Centerline Extraction Research Articles

Automatic Defect Identification Method for Magnetic Particle Inspection of Bearing Rings Based on Visual Characteristics and High-Level Features

Fluorescent magnetic particle inspection (MPI) is a conventional non-destructive testing process for railway bearing rings that still needs to be completed manually. Due to the complexity of bearing ring surfaces in inspection, automatic detection for bearing rings based on image processing is difficult to apply. Therefore, we proposed a bearing ring defect identification method based on visual characteristics and high-level features. Inspired by the mechanism of human visual perception, defects can be identified from the complex background conveniently by human eyes. According to the linear structure characteristics and greyscale distribution characteristics of cracks in the acquired images, we introduce the centerline extraction and Gaussian similarity measure to reduce background noise and obtain the crack candidate regions. Then, an improved MobileNetV3 is used to extract high-level features of the candidate regions and determine whether they are defective, which uses a new attention module, Coordinate Attention (CA), to substitute the Squeeze-and-Excitation (SE) attention to improve the performance. The experimental results show that the detection accuracy rate of the proposed method is 96.5%. Compared with traditional methods, the proposed method can efficiently extract crack defects in a complex textured background and shows high-quality performance in recall and precision.

Cascaded Multi-Task Road Extraction Network for Road Surface, Centerline, and Edge Extraction

Road extraction from very high-resolution (VHR) remote sensing imagery remains a huge challenge, due to the shadows and occlusions of trees and buildings. Such complex backgrounds result in deep networks often producing fragmented roads with poor connectivity. Road extraction has three typical tasks: road surface segmentation (SS), centerline extraction (CE), and edge detection (ED), which are conducted in a wide range of real applications. Also, the three tasks have a symbiotic relationship, i.e., the road SS determines the location of the centerline and edges, and the CE and ED can allow the generation of more continuous road surfaces. However, most of the previous works have completed these three tasks separately, without exploiting the symbiotic relationship between them to boost the road connectivity. In this article, in order to improve road connectivity, a cascaded multitask (CasMT) road extraction framework for simultaneously extracting the road surface, centerline, and edges is proposed. In the proposed framework, topology-aware learning is applied to capture the long-distance topological relationships, and hard example mining (HEM) loss is employed to focus more on hard samples, to further enhance the road completeness. Extensive experiments were conducted on the DeepGlobe road dataset and a large-scale road dataset (called the LSCC dataset) from the three Chinese cities of Beijing, Shanghai, and Wuhan. The experimental results obtained on the public DeepGlobe dataset demonstrate that the proposed CasMT framework can significantly outperform the current state-of-the-art method. Moreover, the generalization capability of the model was verified on the LSCC dataset, where the proposed CasMT framework achieved the best performance in the average path length similarity (APLS) road topology metric, which further confirms the superiority of the proposed framework.

Optimal Time Selection for ISAR Imaging of Ship Target via Novel Approach of Centerline Extraction With RANSAC Algorithm

Optimal time selection for inverse synthetic aperture radar imaging of ship target is significant for the determination of image project plane, and the performance of it is dependent on the accuracy of ship centerline extraction. In this article, a novel optimal time selection method based on the random sample consensus (RANSAC) technique is proposed, which can extract the ship centerline with high accuracy in the case of ship superstructure and low signal to noise ratio. First, a necessary distance threshold is determined for the RANSAC algorithm combined with the ship body width estimation. Then, a novel cost function is proposed according to the amplitude characteristic of radar image, which can alleviate the influence of randomness. Finally, the robustness of the optimal time selection method is demonstrated by the results of simulated and real measured data.

Aerial Remote Sensing Image Cascaded Road Detection Network Based on Edge Sensing Module and Attention Module

Automatic road detection has always been an important research problem in the field of remote sensing image processing, which is of great significance for many applications. However, many road areas cannot be effectively detected and the existing road detection methods suffer from unsmooth edges due to shadow phenomena and the occlusion of objects such as trees. For this reason, we propose a cascaded automatic road detection network based on edge sensing module and attention module, called CasEANet. CasEANet consists of three tasks including road surface detection, road edge detection and road centerline extraction. An encoder-decoder structure integrating edge sensing module and attention module is applied to detect the road surface. In order to obtain smoother road edges, the edge sensing module is designed to enhance the perception of road edges. The attention module is employed to guide the network to reinforce the perception of global information, aiming to solve the problem of discontinuity in the detection of road surfaces that are obscured by trees. The centerline extraction is adopted to assist the training of road surface detection. Experiments performed on the RNBD dataset prove the effectiveness of CasEANet. Specially, the F1 score, overall accuracy and balanced error rates of the CasEANet are 0.946, 0.986 and 0.0219 respectively, outperform the other state-of-the-art methods. The code will release soon on https://github.com/HITLDY/CasEANet.

Aircraft Pipe Gap Inspection on Raw Point Cloud From a Single View

The pipe is a common part of aviation equipment. The pipe gap is one of the important factors that affect the safety of an aviation equipment system. It is crucial to quickly and accurately measure the gaps among the pipes within the aviation equipment. Due to the complexity of the aviation pipeline system, inspecting the spacing efficiently among different pipes is challenging. In this paper, we propose an effective method to inspect the gaps among aircraft pipes over the raw point data from a single view. First, we design a probe function to extract the information of pipe structures based on the cylinder feature consistency among proximity patches. The probe function is intended to avoid the interference of other structures and also provides an appropriate starting location when extracting the pipe structure information. We then design a growth guidance strategy based on the continuity of pipe axial directions, which can ensure the continuity and accuracy of pipe centerline extraction. Since the curved smooth pipe can be subdivided into many straight cylinders, we devise an adaptive growing method which can ensure the centerline extraction efficiency of multi-curvature aviation pipes. Extensive experiments demonstrate that our algorithm outperforms the state-of-the-art methods in terms of inspection accuracy and efficiency.

A Hybrid Algorithm for the Laser Stripe Centreline Extraction

Centreline extraction is one of the critical processes of structured light systems that play an increasingly important role in industry 4.0. This paper proposes a novel algorithm to extract the centreline by combining an improved skeleton thinning algorithm to extract the initial centreline and an improved grey centroid algorithm to establish the final centreline. It consists of several steps: Firstly, an image difference operation and a thresholding operation are undertaken, and the Canny edge detection algorithm is applied to locate the approximate position of the laser stripe to reduce the image matrix calculation cost; Secondly, an initial centreline is extracted via the use of an image morphology operation and a skeleton thinning algorithm; Finally, a high-power grey centroid method is employed to acquire the centreline at the sub-pixel level. Experiments together with a comparison with other mainstream methods is conducted to test the validity and practicability of the proposed hybrid algorithm. The experimental results show that the speed of the proposed algorithm is 50% faster than the Steger algorithm, and the root-mean-square error is 40% lower than the grey centroid method.

An Effective Road Centerline Extraction Method From VHR

Road digitizing is a labor-intensive task. For reducing labor cost, this letter presents an effective semiautomatic road delineation method based on geodesic distance field (GDF) and piecewise polygon fitting. The main components of the approach are optimal circle calculation, soft road center kernel density estimation (KDE), fast GDF generation, and piecewise polygon fitting model. First, the adaptive circular template is proposed to automatically measure the road width. Next, the soft road center kernel density is estimated for fast GDF generation, which supports the extraction of road centerline between two adjacent seeds. Finally, piecewise polygon fitting is used to refine the road centerline. Extensive experiments demonstrate that the proposed algorithm is efficient and robust in road delineation. The proposed approach takes almost the same time to extract any length of road segment given fixed image size, and no hyperparameters needs to be set.

Trajectory Grouping With Curvature Regularization for Tubular Structure Tracking.

Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their strong ability in tracing tubular structures, by which a tubular structure can be naturally modeled as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulties such as the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek suitable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal paths-based tubular structure tracing algorithms.

Vascular centreline extraction for virtual reality interventional training systems

Vascular centreline extraction for virtual reality interventional training systems

Vascular centreline extraction for virtual reality interventional training systems

Vascular centreline extraction for virtual reality interventional training systems

MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction

Automatic segmentation and centerline extraction of blood vessels from retinal fundus images is an essential step to measure the state of retinal blood vessels and achieve the goal of auxiliary diagnosis. Combining the information of blood vessel segments and centerline can help improve the continuity of results and performance. However, previous studies have usually treated these two tasks as separate research topics. Therefore, we propose a novel multitask learning network (MSC-Net) for retinal vessel segmentation and centerline extraction. The network uses a multibranch design to combine information between two tasks. Channel and atrous spatial fusion block (CAS-FB) is designed to fuse and correct the features of different branches and different scales. The clDice loss function is also used to constrain the topological continuity of blood vessel segments and centerline. Experimental results on different fundus blood vessel datasets (DRIVE, STARE, and CHASE) show that our method can obtain better segmentation and centerline extraction results at different scales and has better topological continuity than state-of-the-art methods.

Impact of renal chimney intra-aortic stent length on branch and end-stent angle in chimney endovascular aneurysm repair and endovascular aneurysm sealing configurations.

Practice patterns and durability of parallel stent graft techniques in complex endovascular aneurysm repair (EVAR) remain poorly defined. We aimed to quantify and compare the impact of renal chimney intra-aortic stent length (IASL) on geometric deformations of renal arteries in complex EVAR. Thirty-eight nonconsecutive patients underwent EVAR utilizing parallel stent graft techniques (chimney EVAR [chEVAR], n = 28; chimney endovascular aneurysm sealing [chEVAS], n = 10) between 2010 and 2016. A total of 59 renal chimney stent grafts were used. Geometric quantification was derived from three-dimensional model-based centerline extraction. Renal chimney intra-aortic stent length (IASL) was defined as the length of chimney stent that extended from the proximal edge of the chimney stent to the ostium of the corresponding renal artery. Mean IASL for both left and right renal arteries in the cohort was 35.7mm. Renal arteries containing chimney IASL <30mm trended toward a greater branch angle (135.4 vs. 127.8°, p = .06). Left renal arteries showed significantly greater branch angle among those with IASL <40mm (135.5 vs. 121.7°, p = .045). Mean IASL for renal arteries in chEVAR was significantly longer compared to chEVAS (39.2 vs. 26.3mm, p = .003). No difference was noted in overall branch angle or end-stent angle based on procedure type. ChEVAR with IASL <30mm had significantly greater end-stent angle (48.2 vs. 33.5°, p = .03). In contrast, chEVAS patients showed no difference in end-stent angle based on IASL thresholds, but did have significantly greater branch angle among those with IASL <30mm when grouped by both all renal arteries (133.5 vs. 113.5°, p = .004) and right renal arteries (134.3 vs. 111.6°, p = .02). Renal chimney stents with longer IASL appear to exhibit less renal artery deformation, suggesting a more gradual and perpendicular transition of the chimney stent across the renal ostium.

Abstract 13573: Prediction of Culprit Lesions in Acute Coronary Syndrome Based Upon Myocardial Mass At-Risk From Coronary CT Angiography

Introduction: Stratifying patients with coronary artery disease (CAD) remains challenging. Assessing CAD using myocardial mass at-risk may help identify patients at higher risk of acute coronary syndrome. Hypothesis: This study investigated whether quantitative myocardial mass at-risk (MMAR) can improve prognostication of future culprit coronary lesions at the time of acute coronary syndrome (ACS) events. Methods: From the multicenter, international ICONIC study, 49 patients suspected of coronary artery disease (CAD) going coronary CT angiography imaging (CCTA) were followed to the occurrence of the first ACS. At the time of ACS, culprit lesion was adjudicated by invasive coronary angiography by a group of cardiologists blinded to the previously acquired CCTA. MMAR was calculated for each lesion using the minimum-cost path technique, a previously validated method for calculating myocardial perfusion territories. Left ventricle myocardial segmentation and coronary centerline extraction was performed and used to calculate MMAR distal to all identifiable culprit and non-culprit coronary lesions. Results: In the study sample, 243 coronary lesions were identified on CCTA (194 non-culprit lesions; 49 culprit lesions). The mean MMAR, as a percentage of total left ventricle myocardial mass, for all lesions was 24.6±12.4%. The mean MMAR was 23.1±12.5% and 30.3±10.3% for non-culprit and culprit lesions, respectively (p&lt;0.05). Receiver operating characteristic curve analysis was performed to determine the value of MMAR in predicting a culprit lesion, with an area-under-the-curve analysis for MMAR predicting a culprit lesion of 0.68. Conclusions: MMAR is larger for lesions that will versus will not become culprit at the time of future ACS. The addition of MMAR in the assessment of CAD on CCTA may help stratify the risk of individual coronary lesions.

Improved Centerline Extraction in Fully Automated Coronary Ostium Localization and Centerline Extraction Framework using Deep Learning.

Coronary artery extraction in cardiac CT angiography (CCTA) image volume is a necessary step for any quantitative assessment of stenoses and atherosclerotic plaque. In this work, we propose a fully automated workflow that depends on convolutional networks to extract the centerlines of the coronary arteries from CCTA image volumes, starting from identifying the ostium points and then tracking the vessel till its end based on its radius and direction. First, a regression U-Net is employed to identify the ostium points in the image volume, then these points are fed to an orientation and radius predictor CNN model to track and extract each artery till its end point. Our results show that an average of 96% of the ostium points were identified and located within less than 5mm from their true location. The coronary arteries centerlines extraction was performed with high accuracy and lower number of training parameters making it suitable for real clinical applications and continuous learning.

Coronary Artery Extraction from CT Coronary Angiography with Augmentation on Partially Labelled Data.

Coronary artery disease (CAD) is an important cause of morbidity and mortality. CT coronary angiography is considered as first-line of investigation in patients suspected of having CAD. Coronary artery centerline extraction is a challenging prerequisite for coronary artery stenosis evaluation. These challenges include the small and complex structure, variation of plaques and imaging noise. Deep learning methods often require adequate annotated data to build a good model. This work aims to adopt a dataset that has partial annotation to augment the data to train a Coronary Neural Network (CorNN) to track the coronary artery centerline. We combined a small training dataset with densely labelled centerline and radius, augmented with a larger dataset with only the centerline sparsely labelled to train networks to track centerlines from 3D computed tomography coronary angiography. The vessel orientation estimation is patch based, with or without additional radius prediction. The patch data are carefully positioned and sampled, which are tagged with the orientations computed based on the centerlines. Our experiment results show that, with the augmentation of the new data, although partially annotated, nearly 10% or more improvement has been achieved for the coronary artery extraction by the proposed approach.

Automated vessel centerline extraction and diameter measurement in OCT Angiography

Abstract Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that enables the visualization of perfused vasculature in vivo. In ophthalmology, it allows the physician to monitor diseases affecting the vascular networks of the retina such as agerelated macular degeneration or diabetic retinopathy. Due to the complexity of the vasculature in the retina, it is of interest to automatically extract vascular parameters which describe the condition of the vessels. Suitable parameters could improve the diagnosis and the treatment during the course of therapy. We present an automated algorithm to compute the diameters of the vessels in en face OCTA images. After segmenting the images, the vessel centerline was computed using a thinning algorithm. The centerline was refined by detecting invalid pixels such as spurs and by continuing the centerline until the ends of the vessels. Lastly, the diameter was computed by dilating a discrete circle at the position of the centerline or by measuring the distance between both borders of the vessels. The developed algorithms were applied to in vivo images of human eyes. Certainly, no ground truth was available. Hence, a plausibility check was performed by comparing the measured diameters of two different layers of the retina (Superficial Vascular Complex (SVC) and Deep Vascular Complex (DVC)). Each layer exhibits a different characteristic vasculature. The algorithm clearly reflected the differences from both retinal layers. The measured diameters demonstrate that the DVC consists of more capillaries and considerably smaller vessels compared to the SVC. The presented method enables automated analysis of the retinal vasculature and forms thereby the basis for monitoring diseases influencing the vasculature of the retina. The validation of the method using an artificial ground truth is still needed.

Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images.

Extraction of coronary arteries in coronary computed tomography (CT) angiography is a prerequisite for the quantification of coronary lesions. In this study, we propose a tracking method combining a deep convolutional neural network (DNN) and particle filtering method to identify the trajectories from the coronary ostium to each distal end from 3D CT images. The particle filter, as a non-linear approximator, is an appropriate tracking framework for such thin and elongated structures; however, the robust ‘vesselness’ measurement is essential for extracting coronary centerlines. Importantly, we employed the DNN to robustly measure the vesselness using patch images, and we integrated softmax values to the likelihood function in our particle filtering framework. Tangent patches represent cross-sections of coronary arteries of circular shapes. Thus, 2D tangent patches are assumed to include enough features of coronary arteries, and the use of 2D patches significantly reduces computational complexity. Because coronary vasculature has multiple bifurcations, we also modeled a method to detect branching sites by clustering the particle locations. The proposed method is compared with three commercial workstations and two conventional methods from the academic literature.

Application of image processing technology in cable detection of underwater robot

Manual inspection of underwater cables has many problems, such as heavy labor intensity, high operation difficulty and low work efficiency. It is more effective to use underwater camera to capture images of underwater cables than manual inspection. The addition of subsea detection and construction equipment raises bottom sediment in the water and causes the water to become turbid and cloudy, it also reduces the contrast of the underwater image and adds irrelevant background information. Therefore, a method of cable location in turbid waters based on image processing is proposed. The method firstly applies the dark channel prior dehazing algorithm. An adaptive histogram equalization algorithm is used to improve the light distribution of underwater image after defogging. Then, we use the local adaptive dynamic threshold segmentation method to achieve the rough segmentation, and the fine segmentation of cable image is carried out by selecting each connected domain region. Finally, the cable pixel coordinates are obtained by the method of center line extraction and frequency domain transformation. Experimental result shows that the proposed algorithm has good effects in terms of clarity.

基于Fast-SCNN语义分割的秧苗列中心线提取方法

For the extraction of paddy rice seedling centerline, this study proposed a method based on Fast-SCNN (Fast Segmentation Convolutional Neural Network) semantic segmentation network. By training the FAST-SCNN network, the optimal model was selected to separate the seedling from the picture. Feature points were extracted using the FAST (Features from Accelerated Segment Test) corner detection algorithm after the pre-processing of original images. All the outer contours of the segmentation results were extracted, and feature point classification was carried out based on the extracted outer contour. For each class of points, Hough transformation based on known points was used to fit the seedling row centerline. It has been verified by experiments that this algorithm has high robustness in each period within three weeks after transplanting. In a 1280×1024-pixel PNG format color image, the accuracy of this algorithm is 95.9% and the average time of each frame is 158ms, which meets the real-time requirement of visual navigation in paddy field.

Algorithm of Pulmonary Vascular Segment and Centerline Extraction.

Because pulmonary vascular lesions are harmful to the human body and difficult to detect, computer-assisted diagnosis of pulmonary blood vessels has become the focus and difficulty of the current research. An algorithm of pulmonary vascular segment and centerline extraction which is consistent with the physician's diagnosis process is proposed for the first time. We construct the projection of maximum density, restore the vascular space information, and correct random walk algorithm to satisfy automatic and accurate segmentation of blood vessels. Construct a local 3D model to restrain Hessian matrix when extracting centerline. In order to assist the physician to make a correct diagnosis and verify the effectiveness of the algorithm, we proposed a visual expansion model. According to the 420 high-resolution CT data of lung blood vessels labeled by physicians, the accuracy of segmentation algorithm AOM reached 93%, and the processing speed was 0.05 s/frame, which achieved the clinical application standards.