Dambreville S, Rathi Y, Tannenbaum A Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2006;:977-984 PMID: 23685633 Abstract Segmentation involves separating an object from the background. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, within the level-set framework. Following the work… read more →
Bouix S, Siddiqi K, Tannenbaum A Med Image Anal 2005 Jun;9(3):209-21 PMID: 15854842 Abstract We present a fast, robust and automatic method for computing centerline paths through tubular structures for application to virtual endoscopy. The key idea is to utilize a skeletonization algorithm which exploits properties of the average outward… read more →
Bouix S, Pruessner JC, Louis Collins D, Siddiqi K Neuroimage 2005 May;25(4):1077-89 PMID: 15850726 Abstract In magnetic resonance imaging (MRI) research, significant attention has been paid to the analysis of the hippocampus (HC) within the medial temporal lobe because of its importance in memory and learning, and its role in… read more →
Gao Y, Zhu LJ, Bouix S, Tannenbaum A Proc Soc Photo Opt Instrum Eng 2014 Mar;9034:90342X PMID: 25302008 Abstract Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only… read more →
Gao Y, Tannenbaum A, Bouix S Proc Soc Photo Opt Instrum Eng 2014 Mar;9034:90340V PMID: 25302006 Abstract Techniques in medical image analysis are many times used for the comparison or regression on the intensities of images. In general, the domain of the image is a given Cartesian grids. Shape analysis,… read more →
Hong Y, Gao Y, Niethammer M, Bouix S Med Image Comput Comput Assist Interv 2014;17(Pt 3):17-24 PMID: 25320777 Abstract In this paper we propose a new method for shape analysis based on the depth-ordering of shapes. We use this depth-ordering to non-parametrically define depth with respect to a normal control… read more →
Paniagua B, Lyall AE, Berger JB, Vachet C, Hamer RM, Woolson S, Lin W, Gilmore J, Styner M Proc SPIE Int Soc Opt Eng 2013 Mar;8672 PMID: 23606800 Abstract Statistical shape analysis has emerged as an insightful method for evaluating brain structures in neuroimaging studies, however most shape frameworks are… read more →
Rathi Y, Vaswani N, Tannenbaum A IEEE Trans Image Process 2007 May;16(5):1370-82 PMID: 17491466 Abstract Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters (PFs) have been proposed for tracking such objects,… read more →
Gao Y, Riklin-Raviv T, Bouix S Hum Brain Mapp 2014 Oct;35(10):4965-78 PMID: 24753006 Abstract In the last two decades, the statistical analysis of shape has become an actively studied field and finds applications in a wide range of areas. In addition to algorithmic development, many researchers have distributed end-user orientated… read more →
Gao Y, Bouix S, Shenton ME, Tannenbaum A IEEE Trans Image Process 2013 Oct;22(10):3866-78 PMID: 23799695 Abstract In image segmentation, we are often interested in using certain quantities to characterize the object, and perform the classification based on criteria such as mean intensity, gradient magnitude, and responses to certain predefined… read more →
