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Several marker segmentation techniques for use with a medical AR system—a comparison
International Congress Series 1256 (2003) 1303
Several marker segmentation techniques for use with a medical AR system—a comparison S. Wesarg*, T.H. Lauer b, E.A. Firle a, C. Dold a a
FhG-IGD, Department of Cognitive Comp. and Medical Imaging, Darmstadt, Germany b Contact Software GmbH, Germany
Received 11 March 2003; received in revised form 11 March 2003; accepted 17 March 2003 Keywords: Marker segmentation; Augmented reality; CT
1. Motivation A semitransparent augmented reality (AR) display is developed within the project MEDARPA [1]. For the use of a hybrid tracking system, the registration of the patient (using externally attached markers) is required. 2. Methods This work compares several techniques for segmenting the markers in the data sets. The first algorithm has been developed by Wang et al. [2] (Alg. 1), the second by Capek et al. [3] (Alg. 2) and the last one by ourselves (Alg. 3) [4]. All of the algorithms are based on finding a global threshold automatically. We used synthetic and real CT data sets for testing the algorithms. 3. Results Alg. 1 is quite robust, but it has problems when the markers are very small with respect to the voxel size. Alg. 2 is only able to detect markers which are the brightest objects, and the calculated centers of gravity (cog) differ a lot from the real values. Alg. 3 shows excellent accuracy concerning the calculated cog, but can detect markers only if they are not connected (on the pixel level) to other objects. Regarding speed, Alg. 2 and 3 are the fastest, whereas Alg. 1 is significantly slower. 4. Discussion None of the algorithms worked well with all three data sets. Each of them has advantages and disadvantages due to its design. We favor Alg. 3 due to its accuracy and speed. However, the integration of all of these algorithms into our visualization software gives us the possibility to always find an appropriate algorithm for a given data set. References [1] Medical Augmented Reality for Patients. Project funded by the German Ministry of Education and Research (BMBF), research grant 01IRA09B (http://www.medarpa.de). [2] M.Y. Wang et al., An automatic technique for finding and localizing externally attached markers in CT and MR Volume images of the head, IEEE Trans. Biomed. Eng. 43 (6) 627 – 637. [3] M. Capek et al., Multimodal medical volume registration based on spherical markers. WSCG ’2001. [4] S. Wesarg et al., A method for segmenting markers only by their geometrical shape and size. Submitted for publication. * Corresponding author. Tel.: +49-6151-155-511; fax: +49-6151-155-559. E-mail addresses: [email protected] (S. Wesarg), [email protected] (E.A. Firle), [email protected] (C. Dold). 0531-5131/03 D 2003 Published by Elsevier Science B.V. doi:10.1016/S0531-5131(03)00296-6
Several marker segmentation techniques for use with a medical AR system—a comparison S. Wesarg*, T.H. Lauer b, E.A. Firle a, C. Dold a a
FhG-IGD, Department of Cognitive Comp. and Medical Imaging, Darmstadt, Germany b Contact Software GmbH, Germany
Received 11 March 2003; received in revised form 11 March 2003; accepted 17 March 2003 Keywords: Marker segmentation; Augmented reality; CT
1. Motivation A semitransparent augmented reality (AR) display is developed within the project MEDARPA [1]. For the use of a hybrid tracking system, the registration of the patient (using externally attached markers) is required. 2. Methods This work compares several techniques for segmenting the markers in the data sets. The first algorithm has been developed by Wang et al. [2] (Alg. 1), the second by Capek et al. [3] (Alg. 2) and the last one by ourselves (Alg. 3) [4]. All of the algorithms are based on finding a global threshold automatically. We used synthetic and real CT data sets for testing the algorithms. 3. Results Alg. 1 is quite robust, but it has problems when the markers are very small with respect to the voxel size. Alg. 2 is only able to detect markers which are the brightest objects, and the calculated centers of gravity (cog) differ a lot from the real values. Alg. 3 shows excellent accuracy concerning the calculated cog, but can detect markers only if they are not connected (on the pixel level) to other objects. Regarding speed, Alg. 2 and 3 are the fastest, whereas Alg. 1 is significantly slower. 4. Discussion None of the algorithms worked well with all three data sets. Each of them has advantages and disadvantages due to its design. We favor Alg. 3 due to its accuracy and speed. However, the integration of all of these algorithms into our visualization software gives us the possibility to always find an appropriate algorithm for a given data set. References [1] Medical Augmented Reality for Patients. Project funded by the German Ministry of Education and Research (BMBF), research grant 01IRA09B (http://www.medarpa.de). [2] M.Y. Wang et al., An automatic technique for finding and localizing externally attached markers in CT and MR Volume images of the head, IEEE Trans. Biomed. Eng. 43 (6) 627 – 637. [3] M. Capek et al., Multimodal medical volume registration based on spherical markers. WSCG ’2001. [4] S. Wesarg et al., A method for segmenting markers only by their geometrical shape and size. Submitted for publication. * Corresponding author. Tel.: +49-6151-155-511; fax: +49-6151-155-559. E-mail addresses: [email protected] (S. Wesarg), [email protected] (E.A. Firle), [email protected] (C. Dold). 0531-5131/03 D 2003 Published by Elsevier Science B.V. doi:10.1016/S0531-5131(03)00296-6