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An computer-aided diagnosis system of pathological brain detection PBD is important for help physicians interpret and analyze medical images. We proposed a novel automatic PBD to distinguish pathological brains from healthy brains in magnetic resonance imaging scanning in this paper. The proposed method simplified the PBD problem to a binary classification task. We extracted the wavelet packet Tsallis entropy WPTE from each brain image. The WPTE is the Tsallis entropy of the coefficients of the discrete wavelet packet transform. The, the features were submitted to the fuzzy support vector machine FSVM. We tested the proposed diagnosis method on 3 benchmark datasets with different sizes. A ten runs of K-fold stratified cross validation was carried out. The results demonstrated that the proposed WPTE + FSVM method excelled 17 state-of-the-art methods w.r.t. classification accuracy. The WPTE is superior to discrete wavelet transform. The Tsallis entropy performs better than Shannon entropy. The FSVM excels standard SVM. In closing, the proposed method -WPTE + FSVM- is effective in PBD.

KeywordsPathological brain detection PBD Tsallis entropy Magnetic resonance imaging Computer-aided diagnosis Discrete wavelet packet transform Fuzzy support vector machine Pattern recognition AbbreviationsABPFNNartificial back-propagation feed-forward neural network

DWPTdiscrete wavelet packet transform

kFSVMkernel Fuzzy support vector machine

GWMgray white matter

STEshannon tsallis entropy

ABCartificial bee colony

ADAlzheimer’s disease

BBObiogeography-based optimization

CADcomputer-aided diagnosis

CSFcerebrospinal fluid

FMFfuzzy membership function

HDhuntington’s disease

KNNK-nearest neighbor

MRImagnetic resonance imaging

MSmultiple sclerosis

PiDpick’s disease

PBDpathological brain detection

PNNprobabilistic neural network

QMFquadrature mirror filter

RTripplet transform

SCVstratified cross validation

SDHsubdural hematoma

SOMself-organizing map

WPSTEwavelet packet Shannon Tsallis entropy

Yu-Dong Zhang, Shui-Hua Wang and Xiao-Jun Yang contributed equally

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Autor: Yu-Dong Zhang - Shui-Hua Wang - Xiao-Jun Yang - Zheng-Chao Dong - Ge Liu - Preetha Phillips - Ti-Fei Yuan


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