Multi-Model Image Segmentation for Medical Contour Accuracy
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Solution Overview
Problem
Image classification systems face challenges in accurately identifying target areas within images, especially when the images are cluttered or of non-human subjects, leading to difficulties in segmenting and classifying medical images like X-rays of animals, where traditional extraction models may fail to correctly identify contours.
Innovation Solution
The method employs a plurality of unique extracting models to segment the target area from input images, with a combining model using a majority rule to determine pixel values, generating a combined segment that improves accuracy by averaging out pixels from each model, and a neural network for enhanced classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single extraction model is used to segment target area, then device complexity is low, but segmentation accuracy deteriorates in cluttered images or non-human subjects
Solution Approach 1:
The patent divides the extraction task into multiple specialized models, each trained to extract specific features or aspects of the target area. Instead of relying on a single complex model, multiple simpler models work in parallel to segment different characteristics, which are then combined to achieve high overall segmentation accuracy while keeping individual model complexity manageable.
Solution Approach 2:
The patent combines the output segments from multiple extraction models to produce a final segmented result. By merging the strengths of different models through combination, the system achieves superior segmentation accuracy that exceeds what any single model could achieve alone, while maintaining reasonable complexity through modular architecture.
2Measurement precision
If multiple extracting models are used to improve segmentation accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the overall extraction problem into sub-problems handled by different models, where each model focuses on specific extraction tasks. This division allows the system to use multiple models for improved accuracy while keeping each individual model relatively simple and manageable.
Solution Approach 2:
The combining model automatically integrates the outputs from multiple extracting models without requiring manual intervention. The system self-manages the complexity of coordinating multiple models through automated combination logic, reducing the operational burden despite the increased number of components.
3Measurement precision
If traditional extraction models are used on cluttered medical images, then processing speed is fast, but measurement precision deteriorates
Solution Approach 1:
The patent segments the complex task of identifying contours in cluttered medical images into multiple specialized extraction operations. Each model handles specific aspects of contour detection, and their results are combined to achieve accurate segmentation while maintaining processing efficiency through parallel operation of the models.
Solution Approach 2:
The patent merges the results from multiple extracting models to produce a final accurate segmentation of target areas in cluttered medical images. This combination approach maintains high processing efficiency by using relatively simple individual models that can operate quickly, while achieving high precision through their coordinated output integration.
Data Source
AI summary
An input image that includes a target area may be received. A first segment for extracting the target area from the input image may be generated using a first extracting model. A second segment for extracting the target area from the input image may be generated using a second extracting model. The first segment is compared to the second segment to determine a combined segment of at least the target area.