Multi-atlas dividing method for low-resolution medical image

A low-resolution image and high-resolution image technology, applied in the field of multi-spectrum segmentation of low-resolution medical images, can solve the problems of unguaranteed target image quality, low-resolution target images, etc., and improve the segmentation accuracy is not high , Improve the effect of segmentation accuracy

Inactive Publication Date: 2016-08-31
SHAOXING UNIVERSITY
View PDF4 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In the practical application of the multi-atlas segmentation method, since the atlas is pre-constructed, high-resolution image construction is usually used to obtain a high-quality

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Multi-atlas dividing method for low-resolution medical image
  • Multi-atlas dividing method for low-resolution medical image
  • Multi-atlas dividing method for low-resolution medical image

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0031] The multi-atlas segmentation method of low-resolution medical images includes the following steps:

[0032] Step 1: Given low-resolution target image I d , N high-resolution atlas images A i =(I i ,L i ),i=1,2,...,N, where I i Represents the i-th grayscale image, L i Represents the label image corresponding to the i-th grayscale image, and assumes that the target image I d And atlas image A i =(I i ,L i ), i=1,2,...,N, all have been linearly registered to the same template space;

[0033] Step two, segmentation object area cutting:

[0034] Such as figure 1 As shown, in order to reduce the computational workload, first cut a region containing the segmented object. Since the target image I d And atlas image A i =(I i ,L i ), i = 1, 2,..., N, have been linearly registered to a template space, so the position of the segmentation object in each image is roughly the same, scan all the atlas images A i =(I i ,L i ), i=1,2,...,N, the label image (that is, scan all the segmentation re...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a multi-atlas dividing method for a low-resolution medical image, wherein the multi-atlas dividing method belongs to the field of image processing technology. The method comprises the steps of setting a low-resolution objective image and N high-resolution atlas images, and assuming a fact that the objective image and the atlas images are linearly registered in a same template space; and then successively performing a dividing object area cutting step; an objective image super-resolution recovering step; an image registering and label propagation step; and a label fusion step. According to the multi-atlas dividing method for the low-resolution medical image, an image super-resolution restoring method is merged into a multi-atlas dividing frame; and through improving registering precision between the high-resolution atlas images and the low-resolution to-be-divided image, dividing precision of the multi-atlas dividing method.

Description

technical field [0001] The invention relates to a multi-atlas segmentation method of a low-resolution medical image, belonging to the technical field of image processing. Background technique [0002] With the development and popularization of medical imaging equipment, medical image analysis plays an extremely important role in disease research, surgical planning, and clinical diagnosis. Medical image segmentation is an important part of medical image analysis. Its essence is to label each pixel or voxel of the image to be segmented, and set different label values ​​for pixels or voxels with different attributes, so as to divide the image to be segmented into infinite Overlapping regions of interest and background regions for further analysis and processing of regions of interest. Usually, the segmentation of medical images is completed by manually marking the region of interest. The advantage of this method is that the accuracy of the segmentation results is high. Howeve...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/00G06T5/00
CPCG06T5/001G06T2207/30004
Inventor 祝汉灿范勇
Owner SHAOXING UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products