scholarly journals Segmentation of Tumor in MRI Brain Images using Morphological Operators and Non-Local Means Filter

2019 ◽  
Vol 8 (4) ◽  
pp. 10524-10529
Keyword(s):  
Brain Tumor ◽  
Performance Metrics ◽  
Similarity Index ◽  
Clinical Analysis ◽  
Abnormal Development ◽  
Proton Density ◽  
Rician Noise ◽  
Morphological Operators ◽  
Local Means ◽  
Non Local

Brain Tumor is the abnormal development of tissues in the brain. According to survey report Times of India, 2019 around 5, 00,000 people are diagnosed with brain tumor in India. Among 5, 00,000 people 20 percent are children. Magnetic resonance image (MRI) used for clinical analysis of human body are sensitive to redundant Rician noise. Rician is the type of noise added during the acquisition of MRI. The removal of noise variance can be performed by constructing many filters. Among those filters, non-local means filter is used for denoising the Rician noise. In this project simulated MRI data and real time clinical data of T1, T2 and Proton Density weighted MRI images are de-noised and the performance metrics is analyzed using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Metric). The de-noised image is then subjected to thresholding and morphological operators and the tumor region is segmented.

scholarly journals Brain MR image denoising for Rician noise using pre-smooth non-local means filter

2015 ◽  
Vol 14 (1) ◽  
pp. 2 ◽  
Author(s):  
Jian Yang ◽  
Jingfan Fan ◽  
Danni Ai ◽  
Shoujun Zhou ◽  
Songyuan Tang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Rician Noise ◽  
Mr Image ◽  
Local Means ◽  
Non Local

Fuzzy similarity based non local means filter for Rician noise removal

2014 ◽  
Vol 74 (15) ◽  
pp. 5533-5556 ◽  
Author(s):  
Muhammad Sharif ◽  
Ayyaz Hussain ◽  
Muhammad Arfan Jaffar ◽  
Tae-Sun Choi
Keyword(s):  
Noise Removal ◽  
Rician Noise ◽  
Local Means ◽  
Fuzzy Similarity ◽  
Non Local

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

Computation ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 31
Author(s):  
Lenuta Pana ◽  
Simona Moldovanu ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Luminita Moraru
Keyword(s):  
Brain Tissue ◽  
Right Hemisphere ◽  
Similarity Index ◽  
Region Of Interest ◽  
Structural Similarity ◽  
Noise Removal ◽  
Mirror Reflection ◽  
Proton Density ◽  
Rician Noise ◽  
Evaluation Tool

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)).

Laplacian based non-local means denoising of MR images with Rician noise

2013 ◽  
Vol 31 (9) ◽  
pp. 1599-1610 ◽  
Author(s):  
Hemalata V. Bhujle ◽  
Subhasis Chaudhuri
Keyword(s):  
Rician Noise ◽  
Mr Images ◽  
Local Means ◽  
Non Local

Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

2020 ◽  
Vol 13 (4) ◽  
pp. 14-31
Author(s):  
Nikita Joshi ◽  
Sarika Jain ◽  
Amit Agarwal
Keyword(s):  
Magnetic Resonance ◽  
Total Variation ◽  
Similarity Index ◽  
Noise Removal ◽  
Magnetic Resonance Images ◽  
Variation Method ◽  
Mr Images ◽  
Denoising Method ◽  
Local Means ◽  
Non Local

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

Denoising 3D MR images by the enhanced non-local means filter for Rician noise

2010 ◽  
Vol 28 (10) ◽  
pp. 1485-1496 ◽  
Author(s):  
Hong Liu ◽  
Cihui Yang ◽  
Ning Pan ◽  
Enmin Song ◽  
Richard Green
Keyword(s):  
Rician Noise ◽  
Mr Images ◽  
Local Means ◽  
Non Local

scholarly journals An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Kaixin Chen ◽  
Xiao Lin ◽  
Xing Hu ◽  
Jiayao Wang ◽  
Han Zhong ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Experimental Results ◽  
Rician Noise ◽  
Search Window ◽  
Fixed Size ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Local Means ◽  
Non Local ◽  
Image Details

Abstract Background The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and adaptive non-local means (FANLM), which combined the adaptive non-local means (NLM) with fuzzy c-means (FCM), as a novel method to reduce noise in the study. Method The algorithm chose the optimal size of search window automatically based on the noise variance which was estimated by the improved estimator of the median absolute deviation (MAD) for Rician noise. Meanwhile, it solved the problem that the traditional NLM algorithm had to use a fixed size of search window. Considering the distribution characteristics for each pixel, we designed three types of search window sizes as large, medium and small instead of using a fixed size. In addition, the combination with the FCM algorithm helped to achieve better denoising effect since the improved the FCM algorithm divided the membership degrees of images and introduced the morphological reconstruction to preserve the image details. Results The experimental results showed that the proposed algorithm (FANLM) can effectively remove the noise. Moreover, it had the highest peak signal-noise ratio (PSNR) and structural similarity (SSIM), compared with other three methods: non-local means (NLM), linear minimum mean square error (LMMSE) and undecimated wavelet transform (UWT). Using the FANLM method, the image details can be well preserved with the noise being mostly removed. Conclusion Compared with the traditional denoising methods, the experimental results showed that the proposed approach effectively suppressed the noise and the edge details were well retained. However, the FANLM method took an average of 13 s throughout the experiment, and its computational cost was not the shortest. Addressing these can be part of our future research.

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