Tooth Color Calibration Using Mobile Images for Whiteness Tracking
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Solution Overview
Problem
Conventional imaging techniques fail to accurately determine the whiteness of teeth using images captured by mobile device cameras due to variations in lighting, lens quality, and processor variability, leading to inconsistent color measurements.
Innovation Solution
A method involving a mobile device that captures images of a color calibration object and a color reference object, calculates calibration coefficients, and applies these to correct the color values of teeth using RGB and CIE L*a*b* color spaces to provide accurate whiteness values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging techniques are used to capture tooth images with a mobile device camera, then the imaging process is simple and accessible, but the accuracy of whiteness determination is insufficient due to various factors affecting image quality
Solution Approach 1:
A color reference object (such as a color calibration card or white balance card) is introduced as an intermediary element in the imaging system. This reference object serves as a mediator between the camera and the tooth, providing known color values that enable the system to calibrate and correct color measurements. By capturing the reference object alongside the tooth and comparing measured values against known values, the system can compensate for lighting conditions, camera characteristics, and environmental factors, thereby improving whiteness determination accuracy without requiring complex specialized imaging equipment
Solution Approach 2:
The system transforms the raw color data from the mobile device camera into corrected color values by applying calibration coefficients. These coefficients are derived by comparing measured color values of the reference object against its known color values. The transformation process adjusts the color parameters (such as L*a*b* color space values) to account for systematic errors in the imaging system, enabling accurate whiteness measurement using standard consumer camera hardware
2Measurement precision
If color calibration objects and reference objects are included in images to improve measurement accuracy, then whiteness determination accuracy improves, but the complexity of the imaging process and data processing increases
Solution Approach 1:
The color reference object is merged into the same image frame as the tooth by positioning it within the camera's field of view during capture. This integration allows the system to obtain both the reference color data and the tooth color data in a single imaging operation, eliminating the need for separate calibration shots. The merged image contains all necessary information for calibration and measurement, streamlining the overall process while maintaining accuracy
Solution Approach 2:
The color reference object with known color values is prepared and positioned in advance before the actual tooth measurement is taken. This preliminary setup establishes a reference framework that guides the subsequent measurement process. By having the reference object ready and known values pre-established, the system can directly compare measured values against these predetermined standards, simplifying the calculation process and enabling real-time correction without complex iterative procedures
3Reliability
If multiple color spaces and calibration coefficients are used to correct color values, then the accuracy and reliability of whiteness measurement improves, but the computational complexity and processing time increase
Solution Approach 1:
The system replaces complex physical color measurement instruments (such as spectrophotometers or colorimeters) with computational methods implemented in software. Instead of using sophisticated hardware to directly measure color values, the invention uses algorithms that process standard digital camera images through color space transformations and calibration coefficient applications. This substitution of mechanical/physical measurement systems with computational processing achieves comparable or superior reliability while maintaining compatibility with consumer-grade mobile devices
Solution Approach 2:
The system performs color space transformations, converting color data from the camera's native RGB color space to the CIE L*a*b* color space, which is perceptually uniform and better suited for whiteness measurement. This dimensional transformation in color space allows for more accurate whiteness calculation by separating luminance (L*) from chrominance (a*, b*) components. The transformation matrices and calibration coefficients operate in this transformed space to provide more reliable measurements that account for human color perception characteristics
4Measurement precision
If images are captured with color reference objects to correct for environmental factors, then the effectiveness of tracking teeth whitening progress improves, but the ease of operation decreases due to additional setup requirements
Solution Approach 1:
The color reference object serves multiple functions simultaneously: it provides color calibration data, establishes white balance reference, and enables lighting condition assessment. This multi-functionality means that a single object captures multiple aspects of the imaging environment, reducing the need for separate calibration procedures or multiple reference objects. The universal reference object streamlines the setup process while providing comprehensive data for accurate whiteness measurement and tracking across different lighting conditions and time points
Data Source
AI summary
A method of determining a color value of a target object is provided. The method includes capturing an image having a color calibration object and a color reference object. Computed calibration color values for the color calibration object and a first computed reference color value for the color reference object may be determined based on the single first image. A color calibration coefficient may be determined. A corrected reference color value may be determined based on the color calibration coefficient and the first computed reference color value. A second image may be captured. A second computed reference color value of the color reference object and a computed object color value of the target object may be determined based on the second image. A corrected object color value may be determined based on the corrected reference color value, the second computed reference color value, and the computed object color value.