Automatic segmentation method of cervical cancer images based on t2-mri and dw-mri

Abstract

An automatic segmentation method for a cervical cancer image based on T2-weighted magnetic resonance imaging (T2-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI), including: registering a DW-MR image to a T2-MR image using a nonlinear registration method, and classifying the registered DW-MR image; filtering the T2-MR image using a nonlinear anisotropic diffusion filtering technique, segmenting a bladder and a rectum, and segmenting a region of interest using the segmentation results of the bladder and the rectum; and performing accurate segmentation of a tumour on the region of interest of the T2-MR image and the DW-MR image using a combined maximum a posteriori probability (CMAP) method. The present invention makes full use of the effective information about a T2-MR image and a DW-MR image, can effectively overcome the influences of noise, partial volume effect and intensity overlap in the T2-MR image, is an accurate and effective segmentation method for cervical cancer, and has important clinical significance and application value for the prevention, diagnosis and treatment of cervical cancer.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a method for automatically segmenting cervical cancer images based on T2-weighted magnetic resonance imaging (T2-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI). Background technique [0002] Cervical cancer is one of the common malignant tumors that seriously threaten women's health. Accurate segmentation of cervical cancer has important clinical significance and application value for auxiliary diagnosis and treatment of cervical cancer. [0003] With the development of imaging technology, medical image segmentation has become a key and challenging problem in the field of medical image analysis. Due to the complex tissue structure of the human abdomen, cervical cancer segmentation is difficult, and a single imaging mode T2-MRI cannot fully display the effective information of cervical cancer. Such as figure 1 (a) and (b) are T2-weighted magnetic resonance (T2-M...

AI Technical Summary

Problems solved by technology

from figure 1 (a) It can be seen that the T2-MR image has a high spatial resolution and the tumor boundary is relatively clear, but the normal tissue of the cervix, tumor, bladder wall and rectum overlap with each other; from figure 1 (b) It can be seen that the tumor has a significantly higher gray value in the DW-MR image, but its resolution is low and the tumor boundary is blurred

The automatic realization of cervical cancer segmentation in a single imaging mode is difficult, and some conventional methods such as region growth and thresholding cannot segment tumors well

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