Pulmonary nodule segmentation method based on Hession matrix and three-dimensional shape indexes

Abstract

The present invention discloses a pulmonary nodule segmentation method based on the Hession matrix and three-dimensional shape indexes. According to the method, medical CT images are fully utilized; sequential pulmonary parenchymas are segmented through using an optimal threshold according to the gray values of sequential CT images, and the volume data of the three-dimensional pulmonary parenchymas are constructed; the Hession matrix feature values of each voxel point in the volume data of the three-dimensional pulmonary parenchymas are calculated; the three-dimensional shape indexes are constructed according to the shape features of a three-dimensional nodule model and on the basis of the Hession matrix feature values and two-dimensional shape indexes; and a three-dimensional sphere-like filter, namely, a 3D shape index nodule detection function is constructed finally and is adopted to perform nodule detection on the three-dimensional volume data of the pulmonary parenchymas, and with detected nodule regions adopted as a plurality of seed points of region growth, three-dimensional segmentation is performed on nodules on the basis of a confidence-based region growing algorithm. The method of the invention is simple in operation, can automatically detect and segment different types of suspected pulmonary nodules and has high stability and high accuracy.

Description

technical field [0001] The invention belongs to the field of medical image processing, and in particular relates to a method for automatically segmenting pulmonary nodules based on multi-scale three-dimensional space features of a Hession matrix and a three-dimensional shape index in CT sequence images. Background technique [0002] Lung cancer is one of the malignant tumors with the highest morbidity and mortality. The main reason is that the small lesion features are difficult to be found, and the clinical misdiagnosis rate is high. When clinical symptoms appear, most patients have already reached the middle and late stages of the disease course, and the cure rate is very low. Early detection of lung cancer plays a vital role in improving the cure rate. Computed tomography (CT) has high tissue resolution and is widely used in the screening of pulmonary nodules. With the continuous improvement of the imaging accuracy of the lesion area, the thickness of the CT scan is co...

AI Technical Summary

Problems solved by technology

However, the detection accuracy and efficiency of the pulmonary nodule detection system cannot meet the clinical needs. Inside, some nodules and blood vessels will cross, which will lead to low sensitivity and high false positives in the detection process of nodules, which will affect the accuracy of nodule detection

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