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136 DLeeet alliver surface and the GB surface in addition to that of the vessels. To correlate the US and CTimages in the regions corresponding to these anatomical features, the intensity informationand the gradient information, including the magnitude and orientation, are incorporated intoa cost function using a previous 3D-joint-histogram-based objective function proposed bythe authors. To obtain intermediate parametric transformations, the registration algorithmproposed here sequentially performs vessel-based and surface-based registration processeson the basis of the corresponding cost functions. The algorithm then estimates the finaltransformation based on the obtained parametric transformations. Through quantitativeevaluations, the proposed algorithm was demonstrated to be capable of providing registrationresults which are meaningful for clinical applications such as image-guided surgery andintervention. It should be emphasized that the proposed algorithm can accurately register a 3DCT image to a US image regardless of local deformation which can occur due to the differentrespiratory phases between the two images and due to the pressure from the US probe whenapplied to the contact area of the patient.AcknowledgmentsThe authors would like to thank Medison Co. Ltd for the financial support of this work.Thanks also to Professor B I Choi in the Department of Radiology, Seoul National UniversityHospital, and to D G Hyun in Medison R&D for the helpful discussions, collaboration, andfor their involvement with the data acquisition stages of this study.ReferencesAdams R and Bischof L 1994 Seeded region growing IEEE Trans. Pattern Anal. Mach. Intell. 16 641–7Bitter I, Kaufman A E and Sato M 2001 Penalized-distance volumetric skeleton algorithm IEEE Trans. Vis. Comput.Graphics. 7 195–206Blackall J M, Penney G P, King A P and Hawkes D J 2005 Alignment of sparse freehand 3-D ultrasound withpreoperative images of the liver using models of respiratory motion and deformation IEEE Trans. Med.Imaging 24 1405–16Brandner E D, Wu A, Chen H, Heron D, Kalnicki S, Komanduri K, Gerszten K, Burton S, Ahmed I and Shou Z 2006Abdominal organ motion measured using 4D CT Int. J. Radiat. Oncol. Biol. Phys. 65 554–60Brendel B, Winter S, Rick A, Stockheim M and Ermert H 2002 Registration of 3D CT and ultrasound datasets of thespine using bone structures Comput. Aided Surg. 7 146–55Camara O, Delso G, Colliot O, Moreno-Ingelmo A and Bloch I 2007 Explicit incorporation of prior anatomicalinformation into a nonrigid registration of thoracic and abdominal CT and 18-FDG whole-body emission PETimages IEEE Trans. Med. Imaging 26 164–78Castro-Pareja C R, Zagrodsky V, Bouchet L and Shekhar R 2005 Automated prostate localization in external-beamradiotherapy using mutual information-based registration of treatment planning CT and daily 3D ultrasoundimages Int. Congr. Ser. 1281 435–40Haber E and Modersitzki J 2006 Intensity gradient based registration and fusion of multi-modal images MedicalImage Computing and Computer-Assisted Interventation—MICCAI 2006 (Copenhagen, Denmark, Oct. 2006)Hajnal J V, Hill D L G and Hawkes D J 2001 Medical Image Registration (Boca Raton, FL: CRC Press)Harris C and Stephens M 1988 A combined corner and edge detector Alvey Vision Conf. 1988 (Manchester, UK, Aug.1988)Huang X, Ren J, Guiraudon G, Boughner D and Peters T M 2009 Rapid dynamic image registration of the beatingheart for diagnosis and surgical navigation IEEE Trans. Med. Imaging 28 1802–13Kim Y S, Lee J H and Ra J B 2008 Multi-sensor image registration based on intensity and edge orientation informationPattern Recognit. 41 3356–65Krissian K, Kikinis R, Westin C F and Vosburgh K 2005 Speckle-constrained filtering of ultrasound images IEEEConf. on Computer Vision and Pattern Recognition 2005 (San Diego, California, USA, June 2005)
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