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0)? 1 Nearest neighbour interpolation The easiest solution to the aforementioned problem is to simply use the value at the nearest voxel - in our case the value at position (9, 4, 4). This method is called nearest neighbour or zero-order interpolation. Using this method, there is a region of constant value around each sample in the volume, as seen in Figure 13. This interpolation scheme is the least time consuming, but also the least accurate. Figure 13. Nearest neighbour interpolation. 2 Trilinear interpolation When trilinear interpolation is used, the data value is assumed to vary linearly between voxels along directions parallel to the major axes.
To make a good approximation, a lot of triangles need be used. The rendering complexity and memory requirements increase rapidly with the increased resolution. Second, much of the information in the original data is lost when hollow shells are created out of “solid” data volumes. Third, amorphous phenomena such as fire, fog or clouds cannot really be adequately represented using surfaces. 36 of 76 Volume rendering is an alternative approach which solves the mentioned problems. Transparency and colour values are assigned to all voxels in the data set based on their scalar values, and then the entire set may be viewed from any angle.
This is called maximum intensity projection (mip) and by using this technique, internal features of the data may be revealed that would otherwise be hard to see. Yet another approach is to store the sum of the values encountered along the ray. This is essentially how X-rays work. Or the average of the values along the ray could be calculated and stored at the current pixel. The most generic technique involves defining an opacity and colour for each scalar value, then accumulating intensity along the ray according to some compositing function.