2019
DOI: 10.1002/cta.2591
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Real‐time removal of impulse noise from MR images for radiosurgery applications

Abstract: Summary In the recent years, image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Among these techniques, medical image enhancement algorithms play an essential role in the removal of the noise, which can be produced by medical instruments and during image transfer. Impulse noise is a major type of noise, which is produced by medical imaging systems, such as MRI, computed tomography (CT), and angiography instruments. An embeddable hardware… Show more

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Cited by 4 publications
(2 citation statements)
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“…In the context of Pap smear images, the presence of noise can significantly impact the accuracy and reliability of differentiating between nuclear and non-nuclear features, leading to potential misclassifications and decreased performance in cytoplasm and nucleus segmentation. Examples of different types of noise are shown in Figure 3 [17][18][19][20]. Biomedical images commonly exhibit different types of noise, including Poisson noise, speckle noise, Gaussian noise, and impulse noise.…”
Section: Noise In Pap Smear Imagesmentioning
confidence: 99%
“…In the context of Pap smear images, the presence of noise can significantly impact the accuracy and reliability of differentiating between nuclear and non-nuclear features, leading to potential misclassifications and decreased performance in cytoplasm and nucleus segmentation. Examples of different types of noise are shown in Figure 3 [17][18][19][20]. Biomedical images commonly exhibit different types of noise, including Poisson noise, speckle noise, Gaussian noise, and impulse noise.…”
Section: Noise In Pap Smear Imagesmentioning
confidence: 99%
“…According to the type of filter, it can be divided into linear filters and nonlinear filters. Images generally have such a property: the pixel value at any position in an image is closely related to the pixel value in the neighborhood where the pixel is located, such as spatial correlation or similar pixel values [ 18 ].…”
Section: Related Researchmentioning
confidence: 99%