Fr. 178.00

Fuzzy Sets Methods in Image Processing and Understanding - Medical Imaging Applications

English · Hardback

Shipping usually within 2 to 3 weeks (title will be printed to order)

Description

Read more

This book provides a thorough overview of recent methods using higher level information (object or scene level) for advanced tasks such as image understanding along with their applications to medical images. Advanced methods for fuzzy image processing and understanding are presented, including fuzzy spatial objects, geometry and topology, mathematical morphology, machine learning, verbal descriptions of image content, fusion, spatial relations, and structural representations. For each methodological aspect covered, illustrations from the medical imaging domain are provided. This is an ideal book for graduate students and researchers in the field of medical image processing.

List of contents

Introduction.- Preliminaries.- Fuzzy spatial objects (fuzzy geometry and topology, set theoretic operations).- Mathematical morphology.- Distances and similarities between fuzzy sets.- Machine learning in image processing and understanding.- Fusion.- Spatial relations.- Structural representations.

About the author










Isabelle Bloch is a Professor at Telecom ParisTech.


Anca Ralescu is a Professor at the University of Cincinnati.


Product details

Authors Isabelle Bloch, Anca Ralescu
Publisher Springer, Berlin
 
Languages English
Product format Hardback
Released 02.01.2023
 
EAN 9783031194245
ISBN 978-3-0-3119424-5
No. of pages 302
Dimensions 155 mm x 20 mm x 235 mm
Illustrations XIII, 302 p. 97 illus., 45 illus. in color.
Subject Natural sciences, medicine, IT, technology > Biology > Genetics, genetic engineering

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.