Brain Morphology Calibration for Scan-Condition Robust Image Analysis
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Solution Overview
Problem
Conventional medical image analysis technologies face limitations in providing personalized diagnostic information due to errors in medical images and variations in scan conditions, leading to inconsistent and inaccurate auxiliary diagnostic results.
Innovation Solution
A method and device for analyzing a medical image that calibrates morphological values based on correlations between images taken under different scan conditions, allowing for personalized and accurate diagnostic information by segmenting the image into specific areas and applying correction parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image segmentation technology is used to calculate auxiliary diagnostic indexes, then it is possible to segment medical images and provide diagnostic information, but the results are inaccurate and inconsistent due to artifacts and variations in scan conditions
Solution Approach 1:
The patent introduces a calibration parameter as an intermediary element that mediates between the target medical image and the standard brain model. This calibration parameter, derived from correlation analysis between first and second medical images, serves as a bridge to correct morphological values and compensate for scan condition variations, thereby improving both accuracy and consistency of diagnostic results
Solution Approach 2:
The patent changes the parameter space by introducing calibration parameters that transform morphological values according to scan condition correlations. By calculating calibration parameters based on correlation between different scan conditions and applying them to correct target morphological values, the system adapts parameters to compensate for artifacts and variability, resolving the contradiction between measurement precision and reliability
2Ease of manufacture
If standardization with a standard brain model is performed, then image segmentation can be achieved, but completely personalized auxiliary diagnostic information cannot be provided
Solution Approach 1:
The patent applies local quality by maintaining the standard brain model for overall structural reference while introducing subject-specific calibration parameters that personalize the morphological values. The calibration parameters are calculated based on correlation between different scan conditions of the same subject, allowing personalized correction of specific morphological measurements while retaining the benefits of standardization for segmentation
Solution Approach 2:
The patent segments the diagnostic information into two components: standardized morphological structure from the standard brain model for segmentation purposes, and personalized calibration parameters for accurate measurement. This segmentation allows the system to benefit from both standardization (for ease of segmentation) and personalization (for accurate, subject-specific diagnostic information)
3Productivity
If morphological values are obtained directly from medical images without calibration, then the analysis process is simple, but the values are inaccurate due to scan condition variations
Solution Approach 1:
The patent performs preliminary action by pre-calculating calibration parameters based on correlation between first and second medical images acquired under different scan conditions. These calibration parameters are stored and readily available when analyzing target medical images, allowing quick correction of morphological values without complex real-time calculations, thus maintaining productivity while improving measurement precision
Data Source
AI summary
A brain image analysis method to acquire a brain image and perform a morphological analysis based on the brain image. The method may include acquiring a first correction parameter for correcting a first morphological value related to a first brain element. The method may include acquiring a second correction parameter for correcting a second morphological value related to a second brain element different from the first brain element. The method may include acquiring a target brain image and segmenting the target brain image into a plurality of brain regions including a first region corresponding to the first brain element, a second region corresponding to the second brain element, and a skull region, and acquiring the first region, the second region, and a third region corresponding to an internal region of the skull. The method may include acquiring a first and second brain-related morphological index.