Application of Contrast Limited Adaptive Histogram Equalization (CLAHE) on the Quality and Pathological Information of Chest X-Ray (CXR) Images in Covid-19  Patients in the Intensive Care Unit (ICU) Room

I Wayan Juliasa; Nyoman Supriyani; I Made Lana Prasetya; I Wayan Juliasa; Nyoman Supriyani; I Made Lana Prasetya

doi:10.61096/ijamscr.v11.iss1.2023.1-5

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Vol. 11 No. 1 (2023): 2023 Volume -11 - Issue 1

Date Log

Published

January 6, 2023

Application of Contrast Limited Adaptive Histogram Equalization (CLAHE) on the Quality and Pathological Information of Chest X-Ray (CXR) Images in Covid-19 Patients in the Intensive Care Unit (ICU) Room

https://doi.org/10.61096/ijamscr.v11.iss1.2023.1-5

I Wayan Juliasa

Academy of Radiodiagnostic and Radiotherapy Engineering, Denpasar, Indonesia

Nyoman Supriyani

Academy of Radiodiagnostic and Radiotherapy Engineering, Denpasar, Indonesia

I Made Lana Prasetya

Academy of Radiodiagnostic and Radiotherapy Engineering, Denpasar, Indonesia

Corresponding Author(s) : I Wayan Juliasa

juliasawayan82@gmail.com

International Journal of Allied Medical Sciences and Clinical Research, Vol. 11 No. 1 (2023): 2023 Volume -11 - Issue 1
Article Published : January 6, 2023

Abstract

Background: The advantages of mobile or portable Chest X-Ray (CXR) in Covid-19 patients are the minimal radiation received compared to CT Scan, as well as evaluating or evaluating patient diagnoses in the Intensive Care Unit (ICU) room. CXR in the Anterior Posterior (AP) projection with a Covid-19 patient who sleeps on his back or sits in the ICU room is usually uncooperative in adjusting the patient's position, resulting in low image quality, requiring Digital Imaging (DIP) in an effort to improve image quality. This study used DIP Contrast Limited Adaptive Histogram Equalization (CLAHE) on images of Covid-19 patients in the ICU, and compared before and after CLAHE to quality and pathology information.
Objectives: Improving the quality and pathological information of CXR images in Covid-19 patients in the ICU with the application of CLAHE and to find out the most optimal differences in pathology information.
Methods: Quasi experiment with the Pretest Posttest Without Control Group Design. Sample calculation using purposive sampling with saturation time, Where this study used 20 samples of Covid-19 patients in the ICU room and used 2 Observers, namely radiologists and pulmonologists as Visual Grading Analysis (VGA) assessment of pathology information.
Results: Improving image quality in Covid-19 patients in the ICU before and after CLAHE with MSE values (0.0339), PSNR (111.2541). The after CLAHE image has a higher mean rank than the before CLAHE image.

Keywords

CLAHE, CXR, Covid-19

I Wayan Juliasa, Nyoman Supriyani, I Made Lana Prasetya. Application of Contrast Limited Adaptive Histogram Equalization (CLAHE) on the Quality and Pathological Information of Chest X-Ray (CXR) Images in Covid-19 Patients in the Intensive Care Unit (ICU) Room. Int. J. of Allied Med. Sci. and Clin. Res. [Internet]. 2023 Jan. 6 [cited 2026 Apr. 12];11(1):1-5. Available from: https://ijamscr.com/ijamscr/article/view/1276

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References

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Rangaiah A, Shankar SM, Basawarajappa SG, Shah PA, Chandrashekar A, Munegowda A, et al. Detection of SARS-CoV-2 in Clinical Samples: Target-specific Analysis of Qualitative Reverse Transcription-Polymerase Chain Reaction(RT-PCR) Diagnostic Kits. IJID Reg. 2021;1((Nov)):163-9. doi: 10.1016/j.ijregi.2021.11.004, PMID 35721770.
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Hasni M, Farahat Z, Abdeljelil A, Marzouki K, Aoudad M, Tlemsani Z, et al. An efficient approach based on 3D reconstruction of CT scan to improve the management and monitoring of COVID-19 patients. Heliyon. 2020;6(11):e05453. doi: 10.1016/j.heliyon.2020.e05453, PMID 33195849.
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Giraudo C, Cavaliere A, Fichera G, Weber M, Motta R, Pelloso M, et al. Validation of a composed Covid-19 chest radiography score: the care project. ERJ Open Res. 2020;6(4):1-9. doi: 10.1183/23120541.00359-2020, PMID 33263058.
Yasin R, Gouda W. Chest X-ray findings monitoring COVID-19 disease course and severity. Egypt J Radiol Nucl Med. 2020;51(1). doi: 10.1186/s43055-020-00296-x.
Jacobi A, Chung M, Bernheim A, Eber C. Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review. Clin Imaging. 2020;64(April):35-42. doi: 10.1016/j.clinimag.2020.04.001, PMID 32302927.
Ieracitano C, Mammone N, Versaci M, Varone G, Ali AR, Armentano A, et al. A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images. Neurocomputing. 2022;481:202-15. doi: 10.1016/j.neucom.2022.01.055, PMID 35079203.
Shukla KN. A review on image enhancement techniques. IJEACS. 2017;02(7):232-5. doi: 10.24032/ijeacs/0207/05.
Koonsanit K, Pongnapang N, Thajchayapong P. Science N, agency TD. Image enhancement on digital x-ray images using N-CLAHE National Science and Technology Development Agency (NSTDA), Thailand; 2017;(February 2018). doi: 10.1109/BMEiCON.2017.8229130.
Siracusano G, Corte La A, Gaeta M, Cicero G, Chiappini M, Finocchio G. Pipeline for advanced contrast enhancement (Pace) of chest x-ray in evaluating Covid-19 patients by combining bidimensional empirical mode decomposition and contrast limited adaptive histogram equalization (clahe). Sustainability. 2020;12(20):1-18. doi: 10.3390/su12208573.
Sara U, Akter M, Uddin MS. Image quality assessment through FSIM, SSIM, MSE and PSNR–A comparative study. J Comput Commun. 2019;07(3):8-18. doi: 10.4236/jcc.2019.73002.
Sinecen M. Digital image processing with MATLAB. Appl Eng MATLAB Concepts. 2016:1-42. doi: 10.5772/63028.

References

WHO. Strategy to Achieve Global Covid-19 Vaccination by mid-2022. Who. 2022:1-16.

Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):934-43. doi: 10.1001/jamainternmed.2020.0994, PMID 32167524.

Ayaz A, Demir AGO, Ozturk G, Kocak M. A pooled RT-PCR testing strategy for more efficient COVID-19 pandemic management. Int J Infect Dis. 2022;116:1-6. doi: 10.1016/j.ijid.2021.12.328, PMID 34922006.

Rangaiah A, Shankar SM, Basawarajappa SG, Shah PA, Chandrashekar A, Munegowda A, et al. Detection of SARS-CoV-2 in Clinical Samples: Target-specific Analysis of Qualitative Reverse Transcription-Polymerase Chain Reaction(RT-PCR) Diagnostic Kits. IJID Reg. 2021;1((Nov)):163-9. doi: 10.1016/j.ijregi.2021.11.004, PMID 35721770.

Al-Tawfiq JA, Memish ZA. Diagnosis of SARS-CoV-2 infection based on CT scan vs RT-PCR: reflecting on experience from MERS-CoV. J Hosp Infect. 2020;105(2):154-5. doi: 10.1016/j.jhin.2020.03.001, PMID 32147407.

Hasni M, Farahat Z, Abdeljelil A, Marzouki K, Aoudad M, Tlemsani Z, et al. An efficient approach based on 3D reconstruction of CT scan to improve the management and monitoring of COVID-19 patients. Heliyon. 2020;6(11):e05453. doi: 10.1016/j.heliyon.2020.e05453, PMID 33195849.

Benmalek E, Elmhamdi J, Jilbab A. Comparing CT scan and chest X-ray imaging for COVID-19 diagnosis. Biomed Eng Adv. 2021;1(December 2020):100003. doi: 10.1016/j.bea.2021.100003, PMID 34786568.

Giraudo C, Cavaliere A, Fichera G, Weber M, Motta R, Pelloso M, et al. Validation of a composed Covid-19 chest radiography score: the care project. ERJ Open Res. 2020;6(4):1-9. doi: 10.1183/23120541.00359-2020, PMID 33263058.

Yasin R, Gouda W. Chest X-ray findings monitoring COVID-19 disease course and severity. Egypt J Radiol Nucl Med. 2020;51(1). doi: 10.1186/s43055-020-00296-x.

Jacobi A, Chung M, Bernheim A, Eber C. Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review. Clin Imaging. 2020;64(April):35-42. doi: 10.1016/j.clinimag.2020.04.001, PMID 32302927.

Ieracitano C, Mammone N, Versaci M, Varone G, Ali AR, Armentano A, et al. A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images. Neurocomputing. 2022;481:202-15. doi: 10.1016/j.neucom.2022.01.055, PMID 35079203.

Shukla KN. A review on image enhancement techniques. IJEACS. 2017;02(7):232-5. doi: 10.24032/ijeacs/0207/05.

Koonsanit K, Pongnapang N, Thajchayapong P. Science N, agency TD. Image enhancement on digital x-ray images using N-CLAHE National Science and Technology Development Agency (NSTDA), Thailand; 2017;(February 2018). doi: 10.1109/BMEiCON.2017.8229130.

Siracusano G, Corte La A, Gaeta M, Cicero G, Chiappini M, Finocchio G. Pipeline for advanced contrast enhancement (Pace) of chest x-ray in evaluating Covid-19 patients by combining bidimensional empirical mode decomposition and contrast limited adaptive histogram equalization (clahe). Sustainability. 2020;12(20):1-18. doi: 10.3390/su12208573.

Sara U, Akter M, Uddin MS. Image quality assessment through FSIM, SSIM, MSE and PSNR–A comparative study. J Comput Commun. 2019;07(3):8-18. doi: 10.4236/jcc.2019.73002.

Sinecen M. Digital image processing with MATLAB. Appl Eng MATLAB Concepts. 2016:1-42. doi: 10.5772/63028.

Issue

Vol. 11 No. 1 (2023): 2023 Volume -11 - Issue 1

Date Log

Application of Contrast Limited Adaptive Histogram Equalization (CLAHE) on the Quality and Pathological Information of Chest X-Ray (CXR) Images in Covid-19 Patients in the Intensive Care Unit (ICU) Room

Corresponding Author(s) : I Wayan Juliasa

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