Feature extraction method of three-dimensional point cloud under persistent homology
A three-dimensional point cloud and feature extraction technology, applied in image data processing, instrumentation, computing and other directions, can solve the problem of complex factors of feature description ability, and achieve the effect of wide applicability and strong robustness
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[0028] refer to image 3 The flow chart of the present invention is further elaborated by taking the three-dimensional brain image feature analysis of Alzheimer's disease as an example, and the specific implementation method is as follows:
[0029] First, preprocess the three-dimensional image data of the brain of Alzheimer's disease. The preprocessing steps are as follows:
[0030] S1: Randomly generate three groups of data, each group includes K=40 subjects, and each subject has S=100 three-dimensional scanning images;
[0031] S2: M=90 brain regions are defined by the AAL90 brain atlas, and each brain region is regarded as a node;
[0032] After preprocessing, the following calculation steps are required:
[0033] Step 1: Observe the rank of the homology group of the three-dimensional brain image data after preprocessing, and use the connectivity based on the k-dimensional simplicial complex to distinguish the topological space. The k-th dimension Betti number is the rank...
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