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Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching.

Yan Liu¹, Jiawei Tian¹, Rongrong Hu¹

¹School of Automation, University of Electronic Science and Technology of China, Chengdu, China.

Frontiers in Neurorobotics

|March 4, 2022

Summary

This study introduces an improved algorithm for seamless endoscopic image mosaicing. The method enhances lesion visualization by effectively reducing mismatches in feature point matching, improving diagnostic accuracy.

Keywords:

K-nearest RANSAC SIFT algorithm endoscope feature point matching image mosaic

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Area of Science:

Medical Imaging
Computer Vision
Surgical Technology

Background:

Endoscopic imaging is crucial for diagnosing and treating lesions.
Limited imaging range of endoscopes can hinder comprehensive assessment of lesion scope and detail.
Image mosaic technology offers a solution to overcome the limitations of narrow endoscopic fields of view.

Purpose of the Study:

To develop an improved algorithm for seamless endoscopic image mosaicing.
To enhance the visualization of lesions by creating wider field-of-view images from endoscopic captures.
To improve the accuracy of lesion assessment in endoscopic procedures.

Main Methods:

An improved feature-point pair purification algorithm based on Scale-Invariant Feature Transform (SIFT) was developed.
A combination of K-nearest neighbor (KNN) for initial matching and Random Sample Consensus (RANSAC) for robust outlier elimination was employed.
Image transformation matrices were estimated to achieve seamless image stitching.

Main Results:

The combined KNN and RANSAC approach significantly reduced the mismatching rate of feature points.
The algorithm successfully generated seamless mosaics of endoscopic images.
Validation with real endoscopic images demonstrated the algorithm's effectiveness and good performance.

Conclusions:

The proposed improved feature-point purification algorithm effectively enhances endoscopic image mosaicing.
This technique improves the ability to assess the scope and details of lesions, aiding clinical judgment.
The algorithm offers a valuable tool for improving diagnostic and treatment planning in endoscopy.