Novel methods of image segmentation based on data field

Abstract

The paper introduces data field theory into the field of image segmentation, and proposes novel methods of image segmentation. Simulating some physical fields, such as electromagnetic fields, the fields of gravity, the fields of nuclear force etc., each data point is considered as a material particle with radiate ability, and the interaction of all data points forms a data field. Each image pixel is considered as a data point, all pixels in an image form an image data field which is the basis of image segmentation. The basic steps of image segmentation approaches based on data field include: (1) generate image data field; (2) optimize and adjust influence factor; (3) implement image segmentation based on the optimal image data field. Two kinds of methods are proposed in this paper. One is the method of interactive image segmentation: directly give the isopotential threshold value of the interesting target object, and implement image segmentation and extract the special object; the other one is the method of automatic segmentation: automatically segment the whole image based on the given classification number. Some experiments and analysis confirm the validity of the proposed methods.