scholarly journals Descriptive analysis of dental X-ray images using various practical methods: A review

2021 ◽  
Vol 7 ◽  
pp. e620
Author(s):  
Anuj Kumar ◽  
Harvendra Singh Bhadauria ◽  
Annapurna Singh
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
State Of The Art ◽  
Descriptive Analysis ◽  
Imaging Modalities ◽  
Learning Approaches ◽  
X Ray ◽  
Dental Imaging ◽  
Art Research ◽  
Dental Image Segmentation

In dentistry, practitioners interpret various dental X-ray imaging modalities to identify tooth-related problems, abnormalities, or teeth structure changes. Another aspect of dental imaging is that it can be helpful in the field of biometrics. Human dental image analysis is a challenging and time-consuming process due to the unspecified and uneven structures of various teeth, and hence the manual investigation of dental abnormalities is at par excellence. However, automation in the domain of dental image segmentation and examination is essentially the need of the hour in order to ensure error-free diagnosis and better treatment planning. In this article, we have provided a comprehensive survey of dental image segmentation and analysis by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X-ray, CT (Computed Tomography), CBCT (Cone Beam Computed Tomography), etc. Overall state-of-the-art research works have been classified into three major categories, i.e., image processing, machine learning, and deep learning approaches, and their respective advantages and limitations are identified and discussed. The survey presents extensive details of the state-of-the-art methods, including image modalities, pre-processing applied for image enhancement, performance measures, and datasets utilized.

Image segmentation of nanoscale Zernike phase contrast X-ray computed tomography images

2015 ◽  
Vol 117 (18) ◽  
pp. 183102 ◽  
Author(s):  
Arjun S. Kumar ◽  
Pratiti Mandal ◽  
Yongjie Zhang ◽  
Shawn Litster
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Phase Contrast ◽  
X Ray ◽  
Computed Tomography Images ◽  
X Ray Computed

scholarly journals Mini Review on Dental Imaging Devices and Use of Artificial Intelligence in Dentistry

2020 ◽  
pp. 114-117
Author(s):  
Saadet Sena Egeli ◽  
Yalcin Isler
Keyword(s):  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Imaging Techniques ◽  
Nuclear Imaging ◽  
X Rays ◽  
Resonance Imaging ◽  
X Ray ◽  
Dental Imaging ◽  
Dental Applications

Discovery of X-Rays is the beginning point of the medical imaging which developed and diversified in years. Since early days of X-Ray discovery they are used in also for imaging of teeth, in 1896, Dr. Otto Walkhoff imaged his mouth with X-Ray exposure. X-Rays helped the dentists to diagnose tooth decays and bone loss, examine dental structures and identify abnormalities of these structures. Today developments in technology resulted in different imaging techniques, X-Rays are used for Projectional Radiography and Computed Tomography, besides there are Nuclear Imaging, Magnetic Resonance Imaging and Ultrasound Imaging that widely used. In this review, imaging techniques for dental applications with the extension of artificial intelligence is examined to provide a brief information.

scholarly journals Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals

2021 ◽  
Vol 27 (3) ◽  
pp. 146045822110330
Author(s):  
Diedre Carmo ◽  
Israel Campiotti ◽  
Lívia Rodrigues ◽  
Irene Fantini ◽  
Gustavo Pinheiro ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Decision Support ◽  
State Of The Art ◽  
Natural Images ◽  
Dice Coefficient ◽  
Median Response ◽  
X Ray ◽  
System A ◽  
Picture Archiving And Communication

The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from São Paulo Hospitals. The system used EfficientNet and EfficientDet networks, state-of-the-art convolutional neural networks for natural images classification and segmentation, in a real-time scalable scenario in communication with a Picture Archiving and Communication System (PACS). Additionally, the system could reject non-related images, using header analysis and classifiers. We achieved CT and X-ray classification accuracies of 0.94 and 0.98, respectively, and Dice coefficient for lung and covid findings segmentations of 0.98 and 0.73, respectively. The median response time was 7 s for X-ray and 4 min for CT.

scholarly journals Dual-energy computed tomography in acute ischemic stroke: state-of-the-art

2020 ◽  
Author(s):  
Stephanie Mangesius ◽  
Tanja Janjic ◽  
Ruth Steiger ◽  
Lukas Haider ◽  
Rafael Rehwald ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Patient Management ◽  
State Of The Art ◽  
Dual Energy ◽  
Clinical State ◽  
X Ray ◽  
Dual Energy Computed Tomography ◽  
Future Developments ◽  
Potential Impact

Abstract Dual-energy computed tomography (DECT) allows distinguishing between tissues with similar X-ray attenuation but different atomic numbers. Recent studies demonstrated that this technique has several areas of application in patients with ischemic stroke and a potential impact on patient management. After endovascular stroke therapy (EST), hyperdense areas can represent either hemorrhage or contrast staining due to blood-brain barrier disruption, which can be differentiated reliably by DECT. Further applications are improved visualization of early infarctions, compared to single-energy computed tomography, and prediction of transformation into infarction or hemorrhage in contrast-enhancing areas. In addition, DECT allows detection and evaluation of the material composition of intra-arterial clots after EST. This review summarizes the clinical state-of-the-art of DECT in patients with stroke, and features some prospects for future developments. Key points • Dual-energy computed tomography (DECT) allows differentiation between tissues with similar X-ray attenuation but differentatomic numbers. • DECT has several areas of application in patients with ischemic stroke and a potential impact on patient management. • Prospects for future developments in DECT may improve treatment decision-making.

INITIAL CONTOUR GENERATION APPROACH IN LEVEL SET METHODS FOR DENTAL IMAGE SEGMENTATION

2015 ◽  
Vol 78 (2-2) ◽  
Author(s):  
Abdolvahab Ehsani Rad ◽  
Mohd Shafry Mohd Rahim ◽  
Golnaz Safaian ◽  
Ismail Mat Amin
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Level Set Methods ◽  
Computational Cost ◽  
Human Interaction ◽  
Initial Contour ◽  
X Ray ◽  
Segmentation Process ◽  
Map Function ◽  
Dental Image Segmentation

Segmentation is challenging process in medical images especially on dental x-ray images. Level set methods have best result on medical and dental image segmentation. Initial Contour (IC) is the essential step in level set methods to initialize the efficient process. However, the main issue with IC is how to generate the automatic technique in order to reduce the human interaction and produce accurate result. In this paper a new region-based technique for IC generation, is proposed to generate the most suitable IC. We have utilized the statistical and morphological information inside and outside the contour to establish a region-based map function. This function is able to find the suitable IC on images to perform by level set methods. Experiments on dental x-ray images demonstrate the robustness of segmentation process using proposed method even on noisy images and with weak boundary. Furthermore, computational cost of segmentation process is reduced.

scholarly journals X-ray micro-computed tomography (μCT): an emerging opportunity in parasite imaging

Parasitology ◽  
2017 ◽  
Vol 145 (7) ◽  
pp. 848-854 ◽  
Author(s):  
James D. B. O'Sullivan ◽  
Julia Behnsen ◽  
Tobias Starborg ◽  
Andrew S. MacDonald ◽  
Alexander T. Phythian-Adams ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Internal Structure ◽  
Soft Tissues ◽  
Three Dimensional ◽  
Host Tissue ◽  
Imaging Modalities ◽  
Micro Computed Tomography ◽  
Trichuris Muris ◽  
X Ray ◽  
Three Dimensional Images

AbstractX-ray micro-computed tomography (μCT) is a technique which can obtain three-dimensional images of a sample, including its internal structure, without the need for destructive sectioning. Here, we review the capability of the technique and examine its potential to provide novel insights into the lifestyles of parasites embedded within host tissue. The current capabilities and limitations of the technology in producing contrast in soft tissues are discussed, as well as the potential solutions for parasitologists looking to apply this technique. We present example images of the mouse whipworm Trichuris muris and discuss the application of μCT to provide unique insights into parasite behaviour and pathology, which are inaccessible to other imaging modalities.

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