Adaptive Fusion of Texture-Based Grading: Application to Alzheimer’s Disease Detection
Alzheimer’s disease is a neurodegenerative process leading to irreversible mental dysfunctions. The development of new biomarkers is crucial to perform an early detection of this disease. Among new biomarkers proposed during the last decades, patch-based grading framework demonstrated state-of-the-art results. In this paper, we study the potential using texture information based on Gabor filters to improve patch-based grading method performance, with a focus on the hippocampal structure. We also propose a novel fusion framework to efficiently combine multiple grading maps derived from a Gabor filters bank. Finally, we compare our new texture-based grading biomarker with the state-of-the-art approaches to demonstrate the high potential of the proposed method.
KeywordsPatch-based grading fusion Multi-features Alzheimer’s disease classification Mild Cognitive Impairment
This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HL-MRI ANR-10-LABX-57).
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