Image Processing Improving Image Quality for Digital Radiography

2018 ◽  
Vol 6 (7) ◽  
pp. 803-808
Author(s):  
Madhavi Pingili ◽  
E. G. Rajan
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Digital Radiography

scholarly journals Development of Stand Alone Application Tool for Processing and Quality Measurement of Weld Imperfection Image Captured by μ-Focused Digital Radiography Using MATLAB- Based Graphical User Interface

2012 ◽  
Vol 9 (2) ◽  
pp. 64 ◽  
Author(s):  
PZ Nadila ◽  
YHP Manurung ◽  
SA Halim ◽  
SK Abas ◽  
G Tham ◽  
...  
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Image Quality ◽  
Digital Radiography ◽  
Graphical User Interface ◽  
Signal To Noise Ratio ◽  
Quality Measurement ◽  
Noise Removal ◽  
Signal To Noise ◽  
Noise Ratio

Digital radiography incresingly is being applied in the fabrication industry. Compared to film- based radiography, digitally radiographed images can be acquired with less time and fewer exposures. However, noises can simply occur on the digital image resulting in a low-quality result. Due to this and the system’s complexity, parameters’ sensitivity, and environmental effects, the results can be difficult to interpret, even for a radiographer. Therefore, the need of an application tool to improve and evaluate the image is becoming urgent. In this research, a user-friendly tool for image processing and image quality measurement was developed. The resulting tool contains important components needed by radiograph inspectors in analyzing defects and recording the results. This tool was written by using image processing and the graphical user interface development environment and compiler (GUIDE) toolbox available in Matrix Laboratory (MATLAB) R2008a. In image processing methods, contrast adjustment, and noise removal, edge detection was applied. In image quality measurement methods, mean square error (MSE), peak signal-to-noise ratio (PSNR), modulation transfer function (MTF), normalized signal-to-noise ratio (SNRnorm), sensitivity and unsharpness were used to measure the image quality. The graphical user interface (GUI) wass then compiled to build a Windows, stand-alone application that enables this tool to be executed independently without the installation of MATLAB. 

Digital Radiography Image Quality: Image Processing and Display

2007 ◽  
Vol 4 (6) ◽  
pp. 389-400 ◽  
Author(s):  
Elizabeth A. Krupinski ◽  
Mark B. Williams ◽  
Katherine Andriole ◽  
Keith J. Strauss ◽  
Kimberly Applegate ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Digital Radiography ◽  
Quality Image

scholarly journals Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Jiangang Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Geometric Transformation ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Classification Problems ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

Image quality and dose differences caused by vendor-specific image processing of neonatal radiographs

2016 ◽  
Vol 46 (11) ◽  
pp. 1606-1613 ◽  
Author(s):  
William F. Sensakovic ◽  
M. Cody O’Dell ◽  
Haley Letter ◽  
Nathan Kohler ◽  
Baiywo Rop ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality
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