scholarly journals Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound

2021 ◽  
Vol 4 ◽  
Author(s):  
Conor McDermott ◽  
Maciej Łącki ◽  
Ben Sainsbury ◽  
Jessica Henry ◽  
Mihail Filippov ◽  
...  
Keyword(s):  
Image Processing ◽  
Lung Ultrasound ◽  
Image Interpretation ◽  
Ultrasound Image ◽  
Daily Basis ◽  
Ultrasound Images ◽  
Term Care ◽  
The World ◽  
Small Hand ◽  
Detection And Diagnosis

The sustained increase in new cases of COVID-19 across the world and potential for subsequent outbreaks call for new tools to assist health professionals with early diagnosis and patient monitoring. Growing evidence around the world is showing that lung ultrasound examination can detect manifestations of COVID-19 infection. Ultrasound imaging has several characteristics that make it ideally suited for routine use: small hand-held systems can be contained inside a protective sheath, making it easier to disinfect than X-ray or computed tomography equipment; lung ultrasound allows triage of patients in long term care homes, tents or other areas outside of the hospital where other imaging modalities are not available; and it can determine lung involvement during the early phases of the disease and monitor affected patients at bedside on a daily basis. However, some challenges still remain with routine use of lung ultrasound. Namely, current examination practices and image interpretation are quite challenging, especially for unspecialized personnel. This paper reviews how lung ultrasound (LUS) imaging can be used for COVID-19 diagnosis and explores different image processing methods that have the potential to detect manifestations of COVID-19 in LUS images. Then, the paper reviews how general lung ultrasound examinations are performed before addressing how COVID-19 manifests itself in the images. This will provide the basis to study contemporary methods for both segmentation and classification of lung ultrasound images. The paper concludes with a discussion regarding practical considerations of lung ultrasound image processing use and draws parallels between different methods to allow researchers to decide which particular method may be best considering their needs. With the deficit of trained sonographers who are working to diagnose the thousands of people afflicted by COVID-19, a partially or totally automated lung ultrasound detection and diagnosis tool would be a major asset to fight the pandemic at the front lines.

scholarly journals Enhancing ultrasound texture differences for developing an in vivo 'virtual histology' approach to bovine ovarian imaging

2007 ◽  
Vol 19 (8) ◽  
pp. 910 ◽  
Author(s):  
Mark G. Eramian ◽  
Gregg P. Adams ◽  
Roger A. Pierson
Keyword(s):  
Image Processing ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Virtual Histology ◽  
Form And Function ◽  
Non Invasive ◽  
Candidate Image ◽  
And Function ◽  
Processing Techniques

A ‘virtual histology’ can be thought of as the ‘staining’ of a digital ultrasound image via image processing techniques in order to enhance the visualisation of differences in the echotexture of different types of tissues. Several candidate image-processing algorithms for virtual histology using ultrasound images of the bovine ovary were studied. The candidate algorithms were evaluated qualitatively for the ability to enhance the visual differences in intra-ovarian structures and quantitatively, using standard texture description features, for the ability to increase statistical differences in the echotexture of different ovarian tissues. Certain algorithms were found to create textures that were representative of ovarian micro-anatomical structures that one would observe in actual histology. Quantitative analysis using standard texture description features showed that our algorithms increased the statistical differences in the echotexture of stroma regions and corpus luteum regions. This work represents a first step toward both a general algorithm for the virtual histology of ultrasound images and understanding dynamic changes in form and function of the ovary at the microscopic level in a safe, repeatable and non-invasive way.

scholarly journals Using the method of renormalization with limitation for medical ultrasound image processing.

2020 ◽  
Vol 2020 (10) ◽  
Author(s):  
A.V. Kokoshkin ◽  
Keyword(s):  
Image Processing ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Bilateral Filtering ◽  
Ultrasound Image Processing ◽  
Images Processing

This article proposes the application of the technique of combining the modernized method of renormalization with limitation with subsequent bilateral filtering to improve the quality of medical ultrasound images. Processing according to this algorithm increases the overall contrast of the image, smoothes out speckle noise and makes it possible to cope well with determining the localization of significant objects. The presented results indicate a significant improvement in the quality of ultrasound images, which can serve as an auxiliary tool for medical workers when clarifying the diagnosis.

scholarly journals The Rise of Lung Ultrasound in the Era of COVID-19

2021 ◽  
Vol 33 (4) ◽  
pp. 5-8
Author(s):  
Shailendra Kumar Motwani ◽  
Helen Saunders
Keyword(s):  
Lung Ultrasound ◽  
Image Interpretation ◽  
Ultrasound Images ◽  
The Novel ◽  
Non Invasive ◽  
Global Pandemic ◽  
Pleural Surface ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Huge Challenge

The current global pandemic caused by the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) presents a huge challenge for physicians. Rapid diagnosis, triage and clinical management of these patients is a challenge for physicians but may be aided using lung ultrasound. Lung ultrasound has been in use for over 10 years mainly by critical care practitioners and emergency physicians with variable uptake, but it has gained popularity during the Coronavirus disease-2019 (COVID-19) pandemic as a diagnostic tool and can be easily learned compared to the other ultrasound techniques. Image interpretation is based on identifying artefacts generated by the pleural surface. This technique is non-invasive and can be performed rapidly at the patient’s bedside. It has higher accuracy in diagnosis than auscultation and Chest X-ray (CXR) combined. In this article the authors describe the interpretation of lung ultrasound images, particularly in patients with COVID-19 and discuss indications for this technique. Physicians are recommended to gain familiarity with this technique and use of online resources for guidance.

scholarly journals Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus

2021 ◽  
Vol 11 (1) ◽  
pp. 399-410
Author(s):  
Kaitheri Thacharedath Dilna ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Signal To Noise ◽  
Statistical Measures ◽  
Noise Ratio ◽  
Scanned Images

Abstract Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentation and feature extraction. Many speckle reduction filters are proposed but while combining many techniques some speckle diagnostic information should be preserved. Removal of speckle noise from ultrasound image by preserving edges and added features is a great challenging task in ultrasound image restoration. This paper aims at a comprehensive description and comparison of reduction of speckle noise of ultrasound fibroid image. Many filters are applied on ultrasound scanned images and the performance is marked in terms of some statistical measures. Even though several despeckling filters are there for speckle reduction, all are not good for ultrasound scanned images. A comparison of quality measures such as mean square error, peak signal-to-noise ratio, and signal-to-noise ratio is done in ultrasound images in despeckling.

scholarly journals A Median Filter With Evaluating of Temporal Ultrasound Image for Impulse Noise Removal for Kidney Diagnosis

2020 ◽  
Vol 1 (2) ◽  
pp. 71-77
Author(s):  
Rasheed Ihsan ◽  
Saman Almufti ◽  
Ridwan Marqas
Keyword(s):  
Information Quality ◽  
Kidney Diseases ◽  
Median Filter ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Examination Methods ◽  
Detection And Diagnosis ◽  
Temporal Dimensions

Ultrasound imaging helps the doctor to view the tissues and organs in the body's abdominal area with no ionization risks compared to other internal organ examination methods dependent on radiation. It offers highly precise renal imaging of suspected acute kidney diseases. This paper proposes temporary filtering methods to improve ultrasound images from ultrasonic kidney video. The proposed filters focus on the detection and diagnosis of kidney disease by processing consecutive images of the acquired kidney video. Extending the spatial median image filters to temporal dimensions after the picture frames are manually clipped and aligned in MATLAB by image processing Toolbox to suppress speckle noise, and enhance a doctor's diagnostic information quality.

scholarly journals Ultrasound Image Enhancement Using Structure-Based Filtering

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Shyh-Kuang Ueng ◽  
Cho-Li Yen ◽  
Guan-Zhi Chen
Keyword(s):  
Image Processing ◽  
Diffusion Tensor ◽  
Median Filter ◽  
Ultrasound Image ◽  
Underlying Structure ◽  
Ultrasound Images ◽  
Test Results ◽  
Classification Result ◽  
Linear Filters ◽  
Ultrasound Image Processing

Ultrasound images are prone to speckle noises. Speckles blur features which are essential for diagnosis and assessment. Thus despeckling is a necessity in ultrasound image processing. Linear filters can suppress speckles, but they smooth out features. Median filter based despeckling algorithms produce better results. However, they may produce artifact patterns in the resulted images and oversmooth nonuniform regions. This paper presents an innovative despeckle procedure for ultrasound images. In the proposed method, the diffusion tensor of intensity is computed at each pixel at first. Then the eigensystem of the diffusion tensor is calculated and employed to detect and classify the underlying structure. Based on the classification result, a feasible filter is selected to suppress speckles and enhance features. Test results show that the proposed despeckle method reduces speckles in uniform areas and enhances tissue boundaries and spots.

scholarly journals Interpreting Ultrasound Images for Accurate Epidural Needle Insertion

2017 ◽  
Author(s):  
Neil Vaughan ◽  
Venketesh N. Dubey
Keyword(s):  
Image Processing ◽  
Epidural Space ◽  
Human Error ◽  
Ultrasound Image ◽  
Low Frequency ◽  
Needle Insertion ◽  
Epidural Needle ◽  
Ultrasound Images ◽  
Gradient Based ◽  
2D Ultrasound

This work presents development and testing of image processing algorithms for the automatic detection of landmarks within ultrasound images. The aim was to automate ultrasound analysis, for use during the process of epidural needle insertion. For epidural insertion, ultrasound is increasingly used to guide the needle into the epidural space. Ultrasound can improve the safety of epidural and was recommended by the 2008 NICE guidelines (National Institute for Health and Care Excellence). Without using ultrasound, there is no way for the anaesthetist to observe the location of the needle within the ligaments requiring the use of their personal judgment which may lead to injury. If the needle stops short of the epidural space, the anaesthetic is ineffective. If the needle proceeds too deep, it can cause injuries ranging from headache, to permanent nerve damage or death. Ultrasound of the spine is particularly difficult, because the complex bony structures surrounding the spine limit the ultrasound beam acoustic windows [1]. Additionally, the important structures for epidural that need to be observed are located deeper than other conventional procedures such as peripheral nerve block. This is why a low frequency, curved probe (2–5 MHz) is used, which penetrates deeper but decreases in resolution. The benefits of automating ultrasound are to enable real-time ultrasound analysis on the live video, mitigate human error, and ensure repeatability by avoiding variation in perception by different users. Previous ultrasound image processing for epidural research used speckle image enhancement with canny and gradient based methods for bone detection [2]. A clinical trial with 39 patients had success detecting the ligamentum flavum (LF) from ultrasound by algorithms in 87% of patients. Echogenic needles and catheters are now becoming available which are enhanced for extra ultrasound visibility. The Epimed UltraKath ULTRA-KATH™ [3] has a patented design to maximize visibility under ultrasound [4]. The Echogenic Tuohy Needle also includes imprints on the needle tip that reflects ultrasound, allowing for better visualization. Curved needles can also be detected in 2D ultrasound images [5].

scholarly journals Evaluation of Lung Involvement in COVID-19 Pneumonia Based on Ultrasound Images

2020 ◽  
Author(s):  
Zhaoyu Hu ◽  
Zhenhua Liu ◽  
Yijie Dong ◽  
Jianjian Liu ◽  
Bin Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Ultrasound ◽  
Ultrasound Image ◽  
Assessment Model ◽  
Diagnostic Model ◽  
Ultrasound Images ◽  
Lung Involvement ◽  
Selective Learning ◽  
Imaging Tool ◽  
Multi Mode

Abstract Background: Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. In this study, we determined the ultrasound manifestations of the lung associated with COVID-19 pneumonia, and obtained the ultrasound image changes of the patients from the initial diagnosis to rehabilitation. Methods: The purpose of this study is to establish a lung involvement assessment model based on deep learning. A channel attention classification method based on squeeze-and-excitation network combining with ResNeXt (SE_ResNeXt) is proposed, which can automatically learn the importance of different channel features, so as to achieve selective learning of channels and further achieve more accurate classification results. Results and conclusion: Among 104 patients' data from multicenter and multi-mode ultrasound, the diagnostic model can achieve 97.11% accuracy. The lung involvement severity of COVID-19 pneumonia and the trend of lesion were evaluated quantitatively.

A basic introduction to image processing using NIH-Image as a model

1994 ◽  
Vol 52 ◽  
pp. 392-393
Author(s):  
John Mansfield
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Image Acquisition ◽  
Digital Instrument ◽  
Novice User ◽  
Nih Image ◽  
The Past ◽  
Digital World ◽  
The World ◽  
Instrument Control

Advances in camera technology and digital instrument control have meant that in modern microscopy, the image that was, in the past, typically recorded on a piece of film is now recorded directly into a computer. The transfer of the analog image seen in the microscope to the digitized picture in the computer does not mean, however, that the problems associated with recording images, analyzing them, and preparing them for publication, have all miraculously been solved. The steps involved in the recording an image to film remain largely intact in the digital world. The image is recorded, prepared for measurement in some way, analyzed, and then prepared for presentation.Digital image acquisition schemes are largely the realm of the microscope manufacturers, however, there are also a multitude of “homemade” acquisition systems in microscope laboratories around the world. It is not the mission of this tutorial to deal with the various acquisition systems, but rather to introduce the novice user to rudimentary image processing and measurement.

Detection of Leukemia Using Image Processing

2018 ◽  
Vol 8 (4) ◽  
pp. 61
Author(s):  
A. Sivasangari ◽  
G. Sasikumar
Keyword(s):  
Image Processing ◽  
Statistical Analysis ◽  
Early Detection ◽  
Visual Inspection ◽  
Causes Of Death ◽  
Cure Rate ◽  
Blood Disorders ◽  
Detection And Diagnosis ◽  
The Moment ◽  
Early Detection And Diagnosis

Leukemia   disease   is one   of    the   leading   causes   of death   among   human. Its  cure  rate and  prognosis   depends   mainly   on  the  early  detection   and  diagnosis  of   the  disease. At  the  moment, identification  of  blood  disorders  is  through   visual  inspection  of  microscopic  images  by  examining  changes  like  texture, geometry, colour  and   statistical  analysis  of  images . This  project  aims  to  preliminary  of  developing  a  detection  of  leukemia  types  using   microscopic  blood  sample using MATLAB. Images  are  used  as  they  are  cheap  and  do  not  expensive  for testing  and  lab  equipment.

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