<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Reinhardt Beichel</style></author><author><style face="normal" font="default" size="100%">Thomas Pock</style></author><author><style face="normal" font="default" size="100%">Christian Janko</style></author><author><style face="normal" font="default" size="100%">Roman B Zotter</style></author><author><style face="normal" font="default" size="100%">Bernhard Reitinger</style></author><author><style face="normal" font="default" size="100%">Alexander Bornik</style></author><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author><author><style face="normal" font="default" size="100%">Erich Sorantin</style></author><author><style face="normal" font="default" size="100%">Georg Werkgartner</style></author><author><style face="normal" font="default" size="100%">Horst Bischof</style></author><author><style face="normal" font="default" size="100%">Milan Sonka</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J Michael Fitzpatrick</style></author><author><style face="normal" font="default" size="100%">Milan Sonka</style></author></secondary-authors></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><authors><author><style face="normal" font="default" size="100%">Ying Zhuge</style></author><author><style face="normal" font="default" size="100%">Jayaram K Udupa</style></author><author><style face="normal" font="default" size="100%">László Gábor Nyúl</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J Michael Fitzpatrick</style></author><author><style face="normal" font="default" size="100%">Milan Sonka</style></author></secondary-authors></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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author><author><style face="normal" font="default" size="100%">Juerg Tschirren</style></author><author><style face="normal" font="default" size="100%">Milan Sonka</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%">J Michael Fitzpatrick</style></author></secondary-authors></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><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></authors><secondary-authors><author><style face="normal" font="default" size="100%">Milan Sonka</style></author><author><style face="normal" font="default" size="100%">J Michael Fitzpatrick</style></author></secondary-authors></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></records></xml>