Carious Lesion Extraction via Fluorescence and Reflectance Image Merging
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Solution Overview
Problem
Current dental imaging techniques, particularly fluorescence-based methods, face challenges in accurately detecting early caries due to poor image contrast between healthy and infected areas, leading to delayed detection and potential tooth damage.
Innovation Solution
A method that combines fluorescence and reflectance images using a marker-controlled watershed algorithm and morphological bottom-hat based methods for automatic extraction of carious lesions, enhancing image contrast and accuracy in caries detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If fluorescence imaging is used for caries detection, then non-invasive detection is achieved, but image contrast between healthy and infected areas is poor
Solution Approach 1:
The patent combines fluorescence imaging with reflectance imaging to create a composite image that leverages the advantages of both modalities. The fluorescence component provides non-invasive detection capability, while the reflectance component enhances image contrast by highlighting surface irregularities and demineralization. This merging resolves the contradiction by maintaining non-invasive detection while improving measurement precision through complementary information from both imaging modes.
Solution Approach 2:
The patent employs a composite imaging approach where fluorescence and reflectance data are integrated to form a multi-parameter assessment of tooth structure. This composite material metaphorically represents the combination of different imaging signals to create a more comprehensive and contrast-enhanced image that overcomes the limitations of single-mode fluorescence imaging.
2Ease of operation
If traditional visual and tactile examination methods are used, then detection can be performed, but detection accuracy is subjective and varies due to practitioner expertise and viewing conditions
Solution Approach 1:
The patent implements automated image processing algorithms that objectively analyze fluorescence and reflectance images to identify carious lesions. The system performs self-service through computer vision and machine learning models that automatically detect, segment, and quantify caries without requiring practitioner interpretation. This automation eliminates subjectivity and variability associated with manual examination, providing consistent and reproducible detection accuracy while maintaining ease of operation through automated workflows.
Solution Approach 2:
The patent replaces the mechanical and subjective processes of visual examination and tactile probing with optical imaging and computational analysis. Instead of relying on practitioner expertise and manual manipulation, the system uses fluorescence and reflectance imaging combined with automated image processing to objectively identify caries. This substitution of mechanical/examination-based methods with optical and computational methods resolves the contradiction between ease of operation and measurement precision.
3Object-affected harmful factors
If fluorescence imaging is used for early caries detection, then non-invasive imaging is achieved, but detection is delayed until advanced stages due to insufficient contrast
Solution Approach 1:
The patent merges fluorescence imaging with reflectance imaging to enable earlier detection of caries. The reflectance component provides enhanced contrast for early-stage lesions by detecting surface changes and demineralization that occur before significant fluorescence loss. This combination allows non-invasive imaging to detect caries at earlier stages, reducing the time loss between lesion onset and detection while maintaining the benefits of non-invasive methodology.
4Measurement precision
If manual extraction of lesion regions is performed, then some degree of analysis is achieved, but the process is slow and requires considerable diagnostic experience
Solution Approach 1:
The patent implements automated image processing systems that perform lesion extraction and quantification without requiring manual intervention. The system uses computer vision algorithms, machine learning models, and automated segmentation techniques to rapidly identify and measure carious lesions. This automation provides high measurement precision through objective analysis while dramatically improving productivity by eliminating the time-consuming manual extraction process and reducing dependency on practitioner experience.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and accurate automatic extraction and quantification of carious lesions, allowing for earlier detection and prevention of caries, improving diagnostic accuracy and reducing the need for invasive treatments.
Implementation Method 1
This technique, sometimes termed quantitative light-induced fluorescence (QLF), operates on the principle that sound, healthy tooth enamel yields a higher intensity of fluorescence under excitation from some wavelengths than does de-mineralized enamel that has been damaged by caries infection.
Implementation Method 2
A different relationship has been found for red light excitation, a region of the spectrum for which bacteria and bacterial by-products in carious regions absorb and fluoresce more pronouncedly than do healthy areas.
Implementation Method 3
extracting a suspicious lesion area by using a marker-controlled watershed algorithm or by using a morphological bottom-hat based method
Implementation Method 4
extracting a suspicious lesion area by using a marker-controlled watershed algorithm or by using a morphological bottom-hat based method along with a multi-resolution surface reconstruction method
Data Source
AI summary
A method for extracting a carious lesion area from sound regions of a tooth. In one embodiment, the method includes generating a digital image of the tooth; identifying tooth regions; extracting a suspicious lesion area by using a marker-controlled watershed algorithm or by using a morphological bottom-hat based method along with a multi-resolution surface reconstruction method; and removing false positives. In another embodiment, the method includes generating a digital image of the tooth comprising obtaining a fluorescence image of the tooth, obtaining a reflectance image of the tooth, and combining image data for the fluorescence and reflectance images; identifying tooth regions; extracting suspicious lesion areas; and removing false positives.