scholarly journals Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Nilesh Bhaskarrao Bahadure ◽  
Arun Kumar Ray ◽  
Har Pal Thethi
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Similarity Index ◽  
Medical Image Segmentation ◽  
Experimental Results ◽  
Support Vector ◽  
Primary Concern ◽  
Mr Images ◽  
Tumor Region ◽  
Dice Similarity Index

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

scholarly journals Using Transfer Learning, a Mobile Application Detects Brain Tumors

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
BHARATH BALAJI R ◽  
PRADEPP K V
Keyword(s):  
Magnetic Resonance ◽  
Similarity Index ◽  
Quality Parameters ◽  
Medical Image Segmentation ◽  
Quality Analysis ◽  
Support Vector ◽  
Computer Assisted ◽  
Mr Images ◽  
Tumour Segmentation ◽  
Dice Similarity Index

The segmentation, identification, and extraction of contaminated tumour regions from magnetic resonance (MR) images is a serious problem, but it is a time-consuming and labor-intensive operation carried out by radiologists or clinical experts, whose accuracy is totally reliant on their knowledge. As a consequence, using computer-assisted technologies to circumvent these limits becomes more vital. In this study, we looked into Berkeley wavelet transformation (BWT) based brain tumour segmentation to improve performance and reduce the complexity of the medical image segmentation process. Furthermore, relevant properties are extracted from each segmented tissue to improve the support vector machine (SVM) based classifier's accuracy and quality rate. The experimental results of the recommended technique have been examined and validated for performance and quality analysis on magnetic resonance brain pictures based on accuracy, sensitivity, specificity, and dice similarity index coefficient. With 96.51 percent accuracy, 94.2 percent specificity, and 97.72 percent sensitivity, the recommended technique for discriminating normal and diseased tissues from brain MR images was shown to be effective. The results of the testing revealed an average dice similarity index coefficient of 0.82, showing that the automated (machine) extracted tumour area coincided with the manually determined tumour region by radiologists. The simulation results show the relevance of quality parameters and accuracy when compared to state-of-the-art approaches. The main objective is to develop a smartphone app for identifying brain tumours.

Automated brain tumor segmentation from multimodal MRI data based on Tamura texture feature and an ensemble SVM classifier

2019 ◽  
Vol 12 (4) ◽  
pp. 466-480
Author(s):  
Li Na ◽  
Xiong Zhiyong ◽  
Deng Tianqi ◽  
Ren Kai
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Original Solution ◽  
Content Type ◽  
Tumor Region ◽  
The Brain

Purpose The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred boundaries and edema around the brain tumor region, the brain tumor image has indistinct features in the tumor region, which pose a problem for diagnostics. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine (SVM) structure. In the proposed technique, 124 features of each voxel are extracted, including Tamura texture features and grayscale features. Then, these features are ranked using the SVM-Recursive Feature Elimination method, which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of voxel. Findings The experiments are conducted over a sample data set to be called BraTS2015. The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors, especially the feature of line-likeness. The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods. Originality/value The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.

Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

2013 ◽  
Vol 647 ◽  
pp. 325-330 ◽  
Author(s):  
Yu Fan Zeng ◽  
Xue Jun Zhang ◽  
Wen Yan ◽  
Li Ling Long ◽  
Yu Kun Huang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Hepatic Fibrosis ◽  
Region Of Interest ◽  
Texture Features ◽  
Magnetic Resonance Images ◽  
Optimal Number ◽  
Support Vector ◽  
Mr Images ◽  
Accuracy Rate ◽  
Occurrence Matrix

The fibrous texture in liver is one of important signs for interpreting the chronic liver diseases in radiologists’ routines. In order to investigate the usefulness of various texture features calculated by computer algorithm on hepatic magnetic resonance (MR) images, 15 texture features were calculated from the gray level co-occurrence matrix (GLCM) within a region of interest (ROI) which was selected from the MR images with 6 stages of hepatic fibrosis. By different combination of 15 features as input vectors, the classifier had different performance in staging the hepatic fibrosis. Each combination of texture features was tested by Support Vector Machine (SVM) with leave one case out method. 173 patients’ MR images including 6 stages of hepatic fibrosis were scanned within recent two years. The result showed that optimal number of features was confirmed from 3 to 7 by investigating the classified accuracy rate between each stage/group. It is evident that angular second moment, entropy, sum average and sum entropy played the most significant role in classification.

scholarly journals Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
R. Rajesh Sharma ◽  
P. Marikkannu
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Gravitational Search Algorithm ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Classification Model ◽  
Support Vector ◽  
Preliminary Evaluation ◽  
Distribution Method

A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, andk-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

scholarly journals Recognition of brain cancer and cerebrospinal fluid due to the usage of different MRI image by utilizing support vector machine

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Soobia Saeed ◽  
Afnizanfaizal Abdullah
Keyword(s):  
Support Vector Machine ◽  
Cerebrospinal Fluid ◽  
Brain Tumor ◽  
Brain Cancer ◽  
The Body ◽  
Csf Leak ◽  
Support Vector ◽  
The Future ◽  
Tumor Region ◽  
The Right

Medicinal images assume an important part in the diagnosis of tumors as well as Cerebrospinal fluid (CSF) leak. Similarly, MRI could be the cutting-edge regenerative imaging technology that allows for a sectional angle perspective of the body that gives specialists convenience and will inspect the person-concerned. In this paper, the author has attempted the strategy to classify MRI images at the beginning of production to have a tumor or recognition. The study aims to address the aforementioned problems associated with brain cancer with a CSF leak. This research, the author focuses on brain tumor and applies the statistical model for the testing and also discusses the images of a brain tumor. They can judge the tumor region by conducting a comparative image analysis and applying Histogram function afterwards to construct a classifier that could be prepared to predict tumor and non-tumor MRI examinees based on the support vector machine. Our system is capable of detecting the right region that a pathologist also highlights. In the future, this should be more driven with the objective that tumors can be arranged and describe the solution in the medical terms implementation with gives some predictions about the future generated by modified technology. 

scholarly journals Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1171-1179
Author(s):  
Swaraja Kuraparthi ◽  
Madhavi K. Reddy ◽  
C.N. Sujatha ◽  
Himabindu Valiveti ◽  
Chaitanya Duggineni ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Data Augmentation ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Tumor Type

Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.

scholarly journals A Hybrid Convolutional Neural Network and Deep Belief Network for Brain Tumor Detection in MR Images

2019 ◽  
Vol 8 (2S4) ◽  
pp. 979-985
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Tumor Detection ◽  
Deep Belief Network ◽  
Support Vector ◽  
Mr Images ◽  
Belief Network ◽  
Early Tumor ◽  
Precision Rate

Early tumor detection in brain plays vital role in early tumor detection and radiotherapy. MR images are used as the input image for brain tumor finding and classify the type of brain tumor. For early detection or prediction of the brain tumor, an improved feature extraction technique along with Deep Neural Network (DNN) has been recommended. First, MR image is pre-processed, segmented and classified utilizing image processing techniques. Support Vector Machine (SVM) based brain tumor classifications are achieved previously with less precision rate. By integrating DCNN(Deep Convolutional Neural Network) classifier and DBN(Deep Belief Network), an improvement in precision rate can be achieved. This paper mainly focuses on six features viz., entropy, mean, correlation, contrast, energy and homogeneity. The proposed method is used to identify the place, locality and dimension (size) of the tumor in the cerebrum through MR copy using MATLAB software. The performance metrics recall, precision, sensitivity, accuracy and specificity are achieved.

scholarly journals Brain Tumor Segmentation and Classification using Hybrid clustering technique and SVM Classifier

2019 ◽  
Vol 9 (1S) ◽  
pp. 14-17
Keyword(s):  
Brain Tumor ◽  
Texture Features ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Primary Concern ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Clustering Technique ◽  
Detection And Identification

Brain tumor Detection is a primary concern in today’s life. So a computer aided technology must be implemented for an accurate detection and identification of brain tumor. The tumor can be detected using various classification techniques from brain MR Images. In this paper segmentation process is being done using K means Clustering technique and Binary Thresholding, the features from the images are then extracted using GLCM where six texture features are extracted and SVM Classifier is being used for classification of the images. This proposed method shows an accuracy of 97.12%.

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