Medical Image Segment Adjustment via Classification-Driven Modification
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Solution Overview
Problem
Existing medical imaging technologies require multiple processing operations to achieve desired image appearances, leading to significant delays and disruptions in user workflow due to unawareness of ideal processing parameters.
Innovation Solution
A method using a modification algorithm parameterized by a classification algorithm, such as a Generative Adversarial Network, adjusts the visual appearance of specific image segments based on user preferences or task requirements, allowing quick switching between different visual appearances without interrupting the workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple processing operations are performed to achieve desired image appearances, then the image quality and visual appearance are improved, but the processing time and workflow disruption increase
Solution Approach 1:
The system performs preliminary classification of image segments and pre-determines appropriate processing parameters before the user requests image processing. This allows the modification algorithm to be pre-configured with optimal parameters based on the classified segment types, eliminating the need for multiple iterative processing operations and significantly reducing processing time while maintaining image quality.
Solution Approach 2:
The system dynamically changes processing parameters based on the classified image segment type. Instead of performing multiple processing operations with different parameters, the classification algorithm automatically selects the appropriate parameters for each segment type, allowing a single processing operation to achieve the desired image quality with optimal parameters from the start.
2Adaptability or versatility
If users manually adjust processing parameters to achieve desired visual appearances, then the adaptability to user needs is improved, but the ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically classifying image segments and selecting appropriate processing parameters without requiring user intervention. The classification algorithm autonomously determines the segment type and configures the modification algorithm with suitable parameters, providing adaptability to different image types while maintaining ease of operation through automated decision-making.
Solution Approach 2:
The system uses feedback from the classification algorithm to automatically adjust processing parameters. The classification results provide feedback about the image segment characteristics, which are then used to select optimal processing parameters, enabling the system to adapt to different user needs and image types without requiring manual parameter adjustment by the user.
3Manufacturing precision
If different processing parameters are used for different image segments, then the manufacturing precision of image quality is improved, but the device complexity increases
Solution Approach 1:
The system divides the image into segments and classifies each segment type using a classification algorithm. This segmentation approach allows different processing parameters to be applied to different segment types while maintaining a unified processing framework. The classification algorithm manages the complexity by providing a systematic way to identify and handle different segment types, thereby improving image quality without proportionally increasing device complexity.
Solution Approach 2:
The modification algorithm is designed with multi-functionality to handle different image segment types using a single unified algorithm structure. Instead of requiring separate processing systems for different segment types, the universal modification algorithm can adapt its parameters based on the classified segment type, thereby achieving high manufacturing precision for different image qualities while keeping the device complexity manageable through parameter-based flexibility rather than structural complexity.
Data Source
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AI summary
A method for adjusting the image impression of an image (7), in particular an image (7) obtained in the context of medical imaging, comprising the steps of: - providing an image (7) or input data from which the image (7) is determined, - specifying a respective target image impression class (9) for the image (7) and/or for at least one image segment (16, 17) of the image (7) specified directly or by specifying at least one segment type, wherein a relationship between the image data of the image (7) and/or the respective image segment (16, 17) and an assigned image impression class (14) is specified by a classification algorithm (13), - modifying the image (7) or the input data by a modification algorithm (8) to determine the image impression class assigned to the resulting modified image data (12, 18, 19) of the image (7) or the respective image segment (16, 17). (14) to match the respective target image impression class (9),wherein at least one modification parameter of the modification algorithm (8) is or becomes specified depending on the classification algorithm (13).