Automatic intraocular lens segmentation and detection in optical coherence tomography images.
Identifieur interne : 001411 ( Main/Exploration ); précédent : 001410; suivant : 001412Automatic intraocular lens segmentation and detection in optical coherence tomography images.
Auteurs : Melanie Gillner [Allemagne] ; Timo Eppig [Allemagne] ; Achim Langenbucher [Allemagne]Source :
- Zeitschrift für medizinische Physik [ 1876-4436 ] ; 2014.
English descriptors
- KwdEn :
- Algorithms, Anterior Eye Segment (cytology), Anterior Eye Segment (surgery), Artificial Intelligence, Humans, Image Interpretation, Computer-Assisted (methods), Lenses, Intraocular, Pattern Recognition, Automated (methods), Phantoms, Imaging, Tomography, Optical Coherence (instrumentation), Tomography, Optical Coherence (methods).
- MESH :
- cytology : Anterior Eye Segment.
- instrumentation : Tomography, Optical Coherence.
- methods : Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Tomography, Optical Coherence.
- surgery : Anterior Eye Segment.
- Algorithms, Artificial Intelligence, Humans, Lenses, Intraocular, Phantoms, Imaging.
Abstract
We present a new algorithm for automatic segmentation and detection of an accommodative intraocular lens implanted in a biomechanical eye model. We extracted lens curvature and position. The algorithm contains denoising and fan correction by a multi-level calibration routine. The segmentation is realized by an adapted canny edge detection algorithm followed by a detection of lens surface with an automatic region of interest search to suppress non-optical surfaces like the lens haptic. The optical distortion of lens back surface is corrected by inverse raytracing. Lens geometry was extracted by a spherical fit. We implemented and demonstrated a powerful algorithm for automatic segmentation, detection and surface analysis of intraocular lenses in vitro. The achieved accuracy is within the expected range determined by previous studies. Future improvements will include the transfer to clinical anterior segment OCT devices.
DOI: 10.1016/j.zemedi.2013.07.002
PubMed: 23928353
Affiliations:
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Le document en format XML
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We extracted lens curvature and position. The algorithm contains denoising and fan correction by a multi-level calibration routine. The segmentation is realized by an adapted canny edge detection algorithm followed by a detection of lens surface with an automatic region of interest search to suppress non-optical surfaces like the lens haptic. The optical distortion of lens back surface is corrected by inverse raytracing. Lens geometry was extracted by a spherical fit. We implemented and demonstrated a powerful algorithm for automatic segmentation, detection and surface analysis of intraocular lenses in vitro. The achieved accuracy is within the expected range determined by previous studies. Future improvements will include the transfer to clinical anterior segment OCT devices.</div></front></TEI><affiliations><list><country><li>Allemagne</li></country><region><li>Bavière</li><li>District de Moyenne-Franconie</li></region><settlement><li>Erlangen</li></settlement></list><tree><country name="Allemagne"><region name="Bavière"><name sortKey="Gillner, Melanie" sort="Gillner, Melanie" uniqKey="Gillner M" first="Melanie" last="Gillner">Melanie Gillner</name></region><name sortKey="Eppig, Timo" sort="Eppig, Timo" uniqKey="Eppig T" first="Timo" last="Eppig">Timo Eppig</name><name sortKey="Langenbucher, Achim" sort="Langenbucher, Achim" uniqKey="Langenbucher A" first="Achim" last="Langenbucher">Achim Langenbucher</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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