A Short Survey on MRI Brain Detection <s> III. DISCUSSION <s> Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. We proposed a novel automatic classification system based on particle swarm optimization PSO and artificial bee colony ABC, with the aim of distinguishing abnormal brains from normal brains in MRI scanning. The proposed method used stationary wavelet transform SWT to extract features from MR brain images. SWT is translation-invariant and performed well even the image suffered from slight translation. Next, principal component analysis PCA was harnessed to reduce the SWT coefficients. Based on three different hybridization methods of PSO and ABC, we proposed three new variants of feed-forward neural network FNN, consisting of IABAP-FNN, ABC-SPSO-FNN, and HPA-FNN. The 10 runs of K-fold cross validation result showed the proposed HPA-FNN was superior to not only other two proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the method achieved perfect classification on Dataset-66 and Dataset-160. For Dataset-255, the 10 repetition achieved average sensitivity of 99.37%, average specificity of 100.00%, average precision of 100.00%, and average accuracy of 99.45%. The offline learning cost 219.077 s for Dataset-255, and merely 0.016 s for online prediction. Thus, the proposed SWT+PCA+HPA-FNN method excelled existing methods. It can be applied to practical use. <s> BIB001 </s> A Short Survey on MRI Brain Detection <s> III. DISCUSSION <s> Abstract Background We proposed a novel classification system to distinguish among elderly subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC), based on 3D magnetic resonance imaging (MRI) scanning. Methods The method employed 3D data of 178 subjects consisting of 97 NCs, 57 MCIs, and 24 ADs. First, all these 3D MR images were preprocessed with atlas-registered normalization to form an averaged volumetric image. Then, 3D discrete wavelet transform (3D-DWT) was used to extract wavelet coefficients the volumetric image. The triplets (energy, variance, and Shannon entropy) of all subbands coefficients of 3D-DWT were obtained as feature vector. Afterwards, principle component analysis (PCA) was applied for feature reduction. On the basic of the reduced features, we proposed nine classification methods: three individual classifiers as linear SVM, kernel SVM, and kernel SVM trained by PSO with time-varying acceleration-coefficient (PSOTVAC), with three multiclass methods as Winner-Takes-All (WTA), Max-Wins-Voting, and Directed Acyclic Graph. Results The 5-fold cross validation results showed that the “WTA-KSVM + PSOTVAC” performed best over the OASIS benchmark dataset, with overall accuracy of 81.5% among all proposed nine classifiers. Moreover, the method “WTA-KSVM + PSOTVAC” exceeded significantly existing state-of-the-art methods (accuracies of which were less than or equal to 74.0%). Conclusion We validate the effectiveness of 3D-DWT. The proposed approach has the potential to assist in early diagnosis of ADs and MCIs. <s> BIB002 </s> A Short Survey on MRI Brain Detection <s> III. DISCUSSION <s> (Purpose) Early diagnosis or detection of Alzheimer’s disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions. (Method) First, we used maximum inter-class variance to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch’s t-test. Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC. (Results) The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36±0.94) was better than the linear kernel of 91.47±1.02 and the radial basis function (RBF) kernel of 86.71±1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions. The results were coherent with existing literatures. (Conclusion) The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning. <s> BIB003
In this review, we gave an overview of the state of the art in the MRI-based medical image classification methods. It can be easily observed that the method is becoming mature. The characteristics of different methods are summarized in Table1. The number of the features is too high, and their method was only applied to axial T2-weighted images. Their method was only applied to T2-weighted images. The computation time could be accelerated by using advanced wavelet transforms such as the lift-up wavelet. BIB001 , 3D-DWT method BIB002 , and eigenbrain BIB003 , which will be included in our future research.