Attenuating noise from computed tomography medical images using a coefficients‐driven total variation denoising algorithm. Issue 4 (18th November 2014)

• Record Type: Journal Article
• Authors: Al‐Ameen, Zohair; Sulong, Ghazali
• Is Part Of: International journal of imaging systems and technology. Volume 24:Issue 4(2014:Dec.)
• Page Start: 350
• Page End: 358
• Publication Date: 2014-11-18
• Subjects: Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22112
• Languages: English
• ISSNs: 0899-9457

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>The aim of image denoising is to recover a visually accepted image from its noisy observation with as much detail as possible. The noise exists in computed tomography images due to hardware errors, software faults and/or low radiation dose. Because of noise, the analysis and extraction of accurate medical information is a challenging task for specialists. Therefore, a novel modification on the total variational denoising algorithm is proposed in this article to attenuate the noise from CT images and provide a better visual quality. The newly developed algorithm can properly detect noise from the other image components using four new noise distinguishing coefficients and reduce it using a novel minimization function. Moreover, the proposed algorithm has a fast computation speed, a simple structure, a relatively low computational cost and preserves the small image details while reducing the noise efficiently. Evaluating the performance of the proposed algorithm is achieved through the use of synthetic and real noisy images. Likewise, the synthetic images are appraised by three advanced accuracy methods –Gradient Magnitude Similarity Deviation (GMSD), Structural Similarity (SSIM) and Weighted Signal‐to‐Noise Ratio (WSNR). The empirical results exhibited significant improvement not only in noise reduction but also in preserving the minor image details. Finally, the proposed algorithm provided satisfying results that outperformed all the comparative methods.</p> </abstract>