Bone Image Normalization via Standardized Skeleton Template
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Solution Overview
Problem
Current medical imaging systems face challenges in accurately distinguishing cancerous lesions from background noise in bone images due to varying image intensities across different bones, leading to subjective interpretation and potential errors in disease assessment.
Innovation Solution
A system that uses a standardized skeleton template to normalize bone images by establishing anatomically-based background signal thresholds, allowing for the accentuation of disease-related features and reduction of normal physiological variations, thereby improving lesion identification and diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bone imaging is used to detect lesions, then disease presence can be identified, but image intensity variations in healthy tissue create background noise that reduces detection accuracy
Solution Approach 1:
The patent transforms the image data by applying statistical parameters (mean and standard deviation) to normalize intensity values across different bones. This parameter transformation converts absolute intensity values into standardized scores, eliminating the harmful background noise while preserving lesion information.
Solution Approach 2:
The patent divides the bone image into multiple anatomical regions and applies region-specific normalization parameters. Each bone or anatomical region receives tailored statistical adjustment based on its normal intensity distribution, allowing precise removal of background variations while preserving local lesion characteristics.
2Reliability
If physicians manually analyze images to distinguish lesions from background noise, then disease identification can be performed, but subjective interpretation increases burden and potential for error
Solution Approach 1:
The system performs automatic normalization and lesion detection without requiring manual physician intervention for background correction. The computational algorithm independently processes the image data, applying statistical models to eliminate background noise and highlight lesions, thereby reducing physician burden while maintaining high reliability.
Solution Approach 2:
The patent replaces the manual mechanical process of visual inspection and subjective judgment with an automated computational system. The algorithm objectively processes image data using mathematical transformations, eliminating human subjectivity and reducing the cognitive burden on physicians while improving consistency and reliability.
3Measurement precision
If image intensity variations are used to identify lesions, then disease detection is possible, but quantitative measurements are interfered with by normal physiological variations
Solution Approach 1:
The patent applies statistical parameter transformations (standardization using mean and standard deviation) to convert absolute intensity measurements into relative deviations from normal. This parameter change removes the confounding effect of physiological variations, allowing accurate quantitative measurement of lesions independent of normal bone metabolism differences.
Solution Approach 2:
The patent introduces statistical parameters (mean and standard deviation calculated from healthy regions) as intermediary variables that mediate between raw image intensities and lesion quantification. These intermediaries serve as reference frames to distinguish true lesion signals from normal physiological variations, enabling accurate quantitative assessment.
Data Source
AI summary
A standardized skeleton template is used to normalize medical image data of the skeleton to eliminate variations in the medical image data related to physiological variations in a normal patient thereby better accentuating disease conditions.