A tac clustering method based on Dirichlet process mixture model

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

Active Publication Date: 2018-12-25
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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  • A tac clustering method based on Dirichlet process mixture model
  • A tac clustering method based on Dirichlet process mixture model
  • A tac clustering method based on Dirichlet process mixture model

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Embodiment Construction

[0068] In order to describe the present invention in more detail, the TAC clustering method of the present invention will be described in detail below with reference to the drawings and specific embodiments.

[0069] Such as figure 1 As shown, the TAC clustering method based on the Dirichlet process mixture model of the present invention includes 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 parameters related to the class separation degree ss, s0, and the value of the degree of freedom v of the inverse Vicht covariance prior;

[0071] S2. Initialize the DP hybrid model: initialize the category to which each TAC belongs, and calculate and determine the relevant information q of each category in the hybrid 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 element in z, z i...

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Abstract

The invention discloses a TAC clustering method based on a Dirichlet process mixture model, comprising: initializing the Dirichlet process mixture model, iteratively calculating conditional probability and performing sampling until the iteration stop condition is satisfied. The present invention clusters TACs by using the Dirichlet process mixture model, which effectively solves the problem of clustering TACs when the number of classes is unknown, and the complexity of the Dirichlet mixture model can increase as we obtain This is an advantage that other clustering algorithms do not have.

Description

Technical field [0001] The invention belongs to the technical field of clustering, and specifically relates to a TAC clustering method based on a Dirichlet process hybrid model. Background technique [0002] Positron emission tomography (PET) is a nuclear medicine imaging technique. Dynamic PET imaging acquires the spatial distribution of multiple frames of physiological states through continuous data collection. It can reconstruct the temporal and spatial distribution of radiopharmaceutical-labeled biological matrices in living tissues, and provide quantitative and non-quantitative information about different biological or physiological processes. 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 (time activity curve, TAC) is extracted from each region. TAC can be further analyzed to estimate physiological parameters such as blood flow, metabolism, and receptor...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/2321
Inventor 刘华锋王婷
Owner ZHEJIANG UNIV
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