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Depth–Depth Matching of Virtual and Real Images for a Surgical Navigation System

Hiroshi Noborio1, Katsuhiko Onishi1, Masanao Koeda1, Kaoru Watanabe1, and Miho Asano2
1.Department of Computer Science, Osaka Electro-Communication University, Osaka, Japan
2.Preemptive Medicine and Lifestyle-Related Disease Research Center, Kyoto University Hospital, Kyoto, Japan
Abstract—We propose a surgical navigation system aimed at conducting Depth–Depth Matching (DDM) between virtual and real organ images. The depth image of virtual organs modeled using stereolithography data derived from the Z-buffer of a GPU. In contrast, the depth image of real organs is obtained through an arbitrary depth camera. Therefore, in DDM, we need only non-combinatorial L subtractions and additions between virtual and real 2D depth images with pixel number of L, which is approximately 100,000. The most popular Iterative Closest Point (ICP) algorithm in the point cloud library consumes a considerable amount of time for checking the coincidence of two kinds of point clouds of whole organs. This could be because (1) the ICP needs combinatorial M × N calculation of the Euclidean distances of 3D cloud points (where M and N are usually near 100,000) and (2) considering that a real organ is obstructed by the patient’s body, the directions from which it is captured by a camera are restricted to the top view or near a shadowless lamp.

Index Terms—Digital Imaging and Communications in Medicine (DICOM), surgical navigation, depth–depth matching, Z-buffer, Stereolithography (STL), Cavitron Ultrasonic Surgical Aspirator (CUSA) scalpel

Cite: Hiroshi Noborio, Katsuhiko Onishi, Masanao Koeda, Kaoru Watanabe, and Miho Asano, "Depth–Depth Matching of Virtual and Real Images for a Surgical Navigation System," International Journal of Pharma Medicine and Biological Sciences, Vol. 10, No. 2, pp. 40-48, April 2021. doi: 10.18178/ijpmbs.10.2.40-48

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