Brain Tumour Texture Analysis-A Method

2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Samir Kumar Bandyopadhyay
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Texture Analysis ◽  
Brain Tumour ◽  
Tumor Segmentation ◽  
Biomedical Image Processing ◽  
Biomedical Analysis ◽  
Brain Tumor Segmentation ◽  
Human Brain Tumor ◽  
Biomedical Image

Computer aided technology is used in biomedical image processing. In biomedical analysis features are extracted and then the proposed method will detect any abnormalities present or not in the system to be considered. In recent days the detection of brain tumour through image processing is made in medical diagnosis. The separation of tumor is made by the process of segmentation. Brain in human is the most complicated and delicate anatomical structure. There are various brain ailments in human but the indication of cancer in brain tumour may be fatal for the human. Brain tumor can be malignant or benign. The neurologist or neurosurgeon wants to know the exact location, size, shape and texture of tumor from Magnetic Resonance Imaging (MRI) of brain before going to the operation of the brain tumour or decided whether operation of removing brain tumour is at all necessary or not. The disease is analyzed since operation may cause death to the patient. Initially they took a chance by prescribing medicines to see whether there is any improvement of the condition of the patient. If the result is not satisfactory then there is no option other than operation of the tumor. Doctors also take an attempt to find the texture of the tumor since it may help them to know the progress of the tumour. In addition to Brain tumor segmentation, the detection of surface of the texture of brain tumor is required for proper treatment. The chapter proposed methods for detection of the progressive nature of the texture in the tumor presence in brain. For this process segmentation of tumor from other parts of brain is essential. In the chapter segmentation techniques are presented before the texture analysis process is given. Finally, comparisons of the proposed method with other methods are analyzed.

scholarly journals Improving Brain Tumor Segmentation with Data Augmentation Strategies

2021 ◽  
Author(s):  
Radhika Malhotra ◽  
Jasleen Saini ◽  
Barjinder Singh Saini ◽  
Savita Gupta
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
Large Data ◽  
Training Data ◽  
Tumor Segmentation ◽  
Biomedical Image Processing ◽  
Brain Tumor Segmentation ◽  
Biomedical Image ◽  
Augmentation Strategies ◽  
Unseen Data

In the past decade, there has been a remarkable evolution of convolutional neural networks (CNN) for biomedical image processing. These improvements are inculcated in the basic deep learning-based models for computer-aided detection and prognosis of various ailments. But implementation of these CNN based networks is highly dependent on large data in case of supervised learning processes. This is needed to tackle overfitting issues which is a major concern in supervised techniques. Overfitting refers to the phenomenon when a network starts learning specific patterns of the input such that it fits well on the training data but leads to poor generalization abilities on unseen data. The accessibility of enormous quantity of data limits the field of medical domain research. This paper focuses on utility of data augmentation (DA) techniques, which is a well-recognized solution to the problem of limited data. The experiments were performed on the Brain Tumor Segmentation (BraTS) dataset which is available online. The results signify that different DA approaches have upgraded the accuracies for segmenting brain tumor boundaries using CNN based model.

scholarly journals Brain Tumor Segmentation using K-Means Clustering and Detection using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 1452-1455
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Scans ◽  
Two Phases ◽  
Processing Techniques

This paper presents brain tumor detection and segmentation using image processing techniques. Convolutional neural networks can be applied for medical research in brain tumor analysis. The tumor in the MRI scans is segmented using the K-means clustering algorithm which is applied of every scan and the feed it to the convolutional neural network for training and testing. In our CNN we propose to use ReLU and Sigmoid activation functions to determine our end result. The training is done only using the CPU power and no GPU is used. The research is done in two phases, image processing and applying neural network.

Brain Tumor Segmentation Based on Region of Interest-Aided Localization and Segmentation U-Net

2021 ◽  
Author(s):  
Shidong Li ◽  
Jianwei Liu ◽  
Zhanjie Song
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Brain Mri ◽  
Medical Image Processing ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Brain Tumor Segmentation ◽  
The Impact

Abstract Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-ofinterest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.

scholarly journals Classification of Alzheimer's disease from MRI Images using CNN based Pre-trained VGG-19 Model

2020 ◽  
Vol 1 (2) ◽  
pp. 34-41
Author(s):  
Manimurugan S ◽  
Keyword(s):  
Brain Tumor ◽  
Brain Tumour ◽  
Objective Assessment ◽  
Survival Rates ◽  
Automated Detection ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Non Invasive ◽  
Magnetic Resonance Imaging Mri

Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vectormachine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.

scholarly journals Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

2018 ◽  
Vol 24 (1) ◽  
pp. 43-53
Author(s):  
Behrouz Alizadeh Savareh ◽  
Hassan Emami ◽  
Mohamadreza Hajiabadi ◽  
Mahyar Ghafoori ◽  
Seyed Majid Azimi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Manual Analysis ◽  
The Brain ◽  
Evaluation Parameters

Abstract Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

scholarly journals A Proficient Adaptive K-means based Brain Tumor Segmentation and Detection Using Deep Learning Scheme with PSO

2020 ◽  
Vol 1 (3) ◽  
pp. 9-14
Author(s):  
Anitha T ◽  
◽  
Charlyn Pushpa Latha G ◽  
Surendra Prasad M ◽  
◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumour ◽  
Objective Assessment ◽  
Survival Rates ◽  
Automated Detection ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Non Invasive ◽  
Magnetic Resonance Imaging Mri

Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vector machine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.

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