Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 2.110
Filtrar
1.
BMC Bioinformatics ; 20(Suppl 25): 693, 2019 Dec 24.
Artigo em Inglês | MEDLINE | ID: mdl-31874641

RESUMO

BACKGROUND: Glaucoma is an irreversible eye disease caused by the optic nerve injury. Therefore, it usually changes the structure of the optic nerve head (ONH). Clinically, ONH assessment based on fundus image is one of the most useful way for glaucoma detection. However, the effective representation for ONH assessment is a challenging task because its structural changes result in the complex and mixed visual patterns. METHOD: We proposed a novel feature representation based on Radon and Wavelet transform to capture these visual patterns. Firstly, Radon transform (RT) is used to map the fundus image into Radon domain, in which the spatial radial variations of ONH are converted to a discrete signal for the description of image structural features. Secondly, the discrete wavelet transform (DWT) is utilized to capture differences and get quantitative representation. Finally, principal component analysis (PCA) and support vector machine (SVM) are used for dimensionality reduction and glaucoma detection. RESULTS: The proposed method achieves the state-of-the-art detection performance on RIMONE-r2 dataset with the accuracy and area under the curve (AUC) at 0.861 and 0.906, respectively. CONCLUSION: In conclusion, we showed that the proposed method has the capacity as an effective tool for large-scale glaucoma screening, and it can provide a reference for the clinical diagnosis on glaucoma.


Assuntos
Glaucoma/diagnóstico por imagem , Humanos , Disco Óptico/diagnóstico por imagem , Radônio , Máquina de Vetores de Suporte , Análise de Ondaletas
2.
Sensors (Basel) ; 19(20)2019 Oct 14.
Artigo em Inglês | MEDLINE | ID: mdl-31615162

RESUMO

Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.


Assuntos
Algoritmos , Eletromiografia , Mãos/fisiologia , Movimento/fisiologia , Reconhecimento Automatizado de Padrão , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Análise de Ondaletas
3.
Zhongguo Yi Liao Qi Xie Za Zhi ; 43(5): 341-344, 2019 Sep 30.
Artigo em Chinês | MEDLINE | ID: mdl-31625331

RESUMO

OBJECTIVE: A method for dynamically collecting and processing ECG signals was designed to obtain classification information of abnormal ECG signals. METHODS: Firstly, the ECG eigenvectors were acquired by real-time acquisition of ECG signals combined with discrete wavelet transform, and then the ECG fuzzy information entropy was calculated. Finally, the Euclidean distance was used to obtain the semantic distance of ECG signals, and the classification information of abnormal signals was obtained. RESULTS: The device could effectively identify abnormal ECG signals on an embedded platform based on the Internet of Things, and improved the diagnosis accuracy of heart diseases. CONCLUSIONS: The fuzzy diagnosis device of ECG signal could accurately classify the abnormal signal and output an online signal classification matrix with a high confidence interval.


Assuntos
Eletrocardiografia , Cardiopatias , Algoritmos , Arritmias Cardíacas , Lógica Fuzzy , Cardiopatias/diagnóstico , Humanos , Internet , Processamento de Sinais Assistido por Computador , Análise de Ondaletas
4.
Accid Anal Prev ; 133: 105296, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31563015

RESUMO

Risky driving states such as aggressive driving and unstable driving are the cause of many traffic accidents. Many studies have used either driving data or physiological data such as electroencephalography (EEG) to estimate and monitor driving states. However, few studies made comparison among those driving-feature-based, EEG-feature-based and hybrid-feature-based (combination of driving features and EEG features) models. Further, limited types of EEG features have been extracted and investigated in the existing studies. To fill these research gaps aforementioned, this study adopts two EEG analysis techniques (i.e., independent component analysis and brain source localization), two signal processing methods (i.e., power spectrum analysis and wavelets analysis) to extract twelve kinds of EEG features for the short-term driving state prediction. The prediction performance of driving features, EEG features and hybrid features of them was evaluated and compared. The results indicated that EEG-based model has better performance than driving-data-based model (i.e., 83.84% versus 71.59%) and the integrated model of driving features and the full brain regions features extracted by wavelet analysis outperforms other types of features with the highest accuracy of 86.27%.


Assuntos
Direção Agressiva/psicologia , Encéfalo/fisiologia , Eletroencefalografia/métodos , Processamento de Sinais Assistido por Computador/instrumentação , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Análise Espectral , Análise de Ondaletas
5.
Med Sci Monit ; 25: 6574-6580, 2019 Sep 02.
Artigo em Inglês | MEDLINE | ID: mdl-31474746

RESUMO

BACKGROUND In anterior cervical discectomy and fusion (ACDF) surgery, drilling operation causes a high risk of tissue injury. This study aimed to present a novel feedback system based on sound pressure signals to identify drilling condition during ACDF. MATERIAL AND METHODS ACDF surgery was performed on the C4/5 segments of 6 porcine cervical specimens. The annulus fibrosus, endplate cartilage, sub-endplate cortical bone, and posterior longitudinal ligament (PLL) were drilled until penetration using a 2-mm high-speed burr. Sound pressure signals were collected using a microphone and dynamic signal analyzer. The recorded signals of different tissues were proceeded with lifting wavelet transform for extracting harmonic components. The frequencies of harmonic components are 1, 2, 3, 4, and 5 times higher than the motor frequency. The magnitude of harmonic components was calculated to identify different drilling conditions, along a broad spectrum of frequencies (1-5 kHz). For statistical analysis, one-way ANOVA (analysis of variance) and post hoc test (Dunnett's T3) were performed. RESULTS Very good demarcation was found among the signal magnitudes of different drilling conditions. Different drilling conditions do not present the same rate of variation of frequency. Differences in magnitude among all drilling conditions were statistically significant at certain frequency points (p<0.05). In 3 cases, one tissue could not be identified with respect to another (annulus fibrosus and endplate cartilage at 2 kHz, PLL and penetration at 3 kHz, annulus fibrosus and sub-endplate cortical bone at 5 kHz, p>0.05). CONCLUSIONS Sound pressure signals may provide an auxiliary feedback system for enhancing drilling operation in ACDF surgery, especially in minimally invasive surgery.


Assuntos
Vértebras Cervicais/cirurgia , Discotomia , Pressão , Som , Animais , Feminino , Masculino , Suínos , Análise de Ondaletas
6.
Sensors (Basel) ; 19(17)2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31484303

RESUMO

Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications.


Assuntos
Análise de Ondaletas , Algoritmos
7.
Sensors (Basel) ; 19(16)2019 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-31398903

RESUMO

Gait event detection is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. Recently, the continuous wavelet transform (CWT) based methods have been increasingly proposed for gait event detection due to their robustness. However, few investigations on determining the appropriate mother wavelet with proper selection criteria have been performed, especially for hemiplegic patients. In this study, the performances of commonly used mother wavelets in detecting gait events were systematically investigated. The acceleration signals from the tibialis anterior muscle of both healthy and hemiplegic subjects were recorded during ground walking and the two core gait events of heel strike (HS) and toe off (TO) were detected from the signal recordings by a CWT algorithm with different mother wavelets. Our results showed that the overall performance of the CWT algorithm in detecting the two gait events was significantly different when using various mother wavelets. By using different wavelet selection criteria, we also found that the accuracy criteria based on time-error minimization and F1-score maximization could provide the appropriate mother wavelet for gait event detection. The findings from this study will provide an insight on the selection of an appropriate mother wavelet for gait event detection and facilitate the development of adequate rehabilitation aids.


Assuntos
Marcha/fisiologia , Hemiplegia/fisiopatologia , Análise de Ondaletas , Acelerometria , Adulto , Algoritmos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fatores de Tempo , Adulto Jovem
8.
Comput Methods Programs Biomed ; 178: 135-143, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31416542

RESUMO

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is an important diagnostic tool for the diagnosis of heart disorders. Useful features and well-designed classification method are crucial for automatic diagnosis. However, most of the contributions were in single lead or two-lead ECG signal and only features from single lead were used to classify the ECG beats. In this paper, a cascaded classification system is proposed to extract features and classify heartbeats in order to improve the performance of ECG beat classification via multi-lead ECG. METHODS: In contrast with most of the literatures, ten signal features were chosen and run on each of the 12 leads separately. Based on these features, we developed a novel feature fusion method combining information from all available leads, and then implemented a cascaded classifier utilizing random forest (RF) and multilayer perceptron (MLP). Besides, in order to reduce the feature space dimension, principal component analysis (PCA) was applied in the method. MATERIALS: Four open source databases including MIT-BIH Arrythmia Database, QT Database, MIT-BIH Supraventricular Arrhythmia Database and St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database (INCART Database) were used in this work. These four databases are different in classes of beats, volume of dataset, number of individual volunteers. Except INCART database, in which the recording format is 12-lead, the other three databases consist of 2-lead recordings. Above all, they all have annotations for every single beat including the type of each beat. CONCLUSIONS: Extensive experimental results shown that the average accuracy achieved 99.3%, 99.8%, 97.6% and 99.6% on four databases respectively. Compared with most state-of-the-art methods, our work has better performance and strong generalization capability.


Assuntos
Arritmias Cardíacas/diagnóstico , Eletrocardiografia , Frequência Cardíaca , Processamento de Sinais Assistido por Computador , Análise de Ondaletas , Algoritmos , Bases de Dados Factuais , Humanos , Reconhecimento Automatizado de Padrão , Análise de Componente Principal , Reprodutibilidade dos Testes , Software
9.
J Med Syst ; 43(10): 307, 2019 Aug 16.
Artigo em Inglês | MEDLINE | ID: mdl-31420756

RESUMO

The image restoration has emerged as a very vital investigation technique in the domain of the image processing. The underlying motive behind the image restoration is devoted to the augmentation of the perceived visual impact of image so as to make it almost identical to the original image. A host of exploration approaches are now in vogues which are intended to steer clear of the noise, thereby regaining the images with original quality. In our earlier research, two distinct noise elimination methods like the (OGHP) and SURE shrinkage were effectively employed for the purpose of denoising, though the relative PSNR and SSIM efficiencies did not come up to the desired level. In the innovative approach envisaged in the document, at the outset, the noise is included by means of two processes like the salt and pepper and impulse noise. Subsequently, the pre-processing methods are performed with the able assistance of two novel filters such as the adaptive median filter and adaptive fuzzy switching. Thereafter, the preprocessed image is furnished to the succeeding function of noise elimination like the (OGHP) and SURE shrinkage. In the course of the OGHP noise elimination technique, the GHP constraints are optimized by employing the Cuckoo Search Algorithm. Thereafter, the noise-eliminated image is effectively estimated with the help of the Discrete Wavelet Transform (DWT). The consequential noiseless images are subjected to the image restoration procedure by efficiently employing the AGA approach. The cheering performance outcomes chant the success stories of the novel image restoration method, highlighting its superlative efficiency. Moreover, the efficacy of the innovative approach is assessed by means of a set of noise-polluted images and contrasted with the modern noiseless image restoration technique.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador/métodos , Humanos , Processamento de Sinais Assistido por Computador , Razão Sinal-Ruído , Análise de Ondaletas
10.
BMC Neurosci ; 20(1): 38, 2019 07 31.
Artigo em Inglês | MEDLINE | ID: mdl-31366317

RESUMO

BACKGROUND: In this study, nonlinear based time-frequency (TF) and time domain investigations are employed for the analysis of electroencephalogram (EEG) signals of mild cognitive impairment-Alzheimer's disease (MCI-AD) patients and healthy controls. This study attempts to comprehend the cognitive decline of MCI-AD under both resting and cognitive task conditions. RESULTS: Wavelet-based synchrosqueezing transform (SST) alleviates the smearing of energy observed in the spectrogram around the central frequencies in short-time Fourier transform (STFT), and continuous wavelet transform (CWT). A precise TF representation is assured due to the reassignment of scale variable to the frequency variable. It is discerned from the studies of time domain measures encompassing fractal dimension (FD) and approximate entropy (ApEn), that the parietal lobe is the most affected in MCI-AD under both resting and cognitive states. Alterations in asymmetry in the brain hemispheres are analysed using the homologous areas inter-hemispheric symmetry (HArS). CONCLUSION: Time and time-frequency domain analysis of EEG signals have been used for distinguishing various brain states. Therefore, EEG analysis is highly useful for the screening of AD in its prodromal phase.


Assuntos
Doença de Alzheimer/fisiopatologia , Disfunção Cognitiva/fisiopatologia , Lateralidade Funcional/fisiologia , Idoso , Idoso de 80 Anos ou mais , Doença de Alzheimer/complicações , Estudos de Casos e Controles , Disfunção Cognitiva/complicações , Eletroencefalografia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Análise de Ondaletas
11.
Curr Pharm Biotechnol ; 20(9): 755-765, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31258079

RESUMO

BACKGROUND: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble model distinguishes between two different sets of problems which are group 1: inter-ictal and ictal, group 2: controlled and inter-ictal using approximate entropy as a parameter. METHODS: This work addresses the classification problem for two groups; Group 1: "inter-ictal vs. ictal" for which case 1(C-E), and case 2(D-E) are included and Group 2; "activity from controlled vs. inter-ictal activity" considering four cases which are case 3 (A-C), case 4(B-C), case 5 (A-D) and case 6(B-D) respectively. To divide the EEG into sub-bands, DWT (Discrete Wavelet Transform) was used and approximate Entropy was extracted out of all the five sub-bands of EEG for each case. Bagged SVM was used to classify the different groups considered. RESULTS: The highest accuracy for Group 1 using Bagged SVM Ensemble model for case 1 was observed to be 96.83% with testing data; which was similar to 97% achieved by using training data. For case 2 (D-E) 93.92% accuracy with training and 84.83% with testing data were obtained. For Group 2, there was a large disparity between SVM and Bagged Ensemble model, where 76%, 81.66%, 72.835% and 71.16% for case 3, case 4, case 5 and case 6 were obtained. While for training data set, 92.87%, 91.74%, 92% and 92.64% accuracy was attained, respectively. The results obtained by SVM for Group 2 showed a huge difference from the highest accuracy achieved by bagged SVM for both the training and the test data. CONCLUSION: Bagged Ensemble model outperformed SVM model for every case with a huge difference with both training as well as test dataset for Group 2 and marginally better for Group 1.


Assuntos
Encéfalo/fisiopatologia , Eletroencefalografia/métodos , Epilepsia/diagnóstico , Máquina de Vetores de Suporte , Análise de Ondaletas , Diagnóstico por Computador , Epilepsia/classificação , Epilepsia/fisiopatologia , Humanos
12.
Sensors (Basel) ; 19(13)2019 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-31266226

RESUMO

Electrocardiogram (ECG) signals are crucial for determining the health status of the human heart. A clean ECG signal is critical in analysis and diagnosis of heart diseases. However, ECG signals are often contaminated by motion artifact noise in the non-contact ECG monitoring systems. In this paper, an ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed. This method consists of five steps, namely, spectrum preprocessing, spectrum segmentation, EWT decomposition, wavelet threshold denoising, and EWT reconstruction. The proposed approach was used to process real ECG signals collected by the non-contact ECG monitoring equipment. The results of quantitative study and analysis indicate that this approach produces a better performance in terms of restorage of QRS complexes of the original ECG with reduced distortion, retaining useful information in ECG signals, and improvement of the signal to noise ratio (SNR) value of the signal. The output results of the practical ECG signal test show that motion artifact in the real recorded ECG is effectively filtered out. The proposed method is feasible for reducing motion artifacts from ECG signals, whether from simulation ECG signals or practical non-contact ECG monitoring systems.


Assuntos
Arritmias Cardíacas/diagnóstico , Eletrocardiografia/métodos , Monitorização Fisiológica , Processamento de Sinais Assistido por Computador , Algoritmos , Simulação por Computador , Humanos , Movimento (Física) , Análise de Ondaletas
13.
Ultrasonics ; 99: 105948, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31323561

RESUMO

Multimodal and dispersive characteristics of ultrasonic guided waves (GWs) cause the wave-packet overlapping in time domain and frequency domain, which challenges the signal interpretation. In this study, we propose an automatic method for individual mode extraction. The inversible synchrosqueezed wavelet transform (SWT) is employed to obtain the high-resolution time-frequency representation (TFR) of the GW signal. Then, two image processing steps, i.e., watershed transform and region growing, are used to process the TFR distributions and extract the TFR trajectory of each individual component. After the TFR segmentation, the individual modes are reconstructed by using the inverse SWT. The algorithm performance is investigated by synthesized multimodal signals. The results show that the reconstructed individual modes are consistent with the original ones. The experimental results measured in a bovine tibia plate and a steel plate are further employed to testify the proposed algorithm. Results suggest that the presented study provides a robust tool for processing multimodal ultrasonic GW signals.


Assuntos
Ondas Ultrassônicas , Análise de Ondaletas , Algoritmos , Animais , Bovinos , Processamento de Sinais Assistido por Computador , Razão Sinal-Ruído , Tíbia
14.
J Med Syst ; 43(9): 291, 2019 Jul 23.
Artigo em Inglês | MEDLINE | ID: mdl-31332536

RESUMO

The one of the preprocessing step for hyperspectral imagery is noise reduction. The images are received by the detector and this can be degraded by several factors like atmospherical things and device noises which emit temperature noise, processing noise and explosion noise. There are several strategies are developed already to cut back the signal to noise magnitude relation of the hyperspectral image. However, the stationary noise of the many denoising ways developed cannot be applied on to the gauge boson noise. Thus, the each gauge boson and thermal noise square measure gift within the captured hyperspectral image (HSI). during this paper, we tend to projected a replacement denoising framework known as tensor-based filtering employing a PARAFAC tensor decomposition methodology for scale back each noise. The proposed technique is performs higher in removing noise as compared with different strategies like Multiple linear regression (MLR) algorithm and combined algorithm called multidimensional wavelet transforms with multiway wiener filter (MWPT-MWF) technique. The performance analysis of the new denoising framework has more efficient for reducing signal dependent (PN) and signal independent noise (TN) as compared with other conventional method. Hence this novel denoising approach would be more beneficial for detection of skin allergy and also this algorithm will be very useful for detection of retinal exudates and diagnosis of diabetes mellitus and retinopathy disease in medical application.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador/métodos , Razão Sinal-Ruído , Cor , Humanos , Modelos Lineares , Imagens de Fantasmas , Análise de Componente Principal , Análise de Ondaletas
15.
Biomed Microdevices ; 21(3): 74, 2019 07 22.
Artigo em Inglês | MEDLINE | ID: mdl-31332586

RESUMO

Magnetic field assisted laser fabrication is proposed to process metal dry bioelectrode with surface microstructures. The effects of magnetic flux density on the geometrical dimension of surface microstructures of bioelectrode is investigated. The electrode-skin contact impedance is then studied using the two-electrode measurement method. Finally, electromyography (EMG) signal is recorded using bioelectrodes processed in different magnetic flux density. Our results show that the magnetic field has obvious influences on the height and bottom width of microstructure of bioelectrode. When a magnetic field of 100 mT is selected, larger height-width ratio of microstructures is obtained, which provides a stronger ability to penetrate stratum corneum. Consequently, much lower contact impedance is obtained. Signal-noise ratio (SNR) of EMG signal shows a correlation coefficient of 0.9836 with height-width ratio of microstructures on the surface of metal dry bioelectrodes. Raw EMG signals recorded by metal dry bioelectrodes in 100 mT magnetic field show a high SNR up to 27.350, which is slightly higher than that of traditional Ag/AgCl wet bioelectrodes (26.689). By stationary wavelet transform (SWT) de-noising, noise interfused in raw EMG signals is suppressed effectively. Moreover, the de-noised EMG signal recorded using metal dry bioelectrodes processed in 100 mT magnetic field still remains a fairly high SNR.


Assuntos
Eletricidade , Lasers , Campos Magnéticos , Metais/química , Microtecnologia/instrumentação , Eletrodos , Eletromiografia , Razão Sinal-Ruído , Propriedades de Superfície , Análise de Ondaletas
16.
J Med Syst ; 43(8): 280, 2019 Jul 12.
Artigo em Inglês | MEDLINE | ID: mdl-31300900

RESUMO

A new denoising algorithm using Fast Guided Filter and Discrete Wavelet Transform is proposed to remove Gaussian noise in an image. The Fast Guided Filter removes some part of the details in addition to noise. These details are estimated accurately and combined with the filtered image to get back the final denoised image. The proposed algorithm is compared with other existing filtering techniques such as Wiener filter, Non Local means filter and bilateral filter and it is observed that the performance of this algorithm is superior compared to the above mentioned Gaussian noise removal techniques. The resultant image obtained from this method is very good both from subjective and objective point of view. This algorithm has less computational complexity and preserves edges and other detail information in an image.


Assuntos
Assistência à Saúde , Diagnóstico por Imagem , Razão Sinal-Ruído , Algoritmos , Distribuição Normal , Análise de Ondaletas
17.
Magn Reson Imaging ; 62: 167-173, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-31279772

RESUMO

An inventive scheme for automated tissue segmentation and classification is offered in this paper using Fast Discrete Wavelet Transform (DWT)/Band Expansion Process (BEP), Kernel-based least squares Support Vector Machine (KLS-SVM) and F-score, backed by Principal Component Analysis (PCA). Using input as T1, T2 and Proton Density (PD) scans of patients, CSF (Cerebrospinal Fluid), WM (White matter) and GM (Gray matter) are afforded as output, which act as hallmark for brain atrophy and thus sustaining in diagnosis of Alzheimer's disease (AD) from Mild Cognitive Impairment (MCI) and Healthy Controls (HC). The blending of BEP features from DWT and texture features from Gray Level Co-occurrence Matrix (GLC) promises to be a savior in atrophy revelation of the segmented tissues. Data used for evaluation of this study is taken from the ADNI database that encloses T1-weighted s-MRI (Structural Magnetic Imaging Resonance) scans of 158 patients with AD and 145 HC. Preprocessing steps unearthed five parameters for classification (i.e. cortical thickness, curvature, gray matter volume, surface area, and sulcal depth), in the preliminary step. For challenging the classifier performance, ROC (Receiver operating characteristics) curves are painted and the SVM classifiers of two-dimensional spaces took the top two important features as classification features for separating HC and AD to the maximum extent. The final results revealed that Fast DWT + F-Score + PCA + KLS-SVM + Poly Kernel is giving 100% tissue classification accuracy for test samples under consideration with only 7 input features.


Assuntos
Doença de Alzheimer/diagnóstico por imagem , Atrofia/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Disfunção Cognitiva/diagnóstico por imagem , Imagem por Ressonância Magnética , Idoso , Algoritmos , Doença de Alzheimer/patologia , Atrofia/patologia , Encéfalo/patologia , Mapeamento Encefálico , Disfunção Cognitiva/patologia , Reações Falso-Positivas , Feminino , Humanos , Processamento de Imagem Assistida por Computador/métodos , Análise dos Mínimos Quadrados , Masculino , Pessoa de Meia-Idade , Reconhecimento Automatizado de Padrão , Análise de Componente Principal , Curva ROC , Máquina de Vetores de Suporte , Análise de Ondaletas
18.
Comput Methods Programs Biomed ; 177: 69-79, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31319962

RESUMO

BACKGROUND AND OBJECTIVE: Brain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumor detection using Magnetic Resonance Imaging (MRI) may increase patient's survival rate. In MRI, tumor is shown more clearly that helps in the process of further treatment. This work aims to detect tumor at an early phase. METHODS: In this manuscript, Weiner filter with different wavelet bands is used to de-noise and enhance the input slices. Subsets of tumor pixels are found with Potential Field (PF) clustering. Furthermore, global threshold and different mathematical morphology operations are used to isolate the tumor region in Fluid Attenuated Inversion Recovery (Flair) and T2 MRI. For accurate classification, Local Binary Pattern (LBP) and Gabor Wavelet Transform (GWT) features are fused. RESULTS: The proposed approach is evaluated in terms of peak signal to noise ratio (PSNR), mean squared error (MSE) and structured similarity index (SSIM) yielding results as 76.38, 0.037 and 0.98 on T2 and 76.2, 0.039 and 0.98 on Flair respectively. The segmentation results have been evaluated based on pixels, individual features and fused features. At pixels level, the comparison of proposed approach is done with ground truth slices and also validated in terms of foreground (FG) pixels, background (BG) pixels, error region (ER) and pixel quality (Q). The approach achieved 0.93 FG and 0.98 BG precision and 0.010 ER on a local dataset. On multimodal brain tumor segmentation challenge dataset BRATS 2013, 0.93 FG and 0.99 BG precision and 0.005 ER are acquired. Similarly on BRATS 2015, 0.97 FG and 0.98 BG precision and 0.015 ER are obtained. In terms of quality, the average Q value and deviation are 0.88 and 0.017. At the fused feature based level, specificity, sensitivity, accuracy, area under the curve (AUC) and dice similarity coefficient (DSC) are 1.00, 0.92, 0.93, 0.96 and 0.96 on BRATS 2013, 0.90, 1.00, 0.97, 0.98 and 0.98 on BRATS 2015 and 0.90, 0.91, 0.90, 0.77 and 0.95 on local dataset respectively. CONCLUSION: The presented approach outperformed as compared to existing approaches.


Assuntos
Neoplasias Encefálicas/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Aprendizado de Máquina , Imagem por Ressonância Magnética , Algoritmos , Área Sob a Curva , Teorema de Bayes , Árvores de Decisões , Glioma/diagnóstico por imagem , Humanos , Modelos Estatísticos , Reconhecimento Automatizado de Padrão , Curva ROC , Reprodutibilidade dos Testes , Análise de Ondaletas
19.
J Med Syst ; 43(8): 275, 2019 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-31280416

RESUMO

To solve the problem of location and segmentation of intervertebral discs in spinal MRI images, a method of intervertebral disc segmentation and degeneration classification diagnosis based on wavelet image denoising and independent component analysis-active appearance model (ICA-AAM) was proposed. Firstly, the spinal MRI image is decomposed by wavelet transform, and the noise is filtered by soft threshold method. Then, aiming at the inadequacy of PCA method in AAM in describing data details, ICA is used instead of PCA to model shape and texture models, and an improved AAM segmentation model is formed. Finally, the intervertebral discs in MRI images are segmented by AAM model, and the degeneration classification of intervertebral discs is diagnosed according to the gray level characteristics of the segmented region. The experimental results show that the method can accurately locate and segment the intervertebral disc region and make classification diagnosis.


Assuntos
Disco Intervertebral/diagnóstico por imagem , Imagem por Ressonância Magnética/métodos , Razão Sinal-Ruído , Análise de Ondaletas , Algoritmos , Humanos , Modelos Estatísticos , Traumatismos da Coluna Vertebral/diagnóstico por imagem
20.
Comput Intell Neurosci ; 2019: 9439407, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31239837

RESUMO

The steady-state motion visual evoked potential (SSMVEP) collected from the scalp suffers from strong noise and is contaminated by artifacts such as the electrooculogram (EOG) and the electromyogram (EMG). Spatial filtering methods can fuse the information of different brain regions, which is beneficial for the enhancement of the active components of the SSMVEP. Traditional spatial filtering methods fuse electroencephalogram (EEG) in the time domain. Based on the idea of image fusion, this study proposed an SSMVEP enhancement method based on time-frequency (T-F) image fusion. The purpose is to fuse SSMVEP in the T-F domain and improve the enhancement effect of the traditional spatial filtering method on SSMVEP active components. Firstly, two electrode signals were transformed from the time domain to the T-F domain via short-time Fourier transform (STFT). The transformed T-F signals can be regarded as T-F images. Then, two T-F images were decomposed via two-dimensional multiscale wavelet decomposition, and both the high-frequency coefficients and low-frequency coefficients of the wavelet were fused by specific fusion rules. The two images were fused into one image via two-dimensional wavelet reconstruction. The fused image was subjected to mean filtering, and finally, the fused time-domain signal was obtained by inverse STFT (ISTFT). The experimental results show that the proposed method has better enhancement effect on SSMVEP active components than the traditional spatial filtering methods. This study indicates that it is feasible to fuse SSMVEP in the T-F domain, which provides a new idea for SSMVEP analysis.


Assuntos
Algoritmos , Eletroencefalografia , Potenciais Evocados Visuais , Processamento de Sinais Assistido por Computador , Adulto , Feminino , Humanos , Masculino , Análise de Ondaletas , Adulto Jovem
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA