A Study on Image Output Method in the Radiography Training Simulator
Joon-Koo Choi
Joon-Koo Choi, Department of Radiological Science, Graduate School of Health Science, Far East University, Daehak-Gil, Gamgok-Myeon, Eumsung-Gun, Chungcheongbuk-Do, Korea.
Manuscript received on 01 January 2019 | Revised Manuscript received on 06 January 2019 | Manuscript Published on 07 April 2019 | PP: 43-46 | Volume-8 Issue- 3C January 2019 | Retrieval Number: C10190183C19/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A simulated training can be induced to suit the actual situation by outputting the changed image according to various exposure doses by applying image processing filter technique during the implementation phase of radiography training simulator. The purpose of this study is to present an image output method using image processing filter in radiography training simulator. Methods/Statistical analysis: The applied image processing filter adjusts the Window width of the original image by calculating the difference between the target tube voltage and the applied tube voltage(kVp) and adjusts the image contrast by weighting value. Next, the Window level is adjusted by calculating the difference between the applied tube current and the target tube current(mA) and the enhancement is adjusted according to the weighted value. Findings: Image with adjusted Window Width, Window Level, Contrast, and Enhancement according to the exposure dose by applying the developed image processing filter Improvements/Applications: In future studies, a method that can output the image by reflecting the region of interest (ROI) of the collimator will be proposed.
Keywords: Radiography, Training Simulator, Image Processing, Mock up, X-ray.
Scope of the Article: Health Monitoring and Life Prediction of Structures