On the adaptive detection of blood vessels in retinal images

This paper proposes an automated blood vessel detection scheme based on adaptive contrast enhancement, feature extraction, and tracing. Feature extraction of small blood vessels is performed by using the standard deviation of Gabor filter responses. Tracing of vessels is done via forward detection, bifurcation identification, and backward verification. Tests over twenty images show that for normal images, the true positive rate (TPR) ranges from 80% to 91%, and their corresponding false positive rates (FPR) range from 2.8% to 5.5%. For abnormal images, the TPR ranges from 73.8% to 86.5% and the FPR ranges from 2.1% to 5.3%, respectively. In comparison with two published solution schemes that were also based on the STARE database, our scheme has lower FPR for the reported TPR measure.     

Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels

We describe an automated method to locate the optic nerve in images of the ocular fundus. Our method uses a novel algorithm we call fuzzy convergence to determine the origination of the blood vessel network. We evaluate our method using 31 images of healthy retinas and 50 images of diseased retinas, containing such diverse symptoms as tortuous vessels, choroidal neovascularization, and hemorrhages that completely obscure the actual nerve. On this difficult data set, our method achieved 89% correct detection. We also compare our method against three simpler methods, demonstrating the performance improvement. All our images and data are freely available for other researchers to use in evaluating related methods.     

Enhancement of blood vessels in retinal imaging using the nonsubsampled contourlet transform

This paper presents an enhancement method for blood vessels in retinal images based on the nonsubsampled contourlet transform (NSCT). The NSCT is a shift-invariant version of the contourlet transform built upon the nonsubsampled pyramid filter banks and the nonsubsampled directional filter banks. The proposed method uses the NSCT to decompose the input retinal image into eight directions from coarser to finer scales, and then analyzes and classifies the image pixels into three categories: vessel, uncertainty, and non-vessel pixels, according to the NSCT coefficients. Then, we modify the NSCT coefficients according to the class of each pixel using a nonlinear mapping function, and reconstruct the enhanced image from the modified NSCT coefficients. The experimental results show that the proposed method can obviously increase the contrast of retinal vessels and thus outperform other enhancement methods.     

Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction

This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width dependent morphological filters. Our approach was tested on two publicly available databases and its results are compared with recently published methods. The results demonstrate that our algorithm outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity.     

Detection of new vessels on the optic disc using retinal photographs

Proliferative diabetic retinopathy is a rare condition likely to lead to severe visual impairment. It is characterized by the development of abnormal new retinal vessels. We describe a method for automatically detecting new vessels on the optic disc using retinal photography. Vessel-like candidate segments are first detected using a method based on watershed lines and ridge strength measurement. Fifteen feature parameters, associated with shape, position, orientation, brightness, contrast and line density are calculated for each candidate segment. Based on these features, each segment is categorized as normal or abnormal using a support vector machine (SVM) classifier. The system was trained and tested by cross-validation using 38 images with new vessels and 71 normal images from two diabetic retinal screening centers and one hospital eye clinic. The discrimination performance of the fifteen features was tested against a clinical reference standard. Fourteen features were found to be effective and used in the final test. The area under the receiver operator characteristic curve was 0.911 for detecting images with new vessels on the disc. This accuracy may be sufficient for it to play a useful clinical role in an automated retinopathy analysis system.     

Detection of blood vessels in retinal images using two-dimensional matched filters

Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods.     

Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter

In this paper, the fitness of estimating vessel profiles with Gaussian function is evaluated and an amplitude-modified second-order Gaussian filter is proposed for the detection and measurement of vessels. Mathematical analysis is given and supported by a simulation and experiments to demonstrate that the vessel width can be measured in linear relationship with the "spreading factor" of the matched filter when the magnitude coefficient of the filter is suitably assigned. The absolute value of vessel diameter can be determined simply by using a precalibrated line, which is typically required since images are always system dependent. The experiment shows that the inclusion of the width measurement in the detection process can improve the performance of matched filter and result in a significant increase in success rate of detection.     

A generalized machine learning-based model for the detection of DDoS attacks

As time is progressing, the number and the complexity of methods adopted for launching distributed denial of service (DDoS) attacks are changing. Therefore, we propose a methodology for the development of a generalized machine learning (ML)-based model for the detection of DDoS attacks. After exploring various attributes of the dataset chosen for this study, we propose an integrated feature selection (IFS) method which consists of three stages and integration of two different methods, that is, filter and embedded methods to select features which highly contribute to the detection of various types of DDoS attacks. We use light gradient boosting machine (LGBM) algorithm for training of the model for classification of benign and malicious flows. For ensuring satisfactory performance and generalized behavior of the developed model, we test it by passing records of unseen DDoS attack types. Several performance metrics are employed for the evaluation of the model. By comparing the performance of developed model against state-of-the-art models, we state an improvement of around 20% for almost all the reported metrics. We also show that the performance of the model improves if feature space is reduced by 77%. Furthermore, the generalized behavior of the developed model is justified by demonstrating a trade-off between high variance and high bias ML models.     

Segmentation and quantification of human vessels using a 3-D cylindrical intensity model.

We introduce a new approach for 3-D segmentation and quantification of vessels. The approach is based on a 3-D cylindrical parametric intensity model, which is directly fitted to the image intensities through an incremental process based on a Kalman filter. Segmentation results are the vessel centerline and shape, i.e., we estimate the local vessel radius, the 3-D position and 3-D orientation, the contrast, as well as the fitting error. We carried out an extensive validation using 3-D synthetic images and also compared the new approach with an approach based on a Gaussian model. In addition, the new model has been successfully applied to segment vessels from 3-D MRA and computed tomography angiography image data. In particular, we compared our approach with an approach based on the randomized Hough transform. Moreover, a validation of the segmentation results based on ground truth provided by a radiologist confirms the accuracy of the new approach. Our experiments show that the new model yields superior results in estimating the vessel radius compared to previous approaches based on a Gaussian model as well as the Hough transform.     

Transient characteristics of alloy junction transistors using a generalized charge storage model

A generalized charge storage model for characterizing the transient behavior of asymmetrical alloy junction transistor bases is presented. The model enables one to study quantitatively not only the various transient waveforms encounted in switching from one region to another under diversified circuit conditions, but also provides a means for evaluating the transient parameters of the device analytically from a knowledge of the base geometry, surface recombination velocity of the free base surface and bulk lifetime of the base semiconductor. A sample calculation is presented illustrating the use of the generalized charge storage model.     

Ultrawideband microwave breast cancer detection: a detection-theoretic approach using the generalized likelihood ratio test

Microwave imaging has been suggested as a promising modality for early-stage breast cancer detection. In this paper, we propose a statistical microwave imaging technique wherein a set of generalized likelihood ratio tests (GLRT) is applied to microwave backscatter data to determine the presence and location of strong scatterers such as malignant tumors in the breast. The GLRT is formulated assuming that the backscatter data is Gaussian distributed with known covariance matrix. We describe the method for estimating this covariance matrix offline and formulating a GLRT for several heterogeneous two-dimensional (2-D) numerical breast phantoms, several three-dimensional (3-D) experimental breast phantoms, and a 3-D numerical breast phantom with a realistic half-ellipsoid shape. Using the GLRT with the estimated covariance matrix and a threshold chosen to constrain the false discovery rate (FDR) of the image, we show the capability to detect and localize small (<0.6 cm) tumors in our numerical and experimental breast phantoms even when the dielectric contrast of the malignant-to-normal tissue is below 2:1.     

Effect of testing techniques on software reliability estimatesobtained using a time-domain model

Since the early 1970s, researchers have proposed several models to estimate software-reliability as testing progresses. Among these, the time-domain models are the most common. We present empirical evidence to show that the testing method does affect the reliability estimates using one of these models, viz, the Musa basic execution-time model. The evidence suggests that: (1) reliability models need to consider additional data, generated during testing, such as some form of code coverage, to obtain accurate reliability estimates; and (2) further research is necessary to determine which testing method, or combination thereof, leads to higher reliability     

An in vitro model of a retinal prosthesis

Epiretinal prostheses are being developed to bypass a degenerated photoreceptor layer and excite surviving ganglion and inner retinal cells. We used custom microfabricated multielectrode arrays with 200-microm-diameter stimulating electrodes and 10-microm-diameter recording electrodes to stimulate and record neural responses in isolated tiger salamander retina. Pharmacological agents were used to isolate direct excitation of ganglion cells from excitation of other inner retinal cells. Strength-duration data suggest that, if amplitude will be used for the coding of brightness or gray level in retinal prostheses, shorter pulses (200 micros) will allow for a smaller region in the area of the electrode to be excited over a larger dynamic range compared with longer pulses (1 ms). Both electrophysiological results and electrostatic finite-element modeling show that electrode-electrode interactions can lead to increased thresholds for sites half way between simultaneously stimulated electrodes (29.4 +/- 6.6 nC) compared with monopolar stimulation (13.3 +/- 1.7 nC, p < 0.02). Presynaptic stimulation of the same ganglion cell with both 200- and 10-microm-diameter electrodes yielded threshold charge densities of 12 +/- 6 and 7.66 +/- 1.30 nC/cm2, respectively, while the required charge was 12.5 +/- 6.2 and 19 +/- 3.3 nC.     

Fast detection and characterization of vessels in very large 3-D data sets using geometrical moments

An improved and very fast algorithm dealing with the extraction of vessels in three-dimensional imaging is described. The approach is based on geometrical moments and a local cylindrical approximation. A robust estimation of vessel and background intensity levels, position, orientation, and diameter of the vessels with adaptive control of key parameters, is provided during vessel tracking. Experimental results are presented for lower limb arteries in multidetector computed tomography scanner.     

Sensor fusion in anti-personnel mine detection using a two-level belief function model

A two-level approach for modeling and fusion of antipersonnel mine detection sensors in terms of belief functions within the Dempster-Shafer framework is presented. Three promising and complementary sensors are considered: a metal detector, an infrared camera, and a ground-penetrating radar. Since the metal detector, the most often used mine detection sensor, provides measures that have different behaviors depending on the metal content of the observed object, the first level aims at identifying this content and at providing a classification into three classes. Depending on the metal content, the object is further analyzed at the second level toward deciding the final object identity. This process can be applied to any problem where one piece of information induces different reasoning schemes depending on its value. A way to include influence of various factors on sensors in the model is also presented, as well as a possibility that not all sensors refer to the same object. An original decision rule adapted to this type of application is proposed, as well as a way for estimating confidence degrees. More generally, this decision rule can be used in any situation where the different types of errors do not have the same importance. Some examples of obtained results are shown on synthetic data mimicking reality and with increasing complexity. Finally, applications on real data show promising results.     

In vivo measurement of the oxygen saturation of retinal vessels in healthy volunteers

A new method for the spatially resolved measurement of the oxygen saturation of retinal vessels is described. Imaging spectrometry was used for both measurements of transmission and reflectance spectra of whole blood in cuvettes as well as for fundus reflectance spectra. A model was developed for the calculation of the oxygen saturation, valid in the wavelength range between 510 nm and 586 nm, in that the internal reflectance is constant and only the transmitted light depends on layer thickness and hematocrit. Altogether 265 measurements were performed in different number at 30 eyes. In each measurement, the oxygen saturation was simultaneously determined for 193 locations along a line of 1.5 mm at the fundus. The mean oxygen saturation in retinal arteries was (92.2 +/- 4.1)% and (57.9 +/- 9.9)% in retinal veins. The mean retinal arterio-venous difference of the oxygen saturation was (35.1 +/- 9.5)%. The venous oxygen saturation depended on distance from the optic disc. The measured mean of the arterio-venous difference of the oxygen saturation corresponded well to the value of the brain (34%). The utilization of oxygen in the temporal quadrants (inferior: 39.4 +/- 10.4%) is significantly (p = 0.05) higher than in the nasal quadrants (inferior: 31.3 +/- 6.7%).     

假设检验方法在目标识别算法评估中的应用

提出了一种应用假设检验方法对目标识别算法进行评估的方法。利用该方法，对一个基于一维距离像的目标识别算法的识别率在全姿态角范围内是否达到指标要求以及是否稳定进行了评估。结果表明，该方法具有实用价值。     

Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

We describe an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. Our method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. We evaluate our method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that our method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75% true positive rate. For a baseline, we also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4% false positive detection rate, on average. These numbers suggest there is still room for a 15% true positive rate improvement, with the same false positive rate, over our method. We are making all our images and hand labelings publicly available for interested researchers to use in evaluating related methods.     

Analysis of Stochastic Petri Net model with non-exponential distributions using a generalized Markov Renewal Process

Stochastic Petri Nets have been developed to model and analyze systems involving concurrent activities. However, the firing times of a Stochastic Petri Net model are always exponentially distributed. This paper presents an aggregate approach on how to analyze Stochastic Petri Net model with non-exponential distributions using a generalized Markov Renewal Process. Therefore, the modeling flexibility of Petri Net and the analyzing power of Markov Renewal Process are fully exploited. Moreover, an Abstract Partial Reachability Graph is introduced to simplify the Markov solution. Furthermore, the aggregate approach is applied to evaluate performance of a parallel operation system.