870 slices. 100,800 pixels per slice. Potentially 87,696,000 data points. Of course, we don’t need all of them. Some are background and others represent volumetrically hidden tissue. Since deep tissue tended to be the most red, we employed some color filtering. This was not enough. So, we added some basic edge detection. Within a particular slice, as the loader moves across a row, it checks the color value difference between one column and the next. If the difference is substantial, the pixel is added to the data set for display. Even so, the current implementation will run on my Mac Pro but not my MacBook. The loaded dataset is about 300,000 points, but all 87.6 million candidate pixels must be processed in loading. For stylization, on each iteration of the draw loop, we display about 1/6 of the total number of points, selected randomly on each iteration. This gives the model an animated quality and makes it feel less congested so that as the viewer approaches, he or she may peer into the model’s internal organs and skeletal system.