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Automatic extraction of silicosis nodules from CT images

A technology for automatic extraction of CT images, applied in the field of medical image processing, can solve problems that are difficult to meet the needs of actual medical applications, and achieve the effects of easy processing and display, high application value, and avoiding operation steps

Inactive Publication Date: 2019-03-22
SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI
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  • Application Information

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Problems solved by technology

There are various deficiencies in the above technologies, and it is difficult to meet the needs of actual medical applications.

Method used

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  • Automatic extraction of silicosis nodules from CT images
  • Automatic extraction of silicosis nodules from CT images
  • Automatic extraction of silicosis nodules from CT images

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Embodiment Construction

[0027] The present invention will be further described in detail below in conjunction with the embodiments, so that those skilled in the art can implement it with reference to the description.

[0028] It should be understood that terms such as "having", "comprising" and "including" used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0029] A kind of automatic extraction method of CT image silicosis nodules of the present embodiment, refer to figure 1 , including the following steps:

[0030] Step 1. Obtain the lung CT image of the patient with silicosis nodules, and perform preprocessing to obtain the lung parenchyma CT image:

[0031] 1-1) Binarization processing: scan each pixel value of the lung CT image, set the pixel value to 0 if the value is less than the set threshold value, set the pixel value to 1 if the value is greater than or equal to the set threshold value, and finally obtain two Value image. In this e...

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Abstract

The invention discloses an automatic extraction method of CT image silicosis nodule, which comprises the following steps: 1) obtaining lung CT image of silicosis nodule patient, preprocessing and obtaining lung parenchyma CT image; 2) performing high-pass filtering on the obtained CT images of lung parenchyma to extract images of blood vessels and nodules; 3) Using the filter based on Hessian matrix eigenvalue to process the extracted blood vessel and nodule images, and then extracting the region of interest of silicosis nodules through the threshold selection method. The invention obtains thenodular image of the whole CT sequence, which is convenient for subsequent processing and display, avoids the operation steps of artificially selecting sample data blocks and feature extraction, andgreatly improves the image processing efficiency. the invention can realize automatic extraction aim at a single type of silicosis nodule, the invention is helpful for clinicians to diagnose silicosisdisease, assists clinicians in formulating individualized treatment scheme, and has high application value.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to an automatic extraction method for silicosis nodules in CT images. Background technique [0002] Pulmonary nodules are one of the most important early signs of lung cancer. According to the lesion characteristics of pulmonary nodules, the lesion characteristics of lung lesions can be inferred. Therefore, early detection and treatment of pulmonary nodules in patients with lung diseases is a key measure to reduce lung cancer mortality. Due to its high morbidity and mortality, lung cancer has become the deadliest tumor among cancers. With the change of people's living habits and the deterioration of the environment, the number of people with lung cancer is increasing, and the society is paying more and more attention to it. Combined with the medical characteristics of pulmonary nodules, using deep learning technology to process and study medical images can provide useful ref...

Claims

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Application Information

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IPC IPC(8): G06T7/155G06T7/168G06T7/187G06T7/194G06T5/00
CPCG06T7/155G06T7/168G06T7/187G06T7/194G06T2207/20056G06T2207/30064G06T5/70
Inventor 戴亚康杨婧耿辰戴斌周志勇
Owner SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI
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