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Segmentation is a process to divides the images into its regions or objects that have similar features or characteristics. Segmentation has no single standard procedure and it is very difficult in non-trivial images. The extent, to which segmentation is carried out, depends on the problem specification. Segmentation algorithms are based on two properties of intensity values- discontinuity and similarity. First property is to partition an image based on the abrupt changes in the intensity and the second is to partition the image into regions that are similar according to a set of predefined criteria. In this paper some methods to detect the discontinuity and similarity of digital images will be discussed. The basic techniques for detecting the gray level discontinuities in a digital images and locating the objects and boundaries of images ( points, lines and edges) have also been discussed. The other segmentation techniques like histogram thresholding, filtration, watershed, edge detection, region growing, region splitting and merging are based on the fact of classification with the usage of range functions that are applied to the intensity value of image pixels.

A Exploratory Review on Soft Computing Segmentation Techniques