UWB device for breast microwave imaging: phantom and clinical validations

Vispa, Alessandro; Sani, Lorenzo; Paoli, Martina; Bigotti, Alessandra; Raspa, Giovanni; Ghavami, Navid; Caschera, Stefano; Ghavami, Mohammad; Duranti, Michele; Tiberi, Gianluigi

doi:10.1016/j.measurement.2019.05.109

Cited by 59 publications

(48 citation statements)

References 23 publications

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“…The hub is internally covered by microwave absorbers, and is equipped with a hole with a cup, allowing the insertion of the object to be imaged. The antennas are installed at the same height, in free space and can rotate around the azimuth in order to collect microwave signals in a multi-bistatic fashion from different angular positions [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

“…If the artefact is not removed fully, the inclusions (e.g., the strokes or tumours) will be masked, and thus the detection will not be accurate. Recently, a portable MWI device, which operates in free space with two azimuthally rotating antennas, has been developed and employed for breast cancer detection [ 13 ]. More specifically, the device consists of two antennas which rotate around the breast to collect signals in a multi-bistatic fashion.…”

Section: Introductionmentioning

confidence: 99%

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Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection

Sohani

Puttock

Khalesi

et al. 2020

Sensors

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In this paper, we present an investigation of different artefact removal methods for ultra-wideband Microwave Imaging (MWI) to evaluate and quantify current methods in a real environment through measurements using an MWI device. The MWI device measures the scattered signals in a multi-bistatic fashion and employs an imaging procedure based on Huygens principle. A simple two-layered phantom mimicking human head tissue is realised, applying a cylindrically shaped inclusion to emulate brain haemorrhage. Detection has been successfully achieved using the superimposition of five transmitter triplet positions, after applying different artefact removal methods, with the inclusion positioned at 0°, 90°, 180°, and 270°. The different artifact removal methods have been proposed for comparison to improve the stroke detection process. To provide a valid comparison between these methods, image quantification metrics are presented. An “ideal/reference” image is used to compare the artefact removal methods. Moreover, the quantification of artefact removal procedures through measurements using MWI device is performed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection

Sohani

Puttock

Khalesi

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The device was employed to image 22 healthy patients and 29 patients with abnormalities. The team reported a true positive rate of 0.7 and false negative rate of 0.35 [187]. Researchers from Kobe University led by Kenjiro Kimura have developed a novel device for 3D-microwave mammography [188].…”

Section: State-of-the-art In Mwimentioning

confidence: 99%

An Overview of Microwave Imaging for Breast Tumor Detection

Benny¹,

Anjit²,

Mythili³

2020

PIER B

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Microwave imaging (MWI) is a non-ionizing, non-invasive and an upcoming affordable medical imaging modality. Over the last few decades, MWI has invited active research towards biomedical imaging, with special focus on breast tumor detection. After long years of intense research and clinical trials, a breast tumour monitoring unit based on MWI is finally entering clinical imaging scenarios. In this manuscript, the vast literature in MWI to date has been consolidated, and an indetail study of the state-of-the-art for breast tumor detection has been presented. The hurdles faced during clinical trials are discussed, and their possible solutions and future directions for a fast transition into clinical imaging have been presented. It is hoped that this paper can serve as a guide for MWI researchers and practitioners, especially those new to the field to comprehend the potential of MWI as a viable imaging tool for breast imaging.

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“…There are many existing clinical methods in diagnosing and detecting breast cancers. Common diagnostic methods are mammography, magnetic resonance imaging (MRI) and ultrasound scans [5,[34][35][36]. However, these methods are proven costly, bulky, invasive and are unable to detect the early stages of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

“…Detection of breast cancer in the early stage is very crucial for further medical diagnostics and treatment. Slow detection is indirectly reducing the survival rate of the patients [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

Vijayasarveswari

Andrew

Jusoh

et al. 2020

Preprint

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Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultrawideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi-stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

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UWB device for breast microwave imaging: phantom and clinical validations

Cited by 59 publications

References 23 publications

Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection

Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection

An Overview of Microwave Imaging for Breast Tumor Detection

Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

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