Ghassan Hamarneh

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Automated segmentation and analysis of tree-like structures from 3D medical images are important for many medical applications, such as those dealing with blood vasculature or lung airways. However, there is an absence of large databases of expert segmentations and analyses of such 3D medical images, which impedes the validation and training of proposed(More)
We propose a novel method for applying active learning strategies to interactive 3D image segmentation. Active learning has been recently introduced to the field of image segmentation. However, so far discussions have focused on 2D images only. Here, we frame interactive 3D image segmentation as a classification problem and incorporate active learning in(More)
We present a fully automated multimodal medical image matching technique. Our method extends the concepts used in the computer vision SIFT technique for extracting and matching distinctive scale invariant features in 2D scalar images to scalar images of arbitrary dimensionality. This extension involves using hyperspherical coordinates for gradients and(More)
We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting(More)
We review methods designed to compute correspondences between geometric shapes represented by triangle meshes, contours, or point sets. This survey is motivated in part by recent developments in space-time registration, where one seeks a correspondence between non-rigid and time-varying surfaces, and semantic shape analysis, which underlines a recent trend(More)
We introduce a new approach to medical image analysis that combines deformable model methodologies with concepts from the field of artificial life. In particular, we propose "deformable organisms", autonomous agents whose task is the automatic segmentation, labeling, and quantitative analysis of anatomical structures in medical images. Analogous to natural(More)
This paper incorporates multiscale vesselness filtering into the Livewire framework to simultaneously compute optimal medial axes and boundaries in vascular images. To this end, we extend the existing 2D graph search to 3D space to optimize not only for spatial variables (x, y), but also for radius values r at each node. In addition, we minimize change for(More)
In this work, we propose and compare several methods for the visualization and exploration of time-varying volumetric medical images based on the temporal characteristics of the data. The principle idea is to consider a time-varying data set as a 3D array where each voxel contains a time-activity curve (TAC). We define and appraise three different TAC(More)
Spinal cord analysis is an important problem relating to the study of various neurological diseases. We present a novel approach to spinal cord segmentation in magnetic resonance images. Our method uses 3D "deformable organisms" (DefOrg) an artificial life framework for medical image analysis that complements classical deformable models (snakes and(More)
Medial representations of shapes are useful due to their use of an object-centered coordinate system that directly captures intuitive notions of shape such as thickness, bending, and elongation. However, it is well known that an object's medial axis transform (MAT) is unstable with respect to small perturbations of its boundary. This instability results in(More)