This is a unique situation in which the floating values are fixed (not resulting from various computations) so that we can compare them for exact equality. In this class we abuse the Python dictionary by using a float value as the key. We use the Exhaustive optimizer to sample the possible set of translations and an observer to tabulate the results. The general relationship between the fixed and moving images is assumed (fixed is "below" the moving). Finally we select the best transformation from the exploitation step.Ĭlass Evaluate2DTranslationCorrelation : ''' Class for evaluating the correlation value for a given set of 2D translations between two images.
Panorama x registration#
We then start a standard registration using these initial transformation estimates, our exploitation step. Registration - Exploration Step ¶Īs image overlap has considerable variation we will use the ExhaustiveOptimizer to obtain several starting points, our exploration step. Consequentially, we will use a heuristic exploration-exploitation approach to improve the robustness of our registration approach. While our transformation type is translation, looking at multiple triplets of images we observed that the size of overlapping regions, expected translations, has significant variability. Joskowicz, " Long Bone Panoramas from Fluoroscopic X-ray Images", IEEE Trans Med Imaging. This simplifies the general model from a homography transformation between images to a planar translation. In general, the x-ray machine is modeled as a pinhole camera, with our images acquired using a fronto-parallel setup and the camera undergoing translation. Linte, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, DOI:10.1080/21681163.2018.1537859. This notebook is based in part on the work described in: "A marker-free registration method for standing X-ray panorama reconstruction for hip-knee-ankle axis deformity assessment", Y. It is achievable by acquiring multiple partially overlapping images and aligning, registering, them to the same coordinate system.
Acquisition of such an image with standard x-ray imaging devices is not possible. 2010.įor a robust estimate of the $HKA$ angle we would like to use a single image that contains the anatomy from the femoral head down to the ankle. Kamath et al., " What is Varus or Valgus Knee Alignment?: A Call for a Uniform Radiographic Classification", Clin Orthop Relat Res. Cooke et al., " Frontal plane knee alignment: a call for standardized measurement", J Rheumatol. The three stances defined by the $HKA$ angle are: Hip-Knee-Ankle angle defined by the femoral mechanical axis (solid red line with dashed extension), and tibial mechanical axis (solid blue line). The tibial axis is defined by the center of the tibial plateau to the center of the tibial plafond. The femoral axis is defined by the center of the femur head and the mid condylar point. The angle is defined by the femoral and tibial mechanical axes. Alignment is measured by the hip-knee-ankle ($HKA$) angle in standing, load bearing, x-ray images. Measurement of knee alignment is useful for diagnosis of arthritic conditions and for planning and evaluation of surgical interventions.