Retinal vessel segmentation using textons

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods

An Evaluation of a Battery of Functional and Structural Tests as Predictors of Likely Risk of Progression of Age-Related Macular Degeneration.

Purpose: To evaluate the ability of visual function and structural tests to identify the likely risk of progression from early/intermediate to advanced AMD, using the Age-Related Eye Disease Study (AREDS) simplified scale as a surrogate for risk of progression. The secondary aim was to determine the relationship between disease severity grade and the observed functional and structural deficits. Methods: A total of 100 participants whose AMD status varied from early to advanced were recruited. Visual function was assessed using cone dark adaptation, 14 Hz flicker and chromatic threshold tests and retinal structure was assessed by measuring drusen volume and macular thickness. The predictive value of the tests was estimated using ordinal regression analysis. Group comparisons were assessed using analysis of covariance. Results: Change in cone dark adaptation (cone τ) and yellow-blue (YB) chromatic sensitivity were independent predictors for AMD progression risk (cone τ, pseudo R2 = 0.35, P < 0.001; YB chromatic threshold, pseudo R2 = 0.16, P < 0.001). The only structural predictor was foveal thickness (R2 = 0.05, P = 0.047). Chromatic sensitivity and cone dark adaptation were also the best functional tests at distinguishing between severity groups. Drusen characteristics clearly differentiated between participants with early and advanced disease, but were not able to differentiate between those with early AMD and controls. Mean differences in retinal thickness existed between severity groups at the foveal (P = 0.040) and inner (P = 0.001) subfields. Conclusions: This study indicates that cone τ, YB chromatic threshold and foveal thickness are independent predictors of likely risk of AMD progression

Assessing neurodegeneration of the retina and brain with ultra-widefield retinal imaging

The eye is embryologically, physiologically and anatomically linked to the brain. Emerging evidence suggests that neurodegenerative diseases, such as Alzheimer’s disease (AD), manifest in the retina. Retinal imaging is a quick, non-invasive method to view the retina and its microvasculature. Features such as blood vessel calibre, tortuosity and complexity of the vascular structure (measured through fractal analysis) are thought to reflect microvascular health and have been found to associate with clinical signs of hypertension, diabetes, cardiovascular disease and cognitive decline. Small deposits of acellular debris called drusen in the peripheral retina have also been linked with AD where histological studies show they can contain amyloid beta, a hallmark of AD. Age-related macular degeneration (AMD) is a neurodegenerative disorder of the retina and a leading cause of irreversible vision loss in the ageing population. Increasing number and size of drusen is a characteristic of AMD disease progression. Ultra-widefield (UWF) retinal imaging with a scanning laser ophthalmoscope captures up to 80% of the retina in a single acquisition allowing a larger area of the retina to be assessed for signs of neurodegeneration than is possible with a conventional fundus camera, particularly the periphery. Quantification of changes to the microvasculature and drusen load could be used to derive early biomarkers of diseases that have vascular and neurodegenerative components such as AD and other forms of dementia.Manually grading drusen in UWF images is a difficult, subjective and a time-consuming process because the area imaged is large (around 700mm2) and drusen appear as small spots ( 0.8 and < 0.9), achieving AUC 0.55-0.59, 0.78-0.82 and 0.82-0.85 in the central, perimacular and peripheral zones, respectively. Measurements of the retinal vasculature appearing in UWF images of cognitively healthy (CH) individuals and patients diagnosed with mild cognitive impairment (MCI) and AD were obtained using a previously established pipeline. Following data cleaning, vascular measures were compared using multivariate generalised estimation equations (GEE), which accounts for the correlation between eyes of individuals with correction for confounders (e.g. age). The vascular measures were repeated for a subset of images and analysed using GEE to assess the repeatability of the results. When comparing AD with CH, the analysis showed a statistically significant difference between measurements of arterioles in the inferonasal quadrant, but fractal analysis produced inconsistent results due to differences in the area sampled in which the fractal dimension was calculated.When looking at drusen load, there was a higher abundance of drusen in the inferonasal region of the peripheral retina in the CH and AD compared to the MCI group. Using GEE analysis, there was evidence of a significant difference in drusen count when comparing MCI to CH (p = 0.02) and MCI to AD (p = 0.03), but no evidence of a difference when comparing AD to CH. However, given the low sensitivity of the system (partly the result of only moderate agreement between human observers), there will be a large proportion of drusen that are not detected giving an under estimation of the true amount of drusen present in an image. Overcoming this limitation will involve training the system using larger datasets and annotations from additional observers to create a more consistent reference standard. Further validation could then be performed in the future to determine if these promising pilot results persist, leading to candidate retinal biomarkers of AD

Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining

Age-related Macular Degeneration (AMD) is the most common cause of adult blindness in the developed world. This paper describes a new image mining technique to perform automated detection of AMD from color fundus photographs. The technique comprises a novel hierarchical image decomposition mechanism founded on a circular and angular partitioning. The resulting decomposition is then stored in a tree structure to which a weighted frequent sub-tree mining algorithm is applied. The identified sub-graphs are then incorporated into a feature vector representation (one vector per image) to which classification techniques can be applied. The results show that the proposed approach performs both efficiently and accurately

Digital ocular fundus imaging: a review

Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs.Fundação para a Ciência e TecnologiaFEDErPrograma COMPET