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

K-means

Otsu

Microwave Tomography

Författare

Yuchong Zhang

Chalmers, Data- och informationsteknik, Interaktionsdesign (Chalmers)

Yong Ma

Ludwig-Maximilians-Universität München

Adel Omrani

Karlsruher Institut für Technologie (KIT)

Rahul Yadav

Itä-Suomen Yliopisto

Morten Fjeld

Chalmers, Data- och informationsteknik, Interaktionsdesign (Chalmers)

Marco Fratarcangeli

Chalmers, Data- och informationsteknik, Interaktionsdesign (Chalmers)

Journal of WSCG

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

Vol. 2020 2020 126-136

Ämneskategorier

Mediateknik

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling

DOI

10.24132/CSRN.2020.3001.15

Mer information

Senast uppdaterat

2020-12-29