Quantitative analysis of multi-spectral fundus images

Abstract

This report summarises the work done during the EPSRC-funded project "Physics-based image interpretation to aid the detection of early signs of retinopathies", grant number GR/S09906/01. We have developed a new technique for extracting histological parameters from multispectral images of the ocular fundus. The new method uses a Monte Carlo simulation of the reflectance of the fundus to model how the colouration 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 multispectral 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 from 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.

Comparison of image registration methods for composing spectral retinal images

“…Greyscale or RGB images with high spatial resolution are commonly used in the diagnosis, complemented with more advanced eye imaging methods when necessary. To support further development of the diagnostic tools, methods for spectral re ectance measurements, especially spectral imaging of the retina, have been developed [3,4,5,10]. The aim has been to signi cantly improve the possibility to detect and visualize di erent parts of the retina and lesions related to the eye diseases.…”

Hyperspectral imaging of speleothems

“…Black and white images have been obtained at 5 nm spectral intervals with a circular field of view of 3 00 . The same set-up has been used for research on diagnostic imaging of human tissues including the retina (Styles et al, 2006) and the colon (Hidovic and Claridge, 2005).…”

Physiological Parameter Estimation from Multispectral Images Unleashed

“…To account for a specific camera setup, r i can be transformed to camera reflectances (c i,j ) at the jth spectral band, using the method described in [4]. Zero mean Gaussian noise w was added to model camera noise.…”

“…Model-based approaches that are sufficiently fast for online execution typically suffer from incorrect base assumptions such as constant scattering or low tissue absorption [2]. Machine learning-based alternatives [3,4] need accurate information about the composition of the underlying tissue for the training phase to account for the lack of annotated real data. This knowledge about optical tissue properties is hard to obtain and is dependent on experimental conditions [5].…”