Alzheimer's Disease Diagnosis Using Nonlinear Weighted T1-Mri Classification

Torabi, Meysam; Fatemizadeh, Emadoddin

doi:10.22041/ijbme.2009.13383

Document Type : Full Research Paper

Authors

¹ M.Sc Graduated, Biomedical Engineering Group, Electrical Engineering School, Sharif University of Technology

² Assistant Professor, Biomedical Engineering Group, Electrical Engineering School, Sharif University of Technology

10.22041/ijbme.2009.13383

Abstract

In this paper, an MRI-based diagnosing approach has been proposed which simultaneously analyzes T1-MR and T2-MR images. The dataset contains 120 cross-sectional images of abnormal and also normal brains as control group. Due to inherent proprieties of T1 and T2 images and their principal differences, particular features have been extracted from each image. Then, more meaningful data has been structured by automatically eliminating redundant data and generating a semi-linear combination of the remaining features. Considering the fact that Alzheimer's disease mainly damages the gray and white matter of the brain and knowing that these parts of the brain can be more clearly observed in T1 images, the classifier which works under a nonlinear structure, allocates more weight for processing the T1 images comparing to T2 image. The images, after being registered, have been processed in two groups of training and test sets. According to the results, three forth of the dataset which was obtained from Harvard University's dataset (The Whole Brain Atlas) has been correctly diagnosed.

Keywords

Main Subjects

Biomedical Image Processing / Medical Image Processing

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Iranian Journal of Biomedical Engineering

Alzheimer's Disease Diagnosis Using Nonlinear Weighted T1-Mri Classification

References

References

Volume 3, Issue 3 - Serial Number 3
June 2009
Pages 213-225

Alzheimer's Disease Diagnosis Using Nonlinear Weighted T1-Mri Classification

References

References

Volume 3, Issue 3 - Serial Number 3June 2009Pages 213-225

Volume 3, Issue 3 - Serial Number 3
June 2009
Pages 213-225