01958nas 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/101000435nas 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/1016