Diagnostic Support for Alzheimers Disease through Feature-Based Brain MRI Retrieval and Unsupervised Distance Learning

Author(s)

Padovese, Bruno T. ; Salvadeo, Denis H. P. ; Pedronette, Daniel C. G. ; IEEE

• Document type: Journal article
• Source: 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE); v. N/A, p. 8-pg., 2016-01-01.

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 rank-based 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. (AU)

• FAPESP's process: 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks
• Grantee: Daniel Carlos Guimarães Pedronette
• Support Opportunities: Research Grants - Young Investigators Grants