Publications by Year: 2005

Olivier Clatz, Hervé Delingette, Ion-Florin Talos, Alexandra J Golby, Ron Kikinis, Ferenc A Jolesz, Nicholas Ayache, and Simon K Warfield. 2005. “Hybrid Formulation of the Model-based Non-rigid Registration Problem to Improve Accuracy and Robustness.” Med Image Comput Comput Assist Interv, 8, Pt 2, Pp. 295-302.Abstract
We present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images with intra-operative MR images of the brain. This algorithm relies on a robust estimation of the deformation from a sparse set of measured displacements. We propose a new framework to compute iteratively the displacement field starting from an approximation formulation (minimizing the sum of a regularization term and a data error term) and converging toward an interpolation formulation (least square minimization of the data error term). The robustness of the algorithm is achieved through the introduction of an outliers rejection step in this gradual registration process. We ensure the validity of the deformation by the use of a biomechanical model of the brain specific to the patient, discretized with the finite element method. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift up to 13 mm.
Steven Haker, William M Wells, Simon K Warfield, Ion-Florin Talos, Jui G Bhagwat, Daniel Goldberg-Zimring, Asim Mian, Lucila Ohno-Machado, and Kelly H Zou. 2005. “Combining Classifiers using their Receiver Operating Characteristics and Maximum Likelihood Estimation.” Med Image Comput Comput Assist Interv, 8, Pt 1, Pp. 506-14.Abstract

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Yan Yang, Lei Zhu, Steven Haker, Allen R Tannenbaum, and Don P Giddens. 2005. “Harmonic Skeleton Guided Evaluation of Stenoses in Human Coronary Arteries.” Med Image Comput Comput Assist Interv, 8, Pt 1, Pp. 490-7.Abstract

This paper presents a novel approach that three-dimensionally visualizes and evaluates stenoses in human coronary arteries by using harmonic skeletons. A harmonic skeleton is the center line of a multi-branched tubular surface extracted based on a harmonic function, which is the solution of the Laplace equation. This skeletonization method guarantees smoothness and connectivity and provides a fast and straightforward way to calculate local cross-sectional areas of the arteries, and thus provides the possibility to localize and evaluate coronary artery stenosis, which is a commonly seen pathology in coronary artery disease.

Steven J Haker, Robert V. Mulkern, Joseph R Roebuck, Agnieszka Szot Barnes, Simon DiMaio, Nobuhiko Hata, and Clare M Tempany. 2005. “Magnetic Resonance Guided Prostate Interventions.” Top Magn Reson Imaging, 16, 5, Pp. 355-68.Abstract

We review our experience using an open 0.5-T magnetic resonance (MR) interventional unit to guide procedures in the prostate. This system allows access to the patient and real-time MR imaging simultaneously and has made it possible to perform prostate biopsy and brachytherapy under MR guidance. We review MR imaging of the prostate and its use in targeted therapy, and describe our use of image processing methods such as image registration to further facilitate precise targeting. We describe current developments with a robot assist system being developed to aid radioactive seed placement.

Lei Zhu, Steven Haker, and Allen Tannenbaum. 2005. “Mass Preserving Registration for Heart MR Images.” Med Image Comput Comput Assist Interv, 8, Pt 2, Pp. 147-54.Abstract

This paper presents a new algorithm for non-rigid registration between two doubly-connected regions. Our algorithm is based on harmonic analysis and the theory of optimal mass transport. It assumes an underlining continuum model, in which the total amount of mass is exactly preserved during the transformation of tissues. We use a finite element approach to numerically implement the algorithm.

Delphine Nain, Steven Haker, Aaron Bobick, and Allen R Tannenbaum. 2005. “Multiscale 3D Shape Analysis using Spherical Wavelets.” Med Image Comput Comput Assist Interv, 8, Pt 2, Pp. 459-67.Abstract

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.