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K V Ling

Publications and source records attributed to K V Ling.

3 recordsLinked to original sources

Magnetic tracker calibration for an augmented reality system for therapy.

Magnetic trackers are widely used position/orientation measurement devices in interactive computer systems. These trackers output six-degree-of-freedom position and orientation of their receiver coordinates with respect to their transmitter coordinates. It is a potential solution for real-time position/orientation measurement in an augmented reality system for therapy (ART). However, an obstruction to be overcome is that a magnetic tracker is vulnerable to measurement inaccuracy caused by magnetic field distortions (including ferric and non-ferric distortions) when it is used in metallic environments. The purpose of this paper is to discuss calibration methods used to compensate distortions produced by metallic objects which are moving during the measurement procedure, but fixed with a receiver. Past work on the calibration of magnetic trackers has concentrated on static distortions, which are produced by stationary metallic objects in the environment. Compared with static distortions, in this paper a relatively dynamic situation is considered where the distortion is not constant with time. However, the location of the metallic objects is reflected in the outputs of the receiver so that the distortion can be compensated. This type of distortions, which is called 'quasi-static distortion' in this paper, is common in an ART. Compensation of quasi-static distortion is more complicated than that of static distortion and has not been mentioned in past publications. Some new compensation methods for quasi-static distortion will be introduced here. The results demonstrate that the methods are capable of significantly reducing quasi-static distortions.

Calibration↗

Automatic prostate boundary recognition in sonographic images using feature model and genetic algorithm.

This paper describes the development of a model-based boundary recognition system for transrectal prostate ultrasonographic images. It consists of two techniques: boundary modeling and boundary searching with model constraints. To achieve higher specificity of the model, a method called feature modeling is derived from the existing point distribution modeling method. To improve the robustness of the searching technique, the genetic algorithm is used. Incremental genetic algorithm with crowding replacement and binary string chromosome type was found experimentally to give good search results. It was shown that the system could recognize the boundary with considerable accuracy and consistency within a few minutes in transrectal ultrasonographic images taken from approximate middle position of the prostate.

Algorithms↗