Multi-Window Imaging Composite Image Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current non-invasive imaging technologies, such as CT scans, face challenges in effectively mapping images with large dynamic ranges, making it difficult to simultaneously view multiple structures like bones, organs, and soft tissue in a single image due to limitations in windowing processes.
Innovation Solution
The method involves segmenting an image into multiple windows, applying separate gray level mapping functions to each segment, and combining these segments into a composite image with borders to distinguish between different structures, allowing for the visualization of a large dynamic range in a single image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single window is used to display CT image data, then the image can be displayed within a specific range of CT attenuations, but multiple structures with different radio densities (such as bones, organs, and soft tissue) cannot be simultaneously viewed in a single image
Solution Approach 1:
The image is divided into multiple segments based on CT attenuation ranges, with each segment corresponding to a different window (e.g., lung window, soft tissue window, bone window). Each segment is then processed independently with its own mapping function, allowing multiple structures with different radio densities to be simultaneously visualized with appropriate contrast in a single composite image.
2Adaptability or versatility
If the window range is expanded to cover a large dynamic range, then more structures can be included, but the ability to distinguish details within specific structures deteriorates due to insufficient gray level distribution
Solution Approach 1:
Different segments of the image are assigned different gray level mapping functions tailored to their specific CT attenuation ranges. This allows each local region (segment) to have optimized contrast and detail visibility for its specific tissue type, while the overall image maintains a wide dynamic range covering multiple anatomical structures.
3Adaptability or versatility
If multiple windows are combined into a single image, then multiple structures can be viewed simultaneously, but the complexity of the image processing increases
Solution Approach 1:
The image processing system segments the image data according to CT attenuation ranges and applies specific mapping functions to each segment. This segmentation approach systematically manages the complexity by breaking down the multi-window processing into independent, manageable segments that can be processed and combined efficiently.
Data Source
AI summary
Methods and systems are provided for multi-window imaging. In one embodiment, a method comprises segmenting an image reconstructed from acquired projection data into segments, converting pixel values based on a mapping for each segment to generate converted segments, and outputting, to a display device, a composite image comprising a combination of the converted segments. The composite image includes a border delineating the converted segments. In this way, multiple windows collectively covering a large dynamic range may be combined into a single image.