Efficient Medical Image Adaption via Axis-Aligned Mesh Deformation

Abstract

Content-aware medical image adaptation can make medical images be well presented on different display devices. The existing adaption algorithms mainly consider the visual effect of salient regions, such as specific organ areas of the patient body, but either ignore the quality of unimportant areas or execute more slowly. In order to enhance the effect of adaption and accelerate the speed of adaptation, we propose an efficient medical image adaptation method via axis-aligned mesh deformation. With this method, importance map is firstly produced by combing the weighted edge map and saliency map. Then, integer programming is used to initialize and deform the axis-aligned mesh based on importance map. Finally, image adaptation is operated rapidly by bi-linear interpolation. With the proposed method, the real-time image adaptation can be realized, and not only the visual effect of the significant areas but also the contour integrity and continuity of the non significant areas can be maintained. Experiments on open data-sets show that the proposed method has high efficiency, better effect and strong stability, and is suitable for real-time image adaptation.