Multislice kernel based on spatial and anatomical structure information for Alzheimer's disease classification
Objective To explore fast and efficient algorithms for early and accurate diagnosis of Alzheimer's disease.Methods Utilizing spatial and anatomical information of brain tissue to construct multi-slice kernels and applying them to the classification and discrimination of Alzheimer's disease.Results Cuingnet proposes a framework to include spatial and anatomical structure information in classical single kernel support vector ma-chine for Alzheimer's disease classification,and it generates more interpretable feature maps with high classifica-tion performances.However,in this framework,the spatial regularization parameter is restricted to be equal to the anatomical one for convenience of using single kernel model.In addition,vectorization of a higher-order ten-sorial image destroys the intrinsic structure and a large-scale matrix is also inevitably generated to define the adja-cency relation between every pair of voxels,so it results in intensive computation loads.In this manuscript,the Cuingnet framework is improved by construction of two new types of multislice kernels wherein spatial and ana-tomical Laplacian matrices derived from every slice are used to retain the adjacency relations,and the widespread sequential minimal optimization algorithm is adopted to estimate the parameters in a multiple kernel learning mod-el.In this manner,the above large-scale matrix computation in the original Cuingnet framework is avoided.Conclusion Experimental results demonstrate that computing speed is increased hundreds of times,while high classification accuracy is maintained.
neuroimaginganatomical regularizationspatial regularizationtensorial kernel function
国家科技期刊平台
NSTL
万方数据
维普
