scholarly journals Brain Tumor Detection and Segmentation Using Watershed Segmentation and Morphological Operation

2021 ◽  
pp. 304-312
Author(s):  
V. Supraja ◽  
Kuna Haritha ◽  
Gunjalli Mounika ◽  
Chintha Manideepika ◽  
Kandikeri Sai Jeevani
Keyword(s):  
Brain Tumor ◽  
Critical Role ◽  
Tumor Detection ◽  
Morphological Operation ◽  
Brain Image ◽  
Mri Brain ◽  
Skull Stripping ◽  
Tumor Region ◽  
The Brain ◽  
Mri Brain Image

In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region.

scholarly journals Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN)

2021 ◽  
Vol 11 (1) ◽  
pp. 380-390
Author(s):  
Pradipta Kumar Mishra ◽  
Suresh Chandra Satapathy ◽  
Minakhi Rout
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Gray Wolf ◽  
Deep Convolutional Neural Networks ◽  
Brain Tumor Segmentation ◽  
Swarm Optimization ◽  
Brain Image ◽  
Mri Brain ◽  
Whale Optimization ◽  
The Brain

Abstract Segmentation of brain image should be done accurately as it can help to predict deadly brain tumor disease so that it can be possible to control the malicious segments of brain image if known beforehand. The accuracy of the brain tumor analysis can be enhanced through the brain tumor segmentation procedure. Earlier DCNN models do not consider the weights as of learning instances which may decrease accuracy levels of the segmentation procedure. Considering the above point, we have suggested a framework for optimizing the network parameters such as weight and bias vector of DCNN models using swarm intelligent based algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). The simulation results reveals that the WOA optimized DCNN segmentation model is outperformed than other three optimization based DCNN models i.e., GA-DCNN, PSO-DCNN, GWO-DCNN.

scholarly journals An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

2021 ◽  
Author(s):  
Muhammad Irfan Sharif ◽  
Jian Ping Li ◽  
Javeria Amin ◽  
Abida Sharif
Keyword(s):  
Brain Tumor ◽  
Tumor Detection ◽  
Entropy Method ◽  
Feature Extraction And Selection ◽  
Tumor Region ◽  
Selection For ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Complicated Task

AbstractBrain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

2017 ◽  
pp. 115-130
Author(s):  
Vijay Kumar ◽  
Jitender Kumar Chhabra ◽  
Dinesh Kumar
Keyword(s):  
Image Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Clustering Technique ◽  
Mri Brain ◽  
Gravitational Search ◽  
The Brain ◽  
Mri Brain Image ◽  
Brain Tissues ◽  
Brain Image Segmentation

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

scholarly journals Automated Brain Tumor Detection in MRI Images Using Efficient Deep Learning Methods

2019 ◽  
Vol 8 (11) ◽  
pp. 286-291
Keyword(s):  
Brain Tumor ◽  
Soft Tissues ◽  
Early Stage ◽  
Brain Mri ◽  
Tumor Detection ◽  
Computational Time ◽  
Learning Methods ◽  
Artificial Bee Colony Optimization ◽  
Skull Stripping ◽  
The Brain

Brain tumor is an unusual intensification of cells inside the skull. The brain MRI scanned images is segmented to extract brain tumor to analyze type and depth of tumor. In order to reduce the time consumption of brain tumor extraction, an automatic method for detection of brain tumor is highly recommended. Deep machine learning methods are used for automatic detection of the brain tumor in soft tissues at an early stage which involves the following stages namely: image pre-processing, clustering and optimization. This paper addresses previously adduced pre-processing (Skull stripping, Contrast stretching, clustering (k-Means, Fuzzy c-means) and optimization (Cuckoo search optimization, Artificial Bee Colony optimization) strategies for abnormal brain tumor detection from MRI brain images. Performance evaluation is done based on computational time of clustering output and optimization algorithms are analyzed in terms of sensitivity, specificity, and accuracy

scholarly journals Brain Tumor Detection and Segmentation using Histogram and Optimization Algorithm

2019 ◽  
Vol 8 (10S) ◽  
pp. 125-129
Keyword(s):  
Brain Tumor ◽  
Optimization Algorithm ◽  
Ground Truth ◽  
Tumor Detection ◽  
High Accuracy ◽  
Mathematical Foundation ◽  
Mr Images ◽  
Brain Image ◽  
Segmentation Technique ◽  
The Brain

In this research, an automated and customized neoplasm segmentation methodology is given and valid against ground truth applying simulated T1-weighted resonance pictures in twenty five subjects. a replacement intensity-based segmentation technique known as bar graph primarily based gravitational optimization algorithm is developed to segment the brain image into discriminative sections (segments) with high accuracy. whereas the mathematical foundation of this rule is given in details, the appliance of the projected rule within the segmentation of single T1-weighted pictures (T1-w) modality of healthy and lesion MR images is additionally given. The results show that the neoplasm lesion is divided from the detected lesion slice with eighty nine.6% accuracy..

Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

2014 ◽  
pp. 313-326 ◽  
Author(s):  
Vijay Kumar ◽  
Jitender Kumar Chhabra ◽  
Dinesh Kumar
Keyword(s):  
Image Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Clustering Technique ◽  
Mri Brain ◽  
Gravitational Search ◽  
The Brain ◽  
Mri Brain Image ◽  
Brain Tissues ◽  
Brain Image Segmentation

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

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