Quantitative analysis of multi-spectral fundus images.
We have developed a new technique for extracting histological parameters from multi-spectral images of the ocular fundus. The new method uses a Monte Carlo simulation of the reflectance of the fundus to model how the spectral reflectance of the tissue varies with differing tissue histology. The model is parameterised by the concentrations of the five main absorbers found in the fundus: retinal haemoglobins, choroidal haemoglobins, choroidal melanin, RPE melanin and macular pigment. These parameters are shown to give rise to distinct variations in the tissue colouration. We use the results of the Monte Carlo simulations to construct an inverse model which maps tissue colouration onto the model parameters. This allows the concentration and distribution of the five main absorbers to be determined from suitable multi-spectral images. We propose the use of "image quotients" to allow this information to be extracted from uncalibrated image data. The filters used to acquire the images are selected to ensure a one-to-one mapping between model parameters and image quotients. To recover five model parameters uniquely, images must be acquired in six distinct spectral bands. Theoretical investigations suggest that retinal haemoglobins and macular pigment can be recovered with RMS errors of less than 10%. We present parametric maps showing the variation of these parameters across the posterior pole of the fundus. The results are in agreement with known tissue histology for normal healthy subjects. We also present an early result which suggests that, with further development, the technique could be used to successfully detect retinal haemorrhages.
Styles
I B
IB
School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, West Midlands B15 2TT, United Kingdom. I.B.Styles@cs.bham.ac.uk
Calcagni
A
A
Claridge
E
E
Orihuela-Espina
F
F
Gibson
J M
JM
D000465:Algorithms; D003198:Computer Simulation; D005654:Fundus Oculi; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D010783:Photometry / Q000379:methods*; D015203:Reproducibility of Results; D012164:Retinal Diseases / Q000175:diagnosis*; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity