Registration of images from capsule endoscopy

Łukasz Maciura, Karolina Sieroń-Stołtny, Aleksander Sieroń, Konrad Wojciechowski

Abstract


This paper presents numerical research and experiments giving rise to developed algorithm to connect into form of mosaic, images from the capsule endoscopy. In order to apply the algorithm, combined images must have a common area where the correspondence of points is determined. That allows to determine the transformation parameters to compensate movement of the capsule that occurs between moments when the mosaic images were acquired. The developed algorithm for images from the capsule endoscopy has proved to be faster and comparably accurate as commercial GDB-ICP algorithm.

Keywords


capsule endoscopy; image registration; image mosaic; keypoints matching

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References


Yue W., Yun-donga W., Huia W.: Free Image Registration and Mosaicing Based on Tin and Improved Szeliski Algorithm. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS Congress, Beijing 2008.

Kanazawaa Y., Kanatanib K.: Image mosaicing by stratified matching. Image and Vision Computing, Vol. 22, 2004, s. 93÷103.

Strona internetowa dystrybutora systemu kapsuły endoskopowej w Polsce, firmy Hammer med., http://pillcam.hammer.pl/.

Silva Cunha J. P., Coimbra M., Campos P., Soares J. M.: Automated Topographic Segmentation and Transit Time Estimation in Endoscopic Capsule Exams. IEEE Transactions on Medical Imaging, Vol. 27, No. 1, 2008.

Zitova B., Flusser J.: Image registration methods: a survey. Department of Image Processing, Institute of Information Theory and Automation, Academy of Sciences, Czech Republic 2003.

Maintz A. J. B., Viergever M. A.: A Survey of Medical Image Registration. Image Sciences Institute, Utrecht University Hospital, Utrecht, Netherlands 1997.

Pollefeys M., Van Gool L., Vergauwen M., Verbiest F., Cornelis K., Tops J.: Visual modeling with a hand-held camera. International Journal of Computer Vision, 2004.

Bay H., Ess A., Tuytelaars T., Van Gool L.: Speeded Up Robust Features (SURF). Elsevier, 2008.

Lowe D. G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004.

Chen J., Tian J.: Rapid Multi - modality preRegistration based on SIFT descriptor. Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA 2006.

Yang G., Stewart C.V., Sofka M., Tsai C.-L.: Registration of Challenging Image Pairs: Initialization, Estimation, and Decision. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 11, 2007.

Matas J., Chuma O.: Randomized RANSAC with Td,d test. Image and Vision Computing, Vol. 22, 2004, s. 837÷842.

Xie G., Shen H.: Automated Digital Image Registration and its Parallel Implementation. New Horizons of Parallel and Distributed Computing, Springer US, 2005.

Pluim J. P. W., Maintz A. J. B., Viergever M. A.: Mutual information based registration of medical images: a survey. IEEE Transactions on Medical Imaging, Vol. XX, 2003.

Canny J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986.

Yang G.: Towards General-Purpose Image Registration. PhD Thesis, Rensselaer Polytechnic Institute, Troy, New York, USA 2007.

Goshtasby A. A.: 2-D and 3-D image registration for medical, remote sensing and industrial application. John Wiley and Sons, Inc 2005.

Forsyth D. A., Ponce J.: Computer Vision: A Modern Approach. Prentice-Hall, 2003.

Hartley R., Zisserman A.: Multiple View Geometry in Computer Vision Second Edition. Cambridge University Press, 2003.

Palus H.: Wybrane zagadnienia przetwarzania obrazów barwnych. Wydawnictwo Politechniki Śląskiej, Gliwice 2006.




DOI: http://dx.doi.org/10.21936/si2012_v33.n2B.171