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

Endoscopic imaging plays a very important role in the diagnosis and treatment of lesions. However, the imaging range of endoscopes is small, which may affect the doctors' judgment on the scope and details of lesions. Image mosaic technology can solve the problem well. In this paper, an improved...

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Published inFrontiers in neurorobotics Vol. 16; p. 840594
Main Authors Liu, Yan, Tian, Jiawei, Hu, Rongrong, Yang, Bo, Liu, Shan, Yin, Lirong, Zheng, Wenfeng
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Research Foundation 15.02.2022
Frontiers Media S.A
Subjects
Algorithms
Bladder
endoscope
Endoscopes
Endoscopy
feature point matching
image mosaic
K-nearest
Neuroscience
Purification
RANSAC
Registration
Robots
SIFT algorithm
RANSAC
K-nearest
SIFT algorithm
feature point matching
endoscope
image mosaic
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Summary:Endoscopic imaging plays a very important role in the diagnosis and treatment of lesions. However, the imaging range of endoscopes is small, which may affect the doctors' judgment on the scope and details of lesions. Image mosaic technology can solve the problem well. In this paper, an improved feature-point pair purification algorithm based on SIFT (Scale invariant feature transform) is proposed. Firstly, the K-nearest neighbor-based feature point matching algorithm is used for rough matching. Then RANSAC (Random Sample Consensus) method is used for robustness tests to eliminate mismatched point pairs. The mismatching rate is greatly reduced by combining the two methods. Then, the image transformation matrix is estimated, and the image is determined. The seamless mosaic of endoscopic images is completed by matching the relationship. Finally, the proposed algorithm is verified by real endoscopic image and has a good effect.
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Edited by: Hang Su, Fondazione Politecnico di Milano, Italy
Reviewed by: Jing Luo, Wuhan Institute of Technology, China; Zhihui Lai, Shenzhen University, China; Ding Junhang, Qingdao University, China; Bo Jin, East China Normal University, China
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2022.840594