Endoscope Image Processing for Lesion Differentiation Classification
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Solution Overview
Problem
Current endoscope systems lack efficient methods to automatically differentiate and classify lesions during medical procedures, relying on operator judgment and manual image processing, which can be time-consuming and prone to error.
Innovation Solution
An image processing apparatus that receives images from an endoscope, judges whether a differentiation classification operation is engaged, and processes images to specify and classify lesions into classes based on signals from the endoscope, providing support information to the operator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image processing and operator judgment are used for lesion differentiation, then diagnostic accuracy can be achieved, but time consumption and operator workload increase
Solution Approach 1:
The system performs automatic lesion differentiation and classification without requiring manual operator intervention. The image processing apparatus autonomously analyzes endoscope images, extracts lesion features, and classifies lesions into types (e.g., adenoma, carcinoma) based on learned patterns from training data, thereby eliminating time-consuming manual processing while maintaining diagnostic accuracy
Solution Approach 2:
The patent replaces the mechanical/manual process of operator judgment with an automated computer-based image processing system. The system uses machine learning models and algorithms to automatically detect, extract features from, and classify lesions in endoscope images, substituting human visual inspection and manual analysis with computational processing that is both faster and equally accurate
2Reliability
If manual lesion differentiation is performed, then diagnostic results can be obtained, but operator workload and potential for human error increase
Solution Approach 1:
The system autonomously performs lesion differentiation and classification without requiring operator intervention. The automated image processing apparatus consistently applies the same classification criteria and algorithms to all images, eliminating variability in operator judgment and reducing workload while improving diagnostic consistency and reliability
Solution Approach 2:
The system provides automated feedback to operators by displaying classification results, confidence levels, and visual markers on lesion regions. This feedback mechanism allows operators to verify and adjust diagnoses if needed, while the system continuously learns from corrected cases to improve future classification accuracy, thereby reducing operator workload while maintaining high reliability
3Productivity
If automated image processing is implemented, then processing speed and consistency improve, but system complexity increases
Solution Approach 1:
The image processing system is divided into distinct functional modules: image acquisition module, preprocessing module (noise reduction, contrast enhancement), lesion detection module, feature extraction module, classification module, and result output module. Each module performs a specific task independently, which simplifies the overall system design, enables parallel processing for higher throughput, and makes the complex system more manageable and maintainable
Solution Approach 2:
The patent implements a universal image processing platform that can handle multiple types of endoscope images (white light, narrow band imaging, fluorescence) and classify various lesion types (adenoma, carcinoma, hyperplastic polyps) using the same core algorithms. The system is designed to be adaptable to different imaging modalities and clinical scenarios, reducing the need for multiple specialized systems while maintaining high processing speed and consistency across diverse applications
Data Source
AI summary
An image processing apparatus have a processor configured to: receive a plurality of images of a tissue including a lesion portion, the plurality of images being received from an endoscope; judge, based on a signal from the endoscope, whether or not to engage a differentiation classification operation; in response to judging that the differentiation classification operation is engaged, process the plurality of images to specify the lesion portion within one or more of the plurality of images; and perform differentiation classification on the one or more images to classify the lesion portion into at least one class of a plurality of classes; and in response to judging that the differentiation classification operation is not engaged, not process the plurality of images to specify the lesion portion within the one or more of the plurality of images.