Local Contour Research Articles

Vocal cord anomaly detection based on Local Fine-Grained Contour Features

Laryngoscopy is a popular examination for vocal cord disease diagnosis. The conventional screening of laryngoscopic images is labor-intensive and depends heavily on the experience of the medical specialists. Automatic detection of vocal cord diseases from laryngoscopic images is highly sought to assist regular image reading. In laryngoscopic images, the symptoms of vocal cord diseases are concentrated in the inner vocal cord contour, which is often characterized as vegetation and small protuberances. The existing classification methods pay little, if any, attention to the role of vocal cord contour in the diagnosis of vocal cord diseases and fail to effectively capture the fine-grained features. In this paper, we propose a novel Local Fine-grained Contour Feature extraction method for vocal cord anomaly detection. Our proposed method consists of four stages: image segmentation to obtain the overall vocal cord contour, inner vocal cord contour isolation to obtain the inner contour curve by comparing the changes of adjacent pixel values, extraction of the latent feature in the inner vocal cord contour by taking the tangent inclination angle of each point on the contour as the latent feature, and the classification module. Our experimental results demonstrate that the proposed method improves the detection performance of vocal cord anomaly and achieves an accuracy of 97.21%.

Local contour features contribute to figure-ground segregation in monkey V4 neural populations and human perception

Figure-ground (FG) segregation is a crucial step towards the recognition of objects in natural scenes. Gestalt psychologists have emphasized the importance of contour features in perception of FG. Recent electrophysiological studies have identified a neural population in V4 that shows FG-dependent modulation (FG neurons). However, whether the contour features contribute to the modulation of the response patterns of the neural population remains unclear. In the present study, we quantified the contour features associated with Gestalt factors in local natural stimuli and examined whether salient contour-features evoked reliable perceptual and neural responses by analyzing response consistency (stability) across trials. The results showed the tendency that the more salient contour-features evoked the greater consistencies in the perceptual FG judgments and population-based neural responses in FG determination; a greater partial correlation for curvature and weaker correlations for closure and parallelism. Multiple linear regression analyses demonstrated that the perceptual consistency depended similarly on curvature and closure, and the neural consistency depended significantly on curvature but weakly on closure. We further observed a strong correlation between the consistencies in the perceptual and neural responses, i.e., stimuli that evoked more stable percepts tended to evoke more stable neural responses. These results indicate that local contour-features modulate the responses of the neural population in V4 and contribute to the perception of FG organization.

First results of a newly-built hard-X-ray/soft-Gamma spectrometer imaging system: on the aspect of plasma disruptions

Abstract A new Hard X-ray and soft gamma-ray spectrometer imaging System (HXS) has been built for the 2-Dimensional measurements of plasma emitted photons on the Experimental Advanced Superconducting Tokamak (EAST). The system uses a 2-D Cadmium Zinc Telluride (CZT) detector and an integrated electronics, which is as a whole shielded by a tungsten box with a pinhole and is tangential to the toroidal field. Three classes of typical energy spectra have been summarized in differently experimental scenarios over the past campaigns. After performing tomography calculations, the local emissivity contours have been obtained in different energy ranges which obviously show the asymmetry on the plasma cross section. The spatial perturbation structure is similar to the magnetohydrodynamics (MHD) modes with low mode numbers. In particular, the runaway island found by an infrared camera [R. Jaspers et al, Phys. Rev. Lett. 72, 4093 (1994)] is also measured by the HXS. There exists a reversal population on the energy spectra in both slide-away and strong neutral beam injection (NBI) shots. It is consistently observed that the count rate is increased in the low energy range before the plasma disruptions. Calculations in phase space indicate that the accelerated momentum flux can be deflected back to the low energy region by the large pitch-angle scattering. In the post-disruption phase the plasma current is not replaced by runaway electrons due to tearing modes or transiently bursting instabilities. This paper constructs the basics for the proper use of HXS for Hard X-ray and Soft Gamma-ray measurements in future investigations of plasma disruptions.

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.

The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI). Multiple feature selection methods, including Minimum Redundancy Maximum Relevance (MRMR), Recursive Feature Elimination (RFE), and Boruta, and multiple classifiers, including Multilayer Perceptron (MLP), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF), were employed. Synthetic Minority Oversampling Technique (SMOTE) was also used to determine if it boosts the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Our results indicated that the combination of SMOTE, IT (with 45% threshold), RFE feature selection and LR classifier showed the best performance (AUC = 0.93, ACC = 0.84, SEN = 0.85, SPE = 0.84) followed by SMOTE, FCM segmentation, MRMR feature selection, and LR classifier (AUC = 0.92, ACC = 0.87, SEN = 1, SPE = 0.84). The highest ACC belonged to the IT segmentation (with 45 and 50% thresholds) alongside Boruta feature selection and the NB classifier without SMOTE (ACC = 0.9, AUC = 0.78 and 0.76, SEN = 0.7, and SPE = 0.94, respectively). Our results indicate that selection of appropriate segmentation method and machine learning algorithm may be helpful in successful prediction of LVI in patients with NSCLC with high accuracy using PET radiomics analysis.

A New Local Region-Based Active Contour Model for Image Segmentation Based on Adaptive Double Potential Well Function

A New Local Region-Based Active Contour Model for Image Segmentation Based on Adaptive Double Potential Well Function

Retraction Note: Lip segmentation using localized active contour model with automatic initial contour

Retraction Note: Lip segmentation using localized active contour model with automatic initial contour

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

PurposeThis study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC).MethodsWe included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with ‘n_splits’ set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome.ResultsFrom 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity.ConclusionRadiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

Automatic 3D coronary artery segmentation based on local region active contour model.

Segmentation of coronary arteries in computed tomography angiography (CTA) images plays a key role in the diagnosis and treatment of coronary-related diseases. However, manually analyzing the large amount of data is time-consuming, and interpreting this data requires the prior knowledge and expertise of radiologists. Therefore, an automatic method is needed to separate coronary arteries from a given CTA dataset. Firstly, an anisotropic diffusion filter was employed to smooth the noise while preserving the vessel boundaries. The coronary skeleton was then extracted using a two-step process based on the intensity of the coronary. In the first step, the thick vessel skeleton was extracted by clustering, improved vesselness filtering and region growing, while in the second step, the thin vessel skeleton was extracted by the height ridge traversal method guided by the cylindrical model. Next, the vesselness measure, representing vessel a priori information, was incorporated into the local region active contour model based on the vessel geometry. Finally, the initial contour of the active contour model was generated using the coronary artery skeleton for effective segmentation of the three-dimensional (3D) coronary arteries. Experimental results on chest CTA images show that the method is able to segment coronary arteries effectively with an average precision, recall and dice similarity coefficient (DSC) of 86.64%, 91.26% and 79.13%, respectively, and has a good performance in thin vessel extraction. The method does not require manual selection of vessel seeds or setting of initial contours, and allows for the extraction of a successful coronary artery skeleton and eventual effective segmentation of the coronary arteries.

Composite descriptor based on contour and appearance for plant species identification

In this paper, we propose an effective composite descriptor combining contour and appearance features for plant species identification. The composite descriptor uses two different fusion schemes: the first is a method based on the fusion of low-level local contour and appearance features; that is, we use local triangle features and local speeded-up robust features (SURF) to describe leaf images, respectively. Then, we utilize the Hausdorff distance to measure the leaf discrepancy to complete the plant identification task. The second is a method of global feature fusion based on high-level shape and appearance features. Specifically, the Fisher vector encodes the local triangle, and SURF features into high-level shape and appearance features. Then, the Euclidean distance is used to compute the dissimilarity of leaves to realize plant leaf recognition. The composite descriptor based on these two fusion schemes can well describe the characteristics of leaf images and improve the ability to identify plant leaves. This composite descriptor is extensively tested on several standard plant databases, including simple, compound, and natural scene plant leaf datasets. Experimental results demonstrate that the composite descriptor achieves high recognition accuracy, outperforming existing state-of-the-art benchmarks. In addition, we further applied our method directly to shape recognition and achieved good recognition results, demonstrating the wide range of applications of our method.

An optical-scan method for measuring the as installed surface port incidence angles for flush air data sensing (FADS) systems

The Flush Air Data Sensing (FADS) System, where air data are inferred from non-intrusive surface pressure measurements, uses natural contours of the vehicle forebody, wing leading edge, or probe. Although multiple methods have been developed to derive airdata from the sensed pressure matrix, all methods rely on accurate knowledge of local surface contours at the port locations. One of the most well-developed solution methods curve-fits the surface pressure distribution against the associated surface incidence angles using a quasi-Newtonian model. The well-known "Triples" algorithm extracts airdata from the curve-fit model. This solution method requires precise knowledge of as-installed incidence angles, i.e. the angles between the surface normal and the longitudinal axis of the vehicle. This study investigates the feasibility and accuracy of using an inexpensive optical-scanning system to measure the in-situ FADS pressure ports surface incidence angles. Here, two legacy 3-D printed probe shapes, as previously tested during a series of very low-speed wind tunnel tests, were used to develop and evaluate this method. The shapes 1) a hemispherical head cylindrical forebody, and 2) a Rankine-Body, were scanned along the longitudinal axis and the resulting point-cloud was edited using open-source software to generate three concentric "loops" surrounding each surface port. Each annular loop was assumed as co-planar with the surface port, and the singular-value decomposition (SVD) was used calculate the local surface gradient vector from the null-space solution. From the resulting gradient vector, geometric relationships calculate the port's polar coordinates including the surface incidence angle. For both body contours the resulting calculations are compared to the "known" design surface angles as prescribed for the 3-D prints. Error plots are presented for each individual ring-set, and for the collected set using all three ring together. For the collected data sets, the incidence angle calculations are accurate to within a quarter-degree.

Research on Image Stitching Algorithm Based on Point-Line Consistency and Local Edge Feature Constraints.

Image stitching aims to synthesize a wider and more informative whole image, which has been widely used in various fields. This study focuses on improving the accuracy of image mosaic and proposes an image mosaic method based on local edge contour matching constraints. Because the accuracy and quantity of feature matching have a direct influence on the stitching result, it often leads to wrong image warpage model estimation when feature points are difficult to detect and match errors are easy to occur. To address this issue, the geometric invariance is used to expand the number of feature matching points, thus enriching the matching information. Based on Canny edge detection, significant local edge contour features are constructed through operations such as structure separation and edge contour merging to improve the image registration effect. The method also introduces the spatial variation warping method to ensure the local alignment of the overlapping area, maintains the line structure in the image without bending by the constraints of short and long lines, and eliminates the distortion of the non-overlapping area by the global line-guided warping method. The method proposed in this paper is compared with other research through experimental comparisons on multiple datasets, and excellent stitching results are obtained.

Sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images.

Liver cancer from abdominal CT images must be accurately segmented for the purpose of diagnosis with treatment planning. But, the similarity in gray values between the liver and the surrounding tissues poses a challenge. To address this, a novel sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images (SDBN-ELFAC-LCS-CT) is proposed. This method incorporates dynamic adaptive pooling and residual modules in SDBN to improve the feature selection and generalization ability. Additionally, the 3D reconstruction is performed to refine segmentation results. The proposed SDBN-ELFAC-LCS-CT approach is implemented in MATLAB. The performance of the proposed SDBN-ELFAC-LCS-CT achieves dice coefficients that were up to 96.16% higher and 75.88%, 88.75%, and 71.16% lower. Volumetric overlap error compared with existing models, like basic ensembles of vanilla-style deep learning modes, increases liver segmentation from CT imageries (BEVS-LCS-CT), an incorporated 3 dimensional sparse deep belief network along enriched seagull optimization approach for liver segmentation (3DBN-ESOA-LCS-CT) and iterative convolutional encoder-decoder network and multiple scale context learning for segmenting liver (ICEDN-LCS-CT), respectively.

Computations of J-integral and T*-integral in elastic–plastic fracture by the quadrature element method

In this paper, J-integral and T*-integral in elastic–plastic fracture are computed by the quadrature element method (QEM). Since high stress gradients exist in the immediate vicinity of the crack front, computing these integrals accurately is not a trivial task. The QEM facilitates the construction of arbitrarily high-order elements, which effectively capture the stress gradients, making it particularly suitable for this computation. After performing an elastoplastic stress–strain analysis of the fracture problem, the stress and deformation states are further formulated to compute the integrals. The integrals are formulated in several forms, including the contour and domain forms, as well as the incremental and total forms. Classical numerical examples are solved to demonstrate the effectiveness and high accuracy of the method. Overall, the equivalent domain integral (EDI) form outperforms the contour integral form because errors at the local contour have less effect on the integral. Moreover, the QEM yields more accurate solutions than the popular finite element method (FEM) that employs low-order finite elements and shares the same number of degrees of freedom. Additionally, it is found that the EDI form solution is not very sensitive to the specific type of the S function. Therefore, a linear function is suggested for convenience.

Расчет поливной нормы при капельном орошении древесно-плодовых культур в садовых насаждениях

Purpose: to develop the methodology for calculating irrigation rate for drip irrigation of tree-fruit crops, ensuring the formation of moistened soil contours in soil space with the required geometric and moisture parameters. Materials and methods. The empirical basis for the methodology development was the materials of the author's research devoted to determining the geometric dimensions (parameters) of soil moisture contours. The working hypothesis of the study involved determining the water supply rate for one emitter, one tree-fruit plant and a number of crops in the plantation, with further determining drip irrigation rate per unit area of drip-irrigated agricultural land, taking into account the number of drip micro-water outlets located on it. Results and discussion. When determining the geometric parameters of moisture contours and water supply rate, the main soil and technological parameters of drip irrigation and contour formation (physical clay content in soil, its lowest moisture capacity and bulk density, levels of pre-irrigation and after-irrigation soil moisture, specified depth soil profile moistening and dripper consumption) were considered as influence factors. The dependencies obtained by the author make it possible to determine the linear, areal and volumetric dimensions of local moisture contours and water supply rate, which, under appropriate conditions of drip irrigation, ensures the formation of a local, initially formed moisture contour with the required soil wetting depth in the soil layer. A study of the proposed methodology showed a deviation of the predicted parameters from the experimental soil moisture contours within ±10.0 %. Conclusions. A methodology allowing determining the irrigation rate for drip irrigation of agricultural land as the sum of the water supply rates of all drip water outlets irrigating 1 hectare of agricultural land with practical accuracy has been developed.

Расчет поливной нормы при капельном орошении древесно-плодовых культур в садовых насаждениях

Purpose: to develop the methodology for calculating irrigation rate for drip irrigation of tree-fruit crops, ensuring the formation of moistened soil contours in soil space with the required geometric and moisture parameters. Materials and methods. The empirical basis for the methodology development was the materials of the author's research devoted to determining the geometric dimensions (parameters) of soil moisture contours. The working hypothesis of the study involved determining the water supply rate for one emitter, one tree-fruit plant and a number of crops in the plantation, with further determining drip irrigation rate per unit area of drip-irrigated agricultural land, taking into account the number of drip micro-water outlets located on it. Results and discussion. When determining the geometric parameters of moisture contours and water supply rate, the main soil and technological parameters of drip irrigation and contour formation (physical clay content in soil, its lowest moisture capacity and bulk density, levels of pre-irrigation and after-irrigation soil moisture, specified depth soil profile moistening and dripper consumption) were considered as influence factors. The dependencies obtained by the author make it possible to determine the linear, areal and volumetric dimensions of local moisture contours and water supply rate, which, under appropriate conditions of drip irrigation, ensures the formation of a local, initially formed moisture contour with the required soil wetting depth in the soil layer. A study of the proposed methodology showed a deviation of the predicted parameters from the experimental soil moisture contours within ±10.0 %. Conclusions. A methodology allowing determining the irrigation rate for drip irrigation of agricultural land as the sum of the water supply rates of all drip water outlets irrigating 1 hectare of agricultural land with practical accuracy has been developed.

LeaNet: Lightweight U-shaped architecture for high-performance skin cancer image segmentation

Skin cancer diagnosis often relies on image segmentation as a crucial aid, and a high-performance segmentation can lower misdiagnosis risks. Part of the medical devices often have limited computing power for deploying image segmentation algorithms. However, existing high-performance algorithms for image segmentation primarily rely on computationally intensive large models, making it challenging to meet the lightweight deployment requirement of medical devices. State-of-the-art lightweight models are not able to capture both local and global feature information of lesion edges due to their model structures, result in pixel loss of lesion edge. To tackle this problem, we propose LeaNet, a novel U-shaped network for high-performance yet lightweight skin cancer image segmentation. Specifically, LeaNet employs multiple attention blocks in a lightweight symmetric U-shaped design. Each blocks contains a dilated efficient channel attention (DECA) module for global and local contour information and an inverted external attention (IEA) module to improve information correlation between data samples. Additionally, LeaNet uses an attention bridge (AB) module to connect the left and right sides of the U-shaped architecture, thereby enhancing the model's multi-level feature extraction capability. We tested our model on ISIC2017 and ISIC2018 datasets. Compared with large models like ResUNet, LeaNet improved the ACC, SEN, and SPEC metrics by 1.09 %, 2.58 %, and 1.6 %, respectively, while reducing the model's parameter number and computational complexity by 570x and 1182x. Compared with lightweight models like MALUNet, LeaNet achieved improvements of 2.07 %, 4.26 %, and 3.11 % in ACC, SEN, and SPEC, respectively, reducing the parameter number and computational complexity by 1.54x and 1.04x.

Numerical simulation of the permeable wedge-shaped geometry incorporating electroosmosis, solar radiation and heat source-sink effects

The present work deals with the convective double diffusive magneto-cross-nanofluid flow across the permeable wedge. Inclined magnetic field, electro-osmosis, heat source-sink parameter, and solar radiation process are the manuscript's standout characteristics. The numerical simulation of the given flow results in a system of generic nonlinear differential equations, which are then exactly transformed into ordinary differential equations. The visual results are drawn using the bvp4c approach to see the effects of relevant changing elements across the porous wedge. The velocity field generally behaves as the increasing function of thermal and solutal Grashof number but surges along the nanofluid Grashof number. The Brownian diffusion coefficient surges the temperature and double-diffusivity, while minors the concentration. The radiation increases the temperature while lowering the concentration rate and the double diffusivity due to the relative importance of thermal radiation and conduction heat transfer. The nanofluid Lewis number particularly slows down the double-diffusivity profile. Electro-osmotic flow is a typical technique in chemical analysis, soil analysis and processing, and microfluidic devices employing structures with high-voltage surfaces. In the limiting case, current results are also compared to previously published literature. The graphical sketches of local skin friction, Nusselt number, and Sherwood number contour elaborated our analyses more precisely.

Multi-Stream Fusion Network for Skeleton-Based Construction Worker Action Recognition.

The global concern regarding the monitoring of construction workers' activities necessitates an efficient means of continuous monitoring for timely action recognition at construction sites. This paper introduces a novel approach-the multi-scale graph strategy-to enhance feature extraction in complex networks. At the core of this strategy lies the multi-feature fusion network (MF-Net), which employs multiple scale graphs in distinct network streams to capture both local and global features of crucial joints. This approach extends beyond local relationships to encompass broader connections, including those between the head and foot, as well as interactions like those involving the head and neck. By integrating diverse scale graphs into distinct network streams, we effectively incorporate physically unrelated information, aiding in the extraction of vital local joint contour features. Furthermore, we introduce velocity and acceleration as temporal features, fusing them with spatial features to enhance informational efficacy and the model's performance. Finally, efficiency-enhancing measures, such as a bottleneck structure and a branch-wise attention block, are implemented to optimize computational resources while enhancing feature discriminability. The significance of this paper lies in improving the management model of the construction industry, ultimately aiming to enhance the health and work efficiency of workers.

Image Segmentation based on Energy Fitting Models – A Review

As a result of changes in imaging technology, segmenting the area of interest (ROI) from medical images is an extremely important yet challenging task. It is still difficult for the global energy-based active contour model (ACM) to properly extract the ROI from medical images, despite the fact that many techniques based on the local region-based active contour model have been proposed to deal with intensity inhomogeneity. This brief study aims to assess the performance of current techniques that have been published in the recent years and have been used to image segmentation. The methods under consideration include the various energy fitting models that have been created to drive the active contour are highlighted in this review study. Each model was examined against a medical image, an MRI brain image, and an image that was not taken by a medical professional. According to the results of the comparison study, it can be determined which technique is better appropriate for image segmentation even when there is intensity inhomogeneity in the images.

Checkerboard corner detection method based onneighborhood linear fitting.

To improve the calibration accuracy of a vision measurement system, a checkerboard corner detection method based on linear fitting of the checkerboard local contour is proposed. First, by binarization and morphological dilation of the checkerboard image, the coordinates of two adjacent vertices of adjacent dark squares are obtained; the midpoint of the two vertices is taken as the reference point; the reference dotted array is obtained; and the Zernike moment subpixel method is used to obtain the checkerboard contour data points in the neighborhood of each reference point. Finally, the contour points are classified according to the orientation based on the reference points; two intersecting lines are fitted; and the intersection of the two lines is exactly the corner point that we want to find. A camera calibration experiment was conducted on the same group of checkerboard images. The results show that the calibration results of the corner points obtained based on this method are highly consistent with the OpenCV library function method and the MATLAB Toolbox calibration method, and the reprojection error is smaller. At the same time, it is robust to changes in the light source brightness.