Medical image fusion using multi-level local extrema

The fusion of data for medical imaging has become a central issue in such biomedical applications as image-guided surgery and radiotherapy. The multi-level local extrema (MLE) representation has been shown to have many advantages over conventional image representation methods. In this paper, we propose a new fusion algorithm for multi-modal medical images based on MLE. Our method enables the decomposition of input images into coarse and detailed layers in the MLE schema, and utilizes local energy and contrast fusion rules for coefficient selection in the different layers. This preserves more detail in the source images and further improves the quality of the fused image. The final fused image is obtained from the superposition of selected coefficients in the coarse and detailed layers. We illustrate the performance of the proposed method using three groups of medical images from different sources as our experimental subjects. We also compare our method with other techniques using cumulative mutual information, the objective image fusion performance measure, spatial frequency, and a blind quality index. Experimental results show that our method achieves a superior performance in both subjective and objective assessment criteria.

► Using local extrema to decompose image into coarse and detail layers. ► Local energy and contrast guided fusion make fusion process more accurate. ► Blind quality index, a new image fusion quality metric without reference image, was proposed.