Finding optimal transformation function for image thresholding using genetic programming

In this paper, Genetic Programming (GP) is employed to obtain an optimum transformation function for bi-level image thresholding. The GP utilizes a user-prepared gold sample to learn from. A magnificent feature of this method is that it does not require neither a prior knowledge about the modality of the image nor a large training set to learn from. The performance of the proposed approach has been examined on 147 X-ray lung images. The transformed images are thresholded using Otsu´s method and the results are highly promising. It performs successfully on 99% of the tested images. The proposed method can be utilized for other image processing tasks, such as, image enhancement or segmentation.