Erbou, Søren G.

Ersbøll, Bjarne Kjær

Larsen, Rasmus

Online Research Database In Technology

Registration and shape modelling of porcine bonestructures via CTSøren G. Erbou, Rasmus Larsen, Bjarne K. ErsbøllInformatics and Mathematical Modelling, Technical University of Denmark.DK-2800 Kgs. Lyngby, Denmark. E-mail: {sge,rl,be}@imm.dtu.dkBased on 2D computed tomography (CT) scans of porcine carcasses, a 3D point based statisticalshape model of bone structures connected to the pelvic bone, fig. 1(a), is built. The shape modelis used by the Danish Meat Research Institute (DMRI) to optimize and validate the functionalityof a specific tool in a slaughterhouse robot currently being developed.The data consists of 2D CT scans, fig. 1(b), of 40 porcine carcasses separated along the medialplane. Each scan has a slice thickness of 10mm with a spacing of 10mm between each scan. Voxeldimensions are [x, y, z] = [0.88, 0.88, 10]mm. The full length of the carcasses is scanned resultingin approximately 130 scans per carcass, but only 30 scans per carcass are used in this application,covering the parts around the region of the pelvic bone.Extracting corresponding points on a 3D shape from 2D scans is a tedious and difficult task,calling for (semi-)automated methods. Standard tresholding techniques combined with morphologyensures a robust segmentation of bone contours in the 2D scans. Points on the contours, fig. 1(c), areused as 3D constraints in the reconstruction of the bone surfaces using variational interpolation andradial basis functions (RBF) [3]. Due to the massive amount of data, each contour is approximatedusing fourier basis functions and then resampled. Only points along the contours having highcurvature are selected to be constraints on the 3D surface. The implicit surface, fig. 1(d), is thenresampled much more dense than the original scanning and the shapes are aligned using the iterativeclosest point (ICP) algorithm [1] and point correspondence is achieved. Using principal componentanalysis (PCA) a compact statistical shape model [2] is built describing the shape variation of thedata set.A generative statistical shape allows simulation of bone structures and thereby the differentconditions under which the robotic tool is to be applied. Furthermore, the generative model can beused as a prior for segmentation of new unseen pig carcass scans.The shape model is used by the DMRI to optimize the design of a tool in a new slaughterhouserobot. Applying the knowledge of how the bone structures vary, makes the process of developing newtools to do specific cuts, much less cumbersome compared to the normal trial-and-error approach.(a) Photo. (b) CT scan. (c) Contours. (d) Implicit Surface.Figure 1: Pelvic BoneReferences[1] Besl, P.J. and McKay, N.D. "A method for registration of 3-D shapes". Pattern Analysis andMachine Intelligence, IEEE Trans. on. Vol.14 (2), p. 239-256, 1992.[2] Cootes, T.F. and Taylor, C.J. "Statistical Models of Appearance for Computer Vi-sion". Tech. Report , University of Manchester, http://www.isbe.man.ac.uk/∼bim/Models/app_models.pdf, March 2004.[3] Turk, G. and O′Brien, J. "Shape transformation using variational implicit functions". ComputerGraphics Proceedings, SIGGRAPH. p. 335-342, 1999.

Registration and shape modelling of porcine bone structures via CT

http://orbit.dtu.dk/ws/files/2982007/imm4862.pdf

Registration and shape modelling of porcine bone structures via CT

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