<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianhu Lei</style></author><author><style face="normal" font="default" size="100%">Jayaram K Udupa</style></author><author><style face="normal" font="default" size="100%">Dewei Odhner</style></author><author><style face="normal" font="default" size="100%">László Gábor Nyúl</style></author><author><style face="normal" font="default" size="100%">Punam K Saha</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3DVIEWNIX-AVS: a software package for the separate visualization of arteries and veins in CE-MRA images</style></title><secondary-title><style face="normal" font="default" size="100%">COMPUTERIZED MEDICAL IMAGING AND GRAPHICS</style></secondary-title><short-title><style face="normal" font="default" size="100%">COMPUT MED IMAG GRAP</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2003///</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">351 - 362</style></pages><isbn><style face="normal" font="default" size="100%">0895-6111</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Our earlier study developed a computerized method, based onfuzzy connected object delineation principles and algorithms, 
for artery and vein separation in contrast enhanced Magnetic 
Resonance Angiography (CE-MRA) images. This paper reports its 
current development-a software package-for routine clinical use. 
The software package, termed 3DVIEWNIX-AVS, consists of the 
following major operational parts: (1) converting data from 
DICOM3 to 3DVIEWNIX format, (2) previewing slices and creating 
VOI and MIP Shell, (3) segmenting vessel, (4) separating artery 
and vein, (5) shell rendering vascular structures and creating 
animations.This package has been applied to EPIX Medical Inc's 
CE-MRA data (AngioMark MS-325). One hundred and thirty-five 
original CE-MRA data sets (of 52 patients) from 6 hospitals have 
been processed. In all case studies, unified parameter settings 
produce correct artery-vein separation. The current package is 
running on a Pentium PC under Linux and the total computation 
time per study is about 3 min.The strengths of this software 
package are (1) minimal user interaction, (2) minimal anatomic 
knowledge requirements on human vascular system, (3) clinically 
required speed, (4) free entry to any operational stages, (5) 
reproducible, reliable, high quality of results, and (6) cost 
effective computer implementation. To date, it seems to be the 
only software package (using an image processing approach) 
available for artery and vein separation of the human vascular 
system for routine use in a clinical setting.
</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><notes><style face="normal" font="default" size="100%">UT: 000184800600003ScopusID: 0038122922doi: 10.1016/S0895-6111(03)00029-6</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">László Gábor Nyúl</style></author><author><style face="normal" font="default" size="100%">Jayaram K Udupa</style></author><author><style face="normal" font="default" size="100%">Punam K Saha</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Incorporating a measure of local scale in voxel-based 3-D image registration</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE TRANSACTIONS ON MEDICAL IMAGING</style></secondary-title><short-title><style face="normal" font="default" size="100%">IEEE T MED IMAGING</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2003///</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">228 - 237</style></pages><isbn><style face="normal" font="default" size="100%">0278-0062</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new class of approaches for rigid-body registrationand their evaluation in studying multiple sclerosis (MS) via 
multiprotocol magnetic resonance imaging (MRI). Three pairs of 
rigid-body registration algorithms were implemented, using 
cross-correlation and mutual information (MI), operating on 
original gray-level images, and utilizing the intermediate 
images resulting from our new scale-based method. In the scale 
image, every voxel has the local &quot;scale&quot; value assigned to it, 
defined as the radius of the largest ball centered at the voxel 
with homogeneous intensities. Three-dimensional image data of 
the head were acquired from ten MS patients for each of six MRI 
protocols. Images in some of the protocols were acquired in 
registration. The registered pairs were used as ground truth. 
Accuracy and consistency of the six registration methods were 
measured within and between protocols for known amounts of 
misregistrations. Our analysis indicates that there is no &quot;best&quot; 
method. For medium misregistration, the method using MI, for 
small add large misregistration the method using normalized 
cross-correlation performs best. For high-resolution data the 
correlation method and for low-resolution data the MI method, 
both using the original gray-level images, are the most 
consistent. We have previously demonstrated the use of local 
scale information in fuzzy connectedness segmentation and image 
filtering. Scale may also have potential for image registration 
as suggested by this work.
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><notes><style face="normal" font="default" size="100%">UT: 000182391600009ScopusID: 0038398636doi: 10.1109/TMI.2002.808358</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">László Gábor Nyúl</style></author><author><style face="normal" font="default" size="100%">Jayaram K Udupa</style></author><author><style face="normal" font="default" size="100%">Punam K Saha</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Milan Sonka</style></author><author><style face="normal" font="default" size="100%">Kenneth M Hanson</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Task-specific comparison of 3D image registration methods</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Imaging 2001: Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2001///</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">SPIE</style></publisher><pub-location><style face="normal" font="default" size="100%">Bellingham; Washington</style></pub-location><pages><style face="normal" font="default" size="100%">1588 - 1598</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new class of approaches for rigid-body registrationand their evaluation in studying Multiple Sclerosis via multi 
protocol MRI. Two pairs of rigid-body registration algorithms 
were implemented, using cross- correlation and mutual 
information, operating on original gray-level images and on the 
intermediate images resulting from our new scale-based method. 
In the scale image, every voxel has the local scale value 
assigned to it, defined as the radius of the largest sphere 
centered at the voxel with homogeneous intensities. 3D data of 
the head were acquired from 10 MS patients using 6 MRI 
protocols. Images in some of the protocols have been acquired in 
registration. The co-registered pairs were used as ground truth. 
Accuracy and consistency of the 4 registration methods were 
measured within and between protocols for known amounts of 
misregistrations. Our analysis indicates that there is no best 
method. For medium and large misregistration, methods using 
mutual information, for small misregistration, and for the 
consistency tests, correlation methods using the original gray-
level images give the best results. We have previously 
demonstrated the use of local scale information in fuzzy 
connectedness segmentation and image filtering. Scale may also 
have considerable potential for image registration as suggested 
by this work.
</style></abstract><notes><style face="normal" font="default" size="100%">ScopusID: 0034843423doi: 10.1117/12.431044</style></notes></record></records></xml>