This book describes image segmentation at multiple scales by integrating with different structures. These techniques relying on boundary, textured and non-textured information for image segmentation at multiple scales. This work argues that the issues of scale selection and structure detection cannot be treated separately for segmentation. Soft computing techniques are most suitable for addressing this kind of problems. Fuzzy image segmentation is a task that classifies pixels of an image using different labels so that the image partitioned into non-overlapped labeled regions. In this dissertation fuzzy clustered based techniques are studied and developed Fuzzy Entropy technique, Rule based Type-II fuzzy logic, Edge detection based on gradient fuzzy logic, Generalized Fuzzy C-means and Fuzzy Entropy triangular model for super resolution images. The experiments have been done on well-known image data bases and the results are produced in the form of tables and graphs for objective analysis and outputs of input images are placed for subjective analysis.