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In this paper, we present a novel modification to level set based automatic retinal vasculature segmentation approaches. The method introduces ridge sample extraction for sampling the vasculature centerline and phase map based edge detection for accurate region boundary detection. Segmenting the vasculature in fundus images has been generally challenging for level set methods employing classical edge-detection methodologies. Furthermore, initialization with seed points determined by sampling vessel centerlines using ridge identification makes the method completely automated. The resulting algorithm is able to segment vasculature in fundus imagery accurately and automatically. Quantitative results supplemented with visual ones support this observation. The methodology could be applied to the broader class of vessel segmentation problems encountered in medical image analytics.

Background and Objective Understanding the causes of disagreement among experts in clinical decision making has been a challenge for decades. In particular, a high amount of variability exists in diagnosis of retinopathy of prematurity (ROP), which is a disease affecting low birthweight infants and a major cause of childhood blindness. A possible cause of variability, that has been mostly neglected in the literature, is related to discrepancies in the sets of important features considered by different experts. In this paper we propose a methodology which makes use of machine learning techniques to understand the underlying causes of inter-expert variability. Methods The experiments are carried out on a dataset consisting of 34 retinal images, each with diagnoses provided by 22 independent experts. Feature selection techniques are applied to discover the most important features considered by a given expert. Those features selected by each expert are then compared to the features selected by other experts by applying similarity measures. Finally, an automated diagnosis system is built in order to check if this approach can be helpful in solving the problem of understanding high inter-rater variability. Results The experimental results reveal that some features are mostly selected by the feature selection methods regardless the considered expert. Moreover, for pairs of experts with high percentage agreement among them, the feature selection algorithms also select similar features. By using the relevant selected features, the classification performance of the automatic system was improved or maintained. Conclusions The proposed methodology provides a handy framework to identify important features for experts and check whether the selected features reflect the pairwise agreements/disagreements. These findings may lead to improved diagnostic accuracy and standardization among clinicians, and pave the way for the application of this methodology to other problems which present inter-expert variability.

Segmentation of vasculature in retinal fundus image by level set methods employing classical edge detection methodologies is a tedious task. In this study, a revised level set-based retinal vasculature segmentation approach is proposed. During preprocessing, intensity inhomogeneity on the green channel of input image is corrected by utilizing all image channels, generating more efficient results compared to methods utilizing only one (green) channel. A structure-based level set method employing a modified phase map is introduced to obtain accurate skeletonization and segmentation of the retinal vasculature. The seed points around vessels are selected and the level sets are initialized automatically. Furthermore, the proposed method introduces an improved zero-level contour regularization term which is more appropriate than the ones introduced by other methods for vasculature structures. We conducted the experiments on our own dataset, as well as two publicly available datasets. The results show that the proposed method segments retinal vessels accurately and its performance is comparable to state-of-the-art supervised/unsupervised segmentation techniques.

Ataer-Cansizoglu, E.; Taguchi, Y.; Ramalingam, S.; Garaas, T. TR2013-106 December 2013Abstract Planes are dominant in most indoor and outdoor scenes and the development of a hybrid algorithm that incorporates both point and plane features provides numerous advantages. In this regard, we present a tracking algorithm for RGB-D cameras using both points and planes as primitives. We show how to extend the standard prediction-and-correction framework to include planes in addition to points. By fitting planes, we implicitly take care of the noise in the depth data that is typical in many commercially available 3D sensors. In comparison with the techniques that use only points, our tracking algorithm has fewer failure modes, and our reconstructed model is compact and more accurate. The tracking algorithm is supported by re-localization and bundle adjustment processes to demonstrate a real-time simultaneous localization and mapping (SLAM)system using a hand-held or robot-mounted RGB-D camera. Our experiments show large-scale indoor reconstruction results as point-based and plane-based 3D models, and demonstrate an improvement over the point-based tracking algorithms using a benchmark for RGB-D cameras. IEEE Workshop on Consumer Depth Cameas for Computer VisionThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.

Purpose To examine vascular tortuosity as a function of distance from the optic disc in infants with retinopathy of prematurity (ROP). Methods 34 wide-angle retinal images from infants with ROP were reviewed by 22 experts. A reference standard for each image was defined as the diagnosis (plus vs. not plus) given by the majority of experts. Tortuosity, defined as vessel length divided by straight-line distance between vessel endpoints, was calculated as a function of distance from the disc margin for arteries and veins using computer-based methods developed by the authors. Results Mean cumulative tortuosity increased with distance from the disc margin, both in 13 images with plus disease (p=0.007 for arterial tortuosity (n=62 arteries), p<0.001 for venous tortuosity (n=58 veins) based on slope of best fit line by regression), and in 21 images without plus disease (p<0.001 for arterial tortuosity (n=94 arteries), p<0.001 for venous tortuosity (n=85 veins)). Images with plus disease had significantly higher vascular tortuosity than images without plus disease (p<0.05), up to 7.0 disc diameters from the optic disc margin. Conclusions Vascular tortuosity was higher peripherally than centrally, both in images with and without plus disease, suggesting that peripheral retinal features may be relevant for ROP diagnosis.

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