[show abstract][hide abstract] ABSTRACT: This paper presents a new method for guidewire tracking on fluoroscopic images from endovascular brain intervention. The combination of algorithms chosen can be implemented in real time, so that it can be used in an augmented reality 3D representation to assist physicians performing these interventions. A ribbon-like morphing process combined with a minimal path optimization algorithm is used to track lateral motion between successive frames. Forward motions are then tracked with an endpoint tracking algorithm, based on a circular window processed with the Radon transform. The proposed method was tested on 6 fluoroscopic sequences presenting high-speed motions, which were saved during endovascular brain interventions. The experiments showed above-average precision and robust guidewire tracking, without any permanent error requiring manual correction.
Medical Engineering & Physics 10/2010; 32(8):813-21. · 1.78 Impact Factor
[show abstract][hide abstract] ABSTRACT: Several studies based on biplanar radiography technologies are foreseen as great systems for 3D-reconstruction applications for medical diagnoses. This paper proposes a non-rigid registration method to estimate a 3D personalized shape of bone models from two planar x-ray images using an as-rigid-as-possible deformation approach based on a moving least-squares optimization method. Based on interactive deformation methods, the proposed technique has the ability to let a user improve readily and with simplicity a D reconstruction which is an important step in clinical applications. Experimental evaluations of six anatomical femur specimens demonstrate good performances of the proposed approach in terms of accuracy and robustness when compared to CT-scan.
[show abstract][hide abstract] ABSTRACT: This paper proposes a prior shape segmentation method to create a constant-width ribbon-like zone that runs along the boundary to be extracted. The image data corresponding to that zone is transformed into a rectangular image subspace where the boundary is roughly straightened. Every step of the segmentation process is then applied to that straightened subspace image where the final extracted boundary is transformed back into the original image space. This approach has the advantage of producing very efficient filtering and edge detection using conventional techniques. The final boundary is continuous even over image regions where partial information is missing. The technique was applied to the femoral head segmentation where we show that the final segmented boundary is very similar to the one obtained manually by a trained orthopedist and has low sensitivity to the initial positioning of the prior shape.
[show abstract][hide abstract] ABSTRACT: 3D reconstructions of the spine from a frontal and sagittal radiographs is extremely challenging. The overlying features of soft tissues and air cavities interfere with image processing. It is also difficult to obtain information that is accurate enough to reconstruct complete 3D models. To overcome these problems, the proposed method efficiently combines the partial information contained in two images from a patient with a statistical 3D spine model generated from a database of scoliotic patients. The algorithm operates through two simultaneous iterating processes. The first one generates a personalized vertebra model using a 2D/3D registration process with bone boundaries extracted from radiographs, while the other one infers the position and the shape of other vertebrae from the current estimation of the registration process using a statistical 3D model. Experimental evaluations have shown good performances of the proposed approach in terms of accuracy and robustness when compared to CT-scan.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:1008-11.
[show abstract][hide abstract] ABSTRACT: ObjectiveThis article describes a computer-based method for the classification of spine scoliosis severity. This is a first step toward
an effective computerized tool to assist general practitioners diagnose spine scoliosis. The method progresses away from Cobb
angles toward pattern and magnitude categorization based upon 3D configurations.
Materials and methodsThe purpose is to classify spine shapes reconstructed from a pair of calibrated X-ray images into one of three categories,
namely, normal spine, moderate scoliosis, and severe scoliosis. The spine shape is represented by the three-dimensional coordinates
of a sequence of equidistant points sampled by interpolation on the reconstructed spine shape. Classification is carried out
using a self- organizing Kohonen neural network trained using this representation.
ResultsThe tests were performed using a database of 174 spine biplane X-rays. The classification accuracy was 97%.
ConclusionThe results demonstrate that classification of 3D spine descriptions by a Kohonen neural network affords a solid basis for
an effective tool to assist clinicians in assessing scoliosis severity.
International Journal of Computer Assisted Radiology and Surgery 05/2008; 3(1):55-60. · 1.36 Impact Factor
[show abstract][hide abstract] ABSTRACT: Planar radiographs still are the gold standard for the measurement of the skeletal weight-bearing shape and posture. In this paper, we propose to use an as-rigid-as-possible deformation approach based on moving least squares to obtain 3D personalized bone models from planar x-ray images. Our prototype implementation is capable of performing interactive rate shape editing. The biplane reconstructions of both femur and vertebrae show a good accuracy when compared to CT-scan.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:3967-70.