Medical Image Series Tagging and Stitching for Comprehensive Diagnostics
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Solution Overview
Problem
Medical imaging technologies often produce incomplete image series that do not encompass all relevant information of a subject, necessitating the combination of multiple image series from different scans to provide comprehensive imaging information.
Innovation Solution
A method and system for image processing that classify and stitch multiple image series based on tags, allowing for the selection and combination of image series using various stitching algorithms to create a comprehensive image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple image series are combined to provide comprehensive imaging information, then the completeness of imaging information is improved, but the complexity of image processing increases
Solution Approach 1:
The patent segments the image processing task by classifying image series into different groups based on tags (e.g., anatomical region, imaging modality, contrast agent). This segmentation allows the system to process different groups independently using appropriate stitching algorithms, reducing overall processing complexity while maintaining comprehensive information coverage.
Solution Approach 2:
The patent performs preliminary classification of image series into groups based on tags before stitching. This preliminary action organizes the data structure in advance, making the subsequent stitching process more systematic and manageable, thereby reducing the complexity of handling multiple image series.
2Loss of information
If multiple image series are stitched together, then the comprehensiveness of the final image is improved, but the processing time increases
Solution Approach 1:
By dividing image series into groups based on tags, the patent enables parallel processing of different groups. Each group can be stitched independently and simultaneously, significantly reducing total processing time while still achieving comprehensive image coverage when all groups are combined.
Solution Approach 2:
The patent allows selective stitching of specific groups or combinations of image series based on user needs. Users can choose to process only relevant groups (partial action) rather than all available image series, reducing processing time while maintaining sufficient comprehensiveness for specific diagnostic purposes.
3Manufacturing precision
If image series are classified and grouped by tags, then the accuracy of stitching is improved, but the system complexity increases
Solution Approach 1:
The patent uses a universal tagging system that can categorize image series by multiple attributes (anatomical region, modality, contrast agent). This multi-functional tagging approach improves stitching accuracy by enabling precise matching of complementary image series while using a standardized system that doesn't significantly increase overall system complexity.
Solution Approach 2:
The patent introduces tags as an intermediary element between raw image data and the stitching process. These tags serve as mediators that organize and characterize image series, improving stitching accuracy by providing clear classification criteria without requiring complex direct analysis of image content.
4Loss of information
If all image series are stitched together, then the completeness of information is improved, but the difficulty of managing and selecting algorithms increases
Solution Approach 1:
The patent segments image series into manageable groups based on tags, making it easier to select and apply appropriate stitching algorithms to each group. This segmentation reduces the overwhelming complexity of managing all image series simultaneously while still enabling comprehensive information synthesis through systematic combination of grouped results.
Data Source
AI summary
A method for stitching image for medical imaging device may include obtaining a plurality of image series. Each of the plurality of image series may include one or more scanning images. The method may also include, for each of the plurality of image series, determining a tag of the each of the plurality of image series and classifying the plurality of image series based on the tags of the plurality of image series. The method may further include determining one or more groups of image series based on the classification. Image series in a same group may have a same tag. The method may also include stitching at least one image series of at least one group of the one or more groups of image series.