Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images

Delia Mitrea, Sergiu Nedevschi, Monica Platon-Lupsor, Radu Badea

2016

Abstract

This chapter describes specific, texture-based methods for the detection, characterization and recognition of some severe affections and of their evolution phases, using only information from ultrasound images. We perform the recognition of the considered affections in supervised manner, and we also discover the disease evolution phases in unsupervised manner. In both cases, the imagistic textural model is defined, consisting of: the relevant features for the characterization of the disease, respectively of its evolution phase; the specific values of the relevant textural features: arithmetic mean, standard deviation, probability distribution. Advanced texture analysis techniques, consisting of textural microstructure co-occurrence matrices based on Laws’ features, are involved in this process. At the end, the imagistic textural model is validated through powerful, supervised classifiers, the resulting accuracy being around 90%.

Paper Citation

in Harvard Style

Mitrea D., Nedevschi S., Platon-Lupsor M. and Badea R. (2016). Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images.In European Project Space on Intelligent Technologies, Software engineering, Computer Vision, Graphics, Optics and Photonics - Volume 1: EPS Rome 2016, ISBN 978-989-758-206-6, pages 32-55. DOI: 10.5220/0007903700320055

in Bibtex Style

@conference{eps rome 201616,
author={Delia Mitrea and Sergiu Nedevschi and Monica Platon-Lupsor and Radu Badea},
title={Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images},
booktitle={European Project Space on Intelligent Technologies, Software engineering, Computer Vision, Graphics, Optics and Photonics - Volume 1: EPS Rome 2016,},
year={2016},
pages={32-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007903700320055},
isbn={978-989-758-206-6},
}

in EndNote Style

TY - CONF

JO - European Project Space on Intelligent Technologies, Software engineering, Computer Vision, Graphics, Optics and Photonics - Volume 1: EPS Rome 2016,
TI - Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images
SN - 978-989-758-206-6
AU - Mitrea D.
AU - Nedevschi S.
AU - Platon-Lupsor M.
AU - Badea R.
PY - 2016
SP - 32
EP - 55
DO - 10.5220/0007903700320055