Frequency Domain Skin Artifact Removal Method for Ultra-Wideband Breast Cancer Detection

Maskooki, Arash; Soh, Christina; Gunawan, E.; Low, Kay Soon

doi:10.2528/pier09101302

Cited by 29 publications

(28 citation statements)

References 16 publications

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“…The Frequency Domain Pole Splitting artifact removal algorithm was originally proposed by Maskooki et al [14]. The principle of this algorithm is to represent the frequency response of each received radar signal as a sum of complex exponentials, where each complex exponential represents a pole of the system and each pole corresponds to a specific scatterer in the view of the antenna.…”

Section: Frequency Domain Pole Splittingmentioning

confidence: 99%

Artifact Removal Algorithms for Microwave Imaging of the Breast

Elahi¹,

Glavin²,

Jones³

et al. 2013

PIER

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Abstract-One of the most promising alternative imaging modalities for breast cancer detection involved the use of microwave radar systems. A critical component of any radar-based imaging system for breast cancer detection is the early-stage artifact removal algorithm. Many existing artifact removal algorithms are based on simplifying assumptions about the degree of commonality in the artifact across all channels. However, several real-world clinical scenarios could result in greater variation in the early-stage artifact, making the artifact removal process much more difficult. In this study, a range of existing artifact removal algorithms, coupled with algorithms adapted from Ground Penetrating Radar applications, are compared across a range of appropriate performance metrics.

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Section: Frequency Domain Pole Splittingmentioning

confidence: 99%

Artifact Removal Algorithms for Microwave Imaging of the Breast

Elahi¹,

Glavin²,

Jones³

et al. 2013

PIER

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“…Consequently, the autocorrelation matrix is replaced with a low rank approximation. The generalized cross validation (GCV) method is used to estimate the rank of R [9]. This technique starts by applying eigenvalue decomposition to the autocorrelation matrix as shown,…”

Section: Skin Subtractionmentioning

confidence: 99%

“…The first of these is a frequency domain approach which involves modeling the frequency response of the system and eliminating poles of the response corresponding to the skin reflection [9]. While promising results were obtained when tested on 3D computational models, criteria for determining the poles corresponding to the skin response must be identified.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood-Based Algorithm to Facilitate the Reduction of Skin Reflections in Radar-Based Microwave Imaging

Maklad¹,

Curtis²,

Fear³

et al. 2012

PIER B

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Abstract-Radar-based microwave imaging is being investigated as a complementary diagnostic tool for breast cancer detection. One of the major challenges associated with radar-based breast imaging is the removal of the overwhelming reflection caused by the skin. This paper presents an algorithm that has been designed for realistic 3D scenarios. The algorithm is tested on a variety of realistic 3D numerical breast models, as well as measured data from a phantom and patient. In all cases, the reflections from the skin are significantly reduced, facilitating detection of known tumors.

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“…Preprocessing is an indispensable stage, because it can reduce false-alarm rate and increase detection rate through suppressing background clutter and enhancing target signature. So far, a lot of preprocessing algorithms have been brought up, some focus on space domain and some care about frequency domain [1][2][3][4][5], such as two-dimensional least mean square (TDLMS) filter [6], morphological filter [7], high-pass filter [8], median filter [9], nonlinear filter [10,11], local variance weighted information entropy (WIE) filter [12]. It is well known that performance evaluation is an essential part for an effective algorithm, so we focus on evaluating the performance of preprocessing algorithms for IR small target images.…”

Section: Introductionmentioning

confidence: 99%

Metrics for Performance Evaluation of Preprocessing Algorithms in Infrared Small Target Images

Diao¹,

Mao²,

Gui³

2011

PIER

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Abstract-Image preprocessing is commonly used in infrared (IR) small target detection to suppress background clutter and enhance target signature. To evaluate the performance of preprocessing algorithms, two performance metrics, namely PFTN (potential false targets number) decline ratio and BRI (background relative intensity) decline ratio are developed in this paper. The proposed metrics evaluate the performance of given preprocessing algorithm by comparing the qualities of input and output images. The new performance metrics are based on the theories of PFTN and BRI, which describe the quality of IR small target image, by representing the difficulty degree of target detection. Theoretical analysis and experimental results show that the proposed performance metrics can accurately reflect the effect of the image preprocessing stage on reducing false alarms and target shielding. Compared to the traditional metrics, such as signal-to-noise ratio gain and background suppression factor, the new ones are more intuitive and valid.

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Frequency Domain Skin Artifact Removal Method for Ultra-Wideband Breast Cancer Detection

Cited by 29 publications

References 16 publications

Artifact Removal Algorithms for Microwave Imaging of the Breast

Artifact Removal Algorithms for Microwave Imaging of the Breast

Neighborhood-Based Algorithm to Facilitate the Reduction of Skin Reflections in Radar-Based Microwave Imaging

Metrics for Performance Evaluation of Preprocessing Algorithms in Infrared Small Target Images

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