Enhancing Seismic Image Quality through an Automatic Refraction Static Correction: A Machine Learning Application in Web Based Seismic Processing

2021 ◽  
Author(s):  
P.D. Wardaya ◽  
E. Septama ◽  
R. Pratama ◽  
V. Rossa ◽  
A. Mulawarman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Quality ◽  
Web Based ◽  
Seismic Processing ◽  
Static Correction ◽  
Seismic Image

scholarly journals Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning

2021 ◽  
Vol 7 (1) ◽  
pp. 5
Author(s):  
Douglas Kurrant ◽  
Muhammad Omer ◽  
Nasim Abdollahi ◽  
Pedram Mojabi ◽  
Elise Fear ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dielectric Properties ◽  
Image Quality ◽  
Inverse Scattering ◽  
Quantitative Assessment ◽  
Scattering Problem ◽  
Inverse Scattering Problem ◽  
Malignant Tissue ◽  
Segmentation Technique ◽  
Specific Tissue

Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breast over a reconstruction model consisting of discrete elements. The breast tissue types are characterized by their dielectric properties, so the complex permittivity profile that is reconstructed may be used to distinguish different tissue types. This manuscript presents a robust and flexible medical image segmentation technique to partition microwave breast images into tissue types in order to facilitate the evaluation of image quality. The approach combines an unsupervised machine learning method with statistical techniques. The key advantage for using the algorithm over other approaches, such as a threshold-based segmentation method, is that it supports this quantitative analysis without prior assumptions such as knowledge of the expected dielectric property values that characterize each tissue type. Moreover, it can be used for scenarios where there is a scarcity of data available for supervised learning. Microwave images are formed by solving an inverse scattering problem that is severely ill-posed, which has a significant impact on image quality. A number of strategies have been developed to alleviate the ill-posedness of the inverse scattering problem. The degree of success of each strategy varies, leading to reconstructions that have a wide range of image quality. A requirement for the segmentation technique is the ability to partition tissue types over a range of image qualities, which is demonstrated in the first part of the paper. The segmentation of images into regions of interest corresponding to various tissue types leads to the decomposition of the breast interior into disjoint tissue masks. An array of region and distance-based metrics are applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed. The incorporation of the segmentation that results in a framework that effectively furnishes the quantitative assessment of regions that contain a specific tissue is also demonstrated. The algorithm is applied to reconstructed microwave images derived from breasts with various densities and tissue distributions to demonstrate the flexibility of the algorithm and that it is not data-specific. The potential for using the algorithm to assist in diagnosis is exhibited with a tumor tracking example. This example also establishes the usefulness of the approach in evaluating the performance of the reconstruction algorithm in terms of its sensitivity and specificity to malignant tissue and its ability to accurately reconstruct malignant tissue.

scholarly journals Feasibility and accuracy of the automated software for dynamic quantification of left ventricular and atrial volumes and function in a large unselected population

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
G Italiano ◽  
G Tamborini ◽  
V Mantegazza ◽  
V Volpato ◽  
L Fusini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Image Quality ◽  
Normal Subjects ◽  
Left Ventricular ◽  
Systolic Volume ◽  
Unselected Population ◽  
Lv Volumes ◽  
Time Required ◽  
2D Images

Abstract Funding Acknowledgements Type of funding sources: None. Objective. Preliminary studies showed the accuracy of machine learning based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes. We aimed to evaluate the feasibility and accuracy of machine learning based automated dynamic quantification of LV and LA volumes in an unselected population. Methods. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV and LA volumes were obtained from 2D images; 3D images were analysed using Dynamic Heart Model (DHM) software (Philips) resulting in LV and LA volume-time curves. A subgroup of 140 patients underwent also cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded and results were compared between 2DTTE, DHM and CMR. Results. The use of DHM was feasible in 522/600 cases (87%). When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) borders corrections were needed. The overall time required for DHM datasets was approximately 40 seconds, resulting in physiologically appearing LV and LA volume–time curves in all cases. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55%±12 vs 56%±14; LA volume 89 ± 36 vs 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0.70 and r = 0.82, p < 0.001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r= 0.82, p < 0.001) and LA volumes. Conclusions. The DHM software is feasible, accurate and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation. Table 1 DHM quality Adjustment Feasibility Good Suboptimal Minor Major Total of patients (n, %) 522/600 (87%) 327/522 (62%) 195/522 (28%) 149/522 (29%) 38/522 (6%) Normal subjects (n, %) 39/40 (97%) 23/39 (57%) 16/39 (40%) 9/39 (21%) 1/39 (3%) Atrial Fibrillation (n, %) 59/73 (81%)* 28/59 (47%) 31/59 (53%) 15/59 (25%) 6/59 (10%) Valvular disease (n, %) 271/312 (87%) 120/271 (%) 151/271 (%) 65/271 (24%) 16/271 (6%) Coronary artery disease (n, %) 47/58 (81%)* 26/47 (46%) 21/47 (37%) 16/47 (34%) 5/47 (11%) Miscellaneous (n, %) 24/25 (96%) 18/24 (75%) 6/24 (25%) 5/24 (21%) 3/24 (12%) Feasibility of DHM, image quality and need to adjustments in global population and in each subgroup. Abstract Figure 1

Acquisition/Processing: Machine learning-based deblending: Dispersed source array data example

2021 ◽  
Vol 40 (10) ◽  
pp. 759-767
Author(s):  
Rolf H. Baardman ◽  
Rob F. Hegge
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Low Frequency ◽  
Seismic Interpretation ◽  
Salt Dome ◽  
Training Data ◽  
Array Data ◽  
Seismic Processing ◽  
Processing Machine ◽  
Source Array

Machine learning (ML) has proven its value in the seismic industry with successful implementations in areas of seismic interpretation such as fault and salt dome detection and velocity picking. The field of seismic processing research also is shifting toward ML applications in areas such as tomography, demultiple, and interpolation. Here, a supervised ML deblending algorithm is illustrated on a dispersed source array (DSA) data example in which both high- and low-frequency vibrators were deployed simultaneously. Training data pairs of blended and corresponding unblended data were constructed from conventional (unblended) data from another survey. From this training data, the method can automatically learn a deblending operator that is used to deblend for both the low- and the high-frequency vibrators of the DSA data. The results obtained on the DSA data are encouraging and show that the ML deblending method can offer a good performing, less user-intensive alternative to existing deblending methods.

scholarly journals Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions

2016 ◽  
Vol 2 ◽  
pp. e90 ◽  
Author(s):  
Ranko Gacesa ◽  
David J. Barlow ◽  
Paul F. Long
Keyword(s):  
Machine Learning ◽  
Sequence Data ◽  
Biological Data ◽  
Biological Databases ◽  
Web Based ◽  
Physiological Functions ◽  
Link Type ◽  
Venom Toxins ◽  
Venomous Animals ◽  
Toxin Protein

Ascribing function to sequence in the absence of biological data is an ongoing challenge in bioinformatics. Differentiating the toxins of venomous animals from homologues having other physiological functions is particularly problematic as there are no universally accepted methods by which to attribute toxin function using sequence data alone. Bioinformatics tools that do exist are difficult to implement for researchers with little bioinformatics training. Here we announce a machine learning tool called ‘ToxClassifier’ that enables simple and consistent discrimination of toxins from non-toxin sequences with >99% accuracy and compare it to commonly used toxin annotation methods. ‘ToxClassifer’ also reports the best-hit annotation allowing placement of a toxin into the most appropriate toxin protein family, or relates it to a non-toxic protein having the closest homology, giving enhanced curation of existing biological databases and new venomics projects. ‘ToxClassifier’ is available for free, either to download (https://github.com/rgacesa/ToxClassifier) or to use on a web-based server (http://bioserv7.bioinfo.pbf.hr/ToxClassifier/).

scholarly journals No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Peng Xu ◽  
Man Guo ◽  
Lei Chen ◽  
Weifeng Hu ◽  
Qingshan Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Statistical Features ◽  
Statistical Regression ◽  
Stereoscopic Image ◽  
Machine Learning Technique ◽  
Stereoscopic Images ◽  
Structure Representation ◽  
Learning Technique

Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.

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