Abstract
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.
Highlights
IntroductionFemale breast cancer was the most common cancer diagnosed worldwide in 2020, with over 2.26 million new cases [1]
Introduction published maps and institutional affilFemale breast cancer was the most common cancer diagnosed worldwide in 2020, with over 2.26 million new cases [1]
This paper is organised as follows: firstly, we present related work already conducted concerning breast and breast tumour models; we detail the materials used and the methodology developed for image pre-processing, segmentation, and estimation of dielectric properties; we present the results of our proposed methodology, followed by a discussion, and we highlight the main conclusions of our work
Summary
Female breast cancer was the most common cancer diagnosed worldwide in 2020, with over 2.26 million new cases [1]. Breast cancer was reported the fifth deadliest type of cancer in 2020, and the cancer with the highest mortality rate in the female population [1]. The most common imaging modality for breast cancer detection is X-ray mammography [2,3]. Mammography is still the go-to imaging method for cancer screening, it does not provide reliable results for women with dense breasts, which are common among younger women [4]. Specificity has been reported to range from 89.1% to 96.9% for the same breast types [5].
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