Fully automatic segmentation method for lung parenchyma CT images

Abstract

The invention discloses a fully automatic segmentation method for lung parenchyma CT images, which is characterized in that the method comprises the following steps: 1) preliminary extraction of lungparenchyma template; 2) full automatic separation of left and right lung; 3) smooth mediastinal surface; 4) Rib surface smoothing. The invention designs a full automatic segmentation method for CT images of lung parenchyma, which can realize full automatic and accurate segmentation of CT images of lung, is helpful for assisting clinicians in diagnosing lung diseases, greatly reduces workload and operation time of doctors, and more quickly and accurately detects lung diseases. The invention also discloses an automatic segmentation method for CT images of lung parenchyma. After obtaining the preliminary pulmonary parenchyma template, the invention carries out the full automatic separation of the left and right lungs, smoothes the mediastinal surface and the costal surface, ensures the accuracy, integrity and robustness of the segmentation result, and assists the clinician in diagnosing lung diseases; as that whole process of the invention does not need manual calibration and parameter setting, the universality and universality of the lung parenchyma CT image segmentation method are ensured.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a fully automatic segmentation method for lung parenchyma CT images. Background technique [0002] With the development of computer tomography (CT) technology and computer image processing technology, computer-aided diagnosis system (Computer-Aided Diagnosis, CAD) provides a new means for accurate, fast and automatic analysis and processing of CT image data. , help to improve the sensitivity and specificity of the doctor's diagnosis. In the study of computer-aided diagnosis of lung diseases, the segmentation of lung parenchyma can eliminate the influence of irrelevant factors (such as chest, heart, examination bed, etc.) in CT images, reduce unnecessary calculations, and the accuracy of segmentation is the most important factor. Quantitative analysis of lung disease, detection of pulmonary nodules and key link in lung function assessment. [0003] The application number ...

AI Technical Summary

Problems solved by technology

However, this patent only realizes the elimination of the trachea of ​​the lung parenchyma template in the CT image, and does not involve the elimination of the trachea of ​​the lung parenchyma template and the subsequent boundary repair. It is difficult to achieve a complete and accurate segmentation of the lung parenchyma CT image

Images