Automatic Drusen Detection from Colour Retinal Images

Abstract

Assessment of the risk for development of age-related macular degeneration (ARMD) requires reliable detection and quantitative mapping of retinal abnormalities that are considered as precursors of the disease. Typical signs for the latter are the so-called drusen that appear as abnormal white-yellow deposits on the retina. Colour retinal images are used presently to visually identify the presence of drusens. Segmentation of these features using conventional image analysis methods is quite complicated mainly due to the non-uniform illumination and the variability of the pigmentation of the background tissue. Automated detection and analysis can provide vital information about the quantity and quality of the drusens. In this paper, we report on two methods that we have developed to reliably detect and count drusens. The methods exploit the morphological characteristics of the drusens such as texture and their 3D profiles. We compare the results of using these two methods and make recommendations for automated drusen analysis.

Automated Classification of Severity of Age-Related Macular Degeneration from Fundus Photographs

“…Some AMD detection methods require user intervention. 10 Recently, researchers have emphasized automated approaches by using adaptive equalization and wavelets 11 ; applying mathematical morphology 12 on angiographic images; using adaptive thresholding 13 ; exploiting probabilistic boosting approaches for the classification of nonhomogeneous drusen textures 14 ; using probabilistic modeling and fuzzy logic 15 ; applying histogram normalization and adaptive segmentation 16 ; exploiting texture discrimination and the intensity topographical profile 17 ; utilizing morphologic reconstruction 18 ; employing a histogram-based segmentation method 19 ; or, finally, using basic feature clustering to find bright lesions. 20 The interested reader is also referred to a recent review 9 of ARIA techniques.…”

Drusen Imaging: A Review

“…Soft drusen detection is hindered by their 'feathered', indistinct edges [24]. Clustering-based drusen segmentation aggregates 'similar' image components in clusters [25,26].…”

Automated drusen segmentation and quantification in SD-OCT images

“…Duanggate and Uyyanonvara (Duanggate and Uyyanonvara, 2008) reviewed automatic drusen segmentation from CFPs. Many different approaches to automated segmentation of drusen in CFPs have been developed, which includes histogram-based approaches (Rapantzikos and Zervakis, 2001; Rapantzikos et al, 2003; Checco and Corinto, 2006; Smith et al, 2005a), texture-based approaches (Parvathi and Devi, 2007; Lee et al, 2008; Freund et al, 2009), morphological approaches (Sbeh et al, 1997, 2001), multi-level analysis approach (Brandon and Hoover, 2003) and fuzzy logic approaches (Thaibaoui et al, 2000; Liang et al, 2010; Quellec et al, 2011). A common challenge among these systems is that the margins of the drusen are difficult to discern reliably on CFP since this is a two-dimensional imaging modality.…”