Adaptive Organ Recognition Algorithm for Medical Image Volume Data
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Solution Overview
Problem
Current medical image processing techniques face challenges in accurately and efficiently recognizing multiple organs in three-dimensional medical image data, particularly due to sensitivity in seed point settings, inapplicability to irregular structures, and the need for user input to correct automatic extraction results.
Innovation Solution
An image processing method and apparatus that includes an organ recognition algorithm, a recognition result judging unit, and an organ recognition algorithm revising unit, allowing for easy modification of automatic organ recognition results by acquiring judgment information on structural information correctness and updating reference information to improve recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic organ recognition is performed on large-size medical image volume data, then productivity is improved, but measurement precision deteriorates due to difficulty in manually checking and modifying results
Solution Approach 1:
The patent implements a feedback mechanism where the system automatically judges the correctness of organ recognition results by comparing extracted structural information against reference data, and automatically modifies recognition algorithms based on judgment outcomes, creating a closed-loop system that maintains precision without manual intervention
Solution Approach 2:
The system performs self-correction by automatically judging recognition results and revising algorithms without human intervention. The organ recognition algorithm executing unit, recognition result judging unit, and organ recognition algorithm revising unit work together to enable the system to self-optimize, eliminating the need for manual checking while maintaining accuracy
2Measurement precision
If region growing is performed with manually set seed points, then measurement precision is improved, but ease of operation deteriorates due to sensitivity to seed point placement
Solution Approach 1:
The system automatically sets seed points and performs region growing without requiring manual user input. The organ recognition algorithm automatically identifies starting points for region growth based on image data analysis, eliminating the need for users to manually place seed points while maintaining extraction accuracy
Solution Approach 2:
The system performs preliminary processing to automatically determine optimal seed point locations before executing region growing. By pre-calculating and setting seed points automatically based on image features, the system eliminates the operational complexity of manual seed point placement while ensuring accurate extraction results
3Measurement precision
If reverse region growing is performed to correct excessive growth, then measurement precision is improved, but loss of time increases due to additional correction steps
Solution Approach 1:
The recognition result judging unit continuously monitors region extraction results and provides feedback to the organ recognition algorithm revising unit. When extraction accuracy is judged to be insufficient, the system automatically adjusts parameters and re-executes region growing, eliminating the need for separate reverse region growing correction steps and reducing total processing time
Solution Approach 2:
The system performs preliminary parameter optimization and seed point selection to prevent excessive region growth in the first place. By pre-configuring optimal growth parameters and monitoring conditions, the system avoids the need for corrective reverse region growing operations, thereby reducing overall processing time while maintaining accuracy
4Ease of operation
If automatic organ recognition is performed without algorithm modification, then ease of operation is improved, but measurement precision deteriorates due to inability to adapt to individual variations
Solution Approach 1:
The organ recognition algorithm is made dynamic and adaptive through automatic modification based on judgment results. The system can adjust recognition parameters, thresholds, and reference data automatically to adapt to different patients and individual variations, maintaining both ease of operation and measurement precision
Solution Approach 2:
The system incorporates feedback loops where recognition results are automatically judged and used to modify the organ recognition algorithm for subsequent processing. This adaptive feedback mechanism enables the system to learn from individual cases and improve recognition accuracy for different patients without requiring manual intervention
Data Source
AI summary
Provided is an image processing apparatus that automatically recognizes a plurality of organs to subsequently visualize the automatic recognition results in an easy-to-understand manner, and facilitate a modification of the displayed recognition results. The image processing apparatus (23) uses an image processing algorithm to perform an image processing of medical image volume data. The image processing apparatus (23) comprises an organ recognition algorithm executing unit (11) that applies an organ recognition algorithm to medical image volume data to generate and output, as an organ recognition result, structural information on the plurality of organs, and a recognition result displaying unit (32) that displays the organ recognition result. The image processing apparatus (23) comprises: a recognition result judging unit (12) that acquires, via an input device (21), judgment information on whether or not the structural information is correct enough for each of the plurality of organs to be properly recognized; and an organ recognition algorithm revising unit that acquires the judgment information from the recognition result judging unit (12), changes reference information on each organ according to the acquired judgment information and changes the organ recognition algorithm to recognize the plurality of organs using the changed reference information.