Method of realizing pulmonary nodule sign recognition based on image retrieval with semantic features and supervised Hashing

A semantic feature, image retrieval technology, applied in character and pattern recognition, image enhancement, image analysis and other directions, can solve problems such as misdiagnosis and missed diagnosis

Active Publication Date: 2017-08-25
TAIYUAN UNIV OF TECH
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Problems solved by technology

However, doctors mainly diagnose diseases based on experience, and the diagnosis results are subjective to a certain extent, and misdiagnosis and missed diagnosis often occur.

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  • Method of realizing pulmonary nodule sign recognition based on image retrieval with semantic features and supervised Hashing
  • Method of realizing pulmonary nodule sign recognition based on image retrieval with semantic features and supervised Hashing
  • Method of realizing pulmonary nodule sign recognition based on image retrieval with semantic features and supervised Hashing

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Embodiment Construction

[0064] The present invention will be described in detail below in conjunction with specific embodiments.

[0065] refer to figure 1 , 2 , 3, 5, the realization process of the inventive method is as follows:

[0066] A method of image retrieval based on semantic features and supervised hashing to realize medical sign recognition of pulmonary nodules, comprising the following steps:

[0067] Step A, extracting the mixed sign area of ​​pulmonary nodules in the lung CT image, and intercepting each single sign area, extracting the semantic features expressing the sign information of the pulmonary nodule and retrieving similar pulmonary nodule images, and then identifying the query image Prepare for the medical signs presented;

[0068] Step B, using a convolutional neural network (CNN) based on parameter sharing to extract semantic features expressing pulmonary nodule sign information; first use the first CNN to train single sign data, and adjust network parameters to effectivel...

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Abstract

The invention discloses a method of realizing pulmonary nodule medical sign recognition based on image retrieval with semantic features and supervised Hashing. The method includes a step A of extracting pulmonary nodule mixed sign region in a lung CT image, and cutting off the same for each single sign regions; a step B of extracting semantic features expressing pulmonary nodule sign information using a convolutional neural network (CNN) based on parameter sharing; a step C for realizing similar pulmonary nodule image retrieval; and a step D for recognizing pulmonary nodule signs. The method of the invention is based on the pulmonary nodule image retrieval with the semantic features and supervised Hashing, the sign category shown in the pulmonary nodule image is thus recognized, a doctor is assisted in judging the benign and malignant degrees of pulmonary nodules, and over-reliance of the doctor on diagnostic experience is reduced.

Description

technical field [0001] The invention relates to the identification of pulmonary nodule signs, in particular to a method for realizing the identification of pulmonary nodule signs based on semantic features and supervised hash image retrieval. Background technique [0002] The medical signs of pulmonary nodules are the basis for doctors to diagnose lung diseases. By analyzing various medical signs of lung CT images, it is convenient for doctors to judge the degree of benign and malignant nodules and make corresponding diagnostic decisions. However, doctors mainly diagnose diseases based on experience, and the diagnosis results are subject to some degree, so misdiagnosis and missed diagnosis often occur. Content-based medical image retrieval can help physicians quickly find similar lesion images from medical databases. The diagnostic schemes and lesion characteristics of these confirmed cases can provide references for the diagnosis of query lesions, thereby assisting physicia...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06T7/00G06N3/08G06F19/00
CPCG06T7/0014G16H50/20G06T2207/20084G06T2207/20081G06T2207/30064G06V10/462
Inventor 赵涓涓潘玲强梓林郝晓燕王华强彦
Owner TAIYUAN UNIV OF TECH
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