Implementation of ultra-low-dose CBCT for routine 2D orthodontic diagnostic radiographs: Cephalometric landmark identification and image quality assessment

Assessment of the quality of the images obtained using optimized (low-dose) protocols is the inherent part of the optimization in X-ray diagnostics. To perform the objective quantitative image quality assessment one can use dedicated test-objects, including several components for the simultaneous measurement of the different physical image characteristics (contrast and spatial resolution). The use of such test objects allows estimating and assessing the relations between the patient dose, parameter of the X-ray examination and image quality. That is especially important for the optimization of the digital radiographic examinations performed with automated exposure control. The aim of the current study was to evaluate the possibilities of the patient dose reduction using “contrast-detail” test-object for the digital radiography of the chest in posterior-anterior projection performed with automated exposure control. The study was performed in St-Petersburg Mariinsky hospital on a digital X-ray unit “ARC-Electron” with a flat-panel detector. The combination of a test-object and a tissue-equivalent phantom were imaged on a range of chest X-ray protocols: on a 60–150 kV tube voltage range with automated exposure control; and using fixed 90 kV tube voltage on a range of 2–100 mAs tube current-exposure time product. Dose-area product (cGy×cm2) was measured for each exposure; effective dose (mSv) was estimated for each exposure based on dose-area product. A dedicated software was developed for the automated image quality assessment. The results of the study indicate that the use of a high tube voltage (140–150 kV) with current automated exposure control settings would lead to 60% and 95% reduction of the dose-area product and effective dose, respectively, compared to the standard protocol. The adjustment of the current automated exposure control settings with the reduction of the tube current-exposure time product from 11,2 mAs to the 4,2 mAs for the tube voltage of 90 kV would lead to the reduction of both the dose-area product and effective dose up to a factor of three, compared to the standard protocol. For both scenarios image quality characteristics decreased by less than 15%. The proposed low-dose protocols are under the clinical approbation at Mariinsky hospital. The proposed method of image quality assessment and development of low-dose protocols is recommended for inclusion in the quality assurance program for the radiography examinations.

Download Full-text

A Novel Hybrid Image Quality Assessment Algorithm

Indian Journal Of Applied Research ◽

10.15373/2249555x/apr2014/31 ◽

2011 ◽

Vol 4 (4) ◽

pp. 107-108

Author(s):

Deepa Maria Thomas ◽

◽

S. John Livingston

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment

Download Full-text

Implementation of No Reference Distortion Patch Features Image Quality Assessment Algorithm Based on Human Visual System

International Journal on Communications Antenna and Propagation (IRECAP) ◽

10.15866/irecap.v4i5.4166 ◽

2014 ◽

Vol 4 (5) ◽

pp. 195

Author(s):

Ch. Subrahmanyam ◽

D. Venkata Rao ◽

N. Usha Rani

Keyword(s):

Image Quality ◽

Visual System ◽

Quality Assessment ◽

Human Visual System ◽

Image Quality Assessment

Download Full-text

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.9.iqsp-323 ◽

2020 ◽

Vol 2020 (9) ◽

pp. 323-1-323-8

Author(s):

Litao Hu ◽

Zhenhua Hu ◽

Peter Bauer ◽

Todd J. Harris ◽

Jan P. Allebach

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Research Area ◽

Input Image ◽

Quality Score ◽

Document Image ◽

Digital Cameras ◽

Active Research ◽

Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

Download Full-text

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.1.010505 ◽

2020 ◽

Vol 64 (1) ◽

pp. 10505-1-10505-16

Author(s):

Yin Zhang ◽

Xuehan Bai ◽

Junhua Yan ◽

Yongqi Xiao ◽

C. R. Chatwin ◽

...

Keyword(s):

Neural Network ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Assessment Method ◽

Image Distortion ◽

Image Feature ◽

Reference Image ◽

Gradient Magnitude ◽

Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

Download Full-text