Theophano Mitsa

Digital halftoning technique using a blue-noise mask

A novel digital halftoning technique, by which the halftoning is achieved by a pixelwise comparison of the gray-scale image to an array (halftone screen), the blue-noise mask, is presented. This mask is designed so that the halftone image has blue-noise (high-frequency) characteristics in the frequency domain. The algorithm for the construction of the blue-noise mask and an algorithm for the construction of binary patterns with the same first-order but different second-order statistics are presented. Two psychovisual tests in which human subjects rated halftone patterns and images according to various criteria are also described.

Digital halftoning using a blue noise mask

We propose a new digital halftoning algorithm where the halftoning is achieved by a pixelwise comparison of the gray scale image against a nonimage array, the blue noise mask. The blue noise mask is constructed such that when thresholded at any level, the resulting binary pattern has the correct first order statistics, and also its power spectrum has blue noise (high frequency) characteristics which are visually pleasing. The construction of the blue noise mask is described and experimental results are shown. Also results from a phychovisual study are provided where subjects rated halftoned images that have the same first order but different second order statistics.

A physically based model for the registration of a 2D image sequence

This paper describes a new approach that registers two images based on a physically deformable model. No prior knowledge about the types of geometric transformations is required for registration. Instead, our approach simulates the manual registration process and models the registration as the deformation of an elastic material governed by physical laws. Before registration, the contours of the images are extracted. The registration consists of two steps. The physically deformable model is applied first to deform one contour, called the start contour, to match the other, the goal contour. The result is point correspondences between the contours. Then an intensity transformation is estimated from the matching points to warp one image towards the other.

Modeling data acquisition and the effects of patient motion in magnetic resonance imaging

In this paper, we present a comprehensive model for MR data acquisition in the presence of patient motion to provide a better understanding as to the source of motion artifacts. This model identifies and quantifies various sources of motion artifacts in 2-D Fourier imaging. We verify our model by comparing the results predicted by the model with actual MR images of phantoms subjected to motion with controlled parameters. We expect that the knowledge of the sources of artifacts will lead to new and better methods of compensating for them.

Modeling and suppression of amplitude artifacts due to z motion in MR imaging

Artifacts due to patient motion in the slice-selection direction (Z motion) have been a major source of MR image degradation for many years but have not been addressed as much as in-plane motion due to the complexity of modeling and correcting for the motion. In this paper we present a model and a detection and correction scheme for amplitude aberrations due to motion in the slice-selection direction. 1.