Graphcut as a Segmentation Method of Covid-19 X-Ray Image for Diagnose Purpose

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50019029" target="_blank" >RIV/62690094:18450/21:50019029 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICOCO53166.2021.9673512" target="_blank" >http://dx.doi.org/10.1109/ICOCO53166.2021.9673512</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICOCO53166.2021.9673512" target="_blank" >10.1109/ICOCO53166.2021.9673512</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Graphcut as a Segmentation Method of Covid-19 X-Ray Image for Diagnose Purpose

Popis výsledku v původním jazyce

Medical images are vital for disease detection. The misleading information during the detection will lead to the worst part of diagnosing. Corona Virus or COVID-19 shocked the whole world with the new viral epidemics with a lower respiratory tract febrile illness causes pulmonary syndrome. Chest X-Ray and Chest Computed Tomography Scans (CT Scan) are the imaging tests that can identify the infection. As the COVID-19 virus is dissimilar to bacterial or viral pneumonia consolidation, X-ray analysis is chosen as a discriminative element that helps in assisting in the timely identification of COVID-19 infections. However, there are limitations in detecting the virus on the X-Ray image with raw eyes only. Several types of image processing are used to enhance the capability to detect the disease. Image segmentation is an image processing method that focuses on the abnormalities that appear on the medical image. Graphcut is one of the potential methods that can enhance to produce an understandable and more precise image for analyzing the process that can precisely diagnose the disease. We proposed the Graphcut with the combination of several techniques such as Dilate mask with Disk, Region-based Active Contour, Edge-based Active Contour, and Fill Holes. The experimental results show that the segmented region is the right part of training in the next phase. In conclusion, the enhancement of the Graphcut for the X-ray image helps the affected part be seen clearly for the diagnose purpose. © 2021 IEEE.

Název v anglickém jazyce

Graphcut as a Segmentation Method of Covid-19 X-Ray Image for Diagnose Purpose

Popis výsledku anglicky

Medical images are vital for disease detection. The misleading information during the detection will lead to the worst part of diagnosing. Corona Virus or COVID-19 shocked the whole world with the new viral epidemics with a lower respiratory tract febrile illness causes pulmonary syndrome. Chest X-Ray and Chest Computed Tomography Scans (CT Scan) are the imaging tests that can identify the infection. As the COVID-19 virus is dissimilar to bacterial or viral pneumonia consolidation, X-ray analysis is chosen as a discriminative element that helps in assisting in the timely identification of COVID-19 infections. However, there are limitations in detecting the virus on the X-Ray image with raw eyes only. Several types of image processing are used to enhance the capability to detect the disease. Image segmentation is an image processing method that focuses on the abnormalities that appear on the medical image. Graphcut is one of the potential methods that can enhance to produce an understandable and more precise image for analyzing the process that can precisely diagnose the disease. We proposed the Graphcut with the combination of several techniques such as Dilate mask with Disk, Region-based Active Contour, Edge-based Active Contour, and Fill Holes. The experimental results show that the segmented region is the right part of training in the next phase. In conclusion, the enhancement of the Graphcut for the X-ray image helps the affected part be seen clearly for the diagnose purpose. © 2021 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 IEEE International Conference on Computing, ICOCO 2021

ISBN

978-1-66543-689-2

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

377-381

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Kuala Lumpur

Místo konání akce

Virtual, Online

Datum konání akce

17. 11. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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