13 March 2009 Direct global adjustment methods for endoscopic mosaicking
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Abstract
Endoscopy is an invaluable tool for several surgical and diagnostic applications. It permits minimally invasive visualization of internal structures thus involving little or no injury to internal structures. This method of visualization however restricts the size of the imaging device and therefore compromises on the field of view captured in a single image. The problem of a narrow field of view can be solved by capturing video sequences and stitching them to generate a mosaic of the scene under consideration. Registration of images in the sequence is therefore a crucial step. Existing methods compute frame-to-frame registration estimates and use these to resample images in order to generate a mosaic. The complexity of the appearance of internal structures and accumulation of registration error in frame to frame estimates however can be large enough to cause a cumulative drift that can misrepresent the scene. These errors can be reduced by application of global adjustment schemes. In this paper, we present a set of techniques for overcoming this problem of drift for pixel based registration in order to achieve global consistency of mosaics. The algorithm uses the frame-to-frame estimate as an initialization and subsequently corrects these estimates by setting up a large scale optimization problem which simultaneously solves for all corrections of estimates. In addition we set up a graph and introduce loop closure constraints in order to ensure consistency of registration. We present our method and results in semi global and fully global graph based adjustment methods as well as validation of our results.
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Sharmishtaa Seshamani, Sharmishtaa Seshamani, Michael D. Smith, Michael D. Smith, Jason J. Corso, Jason J. Corso, Marcus O. Filipovich, Marcus O. Filipovich, Ananth Natarajan, Ananth Natarajan, Gregory D. Hager, Gregory D. Hager, "Direct global adjustment methods for endoscopic mosaicking", Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 72611D (13 March 2009); doi: 10.1117/12.812246; https://doi.org/10.1117/12.812246
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