High fidelity system modeling for high quality image reconstruction in clinical CT.

Today, while many researchers focus on the improvement of the regularization term in IR algorithms, they pay less concern to the improvement of the fidelity term. In this paper, we hypothesize that improving the fidelity term will further improve IR image quality in low-dose scanning, which typically causes more noise. The purpose of this paper is to systematically test and examine the role of high-fidelity system models using raw data in the performance of iterative image reconstruction approach minimizing energy functional.

Unified anomaly suppression and boundary extraction in laser radar range imagery based on a joint curve-evolution and expectation-maximization algorithm.

In this paper, we develop a new unified approach for laser radar range anomaly suppression, range profiling, and segmentation. This approach combines an object-based hybrid scene model for representing the range distribution of the field and a statistical mixture model for the range data measurement noise. The image segmentation problem is formulated as a minimization problem which jointly estimates the target boundary together with the target region range variation and background range variation directly from the noisy and anomaly-filled range data.

A real-time algorithm for the approximation of level-set-based curve evolution.

In this paper, we present a complete and practical algorithm for the approximation of level-set-based curve evolution suitable for real-time implementation. In particular, we propose a two-cycle algorithm to approximate level-set-based curve evolution without the need of solving partial differential equations (PDEs). Our algorithm is applicable to a broad class of evolution speeds that can be viewed as composed of a data-dependent term and a curve smoothness regularization term.

A curve evolution approach to object-based tomographic reconstruction.

In this paper, we develop a new approach to tomographic reconstruction problems based on geometric curve evolution techniques. We use a small set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects. Instead of reconstructing pixel values on a fixed rectangular grid, we then find a reconstruction by jointly estimating these unknown contours and texture coefficients of the object and background.

4Pi spectral self-interference microscopy.

Spectral self-interference microscopy (SSM) relies on the balanced collection of light traveling two different paths from the sample to the detector, one direct and the other indirect from a reflecting substrate. The resulting spectral interference effects allow nanometer-scale axial localization of isolated emitters. To produce spectral fringes the difference between the two optical paths must be significant.

Spectral self-interference microscopy for low-signal nanoscale axial imaging.

A theoretical and numerical analysis of spectral self-interference microscopy (SSM) is presented with the goal of expanding the realm of SSM applications. In particular, this work is intended to enable SSM imaging in low-signal applications such as single-molecule studies. A comprehensive electromagnetic model for SSM is presented, allowing arbitrary forms of the excitation field, detection optics, and tensor sample response.

Coupled shape distribution-based segmentation of multiple objects.

In this paper we develop a multi-object prior shape model for use in curve evolution-based image segmentation. Our prior shape model is constructed from a family of shape distributions (cumulative distribution functions) of features related to the shape. Shape distribution-based object representations possess several desired properties, such as robustness, invariance, and good discriminative and generalizing properties.

Line detection in images through regularized Hough transform.

The problem of determining the location and orientation of straight lines in images is of great importance in the fields of computer vision and image processing. Traditionally the Hough transform, (a special case of the Radon transform) has been widely used to solve this problem for binary images. In this paper, we pose the problem of detecting straight lines in gray-scale images as an inverse problem.