Vessel Recognition Research Articles

Klasik ve Bulanık Kenar Algılama Yöntemlerinin Karşılaştırılması

Edge detection is one of the challenging problems in image processing. Four different classical edge detection methods—Sobel, Prewitt, Roberts, and Canny—and type-1 and type-2 fuzzy logic-based edge detection methods are applied to analyze two separate datasets with various properties. The datasets are STARE which contains medical images of the retina and BIPED which contains images of the street. Furthermore, two separate hybrid fuzzy logic methods are implemented. The type-1 and type-2 fuzzy inference techniques are combined to produce the hybrid-1 and hybrid-2 approaches, using the "AND" and "OR" logic operators. We compare the simulation results for each technique using three different image quality metrics. These are Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The type-2 fuzzy technique outperformed the hybrid-1 fuzzy method in visual quality metrics comparison, demonstrating superior blood vessel recognition on the STARE retinal image dataset—a dataset that more closely resembles the human visual system. Using the BIPED street image dataset, the hybrid-1 fuzzy approach outperformed the Roberts method. The hybrid-1 fuzzy technique showed good results in the second order for both kinds of datasets. Any data and general applications can take advantage of it.

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

AVDNet: Joint coronary artery and vein segmentation with topological consistency

Coronary CT angiography (CCTA) is an effective and non-invasive method for coronary artery disease diagnosis. Extracting an accurate coronary artery tree from CCTA image is essential for centerline extraction, plaque detection, and stenosis quantification. In practice, data quality varies. Sometimes, the arteries and veins have similar intensities and locate closely, which may confuse segmentation algorithms, even deep learning based ones, to obtain accurate arteries. However, it is not always feasible to re-scan the patient for better image quality. In this paper, we propose an artery and vein disentanglement network (AVDNet) for robust and accurate segmentation by incorporating the coronary vein into the segmentation task. This is the first work to segment coronary artery and vein at the same time. The AVDNet consists of an image based vessel recognition network (IVRN) and a topology based vessel refinement network (TVRN). IVRN learns to segment the arteries and veins, while TVRN learns to correct the segmentation errors based on topology consistency. We also design a novel inverse distance weighted dice (IDD) loss function to recover more thin vessel branches and preserve the vascular boundaries. Extensive experiments are conducted on a multi-center dataset of 700 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method by comparing it with state-of-the-art methods and different variants. Prediction results of the AVDNet on the Automated Segmentation of Coronary Artery Challenge dataset are avaliabel at https://github.com/WennyJJ/Coronary-Artery-Vein-Segmentation for follow-up research.

Approaches to Upgrading the Performance of Fishing Vessel Recognition Technology

Approaches to Upgrading the Performance of Fishing Vessel Recognition Technology

A small fishing vessel recognition method using transfer learning based on laser sensors

The management of small vessels has always been key to maritime administration. This paper presents a novel method for recognizing small fishing vessels based on laser sensors. Using four types of small fishing vessels as targets, a recognition method for small fishing vessels based on Markov transition field (MTF) time-series images and VGG-16 transfer learning is proposed. In contrast to conventional methods, this study uses polynomial fitting to obtain the contours of a fishing vessel and transforms one-dimensional vessel contours into two-dimensional time-series images using the MTF coding method. The VGG-16 model is used for the recognition process, and migration learning is applied to improve the results. The UCR time-series public dataset is used as a transfer learning dataset for the MTF time-series image encoding. The experiment demonstrates that the proposed method exhibits higher accuracy and performance than 1D-CNN and other general neural network models, and the highest accuracy rate is 98.92%.

A Novel Multispectral Vessel Recognition Based on RGB-to-Thermal Image Translation

In the last decade, advances in deep learning have led to considerable progress in the field of ship classification in Red Green Blue (RGB) and Infra-Red (IR) images. However, ship classification performs poorly on images acquired in weak visible light intensity. Multispectral imaging constitutes a potential solution to address such difficulty. In this paper, we first propose Convolutional Neural Network (CNN) for ship classification in multi-spectral images (RGB, IR, etc.). The proposed architectures were trained from scratch and fine-tuned to another pre-trained network. Validation was carried out on the publically available RGB-IR pairs ship dataset VAIS. Unfortunately, owing to the small size of the dataset, the obtained classification result was 59,09%, hence not satisfactory for most applications. We, therefore, proposed a new image data augmentation approach for the generation of IR ship images from RGB images. The generation process was carried out through an adaptation of a Generative Adversarial Network (GAN) network and a Pix2Pix model. In fact, VAIS dataset was kept aside for validation purposes and KAIST RGB-IR pairs dataset was used for the training of our translator. The augmented IR dataset yielded more than a 9% increase in the performance of VAIS IR-based ship classification.

Blood-seeking horseflies prefer vessel-imitating temperature gradients on host-mimicking targets: Experimental corroboration of a new explanation of the visual unattractiveness of zebras to tabanids

Several hypotheses tried to explain the advantages of zebra stripes. According to the most recent explanation, since the borderlines of sunlit white and black stripes can hamper thermal vessel detection by blood-seeking female horseflies, striped host animals are unattractive to these parasites which prefer hosts with a homogeneous coat, on which the temperature gradients above blood vessels can be detected more easily. This hypothesis has been tested in a field experiment with horseflies walking on a grey barrel with thin black stripes which were slightly warmer than their grey surroundings in sunshine, while in shade both areas had practically the same temperature. To eliminate the multiple (optical and thermal) cues of this test target, we repeated this experiment with improved test surfaces: we attracted horseflies by water- or host-imitating homogeneous black test surfaces, beneath which a heatable wire ran. When heated, this invisible and mechanically impalpable wire imitated thermally the slightly warmer subsurface blood vessels, otherwise it was thermally imperceptible. We measured the times spent by landed and walking horseflies on the test surface parts with and without underlying heated or unheated wire. We found that walking female and male horseflies had no preference for any (wired or wireless) area of the water-imitating horizontal plane test surface on the ground, independent of the temperature (heated or unheated) of the underlying wire. These horseflies looked for water, rather than a host. On the other hand, in the case of host-imitating test surfaces, female horseflies preferred the thin surface regions above the wire only if it was heated and thus warmer than its surroundings. This behaviour can be explained exclusively with the higher temperature of the wire given the lack of other sensorial cues. Our results prove the thermal vessel recognition of female horseflies and support the idea that sunlit zebra stripes impede the thermal detection of a host‘s vessels by blood-seeking horseflies, the consequence of which is the visual (non-thermal) unattractiveness of zebras to horseflies.

A Hybrid Method for Inland Ship Recognition Using Marine Radar and Closed-Circuit Television

Vessel recognition plays important role in ensuring navigation safety. However, existing methods are mainly based on a single sensor, such as automatic identification system (AIS), marine radar, closed-circuit television (CCTV), etc. To this end, this paper proposes a coarse-to-fine recognition method by fusing CCTV and marine radar, called multi-scale matching vessel recognition (MSM-VR). This method first proposes a novel calibration method that does not use any additional calibration target. The calibration is transformed to solve an N point registration model. Furthermore, marine radar image is used for coarse detection. A region of interest (ROI) area is computed for coarse detection results. Lastly, we design a novel convolutional neural network (CNN) called VesNet and transform the recognition into feature extraction. The VesNet is used to extract the vessel features. As a result, the MVM-VR method has been validated by using actual datasets collected along different waterways such as Nanjing waterway and Wuhan waterway, China, covering different times and weather conditions. Experimental results show that the MSM-VR method can adapt to different times, different weather conditions, and different waterways with good detection stability. The recognition accuracy is no less than 96%. Compared to other methods, the proposed method has high accuracy and great robustness.

Vessel recognition using multi-frequencies of skin impedance and signal similarity of photoplethysmography

Vessel recognition using multi-frequencies of skin impedance and signal similarity of photoplethysmography

Vessel Recognition in Induction Heating Appliances—A Deep-Learning Approach

The selection of a vessel by an induction-hob user has a significant impact on the performance of the appliance. Due to the induction heating physical phenomena, there exist many factors that modify the equivalent impedance of induction hobs and, consequently, the operational conditions of the inverter. In particular, the type of vessel, which is a sole decision of the user, strongly affects these parameters. Besides, the ferromagnetic properties of the different materials the vessels are made with, vary differently with the excitation level, and given that most of the domestic induction hobs are based on an ac-bus voltage arrangement, the excitation level continuously varies. The algorithm proposed in this work takes advantage of this fact to identify the equivalent impedance of the load and recognize the pot. This is accomplished through a phase-sensitive detector that was already proposed in the literature and the application of deep learning. Different convolutional neural networks are tested on an augmented experimental-based dataset and the proposed algorithm is implemented in an experimental prototype with a system-on-chip. The proposed implementation is presented as an effective and accurate method to characterize and discriminate between different pots that could enable further functionalities in new generations of induction hobs.

Potential and Use of the Googlenet Ann for the Purposes of Inland Water Ships Classification

Abstract This article presents an analysis of the possibilities of using the pre-degraded GoogLeNet artificial neural network to classify inland vessels. Inland water authorities monitor the intensity of the vessels via CCTV. Such classification seems to be an improvement in their statutory tasks. The automatic classification of the inland vessels from video recording is a one of the main objectives of the Automatic Ship Recognition and Identification (SHREC) project. The image repository for the training purposes consists about 6,000 images of different categories of the vessels. Some images were gathered from internet websites, and some were collected by the project’s video cameras. The GoogLeNet network was trained and tested using 11 variants. These variants assumed modifications of image sets representing (e.g., change in the number of classes, change of class types, initial reconstruction of images, removal of images of insufficient quality). The final result of the classification quality was 83.6%. The newly obtained neural network can be an extension and a component of a comprehensive geoinformatics system for vessel recognition.

Computer-Aided Diagnosis With Monochromatic Light Endoscopy for Scoring Histologic Remission in Ulcerative Colitis

Computer-Aided Diagnosis With Monochromatic Light Endoscopy for Scoring Histologic Remission in Ulcerative Colitis

P269 Mucosal capillary pattern recognition based on real-time computer-aided image analysis adequately detects histological remission in ulcerative colitis

Abstract Background Objective treatment targets are required in a treat-to-target approach for ulcerative colitis (UC). An automated endoscopic system that correlates with histology can be an objective predictor for sustained remission in UC. The infiltration of neutrophils is associated with irregularities of the pericryptal capillaries. For this, we aimed to develop an objective automated endoscopic tool to assess histological remission based on the evaluation of the morphology of the pericryptal capillaries during endoscopy. Methods We used a prototype endoscopic system with short-wave-length monochromatic light from a LED system. This enables to evaluate in real-time the superficial (<200µm) mucosal architecture (crypts, pericryptal capillaries) and mucosal bleeding, Figure 1. An image analysis algorithm was applied to provide a score that quantifies the specific morphology of the mucosal capillaries. The algorithm included two steps. First, bleeding (mucosal/luminal) was assessed by pattern recognition. Samples with bleedings were automatically classified as non-remission. In case of non-bleeding, the degree of congestion of the capillaries was measured (maximal localised density estimation after morphological hessian based vessel recognition) to assess an ideal cut off value that identifies histological remission (Geboes score (GBS) <2B.1; no neutrophils in the lamina propria). Consecutive patients with UC were evaluated with the Mayo endoscopic subscore (MES), ulcerative colitis endoscopic index of severity (UCEIS) and the automated image analysis algorithm. To test the reliability of the algorithm and scores, the results were correlated with the GBS. Biopsies were taken in the matching area of the endoscopic evaluation. Results Fifty-eight patients with UC (53% male, median (IQR) age 41y (38–56), disease duration 7.1y (2.4–16.4)) with 113 evaluable segments (89% rectum or sigmoid) were included. The correlation between GBS and MES, UCEIS was good (r = 0.76, 0.75 respectively). The automated image analysis algorithm (Figure 2) detected histological remission with a higher performance (sens 0.79, spec 0.90) compared with UCEIS (sens 0.95, spec 0.69) and MES (sens 0.98, spec 0.61), resulting in a positive predictive value of 0.83, 0.65 and 0.59 for the automated image analysis algorithm, UCEIS and MES, respectively. The algorithm detected histological remission with high accuracy (86%). Conclusion Mucosal capillary pattern recognition based on an automated image analysis with short-wave-length monochromatic light detects histological remission with high accuracy in UC. This technique provides an objective and quantitative tool to assess histological remission in UC, and excludes inter-reader variability.

Classifiers fusion for improved vessel recognition with application in quantification of generalized arteriolar narrowing

This paper attempts to estimate diagnostically relevant measure, i.e., Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination scheme. The features exploited for retinal vessel characterization are based on statistical measures of histogram, different filter responses of images and local gradient information. The feature selection process is based on two feature ranking approaches (Pearson Correlation Coefficient technique and Relief-F method) to rank the features followed by use of maximum classification accuracy of three supervised classifiers (k-Nearest Neighbor, Support Vector Machine and Naïve Bayes) as a threshold for feature subset selection. Retinal vessels are labeled using the selected feature subset and proposed hybrid classification scheme, i.e., decision fusion of multiple classifiers. The comparative analysis shows an increase in vessel classification accuracy as well as Arteriovenous Ratio calculation performance. The system is tested on three databases, a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies. Overall, an accuracy of 90.45%, 93.90% and 87.82% is achieved in retinal blood vessel separation with 0.0565, 0.0650 and 0.0849 mean error in Arteriovenous Ratio calculation for Local, INSPIRE-AVR and VICAVR dataset, respectively.

Segmenting retinal vessels with revised top-bottom-hat transformation and flattening of minimum circumscribed ellipse.

Retinal vessel automatic segmentation plays a great important role for analyzing fundus pathologies like diabetes, retinopathy, and hypertension. In this paper, a novel unsupervised method to automatically extract the vessels from fundus images is introduced. The method proposed a new vessel enhancement approach that we called revised top-bottom-hat transformation for removing the bright lesions for further enhancing vessels in a fundus image, and provides a novel feature that we call flattening of minimum circumscribed ellipse for recognizing a vessel. This method was tested on two publicly available databases DRIVE and STARE, and achieved an average accuracy of 0.9446 and 0.9503, respectively. For pathological cases, the approach reached an accuracy of 0.9435 and 0.9439, respectively. The time complexity approaches (O(n)), which is significantly lower than the state-of-the-art method. Graphical Abstract (GA)-Overview of the steps of the proposed algorithm Step 1: Input. Input a fundus color image. Step 2: Preprocess. The aim of process is to obtain gray image and to filter noise. Step 3: Enhancement and amendment. For improving the segmentation accuracy, a new enhancement and amendment is applied for enhancing the vessels particularly thin vessels and removing the various disturbances. Step 4: Blood vessel segmentation. Step 4.1: Binarization. To identify the blood vessel, the threshold-based method is applied to gain binary images. Step 4.2: Object decomposition. Before blood vessel recognition, we must decompose the binary image into some independent objects. Step 4.3: Calculate the flattening. Calculate the flattening of each of objects. Step 4.4: Blood vessel recognition. Blood vessels are identified by its flattening. Step 5: Output. Output a blood vessel image Graphical Abstract (GA)-Overview of the proposed approach. (a) Input Image. (b) Preprocessing. (c) Top-bottom-hat transformation. (d) Enhancement. (e) Blood vessel segmentation with different thresholds. (f) Blood vessels.

Mining spatial–temporal motion pattern for vessel recognition

Approaches of vessel recognition are mostly accomplished by sensing targets and extracting target features, without taking advantage of spatial and temporal motion features. With maritime situation management systems widely applied, vessels’ spatial and temporal state information can be obtained by many kinds of distributed sensors, which is easy to achieve long-time accumulation but are often forgotten in databases. In order to get valuable information from large-scale stored trajectories for unknown vessel recognition, a spatial and temporal constrained trajectory similarity model and a mining algorithm based on spatial and temporal constrained trajectory similarity are proposed in this article by searching trajectories with similar motion features. Based on the idea of finding matching points between trajectories, baseline matching points are first defined to provide time reference for trajectories at different time, then the almost matching points are obtained by setting the spatial and temporal constraints, and the similarity of pairwise almost matching points is defined, which derives the spatial and temporal similarity of trajectories. By searching the matching points from trajectories, the similar motion pattern is extracted. Experiments on real data sets show that the proposed algorithm is useful for similar moving behavior mining from historic trajectories, which can strengthen motion feature with the length increases, and the support for vessel with unknown property is larger than other models.

Applying intelligent methods in detecting maritime smuggling

Taiwan is an island state characterized by well-developed maritime traffic and sea transportation. Smuggling by sea increasingly threatens the country’s social and internal security. The Coast Guard Administration is the main actor intercepting smuggling. This study applies classification trees and Bayesian network algorithms to create dependent and independent classification tree models and Bayesian network models for improving the smuggling vessel recognition rates. We focus on relationships of dependence among smuggled goods (for example, fishery goods, firearms and drugs) and variables (such as information on the home port, the age of boat owners, as well as the arrival and departure dates of vessels). The paper presents a selection method for vessels which could be applicable in a number of other maritime instances, such as, for instance, Port State Control inspection. Our findings help in improving recognition accuracy and reduce the potential harm to social and national security by facilitating the identification of vessels with the largest smuggling value.

Implementation of ultra-low-dose lung protocols in CT-guided lung biopsies: feasibility and safety in the clinical setting.

ObjectiveTo evaluate the use of ultra-low-dose computed tomography (ULDCT) for CT-guided lung biopsy versus standard-dose CT (SDCT).MethodsCT-guided lung biopsies from 115 patients (50 ULDCT, 65 SDCT) were analyzed retrospectively. SDCT settings were 120 kVp with automatic mAs modulation. ULDCT settings were 80 kVp with fixed exposure (20 mAs). Two radiologists evaluated image quality (i.e., needle artifacts, lesion contouring, vessel recognition, visibility of interlobar fissures). Complications and histological results were also evaluated.ResultsULDCT was considered feasible for all lung interventions, showing the same diagnostic accuracy as SDCT. Its mean total radiation dose (dose–length product) was significantly reduced to 34 mGy-cm (SDCT 426 mGy-cm). Image quality and complication rates (P = 0.469) were consistent.ConclusionsULDCT for CT-guided lung biopsies appears safe and accurate, with a significantly reduced radiation dose. We therefore recommend routine clinical use of ULDCT for the benefit of patients and interventionalists.

Acute ST-segment elevation myocardial infarction: The prognostic importance of lead augmented vector right and leads V7-V9.

Background:Acute myocardial infarction (MI) is associated with high mortality and among survivors have high morbidity. Electrocardiogram (ECG), a cost-effective and easily available, has traditionally been used not only just for diagnosis of MI but also for culprit vessel recognition and for prognostication. However, the role of lead augmented vector right (aVR) and leads V7–V9 in acute MI are often neglected in clinical practice. We studied the role of lead aVR and leads V7–V9 in ST-elevation MI (STEMI) patients.Methods:A total of 209 patients presenting with STEMI were enrolled in the study. History of comorbid conditions and habits was enquired. Routine blood tests were performed. Full spectrum ECG (including V7–9) and 2D-ECHO was performed on all patients. All the patients underwent revascularization by primary percutaneous coronary intervention. The role of lead aVR, lead V7, and leads V8–9 was analyzed in anterior wall MI (AWMI) and inferior wall MI. All the patients were followed up for 1 month for outcome assessment.Results:Of the 209 patients, 85.1% were males and 35.8% were diabetic, 60.2% were smokers, AWMI accounted for 55.5%. Lead aVR ST deviation was noted in 75.1% of patients (elevation in 17.7% and depression in 47.1%). V7 ST elevation occurred in 27.6% and V8–9 elevation occurred in 7.5% of the study population. Total death was 11.9% in the study (including the in-hospital mortality), all these patients had lead aVR ST segment deviation (P < 0.001).Conclusion:Lead aVR ST deviation and Lead V7 ST deviation helps to prognosticate the STEMI patients as high risk and those with aVR ST depression had higher mortality compared to aVR ST elevation because of larger myocardial involvement.

Extracting remote photoplethysmogram signal from endoscopy videos for vessel and capillary density recognition.

In this paper, we propose a new feature for finding lesions in gastrointestinal tissues. Polyps or cancerous parts have different capillary pattern compared with normal parts. There are polyps which have higher density of vessel or capillary pattern. This feature leads us to extract remote photoplethysmogram signal from different parts of videos from gastrointestinal tissue. Due to the fact that hemoglobin absorbs more light than surrounding tissues, more changes are expected to be observed in the parts with higher density of vessels and capillaries. In the experimental results, rPPG signals is extracted from colonoscopy and endoscopy videos. This feature is used to distinguish between normal and abnormal tissues. It is shown that power of rPPG signal can be used to find lesion areas.