Medical lung MRI image segmentation method based on adaptive contour model, and MRI equipment

Abstract

The invention relates to a medical lung MRI image segmentation method based on an adaptive contour model, and MRI equipment. The equipment comprises a main magnet system, a gradient magnetic field system, a radio frequency system and an operation and image processing system; the radio frequency system comprises a plurality of radio frequency coils which are respectively arranged in coil fixing devices, and different radio frequency receiving coils correspond to different detection parts on a patient. The method comprises the following steps: 1) preprocessing an input fused MRI image; 2) setting an initial contour line as c0 and setting the initial contour line as a circle according to the characteristics of the lung tumor image, and calculating an initial level set function phi 0 accordingto the c0; 3) updating a level set function phi n, and calculating c1 and c2 according to the current phi n; 4) checking whether iteration is convergent or not: if so, determining that c is the optimal contour line, and otherwise, continuing iteration; 5), after the target area is obtained, removing noise and some tiny protruding parts by using image morphological opening operation, smoothing theboundary, connecting the fracture part by using closed operation, and filling a cavity to obtain a target image. The method is high in segmentation precision and clear in edge, and is suitable for segmenting lung MRI images with fuzzy targets, weak edges, smooth boundaries, discontinuous boundaries and complex topological structures.

Description

Technical field [0001] The invention relates to the technical field of MRI image acquisition and medical image processing, in particular to a method for segmenting lung tumors in MRI lung images and MRI equipment. Background technique [0002] At present, in the clinical diagnosis of respiratory diseases, lung cancer can be accurately diagnosed by CT in general, but some cases need to be diagnosed by MRI. This is because MRI can use multi-planar imaging to display the lesions, and The tumor’s invasion of the soft tissues of the chest wall is also easier and clearer to show whether the lymph nodes and blood vessels are invaded; its enhanced scan plays an important role in distinguishing benign and malignant nodules in the lung, and is for staging lung superior sulcus tumors A preferred method for judgment; its enhanced T1WI can distinguish the hilar mass of central lung cancer and the surrounding atelectasis: because the blood supply of atelectasis is relatively abundant compared ...

AI Technical Summary

Problems solved by technology

However, in real scenes, MRI images often have very complex backgrounds, resulting in a relatively high misjudgment rate for these existing MRI image segmentation methods

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