scholarly journals A Hybrid Method for Endocardial Contour Extraction of Right Ventricle in 4-Slices from 3D Echocardiography Dataset

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Faten A. Dawood ◽  
Rahmita W. Rahmat ◽  
Suhaini B. Kadiman ◽  
Lili N. Abdullah ◽  
Mohd D. Zamrin
Keyword(s):  
Hybrid Method ◽  
Search Algorithm ◽  
Region Of Interest ◽  
Ground Truth ◽  
Speckle Noise ◽  
Contour Detection ◽  
3D Echocardiography ◽  
Phase Iii ◽  
Contour Extraction ◽  
Endocardial Contour

This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours.

scholarly journals Automated analysis of 3D-echocardiography using spatially registered patient-specific CMR meshes

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D Zhao ◽  
E Ferdian ◽  
GD Maso Talou ◽  
GM Quill ◽  
K Gilbert ◽  
...  
Keyword(s):  
New Zealand ◽  
Interobserver Variability ◽  
Ground Truth ◽  
Automated Analysis ◽  
3D Echocardiography ◽  
Training Data ◽  
Patient Specific ◽  
Manual Analysis ◽  
Lv Mass ◽  
3D Echo

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart Foundation (NHF) of New Zealand Health Research Council (HRC) of New Zealand Artificial intelligence shows considerable promise for automated analysis and interpretation of medical images, particularly in the domain of cardiovascular imaging. While application to cardiac magnetic resonance (CMR) has demonstrated excellent results, automated analysis of 3D echocardiography (3D-echo) remains challenging, due to the lower signal-to-noise ratio (SNR), signal dropout, and greater interobserver variability in manual annotations. As 3D-echo is becoming increasingly widespread, robust analysis methods will substantially benefit patient evaluation.  We sought to leverage the high SNR of CMR to provide training data for a convolutional neural network (CNN) capable of analysing 3D-echo. We imaged 73 participants (53 healthy volunteers, 20 patients with non-ischaemic cardiac disease) under both CMR and 3D-echo (<1 hour between scans). 3D models of the left ventricle (LV) were independently constructed from CMR and 3D-echo, and used to spatially align the image volumes using least squares fitting to a cardiac template. The resultant transformation was used to map the CMR mesh to the 3D-echo image. Alignment of mesh and image was verified through volume slicing and visual inspection (Fig. 1) for 120 paired datasets (including 47 rescans) each at end-diastole and end-systole. 100 datasets (80 for training, 20 for validation) were used to train a shallow CNN for mesh extraction from 3D-echo, optimised with a composite loss function consisting of normalised Euclidian distance (for 290 mesh points) and volume. Data augmentation was applied in the form of rotations and tilts (<15 degrees) about the long axis. The network was tested on the remaining 20 datasets (different participants) of varying image quality (Tab. I). For comparison, corresponding LV measurements from conventional manual analysis of 3D-echo and associated interobserver variability (for two observers) were also estimated. Initial results indicate that the use of embedded CMR meshes as training data for 3D-echo analysis is a promising alternative to manual analysis, with improved accuracy and precision compared with conventional methods. Further optimisations and a larger dataset are expected to improve network performance. (n = 20) LV EDV (ml) LV ESV (ml) LV EF (%) LV mass (g) Ground truth CMR 150.5 ± 29.5 57.9 ± 12.7 61.5 ± 3.4 128.1 ± 29.8 Algorithm error -13.3 ± 15.7 -1.4 ± 7.6 -2.8 ± 5.5 0.1 ± 20.9 Manual error -30.1 ± 21.0 -15.1 ± 12.4 3.0 ± 5.0 Not available Interobserver error 19.1 ± 14.3 14.4 ± 7.6 -6.4 ± 4.8 Not available Tab. 1. LV mass and volume differences (means ± standard deviations) for 20 test cases. Algorithm: CNN – CMR (as ground truth). Abstract Figure. Fig 1. CMR mesh registered to 3D-echo.

Peripheral mucociliary clearance with high-frequency chest wall compression

1985 ◽  
Vol 58 (4) ◽  
pp. 1157-1163 ◽  
Author(s):  
D. Gross ◽  
A. Zidulka ◽  
C. O'Brien ◽  
D. Wight ◽  
R. Fraser ◽  
...  
Keyword(s):  
Phase I ◽  
Chest Wall ◽  
High Frequency ◽  
Cuff Pressure ◽  
Region Of Interest ◽  
Peripheral Region ◽  
Phase Iii ◽  
Mucus Clearance ◽  
Spontaneously Breathing ◽  
Tracheal Mucus

We investigated the effects of high-frequency chest wall compression (HFCWC) on peripheral and tracheal mucus clearance in anesthetized spontaneously breathing dogs. HFCWC was achieved by oscillating the pressure in a thoracic cuff with a piston pump. Regional lung retention of a technetium-99m sulfur colloid aerosol was monitored with a gamma camera. A peripheral mucus clearance index (PMCI) was defined for each region of interest. The tracheal mucus clearance rate (TMCR) was determined by bronchoscopic visualization of marker particle transport. Phase I: In seven dogs, 30 min of HFCWC at 13 Hz with peak cuff pressure (Pcuff) 100–120 cmH2O was found to significantly enhance PMCI in regions immediately under the cuff. (delta PMCI = 24.4 +/- 4.6 in the basal peripheral region.) Phase II: Because of subpleural hemorrhage in phase I, the effect of HFCWC on TMCR at various Pcuff levels was studied in five dogs. The enhancement of TMCR by HFCWC reached a plateau level at Pcuff = 50 cmH2O. Phase III: HFCWC at 13 Hz with Pcuff = 50–60 cmH2O was found to significantly enhance PMCI in five dogs without the consequence of hemorrhage. Correlations were found between the enhancement of PMCI and TMCR by HFCWC. These results demonstrate that HFCWC is effective in enhancing both peripheral and central mucus clearance in dogs and safe when moderate pressures are applied.

scholarly journals Aplikasi Machine Vision pada Hexacopter untuk Deteksi Survival Kits di Bidang Mitigasi Bencana

2020 ◽  
Vol 12 (2) ◽  
pp. 72-79
Author(s):  
Ismawan Noor Ikhsan ◽  
Son Ali Akbar
Keyword(s):  
Machine Vision ◽  
Color Space ◽  
Region Of Interest ◽  
Contour Detection ◽  
Segmentation Method ◽  
Smart System ◽  
Hsv Color Space ◽  
Aerial Vehicle ◽  
Detection Evaluation ◽  
Flying Robots

Hexacopter belongs to one of flying robots that is used to carry out a special mission such as retrieving and delivering survival kits object. Thus, it should be built by smart system to determine the object accurately. However, there was an interference from other object that made it difficult to recognize the survival kits object. Therefore, the development of machine vision with the integration of the hexacopter control system is expected to improve the object recognition process. This study intends to develop a survival kit detection using the image processing method, which involved 1) segmentation on the Hue, Saturation, Value (HSV) color space, 2) contour detection, and 3) Region of Interest (ROI) selected detection. The evaluation of the segmentation method performances was done through the three-part experiments (i.e., the similar shape, variety of a color object, and an object shape). The result of survival kits object detection evaluation obtained an accuracy of 90.33%, precision of 99.63%, and recall of 91.24%. According to the performances obtained in this study, the development of machine vision systems on Unmanned Aerial Vehicle (UAV) has a high accuracy for the object survival kits detection even with another object interference.

scholarly journals Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

2021 ◽  
Vol 6 (1) ◽  
pp. e000898
Author(s):  
Andrea Peroni ◽  
Anna Paviotti ◽  
Mauro Campigotto ◽  
Luis Abegão Pinto ◽  
Carlo Alberto Cutolo ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Uncertainty Estimation ◽  
Depth Of Field ◽  
Clinical Settings ◽  
Proposed Model ◽  
Validation Experiment ◽  
Segmentation Accuracy ◽  
Ground Truth Image

ObjectiveTo develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysisWe used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.ResultsThe model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.ConclusionThe proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

scholarly journals Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns

2017 ◽  
Author(s):  
Prerna Singh ◽  
Ramakrishnan Mukundan ◽  
Rex De Ryke
Keyword(s):  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ultrasound Image ◽  
Texture Feature ◽  
Ground Truth ◽  
Speckle Noise ◽  
Local Binary Patterns ◽  
Quality Analysis ◽  
Ultrasound Images ◽  
Ground Truth Data

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

Research on Underwater Target Tracking Based on Contour Detection

2011 ◽  
Vol 317-319 ◽  
pp. 890-896
Author(s):  
Ming Jun Zhang ◽  
Yuan Yuan Wan ◽  
Zhen Zhong Chu
Keyword(s):  
Prediction Model ◽  
Target Tracking ◽  
Tracking System ◽  
Contour Detection ◽  
Motion Prediction ◽  
Moving Target ◽  
Contour Extraction ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Underwater Target

The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.

Cardiac Chamber Contour Extraction

2013 ◽  
pp. 270-292
Author(s):  
Diogo Roxo ◽  
José Silvestre Silva ◽  
Jaime B. Santos ◽  
Paula Martins ◽  
Eduardo Castela ◽  
...  
Keyword(s):  
Speckle Noise ◽  
Initial Contour ◽  
Contour Extraction ◽  
Cardiac Chamber ◽  
Additional Tool ◽  
Symmetry Approach ◽  
Segmentation Methods ◽  
Fully Automatic ◽  
Function Similarity ◽  
Phase Symmetry

Segmentation of echocardiography images presents a great challenge since such images contain strong speckle noise and artifacts. Most ultrasound segmentation methods are semi-automatic, requiring initial contour to be manually identified in the images. In this chapter, a level set algorithm based on the phase symmetry approach and on a new logarithmic-based stopping function is used to extract simultaneously the four heart cavities in a fully automatic way. The idea is to evaluate the algorithm potential for the clinical practice as an additional tool helping the physician´s decision. Thus, the extracted contours are compared with the ones sketched by four physicians using for that several metrics, namely distance error, maximum distance, pratt function, similarity angle, similarity region, hausdorff distance, accuracy, overlap, sensitivity, and specificity. The authors show that the proposed algorithm performs well, producing contours very similar to the physicians’ ones. The experimental work was based on echocardiography images of children.

scholarly journals Closing the gap between Earth-based and interplanetary mission observations: Vesta seen by VLT/SPHERE

2019 ◽  
Vol 623 ◽  
pp. A6 ◽  
Author(s):  
R. JL. Fétick ◽  
L. Jorda ◽  
P. Vernazza ◽  
M. Marsset ◽  
A. Drouard ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Spatial Resolution ◽  
Ground Truth ◽  
Space Mission ◽  
Contour Extraction ◽  
Dawn Mission ◽  
The North ◽  
Adaptive Optics Imaging ◽  
Interplanetary Missions

Context. Over the past decades, several interplanetary missions have studied small bodies in situ, leading to major advances in our understanding of their geological and geophysical properties. These missions, however, have had a limited number of targets. Among them, the NASA Dawn mission has characterised in detail the topography and albedo variegation across the surface of asteroid (4) Vesta down to a spatial resolution of ~20 m pixel−1 scale. Aims. Here our aim was to determine how much topographic and albedo information can be retrieved from the ground with VLT/SPHERE in the case of Vesta, having a former space mission (Dawn) providing us with the ground truth that can be used as a benchmark. Methods. We observed Vesta with VLT/SPHERE/ZIMPOL as part of our ESO large programme (ID 199.C-0074) at six different epochs, and deconvolved the collected images with a parametric point spread function (PSF). We then compared our images with synthetic views of Vesta generated from the 3D shape model of the Dawn mission, on which we projected Vesta’s albedo information. Results. We show that the deconvolution of the VLT/SPHERE images with a parametric PSF allows the retrieval of the main topographic and albedo features present across the surface of Vesta down to a spatial resolution of ~20–30 km. Contour extraction shows an accuracy of ~1 pixel (3.6 mas). The present study provides the very first quantitative estimate of the accuracy of ground-based adaptive-optics imaging observations of asteroid surfaces. Conclusions. In the case of Vesta, the upcoming generation of 30–40 m telescopes (ELT, TMT, GMT) should in principle be able to resolve all of the main features present across its surface, including the troughs and the north–south crater dichotomy, provided that they operate at the diffraction limit.

A hybrid method based region of interest segmentation for continuous wave terahertz imaging

2019 ◽  
Vol 53 (9) ◽  
pp. 095403 ◽  
Author(s):  
Yuye Wang ◽  
Zhongcheng Sun ◽  
Degang Xu ◽  
Limin Wu ◽  
Jiying Chang ◽  
...  
Keyword(s):  
Hybrid Method ◽  
Continuous Wave ◽  
Region Of Interest ◽  
Terahertz Imaging
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