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

• Autor(a) principal: Padovese, Bruno T. [UNESP]
• Data de Publicação: 2016
• Outros Autores: Salvadeo, Denis H. P. [UNESP], Pedronette, Daniel C. G. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/BIBE.2016.52; http://hdl.handle.net/11449/169416
• topic: Alzheimers Disease; Unsupervised Distance Learning

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.