Theoretical Analysis of a Passive Acoustic Brain Monitoring System

Abstract-Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this paper, we presented a novel method to classify a given MR brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of Kfold stratified cross validation was used to enhance generalization of KSVM. We chose seven common brain diseases (glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick's disease, sarcoma, and Huntington's disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Harvard Medical School website. We performed our proposed methods with four different kernels, and found that the GRB kernel achieves the highest classification accuracy as 99.38%. The LIN, HPOL, and IPOL kernel achieves 95%, 96.88%, and 98.12%, respectively. We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernel still achieved the best accurate classification results. The averaged processing time for a 256 × 256 size image on a laptop of P4 IBM with 3 GHz processor and 2 GB RAM is 0.0448 s. From the experimental data, our method was effective and rapid. It could be applied to the field of MR brain image classification and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees.

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“…The diagnostic values of MRI are greatly magnified by the automated and accurate classification of the MRI images [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

Zhang¹,

Wu²

2012

PIER

255

146

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“…Soft tissue structures (heart, lungs, liver, brain and other organs) are clearer and more detailed with MRI than other medical imaging modalities. The noninvasive nature of MRI together with its rich information provision, makes MRI the widely used method for diagnosis and treatment planning [2][3][4]. Various researchers are not only trying to improve the MR image quality, but also seeking novel methods for easier and quicker inference from the images [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Das¹,

Chowdhury²,

Kundu³

2013

PIER

110

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Abstract-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 efficient 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 classification accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is efficient in brain MR image classification.

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An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature

Jena

Nayak

Saxena

2021

Machine Vision and Applications

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Brain tumor classification and segmentation for different weighted MRIs are among the most tedious tasks for many researchers due to the high variability of tumor tissues based on texture, structure, and position. Our study is divided into two stages: supervised machine learning-based tumor classification and image processing-based region of tumor extraction. For this job, seven methods have been used for texture feature generation. We have experimented with various state-of-the-art supervised machine learning classification algorithms such as support vector machines (SVMs), K-nearest neighbors (KNNs), binary decision trees (BDTs), random forest (RF), and ensemble methods. Then considering texture features into account, we have tried for fuzzy C-means (FCM), K-means, and hybrid image segmentation algorithms for our study. The experimental results achieved a classification accuracy of 94.25%, 87.88%, 89.57%, 96.99%, and 97% with SVM, KNN, BDT, RF, and Ensemble methods, respectively, on FLAIR-, T1C-, and T2-weighted MRI, and the hybrid segmentation attaining 90.16% mean dice score for tumor area segmentation against ground-truth images.

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Theoretical Analysis of a Passive Acoustic Brain Monitoring System

Cited by 15 publications

References 17 publications

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature

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