Automated microwave tomography (Mwt) image segmentation: State-of-the-art implementation and evaluation
Artikel i vetenskaplig tidskrift, 2020

Inspired by the high performance in image-based medical analysis, this paper explores the use of advanced segmentation techniques for industrial Microwave Tomography (MWT). Our context is the visual analysis of moisture levels in porous foams undergoing microwave drying. We propose an automatic segmentation technique—MWT Segmentation based on K-means (MWTS-KM) and demonstrate its efficiency and accuracy for industrial use. MWTS-KM consists of three stages: image augmentation, grayscale conversion, and K-means implementation. To estimate the performance of this technique, we empirically benchmark its efficiency and accuracy against two well-established alternatives: Otsu and K-means. To elicit performance data, three metrics (Jaccard index, Dice coefficient and false positive) are used. Our results indicate that MWTS-KM outperforms the well-established Otsu and K-means, both in visually observable and objectively quantitative evaluation.

Image Segmentation



Microwave Tomography


Yuchong Zhang

Chalmers, Data- och informationsteknik, Interaktionsdesign

Yong Ma

Ludwig-Maximilians-Universität München (LMU)

Adel Omrani

Karlsruher Institut für Technologie (KIT)

Rahul Yadav

Itä-Suomen Yliopisto

Morten Fjeld

Chalmers, Data- och informationsteknik, Interaktionsdesign

Marco Fratarcangeli

Chalmers, Data- och informationsteknik, Interaktionsdesign

Journal of WSCG

1213-6972 (ISSN) 1213-6964 (eISSN)

Vol. 2020 2020 126-136



Datorseende och robotik (autonoma system)

Medicinsk bildbehandling



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