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Article

Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning

1
Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Department of Electrical and Computer Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
*
Author to whom correspondence should be addressed.
Received: 20 November 2020 / Revised: 20 December 2020 / Accepted: 23 December 2020 / Published: 7 January 2021
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breast over a reconstruction model consisting of discrete elements. The breast tissue types are characterized by their dielectric properties, so the complex permittivity profile that is reconstructed may be used to distinguish different tissue types. This manuscript presents a robust and flexible medical image segmentation technique to partition microwave breast images into tissue types in order to facilitate the evaluation of image quality. The approach combines an unsupervised machine learning method with statistical techniques. The key advantage for using the algorithm over other approaches, such as a threshold-based segmentation method, is that it supports this quantitative analysis without prior assumptions such as knowledge of the expected dielectric property values that characterize each tissue type. Moreover, it can be used for scenarios where there is a scarcity of data available for supervised learning. Microwave images are formed by solving an inverse scattering problem that is severely ill-posed, which has a significant impact on image quality. A number of strategies have been developed to alleviate the ill-posedness of the inverse scattering problem. The degree of success of each strategy varies, leading to reconstructions that have a wide range of image quality. A requirement for the segmentation technique is the ability to partition tissue types over a range of image qualities, which is demonstrated in the first part of the paper. The segmentation of images into regions of interest corresponding to various tissue types leads to the decomposition of the breast interior into disjoint tissue masks. An array of region and distance-based metrics are applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed. The incorporation of the segmentation that results in a framework that effectively furnishes the quantitative assessment of regions that contain a specific tissue is also demonstrated. The algorithm is applied to reconstructed microwave images derived from breasts with various densities and tissue distributions to demonstrate the flexibility of the algorithm and that it is not data-specific. The potential for using the algorithm to assist in diagnosis is exhibited with a tumor tracking example. This example also establishes the usefulness of the approach in evaluating the performance of the reconstruction algorithm in terms of its sensitivity and specificity to malignant tissue and its ability to accurately reconstruct malignant tissue. View Full-Text
Keywords: breast imaging; microwave imaging; image reconstruction; segmentation; unsupervised machine learning; k-means clustering; Kolmogorov-Smirnov hypothesis test; statistical inference; performance metrics; contrast source inversion breast imaging; microwave imaging; image reconstruction; segmentation; unsupervised machine learning; k-means clustering; Kolmogorov-Smirnov hypothesis test; statistical inference; performance metrics; contrast source inversion
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MDPI and ACS Style

Kurrant, D.; Omer, M.; Abdollahi, N.; Mojabi, P.; Fear, E.; LoVetri, J. Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning. J. Imaging 2021, 7, 5. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010005

AMA Style

Kurrant D, Omer M, Abdollahi N, Mojabi P, Fear E, LoVetri J. Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning. Journal of Imaging. 2021; 7(1):5. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010005

Chicago/Turabian Style

Kurrant, Douglas; Omer, Muhammad; Abdollahi, Nasim; Mojabi, Pedram; Fear, Elise; LoVetri, Joe. 2021. "Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning" J. Imaging 7, no. 1: 5. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010005

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