Measure of Singular Value Decomposition (M-SVD) based
Quality Assessment for Medical Images with Degradation
Ersin Elbasi
College of Engineering and Technology, American University of the Middle
East, Kuwait
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Abstract: We use images in several important areas such as military, health,
security, and science. Images can be distorted during the capturing, recording,
processing, and storing. Image quality metrics are the techniques to measure
the quality and quality accuracy level of the images and videos. Most of the
quality measurement algorithms does not affect by small distortions in the
image. Magnetic
Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI)
are widely used in the health sector. Because of several reasons it might be artifacts
in the medical images. Doctor decisions might be affected by these image artifacts.
Image quality measurement is an important and challenging area to work on.
There are several metrics that have been done in the literature such as mean
square error, peak signal-noise ratio, gradient similarity measure, structural
similarity index, and universal image quality. Patient information can be an
embedded corner of the medical image as a watermark. Watermark can be
considered one of the image distortions types. The most common objective
evaluation algorithms are simple pixel based which are very unreliable,
resulting in poor correlation with the human visual system. In this work, we
proposed a new image quality metric which is a Measure of Singular Value
Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm
gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square
Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in
watermarked and distorted images such as histogram equalization, JPEG compression,
Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.
Keywords: Image quality measurement, M-SVD, image distortion, watermarking,
medical image.
Received September 7, 2020; accepted
February 1, 2021