We present a system that reconstructs the 3D motion of the left ventricle (LV) for a full cardiac cycle using a deformable model built from tagged MR images. Two sets of cues are drawn from tagged MRI. The intersections of the three tagging planes, and the intersections of the LV boundary and the tagging planes, are interpolated onto the mesh vertices. We implement a deformable model to track t...

This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. First, a modified scale-invariant feature transform (SIFT) local descriptor is used to characterize the image features in the vicinity of each pixel, so that the deformable model deforms in a way that seeks for the region with similar...

In computer vision, two complementary approaches have been widely used to perform object reconstruction and registration. The deformable model framework locally applies internal and external forces to fit 3D data. The non-rigid registration framework iteratively computes the best global transformation that minimizes the distance between a template and the data. In this paper, first we show that...

The Deformable Parts Model (DPM) has recently emerged as a very useful and popular tool for tackling the intra-category diversity problem in object detection. In this paper, we summarize the key insights from our empirical analysis of the important elements constituting this detector. More specifically, we study the relationship between the role of deformable parts and the mixture model compone...

This paper describes a new approach to the tracking of complex shapes through image sequences, that combines deformable region models and deformable contours. A new deformable region model is presented: its optimisation is based on texture correlation and is constrained by the use of a motion model, such as rigid, aane or homographic. The use of texture information (versus edge information) not...

This paper addresses the problem of describing the significant intra and inter variability of 3D deformable structures within 3D image data sets. In pursuing it, a 3D probabilistic physically based deformable model is defined. The statistically learned deformable model captures the spatial relationships between the different objects surfaces, together with their shape variations. The structures...

Previously, “Deformable organisms” were introduced as a novel paradigm for medical image analysis that uses artificial life modelling concepts. Deformable organisms were designed to complement the classical bottomup deformable models methodologies (geometrical and physical layers), with top-down intelligent deformation control mechanisms (behavioral and cognitive layers). However, a true physic...

We present a method that allows both autonomous and human-robot collaborative manipulation of deformable objects. The method does not require modeling and simulating deformation. Our method is based on the concept of diminishing rigidity, which we use to quickly compute an approximation to the Jacobian of the deformable object without using simulation. This Jacobian is used to drive the points ...

An improved growing deformable surface patches algorithm and its application in medical image analysis are presented in this work. In this proposed method, a growing mechanism is introduced to 3D deformable model. After a surface patch is initialized within the external force field, it is deformed to reach and stop at the boundary of the object and anchored there. New surface patches are initia...

We propose a deformable surface model for matching anatomically homologous points for a specific region of interest (ROI), based on both surrounding intensity structures and shape characteristics. The initial estimate of the deformable mesh for the individual ROI was derived by transforming the template mesh to the individual image space using nonlinear warping. For the deformable surface model...