IMDSP-8.1

A Multiresolution Image Restoration Method for Photon Imaging Systems
Ghada Jammal (Technische Universitat Darmstadt), Albert Bijaoui (Observatoire de la Cote d'Azur)

Nuclear medicine imaging systems rely on photon detection as the basis of image formation. One of the major sources of error in these imaging systems is Poisson noise. In this paper, we develop a novel multiscale image restoration procedure for photon imaging systems. It consists in separating in the wavelet-domain data points which belong to structures from those due to the noisy background. The latter are replaced by coefficients obtained by the introduction of a multiresolution regularity constraint. The restored image represents therefore a compromise between fidelity to the data and fidelity to the a priori knowledge on the smoothness of the solution. The performance of the method is assessed with simulated data experiments.

IMDSP-8.2

ROI Extraction from Motion Affected MRI Images based on Fuzzy and Active Contour Models
Chaminda Weerasinghe, Lilian Ji, Hong Yan (University of Sydney)

A new method for extracting the boundary of the region of interest (ROI) from motion affected magnetic resonance images (MRI) is presented. An image pre-processing stage is included to suppress prominent ghost artifacts and excessive blurring in the background, in order to facilitate the contour extraction algorithm. The pre-processing stage consists of a novel fuzzy model, incorporating a technique of hierarchical view by view image reconstruction. The contour extraction is performed using an intelligent, attractable active contour model (Snakes), which is capable of driving any initial guess in the area of the evolving estimate towards the desired contour, and fitting in to the object without any overrun. The proposed method has been applied to spin echo MRI images affected by rotational motion, producing good results.

IMDSP-8.3

Measurements of Blood Vessel Wall Areas in Black-Blood MR Images Using Global Minimum Snake Algorithm
Eugene Lin, Jenq-Neng Hwang (Department of Electrical Engineering, University of Washington), Chun Yuan (Department of Radiology, University of Washington)

In this paper, we propose a novel boundary detection approach for three-dimensional shape modeling. Our method is based on finding surfaces of minimal weighted area in a Riemannian metric. In order to take advantage of intensity information of images, we further integrate this intensity information into the boundary detection algorithm. We apply this algorithm to identify the inner and outer boundaries of the blood vessel wall in magnetic resonance images, and assess its accuracy and reproducibility. Our algorithm is reasonably accurate (about 2% difference in comparison with the manual method) and highly reproducible.

IMDSP-8.4

SEGMENTING TELOMERES AND CHROMOSOMES IN CELLS
Steven S.S. Poon (Xillix Technologies Corp.), Rabab K Ward (University of British Columbia), Peter M Lansdorp (British Columbia Cancer Research Centre)

The very end of every chromosome is a region called the telomere. Telomeres are nucleo-protein complexes containing specific DNA repeat sequences whose lengths are strongly believed to give indications to aging and tumor progression. In order to study the role these repeat sequences play in the cell, we developed a fluorescence microscopy imaging system and associated image analysis methods to accurately measure these telomere lengths. To visualize the image of the tiny telomeres, we captured 2 spectrally different images of the same cell. One image contains only telomeres and the other contains only chromosomes. We next apply successful and novel methods to segment the telomere and chromosome images and then to link each chromosome with its telomeres. Our system is so far the only existing system available for this purpose and has already been in use in many research laboratories in Western Europe, North America, and Hong Kong.

IMDSP-8.5

Improved Emission Tomography via Multiscale Sinogram Analysis
Klaus E Timmermann, Robert D Nowak, Keith J Jones (Michigan State University)

In this paper, we extend a multiscale Bayesian approach to modeling and estimation of general Poisson processes previously developed in by the first two authors, and apply it to the emission computed tomography (ECT) image reconstruction problem. We develop a practical prior model for the sinogram image, which we use to estimate the underlying sinogram intensity from the raw projection data prior reconstruction. This sinogram estimate is then used in conjunction with the standard filtered-backprojection algorithm to produce an improved image reconstruction. The impact of the new filtering approach on ECT imaging is illustrated with simulated and clinical data.

IMDSP-8.6

Bayesian MAP Restoration of Scintigraphic Images
Mai Khuong Nguyen (ETIS (CNRS URA 2235-ENSEA-Universite de Cergy-Pontoise), 6 Avenue du Ponceau, 95014 Cergy-Pontoise Cedex, FRANCE), Herve Guillemin (Laboratoire de Biophysique, Faculte de Medecine de Besancon, 2 Place St Jacques, 25000 Besancon), Patrick Duvaut (ETIS (CNRS URA 2235-ENSEA-Universite de Cergy-Pontoise), 6 avenue du Ponceau, 95014 Cergy-Pontoise Cedex, FRANCE)

We are interested in the problem of restoring scintigraphic images acquired by the gamma detector in nuclear medicine. The aim is to improve the detectability of possible heterogeneous areas in different organs. We propose to solve the problem in the Bayesian framework with Maximum A Posteriori (MAP) principle. The prior information was modeled by a Markov Random Field (MRF). The optimization is based on two kinds of methods: the stochastic algorithm of simulated annealing with a Gibbs sampler, and the deterministic algorithm of Graduated Non Convexity (GNC). We compared the results to the images restored by the Metz filter, more classical in this field. We applied these methods to the restoration of cold and warm nodules in the thyroid gland. We noticed the superiority of the proposed methods in terms of contrast around the nodules and uniformity in the images.

IMDSP-8.7

Active Contours: An Overview with Applications to Motion Artifact
Diana L Guidry (CVIP Lab, University of Louisville, KY, USA), Aly A Farag (Computer Vision & Image Processing Lab, E. E. dept)

Motion can be estimated by detecting the edges of a moving object using Active Contours, and registering them together to obtain the motion model parameters. This idea can be applied to patient motion during the acquisition of an MRI to eliminate motion artifacts in the image. The data obtained during the MRI acquistion, the k-space, can be divided into several subbands such that each subband is acquired in a small fraction of the full imaging time. These subbands create invariant tissue feature maps called subband images. Using Active Contours, the relative motion is analyzed across the different subband images to determine the motion parameters. Using these motion parameters it is possible to correct the subbands, thus correcting the k-space. This has the potential to yield clear, noise-free MR images

IMDSP-8.8

Modified Fuzzy C-Mean in Medical Image Segmentation
Nevin A. Mohamed, Aly A. Farag, Mohamed N. Ahmed (CVIP Lab, E.E. dept, University of Louisville, USA)

This paper describes the application of fuzzy set theory in medical imaging, namely the segmentation of brain images. We propose a fully automatic technique to obtain image clusters. A fuzzy c-mean classification algorithm is used to provide a fuzzy partition of the image. In this paper we will be introducing a new method of clustering using the FCM. Our new method is inspired from the Markov Random Field (MRF). We applied the new method on a noisy CT scan and on a single channel MR scan. We recommend using a methodology of over segmentation to the textured MR scan and a user guided interface to obtain the final clusters. One of the application of this technique is TBI recovery prediction in which it is important to consider the partial volume. It is shown that after a number of iterations the system stabilizes with the membership value of the region contours reflecting the partial volume value. The final stage of the process is devoted to decision making or the defuzzification process.

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Last Update: February 4, 1999 Ingo Höntsch