00584nam a2200157   4500008004100000020001900041245006300060210006200123260004300185490000900228100002200237700001500259700002800274700001900302856010500321       2009                        eng d  a9780819478054 00aMIPPR 2009: Multispectral Image Acquisition and Processing0 aMIPPR 2009 Multispectral Image Acquisition and Processing  aBellingham; WashingtonbSPIEcOct 20090 v74941 aUdupa, Jayaram, K1 aSang, Nong1 aNyúl, László, Gábor1 aTong, Hengqing  uhttps://www.inf.u-szeged.hu/en/publication/mippr-2009-multispectral-image-acquisition-and-processing01749nas a2200109   4500008004100000245011800041210006900159260004200228300001600270520130800286856004501594       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://www.inf.u-szeged.hu/en/node/101002034nas a2200133   4500008004100000020001400041245010800055210006900163260001200232300001400244490000700258520159100265856004401856       2003                        eng d  a0895-611100a3DVIEWNIX-AVS: a software package for the separate visualization of arteries and veins in CE-MRA images0 a3DVIEWNIXAVS a software package for the separate visualization o  c2003///  a351 - 3620 v273 aOur 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.
  uhttps://www.inf.u-szeged.hu/en/node/99901976nas a2200133   4500008004100000020001400041245008100055210006900136260001200205300001400217490000700231520155900238856004501797       2003                        eng d  a0278-006200aIncorporating a measure of local scale in voxel-based 3-D image registration0 aIncorporating a measure of local scale in voxelbased 3D image re  c2003///  a228 - 2370 v223 aWe 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 "scale" 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 "best" 
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.
  uhttps://www.inf.u-szeged.hu/en/node/100000356nas a2200097   4500008004100000245005800041210005800099260003800157490001800195856004500213       2003                        eng d00aMethod for standardizing the MR image intensity scale0 aMethod for standardizing the MR image intensity scale  aAmerikai Egyesült Államokc20030 vUS19990447781  uhttps://www.inf.u-szeged.hu/en/node/100602324nas a2200133   4500008004100000020001400041245006400055210006300119260001200182300001400194490000700208520193000215856004502145       2002                        eng d  a1524-070300aFuzzy-connected 3D image segmentation at interactive speeds0 aFuzzyconnected 3D image segmentation at interactive speeds  c2002///  a259 - 2810 v643 aImage segmentation techniques using fuzzy connectednessprinciples hake shown their effectiveness in segmenting a 
variety of objects in several large applications in recent 
years. However, one problem with these algorithms has been their 
excessive computational requirements. In an attempt to 
substantially speed them up. in the present paper, we study 
systematically a host of 18 'optimal' graph search algorithms. 
Extensive testing of these algorithms on a variety of 3D medical 
images taken from large ongoing applications demonstrates that a 
20 1000-fold improvement over current speeds is achievable with 
a combination of algorithms and last modern PCs. Utilizing 
efficient algorithms and careful selection of implementations 
can speed up the computation of fuzzy connectedness values by a 
factor of 16 29 (on the same hardware), as compared to the 
implementation previously used in our applications utilizing 
fuzzy object segmentation. The optimality of an algorithm 
depends on the input data as well as on the choice of the fuzzy 
affinity relation. The running time is reduced considerably (by 
a factor up to 34 for brain MR and even more for bone CT), when 
the algorithms make use of predetermined thresholds for the 
fuzz), objects. The reliable recognition (assisted by human 
operators) and the accurate, efficient. and sophisticated 
delineation (automatically performed by the computer) can be 
effectively incorporated into a single interactive process. If 
images having intensities kith tissue-Specific meaning (such Lis 
CT or standardized MR images) are utilized. most of the 
parameters for the segmentation method can be fixed once for 
all. all, intermediate data (feature and fuzzy affinity values 
for the hole scene) can be computed before the user interaction 
is needed and the user can be provided kith more information at 
the little of interaction.
  uhttps://www.inf.u-szeged.hu/en/node/100101731nas a2200109   4500008004100000245005700041210005400098260004200152300001600194520136600210856004501576       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://www.inf.u-szeged.hu/en/node/101600320nas a2200097   4500008004100000245004500041210004500086260003400131300001200165856004500177       2002                        eng d00aTöbbdimenziós MRI képek feldolgozása0 aTöbbdimenziós MRI képek feldolgozása  aSzegedbNJSZT-KÉPAFc2002///  a96 - 97  uhttps://www.inf.u-szeged.hu/en/node/102301653nas a2200133   4500008004100000020001400041245012400055210006900179260001200248300001400260490000800274520119200282856004501474       2001                        eng d  a0033-841900aBrain atrophy in relapsing-remitting multiple sclerosis: Fractional volumetric analysis of gray matter and white matter0 aBrain atrophy in relapsingremitting multiple sclerosis Fractiona  c2001///  a606 - 6100 v2203 aPURPOSE: To determine the fractional brain tissue volume changesin the gray matter and white matter of patients with relapsing-
remitting multiple sclerosis (MS) and to correlate these 
measurements with clinical disability and total lesion load. 
MATERIALS AND METHODS: Thirty patients with relapsing-remitting 
MS and 25 healthy control subjects underwent magnetic resonance 
imaging. Fractional brain tissue volumes (tissue volume relative 
to total intracranial volume) were obtained from the total 
segmented gray matter and white matter in each group and were 
analyzed. RESULTS: The fractional volume of white matter versus 
that of gray matter was significantly lower (-6.4%) in patients 
with MS (P <.0001) than in control subjects. Neither gray matter 
nor white matter fractional volume measurements correlated with 
clinical disability in the patients with MS. CONCLUSION: Loss of 
brain parenchymal volume in patients with relapsing-remitting MS 
is predominantly confined to white matter. Analysis of 
fractional brain tissue volumes provides additional information 
useful in characterizing MS and may have potential in evaluating 
treatment strategies.
  uhttps://www.inf.u-szeged.hu/en/node/100402162nas a2200133   4500008004100000020001400041245009800055210006900153260001200222300001600234490000600250520172700256856004501983       2001                        eng d  a1076-633200aMultiprotocol MR image segmentation in multiple sclerosis: Experience with over 1,000 studies0 aMultiprotocol MR image segmentation in multiple sclerosis Experi  c2001///  a1116 - 11260 v83 aRATIONALE AND OBJECTIVES: Multiple sclerosis (MS) is an acquireddisease of the central nervous system. Several clinical measures 
are commonly used to express the severity of the disease, 
including the Expanded Disability Status Scale and the 
ambulation index. These measures are subjective and may be 
difficult to reproduce. The aim of this research is to 
investigate the possibility of developing more objective 
measures derived from MR imaging. MATERIALS AND METHODS: Various 
magnetic resonance (MR) imaging protocols are being investigated 
for the study of MS. Seeking to replace the Expanded Disability 
Status Scale and ambulation index with an objective means to 
assess the natural course of the disease and its response to 
therapy, the authors have developed multiprotocol MR image 
segmentation methods based on fuzzy connectedness to quantify 
both macrosopic features of the disease (lesions, gray matter, 
white matter, cerebrospinal fluid, and brain parenchyma) and the 
microscopic appearance of diseased white matter. Over 1,000 
studies have been processed to date. RESULTS: By far the 
strongest correlations with the clinical measures were 
demonstrated by the magnetization transfer ratio histogram 
parameters obtained for the various segmented tissue regions. 
These findings emphasize the importance of considering the 
microscopic and diffuse nature of the disease in the individual 
tissue regions. Brain parenchymal volume also demonstrated a 
strong correlation with clinical measures, which suggests that 
brain atrophy is an important disease indicator. CONCLUSION: 
Fuzzy connectedness is a viable, highly reproducible 
segmentation method for studying MS.
  uhttps://www.inf.u-szeged.hu/en/node/100201777nas a2200109   4500008004100000245006200041210006100103260004200164300001600206520140000222856004501622       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://www.inf.u-szeged.hu/en/node/101500379nas a2200085   4500008004100000245012600041210006900167260001200236856004500248       2000                        eng d00aBrain Atrophy in Relapsing-Remitting Multiple Sclerosis: A Fractional Volumetric Analysis of Gray Matter and White Matter0 aBrain Atrophy in RelapsingRemitting Multiple Sclerosis A Fractio  c2000///  uhttps://www.inf.u-szeged.hu/en/node/107601678nas a2200109   4500008004100000245006400041210006300105260004200168300001400210520129900224856004501523       2000                        eng d00aFuzzy-connected 3D image segmentation at interactive speeds0 aFuzzyconnected 3D image segmentation at interactive speeds  aBellingham; WashingtonbSPIEc2000///  a212 - 2233 aImage segmentation techniques using fuzzy connectednessprinciples have shown their effectiveness in segmenting a 
variety of objects in several large applications in recent 
years. However, one problem with these algorithms has been their 
excessive computational requirements. In an attempt to 
substantially speed them up, in the present paper, we study 
systematically a host of 18 algorithms under two categories -- 
label correcting and label setting. Extensive testing of these 
algorithms on a variety of 3D medical images taken from large 
ongoing applications demonstrates that a 20 - 360 fold 
improvement over current speeds is achievable with a combination 
of algorithms and fast modern PCs. The reliable recognition 
(assisted by human operators) and the accurate, efficient, and 
sophisticated delineation (automatically performed by the 
computer) can be effectively incorporated into a single 
interactive process. If images having intensities with tissue 
specific meaning (such as CT or standardized MR images) are 
utilized, all parameters for the segmentation method can be 
fixed once for all, all intermediate data can be computed before 
the user interaction is needed, and the user can be provided 
with more information at the time of interaction.
  uhttps://www.inf.u-szeged.hu/en/node/101400360nas a2200085   4500008004100000245010700041210006900148260001200217856004500229       2000                        eng d00aMagnetization Transfer Ratio Histogram Analysis of Normal Appearing Gray Matter and White Matter in MS0 aMagnetization Transfer Ratio Histogram Analysis of Normal Appear  c2000///  uhttps://www.inf.u-szeged.hu/en/node/107400773nas a2200133   4500008004100000020001400041245004400055210004400099260001200143300001400155490000700169520041900176856004400595       2000                        eng d  a1052-514900aMR image analysis in multiple sclerosis0 aMR image analysis in multiple sclerosis  c2000///  a799 - 8150 v103 aMR imaging is the ubiquitous imaging modality used for studyingmultiple sclerosis (MS). A variety of MR imaging protocols, 
including T2, spin density, T1-weighted, with and without 
gadolinium, and magnetization transfer imaging, have been used 
in studying MS. This article provides an overview of the 
techniques recently developed for quantifying the extent of MS 
through the application of MR imaging.
  uhttps://www.inf.u-szeged.hu/en/node/99300272nas a2200085   4500008004100000245004400041210004400085260001200129856004500141       2000                        eng d00aMR Image Analysis in Multiple Sclerosis0 aMR Image Analysis in Multiple Sclerosis  c2000///  uhttps://www.inf.u-szeged.hu/en/node/107501826nas a2200133   4500008004100000020001400041245010900055210006900164260001200233300001400245490000800259520138100267856004401648       2000                        eng d  a0033-841900aMultiple sclerosis: Magnetization transfer histogram analysis of segmented normal-appearing white matter0 aMultiple sclerosis Magnetization transfer histogram analysis of   c2000///  a351 - 3550 v2163 aPURPOSE: To investigate and characterize the global distributionof magnetization transfer (MT) ratio values of normal-appearing 
white matter (NAWM) in patients with relapsing-remitting 
multiple sclerosis (MS) and test the hypothesis that the MT 
histogram for NAWM reflects disease progression. MATERIALS AND 
METHODS: Conventional and MT magnetic resonance (MR) images were 
obtained in 23 patients and 25 healthy volunteers. Clinical 
tests for comparison with the MT histogram parameters included 
the Extended Disability Status Scale and the ambulation index. 
Lesion load calculated with T2-weighted MR images and whole-
brain and white matter volumes were measured. RESULTS: The 
location of the MT histogram peak and the mean MT ratio for NAWM 
were significantly lower in patients with MS than in control 
subjects. In longitudinal studies, the histogram peak location 
and mean MT ratio shifted in the direction of normal values as 
the duration of disease increased. A mean of 26.5% of the volume 
of new lesions identified on the later studies were demonstrated 
to have originated in NAWM corresponding to "lost" pixels on the 
histogram. CONCLUSION: MT histogram analysis of NAWM, including 
longitudinal analysis, may provide new prognostic information 
regarding lesion formation and increase understanding of the 
course of the disease.
  uhttps://www.inf.u-szeged.hu/en/node/99401840nas a2200109   4500008004100000245009700041210006900138260004200207300001600249520142000265856004501685       2000                        eng d00aMultiprotocol MR image segmentation in multiple sclerosis: experience with over 1000 studies0 aMultiprotocol MR image segmentation in multiple sclerosis experi  aBellingham; WashingtonbSPIEc2000///  a1017 - 10273 aMultiple Sclerosis (MS) is an acquired disease of the centralnervous system. Subjective cognitive and ambulatory test scores 
on a scale called EDSS are currently utilized to assess the 
disease severity. Various MRI protocols are being investigated 
to study the disease based on how it manifests itself in the 
images. In an attempt to eventually replace EDSS by an objective 
measure to assess the natural course of the disease and its 
response to therapy, we have developed image segmentation 
methods based on fuzzy connectedness to quantify various objects 
in multiprotocol MRI. These include the macroscopic objects such 
as lesions, the gray matter (GM), white matter (WM), 
cerebrospinal fluid (CSF), and brain parenchyma as well as the 
microscopic aspects of the diseased WM. Over 1000 studies have 
been processed to date. By far the strongest correlations with 
the clinical measures were demonstrated by the Magnetization 
Transfer Ratio (MTR) histogram parameters obtained for the 
various segmented tissue regions emphasizing the importance of 
considering the microscopic/diffused nature of the disease in 
the individual tissue regions. Brain parenchymal volume also 
demonstrated a strong correlation with the clinical measures 
indicating that brain atrophy is an important indicator of the 
disease. Fuzzy connectedness is a viable segmentation method for 
studying MS.
  uhttps://www.inf.u-szeged.hu/en/node/101301536nas a2200133   4500008004100000020001400041245005800055210005800113260001200171300001400183490000700197520115400204856004401358       2000                        eng d  a0278-006200aNew variants of a method of MRI scale standardization0 aNew variants of a method of MRI scale standardization  c2000///  a143 - 1500 v193 aOne of the major drawbacks of magnetic resonance imaging (MRI)has been the lack of a standard and quantifiable interpretation 
of image intensities. Unlike in other modalities, such as X-ray 
computerized tomography, MR images taken for the same patient on 
the same scanner at different times may appear different from 
each other due to a variety of scanner-dependent variations and, 
therefore, the absolute intensity values do not have a fixed 
meaning. We have devised a two-step method wherein all images 
(independent of patients and the specific brand of the MR 
scanner used) can be transformed in such a way that for the same 
protocol and body region, in the transformed images similar 
intensities will have similar tissue meaning. Standardized 
images can be displayed with fixed windows without the need of 
per-case adjustment. More importantly, extraction of 
quantitative information about healthy organs or about 
abnormalities can be considerably simplified. This paper 
introduces and compares new variants of this standardizing 
method that can help to overcome some of the problems with the 
original method.
  uhttps://www.inf.u-szeged.hu/en/node/99501619nas a2200133   4500008004100000020001400041245009600055210006900151260001200220300001400232490000700246520118700253856004501440       2000                        eng d  a1053-180700aNumerical tissue characterization in MS via standardization of the MR image intensity scale0 aNumerical tissue characterization in MS via standardization of t  c2000///  a715 - 7210 v123 aImage intensity standardization is a recently developedpostprocessing method that is capable of correcting the signal 
intensity variations in MR images. We evaluated signal intensity 
of healthy and diseased tissues in 10 multiple sclerosis (MS) 
patients based on standardized dual fast spin-echo MR images 
using a numerical postprocessing technique. The main idea of 
this technique is to deform the volume image histogram of each 
study to match a standard histogram and to utilize the resulting 
transformation to map the image intensities into standard scale. 
Upon standardization, the coefficients of variation of signal 
intensities for each segmented tissue (gray matter, white 
matter, lesion plaques, and diffuse abnormal white matter) in 
all patients were significantly smaller (2.3-9.2 times) than in 
the original images, and the same tissues from different 
patients looked alike, with similar intensity characteristics. 
Numerical tissue characterizability of different tissues in MS 
achieved by standardization offers a fixed tissue-specific 
meaning for the numerical values and can significantly 
facilitate image segmentation and analysis.
  uhttps://www.inf.u-szeged.hu/en/node/100300380nas a2200097   4500008004100000245009600041210006900137260002300206300000800229856004500237       2000                        eng d00aNumerical Tissue Characterization in MS via Standardization of the MR Image Intensity Scale0 aNumerical Tissue Characterization in MS via Standardization of t  aBerkeleycApr 2000  a579  uhttps://www.inf.u-szeged.hu/en/node/101900427nas a2200109   4500008004100000245006800041210006800109260005500177300000700232490003300239856004500272       2000                        eng d00aStandardizing the MR Image Intensity Scale and Its Applications0 aStandardizing the MR Image Intensity Scale and Its Applications  aSzegedbJózsef Attila TudományegyetemcJuly 2000  a750 vVolume of extended abstracts  uhttps://www.inf.u-szeged.hu/en/node/101701619nas a2200109   4500008004100000245010000041210006900141260004200210300001400252520119800266856004501464       2000                        eng d00aStandardizing the MR image intensity scales: making MR intensities have tissue-specific meaning0 aStandardizing the MR image intensity scales making MR intensitie  aBellingham; WashingtonbSPIEc2000///  a496 - 5043 aOne of the major drawbacks of Magnetic Resonance Imaging (MRI)has been the lack of a standard and quantifiable interpretation 
of image intensities. Unlike in other modalities such as x-ray 
computerized tomography, MR images taken for the same patient on 
the same scanner at different times may appear different from 
each other due to a variety of scanner-dependent variations, and 
therefore, the absolute intensity values do not have a fixed 
meaning. We have devised a two-step method wherein all images 
can be transformed in such a way that for the same protocol and 
body region, in the transformed images similar intensities will 
have similar tissue meaning. Standardized images can be 
displayed with fixed windows without the need of per case 
adjustment. More importantly, extraction of quantitative 
information with fixed windows without the need of per case 
adjustment. More importantly, extraction of quantitative 
information about healthy organs or about abnormalities can be 
considerably simplified. This paper introduces and compares new 
variants of this standardizing method that can help to overcome 
some of the problems with the original method.
  uhttps://www.inf.u-szeged.hu/en/node/101200398nas a2200097   4500008004100000245011300041210006900154260002300223300000900246856004500255       2000                        eng d00aTissue Characterization in Relapsing-remitting and Secondary-progressive MS via Magnetization Transfer Ratio0 aTissue Characterization in Relapsingremitting and Secondaryprogr  aBerkeleycApr 2000  a1189  uhttps://www.inf.u-szeged.hu/en/node/101801656nas a2200109   4500008004100000245005500041210005500096260004200151300001400193520129400207856004501501       1999                        eng d00aApproach to standardizing MR image intensity scale0 aApproach to standardizing MR image intensity scale  aBellingham; WashingtonbSPIEc1999///  a595 - 6033 aDespite the many advantages of MR images, they lack a standardimage intensity scale. MR image intensity ranges and the meaning 
of intensity values vary even for the same protocol (P) and the 
same body region (D). This causes many difficulties in image 
display and analysis. We propose a two-step method for 
standardizing the intensity scale in such a way that for the 
same P and D, similar intensities will have similar meanings. In 
the first step, the parameters of the standardizing 
transformation are 'learned' from an image set. In the second 
step, for each MR study, these parameters are used to map their 
histogram into the standardized histogram. The method was tested 
quantitatively on 90 whole brain FSE T2, PD and T1 studies of MS 
patients and qualitatively on several other SE PD, T2 and SPGR 
studies of the grain and foot. Measurements using mean squared 
difference showed that the standardized image intensities have 
statistically significantly more consistent range and meaning 
than the originals. Fixed windows can be established for 
standardized imags and used for display without the need of per 
case adjustment. Preliminary results also indicate that the 
method facilitates improving the degree of automation of image 
segmentation.
  uhttps://www.inf.u-szeged.hu/en/node/101100314nas a2200085   4500008004100000245006500041210006500106260001200171856004500183       1999                        eng d00aFuzzy Connected 3D Object Segmentation at Interactive Speeds0 aFuzzy Connected 3D Object Segmentation at Interactive Speeds  c1999///  uhttps://www.inf.u-szeged.hu/en/node/107100365nas a2200085   4500008004100000245011200041210006900153260001200222856004500234       1999                        eng d00aMagnetization Transfer Histogram Analysis of Segmented Normal- Appearing White Matter in Multiple Sclerosis0 aMagnetization Transfer Histogram Analysis of Segmented Normal Ap  c1999///  uhttps://www.inf.u-szeged.hu/en/node/107200395nas a2200097   4500008004100000245011100041210006900152260002300221300000800244856004500252       1999                        eng d00aMagnetization Transfer Histogram Analysis of Segmented Normal-Appearing White Matter in Multiple Sclerosis0 aMagnetization Transfer Histogram Analysis of Segmented NormalApp  aBerkeleycMay 1999  a957  uhttps://www.inf.u-szeged.hu/en/node/102001221nas a2200133   4500008004100000020001400041245005600055210005600111260001200167300001400179490000900193520084000202856004501042       1999                        eng d  a0302-974300aNew variants of a method of MRI scale normalization0 aNew variants of a method of MRI scale normalization  c1999///  a490 - 4950 v16133 aOne of the major drawbacks of Magnetic Resonance Imaging (MRI)has been the lack of a standard and quantifiable interpretation 
of image intensities. This causes many difficulties in image 
display and analysis. We have devised a two-step method wherein 
all images can be transformed in such a way that for the same 
protocol and body region, in the transformed images similar 
intensities will have similar tissue meaning. Normalized images 
can be displayed with fixed windows without the need of per case 
adjustment. More importantly, extraction of quantitative 
information about healthy organs or about abnormities, such as 
tumors, can considerably be simplified. This paper introduces 
and compares new variants of this normalization method that can 
help to overcome some of the problems with the original method.
  uhttps://www.inf.u-szeged.hu/en/node/109000300nas a2200085   4500008004100000245005800041210005800099260001200157856004500169       1999                        eng d00aNew Variants of a Method of MRI Scale Standardization0 aNew Variants of a Method of MRI Scale Standardization  c1999///  uhttps://www.inf.u-szeged.hu/en/node/107000349nas a2200085   4500008004100000245009600041210006900137260001200206856004500218       1999                        eng d00aNumerical Tissue Characterization in MS via Standardization of the MR Image Intensity Scale0 aNumerical Tissue Characterization in MS via Standardization of t  c1999///  uhttps://www.inf.u-szeged.hu/en/node/107300281nas a2200085   4500008004100000245005000041210004700091260001200138856004500150       1999                        eng d00aOn Standardizing the MR Image Intensity Scale0 aStandardizing the MR Image Intensity Scale  c1999///  uhttps://www.inf.u-szeged.hu/en/node/106901595nas a2200133   4500008004100000020001400041245005000055210004700105260001200152300001600164490000700180520123000187856004401417       1999                        eng d  a0740-319400aOn standardizing the MR image intensity scale0 astandardizing the MR image intensity scale  c1999///  a1072 - 10810 v423 aThe lack of a standard image intensity scale in MRI causes manydifficulties in image display and analysis. A two-step 
postprocessing method is proposed for standardizing the 
intensity scale in such a way that for the same MR protocol and 
body region, similar intensities will have similar tissue 
meaning. In the first step, the parameters of the standardizing 
transformation are "learned" from a set of images. In the second 
step, for each MR study these parameters are used to map their 
histogram into the standardized histogram. The method was tested 
quantitatively on 90 whole-brain studies of multiple sclerosis 
patients for several protocols and qualitatively for several 
other protocols and body regions. Measurements using mean 
squared difference showed that the standardized image 
intensities have statistically significantly (P < 0.01) more 
consistent range and meaning than the originals. Fixed gray 
level windows can be established for the standardized images and 
used for display without the need of per case adjustment. 
Preliminary results also indicate that the method facilitates 
improving the degree of automation of image segmentation. Magn 
Reson Med 42:1072-1081, 1999.
  uhttps://www.inf.u-szeged.hu/en/node/99601745nas a2200133   4500008004100000020001400041245005000055210004700105260001200152300001400164490000800178520138000186856004501566       1998                        eng d  a0033-841900aOn Standardizing the MR Image Intensity Scale0 aStandardizing the MR Image Intensity Scale  c1998///  a581 - 5820 v2093 aPURPOSE: MR image intensities have varying ranges and meaningeven for the same protocol (P) and body region (D). This causes 
many difficulties in image display and analysis. This exhibit 
describes a method of standardizing the intensity scale, so that 
for the same P and D, similar intensities will have similar 
meaning.
MATERIALS AND METHODS: In the TRAINING phase (done only once for 
a given P and D), the parameters of the standardizing 
transformation are "learnt" from an image set. In the MAPPING 
phase, done for each MR study, these parameters are utilized to 
determine the mapping needed to deform its histogram into the 
standardized histogram. The method was tested quantitatively on 
90 brain FSE T2, PD and T1 studies of MS patients and 
qualitatively on an additional 15 SE PD, T1 and SPGR studies of 
the brain and foot.
RESULTS: As measured by mean squared difference, standardized 
images have statistically significantly (p<0.01) more consistent 
range and meaning than those without. Fixed windows that do not 
require per study adjustment can be established for the 
standardized images.
CONCLUSIONS: Standardizing MR intensity scales to overcome the 
difficulties due to widely varying intensity meaning is feasible 
by protocol and body region. This can be implemented in a PACS 
via DICOM value of interest look up tables.
  uhttps://www.inf.u-szeged.hu/en/node/108700334nas a2200097   4500008004100000245005400041210005400095260003100149300001200180856004400192       1996                        eng d00aMedical image registration based on fuzzy objects0 aMedical image registration based on fuzzy objects  aBudapestbKFKIc1996.08.29  a44 - 48  uhttps://www.inf.u-szeged.hu/en/node/95300353nas a2200097   4500008004100000245006300041210006300104260003000167300001400197856004400211       1996                        eng d00aOrvosi képek fuzzy objektumokon alapuló regisztrációja0 aOrvosi képek fuzzy objektumokon alapuló regisztrációja  aBudapestbNJSZTcNov 1996  a107 - 110  uhttps://www.inf.u-szeged.hu/en/node/962