RETRACTED ARTICLE: Segmentation of spinal cord from computed tomography images based on level set method with Gaussian kernel

Malathy, V.; Anand, M.; Lal, N. Dayanand; Adhoni, Zameer Ahmed

doi:10.1007/s00500-020-05113-1

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In medical image processing, t is a scale parameter. Additionally, the convolution between a Gaussian kernel 4 and I 0 i, j ð Þ can be obtained which is given as…”

Section: Isotropic Diffusion Modelmentioning

confidence: 99%

“…As a result, Equation () can be expressed in another way as

({, \begin{array}{l} \frac{\partial I}{\partial t} goodbreak= \nabla^{2} I, & ((), i, j) \in Ω \\ I ((), i, j, 0) goodbreak= I_{0} ((), i, j), & t goodbreak= 0 \end{array})

In medical image processing, t is a scale parameter. Additionally, the convolution between a Gaussian kernel 4 and

I_{0} ((), i, j)

can be obtained which is given as

I ((), i, j, t) goodbreak= G_{t} ((), i, j) * I_{0} ((), i, j)

where

G_{t} ((), i, j) = {at}^{- 1} \exp ([], \frac{- (i^{2} + j^{2})}{4 t})

denotes the Gaussian kernel and a denotes the constant.…”

Section: Introductionmentioning

confidence: 99%

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Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function

Kollem¹,

Reddy²,

Rao

et al. 2021

Int J Imaging Syst Tech

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Various image denoising algorithms have been developed for medical imaging. But some disadvantages have been found, including the block effect, which increases smoothing, and the loss of image detail. Using the statistical properties of observed noisy images, we propose a new diffusivity function-based partial differential equation method for image denoising. This model incorporates a Quaternion Wavelet Transform for the generation of the various noisy image coefficients, an improved generalized cross-validation function for generating the optimal threshold value via the soft threshold function, and a new diffusivity function for controlling the diffusion process. The fourth-order PDE diffusivity function, which is presented in this article, is a novel diffusion coefficient that is more effective at removing noise and preserving edges than previous approaches. Finally, the performance of the proposed method is evaluated using the peak signal-to-noise ratio, mean square error, structural similarity index, and comparisons to other traditional methods.

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“…In medical image processing, t is a scale parameter. Additionally, the convolution between a Gaussian kernel 4 and I 0 i, j ð Þ can be obtained which is given as…”

Section: Isotropic Diffusion Modelmentioning

confidence: 99%

“…As a result, Equation () can be expressed in another way as