Behdad Dashtbozorg

An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

Optic disc segmentation using the sliding band filter

BACKGROUNDnThe optic disc (OD) centre and boundary are important landmarks in retinal images and are essential for automating the calculation of health biomarkers related with some prevalent systemic disorders, such as diabetes, hypertension, cerebrovascular and cardiovascular diseases.nnnMETHODSnThis paper presents an automatic approach for OD segmentation using a multiresolution sliding band filter (SBF). After the preprocessing phase, a low-resolution SBF is applied on a downsampled retinal image and the locations of maximal filter response are used for focusing the analysis on a reduced region of interest (ROI). A high-resolution SBF is applied to obtain a set of pixels associated with the maximum response of the SBF, giving a coarse estimation of the OD boundary, which is regularized using a smoothing algorithm.nnnRESULTSnOur results are compared with manually extracted boundaries from public databases (ONHSD, MESSIDOR and INSPIRE-AVR datasets) outperforming recent approaches for OD segmentation. For the ONHSD, 44% of the results are classified as Excellent, while the remaining images are distributed between the Good (47%) and Fair (9%) categories. An average overlapping area of 83%, 89% and 85% is achieved for the images in ONHSD, MESSIDOR and INSPIR-AVR datasets, respectively, when comparing with the manually delineated OD regions.nnnDISCUSSIONnThe evaluation results on the images of three datasets demonstrate the better performance of the proposed method compared to recently published OD segmentation approaches and prove the independence of this method when from changes in image characteristics such as size, quality and camera field of view.

RetinaCAD, a system for the assessment of retinal vascular changes

This paper introduces RetinaCAD, a system, for the fast, reliable and automatic measurement of the Central Retinal Arteriolar Equivalent (CRAE), the Central Retinal Venular Equivalent (CRVE), and the Arteriolar-to-Venular Ratio (AVR) values, as well as several geometrical features of the retinal vasculature. RetinaCAD identifies important landmarks in the retina, such as the blood vessels and optic disc, and performs artery/vein classification and vessel width measurement. The estimation of the CRAE, CRVE and AVR values on 480 images from 120 subjects has shown a significant correlation between right and left eyes and also between images of same eye acquired with different camera fields of view. AVR estimation in retinal images of 54 subjects showed the lowest values in people with diabetes or high blood pressure thus demonstrating the potential of the system as a CAD tool for early detection and follow-up of diabetes, hypertension or cardiovascular pathologies.

Automatic Classification of Retinal Vessels Using Structural and Intensity Information

This paper presents an automatic approach for artery/vein (A/V) classification based on the analysis of a graph representing the structure of the retinal vasculature. The entire vascular tree is classified by deciding on the type of each intersection point (graph node) and assigning one of two classes to each vessel segment (graph link). The final label for each vessel segment is accomplished by a combination of structural information taken from the graph (link class) with intensity features measured in the original color image. An accuracy of 88.0% was achieved for the 40 images of the INSPIRE-AVR dataset, thus demonstrating that our method outperforms state-of-the-art approaches for A/V classification.

An automatic method for the estimation of Arteriolar-to-Venular Ratio in retinal images

This paper presents an automatic approach for the estimation of Arteriolar-to-Venular Ratio (AVR) in retinal images. The method was assessed using the images of the INSPIRE-AVR database. A mean error of 0.05 was obtained when the methods results were compared with reference AVR values provided with this dataset, thus demonstrating the adequacy of the proposed solution for AVR estimation.

Assessment of vascular changes in retinal images

The Arteriolar-to-Venular Ratio (AVR) is a well known index for the early diagnosis of diseases such as diabetes, hypertension or cardio-vascular pathologies. This paper presents an automatic approach for the estimation of the AVR in retinal images. The proposed method includes vessel segmentation, vessel caliber estimation, optic disc detection, region of interest determination, artery/vein classification and finally AVR calculation. This method was evaluated using the images of the INSPIRE-AVR dataset. The mean error of the measured AVR values with respect to the reference ones was 0.05, which is identical to the one achieved by a medical expert using a semi-automated system, thus demonstrating the reliability of the herein proposed solution for AVR estimation.

Automatic Estimation of the Arteriolar-to-Venular Ratio in Retinal Images Using a Graph-Based Approach for Artery/Vein Classification

The Arteriolar-to-Venular Ratio (AVR) is a well known index for the diagnosis of diseases such as diabetes, hypertension or cardiovascular pathologies. This paper presents a fully automatic AVR estimation method which uses a graph-based artery/vein classification approach to classify the retinal vessels by a combination of structural information taken from the vasculature graph with intensity features from the original color image. This method was evaluated on the images of the INSPIRE-AVR dataset. The mean error and the correlation coefficient of obtained results with respect to the reference AVR values were identical to the ones obtained by the second observer using a semi-automated system, which demonstrate the potential of the herein proposed solution for clinical application.

Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs

The Arteriolar-to-Venular Ratio (AVR) is a popular dimensionless measure which allows the assessment of patients’ condition for the early diagnosis of different diseases, including hypertension and diabetic retinopathy. This paper presents two new approaches for AVR computation in retinal photographs which include a sequence of automated processing steps: vessel segmentation, caliber measurement, optic disc segmentation, artery/vein classification, region of interest delineation, and AVR calculation. Both approaches have been tested on the INSPIRE-AVR dataset, and compared with a ground-truth provided by two medical specialists. The obtained results demonstrate the reliability of the fully automatic approach which provides AVR ratios very similar to at least one of the observers. Furthermore, the semi-automatic approach, which includes the manual modification of the artery/vein classification if needed, allows to significantly reduce the error to a level below the human error.

Assessment of Retinal Vascular Changes Through Arteriolar-to-Venular Ratio Calculation

The Arteriolar-to-Venular Ratio (AVR) is an index used for the early diagnosis of diseases such as diabetes, hypertension or cardiovascular pathologies. This paper presents three automatic approaches for the estimation of the AVR in retinal images that result from the combination of different methodologies in some of the processing phases used for AVR estimation. Each one of these methods includes vessel segmentation, vessel caliber estimation, optic disc detection or segmentation, region of interest determination, vessel classification into arteries and veins and finally AVR calculation. The values produced by the proposed methods on 40 images of the INSPIRE-AVR dataset were compared with a ground-truth obtained by two medical experts using a semi-automated system. The results showed that the measured AVRs are not statistically different from the reference, with mean errors similar to those achieved by the two experts, thus demonstrating the reliability of the herein proposed approach for AVR estimation.