Method and System for Hierarchical Parsing and Semantic Navigation of Full Body Computed Tomography Data
A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body... more A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body computed tomography CT scan is disclosed. In particular, organs are segmented and anatomic landmarks are detected in a full or partial body CT volume. One or more predetermined slices of the CT volume are detected. A plurality of anatomic landmarks and organ centers are then detected in the CT volume using a discriminative anatomical network, each detected in a portion of the CT volume constrained by at least one of the ...
Long term motion analysis poses many standing challenges that need to be addressed for advancing ... more Long term motion analysis poses many standing challenges that need to be addressed for advancing this field. One of these challenges is finding algorithms that correctly handle occlusion and can detect when a pixel trajectory needs to be stopped. Very few optical algorithms provide an occlusion map and are appropriate for this task. Another challenge is finding a framework for the accurate evaluation of the motion field produced by an algorithm. This work makes two contributions in these directions. First, it presents a RMSE based error measure for evaluating feature tracking algorithms on sequences with rigid motion under the affine camera model. The proposed measure was observed to be consistent with the relative ranking of a number of optical flow algorithms on the Middlebury dataset. Second, it introduces a feature tracking algorithm based on RankBoost that automatically prunes bad trajectories obtained by an optical flow algorithm. The proposed feature tracking algorithm is observed to outperform many feature trackers based on optical flow using both the proposed measure and an indirect measure based on motion segmentation.
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009
Many computer vision problems can be formulated in a Bayesian framework with Markov Random Field ... more Many computer vision problems can be formulated in a Bayesian framework with Markov Random Field (MRF) or Conditional Random Field (CRF) priors. Usually, the model assumes that a full Maximum A Posteriori (MAP) estimation will be performed for inference, which can be really slow in practice. In this paper, we argue that through appropriate training, a MRF/CRF model can be trained to perform very well on a suboptimal inference algorithm. The model is trained together with a fast inference algorithm through an optimization of a loss function on a training set containing pairs of input images and desired outputs. A validation set can be used in this approach to estimate the generalization performance of the trained system. We apply the proposed method to an image denoising application, training a Fields of Experts MRF together with a 1-4 iteration gradient descent inference algorithm. Experimental validation on unseen data shows that the proposed training approach obtains an improved benchmark performance as well as a 1000-3000 times speedup compared to the Fields of Experts MRF trained with contrastive divergence. Using the new approach, image denoising can be performed in real-time, at 8fps on a single CPU for a 256 × 256 image sequence, with close to state-of-the-art accuracy.
System and method for simultaneously subsampling fluoroscopic images and enhancing guidewire visibility
A method for downsampling fluoroscopic images and enhancing guidewire visibility during coronary ... more A method for downsampling fluoroscopic images and enhancing guidewire visibility during coronary angioplasty includes providing a first digitized image, filtering the image with one or more steerable filters of different angular orientations, assigning a weight W and orientation O for each pixel based on the filter response for each pixel, wherein each pixel weight is assigned to a function of a maximum filter response magnitude and the pixel orientation is calculated from the angle producing the maximum filter response if the ...
System and method for coronary digital subtraction angiography
A method and system for extracting motion-based layers from fluoroscopic image sequences are disc... more A method and system for extracting motion-based layers from fluoroscopic image sequences are disclosed. Portions of multiple objects, such as anatomical structures, are detected in the fluoroscopic images. Motion of the objects is estimated between the images is the sequence of fluoroscopic images. The images in the fluoroscopic image sequence are then divided into layers based on the estimated motion. In a particular implementation, the coronary vessel tree and the diaphragm can be extracted in separate motion layers from coronary ...
Journal of Intelligent and Robotic Systems: Theory and Applications, 2013
Stochastic Clustering Auctions (SCAs) constitute a class of cooperative auction methods that enab... more Stochastic Clustering Auctions (SCAs) constitute a class of cooperative auction methods that enable improvement of the global cost of the task allocations obtained with fast greedy algorithms. Prior research had developed Contracts Sequencing Algorithms (CSAs) that are deterministic and enable transfers, swaps, and other types of contracts between team members. In contrast to CSAs, SCAs use stochastic transfers or swaps between the task clusters assigned to each team member and have algorithm parameters that can enable tradeoffs between optimality and computational and communication requirements. The first SCA was based on a "Gibbs Sampler" and constrained the stochastic cluster reallocations to simple single transfers or swaps; it is applicable to heterogeneous teams. Subsequently, a more efficient SCA was developed, based on the generalized Swendsen-Wang method; it achieves the increased efficiency by connecting tasks that appear to be synergistic and then stochastically reassigning these connected tasks, hence enabling more complex and efficient movements between clusters than the first SCA. However, its application was limited to homogeneous teams. The contribution of this work is to present an efficient SCA for heterogeneous teams; it is based on a modified Swendsen-Wang method. For centralized auctioning and homogeneous teams, extensive numerical experiments were used to provide a comparison in terms of costs and computational and communication requirements of the three SCAs and a baseline CSA. It was seen that the new SCA maintains the efficiency of the second SCA and can yield similar performance to the baseline CSA in far fewer iterations. The same metrics were used to evaluate the performance of the new SCA for heterogeneous teams. A distributed version of the new SCA was also evaluated in numerical experiments. The results show that, as expected, the distributed SCA continually improves the global performance with each iteration, but converges to a lower cost solution than the centralized SCA.
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Papers by Adrian Barbu