A case study on medical image enhancement based on II curvelet

A method is presented in this paper for medical image enhancement based on II curvelet. After the wavelet decomposition of medical image, we continue to break down the high-frequency sub-images, in order to get more detailed information. We have also designed the corresponding gain weight function for the edge enhancement of low-frequency sub-images, using II curvelet to extract the edge information, which has advantages over the normal curvelet transform. This method has overcome the disadvantages of the present medical image enhancement algorithm based on wavelet theory, for instance, (1) most of the present methods only decompose the low-frequency sub-images to get detailed information; (2) they can not well extract the tumors or other large areas of edge information of medical images. In simulation experiments, we enhanced the mammography X-ray images of breast provided by Heilongjiang Provincial Tumor Hospital, and compared our algorithm with several traditional image enhancement methods. The results shows that with our enhancement algorithm based on II curvelet, the textures and edges of the image can be reflected clearly, and the calcifications in the image are independent, at the same time, this method has the superiority when enhancing the images which have been added noise to.