Assessment of non-linear filters for MRI images

Chandrashekar, Leena; Sreedevi, A.

doi:10.1109/icecct.2017.8117852

Cited by 16 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The preprocessing stage resizes the image to size 256 × 256 and converts them to gray scale. The images contain Gaussian and Rician noises, that are eliminated; Non-Local Means Filter is used in filtering the CT and MRI images [29]. The preprocessing is followed by image registration-a mandatory step, where both the as are matched for size, orientation and scaling.…”

Section: Proposed Enhancement Technique For Fused Ct and Mrimentioning

confidence: 99%

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

Chandrashekar¹,

Sreedevi²

2021

Emerging Research in Computing, Information, Communication and Applications

Self Cite

View full text Add to dashboard Cite

Section: Proposed Enhancement Technique For Fused Ct and Mrimentioning

confidence: 99%

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

Chandrashekar¹,

Sreedevi²

2021

Emerging Research in Computing, Information, Communication and Applications

Self Cite

View full text Add to dashboard Cite

“…Subsequently, the images are fused using Laplacian Pyramid [31], since it provides excellent contrast for fused images. The fusion process introduces blocking effect and noise in the fused image, thereby reducing the image quality as discussed in section 1 [30]. Consequently, the enhancement technique is required to enhance the fused image with minimum loss of original information.…”

Section: -Enhancement Technique For Fused Mri and Ct Imagementioning

confidence: 99%

“…Experimental Results for Dataset D20 -D50 UIQI) and Mean Square Error (MSE) are considered to measure the quality of the enhanced image and compared with the fused image quality. The Table2shows the expressions for various performance parameters, the detailed definitions are in[30]. The Standard Deviation (SD) represents the contrast of the enhanced image.…”

mentioning

confidence: 99%

A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images

Chandrashekar¹,

Asundi²

2020

jist

Self Cite

View full text Add to dashboard Cite

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a different characteristic of the brain. Therefore, experts have to analyze each of the images independently. This requires more expertise by doctors and delays the detection and diagnosis time. Multimodal Image Fusion is a process of generating image of high visual quality, by fusing different images. However, it introduces blocking effect, noise and artifacts in the fused image. Most of the enhancement techniques deal with contrast enhancement, however enhancing the image quality in terms of edges, entropy, peak signal to noise ratio is also significant. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used enhancement technique. The major drawback of the technique is that it only enhances the pixel intensities and also requires selection of operational parameters like clip limit, block size and distribution function. Particle Swarm Optimization (PSO) is an optimization technique used to choose the CLAHE parameters, based on a multi objective fitness function representing entropy and edge information of the image. The proposed technique provides improvement in visual quality of the Laplacian Pyramid fused MRI and CT images.

show abstract

“…In the case of linear filters, the noise is reduced by changing the image element value of weighted-mean neighborhood pixels and it creates a poor quality of image. The nonlinear denoising methods 6 include a median filter, a bilateral filter, an anisotropic diffusion filter (ADF), and a nonlocal mean filter (NLM). The neighboring pixels are organized along the basis of sample attributes of a windowpane.…”

Section: Introductionmentioning

confidence: 99%

“…5 The best solution for diagnosis made is a nonlinear filter rather than a linear filter as it diminishes the noise as well as keeps the boundaries represented by pixels. The nonlinear denoising methods 6 include a median filter, a bilateral filter, an anisotropic diffusion filter (ADF), and a nonlocal mean filter (NLM).…”

Section: Introductionmentioning

confidence: 99%

Removal of noise in MRI images using a block difference‐based filtering approach

Nagarajan

Priya

2019

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) images are frequently sensitive to certain types of noises and artifacts. The denoising of MRI images is essential for improving visual quality and reliability of the quantitative analysis of diagnosis and treatment. In this article, a new block difference‐based filtering method is proposed to denoise the MRI images. First, the normal MRI image is degraded by a certain percentage of noise. The block difference between the intensity of the normal and noisy MRI is computed, and then it is compared with the intensity of the blocks of the normal MRI image. Based on the comparison, the pixel weights are updated to each block of the denoised MRI image. Observational results are brought out on the BrainWeb and BraTS datasets and evaluated by performance metrics such as peak signal‐to‐noise ratio, structural similarity index measures, universal quality index, and root mean square error. The proposed method outperforms the existing denoising filtering techniques.

show abstract

Assessment of non-linear filters for MRI images

Cited by 16 publications

References 7 publications

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images

Removal of noise in MRI images using a block difference‐based filtering approach

Contact Info

Product

Resources

About