Paper Info
Title
Diagnostic Support for Alzheimers Disease through Feature-Based Brain MRI Retrieval and Unsupervised Distance Learning
Abstract
Initial stages of Alzheimer's disease are easily confused with the normal aging process. Additionally, the methodology involved in the diagnosis by radiologists can be subjective and difficult to document. In this scenario, the development of accessible approaches capable of supporting the early diagnosis of Alzheimer's disease is crucial. Various approaches have been employed with this objective, specially using brain MRI scans. Although certain satisfactory accuracy results have been achieved, most of the approaches requires very specific pre-processing steps based on the brain anatomy. In this paper, we present a novel image retrieval approach for supporting the Alzheimer's disease diagnostic, based on general use features and unsupervised post-processing step. The brain MRI scans are processed and retrieved through general features without any pre-processing step. In the following, a rankbased unsupervised distance learning procedure is performed for improving the effectiveness of the initial results. Experimental results demonstrate that the proposed approach can achieve effective retrieval results, being suitable in aiding the diagnosis of Alzheimer's disease.
Year
DOI
Venue
2016
10.1109/BIBE.2016.52
2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)
Keywords
Field
DocType
Alzheimers Disease,Unsupervised Distance Learning
Disease,Brain mri,Alzheimer's disease,Brain anatomy,Computer science,Image retrieval,Distance education,Artificial intelligence,Feature based,Machine learning
Conference
ISSN
ISBN
Citations 
2471-7819
978-1-5090-3835-0
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Bruno T. Padovese100.34
Denis H. P. Salvadeo262.66
Daniel Carlos Guimarães Pedronette330425.47