Artifact Removal Algorithms for Microwave Imaging of the Breast

Elahi, Muhammad Adnan; Glavin, Martin; Jones, Edward; O'Halloran, and Martin

doi:10.2528/pier13052407

Cited by 37 publications

(40 citation statements)

References 15 publications

(18 reference statements)

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“…This algorithm requires no a priori information and brings no distortion to the useful data. However, as reported in [4], it often fails to accurately estimate the exact portion of signals containing the artifacts. SVD-singular value decomposition: SVD has been used for clutter reduction in GPR (ground-penetrating radar) and through-wall imaging [26].…”

Section: Preprocessing Phasementioning

confidence: 99%

“…The following main approaches for artifact removal in the field on MWI can be found in the literature [4]:…”

Section: Preprocessing Phasementioning

confidence: 99%

“…SVD-singular value decomposition: SVD has been used for clutter reduction in GPR (ground-penetrating radar) and through-wall imaging [26]. Moreover, it has been considered in the comparison performed in [4] for breast cancer. This algorithm is tightly related to the algorithm here proposed.…”

Section: Preprocessing Phasementioning

confidence: 99%

“…PCA has been used for artifact removal in land mines and through the wall UWB radar applications [10,26], but it has not been considered and optimized for stroke detection. For breast cancer detection, the singular value decomposition (SVD) has been used in [4] to separate the artifacts and lesion subspaces and then remove the artifacts. However, as it will be shown in this paper, the algorithm considered in [4] does not perform well in case of stroke detection.…”

Section: Introductionmentioning

confidence: 99%

“…For breast cancer detection, the singular value decomposition (SVD) has been used in [4] to separate the artifacts and lesion subspaces and then remove the artifacts. However, as it will be shown in this paper, the algorithm considered in [4] does not perform well in case of stroke detection.…”

Section: Introductionmentioning

confidence: 99%

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PCA-based artifact removal algorithm for stroke detection using UWB radar imaging

Ricci

Domenico

Cianca

et al. 2016

Med Biol Eng Comput

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Section: Preprocessing Phasementioning

confidence: 99%

“…The following main approaches for artifact removal in the field on MWI can be found in the literature [4]:…”

Section: Preprocessing Phasementioning

confidence: 99%

Section: Preprocessing Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

PCA-based artifact removal algorithm for stroke detection using UWB radar imaging

Ricci

Domenico

Cianca

et al. 2016

Med Biol Eng Comput

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Skin artifact removal technique for breast cancer radar detection

Caorsi

Lenzi

2016

Radio Science

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In this paper we propose a new model‐based skin artifact cleaning technique with the aim to remove skin reflections with good effectiveness, without introducing significant signal distortions, and without assuming a priori information on the real structure of the breast. The reference cleaning model, constituted by a two‐layer geometry skin‐adipose tissue, is oriented to all the ultrawideband radar methods able to detect the tumor starting by the knowledge of each trace recorded around the breast. All the radar signal measurements were simulated by using realistic breast models derived from the University of Wisconsin computational electromagnetic laboratory database and the finite difference time domain (FDTD)‐based open source software GprMax. First, we have searched for the best configuration for the reference cleaning model with the aim to minimize the distortions introduced on the radar signal. Second, the performance of the proposed cleaning technique has been assessed by using a breast cancer radar detection technique based on the use of artificial neural network (ANN). In order to minimize the signal distortions, we found that it was necessary to use the real skin thickness and the static Debye parameters of both skin and adipose tissue. In such a case the ANN‐based radar approach was able to detect the tumor with an accuracy of 87%. By extending the performance assessment also to the case when only average standard values are used to characterize the reference cleaning model, the detection accuracy was of 84%.

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