The Snake Origin

Active contours (snakes) provide a unified account of a number of visual problems, including detection of edges, lines and subjective contours as well as motion tracking and stereo matching. Since their first apparition the researchers of the image processing community have simply applied this model to their problems without explaining where its parameters come from nor how to obtain its motion equations. To get a reality-based interpretation of the classic snake parameters (i.e. elasticity and bending) we propose an approach originates from the theory of the elasticity. Then we derive the motion equations from the variational method and we finally give some examples of segmentation. An alternative way of computing the external force field which increases the performance of snakes in presence of concavities is studied. We finally propose a new approach based on the dynamic modification of fi that improves the snake behavior at corners

Adaptive Hough transform for the detection of natural shapes under weak affine transformations

This paper introduces a two-steps adaptive generalized Hough transform (GHT) for the detection of non-analytic objects undergoing weak affine transformations in images. The first step of our algorithm coarsely locates the region of interest with a GHT for similitudes. The returned detection is then used by an adaptive GHT for affine transformations. The adaptive strategy makes the computation more amenable and ensures high accuracy, while keeping the size of the accumulator array small. To account for the deformable nature of natural objects, local shape variability is incorporated into the algorithm in both the detection and reconstruction steps. Finally, experiments are performed on real medical data showing that both accuracy and reasonable computation times can be reached

Brain Shift Correction Based on a Boundary Element Biomechanical Model with Different Material Properties

Neuronavigation systems are usually subject to inaccuracy due to intraoperative changes like brain shift or tumor resection. In order to correct for these deformations a biomechanical model of the brain is proposed. Not only elastic tissues, but also fluids are modeled, since an important volume of the head contains cerebrospinal fluid, which does not behave like soft tissues. Unlike other approaches, we propose to solve the differential equations of the model by means of the boundary element method, which has the advantage of only considering the boundaries of the different biomechanically homogeneous regions. The size of the matrix to invert is therefore drastically reduced. Finally, our method is assessed with sequences of intraoperative MR images, showing better performances for the elastic/fluid model than for the purely elastic one

Mitochondrial quality control and neurological disease: an emerging connection

The human brain is a highly complex organ with remarkable energy demands. Although it represents only 2% of the total body weight, it accounts for 20% of all oxygen consumption, reflecting its high rate of metabolic activity. Mitochondria have a crucial role in the supply of energy to the brain. Consequently, their deterioration can have important detrimental consequences on the function and plasticity of neurons, and is thought to have a pivotal role in ageing and in the pathogenesis of several neurological disorders. Owing to their inherent physiological functions, mitochondria are subjected to particularly high levels of stress and have evolved specific molecular quality-control mechanisms to maintain the mitochondrial components. Here, we review some of the most recent advances in the understanding of mitochondrial stress-control pathways, with a particular focus on how defects in such pathways might contribute to neurodegenerative disease

Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images

Abstract. Recently, marginal space learning (MSL) was proposed as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities [1]. To accurately localize a 3D object, we need to estimate nine pose parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of exhaustively searching the original nine-dimensional pose parameter space, only low-dimensional marginal spaces are searched in MSL to improve the detection speed. In this paper, we apply MSL to 2D object detection and perform a thorough comparison between MSL and the alternative full space learning (FSL) approach. Experiments on left ventricle detection in 2D MRI images show MSL outperforms FSL in both speed and accuracy. In addition, we propose two novel techniques, constrained MSL and nonrigid MSL, to further improve the efficiency and accuracy. In many real applications, a strong correlation may exist among pose parameters in the same marginal spaces. For example, a large object may have large scaling values along all directions. Constrained MSL exploits this correlation for further speed-up. The original MSL only estimates the rigid transformation of an object in the image, therefore cannot accurately localize a nonrigid object under a large deformation. The proposed nonrigid MSL directly estimates the nonrigid deformation parameters to improve the localization accuracy. The comparison experiments on liver detection in 226 abdominal CT volumes demonstrate the effectiveness of the proposed methods. Our system takes less than a second to accurately detect the liver in a volume.

Towards automatic full heart segmentation in computed-tomography images

We present a robust, fast and fully automatic approach enabling the segmentation of the main anatomical structures of the heart in CT images. The proposed method is based on the adaptation of a 3D triangulated mesh to new unknown images exploiting simultaneously knowledge of organ shape and typical gray level appearance in images, both learned from a training database made of 28 data sets. The described approach was tested on more than 50 volume images at different cardiac phases. Visual inspection by experts reveals that the proposed method is overall robust and succeeds in segmenting the heart up to minor interactive local corrections. 1