Automated Arteriole and Venule Recognition in Retinal Images using Ensemble Classification M. M. Fraz1, A. R. Rudnicka2, C. G. Owen2, D. P. Strachan2 and S. A. Barman1 1School of Computing and Information Systems, Faculty of Science Engineering and Computing, Kingston University London, London, U.K 2Division of Population Health Sciences and Education, St. George’s, University of London, London, U.K Keywords: Medical Image Analysis, Retinal Image Processing, Artery Vein Classification, Ensemble Learning. Abstract: The shape and size of retinal vessels have been prospectively associated with cardiovascular outcomes in adult life, and with cardiovascular precursors in early life, suggesting life course patterning of vascular development. However, the shape and size of arterioles and venules may show similar or opposing associations with disease precursors / outcomes. Hence accurate detection of vessel type is important when considering cardio-metabolic influences on vascular health. This paper presents an automated method of identifying arterioles and venules, based on colour features using the ensemble classifier of boot strapped decision trees. The classifier utilizes pixel based features, vessel profile based features and vessel segment based features from both RGB and HIS colour spaces. To the best of our knowledge, the decision trees based ensemble classifier has been used for the first time for arteriole/venule classification. The classification is performed across the entire image, including the optic disc. The methodology is evaluated on 3149 vessel segments from 40 colour fundus images acquired from an adult population based study in the UK (EPIC Norfolk), resulting in 83% detection rate. This methodology can be further developed into an automated system for measurement of arterio-venous ratio and quantification of arterio-venous nicking in retinal images, which may be of use in identifying those at high risk of cardiovascular events, in need of early intervention. 1 INTRODUCTION et al. 2011). Associations between retinal vessel morphology and disease precursors / outcomes may With the development of digital imaging and be similar or opposing for arterioles and venules. For computational efficiency, image processing, analysis instance, hypertension and atherosclerosis may have and modeling techniques are increasingly used in all different effects in retinal arterioles and venuels, fields of medical sciences, particularly in resulting in a decreased arteriole to venule width ophthalmology (Abràmoff, Garvin et al. 2010). ratio (AVR) (Jack J. Kanski and Brad Bowling Automated detection of micro-vascular disease such 2011). Retinal arteriovenous nicking, a as diabetic retinopathy in the retinal image using pathognomonic sign of hypertension, is another digital image analysis methods has huge potential retinal feature worthy of study, characterized by a benefits in screening programs for early detection of decrease in the venular calibre at both sides of an disease (Fraz, Remagnino et al. 2012). The blood artery-vein crossing (Jack J. Kanski and Brad vessel structure in retinal images is unique in the Bowling 2011). However, more subtle changes in sense that it is the only part of the blood circulation arteriole / venular morphology may be an early system that can be directly observed non-invasively, physio-maker of vascular health, which might can be easily imaged using Fundus cameras. predict those at high risk of disease in middle and Morphological characteristics of retinal blood later life. However, identifying small changes in vessels (particularly width) have been prospectively retinal arterioles and venules is a difficult task to associated with cardiovascular outcomes in adult life perform manually, as it is subjective, open to (Wong, Klein et al. 2001), and with cardio- measurement error, and time consuming, limiting its metabolic risk factors in early life (Owen, Rudnicka use in large population based studies. Automated 194 M. Fraz M., R. Rudincka A., G. Owen C., P. Strachan D. and A. Barman S.. Automated Arteriole and Venule Recognition in Retinal Images using Ensemble Classification. DOI: 10.5220/0004733701940202 In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 194-202 ISBN: 978-989-758-009-3 Copyright c 2014 SCITEPRESS (Science and Technology Publications, Lda.) AutomatedArterioleandVenuleRecognitioninRetinalImagesusingEnsembleClassification segregation of retinal arterioles and venules could be should contain at least one venule and one arteriole. used to assist with this task, which would be a pre- K-Means clustering is used to classify the vessels in requisite for the development of a computer assisted two concentric circumferences around the optic disc. tool for use in large populations to identify those at The quadrant-wise classification enforces a high risk of disease. condition to have at least one arteriole and one The appearance of arterioles and venules in venule per quadrant and it seems more suitable for retinal images are similar. The general assumption is optic disc centered images rather than macula that there is a difference in the colour and size of the centered images. Also, basic K-Means clustering is venules and arterioles; the later one appears to be sensitive to the initialization and may often become thinner, brighter and present more frequently with a stuck at a local optimal. central light reflex. However, there are some In this paper we have presented an automated challenges in building a robust vessel classification method for retinal a/v classification utilizing an system. There is intra-image and intra-subject ensemble classifer of boot strapped decision trees. variance in the blood colour. The size and colour of The classifier based on the boot strapped decision similar blood vessels changes as they move away trees is a classic ensemble classifier, which has been from the optic disc. In the periphery vessels become broadly applied in many application areas of image so this they are almost indistinguishable. The analysis (Fraz, Remagnino et al. 2012), but has not context based features may also fail at these been extensively utilized for retinal vessel locations due to vessel crossings and branching. In classification. To our knowledge, this is the first use addition, the curved shape of the retina and non- of a decision trees based ensemble method for a/v uniform illumination add complexity to the classification. An important feature of the bagged automated vessel classification task. ensemble is that the classification accuracy can be A number of methods have been reported in estimated during the training phase, without literature for retinal arteriole/venule (a/v) supplying the classifier with test data. Moreover, the classification, which can be divided into two broad importance of each feature in classification can also categories; automated and semi-automated methods. be predicted during the training phase, which helps In automated methods (Niemeijer, Xiayu et al. 2011; in identifying the most relevant features used in a/v Huang, Zhang et al. 2012; Dashtbozorg, Mendonca classification thus automatically reducing the et al. 2013; Nguyen, Bhuiyan et al. 2013), the vessel dimensionality of the feature vector and boosting centerline pixels forming the vascular skeleton are computational speed. The method is validated on 40 extracted from the segmented vascular tree, followed macula centered fundus photographs acquired from by the calculation of various distinguishable features 20 middle-aged and elderly adults examined as part for each centerline pixel and finally each pixel is of the latest phase of the European Investigation into assigned as an arteriole or venule by a classifier. In Cancer in Norfolk study (EPIC-Norfolk 2013). The semi-automated methods (Rothaus, Jiang et al. 2009; classification is not only performed near the optic Vázquez, Cancela et al. 2013), the initial pixels on disc but across the entire image. The proposed the main vessels are marked as arteriole or venule by method achieves a high classification rate without an expert, and then these labels are propagated increasing the training samples or adding many across the vascular network through vessel tracking features. using the structural characteristics and connectivity The organization of the paper is as follows. information. Section 2, presents the methodology for automated Grisan’s method (Grisan and Ruggeri 2003) was segmentation of retinal blood vessels. Next, the amongst the first to propose automatic a/v vessel classification methodology is explained in separation. The main idea was to divide the optic section 3. Section 4 presents the validation disc centered images into four quadrants with the methodology and experimental results. Finally, the assumption that each quadrant will contain discussion and conclusions are given in Section 5. approximately the same number of arterioles and venules with significant differences in the features. The variance of the red channel and mean of the hue 2 THE METHODOLOGY are used as vessel features, fuzzy clustering is applied to each partition independently. In another The vascular network is segmented from the method (Saez, González-Vázquez et al. 2012), the coloured retinal image and the vascular skeleton quadrants are rotated in steps of 20 degrees with the consisting of centerline pixels is constructed. Vessel aim of fulfilling the assumption that each quadrant segments are generated by