Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI

Abstract A brain tumor is one of the main reasons for death among other kinds of cancer because the brain is a very sensitive, complex, and central portion of the body. Proper and timely diagnosis can prolong the life of a person to some extent. Consequently, in this paper, we have proposed a brain tumor classification scheme on the basis of combining wavelet texture features and deep neural networks (DNNs). Normally, the system comprises four modules: (i) feature extraction, (ii) feature selection, (iii) tumor classification, and (iv) segmentation. Primarily, we eliminate the noise from the image. Then, the feature matrix is produced by combining wavelet texture features [gray-level co-occurrence matrix (GLCM)+wavelet GLCM]. Following that, we select the relevant features with the help of the oppositional flower pollination algorithm (OFPA) because a high number of features are major obstacles for classification. Then, we categorize the brain image based on the selected features using the DNN. After the classification procedure, the projected scheme extracts the tumor region from the tumor images with the help of the possibilistic fuzzy c-means clustering (PFCM) algorithm. The experimentation results show that the proposed system attains the better result associated with the available methods.

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An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor

10.54216/jchci.010202 ◽

2021 ◽

pp. 57-62

Author(s):

P. Kavitha ◽

◽

R. Subha Shini ◽

...

Keyword(s):

Brain Tumor ◽

Tumor Cell ◽

Correlation Energy ◽

Cell Structure ◽

Texture Features ◽

Statistical Feature ◽

Cell Extracts ◽

Brain Tumor Cell ◽

Fuzzy C Means Clustering ◽

The Brain

A member of a population who is at risk of becoming infected by disease is a susceptible individual. Finding disease susceptibility and generating an alert in advance, is valuable for an individual. The aim of the work presented a feature vector using different statistical texture analyses of brain tumors from an MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of brain tumor cell structure. For this paper, the brain tumor cell segmented using the strip method to implement hybrid Assured Convergence Particle Swarm Optimization (ACPSO) - Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o, and 135o have calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed on different types of images using past years. So, the algorithm proposed statistical texture features are calculated for iterative image segmentation. The algorithm FETC (Feature Extraction Tumor Cell) extracts statistical features of GLCM. These results show that MRI images can be implemented in a system of brain cancer detection.

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Classification of Magnetic Resonance Image and Segmentation of Brain Tissues for Tumor Detection

Classification and Clustering in Biomedical Signal Processing - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-0140-4.ch006 ◽

2016 ◽

pp. 132-148 ◽

Cited By ~ 2

Author(s):

Manjula Pushparaj ◽

Arokia Renjith J ◽

Mohan Kumar P

Keyword(s):

Brain Tumor ◽

Vital Role ◽

Complex Wavelet Transform ◽

Weighted Clustering ◽

Neuro Fuzzy ◽

Tumor Region ◽

Occurrence Matrix ◽

The Brain ◽

Brain Tissues

Advancing techniques in image processing has led to many inventions and provides valuable support especially in medical fields to identify and analyze the diseases. MRI images are chosen for detection of brain tumor as they are used in soft tissue determinations. Brain tumor is one of the severe diseases in the field of medicine. Early identification of disease increases the chances for successful treatment. Classification and Segmentation plays a vital role in identifying the disease. First, image Pre-processing is used to enhance the image quality. Subsequently, Decomposition is performed using Dual-Tree Complex Wavelet Transform to analysis texture of an image and features are extracted using Gray-Level Co-Occurrence Matrix. Then, Neuro-Fuzzy and Neural Network can be used to categorize the types of Brain Tumor such as normal, benign and malignant. Finally, tumor region is detected using Kernel weighted clustering method by segmenting the brain tissues and also to find the size of the tumor.

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Classification of Magnetic Resonance Image and Segmentation of Brain Tissues for Tumor Detection

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch029 ◽

2017 ◽

pp. 724-740

Author(s):

Manjula Pushparaj ◽

Arokia Renjith J ◽

Mohan Kumar P

Keyword(s):

Brain Tumor ◽

Vital Role ◽

Complex Wavelet Transform ◽

Weighted Clustering ◽

Neuro Fuzzy ◽

Tumor Region ◽

Occurrence Matrix ◽

The Brain ◽

Brain Tissues

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Combining Wavelet Statistical Texture And Recurrent Neural Network For Tumour Detection And Classification Over MRI

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9388.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 3769-3778

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Recurrent Neural Network ◽

Detection System ◽

Region Growing ◽

Texture Features ◽

Noise Removal ◽

The Body ◽

Mr Images ◽

The Brain

Brain tumor is one of the major causes of death among other types of the cancer because Brain is a very sensitive, complex and central part of the body. Proper and timely diagnosis can prevent the life of a person to some extent. Therefore, in this paper we have introduced brain tumor detection system based on combining wavelet statistical texture features and recurrent neural network (RNN). Basically, the system consists of four phases such as (i) feature extraction (ii) feature selection (iii) classification and (iii) segmentation. First, noise removal is performed as the preprocessing step on the brain MR images. After that texture features (both the dominant run length and co-occurrence texture features) are extracted from these noise free MR images. The high number of features is reduced based on sparse principle component analysis (SPCA) approach. The next step is to classify the brain image using Recurrent Neural Network (RNN). After classification, proposed system extracts tumor region from MRI images using modified region growing segmentation algorithm (MRG). This technique has been tested against the datasets of different patients received from muthu neuro center hospital. The experimentation result proves that the proposed system achieves the better result compared to the existing approaches

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Designing a Feature Vector for Statistical Texture Analysis of Brain Tumor

10.20944/preprints201906.0166.v1 ◽

2019 ◽

Author(s):

Kavitha Prithiviraj ◽

S Prabakaran

Keyword(s):

Image Segmentation ◽

Brain Tumor ◽

Texture Analysis ◽

Feature Vector ◽

Correlation Energy ◽

Texture Features ◽

Different Types ◽

Fuzzy C Means Clustering ◽

The Brain ◽

Mri Image

This paper presented a feature vector using a different statistical texture analysis of brain tumor from MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of Brain Nodule structure. For this paper, the brain nodule segmented using strips method to implemented marker watershed image segmentation based on PSO (Particle Swarm Optimization) and Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o and 135o are calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed a different types of images using past years. So the algorithm proposed statistical texture features are calculated for iterative image segmentation. These results show that MRI image can be implemented in a system of brain cancer detection.

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A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2063211 ◽

2020 ◽

pp. 967-973

Author(s):

V. Deepika ◽

T. Rajasenbagam

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Soft Tissues ◽

Tumor Detection ◽

Tumor Classification ◽

Normal Brain ◽

Image Processing Techniques ◽

Normal Brain Function ◽

Processing Techniques ◽

The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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

Complex & Intelligent Systems ◽

10.1007/s40747-021-00310-3 ◽

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.

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The Brain Tumor Segmentation Using Fuzzy C-Means Technique

Computer Vision ◽

10.4018/978-1-5225-5204-8.ch106 ◽

2018 ◽

pp. 2402-2419

Author(s):

Jyotsna Rani ◽

Ram Kumar ◽

Fazal A. Talukdar ◽

Nilanjan Dey

Keyword(s):

Image Segmentation ◽

Brain Tumor ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Fuzzy C Means ◽

Mri Brain ◽

Area Of Interest ◽

Fuzzy C Means Clustering ◽

Comparative Results ◽

The Brain

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

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

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-04-2019-0031 ◽

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.

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Brain tumor classification and segmentation using sparse coding and dictionary learning

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2015-0071 ◽

2016 ◽

Vol 61 (4) ◽

pp. 413-429 ◽

Cited By ~ 8

Author(s):

Saif Dawood Salman Al-Shaikhli ◽

Michael Ying Yang ◽

Bodo Rosenhahn

Keyword(s):

Brain Tumor ◽

Dictionary Learning ◽

Sparse Coding ◽

Image Data ◽

Ground Truth ◽

Tumor Classification ◽

Training Data ◽

Tumor Segmentation ◽

Feature Dictionary ◽

The Brain

AbstractThis paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

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