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A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms

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3 Author(s)
Mingrui Zhang ; Dept. of Comput. Sci., Winona State Univ., MN, USA ; L. O. Hall ; D. B. Goldgof

Segmentation of an image into regions and the labeling of the regions is a challenging problem. In this paper, an approach that is applicable to any set of multifeature images of the same location is derived. Our approach applies to, for example, medical images of a region of the body; repeated camera images of the same area; and satellite images of a region. The segmentation and labeling approach described here uses a set of training images and domain knowledge to produce an image segmentation system that can be used without change on images of the same region collected over time. How to obtain training images, integrate domain knowledge, and utilize learning to segment and label images of the same region taken under any condition for which a training image exists is detailed. It is shown that clustering in conjunction with image processing techniques utilizing an iterative approach can effectively identify objects of interest in images. The segmentation and labeling approach described here is applied to color camera images and two other image domains are used to illustrate the applicability of the approach.

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IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:32 ,  Issue: 5 )