Comparative study of retinal vessel segmentation methods on a new publicly available database

A New Blood Vessel Extraction Technique Using Edge Enhancement and Object Classification

“…The proposed method has been verified by the DRIVE database images [28]. The DRIVE database contains 20 color images of the retina with 565×585 pixels and 8 bits per color channel.…”

Detección automática de vasos en retinografías

“…Respecto a la validación con las bases DRIVE y STARE, hemos recurrido a la comparación de los resultados obtenidos por otros algoritmos, que han empleado las mismas bases de datos y que se han validado de forma amplia [8][9][10][11] . El parámetro utilizado para evaluar de forma numérica el resultado del algoritmo es la exactitud, estimada por la razón del número total de puntos correctamente clasificados (suma de verdaderos positivos y verdaderos negativos) por el número de puntos en la imagen dentro del campo de visión.…”

Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy

“…Unsupervised methods in the literature comprise the matched filter responses, edge detectors, grouping of edge pixels, model based locally adaptive thresholding, vessel tracking, topology adaptive snakes, and morphology-based techniques [5]. Supervised methods, which require feature vector for each pixel and manually labelled images for training, are the most recent approaches in vessel segmentation and use the neural networks [1], or the K-nearest neighbour classifier [5,6] for classifying image pixels as blood vessel or nonblood vessel pixels. These methods depend on generating a feature vector for every pixel in the image and then using training samples (with known classes) to design a classifier to classify these training samples into their corresponding classes.…”

“…Also, the 1st and 2nd derivatives -of the green channel image, in x and y directions [6], or with respect to other image coordinates [5] at different scales -are used as features for every pixel in retinal images. Since taking derivatives of discrete images is an ill-posed operation, these are taken at a scale s using the Gaussian scale-space technique [8].…”

“…Since taking derivatives of discrete images is an ill-posed operation, these are taken at a scale s using the Gaussian scale-space technique [8]. Niemeijer et al [6] proposed a pixel classification method where the KNN classifier is used with 31 features to classify the pixels in retinal images to vessel and non-vessel pixels; these features are the green channel image, and the filtered image using the Gaussian and its derivatives at different scale values.…”