An Intelligent Image Processing Method for Lung Shadows

Abstract

The present invention provides an intelligent image processing method for lung shadows, including S1: extracting an original image of the lung, and segmenting it to obtain lung nodules after preprocessing; S2: performing feature extraction on the lung nodules to obtain features parameter; S3: Feature selection of pulmonary nodules using principal component analysis strategy; S4: Positive data set and negative data set are obtained by using classification algorithm to identify pulmonary nodules. The invention uses the edge filling algorithm to capture and supplement the area structure where the lung nodule is adhered to the area outside the lung in the image display, thereby improving the reliability of the data when processing the lung image.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an intelligent image processing method for lung shadows. Background technique [0002] At present, lung cancer accounts for a high mortality rate of cancer diseases. According to statistics in 2015, the total number of newly diagnosed lung cancer cases in China was about 787,000, and about 630,500 lung cancer patients died, which is equivalent to an average of more than 1,700 deaths per day. The total number of patients ranks first in the world and the incidence of lung cancer is increasing day by day, which seriously endangers the health of Chinese people. In recent years, the development of high-resolution computed tomography, PET-CT, bronchoscopy, percutaneous tumor puncture and other technologies has improved the accuracy of precise screening and staging of lung cancer. PET-CT is of high value not only for the overall assessment, especially for possible distant meta...

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Problems solved by technology

[0003] Pulmonary nodules, as the basic representation of lung cancer lesions, are an important basis for lung cancer screening. With the continuous improvement of CT image scanning technology, it is urgent to solve the problem of matching image recognition processing technology with it. In terms of extracting the integrity of lung features It is particularly critical, because the high-density lung edge adhesion affects the edge extraction of the lung image, and the similarity of the lung edge tissue is high. The traditional edge extraction algorithm will miss some features, resulting in The edges of the lungs with characteristic parameters are excluded, resulting in data loss during image processing

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