Retinal vessel caliber changes are associated with several major diseases, such as diabetes and hypertension. These caliber changes can be evaluated using eye fundus images. However, the clinical assessment is tiresome and prone to errors, motivating the development of automatic methods. An automatic method based on vessel crosssection intensity profile model fitting for the estimation of vessel caliber in retinal images is herein proposed. First, vessels are segmented from the image, vessel centerlines are detected and individual segments are extracted and smoothed. Intensity profiles are extracted perpendicularly to the vessel, and the profile lengths are determined. Then, model fitting is applied to the smoothed profiles. A novel parametric model (DoG-L7) is used, consisting on a Difference-of-Gaussians multiplied by a line which is able to describe profile asymmetry. Finally, the parameters of the best-fit model are used for determining the vessel width through regression using ensembles of bagged regression trees with random sampling of the predictors (random forests). The method is evaluated on the REVIEW public dataset. A precision close to the observers is achieved, outperforming other state-of-the-art methods. The method is robust and reliable for width estimation in images with pathologies and artifacts, with performance independent of the range of diameters.
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.
This paper describes a segmentation method for automating the region of interest (ROI) delineation in chromatographic
images, thus allowing the definition of the image area that contains the fundamental information for further processing
while excluding the frame of the chromatographic plate that does not contain relevant data for disease identification. This
is the first component of a screening tool for Fabry disease, which will be based on the automatic analysis of the
chromatographic patterns extracted from the image ROI. Image segmentation is performed in two phases, where each
individual pixel is finally considered as frame or ROI. In the first phase, an unsupervised learning method is used for
classifying image pixels into three classes: frame, ROI or unknown. In the second phase, distance features are used for
deciding which class the unknown pixels belong to. The segmentation result is post-processed using a sequence of
morphological operators in order to obtain the final ROI rectangular area. The proposed methodology was successfully
evaluated in a dataset of 41 chromatographic images.