Image-level tortuosity estimation in wide-field retinal images from infants with Retinopathy of Prematurity

A Hybrid Method to Enhance Thick and Thin Vessels for Blood Vessel Segmentation

Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature.

Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images

Background

Changes in the retinal vessel caliber are associated with a variety of major diseases, namely diabetes, hypertension and atherosclerosis. The clinical assessment of these changes in fundus images is tiresome and prone to errors and thus automatic methods are desirable for objective and precise caliber measurement. However, the variability of blood vessel appearance, image quality and resolution make the development of these tools a non-trivial task.

Metholodogy

A method for the estimation of vessel caliber in eye fundus images via vessel cross-sectional intensity profile model fitting is herein proposed. First, the vessel centerlines are determined and individual segments are extracted and smoothed by spline approximation. Then, the corresponding cross-sectional intensity profiles are determined, post-processed and ultimately fitted by newly proposed parametric models. These models are based on Difference-of-Gaussians (DoG) curves modified through a multiplying line with varying inclination. With this, the proposed models can describe profile asymmetry, allowing a good adjustment to the most difficult profiles, namely those showing central light reflex. Finally, the parameters of the best-fit model are used to determine the vessel width using ensembles of bagged regression trees with random feature selection.

Results and conclusions

The performance of our approach is evaluated on the REVIEW public dataset by comparing the vessel cross-sectional profile fitting of the proposed modified DoG models with 7 and 8 parameters against a Hermite model with 6 parameters. Results on different goodness of fitness metrics indicate that our models are constantly better at fitting the vessel profiles. Furthermore, our width measurement algorithm achieves a precision close to the observers, outperforming state-of-the art methods, and retrieving the highest precision when evaluated using cross-validation. This high performance supports the robustness of the algorithm and validates its use in retinal vessel width measurement and possible integration in a system for retinal vasculature assessment.

A review on automatic analysis techniques for color fundus photographs

In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field.

Computer-aided diagnosis of retinopathy in retinal fundus images of preterm infants via quantification of vascular tortuosity

Retinopathy of prematurity (ROP), a disorder of the retina occurring in preterm infants, is the leading cause of preventable childhood blindness. An active phase of ROP that requires treatment is associated with the presence of plus disease, which is diagnosed clinically in a qualitative manner by visual assessment of the existence of a certain level of increase in the thickness and tortuosity of retinal vessels. The present study performs computer-aided diagnosis (CAD) of plus disease via quantitative measurement of tortuosity in retinal fundus images of preterm infants. Digital image processing techniques were developed for the detection of retinal vessels and measurement of their tortuosity. The total lengths of abnormally tortuous vessels in each quadrant and the entire image were then computed. A minimum-length diagnostic-decision-making criterion was developed to assess the diagnostic sensitivity and specificity of the values obtained. The area ([Formula: see text]) under the receiver operating characteristic curve was used to assess the overall diagnostic accuracy of the methods. Using a set of 19 retinal fundus images of preterm infants with plus disease and 91 without plus disease, the proposed methods provided an overall diagnostic accuracy of [Formula: see text]. Using the total length of all abnormally tortuous vessel segments in an image, our techniques are capable of CAD of plus disease with high accuracy without the need for manual selection of vessels to analyze. The proposed methods may be used in a clinical or teleophthalmological setting.

Super-image mosaic of infant retinal fundus: selection and registration of the best-quality frames from videos

Wide-field retinal fundus cameras are commercially available devices that allow acquiring videos of a wide area of infants' eye, considered of clinical interest in screening for ROP (Retinopathy of Prematurity). Many frames of the video are often altered by defects such as artifacts, interlacing and defocus, which make critical and time consuming the search and choice of the good frames to be analyzed. We developed a computerized system that automatically selects the best still frames from the video and builds a mosaic from these images. It will allow clinicians to examine a single large, best quality image. The best frames are identified using several image quality parameters that measure sharpness and steadiness, and then registered to obtain a single mosaic image. A custom blending procedure is then applied in order to provide a final image with homogeneous luminosity and contrast, devoid of the dark areas typically present in the outer regions of single frames. The best-frame selection module showed a PPV of 0.92, while the visual inspection of resulting mosaics confirmed the remarkable capability of the proposed system to provide higher quality images.

A Novel Curvature-Based Algorithm for Automatic Grading of Retinal Blood Vessel Tortuosity

Tortuosity of retinal blood vessels is an important symptom of diabetic retinopathy or retinopathy of prematurity. In this paper, we propose an automatic image-based method for measuring single vessel and vessel network tortuosity of these vessels. Simplicity of the algorithm, low-computational burden, and an excellent matching to the clinically perceived tortuosity are the important features of the proposed algorithm. To measure tortuosity, we use curvature which is an indicator of local inflection of a curve. For curvature calculation, template disk method is a common choice and has been utilized in most of the state of the art. However, we show that this method does not possess linearity against curvature and by proposing two modifications, we improve the method. We use the basic and the modified methods to measure tortuosity on a publicly available data bank and two data banks of our own. While interpreting the results, we pursue three goals. First, to show that our algorithm is more efficient to implement than the state of the art. Second, to show that our method possesses an excellent correlation with subjective results (0.94 correlation for vessel tortuosity, 0.95 correlation for vessel network tortuosity in diabetic retinopathy, and 0.7 correlation for vessel network tortuosity in retinopathy of prematurity). Third, to show that the tortuosity perceived by an expert and curvature possess a nonlinear relation.

Assessment of vessel tortuosity in retinal images of preterm infants

Diagnosis of plus disease is crucial for timely treatment and management of retinopathy of prematurity. An indicator of the presence of plus disease is an increase in the tortuosity of blood vessels in the retina. In this work, we propose a new angle-variation-based measure for quantification of tortuosity in retinal fundus images of preterm infants. The methods include the use of Gabor filters to detect vessels as well as to obtain their orientation at each pixel. Morphological image processing methods are used to obtain a skeleton image of the vessels for measurement of tortuosity. Out of 11 vessel segments, marked by an expert ophthalmologist as showing high levels of tortuosity due to plus disease, all were correctly identified using the proposed methods.

Towards a multi-site international public dataset for the validation of retinal image analysis software

This paper discusses concisely the main issues and challenges posed by the validation of retinal image analysis algorithms. It is designed to set the discussion for the IEEE EBMC 2013 invited session "From laboratory to clinic: the validation of retinal image processing tools ". The session carries forward an international initiative started at EMBC 2011, Boston, which resulted in the first large-consensus paper (14 international sites) on the validation of retinal image processing software, appearing in IOVS. This paper is meant as a focus for the session discussion, but the ubiquity and importance of validation makes its contents, arguably, of interest for the wider medical image processing community.