We present a new multiscale approach for deformable contour optimization. The method relies on a multigrid minimization method and a coarse-to-fine relaxation algorithm. This approach consists in minimizing a cascade of optimization problems of reduced and increasing complexity instead of considering the minimization problem on the full and original configuration space. Contrary to classical multiresolution algorithms, no reduction of image is applied. The family of defined energy functions are derived from the original (full resolution) objective function, ensuring that the same function is handled at each scale and that the energy decreases at each step of the deformable contour minimization process. The efficiency and the speed of this multiscale optimization strategy is demonstrated in the difficult context of the minimization of a region-based contour energy function ensuring the boundary detection of anatomical structures in ultrasound medical imagery. In this context, the proposed multiscale segmentation method is compared to other classical region-based segmentation approaches such as Maximum Likelihood or Markov Random Field-based segmentation techniques. We also extend this multiscale segmentation strategy to active contour models using a classical edge-based likelihood approach. Finally, time and performance analysis of this approach, compared to the (commonly used) dynamic programming-based optimization procedure, is given and allows to attest the accuracy and the speed of the proposed method.

Algorithms↗

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Self-calibration of a biplane X-ray imaging system for an optimal three dimensional reconstruction.

The purpose of this paper is to elaborate a three dimensional (3D) reconstruction method, using biplane X-ray angiograms acquired daily by the clinician, without any special calibration procedure during the X-ray examination. The absolute geometry of the X-ray imaging system is determined by an iterative procedure based on the minimization of the mean square distance between observed and analytical projections of a set of reference points identified by the clinician on the simultaneous pair of images. Once the geometry of the imaging system is found the 3D structure of interest is retrieved from classical methods of binocular stereovision. This 3D information is a prerequisite for an accurate evaluation of the degree of severity of a vascular structure or motion anomaly and therefore, for establishing an appropriate diagnosis. The proposed 3D reconstruction method is validated on synthetic and real data and is shown to perform robustly and accurately in the presence of noise. The method should be particularly useful in clinical applications as it needs very little intervention from the clinician.

Angiography↗

PubMedDate not supplied