@book {1052,
	title = {MIPPR 2009: Multispectral Image Acquisition and Processing},
	volume = {7494},
	year = {2009},
	note = {doi: 10.1117/12.839775Yichang},
	month = {Oct 2009},
	publisher = {SPIE},
	organization = {SPIE},
	type = {Book},
	address = {Bellingham; Washington},
	isbn = {9780819478054 },
	editor = {Jayaram K Udupa and Nong Sang and L{\'a}szl{\'o} G{\'a}bor Ny{\'u}l and Hengqing Tong}
}
@inbook {1010,
	title = {Multiple Sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness},
	booktitle = {Medical Imaging 2004: Image Processing},
	year = {2004},
	note = {ScopusID: 5644264947doi: 10.1117/12.535655},
	month = {2004///},
	pages = {1764 - 1773},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {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.
}
}
@article {999,
	title = {3DVIEWNIX-AVS: a software package for the separate visualization of arteries and veins in CE-MRA images},
	journal = {COMPUTERIZED MEDICAL IMAGING AND GRAPHICS},
	volume = {27},
	year = {2003},
	note = {UT: 000184800600003ScopusID: 0038122922doi: 10.1016/S0895-6111(03)00029-6},
	month = {2003///},
	pages = {351 - 362},
	abstract = {Our 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{\textquoteright}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.
},
	isbn = {0895-6111}
}
@article {1000,
	title = {Incorporating a measure of local scale in voxel-based 3-D image registration},
	journal = {IEEE TRANSACTIONS ON MEDICAL IMAGING},
	volume = {22},
	year = {2003},
	note = {UT: 000182391600009ScopusID: 0038398636doi: 10.1109/TMI.2002.808358},
	month = {2003///},
	pages = {228 - 237},
	abstract = {We 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.
},
	isbn = {0278-0062}
}
@article {1006,
	title = {Method for standardizing the MR image intensity scale},
	volume = {US19990447781},
	year = {2003},
	month = {2003},
	address = {Amerikai Egyes{\"u}lt {\'A}llamok}
}
@article {1001,
	title = {Fuzzy-connected 3D image segmentation at interactive speeds},
	journal = {GRAPHICAL MODELS},
	volume = {64},
	year = {2002},
	note = {UT: 000182188800001ScopusID: 0038708574doi: 10.1016/S1077-3169(02)00005-9},
	month = {2002///},
	pages = {259 - 281},
	abstract = {Image 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 {\textquoteright}optimal{\textquoteright} 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.
},
	isbn = {1524-0703}
}
@inbook {1016,
	title = {A protocol-independent brain MRI segmentation method},
	booktitle = {Medical Imaging 2002: Image Processing},
	year = {2002},
	note = {ScopusID: 0036030011doi: 10.1117/12.467128},
	month = {2002///},
	pages = {1588 - 1599},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {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.
}
}
@inbook {1023,
	title = {T{\"o}bbdimenzi{\'o}s MRI k{\'e}pek feldolgoz{\'a}sa},
	booktitle = {K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k III. Konfereci{\'a}ja},
	year = {2002},
	month = {2002///},
	pages = {96 - 97},
	publisher = {NJSZT-K{\'E}PAF},
	organization = {NJSZT-K{\'E}PAF},
	address = {Szeged}
}
@article {1004,
	title = {Brain atrophy in relapsing-remitting multiple sclerosis: Fractional volumetric analysis of gray matter and white matter},
	journal = {RADIOLOGY},
	volume = {220},
	year = {2001},
	note = {UT: 000170616700008ScopusID: 0034866802doi: 10.1148/radiol.2203001776},
	month = {2001///},
	pages = {606 - 610},
	abstract = {PURPOSE: 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.
},
	isbn = {0033-8419}
}
@article {1002,
	title = {Multiprotocol MR image segmentation in multiple sclerosis: Experience with over 1,000 studies},
	journal = {ACADEMIC RADIOLOGY},
	volume = {8},
	year = {2001},
	note = {UT: 000171987900006ScopusID: 0034767131doi: 10.1016/S1076-6332(03)80723-7},
	month = {2001///},
	pages = {1116 - 1126},
	abstract = {RATIONALE 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.
},
	isbn = {1076-6332}
}
@inbook {1015,
	title = {Task-specific comparison of 3D image registration methods},
	booktitle = {Medical Imaging 2001: Image Processing},
	year = {2001},
	note = {ScopusID: 0034843423doi: 10.1117/12.431044},
	month = {2001///},
	pages = {1588 - 1598},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {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.
}
}
@booklet {1076,
	title = {Brain Atrophy in Relapsing-Remitting Multiple Sclerosis: A Fractional Volumetric Analysis of Gray Matter and White Matter},
	year = {2000},
	month = {2000///}
}
@inbook {1014,
	title = {Fuzzy-connected 3D image segmentation at interactive speeds},
	booktitle = {Medical Imaging 2000: Image Processing},
	year = {2000},
	note = {ScopusID: 0033687148doi: 10.1117/12.387681},
	month = {2000///},
	pages = {212 - 223},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {Image 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.
}
}
@booklet {1074,
	title = {Magnetization Transfer Ratio Histogram Analysis of Normal Appearing Gray Matter and White Matter in MS},
	year = {2000},
	month = {2000///}
}
@article {993,
	title = {MR image analysis in multiple sclerosis},
	journal = {NEUROIMAGING CLINICS OF NORTH AMERICA},
	volume = {10},
	year = {2000},
	note = {UT: 000168611300013ScopusID: 0034447740},
	month = {2000///},
	pages = {799 - 815},
	abstract = {MR 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.
},
	isbn = {1052-5149}
}
@booklet {1075,
	title = {MR Image Analysis in Multiple Sclerosis},
	year = {2000},
	month = {2000///}
}
@article {994,
	title = {Multiple sclerosis: Magnetization transfer histogram analysis of segmented normal-appearing white matter},
	journal = {RADIOLOGY},
	volume = {216},
	year = {2000},
	note = {UT: 000088430800008ScopusID: 0033894599},
	month = {2000///},
	pages = {351 - 355},
	abstract = {PURPOSE: 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.
},
	isbn = {0033-8419}
}
@inbook {1013,
	title = {Multiprotocol MR image segmentation in multiple sclerosis: experience with over 1000 studies},
	booktitle = {Medical Imaging 2000: Image Processing},
	year = {2000},
	note = {ScopusID: 0033721228doi: 10.1117/12.387606},
	month = {2000///},
	pages = {1017 - 1027},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {Multiple 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.
}
}
@article {995,
	title = {New variants of a method of MRI scale standardization},
	journal = {IEEE TRANSACTIONS ON MEDICAL IMAGING},
	volume = {19},
	year = {2000},
	note = {UT: 000086614000007ScopusID: 0033624997doi: 10.1109/42.836373},
	month = {2000///},
	pages = {143 - 150},
	abstract = {One 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.
},
	isbn = {0278-0062}
}
@article {1003,
	title = {Numerical tissue characterization in MS via standardization of the MR image intensity scale},
	journal = {JOURNAL OF MAGNETIC RESONANCE IMAGING},
	volume = {12},
	year = {2000},
	note = {UT: 000171295400008ScopusID: 0033754689doi: 10.1002/1522-2586(200011)12:5\&lt;715::AID-JMRI8\&gt;3.0.CO;2-D},
	month = {2000///},
	pages = {715 - 721},
	abstract = {Image 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.
},
	isbn = {1053-1807}
}
@conference {1019,
	title = {Numerical Tissue Characterization in MS via Standardization of the MR Image Intensity Scale},
	booktitle = {International Society for Magnetic Resonance in Medicine: Eight Scientific Meeting and Exhibition},
	year = {2000},
	month = {Apr 2000},
	pages = {579},
	address = {Berkeley}
}
@conference {1017,
	title = {Standardizing the MR Image Intensity Scale and Its Applications},
	booktitle = {Conference of PhD Students in Computer Science},
	volume = {Volume of extended abstracts},
	year = {2000},
	month = {July 2000},
	pages = {75},
	publisher = {J{\'o}zsef Attila Tudom{\'a}nyegyetem},
	organization = {J{\'o}zsef Attila Tudom{\'a}nyegyetem},
	address = {Szeged}
}
@inbook {1012,
	title = {Standardizing the MR image intensity scales: making MR intensities have tissue-specific meaning},
	booktitle = {Medical Imaging 2000: Image Display and Visualization},
	year = {2000},
	note = {ScopusID: 0033745402doi: 10.1117/12.383076},
	month = {2000///},
	pages = {496 - 504},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {One 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.
}
}
@conference {1018,
	title = {Tissue Characterization in Relapsing-remitting and Secondary-progressive MS via Magnetization Transfer Ratio},
	booktitle = {International Society for Magnetic Resonance in Medicine: Eight Scientific Meeting and Exhibition},
	year = {2000},
	month = {Apr 2000},
	pages = {1189},
	address = {Berkeley}
}
@inbook {1011,
	title = {Approach to standardizing MR image intensity scale},
	booktitle = {Medical Imaging 1999: Image Display},
	year = {1999},
	note = {ScopusID: 0032677406doi: 10.1117/12.349472},
	month = {1999///},
	pages = {595 - 603},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {Despite 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 {\textquoteright}learned{\textquoteright} 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.
}
}
@booklet {1071,
	title = {Fuzzy Connected 3D Object Segmentation at Interactive Speeds},
	year = {1999},
	month = {1999///}
}
@booklet {1072,
	title = {Magnetization Transfer Histogram Analysis of Segmented Normal- Appearing White Matter in Multiple Sclerosis},
	year = {1999},
	month = {1999///}
}
@conference {1020,
	title = {Magnetization Transfer Histogram Analysis of Segmented Normal-Appearing White Matter in Multiple Sclerosis},
	booktitle = {International Society for Magnetic Resonance in Medicine: Seventh Scientific Meeting and Exhibition},
	year = {1999},
	month = {May 1999},
	pages = {957},
	address = {Berkeley}
}
@article {1090,
	title = {New variants of a method of MRI scale normalization},
	journal = {LECTURE NOTES IN COMPUTER SCIENCE},
	volume = {1613},
	year = {1999},
	note = {UT: 000170515200051doi: 10.1007/3-540-48714-X_51In: Kuba A; Samal M; Todd-Pokropek A (szerk.)Information Processing in Medical Imaging: 16th International 
Conference, IPMI{\textquoteright}99, Visegr{\'a}d, Hungary, June/July 1999. 
Proceedings.
508 p.
Visegr{\'a}d, Magyarorsz{\'a}g, 1999.06.28-1999.07.02.
Berlin; Heidelberg: Springer-Verlag, 1999. pp. 490-495.
(Lecture Notes in Computer Science; 1613.)
(ISBN:3-540-66167-0)
http://link.springer.com/book/10.1007/3-540-48714-X/page/1
},
	month = {1999///},
	pages = {490 - 495},
	abstract = {One 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.
},
	isbn = {0302-9743}
}
@booklet {1070,
	title = {New Variants of a Method of MRI Scale Standardization},
	year = {1999},
	month = {1999///}
}
@booklet {1073,
	title = {Numerical Tissue Characterization in MS via Standardization of the MR Image Intensity Scale},
	year = {1999},
	month = {1999///}
}
@booklet {1069,
	title = {On Standardizing the MR Image Intensity Scale},
	year = {1999},
	month = {1999///}
}
@article {996,
	title = {On standardizing the MR image intensity scale},
	journal = {MAGNETIC RESONANCE IN MEDICINE},
	volume = {42},
	year = {1999},
	note = {UT: 000083959300011doi: 10.1002/(SICI)1522-2594(199912)42:6\&lt;1072::AID-MRM11\&gt;3.0.CO;2-M},
	month = {1999///},
	pages = {1072 - 1081},
	abstract = {The 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.
},
	isbn = {0740-3194}
}
@article {1087,
	title = {On Standardizing the MR Image Intensity Scale},
	journal = {RADIOLOGY},
	volume = {209},
	year = {1998},
	note = {84th Scientific Assembly and Annual Meeting of the RadiologicalSociety of North America (RSNA)
Chicago, IL, USA, 1998.11.29-1998.12.04.
},
	month = {1998///},
	pages = {581 - 582},
	abstract = {PURPOSE: 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.
},
	isbn = {0033-8419}
}
@conference {953,
	title = {Medical image registration based on fuzzy objects},
	booktitle = {SUMMER WORKSHOP ON COMPUTATIONAL MODELLING, IMAGING AND VISUALIZATION IN BIOSCIENCES (COMBIO)},
	year = {1996},
	month = {1996.08.29},
	pages = {44 - 48},
	publisher = {KFKI},
	organization = {KFKI},
	address = {Budapest}
}
@conference {962,
	title = {Orvosi k{\'e}pek fuzzy objektumokon alapul{\'o} regisztr{\'a}ci{\'o}ja},
	booktitle = {A sz{\'a}m{\'\i}t{\'a}stechnika orvosi {\'e}s biol{\'o}giai alkalmaz{\'a}sai: A XX. Neumann Kollokvium Kiadv{\'a}nya},
	year = {1996},
	month = {Nov 1996},
	pages = {107 - 110},
	publisher = {NJSZT},
	organization = {NJSZT},
	address = {Budapest}
}
