Automatic image segmentation for microwave tomography (MWT): From implementation to comparative evaluation
Conference poster, 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.

K-means

Microwave tomography

Image segmentation

Otsu

Author

Yuchong Zhang

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

Yong Ma

Ludwig-Maximilians-Universität München

Adel Omrani

Karlsruhe Institut für Technologie

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)

VINCI'2019: Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Go to ACM Other conferences homepage September 2019 Read More ACM2019 Proceeding
Shanghai, China,

Subject Categories

Computer Science

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

DOI

10.1145/3356422.3356437

More information

Latest update

1/10/2020