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Surgical navigation describes the concept of real-time processing and presentation of preoperative and intraoperative data from different sources to intraoperatively provide surgeons with additional cognitive support. Imaging methods such as 3D ultrasound, magnetic resonance imaging (MRI) and computed tomography (CT) and data from optical, electromagnetic or mechanical tracking methods are used. The resulting information of the navigation system will be presented by the means of visual methods. Mostly virtual reality or augmented reality visualization is used. There are different guidance systems for various disciplines introduced. Mostly it operates on rigid structures (bone, brain). For soft tissue navigation motion compensation and deformation detection are necessary. Therefore, marker-based tracking methods are used in several urological application examples; however, the systems are often still under development and have not yet arrived in the clinical routine. [ABSTRACT FROM AUTHOR]

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The multiple instance classification problem (Dietterich et al., Artif. Intell. 89:31-71, 1998; Auer, Proceedings of 14th International Conference on Machine Learning, pp. 21-29, Morgan Kaufmann, San Mateo, 1997; Long et al., Mach. Learn. 30(1):7-22, 1998) is formulated using a linear or nonlinear kernel as theminimization of a linear function in a finite-dimensional (noninteger) real space subject to linear and bilinear constraints. A linearization algorithm is proposed that solves a succession of fast linear programs that converges in a few iterations to a local solution. Computational results on a number of datasets indicate that the proposed algorithm is competitive with the considerably more complex integer programming and other formulations. A distinguishing aspect of our linear classifier not shared by other multiple instance classifiers is the sparse number of features it utilizes. In some tasks, the reduction amounts to less than one percent of the original features. [ABSTRACT FROM AUTHOR]