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.

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

A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumour segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organising map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method. The proposed system has been validated with the support of real data sets and the experimental results showed enhanced performance.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2014.0193

Brain tumour classification using two-tier classifier with adaptive segmentation technique

Automated tumor characterization has a prominent role in the computer-aided diagnosis (CAD) system for the human brain. Despite being a well-studied topic, CAD of brain tumors poses severe challenges in some specific aspects. One such challenging problem is the category-based classification of brain tumors among glioma, meningioma, and pituitary tumors using magnetic resonance imaging (MRI) images. The emergence of deep learning and machine learning algorithms have addressed image classification tasks with promising results. But an associated limitation with the medical image classification is the small sizes of medical image databases. This limitation, in turn, limits the availability of medical images for training deep neural networks. To mitigate this challenge, we adopt a combination of convolutional neural network (CNN) features with support vector machine (SVM) for classification of the medical images. The fully automated system is evaluated using Figshare open dataset containing MRI images for the three types of brain tumors. CNN is designed to extract features from brain MRI images. For enhanced performance, a multiclass SVM is used with CNN features. Testing and evaluation of the integrated system followed a fivefold cross-validation procedure. The proposed model attained an overall classification accuracy of 95.82%, better than the state-of-the-art method. Extensive experiments are performed on other MRI datasets for the brain to ascertain the improved performance of the proposed system. When the amount of available training data is small, the SVM classifier is observed to perform better than the softmax classifier for the CNN features. Compared to transfer learning-based classification, the adopted strategy of CNN-SVM has lesser computations and memory requirements.

Automated Categorization of Brain Tumor from MRI Using CNN features and SVM

This study presents a new fully automated, fast, and accurate brain tumour segmentation method which automatically detects and extracts whole tumours from 3D-MRI. The proposed method is based on a hybrid approach that relies on a brain symmetry analysis method and a combining region-based and boundary-based segmentation methods. The segmentation process consists of three main stages. In the first one, image pre-processing is applied to remove any noise, and to extract the brain from the head image. In the second stage, automated tumour detection is performed. It is based essentially on FBB method using brain symmetry. The obtained result constitutes the automatic initialisation of a deformable model, thus removing the need of selecting the initial region of interest by the user. Finally, the third stage focuses on the application of region growing combined with 3D deformable model based on geodesic level-set to detect the tumour boundaries containing the initial region, computed previously, regardless of its shape and size. The proposed segmentation system has been tested and evaluated on 3D-MRIs of 285 subjects with different tumour types and shapes obtained from BraTS'2017 dataset. The obtained results turn out to be promising and objective as well as close to ground truth data.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2017.1124

Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

A computer software system is designed for segmentation and classification of benign and malignant tumour slices in brain computed tomography images. In this study, the authors present a method to select both dominant run length and co-occurrence texture features of wavelet approximation tumour region of each slice to be segmented by a support vector machine (SVM). Two-dimensional discrete wavelet decomposition is performed on the tumour image to remove the noise. The images considered for this study belong to 208 tumour slices. Seventeen features are extracted and six features are selected using Student's t-test. This study constructed the SVM and probabilistic neural network (PNN) classifiers with the selected features. The classification accuracy of both classifiers are evaluated using the k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and segmentation error. The proposed system provides some newly found texture features have an important contribution in classifying tumour slices efficiently and accurately. The experimental results show that the proposed SVM classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2012.0073

A. Padma Nanthagopal

Papers

Wavelet statistical texture features-based segmentation and classification of brain computed tomography images