Image analysis method and system

Abstract

The invention discloses an image analysis method, which comprises the steps of: obtaining a group of T1 weighted images including a control group image and an experimental group image, pre-processing all T1 weighted images, carrying out non-linear registration on preprocessed T1 weighted images into a preset template image, dividing the non-linear registered T1 weighted images to generate a plurality of interest regions, extracting at least one feature data of each interest region to obtain feature matrix information, calculating to obtain a reference value according to a preset Bayesian multi-layer model and the feature matrix information, and judging difference significance of the interest regions between the experimental group image and the control group image according to the relation between the reference value and a preset threshold value. The invention also provides a corresponding image analysis system. The image analysis method and the system can extract more and richer parameters to be analyzed and more diverse information in comparison with the conventional technology, and can simultaneously process multi-region multi-feature significance statistical test so that morphology analysis is more complete.

Description

【Technical field】 [0001] The invention relates to the field of magnetic resonance calculation anatomy, in particular to a method and system for analyzing magnetic resonance images. 【Background technique】 [0002] The development of the human brain is almost a replay of the development and evolution of human brain nervous tissue over tens of millions of years. It also has a physiological development process and biological development law from low to high. The development of the human brain can be divided into three periods: the accelerated growth period of the brain from birth to adulthood; the relatively stable period after the brain matures; and the aging period of the brain after people reach old age. Since the structure of the human brain has formed a certain law in its own development process, the parts, time and performance of the human brain mature or decline are also different. Knowing the law of brain development is of great help to the understanding of human intell...

AI Technical Summary

Problems solved by technology

[0006] In addition, from a statistical point of view, general statistical testing methods such as t-test and permutation test are not suitable for the analysis of small-scale large-scale regression. That is to say, due to the limited number of images available for research, for a voxel or a Sub-region, the dimensionality used to describe the features of this target cannot be too high

This limits the scope of morphological analysis to a certain extent. Existing methods only analyze one of the morphological features described, such as the thickness of the cerebral cortex, the width of the sulci, etc., resulting in incomplete analysis results.

Images