Rotation Correction and Color Bar Generation for Endoscope Imaging
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Solution Overview
Problem
Current image processing systems for body-insertable apparatuses, such as endoscopes, face challenges in aligning orientations of captured images and accurately displaying internal subject data, leading to difficulties in diagnosis due to image rotation and color inconsistency.
Innovation Solution
An image processing system that includes a body-insertable apparatus for capturing internal images and an external device for receiving and processing the data, performing rotation correction based on specified orientations, generating a screen with an average color bar, and creating an organ image by superimposing average colors, thereby aligning image orientations and enhancing diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If rotation correction is performed on image data based on specified orientation, then image orientation alignment is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs rotation correction as a preliminary processing step before image display and diagnosis. By correcting the orientation of image data based on the specified orientation of the body-insertable apparatus before the images are viewed by medical professionals, the system ensures that anatomical structures are presented in their correct spatial relationships from the outset, eliminating the need for manual orientation adjustment and reducing overall processing time.
Solution Approach 2:
The system replaces manual mechanical orientation adjustment with automated computational rotation correction. Instead of requiring operators to physically rotate or reposition images manually, the system uses computer algorithms to automatically rotate image data based on orientation metadata from the imaging device, significantly reducing processing time while maintaining precision.
2Measurement precision
If average color bar and organ image generation are performed, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the processed image data into distinct components: the original rotation-corrected images, an average color bar representing overall tissue characteristics, and highlighted organ images emphasizing specific anatomical structures. This segmentation allows each component to be processed and analyzed independently, managing complexity while enhancing diagnostic accuracy through multiple viewing options.
Solution Approach 2:
The system introduces an average color bar as an intermediary visual element that summarizes tissue characteristics across multiple images. This color bar serves as a mediator between the raw image data and the final diagnostic interpretation, providing a simplified visual representation of tissue health that complements the detailed organ images without requiring complex analysis software.
3Loss of information
If multiple image processing operations are performed simultaneously, then information completeness is improved, but processing time increases
Solution Approach 1:
The system performs different image processing operations in periodic cycles: first rotating and aligning all image data based on orientation information, then generating average color bars, followed by creating highlighted organ images. This periodic processing approach ensures that all necessary information is extracted and presented while managing computational load through structured batch processing rather than simultaneous operations.
Data Source
AI summary
An image processing system includes an orientation specifying unit that specifies orientation of the body-insertable apparatus at the time of capturing the image data with respect to a reference direction, a rotation correcting unit that performs rotation correction on image data based on the specified orientation, a screen generating unit that generates a screen displaying the image data, an average color bar generating unit that calculates an average color of the image data, generates an image of the calculated average color, and generates an average color bar in which images of the generated average colors are connected in accordance with order of the image data, and an organ image generating unit that generates an organ image obtained by superimposing the images of the average colors generated by the average color bar generating unit. The screen generating incorporates the average color bar and the organ image into the screen.