A Short Survey on MRI Brain Detection <s> INTRODUCTION <s> Abstract The present paper proposes the development of a new approach for automated diagnosis, based on classification of magnetic resonance (MR) human brain images. Wavelet transform based methods are a well-known tool for extracting frequency space information from non-stationary signals. In this paper, the proposed method employs an improved version of orthogonal discrete wavelet transform (DWT) for feature extraction, called Slantlet transform, which can especially be useful to provide improved time localization with simultaneous achievement of shorter supports for the filters. For each two-dimensional MR image, we have computed its intensity histogram and Slantlet transform has been applied on this histogram signal. Then a feature vector, for each image, is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions, chosen according to a specific logic. The features hence derived are used to train a neural network based binary classifier, which can automatically infer whether the image is that of a normal brain or a pathological brain, suffering from Alzheimer's disease. An excellent classification ratio of 100% could be achieved for a set of benchmark MR brain images, which was significantly better than the results reported in a very recent research work employing wavelet transform, neural networks and support vector machines. <s> BIB001 </s> A Short Survey on MRI Brain Detection <s> INTRODUCTION <s> We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an e-cient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 £ 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classiflcation accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is e-cient in brain MR image classiflcation. <s> BIB002 </s> A Short Survey on MRI Brain Detection <s> INTRODUCTION <s> Importance Impaired mitochondrial function impacts many biological processes that depend heavily on energy and metabolism and can lead to a wide range of neurodevelopmental disorders, including autism spectrum disorder (ASD). Although evidence that mitochondrial dysfunction is a biological subtype of ASD has grown in recent years, no study, to our knowledge, has demonstrated evidence of mitochondrial dysfunction in brain tissue in vivo in a large, well-defined sample of individuals with ASD. Objectives To assess brain lactate in individuals with ASD and typically developing controls using high-resolution, multiplanar spectroscopic imaging; to map the distribution of lactate in the brains of individuals with ASD; and to assess correlations of elevated brain lactate with age, autism subtype, and intellectual ability. Design, Setting, and Participants Case-control study at Columbia University Medical Center and New York State Psychiatric Institute involving 75 children and adults with ASD and 96 age- and sex-matched, typically developing controls. Main Outcomes and Measures Lactate doublets (present or absent) on brain magnetic resonance spectroscopic imaging. Results Lactate doublets were present at a significantly higher rate in participants with ASD (13%) than controls (1%) ( P = .001). In the ASD group, the presence of lactate doublets correlated significantly with age ( P = .004) and was detected more often in adults (20%) than in children (6%), though it did not correlate with sex, ASD subtype, intellectual ability, or the Autism Diagnostic Observation Schedule total score or subscores. In those with ASD, lactate was detected most frequently within the cingulate gyrus but it was also present in the subcortical gray matter nuclei, corpus callosum, superior temporal gyrus, and pre- and postcentral gyri. Conclusions and Relevance In vivo brain findings provide evidence for a possible neurobiological subtype of mitochondrial dysfunction in ASD. <s> BIB003
Magnetic resonance imaging (MRI) is capable of producing highquality images of the anatomical structures of the human body (especially the brain), while it is a low-risk, fast, non-invasive imaging technique.The rich information it provides is of great help for clinical diagnosis and biomedical research BIB003 . However, because of this huge informationrepository associated with MRIs,employing manual methods to interpret each image is tedious and time consuming, and it is of necessity to develop automateddiagnosis tools to draw quicker and easier inferences from the MRimages.These automated tools can benefit themedical personnel in diagnosis, prognosis, pre-surgical and postsurgical procedures, etc. BIB002 . Sophisticated signal/image processing techniques and some computational intelligent techniquesare applied to develop diagnostic tools. In one possible methodology, the problem of automaticclassification of normal/pathological subjects, based on brainMRIs, can be configured as a three-stage problem:1) feature extraction; 2) feature reduction; 3) classification BIB001 . The main contribution of this study is toreview the most recent classification algorithmsand their state-of-the-art. We summarized the advantages anddisadvantages of the reviewed algorithms in tables to provide astructured vision aspects involved in these algorithms. This paper is organized as follows: Section 2 presents the recent classification algorithms and their experiment results. Section 3 discusses the limitations of these methods, while the conclusion and future work are presented in Section 4.