scholarly journals A Bayesian Neo-Normal Mixture Model (Nenomimo) for MRI-Based Brain Tumor Segmentation

2020 ◽  
Vol 10 (14) ◽  
pp. 4892
Author(s):  
Anindya Apriliyanti Pravitasari ◽  
Nur Iriawan ◽  
Kartika Fithriasari ◽  
Santi Wulan Purnami ◽  
Irhamah ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Noise Model ◽  
Tumor Segmentation ◽  
Optimum Number ◽  
Normal Mixture ◽  
Brain Tumor Segmentation ◽  
Normal Mixture Model

The detection of a brain tumor through magnetic resonance imaging (MRI) is still challenging when the image is in low quality. Image segmentation could be done to provide a clear brain tumor area as the region of interest. In this study, we propose an improved model-based clustering approach for MRI-based image segmentation. The main contribution is the use of the adaptive neo-normal distributions in the form of a finite mixture model that could handle both symmetrical and asymmetrical patterns in an MRI image. The neo-normal mixture model (Nenomimo) also resolves the limitation of the Gaussian mixture model (GMM) and the generalized GMM (GGMM), which are limited by the short-tailed form of their distributions and their sensitivity against noise. Model estimation is done through an optimization process using the Bayesian method coupled with a Markov chain Monte Carlo (MCMC) approach, and it employs a silhouette coefficient to find the optimum number of clusters. The performance of the Nenomimo was evaluated against the GMM and the GGMM using the misclassification ratio (MCR). Finally, this study discovered that the Nenomimo provides better segmentation results for both simulated and real data sets, with an average MCR for MRI brain tumor image segmentation of less than 3%.

scholarly journals Bayesian spatially constrained Fernandez-Steel Skew Normal Mixture model for MRI-based brain tumor segmentation

2019 ◽  
Author(s):  
Anindya Apriliyanti Pravitasari ◽  
Nur Indah Nirmalasari ◽  
Nur Iriawan ◽  
Irhamah ◽  
Kartika Fithriasari ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Mixture Model ◽  
Tumor Segmentation ◽  
Normal Mixture ◽  
Brain Tumor Segmentation ◽  
Normal Mixture Model ◽  
Skew Normal

The Brain Tumor Segmentation Using Fuzzy C-Means Technique

2018 ◽  
pp. 2402-2419
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Fazal A. Talukdar ◽  
Nilanjan Dey
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Area Of Interest ◽  
Fuzzy C Means Clustering ◽  
Comparative Results ◽  
The Brain

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

The Brain Tumor Segmentation Using Fuzzy C-Means Technique

2017 ◽  
pp. 40-61 ◽  
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Fazal A. Talukdar ◽  
Nilanjan Dey
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Area Of Interest ◽  
Fuzzy C Means Clustering ◽  
Comparative Results ◽  
The Brain

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

scholarly journals BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder–Decoder Network

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 169
Author(s):  
Mobeen Ur Rehman ◽  
SeungBin Cho ◽  
Jeehong Kim ◽  
Kil To Chong
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Brain Tumor ◽  
State Of The Art ◽  
Small Scale ◽  
Tumor Segmentation ◽  
Mr Image ◽  
Brain Tumor Segmentation ◽  
Mr Image Segmentation ◽  
Mr Brain

Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder–decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches.

scholarly journals Automatic MRI Brain Tumor Segmentation Techniques: A Survey

2021 ◽  
Vol 16 (2) ◽  
pp. 25
Author(s):  
Otman Basir ◽  
Kalifa Shantta
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Medical Science ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Non Invasive ◽  
Science Community

Image segmentation plays a crucial role in recognizing image signification for checking and mining medical image records. Brain tumor segmentation is a complicated assignment in medical image analysis. It is challenging to identify precisely and extract that a portion of the image has abnormal tissues for further diagnosis and analysis. The method of segmenting a tumor from a brain MRI image is a highly concentrated medical science community field, as MRI is non-invasive. In this survey, brain MRI images' latest brain tumor segmentation techniques are addressed a thoroughgoing literature review. Besides, surveys the several approved techniques regularly applied for brain tumor MRI segmentation. Also, highlighting variances among them and reviews their abilities, pros, and weaknesses. Various approaches to image segmentation are described and explicated with the modern participation of several investigators.

scholarly journals MRI-based brain tumor segmentation using Gaussian mixture model with reversible jump Markov chain Monte Carlo algorithm

2019 ◽  
Author(s):  
Anindya Apriliyanti Pravitasari ◽  
Yusuf Puji Hermanto ◽  
Nur Iriawan ◽  
Irhamah ◽  
Kartika Fithriasari ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Brain Tumor ◽  
Markov Chain Monte Carlo ◽  
Gaussian Mixture Model ◽  
Gaussian Mixture ◽  
Monte Carlo Algorithm ◽  
Tumor Segmentation ◽  
Reversible Jump ◽  
Brain Tumor Segmentation
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