Lesion Evaluation Generator Using Pixel Color Correlation
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Solution Overview
Problem
Existing lesion evaluation methods, even with advanced color endoscope technologies, face challenges in objectively and reproducibly assessing the severity of lesions due to reliance on operator skill and experience, especially for inexperienced operators.
Innovation Solution
A lesion evaluation information generator that acquires endoscopic color image data, determines hue and saturation values for each pixel, calculates correlation values based on reference data, and generates an evaluation value by integrating these correlations to objectively assess lesion severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If color conversion process is used to highlight color differences, then lesion identification becomes easier, but objective and reproducible evaluation of lesion severity remains difficult
Solution Approach 1:
The patent replaces the subjective visual assessment mechanism with an automated computational mechanism. The processor automatically calculates hue and saturation values for each pixel, computes correlation values against reference data, and generates objective evaluation values, eliminating reliance on operator experience and visual interpretation skills.
Solution Approach 2:
The patent transforms the lesion evaluation from qualitative visual assessment to quantitative parameter-based measurement. By extracting specific color parameters (hue and saturation values) and computing correlation metrics, the system converts subjective color perception into objective numerical data that can be precisely measured and reproduced.
2Device complexity
If reliance on operator skill and experience is used for lesion evaluation, then evaluation can be performed with simple equipment, but evaluation results become non-reproducible and operator-dependent
Solution Approach 1:
The system enables self-service evaluation where the processing apparatus autonomously performs lesion severity assessment without requiring operator expertise. The processor automatically acquires image data, extracts color parameters, computes correlation values, and generates evaluation results independently, making the system self-sufficient and eliminating operator-dependent variability.
Solution Approach 2:
The system incorporates reference color data as a feedback mechanism to guide the evaluation process. By comparing measured pixel colors against predetermined reference data, the system automatically adjusts and determines correlation values, providing an objective benchmark that ensures consistent and reproducible results across different operators and sessions.
Data Source
AI summary
A lesion evaluation information generator including a processor configured to, when executing processor-executable instructions stored in a memory, determine a hue value and a saturation value of each of pixels of an endoscopic image based on an acquired endoscopic color image data, determine, for at least a part of the pixels of the endoscopic image, a correlation value between color information of each individual pixel and reference color data, based on a hue correlation value between the hue value of each individual pixel and a reference hue value of the reference color data, and a saturation correlation value between the saturation value of each individual pixel and a reference saturation value of the reference color data, and generate an evaluation value for evaluating a severity of a lesion in the endoscopic image, by integrating the correlation value of each individual pixel.