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Fuzzy sets methods in image processing and understandingmedical imaging applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fuzzy sets methods in image processing and understandingby Isabelle Bloch, Anca Ralescu.
Reminder of title:	medical imaging applications /
Author:	Bloch, Isabelle.
other author:	Ralescu, Anca L.
Published:	Cham :Springer International Publishing :2023.
Description:	xiii, 302 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Image processingMathematics.
Online resource:	https://doi.org/10.1007/978-3-031-19425-2
ISBN:	9783031194252$q(electronic bk.)

Fuzzy sets methods in image processing and understandingmedical imaging applications /
Bloch, Isabelle.

Fuzzy sets methods in image processing and understandingmedical imaging applications /[electronic resource] :by Isabelle Bloch, Anca Ralescu. - Cham :Springer International Publishing :2023. - xiii, 302 p. :ill., digital ;24 cm.

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.

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. This book also: Enriches reader understanding of the latest fuzzy sets methods using higher level information (object or scene level) for advanced tasks, such as image understanding Shares many insights into fuzzy spatial objects, including fuzzy geometry and topology as well as set theoretical operations Covers in detail machine learning in image processing and understanding, with special focus on clustering, classification, and basic and advanced fuzzy methods.

ISBN: 9783031194252$q(electronic bk.)

Standard No.: 10.1007/978-3-031-19425-2doiSubjects--Topical Terms:

184606
Image processing
--Mathematics.

LC Class. No.: TA1637.5 / .B56 2023

Dewey Class. No.: 621.367015113223

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505 0 $a 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.
520 $a 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. This book also: Enriches reader understanding of the latest fuzzy sets methods using higher level information (object or scene level) for advanced tasks, such as image understanding Shares many insights into fuzzy spatial objects, including fuzzy geometry and topology as well as set theoretical operations Covers in detail machine learning in image processing and understanding, with special focus on clustering, classification, and basic and advanced fuzzy methods.
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