Brain MRI Symmetry Detection for Rapid Abnormality Screening
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Solution Overview
Problem
The existing methods for brain imaging analysis, particularly using MRI, are time-consuming due to the large amount of imaging data and the need for manual interpretation by radiologists, leading to delayed diagnostic reports and potential missed treatment opportunities.
Innovation Solution
A method and system that utilize the anatomical symmetry of the left and right brain structures by dividing MRI images into left and right brain images, performing grayscale value subtraction after mirror-flipping, and setting thresholds for grayscale differences and pixel counts to quickly identify abnormal brain images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation by radiologists is used, then diagnostic accuracy is improved, but diagnostic time is excessive
Solution Approach 1:
The patent segments the brain image into left and right hemispheres, processes each hemisphere separately, and compares them. This segmentation allows the system to focus on asymmetric abnormalities while reducing overall processing complexity, thereby improving both speed and accuracy simultaneously
Solution Approach 2:
The patent introduces an intermediary processing step that mirror-flips one hemisphere and subtracts it from the other. This intermediary transformation creates a difference image that highlights abnormalities, enabling rapid automated detection without sacrificing diagnostic accuracy
2Extent of automation
If conventional AI models are used, then automation is improved, but processing speed is still slow
Solution Approach 1:
The patent extracts only the essential information needed for abnormality detection by comparing symmetric regions of the brain. Instead of processing the entire complex image data, it extracts differences between left and right hemispheres, achieving fast automated processing while maintaining high detection accuracy
3Measurement precision
If detailed analysis of all pixels is performed, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies local quality analysis by focusing computational resources only on comparing corresponding regions between left and right hemispheres. Instead of analyzing all pixels globally, it performs localized differential analysis where abnormalities are most likely to manifest as asymmetries, reducing system complexity while improving detection accuracy
Data Source
AI summary
A method of brain imaging detection includes: dividing a brain image into a left brain medical image and a right brain medical image and obtaining the grayscale values of multiple pixels thereof, respectively; mirror-flipping one of the left and right brain medical images; performing a grayscale value subtraction with the mirror-flipped medical image and the other medical image to obtain a plurality of grayscale differences of the pixels; determining whether the grayscale difference of each pixel exceeds a grayscale difference threshold and whether a number of the pixels having the grayscale different exceeding the grayscale difference threshold is greater than or equal to a pixel number threshold; and when the number of the pixels having the grayscale difference exceeding the grayscale difference threshold is greater than or equal to the pixel number threshold, determining that the brain image is abnormal.