Brain Image Diagnostic System Using Spherical Harmonic Mapping
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Solution Overview
Problem
Conventional computer-aided diagnostic systems for analyzing medical images of the brain are not accurate in diagnosing neurological conditions like dyslexia and autism due to their reliance on volumetric analysis, which fails to account for factors such as age and gender, leading to inaccuracies in brain volume-based classification.
Innovation Solution
The development of a system that generates a three-dimensional mapping of the brain from medical imaging scans, using spherical harmonic shape analysis to identify significant locations and correlate them with neurological conditions, allowing for more precise classification and severity assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If volumetric analysis is used to classify brains, then the analysis process is simple, but the diagnostic accuracy deteriorates because it fails to account for age and gender differences
Solution Approach 1:
The patent segments the brain into multiple localized regions (e.g., frontal lobe, temporal lobe, occipital lobe, parietal lobe, limbic system, brainstem, cerebellum) and analyzes each region separately using spherical harmonic mapping. This allows the system to account for regional variations due to age and gender while maintaining diagnostic accuracy, resolving the contradiction between simple volumetric analysis and precise diagnostic classification.
Solution Approach 2:
The patent applies spherical harmonic mapping to generate region-specific morphometric features that capture local structural characteristics. By analyzing localized brain regions with age and gender-specific reference data, the system achieves high diagnostic accuracy without requiring complex whole-brain volumetric analysis, thus resolving the contradiction between analytical simplicity and measurement precision.
2Device complexity
If conventional volumetric analysis is used, then the system complexity is low, but the ability to identify localized structural differences deteriorates
Solution Approach 1:
The patent transitions from conventional three-dimensional volumetric analysis to six-dimensional spherical harmonic mapping, which adds angular and spectral dimensions to the analysis. This dimensional expansion enables the system to capture localized structural differences across multiple brain regions while maintaining computational efficiency through the mathematical framework of spherical harmonics, resolving the contradiction between system complexity and measurement precision.
3Measurement precision
If spherical harmonic shape analysis is used to generate three-dimensional mapping, then the diagnostic accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent transforms raw three-dimensional brain images into spherical harmonic parameters, which represent the brain's shape and structure in a compact mathematical form. This parameter transformation reduces the computational burden by converting complex spatial data into a smaller set of meaningful coefficients, enabling high diagnostic accuracy while managing computational complexity through efficient mathematical representation.
4Productivity
If volumetric classification is used, then the processing time is short, but the classification accuracy deteriorates due to inability to account for demographic factors
Solution Approach 1:
The patent incorporates age and gender as preliminary classification parameters before performing the main diagnostic analysis. By pre-segmenting the population into demographic groups and using group-specific reference data, the system maintains efficient processing speeds while significantly improving classification accuracy, as each subject is compared against appropriate age and gender-matched controls rather than a generic volumetric threshold.
Data Source
AI summary
Systems, methods, and computer program products for classifying a brain are disclosed. An embodiment method includes processing image data to generate segmented image data of a brain cortex. The method further includes generating a statistical analysis of the brain based on a three dimensional (3D) model of the brain cortex generated from the segmented image data. The method further includes using the statistical analysis to classify the brain cortex and to identify the brain as being associated with a particular neurological condition. According to a further embodiment, generating the 3D model of the brain further includes registering a 3D volume associated with the model with a corresponding reference volume and generating a 3D mesh associated with the registered 3D volume. The method further includes generating the statistical analysis by analyzing individual mesh nodes of the registered 3D mesh based on a spherical harmonic shape analysis of the 3D model.