01706nas a2200133   4500008004100000020001400041245006200055210006200117260026900179300001400448490000700462520105800469856004501527       2006                        eng d  a0010-482500aQuantitative analysis of pulmonary airway tree structures0 aQuantitative analysis of pulmonary airway tree structures  aANTIGA 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, c2006///  a974 - 9960 v363 aA 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.  uhttps://home.inf.u-szeged.hu/en/node/91701658nas a2200133   4500008004100000020001400041245005800055210005800113260026900171300001600440490000700456520101600463856004501479       2005                        eng d  a0278-006200aMatching and anatomical labeling of human airway tree0 aMatching and anatomical labeling of human airway tree  aBALLARD 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, LEc2005///  a1540 - 15470 v243 aMatching 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.  uhttps://home.inf.u-szeged.hu/en/node/91800660nas a2200121   4500008004100000020001400041245007700055210006900132260026900201300001400470490000900484856004500493       2004                        eng d  a0302-974300aAssessment of intrathoracic airway trees: Methods and in vivo validation0 aAssessment of intrathoracic airway trees Methods and in vivo val  aBLAND 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
MADDAc2004///  a341 - 3520 v3117  uhttps://home.inf.u-szeged.hu/en/node/91901958nas a2200109   4500008004100000245007500041210006900116260027500185300001600460520132700476856004501803       2004                        eng d00aLiver segment approximation in CT data for surgical resection planning0 aLiver segment approximation in CT data for surgical resection pl  aBellingham; 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., LimitbSPIEc2004///  a1435 - 14463 aSurgical 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.  uhttps://home.inf.u-szeged.hu/en/node/93001750nas a2200109   4500008004100000245011800041210006900159260004200228300001600270520130800286856004601594       2004                        eng d00aMultiple Sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness0 aMultiple Sclerosis lesion quantification in MR images by using v  aBellingham; WashingtonbSPIEc2004///  a1764 - 17733 aThis 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.
  uhttps://home.inf.u-szeged.hu/en/node/101000744nas a2200121   4500008004100000020001400041245016100055210006900216260026900285300001600554490000700570856004500577       2003                        eng d  a1076-633200aCharacterization of the interstitial lung diseases via density-based and texture-based analysis of computed tomography images of lung structure and function0 aCharacterization of the interstitial lung diseases via densityba  aBAE 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
COXSONc2003///  a1104 - 11180 v10  uhttps://home.inf.u-szeged.hu/en/node/93400633nas a2200097   4500008004100000245008000041210006900121260028600190300001400476856004500490       2003                        eng d00aQuantitative analysis of intrathoracic airway trees: Methods and validation0 aQuantitative analysis of intrathoracic airway trees Methods and   aBerlin; 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, VbSpringer Verlagc2003///  a222 - 233  uhttps://home.inf.u-szeged.hu/en/node/92000435nas a2200097   4500008004100000245007100041210006900112260009400181300001400275856004800289       2003                        eng d00aQuantitative analysis of three-dimensional tubular tree structures0 aQuantitative analysis of threedimensional tubular tree structure  aBellingham; WashingtonbSPIE - The International Society for Optical Engineeringc2003///  a277 - 287  uhttp://spie.org/x648.html?product_id=45926801732nas a2200109   4500008004100000245005700041210005400098260004200152300001600194520136600210856004601576       2002                        eng d00aA protocol-independent brain MRI segmentation method0 aprotocolindependent brain MRI segmentation method  aBellingham; WashingtonbSPIEc2002///  a1588 - 15993 aWe 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.
  uhttps://home.inf.u-szeged.hu/en/node/101600464nas a2200121   4500008004100000020001400041245014000055210006900195260001200264300001200276490000900288856004500297       2002                        eng d  a0302-974300aSegmentation, skeletonization, and branchpoint matching - A fully automated quantitative evaluation of human intrathoratic airway trees0 aSegmentation skeletonization and branchpoint matching A fully au  c2002///  a12 - 190 v2489  uhttps://home.inf.u-szeged.hu/en/node/96001778nas a2200109   4500008004100000245006200041210006100103260004200164300001600206520140000222856004601622       2001                        eng d00aTask-specific comparison of 3D image registration methods0 aTaskspecific comparison of 3D image registration methods  aBellingham; WashingtonbSPIEc2001///  a1588 - 15983 aWe 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.
  uhttps://home.inf.u-szeged.hu/en/node/1015