Suporte ao diagnóstico da doença de Alzheimer a partir de imagens de ressonância magnética
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Data
2017-05-15
Autores
Padovese, Bruno Tavares [UNESP]
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Editor
Universidade Estadual Paulista (Unesp)
Resumo
Resumo: Os estágios iniciais da doença de Alzheimer são comumente confundidos com o processo natural de envelhecimento. Adicionalmente, a metodologia envolvida no diagnóstico por radiologistas pode ser subjetiva e difícil de documentar. Neste cenário, o desenvolvimento de abordagens acessíveis capazes de auxiliar no diagnóstico precoce da doença de Alzheimer é crucial. Várias abordagens têm sido empregadas com este objetivo, especialmente utilizando imagens de ressonância magnética cerebral. Embora resultados com precisão satisfatória tenham sido obtidos, a maioria das abordagens requer etapas de pré-processamento muito específicas, baseadas na anatomia do cérebro. Neste trabalho, apresentamos uma nova abordagem de recuperação de imagens para auxílio ao diagnóstico da doença de Alzheimer, com base em descritores de propósito geral e uma etapa de pós-processamento não supervisionada. Os exames de ressonância magnética cerebral são processados e recuperados através de descritores de uso geral sem nenhuma etapa de pré-processamento. Dois algoritmos de aprendizado não-supervisionados baseados em ranqueamento foram aplicados para melhorar a eficácia dos resultados iniciais: os algoritmos RL-Sim e ReckNN. Os resultados experimentais demonstram que a abordagem proposta é capaz de atingir resultados de recuperação eficazes, sendo adequada para auxiliar no diagnóstico da doença de Alzheimer.
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 require very specific pre-processing steps based on the brain anatomy. In this work, we present a novel image retrieval approach for supporting the Alzheimer’s disease diagnostic, based on general purpose features and an unsupervised post-processing step. The brain MRI scans are processed and retrieved through general visual features without any pre-processing step. Two rank-based unsupervised distance learning algorithms were used for improving the effectiveness of the initial results: the RL-Sim and ReckNN algorithms. Experimental results demonstrate that the proposed approach can achieve effective retrieval results, being suitable in aiding the diagnosis of Alzheimer’s disease.
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 require very specific pre-processing steps based on the brain anatomy. In this work, we present a novel image retrieval approach for supporting the Alzheimer’s disease diagnostic, based on general purpose features and an unsupervised post-processing step. The brain MRI scans are processed and retrieved through general visual features without any pre-processing step. Two rank-based unsupervised distance learning algorithms were used for improving the effectiveness of the initial results: the RL-Sim and ReckNN algorithms. Experimental results demonstrate that the proposed approach can achieve effective retrieval results, being suitable in aiding the diagnosis of Alzheimer’s disease.
Descrição
Palavras-chave
CBIR, Visual descriptors, Unsupervised learning, Alzheimer, Descritores visuais, Aprendizado não-supervisionado, Auxílio ao diagnóstico