Dynamic Image Segmentation via Machine Learning Parameters
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Solution Overview
Problem
Current image segmentation methods for medical images lack precision, which can impact the accuracy of medical diagnoses.
Innovation Solution
An image segmentation method using a machine learning model to calculate image segmentation parameters for each target image, enabling precise segmentation of medical images into object regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single fixed image segmentation parameter is used for all medical images, then the device complexity is reduced and operation is simplified, but the manufacturing precision and measurement precision of image segmentation deteriorate
Solution Approach 1:
The patent implements dynamic parameter selection by training a machine learning model to predict optimal segmentation parameters based on input image characteristics. Instead of using fixed parameters, the system adapts parameters dynamically according to the specific image being processed, thereby improving segmentation precision while maintaining manageable system complexity through automated parameter selection
Solution Approach 2:
The patent changes the segmentation parameters based on the specific characteristics of each medical image. By using a machine learning model to predict and select appropriate parameters for different image types and conditions, the system achieves high segmentation precision across diverse medical images without requiring manual parameter tuning for each case
2Measurement precision
If manual parameter adjustment is performed for each image segmentation task, then the manufacturing precision improves, but the productivity and ease of operation deteriorate
Solution Approach 1:
The system performs self-service by automatically selecting segmentation parameters through the trained machine learning model. The model predicts optimal parameters based on image characteristics without requiring manual intervention, thereby maintaining high segmentation precision while significantly improving processing efficiency and reducing operational complexity
Solution Approach 2:
The patent applies preliminary action by pre-training the machine learning model on a dataset of medical images and their corresponding optimal segmentation parameters. This pre-computed knowledge enables the system to quickly predict appropriate parameters for new images without manual adjustment, improving both precision and productivity
3Measurement precision
If different image segmentation parameters are used for different target images, then the manufacturing precision improves, but the device complexity and difficulty of operation increase
Solution Approach 1:
The system automatically selects appropriate segmentation parameters for different target images through the machine learning model, eliminating the need for users to manually adjust parameters. The model handles parameter selection based on image characteristics, maintaining high precision while keeping the operation simple and user-friendly
Solution Approach 2:
The machine learning model acts as an intermediary between the input image and the segmentation algorithm. It translates image characteristics into optimal segmentation parameters, shielding users from the complexity of parameter selection while ensuring high segmentation precision through data-driven parameter choices
Data Source
AI summary
An image segmentation method includes the following steps: obtaining a target image; inputting the target image into a machine learning model to obtain an image segmentation parameter value corresponding to the target image; executing an image segmentation algorithm on the target image according to the image segmentation parameter value to obtain an image segmentation result, wherein the image segmentation result is segmenting the target image into object regions; and displaying the image segmentation result. In addition, an electronic device and storage medium using the method are also provided.