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Pulmonary nodule image processing method based on mkl-svm-pso algorithm

A technology of MKL-SVM and pulmonary nodules, which is applied in the field of image processing of pulmonary nodules based on the MKL-SVM-PSO algorithm, can solve the problems of large amount of calculation, long time for parameter search, poor real-time performance, etc., and achieve the average fitness value The effect of fast and easy global optimal solution

Active Publication Date: 2020-12-29
CHANGCHUN UNIV OF TECH +1
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Problems solved by technology

[0004] The existing technology has the disadvantages of large amount of calculation, long time for parameter search, poor real-time performance, and it is not easy to form an online recognition algorithm

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  • Pulmonary nodule image processing method based on mkl-svm-pso algorithm
  • Pulmonary nodule image processing method based on mkl-svm-pso algorithm
  • Pulmonary nodule image processing method based on mkl-svm-pso algorithm

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Embodiment Construction

[0038] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0039] The present invention provides a kind of pulmonary nodule image processing method based on MKL-SVM-PSO algorithm, see figure 1 ,include:

[0040] S100: Extract a region of interest from the image of a pulmonary nodule, perform feature selection on the region of interest, and obtain a data sample;

[0041] Specifically, this embodiment further explains the region of interest (ROI): in machine vision and image processing, the image to be processed is drawn in the form of a box, circle, ellipse, irregular polygon, etc. The processed area is called region of interest, ROI. In the field of image processing, a region of interest (ROI) is an image region selected from an image, and this region is the focus of image analysis. Circle ...

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Abstract

The invention discloses a method for processing a pulmonary nodule image based on the MKL-SVM-PSO algorithm, comprising: extracting a region of interest from a pulmonary nodule image, performing feature selection on the region of interest, and obtaining data samples; wherein, The data sample includes: a training set for parameter optimization and a test set for testing the model; the training set of the data sample is optimized through the MKL-SVM-PSO algorithm to obtain the optimal parameter group and establish A mathematical model of MKL-SVM; applying the optimal parameter set to the mathematical model of the MKL-SVM to perform identification calculations to obtain the identification result of pulmonary nodules. The present invention can quickly and accurately find the optimal parameter group of the MKL-SVM algorithm, and apply it to the identification of pulmonary nodules; introduce the PSO algorithm into the MKL-SVM algorithm, and apply it to the discrimination of benign and malignant pulmonary nodules .

Description

technical field [0001] The present invention relates to the field, in particular to a method for processing images of pulmonary nodules based on the MKL-SVM-PSO algorithm. Background technique [0002] Pulmonary nodules usually refer to round-like dense opacities in the lungs with a diameter not greater than 3 cm, which is also an early manifestation of lung cancer on lung CT images. Computed tomography (CT) technology is an important means to detect early pulmonary nodules. According to the CT manifestations of pulmonary nodules, they can be divided into solid nodules (such as solitary nodules, adherent pulmonary nodules, adherent vascular nodules), ground glass nodules, and cavitary nodules. [0003] Lung computer-aided detection (Computer Aided Detection, CAD) system is the application of machine vision technology, which can reduce the visual fatigue of radiologists caused by super-loaded film reading, and reduce the possibility of misjudgment or missed detection. Auxil...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06K9/32G06K9/62
CPCG06T7/0012G06T2207/30064G06T2207/20081G06T2207/10081G06V10/25G06F18/2411
Inventor 李阳张丽娟赵庆东侯阿临刘丽伟王宏志祝志川田颖
Owner CHANGCHUN UNIV OF TECH
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