Comparative Analysis of Image Enhancement Techniques for Chest X-ray Images
1st International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2022
; : 130-135, 2022.
Article
in English
| Scopus | ID: covidwho-2018636
ABSTRACT
X-ray radiography plays a crucial part in diagnosis of various diseases in human body like Covid-19, Cancer and Pneumonia. The images obtained through X-ray radiography is interpreted by Surgeons, Pathologists and Radiologists for detecting anomaly in scanned body part. Chest X-ray is one of the cheapest and easily accessible tests of functioning of chest and lungs. However, images obtained through X-ray are not very clear, low in contrast and with lesser variation in gray level. Image enhancement is done for better visualization of images and bringing forward the underlying details of image. The Kaggle repository of total 6334 chest X-ray images were used for experimentation and calculation works. In this paper, we have compared various combinations of contrast enhancement techniques such as CLAHE, Morphological operations (black and white hat transforms) and noise reduction techniques like Median filter, DCT and DWT. The Comparison was done on the basis of image quality assessment parameters such as MSE, PSNR, and AMBE. The results showed that fusion of CLAHE and DWT techniques gave best results with highest PSNR value and lowest AMBE among the various models discussed. The proposed methodology shall be very helpful in diagnosis of diseases from chest X-ray images. © 2022 IEEE.
Chest X-rays; Contrast Enhancement; Image assessment parameters; Medical images; Noise Reduction; Diagnosis; Discrete wavelet transforms; Image compression; Image denoising; Image enhancement; Mathematical morphology; Median filters; Medical imaging; X ray radiography; Body parts; Chest X-ray; Chest X-ray image; Comparative analyzes; Gray-level; Human bodies; Image assessment; Image assessment parameter; Medical image; Noise abatement
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
1st International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2022
Year:
2022
Document Type:
Article
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