The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Rajeev Srivastava - One of the best experts on this subject based on the ideXlab platform.
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weibull probability distribution function based Matched Filter approach for retinal blood vessels segmentation
2017Co-Authors: Nagendra Pratap Singh, Rajeev SrivastavaAbstract:Retinal blood vessels contain an important information that is useful for computer-aided diagnosis of various retinal pathologies such as hypertension, diabetes, glaucoma, etc. Therefore, a retinal blood vessel segmentation is a prominent task. In this paper, a novel Weibull probability distribution function-based Matched Filter approach is introduced to improve the performance of retinal blood vessel segmentation with respect to prominent Matched Filter approaches and other Matched Filter-based approaches existing in literature. Moreover, to enhance the quality of input retinal images in pre-processing step, the concept of principal component analysis (PCA)-based gray scale conversion and contrast-limited adaptive histogram equalization (CLAHE) are used. To design a proposed Matched Filter, the appropriate value of parameters are selected on the basis of an exhaustive experimental analysis. The proposed approach has been tested on 20 retinal images of test set taken from the DRIVE database and confirms that the proposed approach achieved better performance with respect to other prominent Matched Filter-based approaches.
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retinal blood vessels segmentation by using gumbel probability distribution function based Matched Filter
Computer Methods and Programs in Biomedicine, 2016Co-Authors: Nagendra Pratap Singh, Rajeev SrivastavaAbstract:Graphical abstractDisplay Omitted HighlightsA novel Matched Filter approach with the Gumbel PDF as its kernel is proposed.Pre-processing includes PCA based gray-scale conversion and contrast enhancement.Post-processing includes the entropy based optimal thresholding and length Filtering.On the basis of exhaustive experiment select the appropriate value of parameters. Background and objectiveRetinal blood vessel segmentation is a prominent task for the diagnosis of various retinal pathology such as hypertension, diabetes, glaucoma, etc. In this paper, a novel Matched Filter approach with the Gumbel probability distribution function as its kernel is introduced to improve the performance of retinal blood vessel segmentation. MethodsBefore applying the proposed Matched Filter, the input retinal images are pre-processed. During pre-processing stage principal component analysis (PCA) based gray scale conversion followed by contrast limited adaptive histogram equalization (CLAHE) are applied for better enhancement of retinal image. After that an exhaustive experiments have been conducted for selecting the appropriate value of parameters to design a new Matched Filter. The post-processing steps after applying the proposed Matched Filter include the entropy based optimal thresholding and length Filtering to obtain the segmented image. ResultsFor evaluating the performance of proposed approach, the quantitative performance measures, an average accuracy, average true positive rate (ATPR), and average false positive rate (AFPR) are calculated. The respective values of the quantitative performance measures are 0.9522, 0.7594, 0.0292 for DRIVE data set and 0.9270, 0.7939, 0.0624 for STARE data set. To justify the effectiveness of proposed approach, receiver operating characteristic (ROC) curve is plotted and the average area under the curve (AUC) is calculated. The average AUC for DRIVE and STARE data sets are 0.9287 and 0.9140 respectively. ConclusionsThe obtained experimental results confirm that the proposed approach performance better with respect to other prominent Gaussian distribution function and Cauchy PDF based Matched Filter approaches.
Nagendra Pratap Singh - One of the best experts on this subject based on the ideXlab platform.
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weibull probability distribution function based Matched Filter approach for retinal blood vessels segmentation
2017Co-Authors: Nagendra Pratap Singh, Rajeev SrivastavaAbstract:Retinal blood vessels contain an important information that is useful for computer-aided diagnosis of various retinal pathologies such as hypertension, diabetes, glaucoma, etc. Therefore, a retinal blood vessel segmentation is a prominent task. In this paper, a novel Weibull probability distribution function-based Matched Filter approach is introduced to improve the performance of retinal blood vessel segmentation with respect to prominent Matched Filter approaches and other Matched Filter-based approaches existing in literature. Moreover, to enhance the quality of input retinal images in pre-processing step, the concept of principal component analysis (PCA)-based gray scale conversion and contrast-limited adaptive histogram equalization (CLAHE) are used. To design a proposed Matched Filter, the appropriate value of parameters are selected on the basis of an exhaustive experimental analysis. The proposed approach has been tested on 20 retinal images of test set taken from the DRIVE database and confirms that the proposed approach achieved better performance with respect to other prominent Matched Filter-based approaches.
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retinal blood vessels segmentation by using gumbel probability distribution function based Matched Filter
Computer Methods and Programs in Biomedicine, 2016Co-Authors: Nagendra Pratap Singh, Rajeev SrivastavaAbstract:Graphical abstractDisplay Omitted HighlightsA novel Matched Filter approach with the Gumbel PDF as its kernel is proposed.Pre-processing includes PCA based gray-scale conversion and contrast enhancement.Post-processing includes the entropy based optimal thresholding and length Filtering.On the basis of exhaustive experiment select the appropriate value of parameters. Background and objectiveRetinal blood vessel segmentation is a prominent task for the diagnosis of various retinal pathology such as hypertension, diabetes, glaucoma, etc. In this paper, a novel Matched Filter approach with the Gumbel probability distribution function as its kernel is introduced to improve the performance of retinal blood vessel segmentation. MethodsBefore applying the proposed Matched Filter, the input retinal images are pre-processed. During pre-processing stage principal component analysis (PCA) based gray scale conversion followed by contrast limited adaptive histogram equalization (CLAHE) are applied for better enhancement of retinal image. After that an exhaustive experiments have been conducted for selecting the appropriate value of parameters to design a new Matched Filter. The post-processing steps after applying the proposed Matched Filter include the entropy based optimal thresholding and length Filtering to obtain the segmented image. ResultsFor evaluating the performance of proposed approach, the quantitative performance measures, an average accuracy, average true positive rate (ATPR), and average false positive rate (AFPR) are calculated. The respective values of the quantitative performance measures are 0.9522, 0.7594, 0.0292 for DRIVE data set and 0.9270, 0.7939, 0.0624 for STARE data set. To justify the effectiveness of proposed approach, receiver operating characteristic (ROC) curve is plotted and the average area under the curve (AUC) is calculated. The average AUC for DRIVE and STARE data sets are 0.9287 and 0.9140 respectively. ConclusionsThe obtained experimental results confirm that the proposed approach performance better with respect to other prominent Gaussian distribution function and Cauchy PDF based Matched Filter approaches.
Nasser M. Nasrabadi - One of the best experts on this subject based on the ideXlab platform.
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regularization for spectral Matched Filter and rx anomaly detector
Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery XIV, 2008Co-Authors: Nasser M. NasrabadiAbstract:This paper describes a new adaptive spectral Matched Filter and a modified RX-based anomaly detector that incorporates the idea of regularization (shrinkage). The regularization has the effect of restricting the possible Matched Filters (models) to a subset which are more stable and have better performance than the non-regularized adaptive spectral Matched Filters. The effect of regularization depends on the form of the regularization term and the amount of regularization is controlled by so called regularization coefficient. In this paper the sum-of-squares of the Filter coefficients is used as the regularization term and several different values for the regularization coefficient are tested. A Bayesian-based derivation of the regularized Matched Filter is also provided. Experimental results for detecting and recognizing targets in hyperspectral imagery are presented for regularized and non-regularized spectral Matched Filters and RX algorithm.
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Regularization for designing spectral Matched Filter target detectors
Proceedings of SPIE, 2007Co-Authors: Nasser M. NasrabadiAbstract:This paper describes a new adaptive spectral Matched Filter that incorporates the idea of regularization (shrinkage) to penalize and shrink the Filter coefficients to a range of values. The regularization has the effect of restricting the possible Matched Filters (models) to a subset which are more stable and have better performance than the non-regularized adaptive spectral Matched Filters. The effect of regularization depends on the form of the regularization term and the amount of regularization is controlled by so called regularization coefficient. In this paper the sum-of-squares of the Filter coefficients is used as the regularization term and several different values for the regularization coefficient are tested. A Bayesian-based derivation of the regularized Matched Filter is also provided. Experimental results for detecting targets in hyperspectral imagery are presented for regularized and non-regularized spectral Matched Filters.
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Kernel Spectral Matched Filter for Hyperspectral Imagery
International Journal of Computer Vision, 2006Co-Authors: Heesung Kwon, Nasser M. NasrabadiAbstract:In this paper a kernel-based nonlinear spectral Matched Filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral Matched Filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the Matched Filter is derived by expressing the spectral Matched Filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral Matched Filter is equivalent to a nonlinear Matched Filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral Matched Filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral Matched Filter outperforms the conventional linear Matched Filter.
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Kernel spectral Matched Filter for hyperspectral target detection
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: Nasser M. Nasrabadi, Heesung KwonAbstract:In this paper a kernel-based nonlinear spectral Matched Filter is introduced for target detection in hyperspectral imagery. The proposed spectral Matched Filter is defined in a kernel feature space which is equivalent to a nonlinear Matched Filter in the original input space. This nonlinear spectral Matched Filter is based on the notion that performing Matched Filtering in the high dimensional feature space increases the separability of spectral data mainly because it exploits the higher order correlation between the spectral bands. It is also shown that the nonlinear spectral Matched Filter can easily be implemented in terms of kernel functions using the so called kernel trick property of the Mercer kernels. The kernel version of the nonlinear spectral Matched Filter is implemented and simulation results on hyperspectral imagery are shown to outperform the linear version.
Heesung Kwon - One of the best experts on this subject based on the ideXlab platform.
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Kernel Spectral Matched Filter for Hyperspectral Imagery
International Journal of Computer Vision, 2006Co-Authors: Heesung Kwon, Nasser M. NasrabadiAbstract:In this paper a kernel-based nonlinear spectral Matched Filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral Matched Filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the Matched Filter is derived by expressing the spectral Matched Filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral Matched Filter is equivalent to a nonlinear Matched Filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral Matched Filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral Matched Filter outperforms the conventional linear Matched Filter.
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Kernel spectral Matched Filter for hyperspectral target detection
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: Nasser M. Nasrabadi, Heesung KwonAbstract:In this paper a kernel-based nonlinear spectral Matched Filter is introduced for target detection in hyperspectral imagery. The proposed spectral Matched Filter is defined in a kernel feature space which is equivalent to a nonlinear Matched Filter in the original input space. This nonlinear spectral Matched Filter is based on the notion that performing Matched Filtering in the high dimensional feature space increases the separability of spectral data mainly because it exploits the higher order correlation between the spectral bands. It is also shown that the nonlinear spectral Matched Filter can easily be implemented in terms of kernel functions using the so called kernel trick property of the Mercer kernels. The kernel version of the nonlinear spectral Matched Filter is implemented and simulation results on hyperspectral imagery are shown to outperform the linear version.
Richard G Spencer - One of the best experts on this subject based on the ideXlab platform.
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The Time-Domain Matched Filter and the Spectral-Domain Matched Filter in 1-Dimensional NMR Spectroscopy.
Concepts in magnetic resonance. Part A Bridging education and research, 2010Co-Authors: Richard G SpencerAbstract:A type of "Matched Filter" (MF), used extensively in the processing of one-dimensional spectra, is defined by multiplication of a free-induction decay (FID) by a decaying exponential with the same time constant as that of the FID. This maximizes, in a sense to be defined, the signal-to-noise ratio (SNR) in the spectrum obtained after Fourier transformation. However, a different entity known also as the Matched Filter was introduced by van Vleck in the context of pulse detection in the 1940's and has become widely integrated into signal processing practice. These two types of Matched Filters appear to be quite distinct. In the NMR case, the "Filter", that is, the exponential multiplication, is defined by the characteristics of, and applied to, a time domain signal in order to achieve improved SNR in the spectral domain. In signal processing, the Filter is defined by the characteristics of a signal in the spectral domain, and applied in order to improve the SNR in the temporal (pulse) domain. We reconcile these two distinct implementations of the Matched Filter, demonstrating that the NMR "Matched Filter" is a special case of the Matched Filter more rigorously defined in the signal processing literature. In addition, two limitations in the use of the MF are highlighted. First, application of the MF distorts resonance ratios as defined by amplitudes, although not as defined by areas. Second, the MF maximizes SNR with respect to resonance amplitude, while intensities are often more appropriately defined by areas. Maximizing the SNR with respect to area requires a somewhat different approach to Matched Filtering.
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Equivalence of the time‐domain Matched Filter and the spectral‐domain Matched Filter in one‐dimensional NMR spectroscopy
Concepts in Magnetic Resonance Part A, 2010Co-Authors: Richard G SpencerAbstract:: A type of "Matched Filter" (MF), used extensively in the processing of one-dimensional spectra, is defined by multiplication of a free-induction decay (FID) by a decaying exponential with the same time constant as that of the FID. This maximizes, in a sense to be defined, the signal-to-noise ratio (SNR) in the spectrum obtained after Fourier transformation. However, a different entity known also as the Matched Filter was introduced by van Vleck in the context of pulse detection in the 1940's and has become widely integrated into signal processing practice. These two types of Matched Filters appear to be quite distinct. In the NMR case, the "Filter", that is, the exponential multiplication, is defined by the characteristics of, and applied to, a time domain signal in order to achieve improved SNR in the spectral domain. In signal processing, the Filter is defined by the characteristics of a signal in the spectral domain, and applied in order to improve the SNR in the temporal (pulse) domain. We reconcile these two distinct implementations of the Matched Filter, demonstrating that the NMR "Matched Filter" is a special case of the Matched Filter more rigorously defined in the signal processing literature. In addition, two limitations in the use of the MF are highlighted. First, application of the MF distorts resonance ratios as defined by amplitudes, although not as defined by areas. Second, the MF maximizes SNR with respect to resonance amplitude, while intensities are often more appropriately defined by areas. Maximizing the SNR with respect to area requires a somewhat different approach to Matched Filtering.