Noise Removal in Microarray Images Using Variational Mode Decomposition Technique

Kumar, G. Sai Chaitanya; Kumar, R. Kiran; Naidu, G. Apparao; Harikiran, J.

doi:10.12928/telkomnika.v15i4.5375

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Smoothing of co-efficients in the wavelet domain was used to achieve denoising. Based on Variational Mode Decomposition, Kumar, et al [32] proposed a novel noise reduction approach capable of Variational Mode Decomposition(VMD). Shao, et al [33] proposed a compressed sensing-based noise reduction approach.…”

Section: Advances In Microarray Image Enhancementmentioning

confidence: 99%

Microarray image enhancement techniques by denoising: Current status and future directions

Ramachandran

2023

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Microarray imaging is a technique for simultaneously detecting the expression of numerous genes. Microarrays are simply a slide with unique Deoxyribonucleic Acid (DNA) probes, often known as gene chips. This paper aims to provide an overview of important contributions to the field of image denoising in the context of microarray imaging. The methodologies discussed in the article include various techniques of transform domain and spatial filtering methods for denoising microarray images that can be used to improve the quality of microarray images. We have identified the strengths and limitations of these techniques and highlights their potential impact. The paper also explores future developments in this area and discusses their potential impact, on microarray imaging.

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Section: Advances In Microarray Image Enhancementmentioning

confidence: 99%

Microarray image enhancement techniques by denoising: Current status and future directions

Ramachandran

2023

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“…However, they suffered from fault estimation especially in high noise levels. In addition, a novel and efficient noise level estimation technique is proposed in [16], also they analyze the change of singular values in order to determine the content related parameter, and they applied their technique on different kinds of images.…”

Section: Introductionmentioning

confidence: 99%

Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

Khmag¹,

Ghoul²,

Al-Haddad³

et al. 2018

TELKOMNIKA

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DNA Microarray Image Segmentation Using Markov Random Field Algorithm

Said

Jambek

2021

J. Phys.: Conf. Ser.

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A deoxyribonucleic acid (DNA) microarray image requires a three-stage process to enhance and preserve the image’s important information. These are gridding, segmentation, and intensity extraction. Of these three processes, segmentation is considered the most difficult, as its function is to differentiate between features in the foreground and background. The elements in the foreground form the object or the vital information of the image, while the background features less critical information for DNA microarray image analysis. This paper presents a study that utilises the Markov random field (MRF) segmentation algorithm on a DNA microarray image. The MRF algorithm evaluates the current pixel depends on its neighbouring pixels. The experimental results show that the MRF algorithm works effectively in the segmentation process for a DNA microarray image.

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Noise Removal in Microarray Images Using Variational Mode Decomposition Technique

Cited by 9 publications

References 11 publications

Microarray image enhancement techniques by denoising: Current status and future directions

Microarray image enhancement techniques by denoising: Current status and future directions

Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

DNA Microarray Image Segmentation Using Markov Random Field Algorithm

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