Snake Model Research Articles

Dense Scene Flow Estimation with the GVF Snake Model for Obstacle Detection Using a 3D Sensor in the Path-Planning Module

Abstract The novel framework for estimating dense scene flow using depth camera data is demonstrated in the article. Using these estimated flow vectors to identify obstacles improves the path planning module of the autonomous vehicle's (AV) intelligence. The primary difficulty in the development of AVs has been thought to be path planning in cluttered environments. These vehicles must possess the intelligence to recognize their surroundings and successfully navigate around obstacles. The AV needs a thorough understanding of the surroundings to detect and avoid obstacles in a cluttered environment. Therefore, when determining the course, it is preferable to be aware of the kinematic behavior (position and the direction) of the obstacles. As a result, by comparing the depth images between different time frames, the position and direction of the obstacles are calculated using a 3D vision sensor. The current study focuses on the extraction of the flow vectors in 3D coordinates from the differential scene flow method. Generally, the evaluation of scene flow algorithms is crucial in determining their accuracy and effectiveness in different applications. The gradient of the vector field snake model, which extracts changes in pixel values in three directions, is combined with the scene flow technique to identify both static and dynamic obstacles. Our goal is to create a single-vision sensor-based real-time obstacle avoidance method based on scene flow estimation. In addition, the common evaluation metrics such as endpoint error (EPE), average angular error (AAE), and standard deviation angular error (STDAE) are used to measure the accuracy of different algorithms in terms of computational errors with the benchmark Middlebury datasets. The proposed technique is validated with different experiments using a Pixel-Mixed-Device (PMD) camera and a Kinect sensor as 3D sensors. Finally, the numerical and experimental results are displayed and reported.

SPLINE MODEL OF GRIDDED DATA OPERATION AS A PRINCIPLE OF ELECTRONIC MAPPING SEABED TOPOGRAPHY

The influence of technologies of automated processing of hydrographic survey results on the potential of the human factor in the transformation of polygraphic cartographic products into digital ones is analyzed. When configuring the digital bathymetric model, the concept of the navigation surface is used as a perspective principle of automated mapping. The approach to the problem of generating bathymetric contours from measurement results in the navigation surface paradigm is construed on continuous spline interpolation of geospatial data for reasonable cartographic generalization when creating electronic cartographic products. From the theoretical positions of the spline approach, the subjectivity of the method of artificial displacement of generalized isobaths to deep-water areas is excluded when creating a safe digital model of the bottom relief, interpreted in a mathematical sense as a navigational isosurface. The principle of electronic mapping based on the use of a spline in tension as an effective approach for the process of generalizing isobaths in order to obtain a wide range of morphometric characteristics of underwater topography has been developed. The generalized line of the active depth contour is estimated in the form of algorithmic reproduction on electronic charts of the safe convexity of the isobate towards the deep-sea area due to the practical implementation of the B-spline “snake model” by analogy with the serpentine configuration of the bathymetric isoline in the form of a piecewise polynomial function. When using the spline approach, an innovative principle of electronic mapping of the underwater landscape based on operating with a set of gridded data is implemented. The latter are interpreted as the results of depth measurements with the formalization of a two-dimensional frame of fixed values of bathymetric measurements for their representation as a navigational isosurface in three-dimensional Euclidean space. The actual synthesis of the seabed topography is implemented on the basis of a proven hybrid spline model for a specific indicative test case based on the processing of experimental gridded data. Hypothetically, the possibility of intellectual assistance to the watch officer in the strategy of instant orientation in conditions of a minimum depth reserve under the keel is organized when using computer three-dimensional visualization of the topography of the underwater relief in an unaffiliated graphic environment with foreign software.

Dynamic Modeling of Underwater Snake Robot by Hybrid Rigid-Soft Actuation

For decades, underwater vehicles have been performing underwater operations, which are critical to the development and upgrading of underwater robots. With the advancement of technology, various types of robots have been developed. The underwater robotic snake is a bioinspired addition to the family of underwater robotic vehicles. In this paper, we propose an innovative underwater snake robot actuated by rigid propulsions and soft joints, which can improve the swimming efficiency and flexibility of the robot and reduce the probability of collision leading to damage. Existing math models of robotic snakes typically incorporate only planar motion, rarely considering spatial motion. So, we formulate a complete three-dimensional dynamic model for the robotic snake, which is extended by deriving expressions for the geometric Jacobians. This modeling approach is well suited since it provides compact matrix expressions and easy implementation. We use the constant curvature method to describe the configuration of the soft joint, use the Lagrangian method to obtain its dynamic characteristics, and focus on deriving the visco-hyperelastic mechanical energy of the soft material. Next, the local dynamics of soft members are extended as a nonholonomic constraint form for modeling the snake robot. Finally, the multi-modal swimming behavior of the robot has been verified by simulations, including forward and backward rectilinear motion, yaw turning, pitch motion, and spiral rising motion. The overall results demonstrate the effectiveness and the versatility of the developed dynamic model in the prediction of the robot trajectory, position, orientation, and velocity.

Improved Active Contour Snake Model for Hollow Filter Bar Roundness Inspection

Cigarette filter rods are a kind of easily deformed and irregularly shaped parts, and the quality of cigarette filter rods is often measured by the filter rod roundness index, although they are affected by factors such as environment and humidity, which can affect the detection of filter rod roundness and show blurred images. For the detection of blurred filter rod image roundness, an improved active contour snake model algorithm is proposed. The experimental results show that in the analysis of the algorithm iteration number on the model performance, the model performance gradually leveled off at the iteration number of 200. Comparing the PR of the improved algorithm with the traditional snake algorithm with the balloon force snake algorithm, the accuracy and recall rates obtained were 0.803 and 0.798, respectively, which were greater than the other two algorithms; the error of the value of the residual under the improved snake algorithm was always smaller than the other algorithms, which indicated that the convergence effect of the improved snake algorithm on the error of filter bar roundness was better than the other experimental algorithms. The abovementioned results indicate that the improved snake algorithm has high feasibility and effectiveness in the application of filter bar roundness error detection and has a significant value.

Pdf 02.Detectability of reptiles in standardised surveys: a test using grass snake Natrix helvetica models

The ability to detect snakes in the field may be influenced by phenotypic and morphological variables attributable to the target species. These variables include body size, colouration, and body posture. To test what effect these variables had on detectability by surveyors, plasticine model grass snakes were distributed along a predetermined transect in reptile habitat. Detections of different types of snake models along the transect were compared between two groups of inexperienced students and those of a single experienced observer. The experienced surveyor detected 72 % of all the snake models, compared to 53 % and 58 % by the inexperienced groups. All groups detected more larger snakes than smaller snakes, and more uncoiled snakes than coiled snakes. The presence of a yellow/black collar did not influence the detection of the snakes. The results demonstrate the observer bias that may be inherent in surveys of snakes due to variation in size and posture of the target animals. Accounting for such biases in the design of reptile surveys and providing appropriate training and experience for volunteers may improve the validity and interpretation of data collected within citizen science programmes. Keywords: population assessment, imperfect detection, citizen science, survey protocol

Efficacy Evaluation of Ultrasound with Active Contour Model for Hemodialysis in Children with Renal Failure.

This study was aimed to explore the efficacy of ultrasound with active contour model (ACM) for hemodialysis in children with renal failure. The pulse coupled neural network (PCNN) was used to extract the initial contour of the ultrasound images, and the cloud model-based ACM was used to accurately segment the images, whose effect was compared with the classic Snake model. 84 children with chronic renal failure who received hemodialysis treatment in hospital were selected as research objects. There were 42 cases in the control group who were diagnosed by conventional ultrasound and 42 cases in the observation group who were diagnosed by ultrasound with the algorithm. Then, 42 children who underwent healthy physical examination (health group) were selected for comparison of related analysis indicators. The error rates of different algorithms were compared to analyze the levels of inflammatory factors in different groups of patients after hemodialysis. The results showed that the error rate of classical Snake model was 18.87% and that of ACM algorithm model was 11.01%, and the error rate of ACM algorithm model was significantly lower (P < 0.05). After hemodialysis, the level of tumor necrosis factor (TNF)-α was 38.76 pg/mL in the observation group and 40.05 pg/mL in the control group, which was notably decreased in both groups, especially in the observation group (P < 0.05). After hemodialysis, transforming growth factor (TGF)-β1 was 7.76 ng/mL in the observation group and 7.60 ng/mL in the control group, which was significantly reduced in both groups. After treatment, UA and Scr in both groups were significantly reduced, and the reduction was more significant in the observation group (P < 0.05). HGB and RBC were significantly increased in both groups, and the increase was more significant in the observation group (P < 0.05). In summary, ACM algorithm had a good segmentation effect on the ultrasonic images of children with renal failure. This study provided guidance for clinicians to choose the algorithm for the application of ultrasonic imaging diagnosis.

MRI Using Artificial Intelligence Algorithm to Evaluate Concurrent Chemoradiotherapy for Local Recurrence and Distant Metastasis of Cervical Squamous Cell Carcinoma.

The aim of this study was to investigate the magnetic resonance imaging (MRI) features of patients with local recurrence and distant metastasis of cervical squamous cell carcinoma before and after concurrent chemoradiotherapy based on artificial intelligence algorithm. In this study, 100 patients with cervical squamous cell carcinoma with local recurrence and distant metastasis who underwent concurrent chemoradiotherapy were collected as the research subjects, and all underwent MRI multisequence imaging scans. At the same time, according to the evaluation criteria of solid tumor efficacy, patients with complete remission were classified into the effective group, and patients with partial remission, progressive disease, and stable disease were classified into the ineffective group. In addition, an image segmentation algorithm based on Balloon Snake model was proposed for MRI image processing, and simulation experiments were carried out. The results showed that the Dice coefficient of the proposed model segmentation of the reconstructed image was significantly higher than that of the level set model and the greedy algorithm, while the running time was the opposite (P < 0.05). The lesion volume (38.76 ± 5.34 cm3) in the effective group after treatment was significantly smaller than that in the noneffective group (46.33 ± 4.64 cm3), and the rate of lesion volume shrinkage (28.71%) was significantly larger than that in the noneffective group (12.49%) (P < 0.05). The relative apparent diffusion coefficient (rADC) value and rADC value change rate of the lesion after treatment in the effective group were significantly greater than those in the noneffective group (P < 0.05). In summary, the image segmentation and reconstruction algorithm based on Balloon Snake model can not only improve the quality of MRI images but also shorten the processing time and improve the diagnostic efficiency. The volume regression rate and rADC value change rate of cervical squamous cell carcinoma lesion can reflect the early efficacy of concurrent chemoradiotherapy for cervical squamous cell carcinoma and have predictive value.

Generasi Berencana Peduli Organ Reproduksi Perempuan (GenRe POP) Dalam Meningkatkan Personal Menstrual Hygiene Pada Remaja

Adolescents are at risk of health problems, especially when they face signs of adolescent development during puberty, namely menstruation. Adolescents tend to be less likely to maintain personal hygiene during menstruation (personal menstrual hygiene). Improper behavior in personal menstrual hygiene in adolescents can cause reproductive tract infections and other health problems. The purpose of this activity is to increase the knowledge and insight of adolescents in general to maintain personal hygiene during menstruation. The method of this activity is divided into several steps, starting with the socialization of the activity agenda, providing educational materials to teenagers through Zoom meetings, edutainment of the snake and ladder game on the carpet in the courtyard of the mosque area and finally a discussion (FGD) for mothers of teenagers about personal menstrual hygiene. The result of this activity is an increase in knowledge of personal menstrual hygiene in adolescents before and after attending education. Providing education that is packaged with a snake and ladder game model is proven to be effective in increasing adolescent knowledge about personal menstrual hygiene.

Using the Split Bregman Algorithm to Solve the Self-repelling Snakes Model

Preserving contour topology during image segmentation is useful in many practical scenarios. By keeping the contours isomorphic, it is possible to prevent over-segmentation and under-segmentation, as well as to adhere to given topologies. The Self-repelling Snakes model (SR) is a variational model that preserves contour topology by combining a non-local repulsion term with the geodesic active contour model. The SR is traditionally solved using the additive operator splitting (AOS) scheme. In our paper, we propose an alternative solution to the SR using the Split Bregman method. Our algorithm breaks the problem down into simpler sub-problems to use lower-order evolution equations and a simple projection scheme rather than re-initialization. The sub-problems can be solved via fast Fourier transform or an approximate soft thresholding formula which maintains stability, shortening the convergence time, and reduces the memory requirement. The Split Bregman and AOS algorithms are compared theoretically and experimentally.

Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery

The extraction of man-made features from high resolution digital satellite imagery is an important step to underpin management of geo-information in any country. Man-made features and buildings in particular are required for various applications such as urban planning, creation of geographic information systems databases and generation of urban models. Manual extraction processes are expensive, labor intensive, need well trained personnel and cannot cope with high demand of geo-information and changing environment. This paper, presents a Radial Casting Algorithm (RCA) used to extract buildings from high resolution digital satellite imagery. The algorithm measures only a single point on an approximate center of the building on an image and the fine measurement is automatically determined. The algorithm is a modification from original snakes model developed by Kass et al whereby the external constraints energy term is removed which negatively affects the convergence properties of the contour to provide the ability of the snake contour to cope with high variability of buildings on an image. The algorithm was tested on three areas of different environment. The quantitative measures were employed to evaluate the accuracy, efficiency and capability of the algorithm which shows that the time of extracting a single building was reduced by 32 percent, the extraction rate was 92 percent and the Area coverage of extracted polygons was 98 percent.

Application of Video Processing Technology Based on Diffusion Equation Model in Basketball Analysis

Video event detection and annotation work is an important content of video analysis and the basis of video content retrieval. Basketball is one of the most popular types of sports. Event detection and labeling of basketball videos can help viewers quickly locate events of interest and meet retrieval needs. This paper studies the application of anisotropic diffusion in video image smoothing, denoising, and enhancement. An improved form of anisotropic diffusion that can be used for video image enhancement is analyzed. This paper studies the anisotropic diffusion method for coherent speckle noise removal and proposes a video image denoising method that combines anisotropic diffusion and stationary wavelet transform. This paper proposes an anisotropic diffusion method based on visual characteristics, which adds a factor of video image detail while smoothing, and improves the visual effect of diffusion. This article discusses how to apply anisotropic diffusion methods and ideas to video image segmentation. We introduced the classic watershed segmentation algorithm and used forward-backward diffusion to process video images to reduce oversegmentation, introduced the active contour model and its improved GVF Snake, and analyzed the idea of how to use anisotropic diffusion and improve the GVF Snake model to get a new GGVF Snake model. In the study of basketball segmentation of close-up shots, we propose an improved Hough transform method based on a variable direction filter, which can effectively extract the center and radius of the basketball. The algorithm has good robustness to basketball partial occlusion and motion blur. In the basketball segmentation research of the perspective shot, the commonly used object segmentation method based on the change area detection is very sensitive to noise and requires the object not to move too fast. In order to correct the basketball segmentation deviation caused by the video noise and the fast basketball movement, we make corrections based on the peak characteristics of the edge gradient. At the same time, the internal and external energy calculation methods of the traditional active contour model are improved, and the judgment standard of the regional optimal solution and segmentation validity is further established. In the basketball tracking research, an improved block matching method is proposed. On the one hand, in order to overcome the influence of basketball’s own rotation, this article establishes a matching criterion that has nothing to do with the location of the area. On the other hand, this article improves the diamond motion search path based on the basketball’s motion correlation and center offset characteristics to reduce the number of searches and improve the tracking speed.

A feature extraction algorithm based on improved Snake model for multi-pass seam tracking in robotic arc welding

Visual sensor based seam tracking technology plays an important part in intelligentized robotic welding, and the feature extraction is an essential step. However, in multi-pass welding, the traditional algorithms are difficult to deal with the welding noises and irregular laser stripe patterns. Aiming at this problem, a feature extraction algorithm based on improved Snake model is proposed. By carefully redesigning the energy functional based on the unique gray-value distribution of the laser stripe, and optimizing the minimization procedure to accelerate the convergence, the Snake model is improved to a stripe extractor. The feature points are then extracted based on the curvature and the local moments of the stripe. Further, the algorithm is modified for butt welding seams, and a Kalman filter is introduced to handle the weaving process, so as to extend its application range. Experimental results suggest that the algorithm has good adaptability and robustness to multiple welding seams even under the interference of welding smoke, strong arc light, and spatter noises. Comparison tests prove the advantage of the algorithm in high stability and flexibility.

Discrete light sheet microscopic segmentation of left ventricle using morphological tuning and active contours.

Left ventricular segmentation using cardiovascular MR scan is required for the diagnosis and further cure of cardiac diseases. Automatic systems for left ventricle segmentation are being studied for attaining more accurate results in a shorter period of time. A novel algorithm introducing discrete segmentation of left ventricle achieves an independent processing of images swiftly. The workflow consists of four segments; first, automated localization is performed on the MR image. Second, performing preprocessing intimately improves and enhances the quality of image using mean contrast adjustment. Central segmentation of endocardium and epicardium layers includes novel MTAC (Morphological tuning using active contours) segmentation algorithm that provides a perfect combination of active contours and morphological tuning to bring an adequate and desirable segmentation. The prospective snake model is a restrained progression, which takes iterations for an impulse throughout the left ventricle contours. At the end, contrast based refining overcomes minor edge problems for both outer and inner boundaries. Proposed algorithm is evaluated via Sunnybrook cardiac MR images by producing an overall average perpendicular distance 2.45 mm, an average dice matrix (endo: 91.3%; epi: 92.16%) and 91.7% dice matrix of overall endocardium and epicardium contours from ground truth contours.

Initial Geometrical Templates with Parameter Sets for Active Contour on Skin Cancer Boundary Segmentation.

For active-contour-based surgery systems, the success of skin cancer boundary segmentation depends on the initialization point of the snake model, which is a task originally performed by skillful experts, and on the parameters set for the algorithms of active contour. This paper proposes initial geometrical templates and parameter sets for the active contour on skin cancer boundary segmentation. To establish initial geometrical templates and parameter sets for the active contour, first, template candidates, which are geometrically designed by users in advance, are simply calculated based on similarity with a skin cancer boundary, and the candidate with the least difference is selected as an initial template. Initially, all candidate templates are performed before the test with some selected skin cancer samples by randomly varying needed parameters to determine parameter sets for each template. The parameter set is therefore implicitly selected as the suitable set with the selected initial template. Experiments with 227 skin cancer samples were performed based on our proposed initial templates and parameter sets, and the results show 99.46% accuracy, 97.43% sensitivity, and 99.87% specificity approximately in which accuracy, sensitivity, and specificity were improved by 0.26%, 0.36%, and 0.26%, respectively, compared with the conventional method.

Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images.

This paper intends to explore the effect of the enhanced snake variable model in the segmentation of cardiac ultrasound images and its adoption in quantitative measurement of cardiac cavity. First, the basic principles of the traditional snake model and the gradient vector flow (GVF) snake model are explained. Then, an ellipsoid model is constructed to obtain the initial contour of the heart based on the three-dimensional volume of cardiac ultrasound image, and a discretized triangular mesh model is generated. Finally, the vortical gradient vector flow (VGVF) external force field is introduced and combined with the greedy algorithm to process the deformation of the initial ellipsoid contour of the heart. The segmentation effect is quantitatively evaluated regarding the area overlap rate (AOR) and the mean contour distance (MCD). The results show that the VGVF snake model can segment the deep recessed area of the “U-shaped map” in contrast to the traditional snake model and the GVF snake model. After being applied to ultrasonic image segmentation, the VGVF snake model obtains the segmentation result that is close to the doctor's manual segmentation result, and the average AOR and MCD are 97.4% and 3.2, respectively. The quantitative evaluation of the cardiac cavity is carried out based on the segmentation results, and the measurement of the volume change of the left ventricle within a cardiac cycle is realized. To sum up, VGVF snake model is superior to the traditional snake and GVF snake models in terms of ultrasonic image segmentation, which realizes the three-dimensional segmentation and quantitative calculation of the cardiac cavity.

Object-Oriented Building Contour Optimization Methodology for Image Classification Results via Generalized Gradient Vector Flow Snake Model

Building boundary optimization is an essential post-process step for building extraction (by image classification). However, current boundary optimization methods through smoothing or line fitting principles are unable to optimize complex buildings. In response to this limitation, this paper proposes an object-oriented building contour optimization method via an improved generalized gradient vector flow (GGVF) snake model and based on the initial building contour results obtained by a classification method. First, to reduce interference from the adjacent non-building object, each building object is clipped via their extended minimum bounding rectangles (MBR). Second, an adaptive threshold Canny edge detection is applied to each building image to detect the edges, and the progressive probabilistic Hough transform (PPHT) is applied to the edge result to extract the line segments. For those cases with missing or wrong line segments in some edges, a hierarchical line segments reconstruction method is designed to obtain complete contour constraint segments. Third, accurate contour constraint segments for the GGVF snake model are designed to quickly find the target contour. With the help of the initial contour and constraint edge map for GGVF, a GGVF force field computation is executed, and the related optimization principle can be applied to complex buildings. Experimental results validate the robustness and effectiveness of the proposed method, whose contour optimization has higher accuracy and comprehensive value compared with that of the reference methods. This method can be used for effective post-processing to strengthen the accuracy of building extraction results.

Automatic Detection and Measurement of Spinous Process Curve on Clinical Ultrasound Spine Images.

The ultrasound (US) imaging technique has been applied to scoliosis assessment, and the proxy Cobb angle can be acquired on the US coronal images. The spinous process angle (SPA) is a valuable parameter to indicate 3-D deformity of spine. However, the SPA cannot be measured on US images since the spinous process (SP) is merged in the soft tissue layer and impossible to be identified on the coronal view directly. A new method based on the gradient vector flow (GVF) snake model was proposed to automatically locate SP position on the US transverse images, and the density-based spatial clustering of application with noise (DBSCAN) was used to remove the outliers out of the detected location results. With marking the SP points on the US coronal image, the SP curve was interpolated and the SPA was measured. The algorithm was evaluated on 50 subjects with various severity of scoliosis, and two raters measured the SPA on both US images and radiographs manually. The mean absolute differences (MADs) of SPAs obtained from the two modalities were 3.4° ± 2.4° and 3.6° ± 2.8° for the two raters, respectively, which were less than the clinical acceptance error (5°), and the results reported a good linear correlation ( ) between the US method and radiography. It indicates that the proposed method can be a promising approach for SPA measurement using the US imaging technique.

The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

With the rapid development of sports science, human motion recognition technology, as a new biometric recognition technology, has many advantages, such as noncontact target, long recognition distance, secret recognition process, and so on. Traditional human motion recognition technology is affected by environmental factors such as motion background, which is prone to rough edges of the recognized objects and loss of motion tracking information, thus further reducing the recognition accuracy. In this paper, the traditional snake model will be improved and optimized to improve the defect of human motion model contour extraction, so as to realize the accurate repair of image contour; in terms of algorithm running time, this paper innovatively improves the construction process of the snake model, further improves the running time of model evaluation, and solves the concave contour problem of corresponding moving objects in the snake model. In order to solve the problem of accurate convergence, this paper improves the snake model of the average moving algorithm and sets the corresponding weight coefficient to distinguish the corresponding moving target background, so as to achieve the convergence of the differential concave contour. In order to verify the superiority of the improved optimized snake model, experiments are carried out in the corresponding database. The experimental results show that the contour of the moving object extracted by the improved snake model algorithm is complete and the segmentation effect is obvious. At the same time, the running speed of the whole algorithm has been significantly improved.

Tracking-based deep learning method for temporomandibular joint segmentation.

BackgroundThe shape, size, and surface information relating to the glenoid fossae and condyles in temporomandibular joints (TMJ) are essential for diagnosing and treating. Patients with TMJ disease often have surface abrasion which may cause fuzzy edges in computed tomography (CT) imaging, especially for low-dose CT, making TMJ segmentation more difficult.MethodsIn this paper, an automatic segmentation algorithm based on deep learning and post-processing was introduced. First, U-Net was used to divide images into 3 categories: glenoid fossae, condyles, and background. For structural fractures in these divided images, the internal force constraint of a snake model was used to replenish the integrity of the fracture boundary in a post-processing operation, and the initial boundary of the snake was obtained based on the basis of the tracking concept. A total of 206 cases of low-dose CT were used to verify the effectiveness of the algorithm, and such indicators as the Dice coefficient (DC) and mean surface distance (MSD) were used to evaluate the agreement between experimental results and the gold standard.ResultsThe proposed method is tested on a self-collected dataset. The results demonstrate that proposed method achieves state-of-the-art performance in terms of DCs = 0.92±0.03 (condyles) and 0.90±0.04 (glenoid fossae), and MSDs =0.20±0.19 mm (condyles) and 0.19±0.08 mm (glenoid fossae).ConclusionsThis study is the first to focus on the simultaneous segmentation of TMJ glenoid fossae and condyles. The proposed U-Net + tracking-based algorithm showed a relatively high segmentation efficiency, enabling it to achieve sought-after segmentation accuracy.

Multi-layer segmentation framework for cell nuclei using improved GVF Snake model, Watershed, and ellipse fitting

Screening of diseases including cervical cancer, breast cancer and colorectal cancer using cell images from pap smears have been widely applied in recent years. Accurate segmentation of cells including nuclei are important steps of the diagnosis and analysis. Separation of touching nuclei in microscopy images has become a challenge in biological and medical fields. Aiming at the specific screening scenes with limited dataset and labels, a novel multi-layer segmentation framework is proposed, by which the nuclei are segmented efficiently and accurately. First the Watershed and the improved GVF (gradient vector flow) Snake model (optimized by the rectified gradient) are combined for rough segmentation, which preliminarily separates the nuclei from background. Then the outputs from rough segmentation are used for the initialization of refined segmentation, overlapped nuclei is addressed by the convex hull detection, concave point detection and ellipse fitting. Two criteria for ellipse verification are presented to ensure the uniqueness of the fitted border. Better accuracy, robustness and lighter structure of nuclei splitting are gained. Two private (BJTU & BIT) and two publicly available dataset (U20S & NIH3T3) are used for the performance evaluation. Experimental results showed the nuclei are segmented by the proposed framework with effective results (sensitivity: 0.812, precision: 0.913, false positive: 1.8, false negative: 1.7). Proposed method outperforms deep learning models with lighter weight and less computation complexity, it can effectively and automatically segment nuclei or cells from smear images, especially for scenes without sufficient dataset and labels.