A New Image Representation Method Using Nonoverlapping Non-symmetry and Anti-packing Model for Medical Images

Inspired by the idea of the S-Tree Coding (STC) and by extending the popular Gouraud shading approach, in this paper, we propose a new image representation method using the Nonoverlapping Non-symmetry and Antipacking Model (NNAM) for medical images, which is called the NNAM method. Also, a raster-first strategy for searching a rectangle subpattern in the NNAM method is put forward. During the procedure of scanning a rectangle subpptern, the value of the horizontal ordinate is firstly increased, and then the value of the vertical ordinate is increased until the rectangle subpattern finally becomes a non-homogeneous block. By comparing our proposed NNAM method with the conventional STC method, the experimental results presented in this paper show that the former can significantly reduce the bit rate and the number of homogenous blocks than the latter whereas remaining the satisfactory image quality. Also, our proposed NNAM method for medical images, as envisaged in this paper, shows a very strong promise and it has good potential in business applications dealing with image processing, such as reducing storage room, increasing processing speed, and improving pattern match efficiency.