Electronic Endoscope Processor Lesion Index Algorithm
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Solution Overview
Problem
Existing electronic endoscope systems struggle to provide objective and reproducible identification of lesion sites, as the distinction between normal and lesion tissues relies heavily on operator skill and experience, especially when color differences are subtle.
Innovation Solution
An electronic endoscope processor that converts pixel data from multiple color components into fewer components, calculates evaluation values for each pixel, and generates a lesion index based on these values, allowing for objective identification of lesions by determining effective pixels and normalizing angles in the color space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If color conversion processing is performed to emphasize color differences, then lesion sites become easier to identify visually, but the color change remains continuous at boundaries and subtle differences persist making objective identification difficult
Solution Approach 1:
The patent converts pixel data from n-color components to m-color components (where m < n) by changing the parameter dimensionality. This dimensionality reduction transforms the continuous color space into a discrete classification space, enabling objective lesion identification. The conversion matrix transforms the color parameters into a new coordinate system where lesion and normal tissues can be clearly distinguished through threshold-based classification.
2Measurement precision
If extensive training is provided to operators, then lesion identification accuracy improves, but operator dependency increases and reproducibility remains limited
Solution Approach 1:
The system performs self-diagnosis by automatically calculating lesion indices through algorithmic processing of pixel data. The processor independently determines lesion presence and characteristics without relying on operator interpretation, making the diagnostic function self-sufficient and eliminating the need for extensive operator training while maintaining high accuracy and reproducibility.
3Illumination intensity
If color emphasis processing is applied, then visual distinction between normal and lesion tissue improves, but subtle color differences may still be missed depending on illness type
Solution Approach 1:
The patent replaces the mechanical/visual inspection method with an automated computational system. Instead of relying on human visual perception of color differences, the system uses mathematical transformations and algorithmic classification to detect lesions. This substitution ensures consistent and reliable detection across different illness types without being affected by subtle color variations that may escape human perception.
Data Source
AI summary
An electronic endoscope processor includes a converting means for converting each piece of pixel data that is made up of n (n≥3) types of color components and constitutes a color image of a biological tissue in a body cavity into a piece of pixel data that is made up of m (m≥2) types of color components, m being smaller than n; an evaluation value calculating means for calculating, for each pixel of the color image, an evaluation value related to a target illness based on the converted pieces of pixel data that are made up of m types of color components; and a lesion index calculating means for calculating a lesion index for each of a plurality of types of lesions related to the target illness based on the evaluation values calculated for the pixels of the color image.