A Slantlet transform based intelligent system for magnetic resonance brain image classification

Maitra, Madhubanti; Chatterjee, Amitava

doi:10.1016/j.bspc.2006.12.001

Cited by 106 publications

(68 citation statements)

References 11 publications

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“…DWT and its variants were used extensively by various researchers for feature extraction in MR brain image classification [12][13][14][15][16][17][18]. But the problem with DWT is that it is inherently non-supportive to directionality and anisotropy.…”

Section: Ripplet Transform Type-i (Rt)mentioning

confidence: 99%

“…They have used discrete wavelet transform (DWT) for feature extraction [13]. In [14], Maitra and Chatterjee have shown that Slantlet transform can be combined with supervised classification (back-propagation neural network (BPNN)) technique to achieve 100% classification accuracy. Principal component analysis (PCA) is used to reduce the dimension of the feature vector obtained through DWT in [15] by El-Dahshan et al They have achieved 97% and 98% successrates through feed-forward BPNN and k-nearest neighbor (kNN) classifiers, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Das¹,

Chowdhury²,

Kundu³

2013

PIER

110

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Abstract-We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an efficient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 × 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classification accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is efficient in brain MR image classification.

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Section: Ripplet Transform Type-i (Rt)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Das¹,

Chowdhury²,

Kundu³

2013

PIER

110

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“…To improve the efficiency of a classifier, the master features in the MRI image is needed to be identified properly. In recent literature [6,[14][15][16][17][18][19][20][21][22][24][25][26][27], there are many different algorithms (such as DWT and Ripplet transform) used to extract the main features of the images. DWT has some advantages over RT, being less computationally complex and also due to the characteristics of brain MRIs.…”

Section: Master Feature Extractionmentioning

confidence: 99%

Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System

et al. 2017

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Abstract:Background: An accurate and automatic computer-aided multi-class decision support system to classify the magnetic resonance imaging (MRI) scans of the human brain as normal, Alzheimer, AIDS, cerebral calcinosis, glioma, or metastatic, which helps the radiologists to diagnose the disease in brain MRIs is created. Methods: The performance of the proposed system is validated by using benchmark MRI datasets (OASIS and Harvard) of 310 patients. Master features of the images are extracted using a fast discrete wavelet transform (DWT), then these discriminative features are further analysed by principal component analysis (PCA). Different subset sizes of principal feature vectors are provided to five different decision models. The classification models include the J48 decision tree, k-nearest neighbour (kNN), random forest (RF), and least-squares support vector machine (LS-SVM) with polynomial and radial basis kernels. Results: The RF-based classifier outperformed among all compared decision models and achieved an average accuracy of 96% with 4% standard deviation, and an area under the receiver operating characteristic (ROC) curve of 99%. LS-SVM (RBF) also shows promising results (i.e., 89% accuracy) when the least number of principal features was used. Furthermore, the performance of each classifier on different subset sizes of principal features was (80%-96%) for most performance metrics. Conclusion: The presented medical decision support system demonstrates the potential proof for accurate multi-class classification of brain abnormalities; therefore, it has a potential to use as a diagnostic tool for the medical practitioners.

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“…Chaplot et al [5] used the approximation coefficients obtained by discrete wavelet transform (DWT), and employed the self-organizing map (SOM) neural network and support vector machine (SVM). Maitra and Chatterjee [6] employed the Slantlet transform, which is an improved version of DWT. Their feature vector of each image is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions chosen according to a specific logic.…”

Section: Existing Pathological Brain Detection Systemsmentioning

confidence: 99%

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

Zhang¹,

Wang²,

Dong³

et al. 2015

PIER

110

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Abstract-(Background)We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.

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A Slantlet transform based intelligent system for magnetic resonance brain image classification

Cited by 106 publications

References 11 publications

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

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