Endoscope Image Processing Apparatus for Clinical Finding Evaluation
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Solution Overview
Problem
Current medical imaging technologies, such as endoscope systems, can detect the occurrence of clinical findings but lack the capability to effectively evaluate them, limiting the efficiency of medical diagnosis.
Innovation Solution
An image processing apparatus that receives image signals from a body cavity, generates diagnostic images, detects feature values, identifies regions corresponding to clinical findings, performs level detection, and generates color-mapped images for display, allowing for both the detection and evaluation of clinical findings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated classification of clinical findings is performed, then diagnostic efficiency is improved, but the ability to evaluate the severity or level of clinical findings is insufficient
Solution Approach 1:
The diagnostic process is segmented into two distinct functional components: (1) automated classification of clinical findings types using pattern matching, and (2) separate evaluation of clinical finding levels using multiple feature values. This segmentation allows each component to specialize, maintaining high diagnostic efficiency while capturing comprehensive evaluation information.
Solution Approach 2:
The system performs multiple functions through a unified diagnostic apparatus: it not only classifies the type of clinical findings (e.g., pit pattern types I, II, III) but also evaluates their severity levels (e.g., penetration depth). This multi-functionality ensures that both classification and evaluation are performed efficiently within the same system.
2Measurement precision
If multiple feature values are calculated for level detection, then evaluation precision is improved, but computational complexity increases
Solution Approach 1:
Multiple feature values (color, shape, texture) are calculated and stored in advance during the image analysis phase, before the actual level detection is performed. This preliminary calculation allows the level detection to efficiently reference pre-computed features, improving evaluation precision without proportionally increasing real-time computational complexity.
Solution Approach 2:
Different feature values are extracted from different local characteristics of the clinical finding regions: color features from spectral analysis, shape features from boundary detection, and texture features from spatial patterns. This local quality approach ensures that each feature captures specific aspects of the clinical findings, improving overall evaluation precision while organizing computational tasks by their specific purposes.
Data Source
AI summary
A region identifier identifies a region within a diagnostic image corresponding to one of plural predetermined clinical findings according to a first feature value from a feature value detector. A level detector performs level detection of the region at one of plural levels associated with the clinical findings for evaluation of the clinical findings according to a second feature value from a feature value detector. A color mapped image generator generates a color mapped image by color mapping of the diagnostic image with a display color associated with respectively the clinical findings and chromaticity of the display color associated with the levels. A monitor display panel is caused to display the color mapped image by control of a display control unit. Preferably, the diagnostic image is an image generated by an endoscope. The color mapped image generator is included in a processing apparatus.