Retinal Image Analysis Using Machine Vision

The topic of this thesis is studying how lesions in retina caused by diabetic retinopathy can be detected from color fundus images by using machine vision methods. Methods for equalizing uneven illumination in fundus images, detecting regions of poor image quality due toinadequate illumination, and recognizing abnormal lesions were developed duringthe work. The developed methods exploit mainly the color information and simpleshape features to detect lesions. In addition, a graphical tool for collecting lesion data was developed. The tool was used by an ophthalmologist who marked lesions in the images to help method development and evaluation. The tool is a general purpose one, and thus it is possible to reuse the tool in similar projects.The developed methods were tested with a separate test set of 128 color fundus images. From test results it was calculated how accurately methods classify abnormal funduses as abnormal (sensitivity) and healthy funduses as normal (specificity). The sensitivity values were 92% for hemorrhages, 73% for red small dots (microaneurysms and small hemorrhages), and 77% for exudates (hard and soft exudates). The specificity values were 75% for hemorrhages, 70% for red small dots, and 50% for exudates. Thus, the developed methods detected hemorrhages accurately and microaneurysms and exudates moderately.

Statistical segmentation methods and color variance analysis of retinal images

In this research, the effectiveness of Naive Bayes and Gaussian Mixture Models classifiers on segmenting exudates in retinal images is studied and the results are evaluated with metrics commonly used in medical imaging. Also, a color variation analysis of retinal images is carried out to find how effectively can retinal images be segmented using only the color information of the pixels.

Spectral retinal image processing and analysis for ophthalmology

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.

Spectral Retinal Images Reconstruction

While red-green-blue (RGB) image of retina has quite limited information, retinal multispectral images provide both spatial and spectral information which could enhance the capability of exploring the eye-related problems in their early stages. In this thesis, two learning-based algorithms for reconstructing of spectral retinal images from the RGB images are developed by a two-step manner. First, related previous techniques are reviewed and studied. Then, the most suitable methods are enhanced and combined to have new algorithms for the reconstruction of spectral retinal images. The proposed approaches are based on radial basis function network to learn a mapping from tristimulus colour space to multi-spectral space. The resemblance level of reproduced spectral images and original images is estimated using spectral distance metrics spectral angle mapper, spectral correlation mapper, and spectral information divergence, which show a promising result from the suggested algorithms.

Pain-evoked alterations on gingival blood flow and gingival crevicular fluid (GCF) neuropeptide SP and collagenase-2 (MMP-8) levels

Previous studies have demonstrated that clinical pulpal pain can induce the expression of pro-inflammatory neuropeptides in the adjacent gingival crevice fluid (GCF). Vasoactive agents such as substance P (SP) are known to contribute to the inflammatory type of pain and are associated with increased blood flow. More recent animal studies have shown that application of capsaicin on alveolar mucosa provokes pain and neurogenic vasodilatation in the adjacent gingiva. Pain-associated inflammatory reactions may initiate expression of several pro- and anti-inflammatory mediators. Collagenase-2 (MMP-8) has been considered to be the major destructive protease, especially in the periodontitis-affected gingival crevice fluid (GCF). MMP-8 originates mostly from neutrophil leukocytes, the first line of defence cells that exist abundantly in GCF, especially in inflammation. With this background, we wished to clarify the spatial extensions and differences between tooth-pain stimulation and capsaicin-induced neurogenic vasodilatation in human gingiva. Experiments were carried out to study whether tooth stimulation and capsaicin stimulation of alveolar mucosa would induce changes in GCF MMP-8 levels and whether tooth stimulation would release neuropeptide SP in GCF. The experiments were carried out on healthy human volunteers. During the experiments, moderate and high intensity painful tooth stimulation was performed by a constant current tooth stimulator. Moderate tooth stimulation activates A-delta fibres, while high stimulation also activates C-fibres. Painful stimulation of the gingiva was achieved by topical application of capsaicin-moistened filter paper on the mucosal surface. Capsaicin is known to activate selectively nociceptive C-fibres of stimulated tissue. Pain-evoked vasoactive changes in gingivomucosal tissues were mapped by laser Doppler imaging (LDI), which is a sophisticated and non-invasive method for studying e.g. spatial and temporal characteristics of pain- and inflammation-evoked blood flow changes in gingivomucosal tissues. Pain-evoked release of MMP-8 in GCF samples was studied by immunofluorometric assay (IFMA) and Western immunoblotting. The SP levels in GCF were analysed by Enzyme immunoassay (EIA). During the experiments, subjective stimulus-evoked pain responses were determined by a visual analogue pain scale. Unilateral stimulation of alveolar mucosa and attached gingiva by capsaicin evoked a distinct neurogenic vasodilatation in the ipsilateral gingiva, which attenuated rapidly at the midline. Capsaicin stimulation of alveolar mucosa provoked clear inflammatory reactions. In contrast to capsaicin stimuli, tooth stimulation produced symmetrical vasodilatations bilaterally in the gingiva. The ipsilateral responses were significantly smaller during tooth stimulation than during capsaicin stimuli. The current finding – that tooth stimulation evokes bilateral vasodilatation while capsaicin stimulation of the gingiva mainly produces unilateral vasodilatation – emphasises the usefulness of LDI in clarifying spatial features of neurogenic vasoactive changes in the intra-oral tissues. Capsaicin stimulation of the alveolar mucosa induced significant elevations in MMP-8 levels and activation in GCF of the adjacent teeth. During the experiments, no marked changes occurred in MMP-8 levels in the GCF of distantly located teeth. Painful stimulation of the upper incisor provoked elevations in GCF MMP-8 and SP levels of the stimulated tooth. The GCF MMP-8 and SP levels of the non-stimulated teeth were not changed. These results suggest that capsaicin-induced inflammatory reactions in gingivomucosal tissues do not cross the midline in the anterior maxilla. The enhanced reaction found during stimulation of alveolar mucosa indicates that alveolar mucosa is more sensitive to chemical irritants than the attached gingiva. Analysis of these data suggests that capsaicin-evoked neurogenic inflammation in the gingiva can trigger the expression and activation of MMP-8 in GCF of the adjacent teeth. In this study, it is concluded that experimental tooth pain at C-fibre intensity can induce local elevations in MMP-8 and SP levels in GCF. Depending on the role of MMP-8 in inflammation, in addition to surrogated tissue destruction, the elevated MMP-8 in GCF may also reflect accelerated local defensive and anti-inflammatory reactions.