Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering

Pal, Chandrajit; Das, Pabitra; Chakrabarti, Amlan; Ghosh, Ranjan

doi:10.1002/ima.22230

Cited by 34 publications

(17 citation statements)

References 31 publications

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“…In MR images low signal intensities (Signal to Noise Ratio (SNR) < 2) are consequently biased to noise which leads to blurring of significant details of images like edges and boundaries of tissue parts or structural details of an organ. A simple scheme is initiated to correct the bias due to the Rician distribution of the noisy magnitude data (15) . The additional noise in MR image is smoothed with the weights that are computed using pre-smoothed MR images.…”

Section: Methodsmentioning

confidence: 99%

Mass Sphering Approach for Three-Dimensional Reconstruction of Brain Tumor using MRI

Radhakrishnan¹,

Vanaja²,

Devi³

et al. 2022

IJST

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Objective: The objective of the research work is to reconstruct the brain tumor three-dimensionally with high degree of accuracy. Methods: This study describes 3D reconstruction of brain tumor using Mass Sphering Approach (MSA) algorithm. 39 weighted features are extracted from the non-tumor and tumor pixels. These weighted features are used to train the Support Vector Machine (SVM) algorithm. Number of training samples taken to train SVM algorithm are 268 and the testing sample are 64. The complete MR image set of a subject (64 axial slices) are detected for tumor pixels and these slices are concatenated to get volumetric tumor information. Findings: 5-step MSA algorithm is proposed which includes filtering, segmentation, classification, optimization and reconstruction. MR images are subjected to Rician noise which can be removed by a simple correction scheme, initiated to change the bias due to the Rician distribution of the noisy magnitude data. The filtered MR image slices are segmented and classified to detect the tumor areas and the tumor pixels are subjected for 3D reconstruction. The improvement in performance of MSA is depicted by comparing the algorithm with traditional SVM. Novelty: The accuracy achieved in detecting glioma and glioblastoma using MSA are 95.24% and 99% respectively which is highly remarkable.

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Section: Methodsmentioning

confidence: 99%

Mass Sphering Approach for Three-Dimensional Reconstruction of Brain Tumor using MRI

Radhakrishnan¹,

Vanaja²,

Devi³

et al. 2022

IJST

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“…Rician noise produced from the original frequency domain measurements (complex Gaussian noise) to corrupt the MR Image [26]. The Rician probability density function ( ) for the noised image is calculated according to equation 1.…”

Section: Proposed Methodsmentioning

confidence: 99%

Deep CNN based MR image denoising for tumor segmentation using watershed transform

Latif¹,

N.F²,

Alghazo³

et al. 2018

IJET

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Magnetic Resonance Imaging (MRI) is considered one of the most effective imaging techniques used in the medical field for both clinical investigation and diagnosis. This is due to the fact that MRI provides many critical features of the tissue including both physiological and chemical information. Rician noise affects MR images during acquisition thereby reducing the quality of the image and complicating the accurate diagnosis. In this paper, we propose a novel technique for MR image denoising using Deep Convolutional Neural Network (Deep CNN) and anisotropic diffusion (AD) which we will refer to as Deep CNN-AD. Watershed transform is then used to segment the tumorous portion of the denoised image. The proposed method is tested on the BraTS MRI datasets. The proposed denoising method produced better results compared to previous methods. As denoising process affect the segmentation process therefore better denoised images by proposed technique produced more accurate segmentation with an average Specificity of 99.85% and dice coefficient of 90.46% thus indicating better performance of proposed technique.

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“…The images provided by the Harvard's Whole Brain Atlas are raw images and are affected by Rician noise. The selected ROIs are denoised by using an anisotropic diffusion filter [23,24]. The anisotropic diffusion filtering provides reliable noise removing while very satisfactory edge-preserving results are achieved.…”

Section: Mathematical Approachesmentioning

confidence: 99%

“…The analysis was performed in two datasets; D1 contains raw images from the Harvard's Whole Brain Atlas that are affected by noises with Rician distribution. D2 contains denoised images by using an anisotropic diffusion filter [23,24]. The results are compared in terms of the noise effect on the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Brain Tissue Evaluation Based on Skeleton Shape and Similarity Analysis between Hemispheres

et al. 2020

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Background: The purpose of this article is to provide a new evaluation tool based on skeleton maps to assess the tumoral and non-tumoral regions of the 2D MRI in PD-weighted (proton density) and T2w (T2-weighted type) brain images. Methods: The proposed method investigated inter-hemisphere brain tissue similarity using a mask in the right hemisphere and its mirror reflection in the left one. At the hemisphere level and for each ROI (region of interest), a morphological skeleton algorithm was used to efficiently investigate the similarity between hemispheres. Two datasets with 88 T2w and PD images belonging to healthy patients and patients diagnosed with glioma were investigated: D1 contains the original raw images affected by Rician noise and D2 consists of the same images pre-processed for noise removal. Results: The investigation was based on structural similarity assessment by using the Structural Similarity Index (SSIM) and a modified Jaccard metrics. A novel S-Jaccard (Skeleton Jaccard) metric was proposed. Cluster accuracy was estimated based on the Silhouette method (SV). The Silhouette coefficient (SC) indicates the quality of the clustering process for the SSIM and S-Jaccard. To assess the overall classification accuracy an ROC curve implementation was carried out. Conclusions: Consistent results were obtained for healthy patients and for PD images of glioma. We demonstrated that the S-Jaccard metric based on skeletal similarity is an efficient tool for an inter-hemisphere brain similarity evaluation. The accuracy of the proposed skeletonization method was smaller for the original images affected by Rician noise (AUC = 0.883 (T2w) and 0.904 (PD)) but increased for denoised images (AUC = 0.951 (T2w) and 0.969 (PD)).

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Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering

Cited by 34 publications

References 31 publications

Mass Sphering Approach for Three-Dimensional Reconstruction of Brain Tumor using MRI

Mass Sphering Approach for Three-Dimensional Reconstruction of Brain Tumor using MRI

Deep CNN based MR image denoising for tumor segmentation using watershed transform

Brain Tissue Evaluation Based on Skeleton Shape and Similarity Analysis between Hemispheres

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