This paper discusses different compression schemes taking advantage of interpolation methods for 3D volumetric reconstruction of medical imaging data. We developed a volume composition system that uses different compression schemes combined with interpolation algorithms to facilitate the rapid visualization of volumetric images. The quality and speed were compared to evaluate the best choice for volume rendering using data compression by region of interest (ROI), discrete cosine transform (DCT) and ROI combined with DCT techniques. A set of volumetric images of human arm was used. The lossless techniques allow for perfect reconstruction of the original images, yield modest compression rates, while the methods that yield higher compression rates are lossy, which is not permissible in medical imaging. In the first technique, we segmented the 2D images depending on our ROI. Thus, it addressed the separation of the imaging volume into important (flesh) and unimportant regions (blue gel), by using absolute threshold as one of the methods. The regions differed in their grayscale characteristics or in their importance levels. Medical applications require that we encode flesh in a lossless manner. Unfortunately, there is no principle way of choosing the absolute threshold, thus another method of ROI implementation was done by means of snake. This energy-minimizing spline guided by external constraint forces and influenced by image forces pulled the snake towards features such as lines and edges. This helped in extracting the specific area, without loosing relevant information.
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