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Autostereoscopic visualization in clinical image-guided interventions and therapy poses constrains on the maximal latency in the visualization chain. In order to use visual feedback in the hand-eye coordinated loop, the latency of the visualization chain should be less than approximately 250–300 ms. This is a challenging constraint for interactive autostereoscopic multi-view volume rendering of large datasets. The various building blocks of such a visualization chain and their latency aspects are explored in this paper. Two complementary strategies to improve the latency of the entire chain are introduced and examined: lowering the resolution of the rendered views and reducing the amount of rendered views by smartly interpolating the missing views at receiver side. These strategies are balanced while optimizing the latency and image quality using a fuzzy logic approach. Furthermore, the optimal view resolution for a multi-view autostereoscopic lenticular display has been determined by investigating the lenticular lattice in the frequency domain. The quantitative aspects of the latency of the proposed building blocks and the resulting image quality have been measured.

In machine learning the so-called curse of dimensionality, pertinent to many classification algorithms, denotes the drastic increase in computational complexity and classification error with data having a great number of dimensions. In this context, feature selection techniques try to reduce dimensionality finding a new more compact representation of instances selecting the most informative features and removing redundant, irrelevant, and/or noisy features. In this paper, we propose a filter-based feature selection method for working in the multiple-instance learning scenario called ReliefF-MI; it is based on the principles of the well-known ReliefF algorithm. Different extensions are designed and implemented and their performance checked in multiple instance learning. ReliefF-MI is applied as a pre-processing step that is completely independent from the multi-instance classifier learning process and therefore is more efficient and generic than wrapper approaches proposed in this area. Experimental results on five benchmark real-world data sets and 17 classification algorithms confirm the utility and efficiency of this method, both statistically and from the point of view of execution time.