Efficient Framework for Magnetic Resonance Image Fysion Using Histogram Equalization Combined with Cross Bilateral Filter

摘要

Medical image fusion plays a great role in image processing because it can increase the visual interpretation of the medical images especially brain images. The images with low interpretation can have influence on diagnostic results because each small detail in medical images is a very important clue, which can help doctors find out a suitable treatment plan. In this paper, we proposed a method for medical image fusion especially brain magnetic resonance image (MRI). The histogram equalization is applied to enhance the contrast of MRI and then cross bilateral filter is used to extract detail images from the source images. The source images are fused by weighted average using the weights calculated from the detail images to make the image result with more information. Our proposed method is better than the other recent methods based on compared results.