Image Processing Apparatus Invalid Region Detection
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Solution Overview
Problem
Automated extraction of object shapes in medical images faces challenges with images having features not included in learning data, such as different capturing settings or lower image quality, leading to errors in shape estimation, and may incorrectly identify invalid regions as object regions.
Innovation Solution
An image processing apparatus and method that acquires an object image, identifies invalid regions outside the image capturing range or where the object is not present, and determines the success of object shape estimation using this information to switch between contour estimation algorithms and provide accurate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated extraction methods are used, then productivity is improved, but measurement precision deteriorates due to errors in shape estimation
Solution Approach 1:
The patent introduces a feedback mechanism where the determination unit checks whether estimated points are located in invalid regions and provides feedback to switch between estimation algorithms. This feedback loop enables the system to detect and correct estimation errors, maintaining high measurement precision while preserving automated extraction productivity.
Solution Approach 2:
The patent changes the parameter of estimation algorithm selection based on the validity of estimated points. When points are found in invalid regions, the system switches from the first estimation algorithm to the second algorithm, dynamically adjusting parameters to maintain precision without manual intervention.
2Ease of operation
If deep learning-based extraction is used, then ease of operation is improved, but reliability deteriorates when images have features not in learning data
Solution Approach 1:
The determination unit provides feedback by detecting when estimation results are unreliable (points in invalid regions) and triggers algorithm switching. This feedback mechanism maintains reliability for images with unseen features while preserving the ease of automated operation.
Solution Approach 2:
The system dynamically switches between different estimation algorithms based on the characteristics of the input image and estimation results. This dynamic adaptation allows the system to maintain high reliability across diverse imaging conditions without requiring manual operation.
3Reliability
If multiple estimation algorithms are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the estimation process into distinct algorithms with specific functions: a first algorithm for general cases and a second algorithm for handling invalid regions. This segmentation allows reliable estimation through specialized algorithms while keeping the overall system complexity manageable through clear functional division.
Solution Approach 2:
The determination unit acts as an intermediary between the estimation algorithms and the final output. It mediates by evaluating estimation results and deciding which algorithm to invoke, simplifying the system architecture while maintaining high reliability through coordinated algorithm execution.
Data Source
AI summary
An image processing apparatus includes: a memory storing a program; and one or more processors which, by executing the program, function as: an image acquisition unit configured to acquire an object image obtained by capturing an image of an object; an invalid-region acquisition unit configured to acquire information related to an invalid region, which is a region that is outside an image capturing range or where the object is not present, in the object image; an acquisition unit configured to acquire a result of estimating a predetermined region based on the object image; and a determination unit configured to determine, by using the information related to the invalid region, whether or not the estimation of the predetermined region is successful.