Raquel C. Conceição

University of Lisbon

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Martin O’Halloran

Ollscoil na Gaillimhe – University of Galway

Daniela M. Godinho

University of Lisbon

Edward Jones

Ollscoil na Gaillimhe – University of Galway

Martin Glavin

Ollscoil na Gaillimhe – University of Galway

Carlos A. Fernandes

Instituto de Telecomunicações

João M. Felício

Instituto de Telecomunicações

Emily Porter

McGill University

Tiago Castela

Hospital da Luz

Matteo Savazzi

Instituto de Telecomunicações

Ana Catarina Pelicano

University of Lisbon

Cooperative Institutions

University of Lisbon

Ollscoil na Gaillimhe – University of Galway

National University of Ireland

University of Aveiro

Universidade Nova de Lisboa

Instituto Politécnico de Lisboa

Sapienza University of Rome

King's College London

University of Malta

University Hospital Galway

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Avoiding unnecessary breast biopsies: Clinically-informed 3D breast tumour models for microwave imaging applications

Bárbara Luz Oliveira Martin Glavin Edward Jones Martin O’Halloran Raquel C. Conceição

By using anatomically accurate models, the potential to improve microwave imaging as a detection and classification technique for breast cancer is very promising. This paper proposes a novel, clinically-informed approach to 3D modelling of breast tumours that significantly enhances flexibility in setting tumour parameters compared to existing approaches.

Microwave Imaging

Breast imaging

10.1109/aps.2014.6904898

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Citations (8)

Preliminary development of anatomically realistic breast tumor models for microwave imaging

2022 16th European Conference on Antennas and Propagation (EuCAP) (2022)

Ana Catarina Pelicano N. A. M. Araújo Raquel C. Conceição

In this work, we present a preliminary study of three classifiers - Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs) and $k$ -Nearest Neighbors ( $k$ NN) - to differentiate between malignant and benign tumors extracted from Magnetic Resonance (MR) images, based on their morphological features. The dataset in this study comprises 24 tumors: 12 malignant and 12 benign. Twelve morphological features were initially considered for tumor classification. The Mann-Whitney test was employed for feature selection, and the performance of the classifiers was evaluated with accuracy, sensitivity, specificity, F1-score and Matthew's Correlation Coefficient (MCC) metrics. kNN (with $k=6$ and Chebyshev distance) outperformed the other classifiers with an accuracy, sensitivity, and specificity of 87.5%, 83.3% and 91.7%, respectively.

10.23919/eucap53622.2022.9769591

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EFFECTS OF DIELECTRIC HETEROGENEITY IN THE PERFORMANCE OF BREAST TUMOUR CLASSIFIERS

Progress In Electromagnetics Research M (2011)

Raquel C. Conceição Martin O’Halloran Martin Glavin Edward Jones

Breast cancer detection using Ultra Wideband Radar has been thoroughly investigated over the last decade. This breast imaging modality is based on the dielectric properties of normal and cancerous breast tissue at microwave frequencies. However, the dielectric properties of benign and malignant tumours are very similar, so tumour classiflcation based on dielectric properties alone is not feasible. Therefore, classiflcation methods based on the Radar Target Signature of tumours need to be further developed to classify tumours as either benign or malignant. Several studies have addressed the issue of tumour classiflcation based on the size, shape and surface texture of the tumour. In general, these studies examined the performance of classiflcation algorithms in primarily dielectrically homogeneous breast models. These relatively simplistic models do not provide a realistic test platform for the evaluation of tumour classiflcation algorithms. This paper examines the classiflcation of tumours under realistic dielectrically heterogeneous conditions. Four difierent heterogeneous scenarios are considered, with varying levels of heterogeneity and complexity. In this paper, the performance and robustness of tumour classiflcation algorithms under these realistic conditions are examined and discussed.

10.2528/pierm10122402

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Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis

Sensors (2021)

Ana Catarina Pelicano Maria C. T. Gonçalves Daniela M. Godinho Tiago Castela M. Lurdes Orvalho

Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.

Breast MRI

Breast imaging

Microwave Imaging

10.3390/s21248265

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Citations (14)

Initial study for the investigation of breast tumour response with classification algorithms using a microwave radar prototype

2022 16th European Conference on Antennas and Propagation (EuCAP) (2016)

Raquel C. Conceição Dallan Byrne J. Alison Noble Ian Craddock

In this paper classification algorithms will be used to investigate the presence of tumours in the breast, from signals collected with a radar microwave imaging prototype from the University of Bristol. A number of features will be extracted from the scattering of breast tumours and will then be used in classification algorithms such as Linear Discriminant Analysis or Quadratic Discriminant Analysis. The results from the classifier will allow creating an image of the considered synthetic breast phantom in which normal breast tissue is classified as a "miss" and tumour tissue is classified as a "hit".

Microwave Imaging

Quadratic classifier

Contextual image classification

Statistical classification

BI-RADS

10.1109/eucap.2016.7481464

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Citations (4)

Impact of Antenna Configuration and Artefact Removal Algorithms for Axillary Microwave Imaging

Daniela M. Godinho Pedro L. Cidadão Raquel C. Conceição

Microwave Imaging

10.46620/ursiatrasc24/dyhb1389

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Breast MRI Multi-tumor Segmentation Using 3D Region Growing

Lecture notes in computer science (2023)

Teresa M. C. Pereira Ana Catarina Pelicano Daniela M. Godinho Maria C. T. Gonçalves Tiago Castela

Region growing

Normalization

Modality (human–computer interaction)

10.1007/978-3-031-49249-5_2

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Effect of Varying Prior Information in Axillary 2D Microwave Tomography

2022 16th European Conference on Antennas and Propagation (EuCAP) (2022)

Matteo Savazzi Olympia Karadima João M. Felício Carlos A. Fernandes Panagiotis Kosmas

We numerically assess the potential of microwave tomography (MWT) for the detection and dielectric properties estimation of axillary lymph nodes (ALNs), and we study the robustness of our system using prior information with varying levels of accuracy. We adopt a 2-dimensional MWT system with 8 antennas (0.5-2.5 GHz) placed around the axillary region. The reconstruction algorithm implements the distorted Born iterative method. We show that: (i) when accurate prior knowledge of the axillary tissues (fat and muscle) is available, our system successfully detects an ALN; (ii) ±30% error in the prior estimation of fat and muscle dielectric properties does not affect image quality; (iii) ±7mm error in muscle position causes slight artifacts, while ± 14mm error in muscle position affects ALN detection. To the best of our knowledge, this is the first paper in the literature to study the impact of prior information accuracy on detecting an ALN using MWT.

Robustness

Microwave Imaging

Prior information

Position (finance)

10.23919/eucap53622.2022.9769372

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Citations (2)

FDTD MODELING OF THE BREAST: A REVIEW

Progress In Electromagnetics Research B (2009)

Martin O’Halloran Raquel C. Conceição Dallan Byrne Martin Glavin Edward Jones

Microwave imaging is one of the most promising emerging imaging technologies for breast cancer detection. Microwave imaging exploits the dielectric contrast between normal and malignant breast tissue at microwave frequencies. Many UWB radar imaging techniques require the development of accurate numerical phantoms to model the propagation and scattering of microwave signals within the breast. The Finite Difierence Time Domain (FDTD) method is the most commonly used numerical modeling technique used to model the propagation of Electromagnetic (EM) waves in biological tissue. However, it is critical that an FDTD model accurately represents the dielectric properties of the constituent tissues and the highly correlated distribution of these tissues within the breast. This paper presents a comprehensive review of the dielectric properties of normal and cancerous breast tissue, and the heterogeneity of normal breast tissue. Furthermore, existing FDTD models of the breast are examined and compared. This paper provides a basis for the development of more geometrically and dielectrically accurate numerical breast phantoms used in the development of robust microwave imaging algorithms.

10.2528/pierb09080505

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Citations (34)

Development of MRI‐based axillary numerical models and estimation of axillary lymph node dielectric properties for microwave imaging

Medical Physics (2021)

Daniela M. Godinho João M. Felício Tiago Castela Nuno André da Silva Maria de Lurdes Orvalho

Microwave imaging (MWI) has been studied as a complementary imaging modality to improve sensitivity and specificity of diagnosis of axillary lymph nodes (ALNs), which can be metastasized by breast cancer. The feasibility of such a system is based on the dielectric contrast between healthy and metastasized ALNs. However, reliable information such as anatomically realistic numerical models and matching dielectric properties of the axillary region and ALNs, which are crucial to develop MWI systems, are still limited in the literature. The purpose of this work is to develop a methodology to infer dielectric properties of structures from magnetic resonance imaging (MRI), in particular, ALNs. We further use this methodology, which is tailored for structures farther away from MR coils, to create MRI-based numerical models of the axillary region and share them with the scientific community, through an open-access repository.We use a dataset of breast MRI scans of 40 patients, 15 of them with metastasized ALNs. We apply image processing techniques to minimize the artifacts in MR images and segment the tissues of interest. The background, lung cavity, and skin are segmented using thresholding techniques and the remaining tissues are segmented using a K-means clustering algorithm. The ALNs are segmented combining the clustering results of two MRI sequences. The performance of this methodology was evaluated using qualitative criteria. We then apply a piecewise linear interpolation between voxel signal intensities and known dielectric properties, which allow us to create dielectric property maps within an MRI and consequently infer ALN properties. Finally, we compare healthy and metastasized ALN dielectric properties within and between patients, and we create an open-access repository of numerical axillary region numerical models which can be used for electromagnetic simulations.The proposed methodology allowed creating anatomically realistic models of the axillary region, segmenting 80 ALNs and analyzing the corresponding dielectric properties. The estimated relative permittivity of those ALNs ranged from 16.6 to 49.3 at 5 GHz. We observe there is a high variability of dielectric properties of ALNs, which can be mainly related to the ALN size and, consequently, its composition. We verified an average dielectric contrast of 29% between healthy and metastasized ALNs. Our repository comprises 10 numerical models of the axillary region, from five patients, with variable number of metastasized ALNs and body mass index.The observed contrast between healthy and metastasized ALNs is a good indicator for the feasibility of a MWI system aiming to diagnose ALNs. This paper presents new contributions regarding anatomical modeling and dielectric properties' characterization, in particular for axillary region applications.

Microwave Imaging

10.1002/mp.15143

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Citations (10)