neurosurgery & Psychiatry.

[4]  Juan Manuel Górriz,et al.  Improved Gauss-Newton optimisation methods in affine registration of SPECT brain images , 2008 .

[5]  Clifford Goodman,et al.  Society of Nuclear Medicine , 1988 .

[6]  Philippe Robert,et al.  Classification of SPECT Images of Normal Subjects versus Images of Alzheimer's Disease Patients , 2001, MICCAI.

[7]  Glenn Fung,et al.  SVM Feature Selection for Classification of SPECT Images of Alzheimer's Disease Using Spatial Information , 2005, ICDM.

[8]  Fabian J. Theis,et al.  Effective Emission Tomography Image Reconstruction Algorithms for SPECT Data , 2008, ICCS.

[9]  B L Holman,et al.  The scintigraphic appearance of Alzheimer's disease: a prospective study using technetium-99m-HMPAO SPECT. , 1992, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[10]  M F Kijewski,et al.  Quantitative brain SPECT in Alzheimer's disease and normal aging. , 1993, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[11]  J. Mazziotta,et al.  Automated image registration , 1993 .

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  J. M. Gorriz,et al.  Clustering approach for the classificarion of SPECT images , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[14]  R. English,et al.  Spect Single-Photon Emission Computed Tomography: A Primer , 1986 .

[15]  R. V. Van Heertum,et al.  SPECT perfusion imaging in the diagnosis of Alzheimer’s disease , 2001, Neurology.

[16]  Marco Aldinucci,et al.  Computational Science - ICCS 2008, 8th International Conference, Kraków, Poland, June 23-25, 2008, Proceedings, Part I , 2008, ICCS.

[17]  Scott T. Grafton,et al.  Automated image registration: I. General methods and intrasubject, intramodality validation. , 1998, Journal of computer assisted tomography.

[18]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[19]  J. Ashburner,et al.  Nonlinear spatial normalization using basis functions , 1999, Human brain mapping.

[20]  Wiro J. Niessen,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001 , 2001, Lecture Notes in Computer Science.

[21]  D W Palmer,et al.  Alzheimer disease: quantitative analysis of I-123-iodoamphetamine SPECT brain imaging. , 1989, Radiology.

[22]  Barry Horwitz,et al.  An Automatic Threshold-Based Scaling Method for Enhancing the Usefulness of Tc-HMPAO SPECT in the Diagnosis of Alzheimer's Disease , 1998, MICCAI.