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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Journal of Information and Optimization Sciences
Open Access Research Article

Low rank sparse coefficient based nuchal translucency image de-noising

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pp. 333–341Vol. 45Issue 2March 2024DOI: 10.47974/JIOS-1550XML
Published Online:
17 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1550
Pages:
333–341

Abstract

The process of eliminating distortion or noise from an image is known as image de-noising. Random noise is introduced to ultrasonic imaging, resulting in reduced contrast in the images. For Nuchal translucency (NT) detection, image de-noising is a crucial stage. Although deep-learning methods have been extensively studied for this problem and have shown compelling results, most networks may result in disappearing or inflating gradients and need more memory and time to attain a spectacular performance. To achieve better overall framework optimization, Novel Methodology of anisotropic filtering followed by a compressed sensing based on Low Rank Sparse Coefficient(LRSC) for ultrasound image de-noising is proposed to achieve better overall framework optimization, this article proposes anisotropic filtering followed by a compressed sensing based on Low Rank Sparse Coefficient(LRSC) for ultrasound image de-noising. This hybrid technique is quite effective at reducing noise while yet retaining fine image details. Real-time hospital images are utilized to assess the efficiency of the proposed model, taking into account clinical accessibility and imaging features.  The peak signal-to-noise ratio (PSNR), mean square error (MSE), and structural similarity index (SSIM) were used to evaluate the proposed method’s performance. Average SSIM, PSNR, MSE values are 0.98, 42.28 and 49 for HCNN[12], GAN[16] and Proposed method respectively. Proposed method have average MSE of 2.6, HCNN have 14 and GAN have 371.

Keywords

Nuchal translucency (NT)Low rank sparse coefficient (LRSC)Anisotropic filteringDe-noisingCompressed sensing

Subject Classifications

26-XX30-XX

References

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