Compression of MRI brain images based on automatic extraction of tumor region

In the compression of medical images, region of interest (ROI) based techniques seem to be promising, as they can result in high compression ratios while maintaining the quality of region of diagnostic importance, the ROI, when image is reconstructed. In this article, we propose a set-up for compression of brain magnetic resonance imaging (MRI) images based on automatic extraction of tumor. Our approach is to first separate the tumor, the ROI in our case, from brain image, using support vector machine (SVM) classification and region extraction step. Then, tumor region (ROI) is compressed using Arithmetic coding, a lossless compression technique. The non-tumorous region, non-region of interest (NROI), is compressed using a lossy compression technique formed by a combination of discrete wavelet transform (DWT), set partitioning in hierarchical trees (SPIHT) and arithmetic coding (AC). The classification performance parameters, like, dice coefficient, sensitivity, positive predictive value and accuracy are tabulated. In the case of compression, we report, performance parameters like mean square error and peak signal to noise ratio for a given set of bits per pixel (bpp) values. We found that the compression scheme considered in our setup gives promising results as compared to other schemes

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Intelligent System For Brain Disease Diagnosis Using Rotation Invariant Features And Fuzzy Neural Network

The characteristic features of the magnetic resonant image (MRI) for Alzheimer’s patient’s brain image and normal image can be distinguished in terms of dimensional features with the help of wavelet decomposition. From the literature review, it is observed that when datasets used are a combination of the MR images having a very mild cognitive impairment and mild cognitive impairment, the performance of the classifier reduces. Because the features of this kind of MR image are difficult to distinguish from normal brain images. To solve this problem, the lossless feature extraction method along with the feature reduction method having a selection approach is suggested as a solution here. In this paper, the 12 directional, rotation invariant two-dimensional discrete-time continuous wavelet transform (R-DTCWT) and a genetic algorithm (GA) are used for feature selection and feature vector size reduction. The fuzzy neural network (FNN) which is suitable for pattern recognition is used here. The FNN with and without feature reduction is evaluated for identification of combinational dataset, shows satisfactory performance over an artificial neural network (ANN), probabilistic neural network (PNN) classifiers. This method is compared with other state of algorithm to prove the enhanced performanc

Satellite Image Fusion in Various Domains

In order to find out the fusion algorithm which is best suited for the panchromatic and multispectral images, fusion algorithms, such as PCA and wavelet algorithms have been employed and analyzed. In this paper, performance evaluation criteria are also used for quantitative assessment of the fusion performance. The spectral quality of fused images is evaluated by the ERGAS and Q4. The analysis indicates that the DWT fusion scheme has the best definition as well as spectral fidelity, and has better performance with regard to the high textural information absorption. Therefore, as the study area is concerned, it is most suited for the panchromatic and multispectral image fusion. an image fusion algorithm based on wavelet transform is proposed for Multispectral and panchromatic satellite image by using fusion in spatial and transform domains. In the proposed scheme, the images to be processed are decomposed into sub-images with the same resolution at same levels and different resolution at different levels and then the information fusion is performed using high-frequency sub-images under the Multi-resolution image fusion scheme based on wavelets produces better fused image than that by the MS or WA schemes

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DLbased CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper

Brain Tumor Diagnosis Support System: A decision Fusion Framework

An important factor in providing effective and efficient therapy for brain tumors is early and accurate detection, which can increase survival rates. Current image-based tumor detection and diagnosis techniques are heavily dependent on interpretation by neuro-specialists and/or radiologists, making the evaluation process time-consuming and prone to human error and subjectivity. Besides, widespread use of MR spectroscopy requires specialized processing and assessment of the data and obvious and fast show of the results as photos or maps for routine medical interpretative of an exam. Automatic brain tumor detection and classification have the potential to offer greater efficiency and predictions that are more accurate. However, the performance accuracy of automatic detection and classification techniques tends to be dependent on the specific image modality and is well known to vary from technique to technique. For this reason, it would be prudent to examine the variations in the execution of these methods to obtain consistently high levels of achievement accuracy. Designing, implementing, and evaluating categorization software is the goal of the suggested framework for discerning various brain tumor types on magnetic resonance imaging (MRI) using textural features. This thesis introduces a brain tumor detection support system that involves the use of a variety of tumor classifiers. The system is designed as a decision fusion framework that enables these multi-classifier to analyze medical images, such as those obtained from magnetic resonance imaging (MRI). The fusion procedure is ground on the Dempster-Shafer evidence fusion theory. Numerous experimental scenarios have been implemented to validate the efficiency of the proposed framework. Compared with alternative approaches, the outcomes show that the methodology developed in this thesis demonstrates higher accuracy and higher computational efficiency

A Better Looking Brain: Image Pre-Processing Approaches for fMRI Data

Researchers in the field of functional neuroimaging have faced a long standing problem in pre-processing low spatial resolution data without losing meaningful details within. Commonly, the brain function is recorded by a technique known as echo-planar imaging that represents the measure of blood flow (BOLD signal) through a particular location in the brain as an array of intensity values changing over time. This approach to record a movie of blood flow in the brain is known as fMRI. The neural activity is then studied from the temporal correlation patterns existing within the fMRI time series. However, the resulting images are noisy and contain low spatial detail, thus making it imperative to pre-process them appropriately to derive meaningful activation patterns. Two of the several standard preprocessing steps employed just before the analysis stage are denoising and normalization. Fundamentally, it is difficult to perfectly remove noise from an image without making assumptions about signal and noise distributions. A convenient and commonly used alternative is to smooth the image with a Gaussian filter, but this method suffers from various obvious drawbacks, primarily loss of spatial detail. A greater challenge arises when we attempt to derive average activation patterns from fMRI images acquired from a group of individuals. The brain of one individual differs from others in a structural sense as well as in a functional sense. Commonly, the inter-individual differences in anatomical structures are compensated for by co-registering each subject\u27s data to a common normalization space, known as spatial normalization. However, there are no existing methods to compensate for the differences in functional organization of the brain. This work presents first steps towards data-driven robust algorithms for fMRI image denoising and multi-subject image normalization by utilizing inherent information within fMRI data. In addition, a new validation approach based on spatial shape of the activation regions is presented to quantify the effects of preprocessing and also as a tool to record the differences in activation patterns between individual subjects or within two groups such as healthy controls and patients with mental illness. Qualititative and quantitative results of the proposed framework compare favorably against existing and widely used model-driven approaches such as Gaussian smoothing and structure-based spatial normalization. This work is intended to provide neuroscience researchers tools to derive more meaningful activation patterns to accurately identify imaging biomarkers for various neurodevelopmental diseases and also maximize the specificity of a diagnosis

Radiomic Features to Predict Overall Survival Time for Patients with Glioblastoma Brain Tumors Based on Machine Learning and Deep Learning Methods

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for patients with Glioblastoma brain tumors from medical imaging and non-imaging data. This system is developed to enhance and speed-up the diagnosis process, as well as to increase understanding of the behavior of Glioblastoma brain tumors. The proposed OST prediction system is developed based on a classification process to categorize a GBM patient into one of the following three survival time groups: short-term (months), mid-term (10-15 months), and long-term (\u3e15 months). The Brain Tumor Segmentation challenge (BraTS) dataset is used to develop the automatic OST prediction system. This dataset consists of multimodal preoperative Magnetic Resonance Imaging (mpMRI) data, and clinical data. The training data is relatively small in size to train an accurate OST prediction model based on DL method. Therefore, traditional ML methods such as Support Vector Machine (SVM), Neural Network, K-Nearest Neighbor (KNN), Decision Tree (DT) were used to develop the OST prediction model for GBM patients. The main contributions in the perspective of ML field include: developing and evaluating five novel radiomic feature extraction methods to produce an automatic and reliable OST prediction system based on classification task. These methods are volumetric, shape, location, texture, histogram-based, and DL features. Some of these radiomic features can be extracted directly from MRI images, such as statistical texture features and histogram-based features. However, preprocessing methods are required to extract automatically other radiomic features from MRI images such as the volume, shape, and location information of the GBM brain tumors. Therefore, a three-dimension (3D) segmentation DL model based on modified U-Net architecture is developed to identify and localize the three glioma brain tumor subregions, peritumoral edematous/invaded tissue (ED), GD-enhancing tumor (ET), and the necrotic tumor core (NCR), in multi MRI scans. The segmentation results are used to calculate the volume, location and shape information of a GBM tumor. Two novel approaches based on volumetric, shape, and location information, are proposed and evaluated in this dissertation. To improve the performance of the OST prediction system, information fusion strategies based on data-fusion, features-fusion and decision-fusion are involved. The best prediction model was developed based on feature fusions and ensemble models using NN classifiers. The proposed OST prediction system achieved competitive results in the BraTS 2020 with accuracy 55.2% and 55.1% on the BraTS 2020 validation and test datasets, respectively. In sum, developing automatic CADiag systems based on robust features and ML methods, such as our developed OST prediction system, enhances the diagnosis process in terms of cost, accuracy, and time. Our OST prediction system was evaluated from the perspective of the ML field. In addition, preprocessing steps are essential to improve not only the quality of the features but also boost the performance of the prediction system. To test the effectiveness of our developed OST system in medical decisions, we suggest more evaluations from the perspective of biology and medical decisions, to be then involved in the diagnosis process as a fast, inexpensive and automatic diagnosis method. To improve the performance of our developed OST prediction system, we believe it is required to increase the size of the training data, involve multi-modal data, and/or provide any uncertain or missing information to the data (such as patients\u27 resection statuses, gender, etc.). The DL structure is able to extract numerous meaningful low-level and high-level radiomic features during the training process without any feature type nominations by researchers. We thus believe that DL methods could achieve better predictions than ML methods if large size and proper data is available

Brain Tumour Classification Using Deep Features from Structural MRI

C