Refinements of damage detection methods based on wavelet analysis of dynamical shapes

Messina, Arcangelo

doi:10.1016/j.ijsolstr.2008.02.015

Cited by 52 publications

(16 citation statements)

References 22 publications

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“…As we filter the image, the mask will extend beyond the image at the edges, so the solution is to pad the pixels outside the images. In our algorithm, symmetric padding method [28] was utilized to calculate the boundary value.…”

Section: D Dwtmentioning

confidence: 99%

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

Zhang¹,

Wu²

2012

PIER

257

146

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Abstract-Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this paper, we presented a novel method to classify a given MR brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of Kfold stratified cross validation was used to enhance generalization of KSVM. We chose seven common brain diseases (glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick's disease, sarcoma, and Huntington's disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Harvard Medical School website. We performed our proposed methods with four different kernels, and found that the GRB kernel achieves the highest classification accuracy as 99.38%. The LIN, HPOL, and IPOL kernel achieves 95%, 96.88%, and 98.12%, respectively. We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernel still achieved the best accurate classification results. The averaged processing time for a 256 × 256 size image on a laptop of P4 IBM with 3 GHz processor and 2 GB RAM is 0.0448 s. From the experimental data, our method was effective and rapid. It could be applied to the field of MR brain image classification and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees.

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Section: D Dwtmentioning

confidence: 99%

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

Zhang¹,

Wu²

2012

PIER

257

146

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show abstract

“…The border distortion should be avoided. In our algorithm, symmetric padding method [21] was utilized to calculate the boundary value.…”

Section: Experiments and Discussionmentioning

confidence: 99%

An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine

Zhang

Wang

et al. 2013

The Scientific World Journal

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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.

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“…5. Symmetric padding method [18] was utilized to calculate the boundary value, with the aim of avoiding border distortion. After decomposition, 10 features were obtained from The wavelet-energy can reduce the dimension of DWT coefficients.…”

Section: Feature Extractionmentioning

confidence: 99%

Automated classification of brain images using wavelet-energy and biogeography-based optimization

Yang

Zhang

Yang

et al. 2015

Multimed Tools Appl

156

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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 5×5-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.

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Refinements of damage detection methods based on wavelet analysis of dynamical shapes

Cited by 52 publications

References 22 publications

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine

Automated classification of brain images using wavelet-energy and biogeography-based optimization

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