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M Barlaud

Publications and source records attributed to M Barlaud.

2 recordsLinked to original sources

Space-time segmentation using level set active contours applied to myocardial gated SPECT.

This paper presents a new variational method for the segmentation of a moving object against a still background, over a sequence of [two-dimensional or three-dimensional (3-D)] image frames. The method is illustrated in application to myocardial gated single photon emission computed tomography (SPECT) data, and incorporates a level set framework to handle topological changes while providing closed boundaries. The key innovation is the introduction of a geometrical constraint into the derivation of the Euler-Lagrange equations, such that the segmentation of each individual frame can be interpreted as a closed boundary of an object (an isolevel of a set of hyper-surfaces) while integrating information over the entire sequence. This results in the definition of an evolution velocity normal to the object boundary. Applying this method to 3-D myocardial gated SPECT sequences, the left ventricle endocardial and epicardial limits can be computed in each frame. This space-time segmentation method was tested on simulated and clinical 3-D myocardial gated SPECT sequences and the corresponding ejection fractions were computed.

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Combined constraints for efficient algebraic regularized methods in fully 3D reconstruction.

SPECT systems incorporate the use of one or more rotating gamma cameras which can be equipped with cone-beam collimators to improve the trade-off between spatial resolution and sensitivity. The geometry of the cone-beam collimators implies that a specific 3D reconstruction algorithm must be applied. Algebraic methods provide the possibility of including the physical characteristics, such as attenuation, Compton scatter and detector response, in the reconstruction process. However, the reconstruction problem is an ill-posed problem which should be regularized. This paper presents a 3D algebraic method that combines three regularizing constraints. These constraints deal respectively with penalizing negative voxels, local noise smoothing and missing data compensation. The results presented were obtained from imaging simulations, phantom data and from a thyroid clinical study of a normal volunteer.

Algorithms↗