Multimodal and multiprotocol image registration refers to the process of alignment of two or more images obtained from different imaging modalities (e.g. digitized histology and MRI) and protocols (e.g. T2-w and PD-w MRI). Registration is a critical component in medical applications including image guided surgery, image fusion for cancer diagnosis and treatment planning, and automated tissue annotation. However, registration is often complicated on account of differences in both the image intensities and the shape of the underlying anatomy. For example, non-linear differences in the overall shape of the prostate between in vivo MRI and ex vivo whole mount histology (WMH) often exist as a result of the presence of an endorectal coil during pre-operative MR imaging and deformations to the specimen during slide preparation. To overcome these challenges, we present new registration techniques termed Combined Feature Ensemble Mutual Information (COFEMI) and Collection of Image-derived Non-linear Attributes for Registration Using Splines (COLLINARUS). The goal COFEMI is to provide a similarity measure that is driven by unique low level textural features, for registration that is more robust to intensity artifacts and modality differences than measures restricted to intensities alone. COLLINARUS offers the robustness of COFEMI to artifacts and modality differences, while allowing fully automated non-linear image warping at multiple scales via a hierarchical B-spline mesh grid. In addition, since routine clinical imaging procedures often involve the acquisition of multiple imaging protocols, we present a technique termed Multi-attribute Combined Mutual Information (MACAMI) to leverage the availability of multiple image sets to improve registration. We apply our registration techniques to a unique clinical dataset comprising 150 sets of in vivo MRI and post-operative WMH images from 25 patient studies in order to retrospectively establish the spatial extent of prostate cancer (CaP) on structural (T2-w) and functional (DCE) in vivo MRI. Accurate mapping of CaP on MRI is used to facilitate the development and evaluation of a system for computer-assisted detection (CAD) of CaP on multiprotocol MRI. We also demonstrate our registration and CAD algorithms in developing radiation therapy treatment plans that provide dose escalation to CaP by elastically registering diagnostic MRI with planning CT.

Modern medicine has become reliant on medical imaging. Multiple modalities, e.g. magnetic resonance imaging (MRI), computed tomography (CT), etc., are used to provide as much information about the patient as possible. The problem of geometrically aligning the resulting images is called image registration. Mutual information, an information theoretic similarity measure, allows for automated intermodal image registration algorithms. In applications such as cancer therapy, diagnosticians are more concerned with the alignment of images over a region of interest such as a cancerous lesion, than over an entire image set. Attempts to register only the regions of interest, defined manually by diagnosticians, fail due to inaccurate mutual information estimation over the region of overlap of these small regions. This thesis examines the region of union as an alternative to the region of overlap. We demonstrate that the region of union improves the accuracy and reliability of mutual information estimation over small regions. We also present two new mutual information based similarity measures which allow for localized image registration by combining local and global image information. The new similarity measures are based on convex combinations of the information contained in the regions of interest and the information contained in the global images. Preliminary results indicate that the proposed similarity measures are capable of localizing image registration. Experiments using medical images from computer tomography and positron emission tomography demonstrate the initial success of these measures. Finally, in other applications, auto-detection of regions of interest may prove useful and would allow for fully automated localized image registration. We examine methods to automatically detect potential regions of interest based on local activity level and present some encouraging results.

International Association for Statistical Computing The International Association for Statistical Computing (IASC) is a Section of the International Statistical Institute. The objectives of the Association are to foster world-wide interest in e?ective statistical computing and to - change technical knowledge through international contacts and meetings - tween statisticians, computing professionals, organizations, institutions, g- ernments and the general public. The IASC organises its own Conferences, IASC World Conferences, and COMPSTAT in Europe. The 17th Conference of ERS-IASC, the biennial meeting of European - gional Section of the IASC was held in Rome August 28 - September 1, 2006. This conference took place in Rome exactly 20 years after the 7th COMP- STAT symposium which was held in Rome, in 1986. Previous COMPSTAT conferences were held in: Vienna (Austria, 1974); West-Berlin (Germany, 1976); Leiden (The Netherlands, 1978); Edimbourgh (UK, 1980); Toulouse (France, 1982); Prague (Czechoslovakia, 1984); Rome (Italy, 1986); Copenhagen (Denmark, 1988); Dubrovnik (Yugoslavia, 1990); Neuchˆ atel (Switzerland, 1992); Vienna (Austria,1994); Barcelona (Spain, 1996);Bristol(UK,1998);Utrecht(TheNetherlands,2000);Berlin(Germany, 2002); Prague (Czech Republic, 2004).

We perform the unbiased affine registration of 5 multimodality images of anatomy, CT, MR PD, T1 and T2 from Visible Human Male Data and the unbiased nonrigid registration of three MR 3D images of the brain with the normalized metric and high-dimensional histogramming . Our results demonstrate the efficacy of the metrics and high-dimensional histogramming in affine and nonrigid multimodality image registration.

Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid