%0 Book %D 2009 %T MIPPR 2009: Multispectral Image Acquisition and Processing %E Jayaram K Udupa %E Nong Sang %E László Gábor Nyúl %E Hengqing Tong %I SPIE %C Bellingham; Washington %V 7494 %8 Oct 2009 %@ 9780819478054 %G eng %9 Book %0 Book Section %B Medical Imaging 2004: Image Processing %D 2004 %T Multiple Sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness %A Ying Zhuge %A Jayaram K Udupa %A László Gábor Nyúl %E J Michael Fitzpatrick %E Milan Sonka %X 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. %B Medical Imaging 2004: Image Processing %I SPIE %C Bellingham; Washington %P 1764 - 1773 %8 2004/// %G eng %0 Journal Article %J COMPUTERIZED MEDICAL IMAGING AND GRAPHICS %D 2003 %T 3DVIEWNIX-AVS: a software package for the separate visualization of arteries and veins in CE-MRA images %A Tianhu Lei %A Jayaram K Udupa %A Dewei Odhner %A László Gábor Nyúl %A Punam K Saha %X 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'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. %B COMPUTERIZED MEDICAL IMAGING AND GRAPHICS %V 27 %P 351 - 362 %8 2003/// %@ 0895-6111 %G eng %N 5 %! COMPUT MED IMAG GRAP %0 Journal Article %J IEEE TRANSACTIONS ON MEDICAL IMAGING %D 2003 %T Incorporating a measure of local scale in voxel-based 3-D image registration %A László Gábor Nyúl %A Jayaram K Udupa %A Punam K Saha %X 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. %B IEEE TRANSACTIONS ON MEDICAL IMAGING %V 22 %P 228 - 237 %8 2003/// %@ 0278-0062 %G eng %N 2 %! IEEE T MED IMAGING %0 Patent %D 2003 %T Method for standardizing the MR image intensity scale %A László Gábor Nyúl %A Jayaram K Udupa %C Amerikai Egyesült Államok %V US19990447781 %8 2003 %G eng %N US6584216 %0 Journal Article %J GRAPHICAL MODELS %D 2002 %T Fuzzy-connected 3D image segmentation at interactive speeds %A László Gábor Nyúl %A Alexandre X. Falcao %A Jayaram K Udupa %X 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 '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. %B GRAPHICAL MODELS %V 64 %P 259 - 281 %8 2002/// %@ 1524-0703 %G eng %N 5 %! GRAPH MODELS %0 Book Section %B Medical Imaging 2002: Image Processing %D 2002 %T A protocol-independent brain MRI segmentation method %A László Gábor Nyúl %A Jayaram K Udupa %E Milan Sonka %E J Michael Fitzpatrick %X 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. %B Medical Imaging 2002: Image Processing %I SPIE %C Bellingham; Washington %P 1588 - 1599 %8 2002/// %G eng %0 Book Section %B Képfeldolgozók és Alakfelismerők III. Konfereciája %D 2002 %T Többdimenziós MRI képek feldolgozása %A László Gábor Nyúl %A Jayaram K Udupa %E Attila Kuba %E Eörs Máté %E Kálmán Palágyi %B Képfeldolgozók és Alakfelismerők III. Konfereciája %I NJSZT-KÉPAF %C Szeged %P 96 - 97 %8 2002/// %G eng %0 Journal Article %J RADIOLOGY %D 2001 %T Brain atrophy in relapsing-remitting multiple sclerosis: Fractional volumetric analysis of gray matter and white matter %A Yiyue Ge %A Robert J Grossman %A Jayaram K Udupa %A James S Babb %A László Gábor Nyúl %A Dennis L Kolson %X 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. %B RADIOLOGY %V 220 %P 606 - 610 %8 2001/// %@ 0033-8419 %G eng %N 3 %! RADIOLOGY %0 Journal Article %J ACADEMIC RADIOLOGY %D 2001 %T Multiprotocol MR image segmentation in multiple sclerosis: Experience with over 1,000 studies %A Jayaram K Udupa %A László Gábor Nyúl %A Yiyue Ge %A Robert J Grossman %X 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. %B ACADEMIC RADIOLOGY %V 8 %P 1116 - 1126 %8 2001/// %@ 1076-6332 %G eng %N 11 %! ACAD RADIOL %0 Book Section %B Medical Imaging 2001: Image Processing %D 2001 %T Task-specific comparison of 3D image registration methods %A László Gábor Nyúl %A Jayaram K Udupa %A Punam K Saha %E Milan Sonka %E Kenneth M Hanson %X 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. %B Medical Imaging 2001: Image Processing %I SPIE %C Bellingham; Washington %P 1588 - 1598 %8 2001/// %G eng %0 Generic %D 2000 %T Brain Atrophy in Relapsing-Remitting Multiple Sclerosis: A Fractional Volumetric Analysis of Gray Matter and White Matter %A Yiyue Ge %A Robert J Grossman %A Jayaram K Udupa %A James S Babb %A László Gábor Nyúl %A Dennis L Kolson %8 2000/// %G eng %0 Book Section %B Medical Imaging 2000: Image Processing %D 2000 %T Fuzzy-connected 3D image segmentation at interactive speeds %A László Gábor Nyúl %A Alexandre X. Falcao %A Jayaram K Udupa %E Kenneth M Hanson %X 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. %B Medical Imaging 2000: Image Processing %I SPIE %C Bellingham; Washington %P 212 - 223 %8 2000/// %G eng %0 Generic %D 2000 %T Magnetization Transfer Ratio Histogram Analysis of Normal Appearing Gray Matter and White Matter in MS %A Yiyue Ge %A Robert J Grossman %A Jayaram K Udupa %A James S Babb %A László Gábor Nyúl %A Dennis L Kolson %A Lois J Mannon %A Joseph C McGowan %8 2000/// %G eng %0 Journal Article %J NEUROIMAGING CLINICS OF NORTH AMERICA %D 2000 %T MR image analysis in multiple sclerosis %A László Gábor Nyúl %A Jayaram K Udupa %X 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. %B NEUROIMAGING CLINICS OF NORTH AMERICA %V 10 %P 799 - 815 %8 2000/// %@ 1052-5149 %G eng %N 4 %! NEUROIMAG CLIN N AM %0 Generic %D 2000 %T MR Image Analysis in Multiple Sclerosis %A László Gábor Nyúl %A Jayaram K Udupa %8 2000/// %G eng %0 Journal Article %J RADIOLOGY %D 2000 %T Multiple sclerosis: Magnetization transfer histogram analysis of segmented normal-appearing white matter %A Isabelle Catalaa %A Robert J Grossman %A Dennis L Kolson %A Jayaram K Udupa %A László Gábor Nyúl %A Lougang Wei %A Xuan Zhang %A Marcia Polansky %A Lois J Mannon %A Joseph C McGowan %X 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. %B RADIOLOGY %V 216 %P 351 - 355 %8 2000/// %@ 0033-8419 %G eng %N 2 %! RADIOLOGY %0 Book Section %B Medical Imaging 2000: Image Processing %D 2000 %T Multiprotocol MR image segmentation in multiple sclerosis: experience with over 1000 studies %A Jayaram K Udupa %A László Gábor Nyúl %A Yiyue Ge %A Robert J Grossman %E Kenneth M Hanson %X 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. %B Medical Imaging 2000: Image Processing %I SPIE %C Bellingham; Washington %P 1017 - 1027 %8 2000/// %G eng %0 Journal Article %J IEEE TRANSACTIONS ON MEDICAL IMAGING %D 2000 %T New variants of a method of MRI scale standardization %A László Gábor Nyúl %A Jayaram K Udupa %A Xuan Zhang %X 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. %B IEEE TRANSACTIONS ON MEDICAL IMAGING %V 19 %P 143 - 150 %8 2000/// %@ 0278-0062 %G eng %N 2 %! IEEE T MED IMAGING %0 Journal Article %J JOURNAL OF MAGNETIC RESONANCE IMAGING %D 2000 %T Numerical tissue characterization in MS via standardization of the MR image intensity scale %A Yiyue Ge %A Jayaram K Udupa %A László Gábor Nyúl %A Lougang Wei %A Robert J Grossman %X 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. %B JOURNAL OF MAGNETIC RESONANCE IMAGING %V 12 %P 715 - 721 %8 2000/// %@ 1053-1807 %G eng %N 5 %! JMRI - J MAGN RESON IM %0 Conference Paper %B International Society for Magnetic Resonance in Medicine: Eight Scientific Meeting and Exhibition %D 2000 %T Numerical Tissue Characterization in MS via Standardization of the MR Image Intensity Scale %A Yiyue Ge %A Jayaram K Udupa %A László Gábor Nyúl %A Lougang Wei %A Robert J Grossman %B International Society for Magnetic Resonance in Medicine: Eight Scientific Meeting and Exhibition %C Berkeley %P 579 %8 Apr 2000 %G eng %0 Conference Paper %B Conference of PhD Students in Computer Science %D 2000 %T Standardizing the MR Image Intensity Scale and Its Applications %A László Gábor Nyúl %A Jayaram K Udupa %E Tibor Csendes %B Conference of PhD Students in Computer Science %I József Attila Tudományegyetem %C Szeged %V Volume of extended abstracts %P 75 %8 July 2000 %G eng %0 Book Section %B Medical Imaging 2000: Image Display and Visualization %D 2000 %T Standardizing the MR image intensity scales: making MR intensities have tissue-specific meaning %A László Gábor Nyúl %A Jayaram K Udupa %E Seong Ki Mun %X 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. %B Medical Imaging 2000: Image Display and Visualization %I SPIE %C Bellingham; Washington %P 496 - 504 %8 2000/// %G eng %0 Conference Paper %B International Society for Magnetic Resonance in Medicine: Eight Scientific Meeting and Exhibition %D 2000 %T Tissue Characterization in Relapsing-remitting and Secondary-progressive MS via Magnetization Transfer Ratio %A Yiyue Ge %A Robert J Grossman %A Jayaram K Udupa %A James S Babb %A László Gábor Nyúl %A Joseph C McGowan %E %B International Society for Magnetic Resonance in Medicine: Eight Scientific Meeting and Exhibition %C Berkeley %P 1189 %8 Apr 2000 %G eng %0 Book Section %B Medical Imaging 1999: Image Display %D 1999 %T Approach to standardizing MR image intensity scale %A László Gábor Nyúl %A Jayaram K Udupa %E Seong Ki Mun %E Yongmin Kim %X 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 '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. %B Medical Imaging 1999: Image Display %I SPIE %C Bellingham; Washington %P 595 - 603 %8 1999/// %G eng %0 Generic %D 1999 %T Fuzzy Connected 3D Object Segmentation at Interactive Speeds %A László Gábor Nyúl %A Alexandre X. Falcao %A Jayaram K Udupa %8 1999/// %G eng %0 Generic %D 1999 %T Magnetization Transfer Histogram Analysis of Segmented Normal- Appearing White Matter in Multiple Sclerosis %A Isabelle Catalaa %A Robert J Grossman %A Jayaram K Udupa %A László Gábor Nyúl %A Dennis L Kolson %A Lougang Wei %A Xuan Zhang %A Marcia Polansky %A Lois J Mannon %A Joseph C McGowan %8 1999/// %G eng %0 Conference Paper %B International Society for Magnetic Resonance in Medicine: Seventh Scientific Meeting and Exhibition %D 1999 %T Magnetization Transfer Histogram Analysis of Segmented Normal-Appearing White Matter in Multiple Sclerosis %A Isabelle Catalaa %A Robert J Grossman %A Dennis L Kolson %A László Gábor Nyúl %A Lougang Wei %A Jayaram K Udupa %A Marcia Polansky %A Joseph C McGowan %E *[International Society fo *Medicine] %B International Society for Magnetic Resonance in Medicine: Seventh Scientific Meeting and Exhibition %C Berkeley %P 957 %8 May 1999 %G eng %0 Journal Article %J LECTURE NOTES IN COMPUTER SCIENCE %D 1999 %T New variants of a method of MRI scale normalization %A László Gábor Nyúl %A Jayaram K Udupa %X 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. %B LECTURE NOTES IN COMPUTER SCIENCE %V 1613 %P 490 - 495 %8 1999/// %@ 0302-9743 %G eng %! LECT NOTES COMPUT SCI %0 Generic %D 1999 %T New Variants of a Method of MRI Scale Standardization %A László Gábor Nyúl %A Jayaram K Udupa %A Xuan Zhang %8 1999/// %G eng %0 Generic %D 1999 %T Numerical Tissue Characterization in MS via Standardization of the MR Image Intensity Scale %A Yiyue Ge %A Jayaram K Udupa %A László Gábor Nyúl %A Lougang Wei %A Robert J Grossman %8 1999/// %G eng %0 Generic %D 1999 %T On Standardizing the MR Image Intensity Scale %A László Gábor Nyúl %A Jayaram K Udupa %8 1999/// %G eng %0 Journal Article %J MAGNETIC RESONANCE IN MEDICINE %D 1999 %T On standardizing the MR image intensity scale %A László Gábor Nyúl %A Jayaram K Udupa %X 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. %B MAGNETIC RESONANCE IN MEDICINE %V 42 %P 1072 - 1081 %8 1999/// %@ 0740-3194 %G eng %N 6 %! MAGN RESON MED %0 Journal Article %J RADIOLOGY %D 1998 %T On Standardizing the MR Image Intensity Scale %A László Gábor Nyúl %A Jayaram K Udupa %X 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. %B RADIOLOGY %V 209 %P 581 - 582 %8 1998/// %@ 0033-8419 %G eng %N SUPPL P %! RADIOLOGY %0 Conference Paper %B SUMMER WORKSHOP ON COMPUTATIONAL MODELLING, IMAGING AND VISUALIZATION IN BIOSCIENCES (COMBIO) %D 1996 %T Medical image registration based on fuzzy objects %A Kálmán Palágyi %A Jayaram K Udupa %E K Tarnay %E Zoltán Fazekas %B SUMMER WORKSHOP ON COMPUTATIONAL MODELLING, IMAGING AND VISUALIZATION IN BIOSCIENCES (COMBIO) %I KFKI %C Budapest %P 44 - 48 %8 1996.08.29 %G eng %0 Conference Paper %B A számítástechnika orvosi és biológiai alkalmazásai: A XX. Neumann Kollokvium Kiadványa %D 1996 %T Orvosi képek fuzzy objektumokon alapuló regisztrációja %A Kálmán Palágyi %A Jayaram K Udupa %E György Kozmann %B A számítástechnika orvosi és biológiai alkalmazásai: A XX. Neumann Kollokvium Kiadványa %I NJSZT %C Budapest %P 107 - 110 %8 Nov 1996 %G eng