Restoration Algorithm for Gaussian Corrupted MRI Using Non-local Averaging

Srivastava, Aditya; Bhateja, Vikrant; Tiwari, Harshit; Satapathy, Suresh Chandra

doi:10.1007/978-81-322-2247-7_84

Cited by 17 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method utilizes an 'Anisotropic diffusion filter' for noise reduction. MR images could be contaminated with noise, which hampers image classification performance [28]. An image corrupts through the noise by replacing the image pixels value with noise value, and, for that reason, noise reduction is an essential step for medical image processing [29].…”

Section: B Preprocessing 1) Image Resizingmentioning

confidence: 99%

A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification

Biswas

Islam

2023

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

Background: Feature extraction process is noteworthy in order to categorize brain tumors. Handcrafted feature extraction process consists of profound limitations. Similarly, without appropriate classifier, the promising improved results can’t be obtained. Objective: This paper proposes a hybrid model for classifying brain tumors more accurately and rapidly is a preferable choice for aggravating tasks. The main objective of this research is to classify brain tumors through Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM)-based hybrid model. Methods: The MRI images are firstly preprocessed to improve the feature extraction process through the following steps: resize, effective noise reduction, and contrast enhancement. Noise reduction is done by anisotropic diffusion filter, and contrast enhancement is done by adaptive histogram equalization. Secondly, the implementation of augmentation enhances the data number and data variety. Thirdly, custom deep CNN is constructed for meaningful deep feature extraction. Finally, the superior machine learning classifier SVM is integrated for classification tasks. After that, this proposed hybrid model is compared with transfer learning models: AlexNet, GoogLeNet, and VGG16. Results: The proposed method uses the ‘Figshare’ dataset and obtains 96.0% accuracy, 98.0% specificity, and 95.71% sensitivity, higher than other transfer learning models. Also, the proposed model takes less time than others. Conclusion: The effectiveness of the proposed deep CNN-SVM model divulges by the performance, which manifests that it extracts features automatically without overfitting problems and improves the classification performance for hybrid structure, and is less time-consuming. Keywords: Adaptive histogram equalization, Anisotropic diffusion filter, Deep CNN, E-health, Machine learning, SVM, Transfer learning.

show abstract

Section: B Preprocessing 1) Image Resizingmentioning

confidence: 99%

A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification

Biswas

Islam

2023

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

show abstract

“…In brain imaging literature, a significant number of conventional and soft-computing (SC) based techniques are discussed and executed to extract and evaluate the brain abnormality. The earlier works also suggests that traditional approaches may implement a single-step or two-step procedure to extract the abnormal section from the chosen MRI slice [11,15,16]. Most of these procedures are modality specific and works well only for few modality cases, such as Flair and T2.…”

Section: Related Previous Workmentioning

confidence: 99%

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Satapathy

Rajinikanth

2018

Journal of Optimization

View full text Add to dashboard Cite

Brain abnormality is a cause for the chief risk factors in human society with larger morbidity rate. Identification of tumor in its early stage is essential to provide necessary treatment procedure to save the patient. In this work, Jaya Algorithm (JA) and Otsu’s Function (OF) guided method is presented to mine the irregular section of brain MRI recorded with Flair and T2 modality. This work implements a two-step process to examine the brain tumor from the axial, sagittal, and coronal views of the two-dimensional (2D) MRI slices. This paper presents a detailed evaluation of thresholding procedure with varied threshold levels (Th=2,3,4,5), skull stripping process before/after the thresholding practice, and the tumor extraction based on the Chan-Vese approach. Superiority of JA is confirmed among other prominent heuristic approaches found in literature. The outcome of implemented study confirms that Jaya Algorithm guided method is capable of presenting superior values of Jaccard-Index, Dice-Coefficient, sensitivity, specificity, accuracy, and precision on the BRATS 2015 dataset.

show abstract

“…Gaussian noise is statistical noise that has the probability density function equivalent to a normal distribution and tends to make the image data Gaussian distributed. This noise has a property of being additive in nature [23]. The vast majority of the de-noising algorithms assume additive white Gaussian noise.…”

Section: Pre-processing Stagementioning

confidence: 99%

3D Brain Tumor Segmentation Based on Hybrid Clustering Techniques Using Multi-views of MRI

Maksoud

Elmogy

2016

Medical Imaging in Clinical Applications

View full text Add to dashboard Cite

3D medical images segmentation is a very difficult task. It may not be accurate and takes extreme time. In this chapter, we accurately detect the brain tumor from 3D MRI image with less time. The 3D image consists of multiple 2D slices. Segmenting each 2D slice by using the 2D techniques gives more accuracy rather than segmenting the whole 3D image. The integration between K-Means and Particle Swarm Optimization was proposed to segment the 2D MRI slices of the 3D MRI image. We solved the time problem of segmenting all 2D slices of the 3D image. The experiments emphasized the effectiveness of our proposed system in segmenting the 2D and 3D medical images. It achieved 100 % accuracy for the tested 3D dataset and 98.75 % average accuracy for all tested 2D and 3D datasets. The proposed integration reduced time by a mean of 10 min for the tested 2D and 3D datasets.

show abstract

Restoration Algorithm for Gaussian Corrupted MRI Using Non-local Averaging

Cited by 17 publications

References 33 publications

A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification

A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

3D Brain Tumor Segmentation Based on Hybrid Clustering Techniques Using Multi-views of MRI

Contact Info

Product

Resources

About