Image Classification in Microwave Tomography using a Parametric Intensity Model

Mohanad Alkhodari, Amer Zakaria, Nasser Qaddoumi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

A study is conducted to investigate the use of a parametric intensity model for the process of image classification in biomedical microwave tomography (MWT). This process allows for extracting structural information about an object-of-interest (OI), which can be incorporated as prior information in an inversion algorithm. The parametric intensity model is based on a supervised Gaussian probabilistic model. The generated intensity model is used to classify three cross-sectional MWT images of human lower leg models. The classification is based on a Bayesian decision classifier. The resulting segments are used to extract structural information about the legs’ contour.

Original languageBritish English
Title of host publicationICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728165356
DOIs
StatePublished - 16 Mar 2021
Event4th International Conference on Communications, Signal Processing, and their Applications, ICCSPA 2020 - Sharjah, United Arab Emirates
Duration: 16 Mar 202118 Mar 2021

Publication series

NameICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications
Volume2021-January

Conference

Conference4th International Conference on Communications, Signal Processing, and their Applications, ICCSPA 2020
Country/TerritoryUnited Arab Emirates
CitySharjah
Period16/03/2118/03/21

Keywords

  • Bayesian decision classifier
  • Contrast source inversion
  • Finite-element method
  • Gaussian probability
  • Microwave tomography
  • Parametric intensity model

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