An adaptive window-based hybrid artifact removal method for Ultra-Wide Band imaging enhancement of early breast cancer detection. (September 2021)

• Record Type: Journal Article
• Authors: Liu, Guancong; Xiao, Xia; Song, Hang; Lu, Min; Kikkawa, Takamaro
• Is Part Of: Biomedical signal processing and control. Volume 70(2021)
• Publication Date: 2021-09
• Subjects: UWB -- Breast cancer detection -- Adaptive time window -- Two-stage filtering -- S-G smoothing -- Microwave imaging enhancement; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102980
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The proposed method is based on multistatic rotational detection theory. Artifacts are suppressed by adaptive time widow dividing and specific calculation. Less irrelevant responses appear in the artifact suppressed signals. Confocal images from different experiments are all significantly enhanced. Abstract: At present, many studies have shown that breast cancer is the primary factor affecting women's health all over the world. In the past decades, ultra-wide band (UWB) microwave imaging has been widely studied as a non-invasive breast cancer detection tool through abundant researches and engineering applications. Generally, the artifact caused by skin and other factors will significantly affect the accuracy of target detection in this technology. Therefore, how to eliminate them is crucial. In this paper, an adaptive window-based hybrid artifact removal (AW-HAR) approach is proposed. It consists of adaptive time window division, revised two-stage filtering, and Savitzky-Golay (S-G) smoothing. This method pre-analyzes the specificity of each channel and divides them into several time windows adaptively. Then adjusted two-stage filtering is carried out on each window to extract partial tumor information. Finally, the fragmented time sequences are integrated and S-G smoothing is applied. The simulation and phantom experiments demonstrate that the AW-HAR has favorable performance in terms of clutter suppression and image clarity. It effectively suppresses surface disturbance and irrelevant response, enhancing the region of interest (ROI) in microwave imaging.