Image segmentation is the problem of determining a partition of an image into its "main" areas and identifying these areas as being associated with different types of objects. This problem is of particular importance in medical imaging, where blur may conceal information of critical importance.
The problem is modeled as minimization of a deviation penalty, from the captured colors of the pixels, and a separation penalty, which is associated with two adjacent images having different colors.

We describe a very efficient and best possible polynomial time algorithm for the problem. This algorithm is more efficient than most procedures based on spectral techniques, partitioning approaches or heuristic clustering. We then demonstrate how to apply the procedure for the purpose of de-blurring medical images.