Automated microwave tomography (Mwt) image segmentation: State-of-the-art implementation and evaluation
Journal article, 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

Author

Yuchong Zhang

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design (Chalmers)

Yong Ma

Ludwig-Maximilian University of Munich

Adel Omrani

Karlsruhe Institute of Technology (KIT)

Rahul Yadav

University of Eastern Finland

Morten Fjeld

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design (Chalmers)

Marco Fratarcangeli

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design (Chalmers)

Journal of WSCG

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

Vol. 2020 2020 126-136

Subject Categories

Media Engineering

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

DOI

10.24132/CSRN.2020.3001.15

More information

Latest update

12/29/2020