S-KA histogram based transfer function for volume rendering

Transfer function plays the role to discriminate features by assigning different colors and opacities. It is crucial to the quality of volume rendered images thus affect people´s understanding of the data. Multidimensional transfer functions are an effective way to visualize features in volume data. We present a multidimensional transfer function based on S-KA histogram. Firstly, we construct S-KA histogram according to voxel´s scalar value and the average scalar value of its K neighbors. Secondly, with the help of S-KA histogram, the internal voxels, which are projected on the diagonal of the histogram, will be directly classified into different classes according to the prescribed scalar range. Thirdly, using the S-KA histogram, we can determine each boundary voxel´s class through finding the corresponding internal voxel that has the largest similarity of local character with it. Furthermore, we develop an intuitive interface to assign specific features, i.e. colors and opacities, to different tissues. The experimental results showed the effectiveness and efficiency of the proposed method.