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An image is digitized to convert it to a form which can be stored in a computer's memory or on some form of storage media such as a hard disk or CD-ROM. Once the image has been digitized, it can be operated upon by various image processing operations like enhancement, restoration, reconstruction, compression. An image defined in the "real world" is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y). An image may be considered to contain sub-images sometimes referred to as regions-of-interest, ROIs, or simply regions. This concept reflects the fact that images frequently contain collections of objects each of which can be the basis for a region. In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition. For performing image processing operations ,the basic structure called skeleton is much more essential and highly adaptive tool. Skeletons are important shape descriptors in object representation and recognition. A skeleton that captures essential topology and shape information of the object in a simple form is extremely useful in solving various problems such as character recognition, 3D model matching and retrieval, and medical image analysis. Medical imaging systems. Due to its compact shape representation, image skeleton has been studied for a long time in computer vision, pattern recognition, and optical character recognition. It is a powerful tool for intermediate representation for a number of geometric operations on solid models. Many image processing applications depend on the skeletons.
Lakshmi et al. (Sun,) studied this question.