<?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></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative analysis of pulmonary airway tree structures</style></title><secondary-title><style face="normal" font="default" size="100%">COMPUTERS IN BIOLOGY AND MEDICINE</style></secondary-title><short-title><style face="normal" font="default" size="100%">COMPUT BIOL MED</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2006///</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">ANTIGA L, 2003, IEEE T MED IMAGING, V22, P674, DOI10.1109/TMI.2003.812261
AYLWARD SR, 2002, IEEE T MED IMAGING, V21, P61
BLAND JM, 1986, LANCET, V1, P307
BORGEFORS G, 1984, COMPUT VISION GRAPH, V27, P321
BOUIX S, 2003, IEEE C COMP VIS PATT, P449
CHEN ZK, </style></pub-location><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">974 - 996</style></pages><isbn><style face="normal" font="default" size="100%">0010-4825</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A method for computationally efficient skeletonization of three-dimensional tubular structures is reported. The method is specifically targeting skeletonization of vascular and airway tree structures in medical images but it is general and applicable to many other skeletonization tasks. The developed approach builds on the following novel concepts and properties: fast curve-thinning algorithm to increase computational speed, endpoint re-checking to avoid generation of spurious side branches, depth-and-length sensitive pruning, and exact tree-branch partitioning allowing branch volume and surface measurements. The method was validated in computer and physical phantoms and in vivo CT scans of human lungs. The validation studies demonstrated sub-voxel accuracy of branch point positioning, insensitivity to changes of object orientation, and high reproducibility of derived quantitative indices of the tubular structures offering a significant improvement over previously reported methods (p ≪ 0.001). © 2005 Elsevier Ltd. All rights reserved.</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue><notes><style face="normal" font="default" size="100%">UT: 000239889900004ScopusID: 33746349840doi: 10.1016/j.compbiomed.2005.05.004</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Matching and anatomical labeling of human airway tree</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%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2005///</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">BALLARD DH, 1982, COMPUTER VISIONBOYDEN EA, 1955, SEGMENTAL ANATOMY LU
CARRAGHAN R, 1990, OPER RES LETT, V9, P375
GAREY MR, 1979, COMPUTERS INTRACTABI
KITAOKA H, 2002, P MICCAI 2002 TOKYO, P1
MORI K, 2000, IEEE T MED IMAGING, V19, P103
PALAGYI K, 2003, LE</style></pub-location><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">1540 - 1547</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%">Matching of corresponding branchpoints between two human airway trees, as well as assigning anatomical names to the segments and branchpoints of the human airway tree, are of significant interest for clinical applications and physiological studies. In the past, these tasks were often performed manually due to the lack of automated algorithms that can tolerate false branches and anatomical variability typical for in vivo trees. In this paper, we present algorithms that perform both matching of branchpoints and anatomical labeling of in vivo trees without any human intervention and within a short computing time. No hand-pruning of false branches is required. The results from the automated methods show a high degree of accuracy when validated against reference data provided by human experts. 92.9% of the verifiable branchpoint matches found by the computer agree with experts' results. For anatomical labeling, 97.1 % of the automatically assigned segment labels were found to be correct. © 2005 IEEE.</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><notes><style face="normal" font="default" size="100%">UT: 000233779000002ScopusID: 29144483584doi: 10.1109/TMI.2005.857653</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of intrathoracic airway trees: Methods and in vivo validation</style></title><secondary-title><style face="normal" font="default" size="100%">LECTURE NOTES IN COMPUTER SCIENCE</style></secondary-title><short-title><style face="normal" font="default" size="100%">LECT NOTES COMPUT SCI</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2004///</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">BLAND JM, 1986, LANCET, V1, P307CHEN ZK, 2003, COMPUT MED IMAG GRAP, V27, P469, DOI
10.1016/S0895-6111(03)00039-9
GERIG G, 1993, LECT NOTES COMPUTER, V687, P94
KITAOKA H, 1999, J APPL PHYSIOL, V87, P2207
KONG TY, 1989, COMPUT VISION GRAPH, V48, P357
MADDA</style></pub-location><volume><style face="normal" font="default" size="100%">3117</style></volume><pages><style face="normal" font="default" size="100%">341 - 352</style></pages><isbn><style face="normal" font="default" size="100%">0302-9743</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">UT: 000224372600029doi: 10.1007/978-3-540-27816-0_29Milan Sonka, Ioannis A. Kakadiaris, Jan Kybic (eds.)Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis 
ECCV 2004 Workshops CVAMIA and MMBIA, Prague, Czech Republic, May 15, 2004 
Revised Selected Papers 
Berlin; Heidelberg; New York : Springer,2004 
DOI: 10.1007/b98995
</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Liver segment approximation in CT data for surgical resection planning</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Imaging 2004: Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2004///</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; WashingtonScheele, J., Anatomical and atypical liver resection (2001) Chirurg, 72 (2), pp. 113-124;Couinaud, C., (1957) Le Foie - Etudes Anatomiques et Chirurgicales, , Masson, Paris; 
Strunk, H., Stuckmann, G., Textor, J., Willinek, W., Limit</style></pub-location><pages><style face="normal" font="default" size="100%">1435 - 1446</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Surgical planning of liver tumor resections requires detailed three-dimensional (3D) understanding of the complex arrangement of vasculature, liver segments and tumors. Knowledge about location and sizes of liver segments is important for choosing an optimal surgical resection approach and predicting postoperative residual liver capacity. The aim of this work is to facilitate such surgical planning process by developing a robust method for portal vein tree segmentation. The work also investigates the impact of vessel segmentation on the approximation of liver segment volumes. For segment approximation, smaller portal vein branches are of importance. Small branches, however, are difficult to segment due to noise and partial volume effects. Our vessel segmentation is based on the original gray-values and on the result of a vessel enhancement filter. Validation of the developed portal vein segmentation method in computer generated phantoms shows that, compared to a conventional approach, more vessel branches can be segmented. Experiments with in vivo acquired liver CT data sets confirmed this result. The outcome of a Nearest Neighbor liver segment approximation method applied to phantom data demonstrates, that the proposed vessel segmentation approach translates into a more accurate segment partitioning.</style></abstract><notes><style face="normal" font="default" size="100%">ScopusID: 5644267870doi: 10.1117/12.535514</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple Sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Imaging 2004: Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2004///</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%">1764 - 1773</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a methodology for segmenting PD- andT2-weighted brain magnetic resonance (MR) images of 
multiplesclerosis (MS) patients into white matter (WM), gray 
matter (GM),cerebrospinal fluid (CSF), and MS lesions. For a 
given vectorialimage (with PD- and T2-weighted components) to be 
segmented, weperform first intensity inhomogeneity correction 
andstandardization prior to segmentation. Absolute 
fuzzyconnectedness and certain morphological operations are 
utilized togenerate the brain intracranial mask. The optimum 
thresholdingmethod is applied to the product image (the image in 
which voxelvalues represent T2 value x PD value) to 
automaticallyrecognize potential MS lesion sites. Then, the 
recently developedtechnique -- vectorial scale-based relative 
fuzzy connectedness --is utilized to segment all voxels within 
the brain intracranialmask into WM, GM, CSF, and MS lesion 
regions. The number ofsegmented lesions and the volume of each 
lesion are finally outputas well as the volume of other tissue 
regions. The method has beentested on 10 clinical brain MRI data 
sets of MS patients. Anaccuracy of better than 96% has been 
achieved. The preliminaryresults indicate that its performance 
is better than that of thek-nearest neighbors (kNN) method.
</style></abstract><notes><style face="normal" font="default" size="100%">ScopusID: 5644264947doi: 10.1117/12.535655</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of the interstitial lung diseases via density-based and texture-based analysis of computed tomography images of lung structure and function</style></title><secondary-title><style face="normal" font="default" size="100%">ACADEMIC RADIOLOGY</style></secondary-title><short-title><style face="normal" font="default" size="100%">ACAD RADIOL</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><pub-location><style face="normal" font="default" size="100%">BAE KT, 1997, RADIOLOGY, V203, P705BENTLEY MD, 1994, CIRC RES, V74, P945
CHULHO W, 2003, J APPL PHYSIOL, V94, P2483
CLARKE LP, 2001, ACAD RADIOL, V8, P447
COXSON H, 2003, AM J RESP CRIT CARE, V167, A81
COXSON H, 2003, AM J RESP CRIT CARE, V167, A81
COXSON</style></pub-location><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">1104 - 1118</style></pages><isbn><style face="normal" font="default" size="100%">1076-6332</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">10</style></issue><notes><style face="normal" font="default" size="100%">UT: 000185944700005doi: 10.1016/S1076-6332(03)00330-1Workshop on Pulmonary Functional ImagingJUN, 2002
PHILADELPHIA, PENNSYLVANIA
</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative analysis of intrathoracic airway trees: Methods and validation</style></title><secondary-title><style face="normal" font="default" size="100%">INFORMATION PROCESSING IN MEDICAL IMAGING, PROCEEDINGS</style></secondary-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><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin; HeidelbergBLAND JM, 1986, LANCET, V1, P307BORGEFORS G, 1984, COMPUT VISION GRAPH, V27, P321
CORMEN TH, 1990, INTRO ALGORITHMS
GONZALES RC, 1992, DIGITAL IMAGE PROCES
KITAOKA H, 1999, J APPL PHYSIOL, V87, P2207
KONG TY, 1989, COMPUT VISION GRAPH, V</style></pub-location><pages><style face="normal" font="default" size="100%">222 - 233</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">UT: 000185604900019ScopusID: 29144477913doi: 10.1007/978-3-540-45087-0_19</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative analysis of three-dimensional tubular tree structures</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Imaging 2003</style></secondary-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><urls><web-urls><url><style face="normal" font="default" size="100%">http://spie.org/x648.html?product_id=459268</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">SPIE - The International Society for Optical Engineering</style></publisher><pub-location><style face="normal" font="default" size="100%">Bellingham; Washington</style></pub-location><pages><style face="normal" font="default" size="100%">277 - 287</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">doi: 10.1117/12.481127</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">A protocol-independent brain MRI segmentation method</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Imaging 2002: Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2002///</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 - 1599</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a segmentation method that combines the robust,accurate, and efficient techniques of fuzzy connectedness with 
standardized MRI intensities and fast algorithms. The result is 
a general segmentation framework that more efficiently utilizes 
the user input (for recognition) and the power of computer (for 
delineation). This same method has been applied to segment brain 
tissues from a variety of MRI protocols. Images were corrected 
for inhomogeneity and standardized to yield tissue-specific 
intensity values. All parameters for the fuzzy affinity 
relations were fixed for a specific input protocol. Scale-based 
fuzzy affinity was used to better capture fine structures. Brain 
tissues were segmented as 3D fuzzy-connected objects by using 
relative fuzzy connectedness. The user can specify seed points 
in about a minute and tracking the 3D fuzzy-connected objects 
takes about 20 seconds per object. All other computations were 
performed before any user interaction took place. Segmentation 
of brain tissues as 3D fuzzy-connected objects from MRI data is 
feasible at interactive speeds. Utilizing the robust fuzzy 
connectedness principles and fast algorithms, it is possible to 
interactively select fuzzy affinity, seed point, and threshold 
parameters and perform efficient, precise, and accurate 
segmentations.
</style></abstract><notes><style face="normal" font="default" size="100%">ScopusID: 0036030011doi: 10.1117/12.467128</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Segmentation, skeletonization, and branchpoint matching - A fully automated quantitative evaluation of human intrathoratic airway trees</style></title><secondary-title><style face="normal" font="default" size="100%">LECTURE NOTES IN COMPUTER SCIENCE</style></secondary-title><short-title><style face="normal" font="default" size="100%">LECT NOTES COMPUT SCI</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2002///</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">2489</style></volume><pages><style face="normal" font="default" size="100%">12 - 19</style></pages><isbn><style face="normal" font="default" size="100%">0302-9743</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors></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>