A comparative study of transformation models for the sequential mosaicing of long retinal sequences of slit-lamp images obtained in a closed-loop motion

Autor: Kristina Prokopetc, Adrien Bartoli
Rok vydání: 2016
Předmět:
Computer science
Biomedical Engineering
Normalization (image processing)
Image registration
Health Informatics
02 engineering and technology
Slit Lamp Microscopy
Retina
Pattern Recognition
Automated
03 medical and health sciences
Imaging
Three-Dimensional
0302 clinical medicine
Image Interpretation
Computer-Assisted
0202 electrical engineering
electronic engineering
information engineering
Humans
Radiology
Nuclear Medicine and imaging
Point (geometry)
Computer vision
Diabetic Retinopathy
Estimation theory
business.industry
Geometric transformation
Pattern recognition
General Medicine
Models
Theoretical
Computer Graphics and Computer-Aided Design
Computer Science Applications
Transformation (function)
Metric (mathematics)
030221 ophthalmology & optometry
020201 artificial intelligence & image processing
Surgery
Pairwise comparison
Computer Vision and Pattern Recognition
Artificial intelligence
business
Algorithms
Zdroj: International Journal of Computer Assisted Radiology and Surgery. 11:2163-2172
ISSN: 1861-6429
1861-6410
DOI: 10.1007/s11548-016-1439-7
Popis: Navigated panretinal photocoagulation is a standard care for proliferative diabetic retinopathy. Slit-lamp-based systems used for this treatment provide a narrow view of the retina. Retinal mosaics are used for view expansion and treatment planning. Mosaicing slit-lamp images is a hard task due to the absence of a physical model of the imaging process, large textureless regions and imaging artifacts, mostly reflections. We present a comparative study of various geometric transformation models applied to retinal image mosaicing in computer-assisted slit-lamp imaging. We propose an efficient point correspondence-based framework for transformation model evaluation in a typical closed-loop motion scenario. We compare the performance of multiple linear and nonlinear models of different complexities and assess the effect of the number of points used for parameter estimation. We use a local fitting error (LFE) metric to estimate the models’ performance in pairwise registration. Because LFE alone is not conclusive regarding the problem of accumulated drift, we propose a loop closure error (LCE) metric to quantify the effect of accumulated local registration errors. We also provide a new normalization procedure for the quadratic transformation model, widely used in retinal image registration. In total, seven transformation models were evaluated on three datasets of long image sequences. LFE decreases with increasing complexity of the model, while LCE, in contrast, shows superior performance of simple models. Varying the number of point correspondences did not reveal a common trend for the LCE metric, showing an increase in the error for simple models and an unstable behavior of the complex models. Our results show that simple models are less sensitive to drift and preferable for sequential mosaicing in slit-lamp imaging, while more complex models are the best choice for short-term registration.
Databáze: OpenAIRE
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