Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
Author(s) -
Daniil I. Nikitichev,
Dzhoshkun I. Shakir,
François Chadebecq,
Marcel Tella,
Jan Deprest,
Danail Stoyanov,
Sébastien Ourselin,
Tom Vercauteren
Publication year - 2017
Publication title -
journal of visualized experiments
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.596
H-Index - 91
ISSN - 1940-087X
DOI - 10.3791/55298
Subject(s) - calibration , computer science , distortion (music) , artificial intelligence , computer vision , camera resectioning , software , context (archaeology) , biomedical engineering , medicine , physics , amplifier , paleontology , bandwidth (computing) , quantum mechanics , biology , programming language , computer network
We have developed a calibration target for use with fluid-immersed endoscopes within the context of the GIFT-Surg (Guided Instrumentation for Fetal Therapy and Surgery) project. One of the aims of this project is to engineer novel, real-time image processing methods for intra-operative use in the treatment of congenital birth defects, such as spina bifida and the twin-to-twin transfusion syndrome. The developed target allows for the sterility-preserving optical distortion calibration of endoscopes within a few minutes. Good optical distortion calibration and compensation are important for mitigating undesirable effects like radial distortions, which not only hamper accurate imaging using existing endoscopic technology during fetal surgery, but also make acquired images less suitable for potentially very useful image computing applications, like real-time mosaicing. In this paper proposes a novel fabrication method to create an affordable, sterilizable calibration target suitable for use in a clinical setup. This method involves etching a calibration pattern by laser cutting a sandblasted stainless steel sheet. This target was validated using the camera calibration module provided by OpenCV, a state-of-the-art software library popular in the computer vision community.
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