Finger Joint Detection Method in Hand X-ray Radiograph Images Using Statistical Shape Model and Support Vector Machine

2020 ◽  
Author(s):  
Kohei Nakatsu ◽  
Kento Morita ◽  
Naomi Yagi ◽  
Syoji Kobashi
Keyword(s):  
Support Vector Machine ◽  
Detection Method ◽  
Finger Joint ◽  
Statistical Shape Model ◽  
Support Vector ◽  
Joint Detection ◽  
Shape Model ◽  
X Ray ◽  
Statistical Shape

Towards Rapid 3D Reconstruction using Conventional X-Ray for Intraoperative Orthopaedic Applications

2011 ◽  
pp. 309-323 ◽  
Author(s):  
Simant Prakoonwit
Keyword(s):  
3D Reconstruction ◽  
Statistical Shape Model ◽  
Shape Model ◽  
X Ray ◽  
Physical Constraints ◽  
Surgical Tools ◽  
Reconstructed Surfaces ◽  
Statistical Shape ◽  
Post Operation ◽  
2D Images

A rapid 3D reconstruction of bones and other structures during an operation is an important issue. However, most of existing technologies are not feasible to be implemented in an intraoperative environment. Normally, a 3D reconstruction has to be done by a CT or an MRI pre operation or post operation. Due to some physical constraints, it is not feasible to utilise such machine intraoperatively. A special type of MRI has been developed to overcome the problem. However, all normal surgical tools and instruments cannot be employed. This chapter discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct 3D bone and other structures intraoperatively. A statistical shape model is used to fit a set of optimal landmarks vertices, which are automatically created from the 2D images, to reconstruct a full surface. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems.

Correspondence free 3D statistical shape model fitting to sparse x-ray projections

2010 ◽  
Author(s):  
N. Baka ◽  
W. J. Niessen ◽  
B. L. Kaptein ◽  
T. van Walsum ◽  
L. Ferrarini ◽  
...  
Keyword(s):  
Model Fitting ◽  
Statistical Shape Model ◽  
Shape Model ◽  
X Ray ◽  
Statistical Shape

scholarly journals Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications

2014 ◽  
Vol 4 (1) ◽  
pp. 13-31 ◽  
Author(s):  
Simant Prakoonwit
Keyword(s):  
Statistical Shape Model ◽  
Distribution Model ◽  
Shape Model ◽  
Point Distribution ◽  
X Ray ◽  
Landmark Point ◽  
Reconstructed Surfaces ◽  
Statistical Shape ◽  
Dense Point ◽  
Point Distribution Model

This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone's edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems.

Spatiotemporal Statistical Shape Model for Temporal Shape Change Analysis of Adult Brain

2020 ◽  
Vol 16 (5) ◽  
pp. 499-506
Author(s):  
Saadia Binte Alam ◽  
Manabu Nii ◽  
Akinobu Shimizu ◽  
Syoji Kobashi
Keyword(s):  
Weight Function ◽  
Shape Change ◽  
Gaussian Function ◽  
Adult Brain ◽  
Statistical Shape Model ◽  
Support Vector ◽  
Shape Model ◽  
Statistical Shape ◽  
Brain Shape ◽  
Time Point

Background: This study presents a novel method of constructing a spatiotemporal statistical shape model (st-SSM) for adult brain. St-SSM is an extension of statistical shape model (SSM) in the temporal domain which will represent the statistical variability of shape as well as the temporal change of statistical variance with respect to time. Aims: Expectation-Maximization (EM) based weighted principal component analysis (WPCA) using a temporal weight function is applied where the eigenvalues of each data are estimated by Estep using temporal eigenvectors, and M-step updates Eigenvectors in order to maximize the variance. Both E and M-step are iterated until updating vectors reaches the convergence point. A weight parameter for each subject is allocated in accordance with the subject’s age to calculate the weighted variance. A Gaussian function is utilized to define the weight function. The center of the function is a time point while the variance is a predefined parameter. Methods: The proposed method constructs adult brain st-SSM by changing the time point between minimum to maximum age range with a small interval. Here, the eigenvectors changes with aging. The feature vector of representing adult brain shape is extracted through a level set algorithm. To validate the method, this study employed 103 adult subjects (age: 22 to 93 y.o. with Mean ± SD = 59.32±16.89) from OASIS database. st-SSM was constructed for time point 40 to 90 with a step of 2. Results: We calculated the temporal deformation change between two-time points and evaluated the corresponding difference to investigate the influence of analysis parameter. An application of the proposed model is also introduced which involves Alzheimer’s disease (AD) identification utilizing support vector machine. Conclusion: In this study, st-SSM based adult brain shape feature extraction and classification techniques are introduced to classify between normal and AD subject as an application.

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