Cervical caner image automatic partition method based on T2-magnetic resonance imaging (MRI) and dispersion weighted (DW)-MRI

Abstract

A cervical caner image automatic partition method based on T2 weighted magnetic resonance imaging (MRI) T2-MRI and dispersion weighted (DW)-MRI includes that a DW-MR image is registered to a T2-MR image by using a non-linear register method, and the registered DW-MR image is sorted; the T2-MR image is filtered through non-linear anisotropic diffusion filtering technology, a bladder and a rectum are partitioned, and an interested area is partitioned through a partition result of the bladder and the rectum; and a combined maximum a posterior (CMAP) method is adopted for an interested area of the T2-MR image and the DW-MR image to conduct precise partition of a tumor. The cervical caner image automatic partition method fully uses effective information of the T2-MR image and the DW-MR image, can effectively overcome effects of noise, partial volume effect and strength overlapping in the T2-MR image, is precise and effective, and has important clinical and application value on prevention, diagnosis and treatment of the cervical cancer.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to an automatic segmentation method for cervical cancer images based on T2-weighted nuclear magnetic resonance imaging T2-MRI and diffusion-weighted nuclear magnetic resonance imaging DW-MRI. Background technique [0002] Cervical cancer is one of the common malignant tumors that seriously threaten women's life and health. Accurate segmentation of cervical cancer has important clinical significance and application value for the prevention, 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. Cervical cancer segmentation is particularly complex due to the complex structure of human abdominal tissue, and a single imaging mode T2-MRI cannot fully display the effective information of cervical cancer. Such as figure 1 (a) an...

AI Technical Summary

Problems solved by technology

from figure 1 (a) It can be seen that the T2-MR image has a relatively high spatial resolution and the tumor border is relatively clear, but the normal tissue of the cervix, tumor, bladder wall and rectum have serious intensity overlap; 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

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

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