Ellipse Fitting Research Articles

Estimating Tree Diameter at Breast Height (DBH) Using iPad Pro LiDAR Sensor in Boreal Forests

Traditional Diameter at Breast Height (DBH) mensuration is labor-intensive and costly. This scoping study explored the possibility of using the Apple iPad Pro Light Detection And Ranging (LiDAR) sensor to estimate DBH. Three plots were scanned in a research plantation near Thunder Bay, Canada. Sites consisted of either Black Spruce (Picea mariana) or Red Pine (Pinus resinosa) planted with different initial densities. DBH was manually measured for validation. Point clouds were acquired for each plot using three scanning patterns; circular, figure-8, and transect. Single and five cross sections of 4 or 10 cm in thickness were extracted from each point cloud, centered at 1.3 m above the ground. Two circle fitting algorithms (Pratt, Taubin) and two ellipse fitting algorithms (Taubin, Szpak) were applied to the extracted cross-sections to estimate DBH. Scanning pattern and curve-fitting formula significantly impacted DBH estimate accuracy (p-value ≤ 0.001), while cross-section count and thickness did not. The circular scanning pattern with a single 4 cm cross-section and a combination of circle- and ellipse-fitting formulas was the most accurate DBH estimation method (RMSE = 1.1 cm; 6.17%).

Fetal Ultrasound Segmentation and Measurements Using Appearance and Shape Prior Based Density Regression with Deep CNN and Robust Ellipse Fitting.

Accurately segmenting the structure of the fetal head (FH) and performing biometry measurements, including head circumference (HC) estimation, stands as a vital requirement for addressing abnormal fetal growth during pregnancy under the expertise of experienced radiologists using ultrasound (US) images. However, accurate segmentation and measurement is a challenging task due to image artifact, incomplete ellipse fitting, and fluctuations due to FH dimensions over different trimesters. Also, it is highly time-consuming due to the absence of specialized features, which leads to low segmentation accuracy. To address these challenging tasks, we propose an automatic density regression approach to incorporate appearance and shape priors into the deep learning-based network model (DR-ASPnet) with robust ellipse fitting using fetal US images. Initially, we employed multiple pre-processing steps to remove unwanted distortions, variable fluctuations, and a clear view of significant features from the US images. Then some form of augmentation operation is applied to increase the diversity of the dataset. Next, we proposed the hierarchical density regression deep convolutional neural network (HDR-DCNN) model, which involves three network models to determine the complex location of FH for accurate segmentation during the training and testing processes. Then, we used post-processing operations using contrast enhancement filtering with a morphological operation model to smooth the region and remove unnecessary artifacts from the segmentation results. After post-processing, we applied the smoothed segmented result to the robust ellipse fitting-based least square (REFLS) method for HC estimation. Experimental results of the DR-ASPnet model obtain 98.86% dice similarity coefficient (DSC) as segmentation accuracy, and it also obtains 1.67mm absolute distance (AD) as measurement accuracy compared to other state-of-the-art methods. Finally, we achieved a 0.99 correlation coefficient (CC) in estimating the measured and predicted HC values on the HC18 dataset.

An approach of separating the overlapped cells or nuclei based on the outer Canny edges and morphological erosion.

In biomedicine, the automatic processing of medical microscope images plays a key role in the subsequent analysis and diagnosis. Cell or nucleus segmentation is one of the most challenging tasks for microscope image processing. Due to the frequently occurred overlapping, few segmentation methods can achieve satisfactory segmentation accuracy yet. In this paper, we propose an approach to separate the overlapped cells or nuclei based on the outer Canny edges and morphological erosion. The threshold selection is first used to segment the foreground and background of cell or nucleus images. For each binary connected domain in the segmentation image, an intersection based edge selection method is proposed to choose the outer Canny edges of the overlapped cells or nuclei. The outer Canny edges are used to generate a binary cell or nucleus image that is then used to compute the cell or nucleus seeds by the proposed morphological erosion method. The nuclei of the Human U2OS cells, the mouse NIH3T3 cells and the synthetic cells are used for evaluating our proposed approach. The quantitative quantification accuracy is computed by the Dice score and 95.53% is achieved by the proposed approach. Both the quantitative and the qualitative comparisons show that the accuracy of the proposed approach is better than those of the area constrained morphological erosion (ACME) method, the iterative erosion (IE) method, the morphology and watershed (MW) method, the Generalized Laplacian of Gaussian filters (GLGF) method and ellipse fitting (EF) method in separating the cells or nuclei in three publicly available datasets.

Diagnosis of Power Switch Faults in Three-Phase Permanent Magnet Synchronous Motors via Current-Signature Technique

The paper deals with the development of a model-based current-signature algorithm for the detection and isolation of power switch faults in three-phase Permanent Magnet Synchronous Motors (PMSMs). The algorithm, by elaborating the motor currents feedbacks, reconstructs the current phasor trajectories in the Clarke plane through elliptical fittings, up to detecting and isolating the fault depending on the characteristics of the signature deviation from the nominal one. As a rough approximation, as typically proposed in the literature, the fault of one out of six power switches implies that, at constant speed operation, the phasor trajectory deviates from the nominal circular path up to a semi-circular “D-shape” signature, the inclination of which depends on the failed converter leg. However, this evolution can significantly deviate in practical cases, due to the dynamics related to the transition of motor phase connections from failed to active switches. The study demonstrates that an online ellipse fitting of the current signature can be effective for diagnosis, through correlating the ellipse centre to the location of the failed switch. The performances of the proposed monitoring technique are here assessed via the nonlinear simulation of a PMSM employed for the propulsion of a lightweight fixed-wing Unmanned Aerial Vehicle (UAV), by quantifying the fault latencies and the related transients.

A Method for Measuring Shaft Diameter Based on Light Stripe Image Enhancement.

When the workpiece surface exhibits strong reflectivity, it becomes challenging to obtain accurate key measurements using non-contact, visual measurement techniques due to poor image quality. In this paper, we propose a high-precision measurement method shaft diameter based on an enhanced quality stripe image. By capturing two stripe images with different exposure times, we leverage their different characteristics. The results extracted from the low-exposure image are used to perform grayscale correction on the high-exposure image, improving the distribution of stripe grayscale and resulting in more accurate extraction results for the center points. The incorporation of different measurement positions and angles further enhanced measurement precision and robustness. Additionally, ellipse fitting is employed to derive shaft diameter. This method was applied to the profiles of different cross-sections and angles within the same shaft segment. To reduce the shape error of the shaft measurement, the average of these measurements was taken as the estimate of the average diameter for the shaft segment. In the experiments, the average shaft diameters determined by averaging elliptical estimations were compared with shaft diameters obtained using a coordinate measuring machine (CMM) the maximum error and the minimum error were respectively 18 μm and 7 μm; the average error was 11 μm; and the root mean squared error of the multiple measurement results was 10.98 μm. The measurement accuracy achieved is six times higher than that obtained from the unprocessed stripe images.

Ellipse Fitting Method based on Pendulum Genetic Algorithm Particle Swarm Optimization for Well Diameter Measurement by Laser Distance Sensor

Ellipse Fitting Method based on Pendulum Genetic Algorithm Particle Swarm Optimization for Well Diameter Measurement by Laser Distance Sensor

Anisotropic Scale-Invariant Ellipse Detection.

Detecting ellipses poses a challenging low-level task indispensable to many image analysis applications. Existing ellipse detection methods commonly encounter two fundamental issues. First, inferior detection accuracy could be incurred on a small ellipse than that on a large one; this introduces the scale issue. Second, inferior detection accuracy could be yielded along the minor axis than along the major one of the same ellipse; this leads to the anisotropy issue. To address these issues simultaneously, a novel anisotropic scale-invariant (ASI) ellipse detection methodology is proposed. Our basic idea is to perform ellipse detection in a transformed image space referred to as the ellipse normalization (EN) space, in which the desired ellipse from the original image is 'normalized' to the unit circle. With the establishment of the EN-space, an analytical ellipse fitting scheme and a set of distance measures are developed. Theoretical justifications are then derived to prove that both our ellipse fitting scheme and distance measures are invariant to anisotropic scaling, and thus each ellipse can be detected with the same accuracy regardless of its size and ellipticity. By incorporating these components into two recent state-of-the-art algorithms, two ASI ellipse detectors are finally developed and exploited to verify the effectiveness of our proposed methodology.

Extraction of Laser Spot Centroid of Turbulent Disturbance Based on Ellipse Fitting Method

Extraction of Laser Spot Centroid of Turbulent Disturbance Based on Ellipse Fitting Method

Gabor fully convolutional network and ellipse fitting technique for foetal head segmentation and biometry measurement

Gabor fully convolutional network and ellipse fitting technique for foetal head segmentation and biometry measurement

Morphometric Measurement of Fish Blood Cell: An Image Processing and Ellipse Fitting Technique

Morphometric Measurement of Fish Blood Cell: An Image Processing and Ellipse Fitting Technique

A novel image processing procedure for the quantitative evaluation of dental enamel prism arrangement.

Enamel prism is the main microstructural unit of mammalian enamel which composed of hundreds of bioapatite nanocrystals. Prism structure plays a key role in the excellent mechanical performance of dental enamel during millions of chewing cycles without significant remodeling. Thus, quantitative understanding of prism architecture is of utmost importance for biomechanical materials design. To characterize enamel prism orientation quantitatively, a novel image processing method has been developed. Our method is based on scanning electron microscopy images of etched enamel surface and consists of an ellipse fitting procedure, which provides a numerical approximation of prism shape and orientation in the studied cross section. The obtained analytical data allow to construct color coded orientation maps, which provide quick and useful insight into the microstructure of enamel. Besides striking visualization, orientation maps allow to extract and plot the rich information on the azimuthal and inclination angles of the prisms as function of location. Numerical data on prism arrangement can be analyzed using statistical tools over large areas, which paves the way towards quantifying comparative investigation of prism arrangement either in dentistry research or evolution biology. The application of the method is demonstrated for a distal-mesial cross-section of sound human tooth enamel. HIGHLIGHTS: Scanning electron microscopy images of etched enamel surface are analyzed using ellipse fitting. Geometrical parameters of the fitted ellipses provide numerical data of thousands of prisms. Prism arrangement is visualized on color coded orientation maps and analyzed using statistical tools.

Dependence of the ellipse fitting noise on the differential phase between interferometers in atom gravity gradiometers.

Ellipse fitting is widely used in the extraction of the differential phase between atom interferometers amid substantial common phase noise. This study meticulously examines the dependency of extraction noise on the differential phase between atom interferometers during ellipse fitting. It reveals that the minimum extraction noise can manifest at distinct differential phases, contingent upon the dominance of different noise types. Moreover, the outcomes are influenced by whether the interferometers undergo simultaneous detection or not. Our theoretical simulations find empirical validation in a compact horizontal atom gravity gradiometer. The adjustment of the differential phase significantly enhances measurement sensitivity, culminating in a differential gravity resolution of 1.6 × 10-10 g @ 4800 s.

Prior information-based ellipse fitting optimization method for optical navigation with a short-arc horizon

Optical navigation is a key technology in deep-space exploration. Previous studies have struggled to accurately calculate the celestial model in short-arc horizon scenes, greatly reducing navigation accuracy. To address this issue, this article proposes a high-precision short-arc fitting optimization algorithm based on prior shape information. The suggested technique is a two-step estimation process that uses minimal horizon features to solve the optimal model parameters iteratively. First, the random sample consensus (RANSAC) algorithm is applied to compute the prior shape information of celestial bodies and suppress the interference of outliers. Then, an improved ellipse optimization equation is designed to integrate the shape information while ensuring efficiency. Finally, to perform precise short-arc fitting, the model parameters are iteratively optimized using an adaptive trust region. Numerous comparison tests demonstrate that the proposed approach achieves rapid convergence in three steps and requires approximately 15–20% less minimum arc length than Christian’s method.

Concise extraction and characterization of the pore-throat network in unconventional hydrocarbon reservoirs: A new perspective

In this study, a new image-based method for the extraction and characterization of pore-throat network for unconventional hydrocarbon storage and exploitation is proposed. “Pore-throat solidity”, which is analogous to particle solidity, and a new method for automatic identification of pores and throats in tight sandstone oil reservoirs are introduced. Additionally, the “pore-throat combination” and “pure pore” are defined and distinguished by drawing the cumulative probability curve of the pore-throat solidity and by selecting an appropriate cutoff point. When the discrete grid set is recognized as a pore-throat combination, Legendre ellipse fitting and minimum Feret diameter are used. When the pore and throat grid sets are identified as pure pores, the pore diameter can be directly calculated. Using the new method, the analytical results for the physical parameters and pore radius agree well with most prior studies. The results comparing the maximum ball and the new model could also prove the accuracy of the latter's in micro and nano scales. The new model provides a more practical theoretical basis and a new calculation method for the rapid and accurate evaluation of the complex processes of oil migration.

3D flow and fibre orientation modelling of compression moulding of A-SMC: simulations and experimental validation in squeeze flow

Sheet Moulding Compound (SMC) based composites have a large potential in industrial contexts due to the possibility of achieving comparatively short manufacturing times. It is however necessary to be able to numerically predict both mechanical properties as well as manufacturability of parts.In this paper a fully 3D, semi-empirical model based on fluid mechanics for the compression moulding of SMC is described and discussed, in which the fibres and the resin are modelled as a single, inseparable fluid with a viscosity that depends on volume fraction of fibres, shear strain rate and temperature. This model is applied to an advanced carbon-fibre SMC with a high fibre volume fraction (35%). Simulations are run on a model of a squeeze test rig, allowing comparison to experimental results from such a rig. The flow data generated by this model is then used as input for an Advani-Tucker type of model for the evolution of the fibre orientation during the pressing process. Numerical results are also obtained from the software 3DTimon. The resulting fibre orientation distributions are then compared to experimental results that are obtained from microscopy. The experimental measurement of the orientation tensors is performed using the Method of Ellipses. A new, automated, accurate and fast method for the ellipse fitting is developed using machine learning. For the studied case, comparison between the experimental results and numerical methods indicate that 3D Timon better captures the random orientation at the outer edges of the circular disc, while 3D CFD show larger agreement in terms of the out-of-plane component. One of the advantages of the new image technique is that less work is required to obtain microscope images with a quality good enough for the analysis.

Breast MRI radiomics and machine learning radiomics-based predictions of response to neoadjuvant chemotherapy - how are they affected by variations in tumour delineation?

Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumour VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affects different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.

Position measurement technology of boom-type roadheader based on binocular vision

Positioning of underground mining equipment in coal mines is a primary challenge in the intelligent development of tunnel excavation. Accurate and reliable position measurement plays a crucial role in improving excavation efficiency. However, the stability and accuracy of traditional measurement methods are difficult to guarantee due to factors such as vibration, magnetic interference, and the absence of GPS signals in coal mine environments. To address the problem of unstable measurement of the boom-type roadheader‘s position, this paper proposes a binocular vision-based measurement technique that utilizes four light spots as characteristic points. By processing the target images captured by the binocular camera and combining the minimum bounding rectangle and ellipse fitting of the spot regions, the method successfully obtains the four light spot characteristics. Subsequently, precise matching and stereo distance measurement of the target in the left and right images enable the determination of the boom-type roadheader’s posi-tion information. A positioning platform based on binocular vision is built and experimentally evaluated. The results demonstrate that this method can achieve accurate spot extraction and stable measurement of the machine’s position, even in complex backgrounds such as mixed lighting and low illumination. The planar measurement errors within a distance range of 50 m are all within ±25 mm, which basically meets the required construction precision for tunnel excavation.

Optic Canal Size is an Indicator for the Accessory Optic Canal: Applications for Anterior Clinoidectomy

BackgroundThe ophthalmic artery normally travels with the optic nerve through the optic canal. However, sometimes, the ophthalmic artery travels through a foramen within the optic strut named an accessory optic canal, double optic canal, or ophthalmic canal. This variant puts individuals at an increased risk for blindness or death during anterior clinoidectomy due to unforeseen hemorrhage of the ophthalmic artery or internal carotid artery when the optic strut is separated from the body of the sphenoid bone. Several features make the accessory optic canal difficult to recognize on imaging: its variant nature, small size, and ability to masquerade as a caroticoclinoid foramen or a pneumatized sphenoidal structure. Hence, improved methods of presurgical identification are warranted. The aim of this study was to assess the size and shape of the optic canal, with and without a concomitant accessory optic canal, to determine whether measurement of the optic canal may provide useful information regarding the presence of an accessory optic canal. MethodsIn 191 dry crania, optic canals with and without concomitant accessory optic canals were assessed for the following parameters: canal area, canal perimeter, circularity, solidity, the axes and aspect ratio of a best fit ellipse, and roundness. ResultsNormal optic canals were found to have a larger area (p=0.036), perimeter (p=0.043), and minor axis of a best fit ellipse (p=0.031) than the optic canals that occurred alongside accessory optic canals. ConclusionsAsymmetry in optic canal size can help indicate the presence of a unilateral accessory optic canal prior to surgery.

A computer-aided determining method for the myometrial infiltration depth of early endometrial cancer on MRI images

To classify early endometrial cancer (EC) on sagittal T2-weighted images (T2WI) by determining the depth of myometrial infiltration (MI) using a computer-aided diagnosis (CAD) method based on a multi-stage deep learning (DL) model. This study retrospectively investigated 154 patients with pathologically proven early EC at the institution between January 1, 2018, and December 31, 2020. Of these patients, 75 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA and 79 were in FIGO stage IB. An SSD-based detection model and an Attention U-net-based segmentation model were trained to select, crop, and segment magnetic resonance imaging (MRl) images. Then, an ellipse fitting algorithm was used to generate a uterine cavity line (UCL) to obtain MI depth for classification. In the independent test datasets, the uterus and tumor detection model achieves an average precision rate of 98.70% and 87.93%, respectively. Selecting the optimal MRI slices method yields an accuracy of 97.83%. The uterus and tumor segmentation model with mean IOU of 0.738 and 0.655, mean PA of 0.867 and 0.749, and mean DSC of 0.845 and 0.779, respectively. Finally, the CAD method based on the calculated MI depth reaches an accuracy of 86.9%, a sensitivity of 81.8%, and a specificity of 91.7% for early EC classification. In this study, the CAD method implements an end-to-end early EC classification and is found to be on par with radiologists in terms of performance. It is more intuitive and interpretable than previous DL-based CAD methods.

An automatic reduction method of 3D bone fragments based on a novel section contour point descriptor.

Comminuted fractures are orthopedic traumas with greater surgical difficulty. In clinical treatment, a great challenge is precise reduction of multiple broken bone fragments; Another great challenge is personalized and precise internal fixation after reduction. For these two issues, we designed an automated method framework for precise reduction and internal fixation of comminuted fractures. First, the Gaussian mixture model (GMM) is used to distinguish section points and noise points in a broken bone model; Second, ellipse fitting is carried out to achieve section points matching and a descriptor is proposed to describe the section features; Then, the Convolution Auto-Encoder (CAE) and genetic algorithm are used to extract feature vectors; Finally, after broken bone models registration, internal fixed plate can be reconstructed. Three verification experiments for comminuted bone fracture show this method has high accuracy and good efficiency. It can provide support for minimally invasive treatment for comminuted fractures.