Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Das, Susmita; Chowdhury, Manish; Kundu, Malay K.

doi:10.2528/pier13010105

Cited by 110 publications

(88 citation statements)

References 21 publications

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“…Zhang et al [13] suggested that removing spiderweb-plot yielded the same classification performance. Das et al [14] proposed to use Ripplet transform (RT) + PCA + least square SVM (LS-SVM), and the 5 × 5 CV shows high classification accuracies. Kalbkhani et al [15] modelled the detail coefficients of 2-level DWT by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model, and the parameters of GARCH model are considered as the primary feature vector.…”

Section: Existing Pathological Brain Detection Systemsmentioning

confidence: 99%

“…They consist of normal brain images and pathological brain images from seven types of diseases: glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick's disease, sarcoma, and Huntington's disease. In addition, Das et al [14] proposed a third dataset "Dataset-255", which contains 11 types of diseases, among which 7 types are the same as above, and 4 new diseases (chronic subdural hematoma, cerebral toxoplasmosis, herpes encephalitis, and multiple sclerosis) were included. Figure 2 shows samples of a normal brain and 11 types of diseases.…”

Section: Dataset and CV Settingmentioning

confidence: 99%

“…We compared the proposed HBP with BP [37], MBP [38], GA [39], SA [40], 2.4.1 [41], BBO [42], PSO [43], and BPSO [50] III. We compared the proposed HBP-FNN with fourteen state-of-the-art classification methods as DWT + PCA + FP-ANN [7], DWT + PCA + KNN [7], DWT + PCA + SCABC-FNN [8], DWT + PCA + SVM + HPOL [11], DWT + PCA + SVM + IPOL [11], DWT + PCA + SVM + GRB [11], WE + SWP + PNN [12], RT + PCA + LS-SVM [14], PCNN + DWT + PCA + BPNN [17], DWPT + SE + GEPSVM [18], DWPT + TE + GEPSVM [18], WE + NBC [19], WEnergy + SVM [22], and SWT + PCA + HPA-FNN [26]. IV.…”

Section: Experiments Designmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Existing Pathological Brain Detection Systemsmentioning

confidence: 99%

Section: Dataset and CV Settingmentioning

confidence: 99%

Section: Experiments Designmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…For a comprehensive comparison of decision models' performance on the multi-classification of brain MRIs, the proposed research compared five different decision models (J48, kNN, RF, and LS-SVM with polynomial (Poly) and radial basis functions (RBF)). For comparative analysis with the proposed system, some of the other published methods from recent literature [6,18,20,21] were also tested using the same large datasets.…”

Section: Introductionmentioning

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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“…Image fusion methods have been proposed by different researcher in literature [2]- [9]. Image fusion techniques divided into unite state as pixel, feature and decision level.…”

Section: Introductionmentioning

confidence: 99%

A Computational Approach for Image Fusion using Medical Images based on Ripplet Transform

Goswami¹,

Baji²

2015

IJCA

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Image fusion is the process of combine different images from one or multiple imaging to raise the imaging quality. The concept is to improve the image content by combing images. Image fusion is important for improving the image quality and clinical application of medical image. Computed Tomography best suited for bone rift and reduction in providing information of the tissues, at the same time Magnetic Resolution Imaging gives soft tissue information and lacks in boundary information. In this paper Ripplet transform is resolving 2 dimensional singularities and show image edges more efficiently. At beginning the medical images are reconstruct by discrete ripplet transform. Fusion rules are applied to low frequency and high frequency subband. Apply inverse discrete ripplet transform to fused coefficient of low frequency and high frequency, for getting fused image. The performance of proposed system is calculated by quantitative approach such as spatial frequency, entropy, mutual information, peak signal to noise ratio, and root mean square error.

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Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Cited by 110 publications

References 21 publications

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

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

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

A Computational Approach for Image Fusion using Medical Images based on Ripplet Transform

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