Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients

Abstract Aim: To develop an automatic magnetic resonance (MR) brain classification that can assist physicians to make a diagnosis and reduce wrong decisions. Method: This article investigated the binary particle swarm optimization (BPSO) approach and proposed its three new variants: BPSO with mutation and time-varying acceleration coefficients (BPSO-MT), BPSO with mutation (BPSO-M), and BPSO with time-varying acceleration coefficients (BPSO-T). We first extracted wavelet entropy (WE) features from both approximation and detail sub-bands of eight-level decomposition. Afterwards, we used the proposed BPSO-M, BPSO-T, and BPSO-MT to select features. Finally, the selected features were fed into a probabilistic neural network (PNN). Results: The proposed BPSO-MT performed better than BPSO-T and BPSO-M. It finally selected two features of entropies of the following two sub-bands (V1, D1). The proposed system “WE + BPSO-MT + PNN” yielded perfect classification on Data160 and Data66. In addition, it yielded 99.53% average accuracy for the Data255, over 10 repetitions of k-fold stratified cross validation (SCV), higher than state-of-the-art approaches. Conclusions: The proposed method is effective for MR brain classification.

[1]  S. Dimauro,et al.  Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: evidence from brain imaging. , 2014, JAMA psychiatry.

[2]  Max A. Viergever,et al.  Automatic segmentation of MR brain images of preterm infants using supervised classification , 2015, NeuroImage.

[3]  Carlos Fernandez-Lozano,et al.  Classification of mild cognitive impairment and Alzheimer's Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data , 2015, Expert Syst. Appl..

[4]  J. Mukund Nilakantan,et al.  Robotic U-shaped assembly line balancing using particle swarm optimization , 2016 .

[5]  Yudong Zhang,et al.  A hybrid method for MRI brain image classification , 2011, Expert Syst. Appl..

[6]  M. Marchese,et al.  An ant colony optimization method for generalized TSP problem , 2008 .

[7]  Katherine W Ferrara,et al.  Multimodal imaging enables early detection and characterization of changes in tumor permeability of brain metastases. , 2013, Journal of controlled release : official journal of the Controlled Release Society.

[8]  Yudong Zhang,et al.  An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine , 2013, TheScientificWorldJournal.

[9]  Claudio De Stefano,et al.  A GA-based feature selection approach with an application to handwritten character recognition , 2014, Pattern Recognit. Lett..

[10]  K Radermacher,et al.  Computer-assisted single- or double-cut oblique osteotomies for the correction of lower limb deformities , 2007, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[11]  Yudong Zhang,et al.  Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-based Optimization and Particle Swarm Optimization , 2015 .

[12]  Zne-Jung Lee,et al.  Parameter determination of support vector machine and feature selection using simulated annealing approach , 2008, Appl. Soft Comput..

[13]  Yudong Zhang,et al.  Binary PSO with mutation operator for feature selection using decision tree applied to spam detection , 2014, Knowl. Based Syst..

[14]  W. Whittow,et al.  Printability of elastomer latex for additive manufacturing or 3D printing , 2016 .

[15]  G. Nagamani,et al.  Dissipativity and passivity analysis of T–S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach , 2015 .

[16]  Minrui Fei,et al.  Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design , 2012, Expert Syst. Appl..

[17]  Yudong Zhang,et al.  A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications , 2015 .

[18]  Yudong Zhang,et al.  Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning , 2015, Front. Comput. Neurosci..

[19]  Dong Liu,et al.  Binary inheritance learning particle swarm optimisation and its application in thinned antenna array synthesis with the minimum sidelobe level , 2015 .

[20]  Yudong Zhang,et al.  Automated classification of brain images using wavelet-energy and biogeography-based optimization , 2016, Multimedia Tools and Applications.

[21]  Yudong Zhang,et al.  A Novel Method for Magnetic Resonance Brain Image Classification Based on Adaptive Chaotic PSO , 2010 .

[22]  Paul K. Joseph,et al.  Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network , 2013, Pattern Recognit. Lett..

[23]  Mohammad Soleimani,et al.  Topology Design of Dual-Band Antennas Using Binary Particle Swarm Optimization and Discrete Green’s Functions , 2015 .

[24]  Karim Faez,et al.  GA-based optimal selection of PZMI features for face recognition , 2008, Appl. Math. Comput..

[25]  Hojjat Adeli,et al.  Computer-Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network , 2015, Journal of Medical Systems.

[26]  Yudong Zhang,et al.  Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging , 2015, Inf. Sci..

[27]  Yu-Chi Sung,et al.  Application of particle swarm optimisation to construction planning for cable-stayed bridges by the cantilever erection method , 2016 .

[28]  Behnam Mohammadi-Ivatloo,et al.  Energy procurement management for electricity retailer using new hybrid approach based on combined BICA-BPSO , 2015 .

[29]  Yudong Zhang,et al.  Feed‐forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection , 2015, Int. J. Imaging Syst. Technol..

[30]  Ahmed A. El-Sattar,et al.  Improved particle swarm optimization for photovoltaic system connected to the grid with low voltage ride through capability , 2016 .

[31]  Yudong Zhang,et al.  Effect of spider-web-plot in MR brain image classification , 2015, Pattern Recognit. Lett..

[32]  Kenneth Revett,et al.  Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm , 2014, Expert Syst. Appl..

[33]  Yudong Zhang,et al.  Detection of Alzheimer's Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging. , 2015, Journal of Alzheimer's disease : JAD.

[34]  Yudong Zhang,et al.  Binary Structuring Elements Decomposition Based on an Improved Recursive Dilation-Union Model and RSAPSO Method , 2014 .

[35]  N. P. Patidar,et al.  Different Penalty Handling Based Economic Dispatch Using Time Varying Acceleration Coefficients , 2014, SEMCCO.

[36]  Ashok Panigrahy,et al.  Combined MRI and MRS improves pre-therapeutic diagnoses of pediatric brain tumors over MRI alone , 2015, Neuroradiology.

[37]  Yudong Zhang,et al.  Spam Detection via Feature Selection and Decision Tree , 2012 .

[38]  Huazhong Shu,et al.  Curve-Like Structure Extraction Using Minimal Path Propagation With Backtracking , 2016, IEEE Transactions on Image Processing.

[39]  Lalit M. Patnaik,et al.  Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network , 2006, Biomed. Signal Process. Control..

[40]  Yudong Zhang,et al.  Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC , 2015, Biomed. Signal Process. Control..

[41]  A. Jayachandran,et al.  Abnormality Segmentation and Classification of Multi-class Brain Tumor in MR Images Using Fuzzy Logic-Based Hybrid Kernel SVM , 2015, International Journal of Fuzzy Systems.

[42]  S. Lahmiri.  Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques , 2016 .

[43]  Klaus Radermacher,et al.  Fluoroscopy-based 3-D reconstruction of femoral bone cement: a new approach for revision total hip replacement , 2005, IEEE Transactions on Biomedical Engineering.

[44]  Narasimhan Sundararajan,et al.  Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems , 2016, Inf. Sci..

[45]  Yudong Zhang,et al.  Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree , 2014 .

[46]  Wei Xu,et al.  Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier , 2015, IWBBIO.

[47]  Sudeb Das,et al.  Brain Mr Image Classification Using Multiscale Geometric Analysis of Ripplet , 2013 .

[48]  Yudong Zhang,et al.  A Novel Demodulation System Based on Continuous Wavelet Transform , 2015 .

[49]  H. Attar,et al.  Comparison across growth kinetic models of alkaline protease production in batch and fed-batch fermentation using hybrid genetic algorithm and particle swarm optimization , 2015 .

[50]  Preetha Phillips,et al.  Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine , 2015, SpringerPlus.

[51]  H. Larsson,et al.  Simultaneous evaluation of brain tumour metabolism, structure and blood volume using [18F]-fluoroethyltyrosine (FET) PET/MRI: feasibility, agreement and initial experience , 2015, European Journal of Nuclear Medicine and Molecular Imaging.

[52]  Banshidhar Majhi,et al.  Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests , 2016, Neurocomputing.

[53]  Yudong Zhang,et al.  A Robust Hybrid Restarted Simulated Annealing Particle Swarm Optimization Technique , 2012, CSA 2012.

[54]  Yudong Zhang,et al.  Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization , 2011, Sensors.

[55]  Paulo Cesar Marques de Carvalho,et al.  Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region , 2016 .

[56]  Maoguo Gong,et al.  Discrete particle swarm optimization for high-order graph matching , 2016, Inf. Sci..

[57]  Yudong Zhang,et al.  AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT , 2012 .

[58]  Abdel-Badeeh M. Salem,et al.  Hybrid intelligent techniques for MRI brain images classification , 2010, Digit. Signal Process..

[59]  Gnanou Florence Sudha,et al.  Adaptive speckle reduction in ultrasound images using fuzzy logic on Coefficient of Variation , 2016, Biomed. Signal Process. Control..

[60]  Yudong Zhang,et al.  Pathological Brain Detection by a Novel Image Feature - Fractional Fourier Entropy , 2015, Entropy.

[61]  Yudong Zhang,et al.  Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM) , 2015, Entropy.

[62]  Lenan Wu,et al.  UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization , 2013 .