Thin Plate Spline Feature Point Matching for Organ Surfaces in Minimally Invasive Surgery Imaging
Document Type
Conference Proceeding
Publication Date
3-2013
Digital Object Identifier (DOI)
http://dx.doi.org/10.1117/12.2007687
Abstract
Robust feature point matching for images with large view angle changes in Minimally Invasive Surgery (MIS) is a challenging task due to low texture and specular reflections in these images. This paper presents a new approach that can improve feature matching performance by exploiting the inherent geometric property of the organ surfaces. Recently, intensity based template image tracking using a Thin Plate Spline (TPS) model has been extended for 3D surface tracking with stereo cameras. The intensity based tracking is also used here for 3D reconstruction of internal organ surfaces. To overcome the small displacement requirement of intensity based tracking, feature point correspondences are used for proper initialization of the nonlinear optimization in the intensity based method. Second, we generate simulated images from the reconstructed 3D surfaces under all potential view positions and orientations, and then extract feature points from these simulated images. The obtained feature points are then filtered and re-projected to the common reference image. The descriptors of the feature points under different view angles are stored to ensure that the proposed method can tolerate a large range of view angles. We evaluate the proposed method with silicon phantoms and in vivo images. The experimental results show that our method is much more robust with respect to the view angle changes than other state-of-the-art methods.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Proceedings SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, p. 867112.
Scholar Commons Citation
Lin, Bingxiong; Sun, Yu; and Qian, Xiaoning, "Thin Plate Spline Feature Point Matching for Organ Surfaces in Minimally Invasive Surgery Imaging" (2013). Computer Science and Engineering Faculty Publications. 96.
https://digitalcommons.usf.edu/esb_facpub/96
