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Retinal images can be used in several applications, such as ocular fundus operations as well as human recognition. Also, they play important roles in detection of some diseases in early stages, such as diabetes, which can be performed by comparison of the states of retinal blood vessels. Intrinsic characteristics of retinal images make the blood vessel detection process difficult. Here, we proposed a new algorithm to detect the retinal blood vessels effectively. Due to the high ability of the curvelet transform in representing the edges, modification of curvelet transform coefficients to enhance the retinal image edges better prepares the image for the segmentation part. The directionality feature of the multistructure elements method makes it an effective tool in edge detection. Hence, morphology operators using multistructure elements are applied to the enhanced image in order to find the retinal image ridges. Afterward, morphological operators by reconstruction eliminate the ridges not belonging to the vessel tree while trying to preserve the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they were applied using multistructure elements. A simple thresholding method along with connected components analysis (CCA) indicates the remained ridges belonging to vessels. In order to utilize CCA more efficiently, we locally applied the CCA and length filtering instead of considering the whole image. Experimental results on a known database, DRIVE, and achieving to more than 94% accuracy in about 50 s for blood vessel detection, proved that the blood vessels can be effectively detected by applying our method on the retinal images. [ABSTRACT FROM PUBLISHER]

Detection of vascular bifurcations is a challenging task in multimodal retinal image registration. Existing algorithms based on bifurcations usually fail in correctly aligning poor quality retinal image pairs. To solve this problem, we propose a novel highly distinctive local feature descriptor named partial intensity invariant feature descriptor (PIIFD) and describe a robust automatic retinal image registration framework named Harris-PIIFD. PIIFD is invariant to image rotation, partially invariant to image intensity, affine transformation, and viewpoint/perspective change. Our Harris-PIIFD framework consists of four steps. First, corner points are used as control point candidates instead of bifurcations since corner points are sufficient and uniformly distributed across the image domain. Second, PIIFDs are extracted for all corner points, and a bilateral matching technique is applied to identify corresponding PIIFDs matches between image pairs. Third, incorrect matches are removed and inaccurate matches are refined. Finally, an adaptive transformation is used to register the image pairs. PIIFD is so distinctive that it can be correctly identified even in nonvascular areas. When tested on 168 pairs of multimodal retinal images, the Harris-PIIFD far outperforms existing algorithms in terms of robustness, accuracy, and computational efficiency. [ABSTRACT FROM AUTHOR]

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained. [ABSTRACT FROM AUTHOR]

This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis. [ABSTRACT FROM PUBLISHER]

The presence of microaneurysms (MAs) is usually an early sign of diabetic retinopathy and their automatic detection from color retinal images is of clinical interest. In this paper, we present a new approach for automatic MA detection from digital color fundus images. We formulate MA detection as a problem of target detection from clutter, where the probability of occurrence of target is considerably smaller compared to the clutter. A successive rejection-based strategy is proposed to progressively lower the number of clutter responses. The processing stages are designed to reject specific classes of clutter while passing majority of true MAs, using a set of specialized features. The true positives that remain after the final rejector are assigned a score which is based on its similarity to a true MA. Results of extensive evaluation of the proposed approach on three different retinal image datasets are reported, and used to highlight the promise in the presented strategy. [ABSTRACT FROM PUBLISHER]

Proposes a real-time retinal tracking system for image-guided laser treatment. Strategy for retinal image segmentation; Detection of blood vessel boundaries using deformable models; Segmentation of the optic disc; Estimation of laser shot locations.

The tortuosity of retinal blood vessels is a diagnostic parameter assessed by ophthalmologists on the basis of examples and experience; no quantitative model is specified in clinical practice. All quantitative measures proposed to date for automatic image analysis purposes are functions of the curvature of the vessel skeleton. We suggest in this paper that curvature may not be the only quantity involved in modeling tortuosity, and that vessel thickness, or caliber, may also play a role. To support this statement, we devise a novel measure of tortuosity, depending on both curvature and thickness, and test it with 200 vessels selected by our clinical author from the public digital retinal images for vessel extraction database. Results are in good accordance with clinical judgment. Comparative experiments show performance similar to or better than that of four measures reported in the literature. We conclude that there is reasonable evidence supporting the investigation of tortuosity models incorporating more measurements than just skeleton curvature, and specifically vessel caliber. [ABSTRACT FROM AUTHOR]

Cancers located on the internal wall of bladders can be detected in image sequences acquired with endoscopes. The clinical diagnosis and follow-up can be facilitated by building a unique panoramic image of the bladder with the images acquired from different viewpoints. This process, called image mosaicing, consists of two steps. In the first step, consecutive images are pairwise registered to find the local transformation matrices linking geometrically consecutive images. In the second step, all images are placed in a common and global coordinate system. In this contribution, a mutual information-based similarity measure and a stochastic gradient optimization method were implemented in the registration process. However, the images have to be preprocessed in order to register the data in a robust way. Thus, a simple correction method of the distortions affecting endoscopic images is presented. After the placement of all images in the global coordinate system, the parameters of the local transformation matrices are all adjusted to improve the visual aspect of the panoramic images. Phantoms are used to evaluate the global mosaicing accuracy and the limits of the registration algorithm. The mean distances between ground truth positions in the mosaiced image range typically in 1-3 pixels. Results given for in vivo patient data illustrate the ability of the algorithm to give coherent panoramic images in the case of bladders. [ABSTRACT FROM AUTHOR]

This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups. The updating process combines both the local and global information of the contour to achieve the balance of contour stability and accuracy. The modifications make the proposed algorithm more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results show that the proposed method can estimate the disk boundaries of 100 test images closer to the groundtruth, as measured by mean distance to closest point (MDCP) < 3 pixels, with the better success rate when compared to those obtained by gradient vector flow snake (GVF-snake) and modified active shape models (ASM). [ABSTRACT FROM AUTHOR]