Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively

Angiography and Monitoring of Hemodynamic Signals in the Brain via Optical Coherence Tomography

The brain is a complex network of interconnected neurons with each cell functioning as a nonlinear processing unit. Neural responses to stimulus can be described by activity in neurons. While blood flow changes have been associated with neural activity and are critical to brain function, this neurovascular coupling is not well understood. This work presents a technique for neurovascular interrogation, combining optogenetics and optical coherence tomography. Optogenetics is a recently developed neuromodulation technique to control activity in the brain using light with precise spatial neuronal control and high temporal resolution. Using this method, cells act as light-gated ion channels and respond to photo stimulation by increasing or decreasing activity. Spectral-domain optical coherence tomography (SD-OCT) is a noninvasive imaging modality that has the ability to image millimeter range depth and with micrometer resolution. SD-OCT has been shown to image rodent cortical microvasculature in-vivo and detect hemodynamic changes in blood vessels. Our proposed system combines optogenetics and SD-OCT to image cortical patches of the brain with the capability of simultaneously stimulating the brain. The combination allows investigation of the hemodynamic changes in response to neural stimulation. Our results detected changes in blood vessel diameter and velocity before, during and after optogenetic stimulation and is presented

Quantitative cerebral blood flow with optical coherence tomography

Absolute measurements of cerebral blood flow (CBF) are an important endpoint in studies of cerebral pathophysiology. Currently no accepted method exists for in vivo longitudinal monitoring of CBF with high resolution in rats and mice. Using three-dimensional Doppler Optical Coherence Tomography and cranial window preparations, we present methods and algorithms for regional CBF measurements in the rat cortex. Towards this end, we develop and validate a quantitative statistical model to describe the effect of static tissue on velocity sensitivity. This model is used to design scanning protocols and algorithms for sensitive 3D flow measurements and angiography of the cortex. We also introduce a method of absolute flow calculation that does not require explicit knowledge of vessel angles. We show that OCT estimates of absolute CBF values in rats agree with prior measures by autoradiography, suggesting that Doppler OCT can perform absolute flow measurements in animal models.National Institutes of Health (U.S.) (Grant number R01-NS057476)National Institutes of Health (U.S.) (Grant number P01NS055104)National Institutes of Health (U.S.) (Grant number P50NS010828)ational Institutes of Health (U.S.) (Grant number K99NS067050)National Institutes of Health (U.S.) (Grant number R01-CA075289-13)United States. Air Force Office of Scientific Research (FA9550-07-1-0014)United States. Dept. of Defense. Medical Free Electron Laser Program (FA9550-07-1-0101

Process supervision using hybrid modelling under Foundation Fieldbus

World Automation Congress, WAC 2004, Sevilla, SpainThe objective of this work is to describe a coherent methodology for detect deviations between plant and model responses assuming multivariable and non linear processes. It is proposed to supervise a process modelled by applying Hybrid Modelling (HM). Here hybrid modelling is understood as the process model achieved experimentally on the basis of backpropagation NN associated to first order plus delay modelsMinisterio de Ciencia y Tecnología; DPI2003-0051

Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter

Hybrid modelling using neural network based prediction under Foundation Fieldbus

World Automation Congress, WAC 2004, Sevilla, SpainThe objective of this work is to describe a novel methodology for modelling MV and NL processes, useful among other applications, in fault detection tasks, possibly to be applied on plant supervision, including transient state fault detection and decision making according the well known method based on parity equations and rule based residuals evaluationMinisterio de Ciencia y Tecnología; DPI2003-0051

Static compensation on the basis of NNBM predictor under Foundation Fieldbus

World Automation Congress, WAC 2004, Sevilla, SpainThis paper deals with the problem of disturbance compensation by means of a novel feedforward control procedure. It consists in the additive association of a conventional feedback control action with the prediction of the steady state control effort necessary to keep the controlled plant under setpoint requirements. Such steady state control effort is achieved by means of inverse neural network based modelling prediction. Predictors are based in an inverse neural network steady state plant model. Implementation procedure is carried out with the facilities supplied by a FOUNDATION Fieldbus compliant tool which manage databases, neural network structures and training algorithmsMinisterio de Ciencia y Tecnología; DPI2003-0051