An automatic graph-based method for retinal blood vessel classification

In this paper, we present an automatic approach to classify retinal vessels intoartery and vein classes by analyzing the extracted graph from the vasculature treeand deciding on the type of intersection points (bifurcation, crossing or meetingpoints). The results obtained by the proposed method were compared withmanual classification on 40 images of the INSPIRE-AVR dataset

Measurement of retinal blood vessel caliber using two different segmentation methods

In this paper, we explore two different retinal vessel segmentationmethods for the reliable estimation of vessels caliber in retinal images inorder to assess vascular changes as an aid for the diagnosis of the ocularmanifestations of several systemic diseases, namely diabetic retinopathyand hypertensive retinopathy

Computer-aided diagnosis system for the assessment of retinal vascular changes

This paper presents an automatic application that provides several retinal image analysis functionalities, namely vessel segmentation, vessel width estimation, artery/vein classification and optic disc segmentation. A pipeline of these methods allows the computation of important vessel related indexes, namely the Central Retinal Arteriolar Equivalent (CRAE), Central Retinal Venular Equivalent (CRVE) and Arteriolar-to-Venular Ratio (AVR), as well as various geometrical features associated with vessel bifurcations. The results for AVR estimation were assessed using the images of INSPIRE-AVR dataset; for this dataset, the mean error of the measured AVR values with respect to the reference was identical to the one achieved by a medical expert. The estimation of the CRAE, CRVE and AVR values on 480 images from 120 subjects have shown a significant correlation between right and left eyes and also between images of same eye acquired with different camera fields of view

An Automatic Method for Assessing Retinal Vessel Width Changes

The Arteriolar-to-Venular Ratio (AVR) is commonly used in studies forthe diagnosis of diseases such as diabetes, hypertension or cardio-vascularpathologies. This paper presents an automatic approach for the estimationof the Arteriolar-to-Venular Ratio (AVR) in retinal images. The proposedmethod includes vessel segmentation, vessel caliber estimation, opticdisc detection, region of interest determination, artery/vein classificationand AVR calculation. The method was assessed using the images ofthe INSPIRE-AVR database. A mean error of 0.05 was obtained when themethods results were compared with reference AVR values provided withthis dataset, thus demonstrating the adequacy of the proposed solution forAVR estimation

Diffuse reflection spectroscopy at the fingertip:design and performance of a compact side-firing probe for tissue discrimination during colorectal cancer surgery

Optical technologies are widely used for tissue sensing purposes. However, maneuvering conventional probe designs with flat-tipped fibers in narrow spaces can be challenging, for instance during pelvic colorectal cancer surgery. In this study, a compact side-firing fiber probe was developed for tissue discrimination during colorectal cancer surgery using diffuse reflectance spectroscopy. The optical behavior was compared to flat-tipped fibers using both Monte Carlo simulations and experimental phantom measurements. The tissue classification performance was examined using freshly excised colorectal cancer specimens. Using the developed probe and classification algorithm, an accuracy of 0.92 was achieved for discriminating tumor tissue from healthy tissue

Stability Analysis of Fractal Dimension in Retinal Vasculature

Fractal dimension (FD) has been considered as a potential biomarker for retina-based disease detection. However, conflicting findings can be found in the reported literature regarding the association of the biomarker with diseases. This motivates us to examine the stability of the FD on different (1) vessel segmentations obtained from human observers, (2) automatic segmentation methods, (3) threshold values, and (4) region-of-interests. Our experiments show that the corresponding relative errors with respect to reference ones, computed per patient, are generally higher than the relative standard deviation of the reference values themselves (among all patients). The conclusion of this paper is that we cannot fully rely on the studied FD values, and thus do not recommend their use in quantitative clinical applications

Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen