Teeth Whiteness Measurement via Pixel Analysis and Reference Normalization
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Solution Overview
Problem
Current systems for measuring teeth whiteness are confusing and subjective, failing to cover all colors of the visible electromagnetic spectrum, and struggle with comparing whiteness values across different lighting conditions and exposure settings.
Innovation Solution
A method using a mobile device to capture images of teeth, select pixels, reject outliers, and apply algorithms to generate a standardized 1-100 whiteness score, with a reference item for normalization, allowing comparison across users and tracking historical changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color-based measurement systems (e.g., Vita guide) are used, then color variations can be assessed, but the systems become confusing and subjective without covering all colors of the visible spectrum
Solution Approach 1:
The patent transforms the complex multi-dimensional color measurement problem into a simplified single-parameter whiteness score (1-100 scale). By changing the measurement parameter from comprehensive color values to a focused whiteness metric, the system achieves precise and objective measurement without complexity. The algorithm processes RGB values and converts them into a standardized whiteness score that is easy to interpret and compare.
2Adaptability or versatility
If images are taken under different lighting conditions and exposure settings, then more flexible photography is possible, but comparing whiteness values across different images becomes difficult
Solution Approach 1:
The patent introduces a reference item (such as a gray card or reference paper with known reflectivity) as an intermediary element captured in each image. This reference serves as a mediator that allows the system to calculate correction factors for lighting conditions and exposure settings. By comparing the actual captured reference values against known reference values, the algorithm compensates for environmental variations and enables accurate whiteness comparison across different imaging conditions.
3Ease of operation
If a standardized whiteness score system is implemented, then easy comparison and tracking are achieved, but handling and normalizing variations in lighting and exposure requires additional processing
Solution Approach 1:
The system implements self-service through automated processing. The algorithm automatically detects the reference item in each image, calculates lighting and exposure correction factors, applies normalization to the teeth region, and generates the standardized whiteness score without requiring manual intervention. This automation handles the complex image processing tasks while maintaining ease of operation for the user, who simply needs to capture the image and receive the result.
Data Source
AI summary
A method and system are presented for measuring the whiteness of teeth. This is accomplished by analyzing an average pixel value of teeth taken from a digital image. The pixel value is mathematically standardized to create an indicator that quantifies teeth whiteness. This eliminates subjectivity in measuring teeth whiteness and permits precise communication of the level of teeth whiteness.