A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> In this paper, we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% from support vector machine. We observed that the classification rate is high for a support vector machine classifier compared to self-organizing map-based approach. <s> BIB001 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <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> BIB002 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> This paper presents a hybrid technique for the classification of the magnetic resonance images (MRI). The proposed hybrid technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related to MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images have been reduced, using principal component analysis (PCA), to the more essential features. In the classification stage, two classifiers have been developed. The first classifier based on feed forward back-propagation artificial neural network (FP-ANN) and the second classifier is based on k-nearest neighbor (k-NN). The classifiers have been used to classify subjects as normal or abnormal MRI human images. A classification with a success of 97% and 98% has been obtained by FP-ANN and k-NN, respectively. This result shows that the proposed technique is robust and effective compared with other recent work. <s> BIB003 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> Automated and accurate classiflcation of magnetic resonance (MR) brain images is a hot topic in the fleld of neuroimaging. Recently many difierent and innovative methods have been proposed to improve upon this technology. In this study, we presented a hybrid method based on forward neural network (FNN) to classify an MR brain image as normal or abnormal. The method flrst employed a discrete wavelet transform to extract features from images, and then applied the technique of principle component analysis (PCA) to reduce the size of the features. The reduced features were sent to an FNN, of which the parameters were optimized via an improved artiflcial bee colony (ABC) algorithm based on both fltness scaling and chaotic theory. We referred to the improved algorithm as scaled chaotic artiflcial bee colony (SCABC). Moreover, the K-fold stratifled cross validation was employed to avoid overfltting. In the experiment, we applied the proposed method on the data set of T2-weighted MRI images consisting of 66 brain images (18 normal and 48 abnormal). The proposed SCABC was compared with traditional training methods such as BP, momentum BP, genetic algorithm, elite genetic algorithm with migration, simulated annealing, and ABC. Each algorithm was run 20times to reduce randomness. The results show that our SCABC can obtain the least mean MSE and 100% classiflcation accuracy. <s> BIB004 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> Automated and accurate classification of MR brain images is of importance for the analysis and interpretation of these images and many methods have been proposed. In this paper, we present a neural network (NN) based method to classify a given MR brain image as normal or abnormal. This method first employs wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to a back propagation (BP) NN, with which scaled conjugate gradient (SCG) is adopted to find the optimal weights of the NN. We applied this method on 66 images (18 normal, 48 abnormal). The classification accuracies on both training and test images are 100%, and the computation time per image is only 0.0451s. <s> BIB005 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> Magnetic resonance imaging (MRI) is a non-invasive diagnostic tool very frequently used for brain imaging. The classification of MRI images of normal and pathological brain conditions pose a challenge from technological and clinical point of view, since MR imaging focuses on soft tissue anatomy and generates a large information set and these can act as a mirror reflecting the conditions of the brain. A new approach by integrating wavelet entropy based spider web plots and probabilistic neural network is proposed for the classification of MRI brain images. The two step method for classification uses (1) wavelet entropy based spider web plots for the feature extraction and (2) probabilistic neural network for the classification. The spider web plot is a geometric construction drawn using the entropy of the wavelet approximation components and the areas calculated are used as feature set for classification. Probabilistic neural network provides a general solution to the pattern classification problems and the classification accuracy is found to be 100%. <s> BIB006 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <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> BIB007 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA) to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM) with RBF kernel, using particle swarm optimization (PSO) to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick's disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM. <s> BIB008 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> Abstract In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper features and remove the redundancy from the primary feature vector. Finally, the extracted features are applied to the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers separately to determine the normal image or disease type. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs less number of features for classification. <s> BIB009 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a method to find and select both the dominant run length and co-occurrence texture features of the wavelet approximation tumor region of each slice to be segmented by support vector machine. Two dimensional discrete wavelet decomposition is performed on the tumor image to remove the noise. The images considered for this study belong to 192 benign and malignant tumor slices. A total of 17 features are extracted and six features are selected using Student’s t test. The reduced optimal features are used to model and train the probabilistic neural network classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features that have important contribution in classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed system is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity. <s> BIB010 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> In this paper we proposed a novel classiflcation system to distinguish among elderly subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC). The method employed the magnetic resonance imaging (MRI) data of 178 subjects consisting of 97NCs, 57MCIs, and 24ADs. First, all these three dimensional (3D) MRI images were preprocessed with atlas-registered normalization. Then, gray matter images were extracted and the 3D images were under-sampled. Afterwards, principle component analysis was applied for feature extraction. In total, 20 principal components (PC) were extracted from 3D MRI data using singular value decomposition (SVD) algorithm, and 2 PCs were extracted from additional information (consisting of demographics, clinical examination, and derived anatomic volumes) using alternating least squares (ALS). On the basic of the 22 features, we constructed a kernel support vector machine decision tree (kSVM-DT). The error penalty parameter C and kernel parameter ae were determined by Particle Swarm Optimization (PSO). The weights ! and biases b were still obtained by quadratic programming method. 5-fold cross validation was employed to obtain the out-of-sample estimate. The results show that the proposed kSVM-DT achieves 80% classiflcation accuracy, better than 74% of the method without kernel. Besides, the PSO exceeds the random selection method in choosing the parameters of the classifler. The computation time to predict a new patient is only 0.022s. <s> BIB011 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> This letter researched on the effect of spider-web-plot, which was proposed in the literature "M. Saritha, K.P. Joseph, A.T. Mathew, Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network, Pattern Recognit. Lett. 34 (2013) 2151-2156". We found it was unnecessary to use spider-web-plot as feature transform method. In addition, our simulation results showed that removing the procedure of spider-web-plot yielded the same classification accuracy of 100%. <s> BIB012 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> An accurate diagnosis is important for the medical treatment of patients suffered from brain disease. Nuclear magnetic resonance images are commonly used by technicians to assist the pre-clinical diagnosis, rating them by visual evaluations. The classification of NMR images of normal and pathological brains poses a challenge from technological point of view, since NMR imaging generates a large information set that reflects the conditions of the brain. In this work, we present a computer assisted diagnosis method based on a wavelet-entropy (In this paper 2D-discrete wavelet transform has been used, in that it can extract more information) of the feature space approach and a Naive Bayes classifier classification method for improving the brain diagnosis accuracy by means of NMR images. The most relevant image feature is selected as the wavelet entropy, which is used to train a Naive Bayes classifier. The results over 64 images show that the sensitivity of the classifier is as high as 94.50%, the specificity 91.70%, the overall accuracy 92.60%. It is easily observed from the data that the proposed classifier can detect abnormal brains from normal controls within excellent performance, which is competitive with latest existing methods. <s> BIB013 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> Background: Developing an accurate computer-aided diagnosis (CAD) system of MR brain images is essential for medical interpretation and analysis. In this study, we propose a novel automatic CAD system to distinguish abnormal brains from normal brains in MRI scanning. Methods: The proposed method simplifies the task to a binary classification problem. We used discrete wavelet packet transform (DWPT) to extract wavelet packet coefficients from MR brain images. Next, Shannon entropy (SE) and Tsallis entropy (TE) were harnessed to obtain entropy features from DWPT coefficients. Finally, generalized eigenvalue proximate support vector machine (GEPSVM), and GEPSVM with radial basis function (RBF) kernel, were employed as classifier. We tested the four proposed diagnosis methods (DWPT + SE + GEPSVM, DWPT + TE + GEPSVM, DWPT + SE + GEPSVM + RBF, and DWPT + TE + GEPSVM + RBF) on three benchmark datasets of Dataset-66, Dataset-160, and Dataset-255. Results: The 10 repetition of K-fold stratified cross validation results showed the proposed DWPT + TE + GEPSVM + RBF method excelled not only other three proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the DWPT + TE + GEPSVM + RBF method achieved accuracy of 100%, 100%, and 99.53% on Dataset-66, Dataset-160, and Dataset-255, respectively. For Dataset-255, the offline learning cost 8.4430s and online prediction cost merely 0.1059s. Conclusions: We have proved the effectiveness of the proposed method, which achieved nearly 100% accuracy over three benchmark datasets. <s> BIB014 </s> A Short Survey on MRI Brain Detection <s> II. METHODS AND RESULTS <s> It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in various applications; hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5ź×ź5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance. <s> BIB015
Chaplot, Patnaik, and Jagannathan BIB001 used the approximation coefficients obtained by discrete wavelet transform (DWT), and employed the self-organizing map (SOM) neural network and support vector machine (SVM). The classification percentage of more than 94% in case of self-organizing maps and 98% in case of support vector machine. However, the number of their extracted features reached as high as 4761 which increases the computational time. Meanwhile, their method was only applied to axial T2-weighted imagesat a particular depth inside the brain, and the dataset in their experiment is too small only containing 52 images. Maitra and Chatterjee BIB002 employed the Slantlet transform, which is an improved version of DWT for feature extraction. Their feature vector of each image is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions chosen according to a specific logic. Then, they used the common back-propagation neural network (BPNN). The system they developed could achieve an excellent classification accuracy of100%, while the number of the features for the classifierinput is only six, which obviouslylightened computational burden.However, their method was only tested on AD. El-Dahshan, Hosny, and Salem BIB003 extracted the approximation and detail coefficients of 3-level DWT, reduced the coefficients by principal component analysis (PCA), and used feed-forward back-propagation artificial neural network (FP-ANN) and K-nearest neighbor (KNN) classifiers. The FP-ANN and KNN achieved accuracy of 97% and 98%, respectively. However, whenever the image database increased, a fresh training is required for the proposed system before it works again,which is inconvenient in practical use, and the classification performance can be improved. Zhang, Wu, and Wang BIB004 proposed using DWT for feature extraction, PCA for feature reduction, and FNN with scaled chaotic artificial bee colony (SCABC) as classifier. Based on it, Zhang, Dong, Wu, and Wang BIB005 suggested to replace SCABC with scaled conjugate gradient (SCG) method. Both algorithms achieved 100% accuracy, and the computation time per image is only 0.0451 s.However, the dataset is too small only containing 66 images, and the requirement for International Conference on Chemical, Material and Food Engineering (CMFE-2015) computationtime could be decreasedif they used advanced wavelet transformssuch as lift-up wavelet. Zhang and Wu  proposed to use kernel SVM as classification method, and found the GRB kernel achieved the highest accuracy of 99.38%. The prediction time of a 256x256 image was only 0.0448s. Future scope of their paper is that, they'll test novel kernels to increase the classification accuracy. Meanwhile, multi-classificationcan also be explored, which focuses onspecific disorders utilizing brain MRI. Saritha, Joseph, and Mathew BIB006 proposed a novel feature of wavelet-entropy (WE), employed spider-web-plot (SWP) to further reduce features, and used the probabilistic neural network (PNN). The classification accuracy was found to be 100% on a 75-image dataset with four diseases. The limitation of their work is that oncethere is an increase in image database, fresh training is required. The size of dataset was small and the number of categories of diseases is too little. The efficiency of the developed system could be improved, sinceZhang, Dong, Ji, and Wang BIB012 suggested that removing spider-web-plot yielded the same classification performance later. Das, Chowdhury, and Kundu BIB007 proposed to use a hybrid method for classification. First they employed Ripplet transform (RT) to extract features from MRIs.And then the feature of Magnetic Resonance Image has been reducedby using Principal Component Analysis (PCA). Afterwards, they developed a classifier based on least square SVM (LS-SVM). Five-fold cross-validation was utilized to avoidoverfitting. With this combination, theynot only achieved higher feature reduction, but also acquired superiorperformance, which showed high classification accuracy of 99.39% over a 255-image dataset. However, the time requirement for feature extraction through RT is slightly greater than DWT. Zhang, Wang, Ji, and Dong BIB008 suggested a hybrid system, integrating DWT, PCA, PSO,KSVM, and CV, to identify normal MR brains fromabnormal MR brains.The proposed method first employed digital wavelet transform to extract features, and then used principal componentanalysis (PCA) to reduce the feature space. Afterwards, they constructed a kernel support vector machine (KSVM) with RBF kernel,using particle swarm optimization (PSO) to optimize the parameters C and . Fivefold cross-validation was utilized to avoidoverfitting. The classification result on a 90-image database achieved 97.11% accuracy,which shows that the PSO is more effective to findthe optimal values than random selection method. Theclassification capability of KSVMcan be improved by integrating PSO. However, advanced wavelet transforms, such as the lift-up wavelet, can be helpful to accelerate the computation time. Kalbkhani, Shayesteh, and Zali-Vargahan BIB009 modelled the detail coefficients of 2-level DWT by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model, and the parameters of GARCH model are considered as the primary feature vector. Their classifier was chosen as KNN and SVM models. KNNand SVM classifiers achieved 97.62% and 98.21% accuracy for theeight-class classification, respectively. Also, for two-class classification both classifiers achieve 100% accuracy. Considering that the problem is multi-classification, and the number of features is relatively low, the result is outstanding. However, the feature extraction process of the system requires relatively more time. Padma and Sukanesh BIB010 used combined wavelet statistical texture features, to segment and classify AD benign and malignant tumor slices. First, Two dimensional discretewavelet decomposition is performed on the tumor image to remove the noise. Than a total of17 features are extracted and six features are selected usingStudent's t test. Finally, the reduced optimal features are used tomodel and train the probabilistic neural network classifier and the classification accuracy is evaluated using k fold cross validation method. Their classification model achieved 97.39 % classification accuracy based on SVM tumor segmentation and 2D-DWT preprocessing. However, the limitation ofthis method is that it needs new training for the PNN classifier whenever there is change in the slice data set and itcan't be applied to brain imaging techniques but CT. El-Dahshan, Mohsen, Revett, and Salem  used the feedback pulse-coupled neural network for image segmentation, the DWT for features extraction, the PCA for reducing the dimensionality of the wavelet coefficients, and the FBPNN to classify inputs into normal or abnormal. The accuracy achieved 99% on a 101-image dataset. However, if they extract moreefficient features and increase the training dataset, the classification can be more accurate. Zhang, Wang, and Dong BIB011 presented a diagnosis method to distinguish Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal controls, based on structural MR images by kernel SVM Decision Tree. A 5-fold cross validation showed their method yielded 80% accuracy for these three classes, and the computationtime to predict a new patient is only 0.022 s. The limitation of their method is thatthe meanings of weights/biases of the kSVM-DT mean can't be understood by ordinary technicians, since the classifier establishes machine-oriented rules instead of human-oriented rules. Another limitation is that the classification accuracy needs to be improved. However, structural MRI may not cover the sufficient information containing the cause of AD. Zhou, et al. BIB013 used wavelet-entropy as the feature space, then they employed a Naive Bayes classifier (NBC) classification method. Their results over 64 images showed that the sensitivity of the classifier is 94.50%, the specificity 91.70%, the overall accuracy 92.60%. However, ordinary technicians have difficulty interpreting the entropy values or theweights/biases of NBC.Meanwhile,multi-disease classification was the future research direction, since their classifier can only perform two-class classification. Finally, the small dataset may introduce error to the reported classificationaccuracy. Zhang, Dong, Wang, Ji, and Yang BIB014 used discrete wavelet packet transform (DWPT), and harnessed Tsallis entropy to obtain features from DWPT coefficients. Then, they used a generalized eigenvalue proximal SVM (GEPSVM) with RBF kernel. This combination yielded superior performance, achieving 99.61% accuracy. However, the limitation was that the classifier was machine-oriented instead of human-oriented. Although machine-orientedclassifiers are better in classification performance than human-oriented classifiers, it is difficult for technicians tounderstand or interpret the meanings of weights/biases of the classifier, which is inconvenient in practical use.And the computation of DWPT is relatively time-consuming. Yang, et al. BIB015 selected wavelet-energy as the features, and introduced biogeography-based optimization (BBO) to train the SVM. Their method reached 97.78% accuracy on 90 T2-weighted MR brain images. Future scope of their paper is that, they'll replace wavelet-energywith more efficient feature descriptors, suchas scale-invariant features and employ advanced pattern recognition techniques,such as deep learning and RBFNN. Multiple slices can be employed in order to improve theclassification performance.They'll also test whether higher level can lead to better classificationperformance by increasing the decompositionlevel of 2D-DWT.