Medical field image semantic similarity matrix generation method

A semantic similarity, image technology, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of waste of limited storage space, reducing the efficiency and accuracy of semantic retrieval of medical images, and inability to interoperate. The effect of reducing the incidence and improving the accuracy

Active Publication Date: 2015-12-23
BENGBU MEDICAL COLLEGE
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Problems solved by technology

Although these different domain knowledge base systems are concentrated descriptions of knowledge in the same domain, they still inevitably contain a lot of image information with repeated semantics, resulting in a waste of limited storage space and seriously reducing the efficiency and efficiency of medical image semantic retrieval. Accuracy, which ultimately makes interoperability between knowledge entities in the field impossible, greatly restricting the efficiency of knowledge use

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  • Medical field image semantic similarity matrix generation method
  • Medical field image semantic similarity matrix generation method
  • Medical field image semantic similarity matrix generation method

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Embodiment Construction

[0023] The design concept of the present invention is: using the Bayesian probability model to explicitly represent the semantic information hidden in the image in the form of a tagged word set. Semantic weights of image concepts are adjusted using attributes, and discrete attribute values ​​are obtained by constructing binary conditional attribute decision tables. The method of distinguishable difference matrix is ​​adopted to reduce the calculation scale of tagged words. The matrix calculation of multi-angle semantic distance is introduced to generate a semantic similarity matrix.

[0024] The system of this embodiment includes a domain image semantic information tagging module, a conditional decision entropy generating module, a tagged word reduction module, and a matrix calculation module. The present invention will be further described below in conjunction with the accompanying drawings.

[0025] see figure 1 , a method for generating a semantic similarity matrix of ima...

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Abstract

The invention relates to a generation method for a semantic similarity matrix between field images taking semantic distance between medical field images as a research object, and discloses a medical image similarity matrix extraction modeling method based on a rough semantic probability model through multi-strategy matched similarity relational mapping. The medical field image semantic similarity matrix generation method disclosed by the invention mainly comprises four steps of: semantic annotation based on a Bayes probability model, image feature discretization, semantic feature reduction and field similarity model calculation based on a polymorphic theory. The medical field image semantic similarity matrix generation method can effectively increase combination accuracy of semantic information between the medical field images, improve quality of an integrated medical clinical diagnosis field knowledge base, and reduces calculation scale required for mining image semantic information in large scale.

Description

technical field [0001] The invention belongs to the technical field of medical semantic network and knowledge grid computing and retrieval, and in particular relates to a method for generating a semantic similarity matrix of images in the medical field. Background technique [0002] Due to its extensive application, knowledge in the medical field has been paid more and more attention by relevant scholars. Medical information resources are relatively isolated due to their complexity, dispersion, and heterogeneity, and it is difficult to meet the information needs of users, resulting in the diversity and conflict of image databases in the same field, making it impossible for knowledge bases in the field to interact. operate. [0003] With the rapid development of technologies such as network communication and cloud storage, the scale of information sources including various medical images has gradually expanded. How to obtain implicit and valuable information from massive da...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24155
Inventor 王凯
Owner BENGBU MEDICAL COLLEGE
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