A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images

Zheng, Guoyan; Gollmer, Sebastian T.; Schumann, Steffen; Dong, Xiao; Feilkas, T.; Ballester, Miguel Ángel González

doi:10.1016/j.media.2008.12.003

Cited by 136 publications

(90 citation statements)

References 54 publications

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“…Zheng et.al [26] in their paper presented a method for building a 2D/3D correspondence which is based upon a non-rigid 2D point matching process, in which a symmetric nearest neighbor mapping named operator is used along with 2D thin-plate spline based deformations so that a fraction of best matched pairs of 2D points between features being extracted from the bi-planar X-ray images and the features being extracted from the 3D model can be found. The pairs of points being estimated are further used to build up a set of 3D pair of points so that a 2D/3D reconstruction problem turns into a 3D/3D problem, whose solutions are well studied.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

3D Modeling of X-Ray Images: A Review

Goswami¹,

Misra²

2015

IJCA

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Accurate geometrical reconstruction of human bones into three-dimensional(3D) view is currently required for clinical studies such as enabling the radiologists to well analyze the fractures or infections in the bones, evidence of arthritis, presence of dental decays, lung infections etc. CT-scan is commonly used to obtain accurate reconstruction of the human body. However, this method is quite relucent for the patients as it demands a large number of image data sets, typically, more than 100s of images for a single bone to reconstruct. Analysis using MRI are also meant especially to investigate the anatomy and physiology of the body in both health and disease. However, although quite accurate, CTscan is not an appropriate 3D reconstruction method because of the high irradiating dose, high price and large input data volume. Thus, a 3D model reconstructed from 2D X-ray images can be a useful alternative. The generation of the 3D model is termed as 3D reconstruction from 2D X-Ray images. The reconstruction of the X-ray images can be achieved from both single and multiple X-Ray images. Many researches have been carried out in this field and the reconstruction has been carried out with varying accuracy. This paper presents a review of the existing methods for accurate 3D reconstruction from bi-planar X-rays. General Terms3D Reconstruction, X-Ray images.

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

3D Modeling of X-Ray Images: A Review

Goswami¹,

Misra²

2015

IJCA

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“… 3D reconstruction of anatomic structures from sparse patient-specific input data (Barratt et al 2008, Benameur et al 2003, Blanz et al 2004, Fleute et al 1998, Kurazume et al 2009, Rajamani et al 2007, Stindel et al 2002, Zheng et al 2008, Zheng and Schumann 2009. While 3D image datasets (e.g.…”

Section: Statistical Shape Modelsmentioning

confidence: 99%

Patient-Specific Modelling in Orthopedics: From Image to Surgery

Gomes

Cauter

Beule

et al. 2012

Biomedical Imaging and Computational Modeling in Biomechanics

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In orthopedic surgery, to decide upon intervention and how it can be optimized, surgeons usually rely on subjective analysis of medical images of the patient, obtained from computed tomography, magnetic resonance imaging, ultrasound or other techniques. Recent advancements in computational performance, image analysis and in silico modeling techniques have started to revolutionize clinical practice through the development of quantitative tools, including patient-specific models aiming at improving clinical diagnosis and surgical treatment. Anatomical and surgical landmarks as well as features extraction can be automated allowing for the creation of general or patient-specific models based on statistical shape models. Preoperative virtual planning and rapid prototyping tools allow the implementation of customized surgical solutions in real clinical environments. In the present chapter we discuss the applications of some of these techniques in orthopedics and present new computer-aided tools that can take us from image analysis to customized surgical treatment.

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“…Significant progress has been made during the past two decades [1][2][3]. However, most of the efforts have been focused on the development of high accuracy and complete modeling of complex scenes based on matching primitive features, such as points and lines regardless of their relationships or context in the scene.…”

Section: Introductionmentioning

confidence: 99%

3D Reconstruction and Self-calibration based on Binocular Stereo Vision

Hou¹,

Jeong

2012

Journal of the Korea Academia-Industrial cooperation Society

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A 3D reconstruction technique from stereo images that requires minimal intervention from the user has been developed. The reconstruction problem consists of three steps of estimating specific geometry groups. The first step is estimating the epipolar geometry that exists between the stereo image pairs which includes feature matching in both images. The second is estimating the affine geometry, a process to find a special plane in the projective space by means of vanishing points. The third step, which includes camera self-calibration, is obtaining a metric geometry from which a 3D model of the scene could be obtained. The major advantage of this method is that the stereo images do not need to be calibrated for reconstruction. The results of camera calibration and reconstruction have shown the possibility of obtaining a 3D model directly from features in the images.

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A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images

Cited by 136 publications

References 54 publications

3D Modeling of X-Ray Images: A Review

3D Modeling of X-Ray Images: A Review

Patient-Specific Modelling in Orthopedics: From Image to Surgery

3D Reconstruction and Self-calibration based on Binocular Stereo Vision

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