00577nas a2200181 4500008004100000245008800041210006900129260003400198100001600232700001400248700001400262700001200276700001900288700001900307700001300326700001600339856004000355 2015 eng d00aHypothesis-Exploring Methods for Automated Meta-Analyes of Brain Imaging Literature0 aHypothesisExploring Methods for Automated MetaAnalyes of Brain I aMonterey, Californiac06/20151 aTaswell, K.1 aCraig, A.1 aLeung, D.1 aLoh, S.1 aSkarzynski, M.1 aGharabaghi, S.1 aZhou, B.1 aTaswell, C. uhttp://knoesis.wright.edu/node/273401002nas a2200205 4500008004100000245005300041210005300094520041800147100002100565700002200586700002000608700001600628700001800644700001900662700002100681700002000702700001700722700001700739856004000756 2014 eng d00aInteractive Visualization of GRT and BioHTS Data0 aInteractive Visualization of GRT and BioHTS Data3 aThe scope of this project is to provide better tools for statistical and informational visual analysis for High Throughput Screening of Biological Infectious Agents (BioHTS), General Recognition Theory (GRT) modeling, and areas where pipelines of unstructured datasets of all types must be analyzed. A parallel coordinates plot is one of the more effective visualization methods for visualizing multivariate data.1 aGharabaghi, Sara1 aWischgoll, Thomas1 aVickery, Rhonda1 aSmith, Ross1 aBlaha, Leslie1 aLamkin, Thomas1 aKawamoto, Steven1 aTrevino, Robert1 aBardes, Eric1 aTabar, Scott uhttp://knoesis.wright.edu/node/223501489nas a2200157 4500008004100000245005800041210005800099260001200157300001200169490000700181520104900188100001901237700001801256700001701274856004001291 2012 eng d00aRetinal Image Registration Using Geometrical Features0 aRetinal Image Registration Using Geometrical Features c06/2012 a248-2580 v263 aIn this study, we have introduced an accurate retinal images registration method using affine moment invariants (AMI's) which are the shape descriptors. First, some closed-boundary regions are extracted in both reference and sensed images. Then, AMI's are computed for each of those regions. The centers of gravity of three pairs of regions which have the minimum of distances are selected as the control points. The region matching is performed by the distance measurements of AMI's. The evaluation of region matching is performed by comparing the angles of three triangles which are built on these three-point pairs in reference and sensed images. The parameters of affine transform can be computed using these three pairs of control points. The proposed algorithm is applied on the valid DRIVE database. In general (for the case, each sensed image is produced by rotating, scaling, and translating the reference image with different angles, scale factors, and translation factors), the success rate and accuracy is 95 and 96 %, respectively.1 aGharabaghi, S.1 aDaneshvar, S.1 aSedaaghi, M. uhttp://knoesis.wright.edu/node/2735