阿尔茨海默病格林函数经验模式分解研究的生物医学图像研究

Title:Functional Biomedical Images of Alzheimer's Disease a Green's Functionbased Empirical Mode Decomposition Study

Volume: 13 Issue: 6

Author(s): S. Al-Baddai, A. Neubauer, A.M. Tomé, V. Vigneron, D. Salas-Gonzalez, J.M. Górriz, C.G. Puntonet and E.W. Lang, the Alzheimer's Disease Neuroimaging Initiative

Affiliation:

关键词: 阿尔茨海默病，经验模式分解，格林函数，支持向量机，射频。

摘要: Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D-EMD) provides means to analyze such images. It decomposes the latter into characteristic modes which represent textures on different spatial scales. These textures provide informative features for subsequent classification purposes. The study proposes a new EMD variant which relies on a Green's function based estimation method including a tension parameter to fast and reliably estimate the envelope hypersurfaces interpolating extremal points of the twodimensional intensity distrubution of the images. The new method represents a fast and stable bi-dimensional EMD which speeds up computations roughly 100-fold. In combination with proper classifiers these exploratory feature extraction techniques can form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from Alzheimer's disease are taken to illustrate this ability.

Cite this article as:

S. Al-Baddai, A. Neubauer, A.M. Tomé, V. Vigneron, D. Salas-Gonzalez, J.M. Górriz, C.G. Puntonet and E.W. Lang, the Alzheimer's Disease Neuroimaging Initiative , Functional Biomedical Images of Alzheimer's Disease a Green's Functionbased Empirical Mode Decomposition Study, Current Alzheimer Research 2016; 13 (6) . https://dx.doi.org/10.2174/1567205013666160322141726