Non‐parametric detector in non‐homogeneous clutter environments with knowledge‐aided permutation test

Zhang, Xuwang; Sun, Jinping; Wang, Guohua

doi:10.1049/iet-rsn.2015.0566

Cited by 8 publications

(7 citation statements)

References 21 publications

(41 reference statements)

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“…Subsequently, the label L C (Y ) can precisely decide whether Y ∈ G i if less improbability G(Y ) is developed, or additional details concerning F (Y ) the description will have. The reciprocal data M F (Y ) :L C (Y ) is used as a criterion of segmentation [26]. The mutual data is formally considered as…”

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confidence: 99%

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: Figure 3 Explanation Of the Curves And Intersection Between Themmentioning

confidence: 99%

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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“…These cheap aircraft mainly fly on the ground and the illegal manipulation on these aircraft can result in serious security issues. However, there are few literatures focusing on the slow moving target detection in the ground clutter [13][14][15][16][17]. A space-time adaptive processing method was proposed to detect the ground moving target with range migration (RM) [14].…”

Section: Introductionmentioning

confidence: 99%

Low-Altitude and Slow-Speed Small Target Detection Based on Spectrum Zoom Processing

Zhang

Sun

Shangguan

2018

Mathematical Problems in Engineering

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This paper proposes a spectrum zoom processing based target detection algorithm for detecting the weak echo of low-altitude and slow-speed small (LSS) targets in heavy ground clutter environments, which can be used to retrofit the existing radar systems. With the existing range-Doppler frequency images, the proposed method firstly concatenates the data from the same Doppler frequency slot of different images and then applies the spectrum zoom processing. After performing the clutter suppression, the target detection can be finally implemented. Through the theoretical analysis and real data verification, it is shown that the proposed algorithm can obtain a preferable spectrum zoom result and improve the signal-to-clutter ratio (SCR) with a very low computational load.

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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%

“…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. This method incorporates the non‐parametric permutation test with a data selector, exploits the prior geographic information system to select the reference cells, can significantly improve detection performance in practice.…”

Section: Introductionmentioning

confidence: 99%

“…It has not been based upon the Neyman–Pearson lemma, but was produced based upon properties of Gaussian processes. In this sense, our detection method is different from the normalised matched filter detector and the non‐parametric permutation test detection method [13], which use the Neyman–Pearson criterion. The goal of this paper is to devise geometric measures‐based matrix CFAR detectors, and investigate the relationship between the detection performance and the anisotropy of the geometric measure.…”

Section: Introductionmentioning

confidence: 99%

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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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Non‐parametric detector in non‐homogeneous clutter environments with knowledge‐aided permutation test

Cited by 8 publications

References 21 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

Low-Altitude and Slow-Speed Small Target Detection Based on Spectrum Zoom Processing

Geometric means and medians with applications to target detection

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