A data generation method for correcting endoscopic probe rotation non-uniformity

By constructing a mathematical model to simulate the non-uniform rotational distortion law and generating distortion-reference image data pairs with known labels, the problem of insufficient training data for the non-uniform rotational distortion correction model of the endoscope probe is solved, and a low-cost and high-precision correction effect is achieved.

CN117911293BActive Publication Date: 2026-06-30SHANGHAI JIAOTONG UNIV
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Patent Information

Application Number
CN202410101617.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-01-24
Publication Date
2026-06-30
Estimated Expiration
2044-01-24

AI Technical Summary

Technical Problem

Existing technologies struggle to efficiently generate diverse distortion-reference image data pairs for training endoscopic probe non-uniform rotational distortion correction models, resulting in high correction costs and time-consuming processes that negatively impact imaging quality and treatment outcomes.

Method used

By constructing a mathematical model to simulate the non-uniform rotational distortion pattern, distortion-reference image data pairs are randomly generated, including random image transformation and sine function synthesis of offset vectors, to generate distortion-reference image data pairs with known labels for neural network training.

Benefits of technology

It enables the low-cost, high-precision generation of diverse distortion-reference image data pairs, improving the efficiency and generalization ability of model training, reducing human and financial costs, and enhancing the accuracy of endoscopic imaging.

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Abstract

This invention discloses a data generation method for correcting rotational nonuniformity of endoscopic probes, relating to the field of medical image processing technology. The method includes: selecting an acquired image composed of multiple scan line data; performing a random image transformation on the acquired image to obtain a reference image; simulating diverse nonuniform rotational distortion patterns using a mathematical model to randomly synthesize an offset vector corresponding to the number of line data in the reference image; applying the offset vector to the reference image, obtaining corresponding line data based on the offset value at each position, and arranging the newly arranged line data to form a distorted image; and generating a distorted-reference image data pair with known labels from the reference image and the distorted image. This invention can meet the quantitative and diverse requirements for training data in nonuniform rotational distortion correction at extremely low cost, making the training set more complete and exhibiting good applicability and generalization for correcting endoscopic probe imaging of different modalities and systems.
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