In order to accelerate rendering of 3D scenes by path tracing， a visual?saliency?driven reuse algorithm of indirect lighting in 3D scene rendering was proposed. Firstly， according to the characteristics of visual perception that the regions of interest have high saliency， while other regions have low saliency， a 2D saliency map of the scene image was obtained， which consists of color information， edge information， depth information and motion information of the image. Then， the indirect lighting in the high?saliency area was re?rendered， while the indirect lighting of the previous frame was reused in the low?saliency area under certain conditions， thereby accelerating the rendering. Experimental results show that the global lighting effect of the image generated by this method is real， and the rendering speed of the method is improved in several experimental scenes， and the speed can reach up to 5.89 times of that of the high?quality rendering.

In response to the issue that the intervertebral disk manual modeling was time-consuming and subjective, and the existing segmentation method was not accurate enough, a new method named two-diememsional Automatic Active Shape Model (2D-AASM) was proposed. It included three parts: automatic statistical shape modeling of intervertebral disk based on minimum description length, 2D local gradient modeling and segmentation. Adopting the manual segmentation results of 25 sets of spinal MR images as the training set, the study used minimum description length method to determine the point correspondence, built statistical shape model and 2D local gradient model for intervertebral disk T4-5. The generated shape model had lower variance and the objective function value than the manual and arc length parameter method. After the model was built, three sets of Magnetic Resonance Image (MRI) images were used to test the proposed method. Compared with the traditional ASM and 1D-ASM, the segmentation result of the proposed method had a higher Dice coefficient and lower over-segmentation and under-segmentation rate. The experiment results indicate that the proposed method generates a better model and more accurate segmentation result.