Automatic image segmentation for microwave tomography (MWT): From implementation to comparative evaluation
Paper i proceeding, 2019

Inspired by its high performance in image-based medical analysis, this poster 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, grey-scale 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. Based on our experiments, our results indicate that MWTS-KM outperforms the well-established Otsu and K-means.

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

ACM International Conference Proceeding Series

Vol. 2019 3356437
978-145037626-6 (ISBN)

12th International Symposium on Visual Information Communication and Interaction, VINCI 2019
Shanghai, China,


Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling



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