http://www.midasjournal.org The Midas Journal Copyright www.midasjournal.org http://www.midasjournal.org/images/IJLogo2.gif http://www.midasjournal.org Thu, 04 Dec 2008 11:46:49 -0500 Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/291 We have created open-source Simulink block libraries for ITK and VTK that allow pipelines for these toolkits to be built in a visual, drag-and-drop style within MATLAB. Each block contains an instance of an ITK or VTK class. Any block connections and parameters that the user makes within MATLAB's Simulink visual environment are converted into connections and parameters for the ITK and VTK pipelines. In addition, we provide conversion of images to and from MATLAB arrays to allow MATLAB image processing blocks to be mixed with ITK and VTK blocks. The code for our block libraries is generated automatically from XML descriptions of the inputs, outputs, and parameters of the ITK and VTK classes. We have used these block libraries to build some example pipelines and believe that they will be useful for developing applications in image analysis and image-guided therapy. Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/309 This paper describes the setup of a segmentation competition for the automatic extraction of Multiple Sclerosis (MS) lesions from brain Magnetic Resonance Imaging (MRI) data. This competition is one of three competitions that make up a comparison workshop at the 2008 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference and was modeled after the successful comparison workshop on liver and caudate segmentation at the 2007 MICCAI conference. In this paper, the rationale for organizing the competition is discussed, the training and test data sets for both segmentation tasks are described and the scoring system used to evaluate the segmentation is presented. Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/309 This paper describes the setup of a segmentation competition for the automatic extraction of Multiple Sclerosis (MS) lesions from brain Magnetic Resonance Imaging (MRI) data. This competition is one of three competitions that make up a comparison workshop at the 2008 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference and was modeled after the successful comparison workshop on liver and caudate segmentation at the 2007 MICCAI conference. In this paper, the rationale for organizing the competition is discussed, the training and test data sets for both segmentation tasks are described and the scoring system used to evaluate the segmentation is presented. Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/309 This paper describes the setup of a segmentation competition for the automatic extraction of Multiple Sclerosis (MS) lesions from brain Magnetic Resonance Imaging (MRI) data. This competition is one of three competitions that make up a comparison workshop at the 2008 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference and was modeled after the successful comparison workshop on liver and caudate segmentation at the 2007 MICCAI conference. In this paper, the rationale for organizing the competition is discussed, the training and test data sets for both segmentation tasks are described and the scoring system used to evaluate the segmentation is presented. Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/276 This paper presents a new fully automatic method for segmentation of brain images that possess multiple sclerosis (MS) lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing with cortical unfolding or diffeomorphic shape analysis techniques. Validation on data from two studies demonstrates that the method has an accuracy comparable with other MS lesion segmentation methods, while simultaneously segmenting the whole brain. Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/245 In this paper the Coronary Artery Tracking competition, which was part of the workshop: "3D Segmentation in the Clinic: A Grand Challenge II" is described. This workshopwas held during the 2008 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference. An introduction is given to underline the importance of (semi-)automatic coronary artery centerline extraction methods and the advantages of an online framework facilitating a fair comparison of these methods. Furthermore, information is provided about the set-up of the workshop, the evaluation measures used and the online framework. Results for the algorithms, submitted by both industrial and academic research institutes, are presented as well. Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/290 This document describes a novel scheme for the automated extraction of the central lumen lines of coronary arteries from computed tomography angiography (CTA) data. The scheme first obtains a seg- mentation of the whole coronary tree and subsequently extracts the centerlines from this segmentation. The first steps of the segmentation algorithm consist of the detection of the aorta and the entire heart region. Next, candidate coronary artery components are detected in the heart region after the masking of the cardiac blood pools. Based on their location and geometrical properties the structures representing the right and left arterties are selected from the candidate list. Starting from the aorta, connections between these structures are made resulting in a final segmentation of the whole coronary artery tree, A fast-marching level set method combined with a backtracking algorithm is employed to obtain the initial centerlines within this segmentation. For all vessels a curved multiplanar reformatted image (CMPR) is constructed and used to detect the lumen contours. The final centerline was then defined by determining the center of gravity of the detected lumen in the transversal CMPR slices. Within the scope of the MICCAI Challenge "Coronary Artery Tracking 2008", the coronary tree segmentation and centerline extraction scheme was used to automatically detect a set of centerlines in 24 datasets. For 8 data sets reference centerlines were available. This training data was used during the development and tuning of the algorithm. Sixteen other data sets were provided as testing data. Evaluation of the proposed methodology was performed through submission of the resulting centerlines to the MICCAI Challenge website Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/303 A new Combinatorial Ricci curvature and Laplacian operators for grayscale images are introduced and tested on 2D medical images. These notions are based upon more general concepts developed by R. Forman. Further applications are also suggested. Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/305 The task of classifying or labeling cortical sulci is made difficult by the fact that individual sulci may not have unique distinguishing features and usually need to be identified by a multivariate feature set that takes the relative spatial arrangement into account. In this paper, classical multidimensional scaling (MDS), which gives a geometric interpretation to input dissimilarity data, is used to classify 180 sulci drawn from the ten major classes of sulci. Using a leave-one-out validation strategy, we acheive a success rate of 100% in the best case and 78% in the worst case. For these more difficult cases, we propose a second stage of classification using shape based features. One of these features is the geodesic distance between sulcal curves obtained from a new open curve representation in a geometric framework. With MDS, we offer a simple and intuitive approach to a challenging problem. Not only can we easily separate left and right brain sulci, but we also narrow the classification problem from, in this case, a 10-class to a 2-class problem. More generally, we can identify a region-of-interest (ROI) within which one can carry out further classification. Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/308 The field of computational anatomy has developed rigorous frameworks for analyzing anatomical shape, based on diffeomorphic transformations of a template. However, differences in algorithms used for template warping, in regularization parameters, and in the template itself, lead to different representations of the same anatomy. Variations of these parameters are considered as confounding factors. Recently, extensions of the conventional computational anatomy framework to account for such confounding variations has shown that learning the equivalence class derived from the multitude of representations can lead to improved and more stable morphological descriptors. Herein, we follow that approach, estimating the morphological appearance manifold obtained by varying parameters of the template warping procedure. Our approach parallels work in the computer vision field, in which variations lighting, pose and other parameters leads to image appearance manifolds representing the exact same figure in different ways. The proposed framework is then used for groupwise registration and statistical analysis of biomedical images, by employing a minimum variance criterion to perform manifold-constrained optimization, i.e. to traverse each individual’s morphological appearance manifold until all individuals' representations come as close to each other as possible. Effectively, this process removes the aforementioned confounding effects and potentially leads to morphological representations reflecting purely biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local approximations of the manifold via PCA. Thu, 04 Dec 2008 11:46:49 -0500 http://www.midasjournal.org//browse/publication/307 We propose a new clustering algorithm. This algorithm performs clustering and manifold learning simultaneously by using a graph-theoretical approach to manifold learning. We apply this algorithm in order to cluster white matter fiber tracts obtained fromDiffusion TensorMRI (DT-MRI) through streamline tractography. Our algorithm is able perform clustering of these fiber tracts incorporating information about the shape of the fiber and a priori knowledge as the probability of the fiber belonging to known anatomical structures. This anatomical knowledge is incorporated as a volumetric white matter atlas, in this case LONI's ICBM DTI-81 Thu, 04 Dec 2008 11:46:49 -0500