scholarly journals Validation of Performance Homogeneity of Chan-Vese Model on Selected Tumour Cells

2021 ◽  
Vol 12 (6) ◽  
pp. 1-17
Author(s):  
Justice Kwame Appati ◽  
Franklin Iron Badzi ◽  
Michael Agbo Tettey Soli ◽  
Stephane Jnr Nwolley ◽  
Ismail Wafaa Denwar
Keyword(s):  
Breast Tumor ◽  
Tumour Cells ◽  
Tumor Segmentation ◽  
Segmented Image ◽  
Tumor Region ◽  
The Brain

This study aims to analyze the Chan-Vese model's performance using a variety of tumor images. The processes involve the tumors' segmentation, detecting the tumors, identifying the segmented tumor region, and extracting the features before classification occurs. In the findings, the Chan-Vese model performed well with brain and breast tumor segmentation. The model on the skin performed poorly. The brain recorded DSC 0.6949903, Jaccard 0.532558; the time elapsed 7.389940 with an iteration of 100. The breast recorded a DSC of 0.554107, Jaccard 0.383228; the time elapsed 9.577161 with an iteration of 100. According to this study, a higher DSC does not signify a well-segmented image, as the breast had a lower DSC than the skin. The skin recorded a DSC of 0.620420, Jaccard 0.449717; the time elapsed 17.566681 with an iteration of 200.

scholarly journals Validation of Performance Homogeneity of Chan-Vese Model on Selected Tumour Cells

2021 ◽  
Vol 12 (6) ◽  
pp. 0-0
Keyword(s):  
Breast Tumor ◽  
Tumour Cells ◽  
Tumor Segmentation ◽  
Segmented Image ◽  
Tumor Region ◽  
The Brain

This study aims to analyze the Chan-Vese model's performance using a variety of tumor images. The processes involve the tumors' segmentation, detecting the tumors, identifying the segmented tumor region, and extracting the features before classification occurs. In the findings, the Chan-Vese model performed well with brain and breast tumor segmentation. The model on the skin performed poorly. The brain recorded DSC 0.6949903, Jaccard 0.532558; the time elapsed 7.389940 with an iteration of 100. The breast recorded a DSC of 0.554107, Jaccard 0.383228; the time elapsed 9.577161 with an iteration of 100. According to this study, a higher DSC does not signify a well-segmented image, as the breast had a lower DSC than the skin. The skin recorded a DSC of 0.620420, Jaccard 0.449717; the time elapsed 17.566681 with an iteration of 200.

scholarly journals Brain Tumor Segmentation using FCM and Symbolic Feature

2019 ◽  
Vol 8 (6S) ◽  
pp. 654-658
Keyword(s):  
Brain Tumor ◽  
Malignant Tumors ◽  
Median Filter ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Normal Tissues ◽  
Tumor Region ◽  
Mr Brain Images ◽  
The Brain

The brain tumor segmentation from image is interesting and challenging in the field of image processing and pattern recognition. An early detection of a brain tumor region helps the patient to take the correct medicine and increase the rate of the survival.The brain tumor segmentation is a process of differentiating the abnormal tissues and normal tissues. most common types of brain tumors are Benign and Malignant tumors. In this paper, the Fuzzy C-Means (FCM) approach is used to cluster the abnormal cells region and normal cells region in the brain image. The possible noises are removed by employing the median filter and morphological function is applied to extract the possible tumor region. The true tumor region is extracted with the help of symbolic features. Finally, the proposed methods is tested on T2- weighted MR brain images

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 Aggregation-and-Attention Network for brain tumor segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chih-Wei Lin ◽  
Yu Hong ◽  
Jinfu Liu
Keyword(s):  
Brain Tumor ◽  
Semantic Information ◽  
Spatial Relationship ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Assisted Diagnosis ◽  
The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

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.

scholarly journals Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
Yue Zhao ◽  
Xiaoqiang Ren ◽  
Kun Hou ◽  
Wentao Li
Keyword(s):  
Brain Tumor ◽  
Network Architecture ◽  
Contextual Information ◽  
Computational Cost ◽  
Disease Diagnosis ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fiber Network ◽  
3D Network ◽  
The Brain

Automated brain tumor segmentation based on 3D magnetic resonance imaging (MRI) is critical to disease diagnosis. Moreover, robust and accurate achieving automatic extraction of brain tumor is a big challenge because of the inherent heterogeneity of the tumor structure. In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder–decoder architecture to segment the brain tumor accurately. 3D RMFNet is applied in our paper to solve the problem of brain tumor segmentation, including a 3D recurrent unit and 3D multi-fiber unit. First of all, we propose that recurrent units segment brain tumors by connecting recurrent units and convolutional layers. This quality enhances the model’s ability to integrate contextual information and is of great significance to enhance the contextual information. Then, a 3D multi-fiber unit is added to the overall network to solve the high computational cost caused by the use of a 3D network architecture to capture local features. 3D RMFNet combines both advantages from a 3D recurrent unit and 3D multi-fiber unit. Extensive experiments on the Brain Tumor Segmentation (BraTS) 2018 challenge dataset show that our RMFNet remarkably outperforms state-of-the-art methods, and achieves average Dice scores of 89.62%, 83.65% and 78.72% for the whole tumor, tumor core and enhancing tumor, respectively. The experimental results prove our architecture to be an efficient tool for brain tumor segmentation accurately.

scholarly journals Cancer Cell Invasion of Brain Tissue: Guided by a Prepattern?

2005 ◽  
Vol 6 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Michael Wurzel ◽  
Carlo Schaller ◽  
Matthias Simon ◽  
Andreas Deutsch
Keyword(s):  
Lattice Gas ◽  
Individual Cell ◽  
Physical Structure ◽  
Tumour Cells ◽  
Malignant Cells ◽  
Malignant Brain Tumour ◽  
Tumour Bulk ◽  
Cell Motion ◽  
The Brain ◽  
Global Growth

The malignant brain tumourGlioblastoma multiforme(GBM) displays a highly invasive behaviour. Spreading of the malignant cells appears to be guided by the white matter fibre tracts within the brain. In order to understand the global growth process we introduce a lattice-gas cellular automaton model which describes the local interaction between individual malignant cells and their neighbourhood. We consider interactions between cells (brain cells and tumour cells) and between malignant cells and the fibre tracts in the brain, which are considered as a prepattern. The prepattern implies persistent individual cell motion along the fibre structure. Simulations with the model show that only the inclusion of the prepattern results in invading tumour and growing tumour islets in front of the expanding tumour bulk (i.e. the growth pattern observed in clinical practice). Our results imply that the infiltrative growth of GBMs is, in part, determined by the physical structure of the surrounding brain rather than by intrinsic properties of the tumour cells.

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