Permutation test algorithms for nonparametric radar detection

Sanz-González, J.L.; Álvarez-Vaquero, F.; Gonzalez-Garcia, Jose E.

doi:10.1049/cp:20070542

Cited by 6 publications

(9 citation statements)

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“…The DSC is obtained by using equation (21). The Q is obtained by using equation ( 20), PSNR metric is obtained by using equation ( 22), HD is obtained by using equation (23), and the probability of tumor detection (PTD) is obtained by using equation (27). The Q, PSNR, HD, and PTD values were calculated, and the mean values of logarithmic transformed values were compared with the analysis of variance.…”

Section: Performance Discussion and Comparative Resultsmentioning

confidence: 99%

“…where k(W ) is the difference in entropy of a random variable W with support S and is calculated by: k (W ) = − S u W (w) log u W (w)dw [27], [28]. Three entropies in equation ( 2…”

Section: Figure 3 Explanation Of the Curves And Intersection Between Themmentioning

confidence: 99%

“…Each dependent entropy calculates the heterogeneity degree in each zone given by the curve C. The more similar the segmented regions, the greater the shared data, and the restricted entropies, which is a qualified property for segmentation [27], [28]. The mutual data is exploited if and only if it is the true segmentation, if, G s…”

Section: Figure 3 Explanation Of the Curves And Intersection Between Themmentioning

confidence: 99%

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A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

Geweid

Abdallah

2019

IEEE Access

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Breast cancer identification is the first and foremost step in the journey of proper diagnosis and treatment of this disease. Therefore, many medical examinations and applications are devised and used including different approaches for breast imaging. For medical image analysis, nonparametric algorithm has been used. This work focuses on improving breast cancer recognition in mammogram images using a nonparametric approach based on image pixel intensities (IBCNP). A nonparametric approach is a new expression to deal with uncertainty. In this paper, the nonparametric approach is used to solve the uncertainty in breast cancer detection; uncertainty means no one knows for sure how to detect breast cancer without imaging, which creates an uncertainty for the oncologists. This paper investigates the feasibility of applying the nonparametric approach in mammography (x-ray breast imaging) to detect and hence diagnose and treat breast cancer. Analysing breast images is a very complicated process and requires distinguishing normal and malignant pixel intensities. Maximizing the shared data between image pixel intensities and the desired areas (infected region) is defined as the identification problem, subject to a constraint on the total size of the desired area borders. As part of this research, a comparative study was conducted on the proposed algorithm, Breast Cancer Cells Infestation based on a set of partial differential equations (PDEs), Incoherent Motion in Breast Cancer (IMBC), Global Dynamics of a Breast Cancer Competition Model using five simple differential equations (SDFs), and Estimation of Intensity Using Nonparametric Method (EINP) techniques. These five techniques of medical image segmentation are applied to 156 different mammogram images to obtain the exact measurement of the efficiency of the identification process. This process uses multiple performance metrics such as the overall mammogram image quality (Q), the dice similarity coefficient (DSC), Peak signalto-noise ratio (PSNR) metric, Haussdorff Distance (HD), and probability of tumor detection (PTD). This paper assumes lack of knowledge of the probability densities in relation to pixel intensities of an image within a given area. It proposes an estimation of non-parametric density that formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm was applied to 156 sets of different mammogram images from twelve groups with both malignant and/or normal. The experimental findings showed that the proposed algorithm is 94.937% accurately effective in breast cancer detection. INDEX TERMSMammograms image, pixel intensities, nonparametric technique, breast cancer detection.

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Section: Performance Discussion and Comparative Resultsmentioning

confidence: 99%

“…where k(W ) is the difference in entropy of a random variable W with support S and is calculated by: k (W ) = − S u W (w) log u W (w)dw [27], [28]. Three entropies in equation ( 2…”

Section: Figure 3 Explanation Of the Curves And Intersection Between Themmentioning

confidence: 99%

Section: Figure 3 Explanation Of the Curves And Intersection Between Themmentioning

confidence: 99%

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A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

Geweid

Abdallah

2019

IEEE Access

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“…Another example is provided in [7], the Bayesian approach is employed to assume a suitable distribution about the unknown clutter covariance matrix, and similar methods also found in [8–10]. Furthermore, there are many non‐parametric detection methods for the problem of performance deterioration in non‐homogeneous clutter environments, such as the permutation tests [11–13] and the rank tests [14, 15]. For instance, in [13], the authors have proposed a new kind of knowledge‐aided non‐parametric detection algorithm in complex non‐homogeneous clutter environments.…”

Section: Introductionmentioning

confidence: 99%

Geometric means and medians with applications to target detection

Hua

Cheng

Wang

et al. 2017

IET signal process.

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This study explores the application of geometric measures‐based means and medians on the Riemannian manifold of Hermitian positive‐definite (HPD) matrix to target detection problems in radar systems. Firstly, the slow‐time dimension of radar received clutter data in each cell is modelled and mapped to HPD matrix space, which can be described as a complex Riemannian manifold. Each point of this manifold is an HPD matrix. Then, several geometric measures are presented for measuring closeness between two HPD matrices. According to these measures, the means and medians of a finite collection of HPD matrices are deduced, and various matrix constant false alarm rate (CFAR) detectors are designed. The principle of target detection is that if a location has enough dissimilarity from the geometric mean or median estimated by its neighbouring locations, targets are supposed to appear at this location. Moreover, different distance measures adopted in detector can result in different performance of detection. These differences are owing to different measure structure, reflected by the anisotropy of a location on the Riemannian manifold. Numerical experiments are given to demonstrate the relationship between anisotropy of the geometric measures and the detection performance of their corresponding matrix CFAR detectors.

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“…Some examples of nonparametric radar detectors can be found in [1][2][3]. In the past, some nonparametric detectors have been applied to radar detection [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

New nonparametric detectors under K-distributed sea clutter in radar applications

Zhao

Ruilai

Huang

et al. 2011

Proceedings of 2011 IEEE CIE International Conference on Radar

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Based on modified rank test, this paper proposes two new nonparametric detectors by using the K-distributed sea clutter model. These two detectors are Modified-Rank-based Generalized Sign detector (MRGS) and Modified-Rank-based Mann-Whitney detector (MRMW). The new detectors are achieved by designing the inverse-normal-score functions of the rank statistics. They are CFAR (Constant False Alarm Rate) for the data with continuous CDF (Cumulative Distribution Function) and with PDF (Probability Density Function) bing symmetric about zero. The performance of the new detectors is analyzed under the Swerling II target model by Monte-Carlo simulation.We compare the results of our proposed detectors with two classical nonparametric detectors: Generalized Sign (GS) detector and Mann-Whitney (MW) Detector. The most important conclusion is that the detectability of nonparametric detector is insensitive to the change of the scale parameter b but just depends on the shape parameter Ȟ of the K-distributed clutter for the Swerling II target model. Moreover, nonparametric detector especially for the proposed MRMW detector in this paper is quite suitable for low observable targets detection under spiky Kdistributed sea clutter.

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Permutation test algorithms for nonparametric radar detection

Cited by 6 publications

References 0 publications

A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

Geometric means and medians with applications to target detection

New nonparametric detectors under K-distributed sea clutter in radar applications

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