Enhancing view consistency in collaborative medical visualization systems using predictive-based attitude estimation

Abstract

Nowadays, medical diagnosis in critical diseases are seldom executed by only one person. Often, in difficult cases, two or more physicians are involved to reach a diagnosis. The growth of the World Wide Web and the modern trend of cooperative work in scientific research gave rise to a new class of systems, the so-called collaborative visualization systems. We present a new attitude dead reckoning mechanism in a collaborative medical visualization system (CMVS). Using quaterions as the description of attitude, we derive a general trajectory construction scheme of attitude that extrapolates a number of previous packets in order to form the future trajectory of objects. An adaptive prediction and convergence approach is developed based on this cumulative trajectory. The method allows smooth transition between consecutive attitudes obtained from the network.