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Rough Fuzzy Image Analysis

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advanced medical image segmentation techniques
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B01=James F. Peters
B01=Sankar K. Pal
Category1=Non-Fiction
Category=PBW
Category=THR
Category=TJFM
Category=TQ
Category=UYQ
Category=UYT
computational image analysis
COP=United States
Delivery_Delivery within 10-20 working days
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eq_computing
eq_isMigrated=0
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feature extraction methods
Language_English
mathematical morphology
medical image processing
PA=Available
pattern classification
perceptual similarity measures
Price_€100 and above
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softlaunch

Product details

  • ISBN 9781439803295
  • Weight: 676g
  • Dimensions: 178 x 254mm
  • Publication Date: 04 May 2010
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Description

Fuzzy sets, near sets, and rough sets are useful and important stepping stones in a variety of approaches to image analysis. These three types of sets and their various hybridizations provide powerful frameworks for image analysis. Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and Methodologies introduces the fundamentals and applications in the state of the art of rough fuzzy image analysis.

In the first chapter, the distinguished editors explain how fuzzy, near, and rough sets provide the basis for the stages of pictorial pattern recognition: image transformation, feature extraction, and classification. The text then discusses hybrid approaches that combine fuzzy sets and rough sets in image analysis, illustrates how to perform image analysis using only rough sets, and describes tolerance spaces and a perceptual systems approach to image analysis. It also presents a free, downloadable implementation of near sets using the Near Set Evaluation and Recognition (NEAR) system, which visualizes concepts from near set theory. In addition, the book covers an array of applications, particularly in medical imaging involving breast cancer diagnosis, laryngeal pathology diagnosis, and brain MR segmentation.

Edited by two leading researchers and with contributions from some of the best in the field, this volume fully reflects the diversity and richness of rough fuzzy image analysis. It deftly examines the underlying set theories as well as the diverse methods and applications.
About

Sankar K. Pal is the director and a distinguished scientist of the Indian Statistical Institute in Kolkata.

James F. Peters is a professor in the Department of Electrical and Computer Engineering and group leader of the Computational Intelligence Laboratory at the University of Manitoba in Winnipeg, Canada.