Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

2018 ◽  
Vol 19 (4) ◽  
pp. 471-480 ◽  
Author(s):  
Ji-jun Tong ◽  
Peng Zhang ◽  
Yu-xiang Weng ◽  
Dan-hua Zhu
Keyword(s):  
Image Analysis ◽  
Brain Tumor ◽  
Sparse Representation ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Image

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

Brain Tumor Detection using Hybrid Deep Learning with Genetic Algorithm

2021 ◽  
Vol 23 (09) ◽  
pp. 981-993
Author(s):  
T. Balamurugan ◽  
◽  
E. Gnanamanoharan ◽  
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Brain Tumor ◽  
Advanced Technology ◽  
Great Promise ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Feature Subset ◽  
Brain Tumor Segmentation ◽  
Mri Image

Brain tumor segmentation is a challenging task in the medical diagnosis. The primary aim of brain tumor segmentation is to produce precise characterizations of brain tumor areas using adequately placed masks. Deep learning techniques have shown great promise in recent years for solving various computer vision problems such as object detection, image classification, and semantic segmentation. Numerous deep learning-based approaches have been implemented to achieve excellent system performance in brain tumor segmentation. This article aims to comprehensively study the recently developed brain tumor segmentation technology based on deep learning in light of the most advanced technology and its performance. A genetic algorithm based on fuzzy C-means (FCM-GA) was used in this study to segment tumor regions from brain images. The input image is scaled to 256×256 during the preprocessing stage. FCM-GA segmented a preprocessed MRI image. This is a versatile advanced machine learning (ML) technique for locating objects in large datasets. The segmented image is then subjected to hybrid feature extraction (HFE) to improve the feature subset. To obtain the best feature value, Kernel Nearest Neighbor with a genetic algorithm (KNN-GA) is used in the feature selection process. The best feature value is fed into the RESNET classifier, which divides the MRI image into meningioma, glioma, and pituitary gland regions. Real-time data sets are used to validate the performance of the proposed hybrid method. The proposed method improves average classification accuracy by 7.99 % to existing Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) classification algorithms

Hybrid active contour model and deep belief network based approach for brain tumor segmentation and classification

Sensor Review ◽  
2019 ◽  
Vol 39 (4) ◽  
pp. 473-487 ◽  
Author(s):  
Ayalapogu Ratna Raju ◽  
Suresh Pabboju ◽  
Ramisetty Rajeswara Rao
Keyword(s):  
Brain Tumor ◽  
Sensitivity And Specificity ◽  
Active Contour ◽  
Active Contour Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Content Type ◽  
Contour Model ◽  
The Brain ◽  
Mri Image

Purpose Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Design/methodology/approach The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training. Findings The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Originality/value This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

Low Volume Brain Tumor Extraction

2019 ◽  
Vol 16 (8) ◽  
pp. 3270-3275
Author(s):  
S. L. Jany Shabu ◽  
C. Jayakumar ◽  
A. Christy ◽  
P. Naveena ◽  
G. Kavya Sri
Keyword(s):  
Brain Tumor ◽  
Embedded System ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Manual Task ◽  
Life Threatening ◽  
Tumor Extraction ◽  
Wired Communication ◽  
Mri Image

The segmentation of magnetic resonance images plays a very important role in medical fields because it extracts the required area from the image. By and large there is no novel methodology for the division of picture. Tumor segmentation from MRI data is an important but time consuming manual task performed by medical experts. This paper focuses on a new and very famous algorithm for brain tumor segmentation of MRI image by ANN algorithm to diagnose accurately the region of cancer because of its simplicity and computational efficiency. The MATLAB output will be displayed in pc and also see the output to embedded system using wired communication. Brain tumor is a life threatening disease. The cerebrum contains in excess of 10 billion working mind cells. The damaged brain cells are diagnosed themselves by splitting to make more cells. This regeneration takes place in an orderly and controlled manner. If the regeneration of the cells gets out of control, the cells will continue to divide developing a lump which is called tumor. In this paper a Brain Tumor Detection and Classification System has been designed and developed. The framework utilizes PC based strategies to recognize tumor squares and order the sort of tumor utilizing Artificial Neural Network in MRI pictures of various patients with astrocytoma kind of mind tumors.

Automated Brain Tumor Segmentation from Magnetic Resonance Images using Morphometric Algorithms

2019 ◽  
Vol 14 ◽  
Author(s):  
Senthilkumar C ◽  
Gnanamurthy RK
Keyword(s):  
Brain Tumor ◽  
Clinical Study ◽  
Region Of Interest ◽  
Vital Role ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Novel Method ◽  
Digital Film ◽  
Mri Image

Image segmentation plays a vital role in clinical study and in diagnosing the stages of diseases. The hospitals are marching towards filmless imaging process in par with digital film technology. The paper proposes a novel method based on morphometirc segmentation algorithm with Region of Interest (ROI). The core objective is to detect better segmentation of brain tumor from MRI image for clinical study. A set of experiment has been performed for the analysis of the proposed work on the several MRI images. The proposed morphometric segmentation method provides better accuracy on segmentation of tumor image from the input MRI image.

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