Image appearance cues are often used to derive object shapes, which is usually one of the key steps of image understanding tasks. However, when image appearance cues are weak or misleading, shape priors become critical to infer and refine the shape derived by these appearance cues. Effective modeling of shape priors is challenging because: 1) shape variation is complex and cannot always be modeled by a parametric probability distribution; 2) a shape instance derived from image appearance cues (input shape) may have gross errors; and 3) local details of the input shape are difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to deal with these three challenges in a unified framework. In our method, training shapes are adaptively composed to infer/refine an input shape. The a-priori information is thus implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: 1) the input shape can be approximately represented by a sparse linear combination of training shapes; 2) parts of the input shape may contain gross errors but such errors are usually sparse. Using L1 norm relaxation, our model is formulated as a convex optimization problem, which is solved by an efficient alternating minimization framework. Our method is extensively validated on two real world medical applications, 2D lung localization in X-ray images and 3D liver segmentation in low-dose CT scans. Compared to state-of-the-art methods, our model exhibits better performance in both studies.

Diagnostic magnetic resonance (MR) image quality is highly dependent on the position and orientation of the slice groups, due to the intrinsic high in-slice and low through-slice resolutions of MR imaging. Hence, the higher speed, accuracy, and reproducibility of automatic slice positioning, make it highly desirable over manual slice positioning. However, imaging artifacts, diseases, joint articulation, variations across ages and demographics as well as the extremely high performance requirements prevent state-of-the-art methods, such as volumetric registration, to be an off-the-shelf solution. In this paper, we address all these issues through an automatic slice positioning framework based on redundant and hierarchical learning. Our method has two hallmarks that are specifically designed to achieve high robustness and accuracy. 1) A redundant set of anatomy detectors are learned to provide local appearance cues. These detections are pruned and assembled according to a distributed anatomy model, which captures group-wise spatial configurations among anatomy primitives. This strategy brings about a high level of robustness and works even if a large portion of the target is distorted, missing, or occluded. 2) The detectors are learned and invoked in a hierarchical fashion, with each local detection scheduled and iterated according to its intrinsic invariance property. This iterative alignment process is shown to dramatically improve alignment accuracy. The proposed system is extensively validated on a large dataset including 744 clinical MR scans. Compared to state-of-the-art methods, our method exhibits superior performance in terms of robustness, accuracy, and reproducibility. The methodology is general and can be applied to other anatomies and other imaging modalities.

The speed and quality of imaging cardiac structures (coronary arteries, cardiac valves etc) in MR can be improved by tracking and predicting their motion in MR images. The problem is challenging not only due to the complex motion of these structures that significantly changes the appearance of the region of interest, but also the ability to track at different spatial and temporal resolutions depending on the application. We have developed a multiple-template based tracking approach to track the cardiac structures in MR images. The algorithm has two novel features. First a bidirectional coordinate-descent algorithm is derived to improve accuracy and performance of tracking. Second we propose a method for choosing an optimal set of templates for tracking. The efficacy of the algorithm has been validated by tracking the coronary artery and cardiac valves reliably and accurately in thousands of high resolution cine and low-re solution real-time MR images.

Traditionally, visual servoing is separated into tracking and control subsystems. This separation, though convenient, is not necessarily well justified. When tracking and control strategies are designed independently, it is not clear how to optimize them to achieve a certain task. In this work, we propose a framework in which spatial sampling kernels - borrowed from the tracking and registration literature - are used to design feedback controllers for visual servoing. The use of spatial sampling kernels provides natural hooks for Lyapunov theory, thus unifying tracking and control and providing a framework for optimizing a particular servoing task. As a first step, we develop kernel-based visual servos for a subset of relative motions between camera and target scene. The subset of motions we consider are 2D translation, scale, and roll of the target relative to the camera. Our approach provides formal guarantees on the convergence/stability of visual servoing algorithms under putatively generic conditions.

The design and development of methods for tracking targets in visual images has developed rapidly in the past decade. However, in practice the design of tracking algorithms is still largely ad-hoc, based on trial and error. As a result, the performance of such algorithms can vary widely based on the properties of the target of interest and the choice of design. The use of spatial sampling kernels on multiple feature spaces has recently emerged as a promising approach to visual target tracking. In particular, it is possible to show that most popular tracking algorithms can be expressed within this framework. As a result, sampling kernels can be viewed as a flexible design space for tracking algorithms. However, in the current approaches, the kernels are placed in an adhoc fashion at the center of the target with a scale equal to the size of the target. This can lead to sub-optimal tracking results. In this paper, we present results pointing toward the design of optimal and approximately optimal target-specific tracking algorithms. The target tracking problem is formulated in terms of an optimization over a family of kernelbased sampling functions. This optimization is solved to produce an optimal target-specific kernel configuration. Experimental results show greatly improved performance over classical template tracking and naive kernel-based tracking.