Rough Fuzzy Image Analysis: Foundations and Methodologies

Sankar K. Pal, James F. Peters

June 30, 2017 by CRC Press
Reference - 266 Pages - 113 B/W Illustrations
ISBN 9781138116238 - CAT# K35474
Series: Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series


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  • Focuses on fuzzy, near, and rough sets, showing how they are different incarnations of Cantor sets
  • Presents a new approach to image analysis using near sets and tolerance spaces
  • Gives a complete implementation of near sets and offers the NEAR system for download on
  • Explores how a rough fuzzy clustering algorithm is used in brain image segmentation and how rough fuzzy hybrids are applied in the analysis of breast cancer images


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.

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