Naïve Bayesian Classification Based Glioma Brain Tumor Segmentation Using Grey Level Co-occurrence Matrix Method

Brain tumors vary widely in size and form, making detection and diagnosis difficult. This study's main aim is to identify abnormal brain images., classify them from normal brain images, and then segment the tumor areas from the categorised brain images. In this study, we offer a technique based on the Nave Bayesian classification approach that can efficiently identify and segment brain tumors. Noises are identified and filtered out during the preprocessing phase of tumor identification. After preprocessing the brain image, GLCM and probabilistic properties are extracted. Naive Bayesian classifier is then used to train and label the retrieved features. When the tumors in a brain picture have been categorised, the watershed segmentation approach is used to isolate the tumors. This paper's brain pictures are from the BRATS 2015 data collection. The suggested approach has a classification rate of 99.2% for MR pictures of normal brain tissue and a rate of 97.3% for MR images of aberrant Glioma brain tissue. In this study, we provide a strategy for detecting and segmenting tumors that has a 97.54% Probability of Detection (POD), a 92.18% Probability of False Detection (POFD), a 98.17% Critical Success Index (CSI), and a 98.55% Percentage of Corrects (PC). The recommended Glioma brain tumour detection technique outperforms existing state-of-the-art approaches in POD, POFD, CSI, and PC because it can identify tumour locations in abnormal brain images

Efficient Algorithm for Distinction Mild Cognitive Impairment from Alzheimer’s Disease Based on Specific View FCM White Matter Segmentation and Ensemble Learning

Purpose: Alzheimer's Disease (AD) is in the dementia group and is one of the most prevalent neurodegenerative disorders. Between existing characteristics, White Matter (WM) is a known marker for AD tracking, and WM segmentation in MRI based on clustering can be used to decrease the volume of data. Many algorithms have been developed to predict AD, but most concentrate on the distinction of AD from Cognitive Normal (CN). In this study, we provided a new, simple, and efficient methodology for classifying patients into AD and MCI patients and evaluated the effect of the view dimension of Fuzzy C Means (FCM) in prediction with ensemble classifiers. Materials and Methods: We proposed our methodology in three steps; first, segmentation of WM from T1 MRI with FCM according to two specific viewpoints (3D and 2D). In the second, two groups of features are extracted: approximate coefficients of Discrete Wavelet Transform (DWT) and statistical (mean, variance, skewness) features. In the final step, an ensemble classifier that is constructed with three classifiers, K-Nearest Neighbor (KNN), Decision Tree (DT), and Linear Discriminant Analysis (LDA), was used. Results: The proposed method has been evaluated by using 1280 slices (samples) from 64 patients with MCI (32) and AD (32) of the ADNI dataset. The best performance is for the 3D viewpoint, and the accuracy, precision, and f1-score achieved from the methodology are 94.22%, 94.45%, and 94.21%, respectively, by using a ten-fold Cross-Validation (CV) strategy. Conclusion: The experimental evaluation shows that WM segmentation increases the performance of the ensemble classifier, and moreover the 3D view FCM is better than the 2D view. According to the results, the proposed methodology has comparable performance for the detection of MCI from AD. The low computational cost algorithm and the three classifiers for generalization can be used in practical application by physicians in pre-clinical

Advances in Statistical and Machine Learning Methods for Image Data, with Application to Alzheimer's Disease

The revolutionary development of neuroimage technology allows for the generation of large-scale neuroimage data in modern medical studies. For example, structural magnetic resonance imaging (sMRI) is widely used in segmenting neurodegenerative regions in the brain and positron-emission tomography (PET) is commonly used by clinicians and researchers to quantify the severity of Alzheimer's disease. In the first part of this dissertation, we build “OASIS-AD”, which is a supervised learning model based on a well-validated automated segmentation tool “OASIS” in multiple sclerosis (MS). OASIS-AD considers the specific challenges raised by WMH in Alzheimer's Disease (AD) to reduce false discoveries. We show that OASIS-AD performs better than several existing automated white matter hyperintensity segmentation approaches. In the second part of this dissertation, we develop an interpretable penalized multivariate high-dimensional method for image-on-scalar regression that can be used for association studies between high-dimensional PET images and patients' scalar measures. This method overcomes the lack of interpretability in regularized regression after reduced-rank decomposition through a novel encoder-decoder based penalty to regularize interpretable image characteristics. Empirical properties of the proposed approach are examined and compared to existing methods in simulation studies and in the analysis of PET images from subjects in a study of Alzheimer's Disease. In the third part of this dissertation, we developed ACU-Net, an efficient convolutional network for medical image segmentation. The proposed deep learning network overcomes the small sample size problem of training a deep neural network when used for medical image segmentation. It also decreases computation cost by increasing the effective degrees of freedom through data augmentation and the novel use of convolutional layers blocks to compress the model. We show that ACU-Net can achieve competitive performance while dramatically decreases the computation cost compared with modern CNNs. Public health significance: This dissertation proposes new statistical and machine learning methods for two aging-related problems: (1) automatically segmenting white matter hyperintensity (WMH), a biomarker of neurodegenerative pathology, and (2) estimating the association between neurodegeneration pathology and vascular measures, which are important to aging population living quality and can be studied by clinical neuroimage data

The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke

Competing theories of short-term memory function make specific predictions about the functional anatomy of auditory short-term memory and its role in language comprehension. We analysed high-resolution structural magnetic resonance images from 210 stroke patients and employed a novel voxel based analysis to test the relationship between auditory short-term memory and speech comprehension. Using digit span as an index of auditory short-term memory capacity we found that the structural integrity of a posterior region of the superior temporal gyrus and sulcus predicted auditory short-term memory capacity, even when performance on a range of other measures was factored out. We show that the integrity of this region also predicts the ability to comprehend spoken sentences. Our results therefore support cognitive models that posit a shared substrate between auditory short-term memory capacity and speech comprehension ability. The method applied here will be particularly useful for modelling structure–function relationships within other complex cognitive domains

Computational Imaging Methods for Analysis of DaTScan SPECT Images

There is an important need to develop biomarkers to improve the diagnosis and assess the severity of Parkinson’s disease (PD). The potential to derive such biomarkers from quantitative dopamine transporter scan (DaT-Scan) single-photon emission computed tomography (SPECT) imaging, in particular the uptake of DaT in the caudate, putamen, and globus pallidus regions, is highly appealing as imaging is non-invasive and DaTScan is already used in the management of patients with PD. However, reliable quantification requires reliable segmentation of these regions in these images. Reliable segmentation is challenging due to the limited spatial resolution and high image noise in SPECT images and the physiological variability in these regions. To address this issue, we propose a three-dimensional physics-guided estimation-based method for segmenting SPECT images. The method implicitly incorporates the prior distribution of boundaries of caudate, putamen and globus pallidus, as can be obtained from high-resolution MR images of patients scanned previously, during the training process. Our approach is guided by the physics of the SPECT imaging, and thus inherently accounts for the two sources of partial volume effects in SPECT images, namely limited system resolution and tissue-fraction effects. The proposed method was evaluated both qualitatively and quantitatively using highly realistic simulation studies. The method yielded accurate boundaries of the caudate, putamen, and globus pallidus regions, provided reliable estimates of the specific binding ratios of these regions, and significantly out-performed several commonly used segmentation methods. We have implemented geometric transfer matrix (GTM) method that uses this delineated boundary to compensate for partial volume effects, with the goal of estimating more accurate quantification results

Evaluation of Cerebral Lateral Ventricular Enlargement Derived from Magnetic Resonance Imaging: A Candidate Biomarker of Alzheimer Disease Progression in Vivo

Alzheimer disease (AD) is the most common form of dementia and has grievous mortality rates. Measuring brain volumes from structural magnetic resonance images (MRI) may be useful for illuminating disease progression. The goal of this thesis was to (1) help refine a novel technique used to segment the lateral cerebral ventricles from MRI, (2) validate this tool, and determine group-wise differences between normal elderly controls (NEC) and subjects with mild cognitive impairment (MCI) and AD and (3) determine the number of subjects necessary to detect a 20 percent change from the natural history of ventricular enlargement with respect to genotype. Three dimensional Ti-weighted MRI and cognitive measures were acquired from 504 subjects (NEC n = 152, MCI n = 247 and AD n = 105) participating in the multi-centre Alzheimer\u27s Disease Neuroimaging Initiative. Cerebral ventricular volume was quantified at baseline and after six months. For secondary analyses, all groups were dichotomized for Apolipoprotein E genotype based on the presence of an e4 polymorphism. The AD group had greater ventricular enlargement compared to both subjects with MCI (P = 0.0004) and NEC (P \u3c 0.0001), and subjects with MCI had a greater rate of ventricular enlargement compared to NEC (P =0.0001). MCI subjects that progressed to clinical AD after six months had greater ventricular enlargement than stable MCI subjects (P = 0.0270). Ventricular enlargement was different between apolipoprotein E genotypes within the AD group (P = 0.010). The number of subjects required to demonstrate a 20% change in ventricular enlargement (AD: N=342, MCI: N=1180) was substantially lower than that required to demonstrate a 20% change in cognitive scores (MMSE) (AD: N=7056, MCI: N=7712). Therefore, ventricular enlargement represents a feasible short-term marker of disease progression in subjects with MCI and subjects with AD for multi-centre studie

Automated detection of Alzheimer disease using MRI images and deep neural networks- A review

Early detection of Alzheimer disease is crucial for deploying interventions and slowing the disease progression. A lot of machine learning and deep learning algorithms have been explored in the past decade with the aim of building an automated detection for Alzheimer. Advancements in data augmentation techniques and advanced deep learning architectures have opened up new frontiers in this field, and research is moving at a rapid speed. Hence, the purpose of this survey is to provide an overview of recent research on deep learning models for Alzheimer disease diagnosis. In addition to categorizing the numerous data sources, neural network architectures, and commonly used assessment measures, we also classify implementation and reproducibility. Our objective is to assist interested researchers in keeping up with the newest developments and in reproducing earlier investigations as benchmarks. In addition, we also indicate future research directions for this topic.Comment: 22 Pages, 5 Figures, 7 Table