Abstract
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.
Highlights
Pathological brain detection (PBD) was of essential importance
Wavelet packet transform Compared to standard discrete wavelet transform (DWT), the wavelet packet transform (WPT) is an extension where the signal is passed through more filters than DWT
The similar results occur between “wavelet packet Tsallis entropy (WPTE) + fuzzy sup‐ port vector machine (FSVM)” and “WPTE + support vector machine (SVM)” in the way that the classification accuracy increases after SVM is replaced with FSVM
Summary
Pathological brain detection (PBD) was of essential importance. It can help physicians make decisions, and to avoid wrong judgements on subjects. Das et al (2013) developed a novel method as Ripplet transform (RT) + principal component analysis (PCA) + least square support vector machine (LS-SVM) Their five-fold cross validation results showed promising classification accuracies. Zhou et al (2015) used wavelet-entropy as the feature space, they employed a Naive Bayes classifier (NBC) classification method Their results over 64 images showed that the sensitivity of the classifier was 94.50 %, the specificity 91.70 %, the overall accuracy 92.60 %. Yang et al (2015) 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. Feature extraction Co-registration was unnecessary since many publications about PBD did not use it with excellent classification results, comparative with the results that employed coregistration (Ribbens et al 2014; Schwarz and Kasparek 2014)
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