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Eye fundus imaging is a useful, non-invasive tool in disease progress tracking, in early detection of disease and other cases. Often, the disease diagnosis is made by an ophthalmologist and automatic analysis systems are used only for support. There are several commonly used features for disease detection, one of them is the artery and vein ratio measured according to the width of the main vessels. Arteries must be separated from veins automatically in order to calculate the ratio, therefore, vessel classification is a vital step. For most analysis methods high quality images are required for correct classification. This paper presents an adaptive algorithm for vessel measurements without the necessity to tune the algorithm for concrete imaging equipment or a specific situation. The main novelty of the proposed method is the extraction of blood vessel features based on vessel width measurement algorithm and vessel spatial dependency. Vessel classification accuracy rates of 0.855 and 0.859 are obtained on publicly available eye fundus image databases used for comparison with another state of the art algorithms for vessel classification in order to evaluate artery-vein ratio (AV R). The method is also evaluated with images that represent artery and vein size changes before and after physical load. Optomed OY digital mobile eye fundus camera SmartscopeM5 PRO is used for image gathering.

Many diseases can be early detected from eye fundus images by several different features. One of the features is the artery and vein ratio. Width measurement is made on the main vessels. The aim of this automatization process is to create a fully automated method for eye fundus analysis. The fully automated system consists of blood vessel tree extraction and optic nerve disc detection in order to perform measurements at the standard place. Vessel tree extraction is complicated in some situations, so the vessel measurement algorithm is developed independently of the extracted blood vessel tree, and the tree is used only for direction enhancement and only when it is available. Vessel measurements are compared with manual measurements, performed by an expert. Automated measurements are not different from the expert’s measurements with the confidence level of 95%. For this investigation, the Optomed OY digital mobile eye fundus camera Smartscope M5 PRO was used, but the algorithm can be used on any type of eye fundus images.

When using Spatial Correlation Rule with Distance (SCRD) the selection of the neighborhood scheme influences classification accuracy. Spatial dependency in different situations remains at various distances, so, according to this, in applications it is important to choose a suitable neighborhood scheme. In the earlier papers of the authors, the nearest neighbor scheme was used. In this paper, several different neighborhood schemes are examined by large experiment.

In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods.