Neuroscience International

Efficient Morphometric Techniques in Alzheimer’s Disease Detection: Survey and Tools

Vinutha N., P. Deepa Shenoy, P. Deepa Shenoy and K.R. Venugopal

DOI : 10.3844/amjnsp.2016.19.44

Neuroscience International

Volume 7, Issue 2

Pages 19-44

Abstract

The development of advance techniques in the multiple fields such as image processing, data mining and machine learning are required for the early detection of Alzheimer’s Disease (AD) and to prevent the progression of the disease to the later stages. The longitudinal and cross sectional images of elderly subjects were obtained from the standard datasets like ADNI, OASIS, MIRIAD and ICBM. The subject image obtained from the dataset, can be geometrically aligned to the template image through the process of registration. The registration techniques like Mutual Information Registration, Fluid registration, Rigid registration, Spatial Transformation algorithm for registration, Elastic Registration are selected based on type of transformation and similarity measures to suit the required application. The registered images are then subjected to the process of segmentation in order to segment relevant tissues or desired region of interest that are significant in AD detection. The different types of segmentation techniques such as Tissue Segmentation, Atlas based Segmentation, Hippocampus Segmentation and other segmentation techniques have been discussed. The segmented images are then subjected to morphometry techniques to identify the morphological changes developed in an abnormal image. The different types of morphometry techniques used are Voxel Based Morphometry (VBM), Deformation Based Morphometry (DBM), Shape Based Morphometry (SBM) and Feature Based Morphometry (FBM). But in recent years, the main focus of researchers is towards the FBM and SBM to overcome the disadvantage of group analysis that existed in VBM and DBM. Further the data is classified into healthy normal and AD by supervised, unsupervised or probabilistic methods.

Copyright

© 2016 Vinutha N., P. Deepa Shenoy, P. Deepa Shenoy and K.R. Venugopal. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.