Image Processing Apparatus Higher-Order Energy Function Segmentation
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Solution Overview
Problem
Current image processing methods are inadequate for accurately and robustly extracting target regions with predictable shapes, such as organ or tumor regions, from medical images, as they do not effectively utilize high-order energy based on shape characteristics.
Innovation Solution
An image processing apparatus and method that sets an energy function with higher-order terms to minimize energy when pixels belong to a target region, and increases energy as pixels are assigned to a different label, allowing for the selection of pixel groups based on predicted shapes like ellipses, relaxation curves, or organ shapes, using submodular or non-submodular methods to optimize labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional energy minimization methods (graph cut, QPBO) are used for image segmentation, then the segmentation can be solved globally or optimally, but the extraction accuracy of target regions with predictable shapes is insufficient
Solution Approach 1:
The patent applies local quality by introducing shape-specific energy terms that treat different spatial locations and configurations differently. The higher-order energy terms are designed to specifically penalize or reward certain spatial patterns of labels, making the energy function adapt to local shape characteristics rather than treating all pixel configurations uniformly.
Solution Approach 2:
The patent changes the parameters of the energy function by incorporating higher-order terms (third-order and above) that capture shape characteristics. These higher-order parameters enable the energy function to encode predictable shape patterns, transforming the segmentation problem from a generic optimization to one that is tailored to specific shape constraints.
2Measurement precision
If higher-order energy terms are introduced to capture shape characteristics, then the extraction accuracy of predictable shape regions improves, but the complexity of the energy function and optimization problem increases
Solution Approach 1:
The patent segments the energy function into distinct components: conventional lower-order terms (first-order for data fidelity, second-order for smoothness) and additional higher-order shape-specific terms. This segmentation allows each term to have a specific, localized function, making the overall complex energy function more manageable and interpretable.
Solution Approach 2:
The patent performs preliminary action by pre-defining the structure and parameters of higher-order energy terms based on expected shape characteristics before the actual segmentation optimization. This allows the optimization algorithm to work with a structured energy function rather than dealing with arbitrary high-dimensional complexity.
3Reliability
If higher-order energy terms are used to enforce shape consistency, then the robustness of segmentation for predictable shapes improves, but the computational complexity of solving the energy minimization problem increases
Solution Approach 1:
The patent applies partial action by selectively introducing higher-order energy terms only for regions or configurations where shape constraints are most relevant, rather than applying uniform high-order constraints across the entire image. This reduces the overall computational burden while maintaining robustness where needed.
Data Source
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AI summary
In an image processing apparatus, and an operation method and a program therefor, a target region having a predictable shape is extracted more accurately and more robustly. The image processing apparatus configured to assign a binary label representing belonging to a target region or not to each pixel in an image includes: a shape setting unit configured to set a predicted shape of the target region; an energy function setting unit configured to: select a pixel group including N pixels in the image, where N is a natural number of 4 or more, which have a positional relationship representing the set predicted shape; and set an energy function including an N-th order term in which a variable is a label of each of the N pixels of the selected pixel group, so that a value of the N-th order term is at a minimum value when a combination of the labels assigned to the N pixels of the selected pixel group is a pattern matching the set predicted shape of the target region, and increases in stages from the minimum value along with an increase in a number of pixels to which a label different from the pattern is assigned; and a labeling unit configured to perform the labeling by minimizing the set energy function.