Spectral retinal image processing and analysis for ophthalmology
Laaksonen, Lauri (2016-05-27)
Väitöskirja
Laaksonen, Lauri
27.05.2016
Lappeenranta University of Technology
Acta Universitatis Lappeenrantaensis
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-265-957-6
https://urn.fi/URN:ISBN:978-952-265-957-6
Tiivistelmä
Diabetic retinopathy, age-related macular degeneration and glaucoma are the leading
causes of blindness worldwide. Automatic methods for diagnosis exist, but their performance
is limited by the quality of the data. Spectral retinal images provide a significantly
better representation of the colour information than common grayscale or red-green-blue
retinal imaging, having the potential to improve the performance of automatic diagnosis
methods.
This work studies the image processing techniques required for composing spectral retinal
images with accurate reflection spectra, including wavelength channel image registration,
spectral and spatial calibration, illumination correction, and the estimation of depth information
from image disparities. The composition of a spectral retinal image database
of patients with diabetic retinopathy is described. The database includes gold standards
for a number of pathologies and retinal structures, marked by two expert ophthalmologists.
The diagnostic applications of the reflectance spectra are studied using supervised
classifiers for lesion detection. In addition, inversion of a model of light transport is used
to estimate histological parameters from the reflectance spectra.
Experimental results suggest that the methods for composing, calibrating and postprocessing
spectral images presented in this work can be used to improve the quality
of the spectral data. The experiments on the direct and indirect use of the data show
the diagnostic potential of spectral retinal data over standard retinal images. The use of
spectral data could improve automatic and semi-automated diagnostics for the screening
of retinal diseases, for the quantitative detection of retinal changes for follow-up, clinically
relevant end-points for clinical studies and development of new therapeutic modalities.
causes of blindness worldwide. Automatic methods for diagnosis exist, but their performance
is limited by the quality of the data. Spectral retinal images provide a significantly
better representation of the colour information than common grayscale or red-green-blue
retinal imaging, having the potential to improve the performance of automatic diagnosis
methods.
This work studies the image processing techniques required for composing spectral retinal
images with accurate reflection spectra, including wavelength channel image registration,
spectral and spatial calibration, illumination correction, and the estimation of depth information
from image disparities. The composition of a spectral retinal image database
of patients with diabetic retinopathy is described. The database includes gold standards
for a number of pathologies and retinal structures, marked by two expert ophthalmologists.
The diagnostic applications of the reflectance spectra are studied using supervised
classifiers for lesion detection. In addition, inversion of a model of light transport is used
to estimate histological parameters from the reflectance spectra.
Experimental results suggest that the methods for composing, calibrating and postprocessing
spectral images presented in this work can be used to improve the quality
of the spectral data. The experiments on the direct and indirect use of the data show
the diagnostic potential of spectral retinal data over standard retinal images. The use of
spectral data could improve automatic and semi-automated diagnostics for the screening
of retinal diseases, for the quantitative detection of retinal changes for follow-up, clinically
relevant end-points for clinical studies and development of new therapeutic modalities.
Kokoelmat
- Väitöskirjat [1036]