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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