Alzheimer’s disease brain image segmentation using multi-feature fusion in 3D Rényi entropy model and quantum hybrid optimization

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder, and its early diagnosis critically depends on accurate segmentation of brain pathological images. However, conventional multi-threshold image segmentation (MIS) methods often exhibit high sensitivity to noise and insufficient exploitation of spatial structural information, particularly when applied to AD images with complex textures and dense information content. To overcome these limitations, this study introduces a novel three-dimensional (3D) Rényi entropy model that integrates grayscale intensity, non-local means (NLM), and local entropy. The resulting joint histogram simultaneously captures grayscale, spatial, and texture features,…

• NS
  Natural Science Foundation of Tianjin City

  Award: 21JCYBJC00110