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Image-Guided Surgery
Published in John G Webster, Minimally Invasive Medical Technology, 2016
Any image-guided surgical procedure starts with the registration of the image data with the surgical field. This intraoperative registration can be accomplished with fiducial markers that are visible in the images and the operative field and therefore can establish the transformation matrix. The fiducials are fixed to the patient and represent fixed reference points in both coordinate systems. Alternatively, anatomical landmarks can be mapped with a position-sensing pointing device that is physically held to the position of the landmark and also identified in image space, e.g. with the placement of a cursor. Point-based registration methods can be used for the mapping. Yet another approach is surface-based methods, where 40 or more fiducial markers are attached to the skin. The mapping can be performed with surfaces extracted from the images and fiducials without the need for a pointing device. If the patient is moved, the coordinate systems have to be reregistered.
A review on the applications of virtual reality, augmented reality and mixed reality in surgical simulation: an extension to different kinds of surgery
Published in Expert Review of Medical Devices, 2021
Abel J Lungu, Wout Swinkels, Luc Claesen, Puxun Tu, Jan Egger, Xiaojun Chen
Image registration is required to align the virtual data accurately with the physical scene. In [19,20], a multi-step co-registration strategy has been adopted in which four fiducial markers are placed around the surgical site. During navigation, these markers track the tumor margin that needs to be resected. To register the virtual coordinate system to that of the physical coordinate system, Wang et al. [23] adopted a point-to-point registration method. For the extraction of the fiducial landmark coordinates in the virtual coordinate system, image processing is applied. The fiducial landmark coordinates have been obtained using a positioning probe in the physical coordinate system. Liu et al. [24] adopted a similar point-based registration approach. To improve the registration accuracy, Chen et al. [22] combine fiducial point-based registration with surface-based registration. These registration methods are based upon visible markers. However, there are also marker-less methods that can be used for image registration. It has been demonstrated that the SIFT, SURF, BRISK and ORB algorithms can be used in fluorescence-to-color image registration for intraoperative AR [39]. It is also possible to use anatomical landmarks to perform image registration. Wang et al. [34] use patient tracking in combination with 3D contour matching of the teeth to obtain automatic marker-free patient-image registration. Similar approaches have been used by Suenaga et al. [36] and Wang et al. [37,38].
Three-dimensional human head modelling: a systematic review
Published in Theoretical Issues in Ergonomics Science, 2018
To study multiple 3D head models to understand the shape variance and to develop generalised head model for a specific ethnic group or based on countries or region, there is a need of proper alignment of all the 3D head models. Manual alignment is time consuming and leads to errors. Many studies use multiple reference anatomical landmarks or use reference planes to achieve a better alignment. Most commonly used plane is Frankfurt plane consisting of 3 anatomical landmarks including left infraorbitale, left and right tragions (Luximon, Ball, and Justice 2010; Kouchi and Mochimaru 2004). This is implemented using 3D modelling software. The other technique used to achieve accurate alignment uses ICP method (Luximon et al. 2016; Amor et al. 2005). ICP method consists of two set of point cloud data where one set acts as a reference set, whereas the second point cloud data set tries to iteratively transform to minimise the difference between the two sets to achieve the best match or overlapping. To achieve ICP-based alignment coding can be done in software like MATLAB for simplification.