scholarly journals Brain MRI Images Segmentation Based on U-Net Architecture

2021 ◽  
Vol 18 (1) ◽  
pp. 21-27
Author(s):  
Assalah Atiyah ◽  
Khawla Ali
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Brain Mri ◽  
Regular Function ◽  
Survival Rates ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Edge Based ◽  
The Brain

Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

IBRDM: An Intelligent Framework for Brain Tumor Classification Using Radiomics- and DWT-based Fusion of MRI Sequences

2022 ◽  
Vol 22 (1) ◽  
pp. 1-30
Author(s):  
Rahul Kumar ◽  
Ankur Gupta ◽  
Harkirat Singh Arora ◽  
Balasubramanian Raman
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Tumor Classification ◽  
Training Dataset ◽  
Tumor Segmentation ◽  
Discrete Wavelet ◽  
Low Grade ◽  
Brain Tumor Segmentation ◽  
Mri Sequences

Brain tumors are one of the critical malignant neurological cancers with the highest number of deaths and injuries worldwide. They are categorized into two major classes, high-grade glioma (HGG) and low-grade glioma (LGG), with HGG being more aggressive and malignant, whereas LGG tumors are less aggressive, but if left untreated, they get converted to HGG. Thus, the classification of brain tumors into the corresponding grade is a crucial task, especially for making decisions related to treatment. Motivated by the importance of such critical threats to humans, we propose a novel framework for brain tumor classification using discrete wavelet transform-based fusion of MRI sequences and Radiomics feature extraction. We utilized the Brain Tumor Segmentation 2018 challenge training dataset for the performance evaluation of our approach, and we extract features from three regions of interest derived using a combination of several tumor regions. We used wrapper method-based feature selection techniques for selecting a significant set of features and utilize various machine learning classifiers, Random Forest, Decision Tree, and Extra Randomized Tree for training the model. For proper validation of our approach, we adopt the five-fold cross-validation technique. We achieved state-of-the-art performance considering several performance metrics, 〈 Acc , Sens , Spec , F1-score , MCC , AUC 〉 ≡ 〈 98.60%, 99.05%, 97.33%, 99.05%, 96.42%, 98.19% 〉, where Acc , Sens , Spec , F1-score , MCC , and AUC represents the accuracy, sensitivity, specificity, F1-score, Matthews correlation coefficient, and area-under-the-curve, respectively. We believe our proposed approach will play a crucial role in the planning of clinical treatment and guidelines before surgery.

scholarly journals MRI-based brain tumor segmentation using FPGA-accelerated neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siyu Xiong ◽  
Guoqing Wu ◽  
Xitian Fan ◽  
Xuan Feng ◽  
Zhongcheng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Cad Systems ◽  
Segmentation Accuracy ◽  
The Brain

Abstract Background Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. Results Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. Conclusion We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

scholarly journals Recognization and Systematization of MR Images using K Means Clustering and DNN

2020 ◽  
Vol 9 (6) ◽  
pp. 924-927
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Digital Health ◽  
High Accuracy ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Brain Tumor Diagnosis ◽  
The Brain

Brain tumors are the result of unusual growth and unrestrained cell disunity in the brain. Most of the medical image application lack in segmentation and labeling. Brain tumors can lead to loss of lives if they are not detected early and correctly. Recently, deep learning has been an important role in the field of digital health. One of its action is the reduction of manual decision in the diagnosis of diseases specifically brain tumor diagnosis needs high accuracy, where minute errors in judgment may lead to loss therefore, brain tumor segmentation is an necessary challenge in medical side. In recent time numerous ,methods exist for tumor segmentation with lack of accuracy. Deep learning is used to achieve the goal of brain tumor segmentation. In this work, three network of brain MR images segmentation is employed .A single network is compared to achieve segmentation of MR images using separate network .In this paper segmentation has improved and result is obtained with high accuracy and efficiency.

Brain Tumor and Its Segmentation From Brain MRI Sequences

2019 ◽  
pp. 39-60
Author(s):  
Sanjay Saxena ◽  
Puspanjali Mohapatra ◽  
Swati Pattnaik
Keyword(s):  
Brain Tumor ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Non Invasive ◽  
Main Motive ◽  
Tissue Contrast ◽  
Brain Magnetic Resonance Image ◽  
Mri Sequences ◽  
The Brain

Automated segmentation of tumorous region from the brain magnetic resonance image (MRI) is the procedure of extrication anomalous tissues from regular tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The process of accurate and efficient segmentation is still exigent because of the diversity of location, size, and shape of the tumorous region. Brain MRI provides metabolic process, psychological process, and descriptive information of the brain. Brain tumor segmentation using MRI is drawing the attention of the researchers due to its non-invasive nature and good soft tissue contrast of MRI sequences. The main motive of this chapter is to provide a broad overview of the methods of brain tumor segmentation based on MRI. This chapter provides the information of the brain tumor, its types, brief introduction of the MRI, and its diverse types, and lastly, this chapter gives the brief overview with benefits and limitations about diverse techniques used for brain tumor segmentation by different researchers and scientists.

Brain Segmentation Using Deep Neural Networks

2020 ◽  
Vol 10 ◽  
Author(s):  
Vandana Mohindru ◽  
Ashutosh Sharma ◽  
Apurv Mathur ◽  
Anuj Kumar Gupta
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Convolutional Neural Networks ◽  
Research Work ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Brain Tumor Segmentation ◽  
The Brain

Background: The determination of tumor extent is a major challenging task in brain tumor planning and quantitative evaluation. Magnetic Resonance Imaging (MRI) is one of the non-intellectual technique has emerged as a front- line diagnostic tool for a brain tumor with non-ionizing radiation. <P> Objectives: In Brain tumors, Gliomas is the very basic tumor of the brain; they might be less aggressive or more aggressive in a patient with a life expectancy of not more than 2 years. Manual segmentation is time-consuming so we use a deep convolutional neural network to increase the performance is highly dependent on the operator&#039;s experience. <P> Methods: This paper proposed a fully automatic segmentation of brain tumors using deep convolutional neural networks. Further, it uses high-grade gliomas brain images from BRATS 2016 database. The suggested work achieve brain tumor segmentation using tensor flow, in which the anaconda frameworks are used to execute high-level mathematical functions. <P> Results: Hence, the research work segments brain tumors into four classes like edema, non-enhancing tumor, enhancing tumor and necrotic tumor. Brain tumor segmentation needs to separate healthy tissues from tumor regions such as advancing tumor, necrotic core, and surrounding edema. We have presented a process to segment 3D MRI image of a brain tumor into healthy and area where the tumor is present, including their separate sub-areas. We have applied an SVM based classification. Categorization is complete using a soft-margin SVM classifier. <P> Conclusion: We are using deep convolutional neural networks for presenting the brain tumor segmentation. Outcomes of the BRATS 2016 online judgment method assure us to increase the performance, accuracy, and speed with our best model. The fuzzy c-mean algorithm provides better accuracy and train on the SVM based classifier. We can achieve the finest performance and accuracy by using the novel two-pathway architecture i.e. encoder and decoder as well as the modeling local label that depends on stacking two CNN's

scholarly journals K-means algorithm with level set for brain tumor segmentation

2019 ◽  
Vol 15 (2) ◽  
pp. 991
Author(s):  
Samah Abdelaziz ◽  
Songfeng Lu
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Level Set ◽  
Medical Image ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Accurate Information ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Magnetic Resonance Imaging Mri

<p>Brain is a complicated structure consisting of millions of millions cells so that, it’s difficult to identify any diseases without using any computerized technology. Magnetic resonance imaging (mri) is one of the main assessments of brain tumors. One of the most important steps on medical image processing is segmentation. Segmenting brain mri images, which provide accurate information for the diagnosis and therapy decisions of brain tumors. We proposed to segment brain tumor mri images into three parts (wm (white matter), gm (gray matter), and background). The first algorithm is for applying median filtering on brain mri image for removing the noise from the image for achieving accurate results. The second algorithm is for applying k-means algorithm for accuracy in time consuming and for clustering into regions and  the third algorithm indicate the detecting the boundary of the image with the use of level set. By comparison, our proposed method, its efficiency to segment perfectly more than other previous used algorithms especially on time consuming.</p>

Brain Tumor Segmentation and Prediction using Fuzzy Neighborhood Learning Approach for 3D MRI Images

2021 ◽  
Author(s):  
Pitchai R ◽  
Supraja P ◽  
Razia Sulthana A ◽  
Veeramakali T
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Network Architecture ◽  
Tumor Detection ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
3D Images ◽  
Learning Techniques ◽  
The Brain

Abstract Segmentation of brain tumors is a daunting process comprising the delineation of heterogeneous cancerous tissues and diffuse types in anatomical representations of the brain. Deep learning techniques have recently made important strides in the segmentation of brain tumors. However, owing to the irregularity of the tumor, most of the deep learning-based segmentation techniques are not used directly for tumor detection. Although recent studies are capable of addressing the irregularity issue and retaining permutation invariance, many approaches struggle to catch the valuable high-dimensional local features of finer resolution. Inspired by the fuzzy learning methods and an analysis of the shortcomings of existing methods, an automated fuzzy neighborhood learning-based 3D segmentation technique has been proposed for the detection of cerebrum tumors in 3D images. In this technique, the fuzzy neighborhood function is deeply integrated with the proposed network architecture. This technique has been evaluated on BRATS 2013dataset. The simulation results show that the proposed brain tumor detection technique is superior to other methods in the diagnosis of brain tumors with the dice coefficient of 0.85 and the Jaccard index of 0.74.

Brain Tumor segmentation of T1w MRI images based on Clustering using Dimensionality Reduction Random Projection Technique

2020 ◽  
Vol 16 ◽  
Author(s):  
K. Rajesh Babu ◽  
P. V. Nagajaneyulu ◽  
K. Satya Prasad
Keyword(s):  
Brain Tumor ◽  
Life Expectancy ◽  
Performance Metrics ◽  
Image Data ◽  
Random Projection ◽  
Tumor Segmentation ◽  
Projection Technique ◽  
Brain Tumor Segmentation ◽  
Fuzzy C Means ◽  
The Brain

Background: Early diagnosis of a brain tumor may increase life expectancy. If not diagnosed at an early stage, the brain tumor shortens the life expectancy of the diseased. Accompanied by several segmentation algorithms, Magnetic Resonance Imaging (MRI) preferred as a reliable assessment technique. The availability of high-dimensional medical image data during the identification procedure can place a heavy computational burden and require a suitable preprocessing step for lower-dimensional representation. At the same time, to reduce the storage requirement and complexity of the image data Random Projection Technique (RPT) is widely accepted as the multivariate approach for data reduction. Aims: This paper mainly focuses on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by using a conventional Principle Component Analysis (PCA) and RPT. Methods: Two clustering algorithms, K-Means and Fuzzy C-Means, are used to detect the brain tumor. The primary objective is to present a comparison of two cluster methods between the PCA algorithm and RPT on MRIs. Apart from the original dimension of 512×512, the analysis used three other sizes, 256×256, 128×128, and 64×64, to study the effect of methods. Results: As per the average reconstruction, Euclidean distance, and segmentation distance errors, the RPT attained better results as compared with PCA along with clustered images. According to the performance metrics, the RPT supported the Fuzzy C-Means algorithm in achieving the best clustering performance, and significant results for each resize of MRI 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.

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