Image segmentation involves the separation of mutually exclusive regions/objects of interest (Gonzalez and Woods, 2002), and is integral to the image processing, coding and interpretation domains, with examples of some of the eclectic range of applications including: image analysis, robot vision, automatic car assembly, security surveillance systems, object recognition and medical imaging (Gonzalez and Woods, 2002; Hoppner et al., 1999; Pham and Prince, 1999; Gath and Geva, 1989; Pal and Pal, 1993). As there are potentially a very large number of perceptual objects in an image, with subtle variations between them, this makes generalised object-based segmentation an especially challenging task.
The performance of clustering algorithms for image segmentation are highly sensitive to the features used and types of objects in the image, which ultimately limits their generalization capability. This provides strong motivation to investigate integrating shape information into the clustering framework to improve the generality of these algorithms. Existing shape-based clustering techniques mainly focus on circular and elliptical clusters and so are unable to segment arbitrarily-shaped objects. To address this limitation, this paper presents a new shape-based algorithm called fuzzy clustering for image segmentation using generic shape information (FCGS), which exploits the B-spline representation of an object’s shape in combination with the Gustafson-Kessel clustering algorithm. Qualitative and quantitative results for FCGS confirm its superior segmentation performance consistently compared to well-established shape-based clustering techniques, for a wide range of test images comprising various regular and arbitrary-shaped objects