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Dirichlet process mixture model based TAC clustering method

A hybrid model and clustering method technology, applied in the field of clustering, can solve problems such as overfitting, difficult model selection, and modeling underfitting

Active Publication Date: 2016-03-23
ZHEJIANG UNIV
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Problems solved by technology

[0005] The present invention provides a TAC clustering method based on the Dirichlet process mixture model, which can solve the difficulty in model selection and the problems of underfitting and overfitting that are prone to occur in the modeling of existing clustering methods

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  • Dirichlet process mixture model based TAC clustering method
  • Dirichlet process mixture model based TAC clustering method
  • Dirichlet process mixture model based TAC clustering method

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Embodiment Construction

[0068] In order to describe the present invention more specifically, the following describes the TAC clustering method of the present invention in detail in conjunction with the accompanying drawings and specific embodiments.

[0069] Such as figure 1 Shown, the present invention is based on the TAC clustering method of Dirichlet process mixed model, comprises the following steps:

[0070] S1. Initialize various parameters: the parameters that need to be initialized include the number of classes K, the aggregation parameter α, the class separation related parameters ss, s0, and the value of the degree of freedom v of the inverse Vichter covariance prior;

[0071] S2. Initialize the DP mixed model: initialize the category to which each TAC belongs, and calculate and determine the relevant information q of each category in the mixed model. The specific process is as follows:

[0072] 2.1 Use z to represent the value of the class to which all TACs belong, z i is the i-th elemen...

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Abstract

The invention discloses a Dirichlet process mixture model based TAC clustering method. The method comprises: initializing a Dirichlet process mixture model; iteratively calculating a conditional probability; and performing sampling until an iterative stop condition is met. According to the method, the TAC clustering is performed by using the Dirichlet process mixture model, so that the problem in TAC clustering performed under the condition of an unknown class number is effectively solved; and compared with other clustering algorithms, the method has the advantage that the complexity of the Dirichlet process mixture model can be increased with an increase in volume of obtained data.

Description

technical field [0001] The invention belongs to the technical field of clustering, and in particular relates to a TAC clustering method based on a Dirichlet process mixture model. Background technique [0002] Positron emission tomography (PET) is a nuclear medical imaging technique. Dynamic PET imaging obtains the spatial distribution of multi-frame physiological states through continuous data acquisition, which can provide quantitative and non-destructive information about different biological or physiological processes by reconstructing the temporal and spatial distribution of radiopharmaceutical-labeled biological matrices in living tissues. Intrusive information. In practice, dynamic PET images are often segmented into different regions of interest (region of interest, ROI), and then a time activity curve (timeactivity curve, TAC) is extracted from each region. TAC can be further analyzed to estimate physiological parameters such as blood flow, metabolism, and recepto...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2321
Inventor 刘华锋王婷
Owner ZHEJIANG UNIV
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