Endoscope Image Screening with PCA and RMSE Redundancy Removal
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Solution Overview
Problem
Existing endoscopic image processing systems face challenges with large volumes of data, leading to computational difficulties and inefficiencies in image recognition, particularly in detecting subtle changes in esophageal tissues.
Innovation Solution
A method involving dimensionality reduction and root-mean-square error (RMSE) analysis is employed to identify and remove redundant images, utilizing principal component analysis (PCA) and hyperspectral conversion to reduce data volume and improve image processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of endoscopic images are captured to ensure comprehensive detection of esophageal lesions, then the detection coverage is improved, but the data processing complexity and computational burden increase significantly
Solution Approach 1:
The patent extracts only the essential feature information from endoscopic images using dimensionality reduction techniques. By converting images to hyperspectral representation and applying PCA, the system extracts key spectral and spatial features while discarding redundant pixel data, thus maintaining detection coverage while reducing processing complexity.
Solution Approach 2:
The patent transforms the parameter representation of image data by converting standard RGB images to hyperspectral images and then reducing them to principal component scores. This parameter transformation allows the system to work with compressed feature representations rather than full-resolution images, reducing computational burden while preserving diagnostic information.
2Measurement precision
If high-resolution images are used to improve the accuracy of lesion detection, then the measurement precision is improved, but the volume of data to be processed increases
Solution Approach 1:
The patent extracts essential diagnostic features from high-resolution images through hyperspectral conversion and PCA dimensionality reduction. By taking out only the most informative spectral and spatial characteristics, the system maintains lesion detection accuracy while dramatically reducing the volume of data that needs to be stored and processed.
Solution Approach 2:
The patent introduces a new dimensional representation by converting images to hyperspectral space and then projecting them onto a reduced set of principal components. This dimensionality change transforms the data from a high-dimensional pixel grid to a lower-dimensional feature space that preserves diagnostic information while reducing data volume.
3Reliability
If redundant images are retained to ensure no potential lesions are missed, then the reliability of detection is improved, but the efficiency of image recognition decreases
Solution Approach 1:
The patent implements a feedback mechanism by comparing consecutive images after dimensionality reduction and using RMSE to determine whether to retain or discard images. This feedback loop allows the system to selectively remove redundant images while maintaining detection reliability by keeping images that contain meaningful diagnostic information.
Solution Approach 2:
The patent discards redundant images identified through RMSE comparison while recovering and retaining images that contain significant diagnostic information. This selective discarding and recovering process improves image recognition efficiency by reducing the number of images to process while maintaining detection reliability through intelligent retention of meaningful images.
Data Source
AI summary
The present application related to a method for deleting redundant images of an endoscope. Firstly, a host receives an input image datum from an endoscopy, hereby, capturing a plurality of corresponded input images from the input image data. Next, a plurality of dimension reduction data are obtained to operate with at least one adjacent dimension reduction datum, and then, a plurality of root mean squared error values are obtained to compare with an error threshold value. Hereby, at least one image unarrived the error threshold value are deleted to obtain a plurality of screened images. Thus, redundant images in the input image data of the endoscopy will be deleted to prevent from performing an image analysis of same images or approximated images.