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Sample records for resonance image segmentation

  1. Neural network segmentation of magnetic resonance images

    International Nuclear Information System (INIS)

    Frederick, B.

    1990-01-01

    Neural networks are well adapted to the task of grouping input patterns into subsets which share some similarity. Moreover, once trained, they can generalize their classification rules to classify new data sets. Sets of pixel intensities from magnetic resonance (MR) images provide a natural input to a neural network; by varying imaging parameters, MR images can reflect various independent physical parameters of tissues in their pixel intensities. A neural net can then be trained to classify physically similar tissue types based on sets of pixel intensities resulting from different imaging studies on the same subject. This paper reports that a neural network classifier for image segmentation was implanted on a Sun 4/60, and was tested on the task of classifying tissues of canine head MR images. Four images of a transaxial slice with different imaging sequences were taken as input to the network (three spin-echo images and an inversion recovery image). The training set consisted of 691 representative samples of gray matter, white matter, cerebrospinal fluid, bone, and muscle preclassified by a neuroscientist. The network was trained using a fast backpropagation algorithm to derive the decision criteria to classify any location in the image by its pixel intensities, and the image was subsequently segmented by the classifier

  2. Segmentation of neuroanatomy in magnetic resonance images

    Science.gov (United States)

    Simmons, Andrew; Arridge, Simon R.; Barker, G. J.; Tofts, Paul S.

    1992-06-01

    Segmentation in neurological magnetic resonance imaging (MRI) is necessary for feature extraction, volume measurement and for the three-dimensional display of neuroanatomy. Automated and semi-automated methods offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. We have used dual echo multi-slice spin-echo data sets which take advantage of the intrinsically multispectral nature of MRI. As a pre-processing step, a rf non-uniformity correction is applied and if the data is noisy the images are smoothed using a non-isotropic blurring method. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing has been used to significantly simplify edge images and thus allow simple postprocessing to pick out the brain contour in each slice of the data set. Edge- focusing is a technique which locates significant edges using a high degree of smoothing at a coarse level and tracks these edges to a fine level where the edges can be determined with high positional accuracy. Both 2-D and 3-D edge-detection methods have been compared. Once isolated, the brain is further processed to identify CSF, and, depending upon the MR pulse sequence used, the brain itself may be sub-divided into gray matter and white matter using semi-automatic contrast enhancement and clustering methods.

  3. Automated Segmentation of Cardiac Magnetic Resonance Images

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Nilsson, Jens Chr.; Grønning, Bjørn A.

    2001-01-01

    Magnetic resonance imaging (MRI) has been shown to be an accurate and precise technique to assess cardiac volumes and function in a non-invasive manner and is generally considered to be the current gold-standard for cardiac imaging [1]. Measurement of ventricular volumes, muscle mass and function...

  4. Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

    International Nuclear Information System (INIS)

    Neubert, A.; Yang, Z.; Engstrom, C.; Xia, Y.; Strudwick, M. W.; Chandra, S. S.; Crozier, S.; Fripp, J.

    2016-01-01

    Purpose: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. Methods: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone–cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Results: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head

  5. Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Neubert, A., E-mail: ales.neubert@csiro.au [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane 4029 (Australia); Yang, Z. [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 (China); Engstrom, C. [School of Human Movement Studies, University of Queensland, Brisbane 4072 (Australia); Xia, Y.; Strudwick, M. W.; Chandra, S. S.; Crozier, S. [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072 (Australia); Fripp, J. [The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, 4029 (Australia)

    2016-10-15

    Purpose: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. Methods: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone–cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Results: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head

  6. Applications of magnetic resonance image segmentation in neurology

    Science.gov (United States)

    Heinonen, Tomi; Lahtinen, Antti J.; Dastidar, Prasun; Ryymin, Pertti; Laarne, Paeivi; Malmivuo, Jaakko; Laasonen, Erkki; Frey, Harry; Eskola, Hannu

    1999-05-01

    After the introduction of digital imagin devices in medicine computerized tissue recognition and classification have become important in research and clinical applications. Segmented data can be applied among numerous research fields including volumetric analysis of particular tissues and structures, construction of anatomical modes, 3D visualization, and multimodal visualization, hence making segmentation essential in modern image analysis. In this research project several PC based software were developed in order to segment medical images, to visualize raw and segmented images in 3D, and to produce EEG brain maps in which MR images and EEG signals were integrated. The software package was tested and validated in numerous clinical research projects in hospital environment.

  7. Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation

    Directory of Open Access Journals (Sweden)

    Shuiping Gou, PhD

    2016-07-01

    Conclusions: Our study demonstrated potential feasibility of automated segmentation of the pancreas on MRI scans with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization.

  8. Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm

    DEFF Research Database (Denmark)

    Letteboer, Marloes M J; Olsen, Ole F; Dam, Erik B

    2004-01-01

    RATIONALE AND OBJECTIVE: This article presents the evaluation of an interactive multiscale watershed segmentation algorithm for segmenting tumors in magnetic resonance brain images of patients scheduled for neuronavigational procedures. MATERIALS AND METHODS: The watershed method is compared...... delineation shows that the two methods are interchangeable according to the Bland and Altman criterion, and thus equally accurate. The repeatability of the watershed method and the manual method are compared by looking at the similarity of the segmented volumes. The similarity for intraobserver...

  9. Automated Segmentation of in Vivo and Ex Vivo Mouse Brain Magnetic Resonance Images

    Directory of Open Access Journals (Sweden)

    Alize E.H. Scheenstra

    2009-01-01

    Full Text Available Segmentation of magnetic resonance imaging (MRI data is required for many applications, such as the comparison of different structures or time points, and for annotation purposes. Currently, the gold standard for automated image segmentation is nonlinear atlas-based segmentation. However, these methods are either not sufficient or highly time consuming for mouse brains, owing to the low signal to noise ratio and low contrast between structures compared with other applications. We present a novel generic approach to reduce processing time for segmentation of various structures of mouse brains, in vivo and ex vivo. The segmentation consists of a rough affine registration to a template followed by a clustering approach to refine the rough segmentation near the edges. Compared with manual segmentations, the presented segmentation method has an average kappa index of 0.7 for 7 of 12 structures in in vivo MRI and 11 of 12 structures in ex vivo MRI. Furthermore, we found that these results were equal to the performance of a nonlinear segmentation method, but with the advantage of being 8 times faster. The presented automatic segmentation method is quick and intuitive and can be used for image registration, volume quantification of structures, and annotation.

  10. Magnetic resonance imaging of water ascent in embolized xylem vessels of grapevine stem segments

    Science.gov (United States)

    Mingtao Wang; Melvin T. Tyree; Roderick E. Wasylishen

    2013-01-01

    Temporal and spatial information about water refilling of embolized xylem vessels and the rate of water ascent in these vessels is critical for understanding embolism repair in intact living vascular plants. High-resolution 1H magnetic resonance imaging (MRI) experiments have been performed on embolized grapevine stem segments while they were...

  11. The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

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    Korsager, Anne Sofie, E-mail: asko@hst.aau.dk; Østergaard, Lasse Riis [Department of Health Science and Technology, Aalborg University, Aalborg 9220 (Denmark); Fortunati, Valerio; Lijn, Fedde van der; Niessen, Wiro; Walsum, Theo van [Biomedical Imaging Group of Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC, Rotterdam 3015 GE Rotterdam (Netherlands); Carl, Jesper [Department of Medical Physics, Oncology, Aalborg University Hospital, Aalborg 9220 (Denmark)

    2015-04-15

    Purpose: An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer. Methods: A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T{sub 2}-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas and intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. Results: A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. Conclusions: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.

  12. Segmentation of head magnetic resonance image using self-mapping characteristic

    International Nuclear Information System (INIS)

    Madokoro, Hirokazu; Sato, Kazuhito; Ishii, Masaki; Kadowaki, Sakura

    2004-01-01

    In this paper, we proposed a segmentation method, for head magnetic resonance (MR) images. Our method used self mapping characteristic of a self-organization map (SOM), and it does not need the setting of the representative point by the operator. We considered the continuity and boundary in the brain tissues by the definition of the local block. In the evaluation experiment, we obtained the segmentation result of matching anatomical structure information. In addition, our method applied the clinical MR images, it was possible to obtain the effective and objective result for supporting the diagnosis of the brain atrophy by the doctor. (author)

  13. Denoising human cardiac diffusion tensor magnetic resonance images using sparse representation combined with segmentation

    International Nuclear Information System (INIS)

    Bao, L J; Zhu, Y M; Liu, W Y; Pu, Z B; Magnin, I E; Croisille, P; Robini, M

    2009-01-01

    Cardiac diffusion tensor magnetic resonance imaging (DT-MRI) is noise sensitive, and the noise can induce numerous systematic errors in subsequent parameter calculations. This paper proposes a sparse representation-based method for denoising cardiac DT-MRI images. The method first generates a dictionary of multiple bases according to the features of the observed image. A segmentation algorithm based on nonstationary degree detector is then introduced to make the selection of atoms in the dictionary adapted to the image's features. The denoising is achieved by gradually approximating the underlying image using the atoms selected from the generated dictionary. The results on both simulated image and real cardiac DT-MRI images from ex vivo human hearts show that the proposed denoising method performs better than conventional denoising techniques by preserving image contrast and fine structures.

  14. Segmentation of internal brain structures in three-dimensional nuclear magnetic resonance imaging

    International Nuclear Information System (INIS)

    Geraud, Th.

    1998-01-01

    For neurological studies, the in vivo aspect of imaging systems is very attractive. Brain images are currently a classical tool used in clinical routine and research. The most appropriate system to observe brain anatomy is tridimensional magnetic resonance imaging, and a major issue of image processing is to segment automatically cerebral structures. This is the scope of our thesis. The number of applications is steadily growing: morphometric measurements, pathology detection, surgery planning, getting a reference for functional studies,a and so forth. The use of pattern recognition to classify the different cerebral tissues from the only radiometric levels of the images is limited. Even supervised, these methods can not lead to distinguish easily several classes of grey matter. When these methods are automatic, their use has to be empirical in order to ensure robust results, and has to be restricted to regions of interest in order to get reliable results. As these methods do not fully respect the spatial consistency of classes in the images, we have introduced contextual information with the help of different formalisms. With Markovian regularization, we have shown that energetic terms of localization permit the separation of two grey classes: cortex and central nuclei. With mathematical morphology, we have proposed processing chains dedicated to several cerebral objects; in particular, brain segmentation is robust and reproducible, and we have successfully obtained individual markers for lateral ventricles, caudate nuclei, putamen and thalami. We have also proposed a contextual method to estimate pure tissue characteristics from a rough segmentation. Our main contribution has been to present a recognition method which is progressive and atlas guided. The originality of this method is manifold. At first, it takes into account structural information processed as flexible spatial constraints the formalism of which relies on fuzzy set theory and information fusion

  15. Kinetic magnetic resonance imaging analysis of lumbar segmental mobility in patients without significant spondylosis.

    Science.gov (United States)

    Tan, Yanlin; Aghdasi, Bayan G; Montgomery, Scott R; Inoue, Hirokazu; Lu, Chang; Wang, Jeffrey C

    2012-12-01

    The purpose of this study was to examine lumbar segmental mobility using kinetic magnetic resonance imaging (MRI) in patients with minimal lumbar spondylosis. Mid-sagittal images of patients who underwent weight-bearing, multi-position kinetic MRI for symptomatic low back pain or radiculopathy were reviewed. Only patients with a Pfirrmann grade of I or II, indicating minimal disc disease, in all lumbar discs from L1-2 to L5-S1 were included for further analysis. Translational and angular motion was measured at each motion segment. The mean translational motion of the lumbar spine at each level was 1.38 mm at L1-L2, 1.41 mm at L2-L3, 1.14 mm at L3-L4, 1.10 mm at L4-L5 and 1.01 mm at L5-S1. Translational motion at L1-L2 and L2-L3 was significantly greater than L3-4, L4-L5 and L5-S1 levels (P lumbar spine was highest at L2-L3 (22.45 %) and least at L5/S1 (14.71 %) (P lumbar segmental mobility in patients without significant degenerative disc disease and found that translational motion was greatest in the proximal lumbar levels whereas angular motion was similar in the mid-lumbar levels but decreased at L1-L2 and L5-S1.

  16. Automatic falx cerebri and tentorium cerebelli segmentation from magnetic resonance images

    Science.gov (United States)

    Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L.; Butman, John A.; Prince, Jerry L.

    2017-03-01

    The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

  17. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

    Science.gov (United States)

    Liu, Fang; Zhou, Zhaoye; Jang, Hyungseok; Samsonov, Alexey; Zhao, Gengyan; Kijowski, Richard

    2018-04-01

    To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379-2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  18. Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging

    DEFF Research Database (Denmark)

    Dam, Erik Bjørnager

    2018-01-01

    Scanner drift is a well-known magnetic resonance imaging (MRI) artifact characterized by gradual signal degradation and scan intensity changes over time. In addition, hardware and software updates may imply abrupt changes in signal. The combined effects are particularly challenging for automatic...... image analysis methods used in longitudinal studies. The implication is increased measurement variation and a risk of bias in the estimations (e.g. in the volume change for a structure). We proposed two quite different approaches for scanner drift normalization and demonstrated the performance...... for segmentation of knee MRI using the fully automatic KneeIQ framework. The validation included a total of 1975 scans from both high-field and low-field MRI. The results demonstrated that the pre-processing method denoted Atlas Affine Normalization significantly removed scanner drift effects and ensured...

  19. Improving Brain Magnetic Resonance Image (MRI Segmentation via a Novel Algorithm based on Genetic and Regional Growth

    Directory of Open Access Journals (Sweden)

    Javadpour A.

    2016-06-01

    Full Text Available Background: Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective: This study describes a new method for brain Magnetic Resonance Image (MRI segmentation via a novel algorithm based on genetic and regional growth. Methods: Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results: By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion: The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases.

  20. Soft tissue segmentation and 3D display from computerized tomography and magnetic resonance imaging

    International Nuclear Information System (INIS)

    Fan, R.T.; Trivedi, S.S.; Fellingham, L.L.; Gamboa-Aldeco, A.; Hedgcock, M.W.

    1987-01-01

    Volume calculation and 3D display of human anatomy facilitate a physician's diagnosis, treatment, and evaluation. Accurate segmentation of soft tissue structures is a prerequisite for such volume calculations and 3D displays, but segmentation by hand-outlining structures is often tedious and time-consuming. In this paper, methods based on analysis of statistics of image gray level are applied to segmentation of soft tissue in medical images, with the goal of making segmentation automatic or semi-automatic. The resulting segmented images, volume calculations, and 3D displays are analyzed and compared with results based on physician-drawn outlines as well as actual volume measurements

  1. Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

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    Diniz, P.R.B.; Brum, D.G. [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Medicina. Dept. de Neurociencias e Ciencias do Comportamento; Santos, A. C. [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Medicina. Dept. de Clinica Medica; Murta-Junior, L.O.; Araujo, D.B. de, E-mail: murta@usp.b [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Filosofia, Ciencias e Letras. Dept. de Fisica e Matematica

    2010-01-15

    The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously. (author)

  2. Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

    Directory of Open Access Journals (Sweden)

    P.R.B. Diniz

    2010-01-01

    Full Text Available The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.

  3. Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

    International Nuclear Information System (INIS)

    Diniz, P.R.B.; Brum, D.G.; Santos, A. C.; Murta-Junior, L.O.; Araujo, D.B. de

    2010-01-01

    The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously. (author)

  4. Anatomical pulmonary magnetic resonance imaging segmentation for regional structure-function measurements of asthma

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    Guo, F. [Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5B7 (Canada); Graduate Program in Biomedical Engineering, University of Western Ontario, London, Ontario N6A 5B9 (Canada); Svenningsen, S.; Eddy, R. L.; Capaldi, D. P. I.; Sheikh, K. [Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5B7 (Canada); Department of Medical Biophysics, University of Western Ontario, London, Ontario N6A 5C1 (Canada); Fenster, A.; Parraga, G., E-mail: gparraga@robarts.ca [Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5B7 (Canada); Graduate Program in Biomedical Engineering, University of Western Ontario, London, Ontario N6A 5B9 (Canada); Department of Medical Biophysics, University of Western Ontario, London, Ontario N6A 5C1 (Canada)

    2016-06-15

    Purpose: Pulmonary magnetic-resonance-imaging (MRI) and x-ray computed-tomography have provided strong evidence of spatially and temporally persistent lung structure-function abnormalities in asthmatics. This has generated a shift in their understanding of lung disease and supports the use of imaging biomarkers as intermediate endpoints of asthma severity and control. In particular, pulmonary {sup 1}H MRI can be used to provide quantitative lung structure-function measurements longitudinally and in response to treatment. However, to translate such biomarkers of asthma, robust methods are required to segment the lung from pulmonary {sup 1}H MRI. Therefore, their objective was to develop a pulmonary {sup 1}H MRI segmentation algorithm to provide regional measurements with the precision and speed required to support clinical studies. Methods: The authors developed a method to segment the left and right lung from {sup 1}H MRI acquired in 20 asthmatics including five well-controlled and 15 severe poorly controlled participants who provided written informed consent to a study protocol approved by Health Canada. Same-day spirometry and plethysmography measurements of lung function and volume were acquired as well as {sup 1}H MRI using a whole-body radiofrequency coil and fast spoiled gradient-recalled echo sequence at a fixed lung volume (functional residual capacity + 1 l). We incorporated the left-to-right lung volume proportion prior based on the Potts model and derived a volume-proportion preserved Potts model, which was approximated through convex relaxation and further represented by a dual volume-proportion preserved max-flow model. The max-flow model led to a linear problem with convex and linear equality constraints that implicitly encoded the proportion prior. To implement the algorithm, {sup 1}H MRI was resampled into ∼3 × 3 × 3 mm{sup 3} isotropic voxel space. Two observers placed seeds on each lung and on the background of 20 pulmonary {sup 1}H MR images

  5. Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images.

    Science.gov (United States)

    Jian, Junming; Xiong, Fei; Xia, Wei; Zhang, Rui; Gu, Jinhui; Wu, Xiaodong; Meng, Xiaochun; Gao, Xin

    2018-06-01

    Segmentation of colorectal tumors is the basis of preoperative prediction, staging, and therapeutic response evaluation. Due to the blurred boundary between lesions and normal colorectal tissue, it is hard to realize accurate segmentation. Routinely manual or semi-manual segmentation methods are extremely tedious, time-consuming, and highly operator-dependent. In the framework of FCNs, a segmentation method for colorectal tumor was presented. Normalization was applied to reduce the differences among images. Borrowing from transfer learning, VGG-16 was employed to extract features from normalized images. We conducted five side-output blocks from the last convolutional layer of each block of VGG-16 along the network, these side-output blocks can deep dive multiscale features, and produced corresponding predictions. Finally, all of the predictions from side-output blocks were fused to determine the final boundaries of the tumors. A quantitative comparison of 2772 colorectal tumor manual segmentation results from T2-weighted magnetic resonance images shows that the average Dice similarity coefficient, positive predictive value, specificity, sensitivity, Hammoude distance, and Hausdorff distance were 83.56, 82.67, 96.75, 87.85%, 0.2694, and 8.20, respectively. The proposed method is superior to U-net in colorectal tumor segmentation (P colorectal tumor segmentation (P > 0.05). The results indicate that the introduction of FCNs contributed to accurate segmentation of colorectal tumors. This method has the potential to replace the present time-consuming and nonreproducible manual segmentation method.

  6. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering

    Directory of Open Access Journals (Sweden)

    Ahmed Elazab

    2015-01-01

    Full Text Available An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

  7. Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Shih, Tzu-Ching [Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, 40402, Taiwan (China); Chen, Jeon-Hor; Nie Ke; Lin Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying [Tu and Yuen Center for Functional Onco-Imaging and Radiological Sciences, University of California, Irvine, CA 92697 (United States); Liu Dongxu; Sun Lizhi, E-mail: shih@mail.cmu.edu.t [Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697 (United States)

    2010-07-21

    This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo (registered) 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc (registered) software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under

  8. Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction.

    Science.gov (United States)

    Larroza, Andrés; López-Lereu, María P; Monmeneu, José V; Gavara, Jose; Chorro, Francisco J; Bodí, Vicente; Moratal, David

    2018-04-01

    To investigate the ability of texture analysis to differentiate between infarcted nonviable, viable, and remote segments on cardiac cine magnetic resonance imaging (MRI). This retrospective study included 50 patients suffering chronic myocardial infarction. The data were randomly split into training (30 patients) and testing (20 patients) sets. The left ventricular myocardium was segmented according to the 17-segment model in both cine and late gadolinium enhancement (LGE) MRI. Infarcted myocardium regions were identified on LGE in short-axis views. Nonviable segments were identified as those showing LGE ≥ 50%, and viable segments those showing 0 cine images. A support vector machine (SVM) classifier was trained with different combination of texture features to obtain a model that provided optimal classification performance. The best classification on testing set was achieved with local binary patterns features using a 2D + t approach, in which the features are computed by including information of the time dimension available in cine sequences. The best overall area under the receiver operating characteristic curve (AUC) were: 0.849, sensitivity of 92% to detect nonviable segments, 72% to detect viable segments, and 85% to detect remote segments. Nonviable segments can be detected on cine MRI using texture analysis and this may be used as hypothesis for future research aiming to detect the infarcted myocardium by means of a gadolinium-free approach. © 2018 American Association of Physicists in Medicine.

  9. Feedforward and recurrent processing in scene segmentation: electroencephalography and functional magnetic resonance imaging.

    Science.gov (United States)

    Scholte, H Steven; Jolij, Jacob; Fahrenfort, Johannes J; Lamme, Victor A F

    2008-11-01

    In texture segregation, an example of scene segmentation, we can discern two different processes: texture boundary detection and subsequent surface segregation [Lamme, V. A. F., Rodriguez-Rodriguez, V., & Spekreijse, H. Separate processing dynamics for texture elements, boundaries and surfaces in primary visual cortex of the macaque monkey. Cerebral Cortex, 9, 406-413, 1999]. Neural correlates of texture boundary detection have been found in monkey V1 [Sillito, A. M., Grieve, K. L., Jones, H. E., Cudeiro, J., & Davis, J. Visual cortical mechanisms detecting focal orientation discontinuities. Nature, 378, 492-496, 1995; Grosof, D. H., Shapley, R. M., & Hawken, M. J. Macaque-V1 neurons can signal illusory contours. Nature, 365, 550-552, 1993], but whether surface segregation occurs in monkey V1 [Rossi, A. F., Desimone, R., & Ungerleider, L. G. Contextual modulation in primary visual cortex of macaques. Journal of Neuroscience, 21, 1698-1709, 2001; Lamme, V. A. F. The neurophysiology of figure ground segregation in primary visual-cortex. Journal of Neuroscience, 15, 1605-1615, 1995], and whether boundary detection or surface segregation signals can also be measured in human V1, is more controversial [Kastner, S., De Weerd, P., & Ungerleider, L. G. Texture segregation in the human visual cortex: A functional MRI study. Journal of Neurophysiology, 83, 2453-2457, 2000]. Here we present electroencephalography (EEG) and functional magnetic resonance imaging data that have been recorded with a paradigm that makes it possible to differentiate between boundary detection and scene segmentation in humans. In this way, we were able to show with EEG that neural correlates of texture boundary detection are first present in the early visual cortex around 92 msec and then spread toward the parietal and temporal lobes. Correlates of surface segregation first appear in temporal areas (around 112 msec) and from there appear to spread to parietal, and back to occipital areas. After 208

  10. Fast and robust multi-atlas segmentation of brain magnetic resonance images

    DEFF Research Database (Denmark)

    Lötjönen, Jyrki Mp; Wolz, Robin; Koikkalainen, Juha R

    2010-01-01

    We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of stand......We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead...... of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity...

  11. Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

    International Nuclear Information System (INIS)

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K; Ourselin, Sebastien

    2007-01-01

    The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis

  12. Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

    Energy Technology Data Exchange (ETDEWEB)

    Fripp, Jurgen [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia); Crozier, Stuart [School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD 4072 (Australia); Warfield, Simon K [Computational Radiology Laboratory, Harvard Medical School, Children' s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115 (United States); Ourselin, Sebastien [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia)

    2007-03-21

    The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

  13. Automated medical image segmentation techniques

    Directory of Open Access Journals (Sweden)

    Sharma Neeraj

    2010-01-01

    Full Text Available Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT and Magnetic resonance (MR imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

  14. Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Byung Il; Han, Ji Won; Oh, San Yeo Wool; Kim, Tae Hui [Seoul National University Bundang Hospital, Department of Neuropsychiatry, Seongnam, Gyeonggi-do (Korea, Republic of); Lee, Jung Jae; Lee, Eun Young [Kyungbook National University Chilgok Hospital, Department of Psychiatry, Buk-gu, Daegu (Korea, Republic of); MacFall, James R. [Duke University Medical Center, Neuropsychiatric Imaging Research Laboratory, Durham, NC (United States); Duke University Medical Center, Department of Radiology, Durham, NC (United States); Payne, Martha E. [Duke University Medical Center, Neuropsychiatric Imaging Research Laboratory, Durham, NC (United States); Duke University Medical Center, Department of Psychiatry and Behavioral Sciences, Durham, NC (United States); Kim, Jae Hyoung [Seoul National University Bundang Hospital, Department of Radiology, Seongnam, Gyeonggi-do (Korea, Republic of); Seoul National University College of Medicine, Department of Radiology, Jongno-gu, Seoul (Korea, Republic of); Kim, Ki Woong [Seoul National University Bundang Hospital, Department of Neuropsychiatry, Seongnam, Gyeonggi-do (Korea, Republic of); Seoul National University College of Medicine, Department of Psychiatry, Jongno-gu, Seoul (Korea, Republic of); Seoul National University College of Natural Sciences, Department of Brain and Cognitive Science, Gwanak-gu, Seoul (Korea, Republic of)

    2014-04-15

    White matter hyperintensities (WMHs) are regions of abnormally high intensity on T2-weighted or fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI). Accurate and reproducible automatic segmentation of WMHs is important since WMHs are often seen in the elderly and are associated with various geriatric and psychiatric disorders. We developed a fully automated monospectral segmentation method for WMHs using FLAIR MRIs. Through this method, we introduce an optimal threshold intensity (I{sub O}) for segmenting WMHs, which varies with WMHs volume (V{sub WMH}), and we establish the I{sub O} -V{sub WMH} relationship. Our method showed accurate validations in volumetric and spatial agreements of automatically segmented WMHs compared with manually segmented WMHs for 32 confirmatory images. Bland-Altman values of volumetric agreement were 0.96 ± 8.311 ml (bias and 95 % confidence interval), and the similarity index of spatial agreement was 0.762 ± 0.127 (mean ± standard deviation). Furthermore, similar validation accuracies were obtained in the images acquired from different scanners. The proposed segmentation method uses only FLAIR MRIs, has the potential to be accurate with images obtained from different scanners, and can be implemented with a fully automated procedure. In our study, validation results were obtained with FLAIR MRIs from only two scanner types. The design of the method may allow its use in large multicenter studies with correct efficiency. (orig.)

  15. Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images

    International Nuclear Information System (INIS)

    Yoo, Byung Il; Han, Ji Won; Oh, San Yeo Wool; Kim, Tae Hui; Lee, Jung Jae; Lee, Eun Young; MacFall, James R.; Payne, Martha E.; Kim, Jae Hyoung; Kim, Ki Woong

    2014-01-01

    White matter hyperintensities (WMHs) are regions of abnormally high intensity on T2-weighted or fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI). Accurate and reproducible automatic segmentation of WMHs is important since WMHs are often seen in the elderly and are associated with various geriatric and psychiatric disorders. We developed a fully automated monospectral segmentation method for WMHs using FLAIR MRIs. Through this method, we introduce an optimal threshold intensity (I O ) for segmenting WMHs, which varies with WMHs volume (V WMH ), and we establish the I O -V WMH relationship. Our method showed accurate validations in volumetric and spatial agreements of automatically segmented WMHs compared with manually segmented WMHs for 32 confirmatory images. Bland-Altman values of volumetric agreement were 0.96 ± 8.311 ml (bias and 95 % confidence interval), and the similarity index of spatial agreement was 0.762 ± 0.127 (mean ± standard deviation). Furthermore, similar validation accuracies were obtained in the images acquired from different scanners. The proposed segmentation method uses only FLAIR MRIs, has the potential to be accurate with images obtained from different scanners, and can be implemented with a fully automated procedure. In our study, validation results were obtained with FLAIR MRIs from only two scanner types. The design of the method may allow its use in large multicenter studies with correct efficiency. (orig.)

  16. Registration-based segmentation with articulated model from multipostural magnetic resonance images for hand bone motion animation.

    Science.gov (United States)

    Chen, Hsin-Chen; Jou, I-Ming; Wang, Chien-Kuo; Su, Fong-Chin; Sun, Yung-Nien

    2010-06-01

    The quantitative measurements of hand bones, including volume, surface, orientation, and position are essential in investigating hand kinematics. Moreover, within the measurement stage, bone segmentation is the most important step due to its certain influences on measuring accuracy. Since hand bones are small and tubular in shape, magnetic resonance (MR) imaging is prone to artifacts such as nonuniform intensity and fuzzy boundaries. Thus, greater detail is required for improving segmentation accuracy. The authors then propose using a novel registration-based method on an articulated hand model to segment hand bones from multipostural MR images. The proposed method consists of the model construction and registration-based segmentation stages. Given a reference postural image, the first stage requires construction of a drivable reference model characterized by hand bone shapes, intensity patterns, and articulated joint mechanism. By applying the reference model to the second stage, the authors initially design a model-based registration pursuant to intensity distribution similarity, MR bone intensity properties, and constraints of model geometry to align the reference model to target bone regions of the given postural image. The authors then refine the resulting surface to improve the superimposition between the registered reference model and target bone boundaries. For each subject, given a reference postural image, the proposed method can automatically segment all hand bones from all other postural images. Compared to the ground truth from two experts, the resulting surface image had an average margin of error within 1 mm (mm) only. In addition, the proposed method showed good agreement on the overlap of bone segmentations by dice similarity coefficient and also demonstrated better segmentation results than conventional methods. The proposed registration-based segmentation method can successfully overcome drawbacks caused by inherent artifacts in MR images and

  17. Neural - levelset shape detection segmentation of brain tumors in dynamic susceptibility contrast enhanced and diffusion weighted magnetic resonance images

    International Nuclear Information System (INIS)

    Vijayakumar, C.; Bhargava, Sunil; Gharpure, Damayanti Chandrashekhar

    2008-01-01

    A novel Neuro - level set shape detection algorithm is proposed and evaluated for segmentation and grading of brain tumours. The algorithm evaluates vascular and cellular information provided by dynamic contrast susceptibility magnetic resonance images and apparent diffusion coefficient maps. The proposed neural shape detection algorithm is based on the levels at algorithm (shape detection algorithm) and utilizes a neural block to provide the speed image for the level set methods. In this study, two different architectures of level set method have been implemented and their results are compared. The results show that the proposed Neuro-shape detection performs better in differentiating the tumor, edema, necrosis in reconstructed images of perfusion and diffusion weighted magnetic resonance images. (author)

  18. Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.

    Science.gov (United States)

    Agner, Shannon C; Xu, Jun; Madabhushi, Anant

    2013-03-01

    Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. On a cohort of 50 breast DCE-MRI studies, Pr

  19. Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients.

    Science.gov (United States)

    Hosseini, Mohammad-Parsa; Nazem-Zadeh, Mohammad-Reza; Pompili, Dario; Jafari-Khouzani, Kourosh; Elisevich, Kost; Soltanian-Zadeh, Hamid

    2016-01-01

    Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus. A template database of 195 (81 males, 114 females; age range 32-67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark. Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, -4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and

  20. Functional measurements based on feature tracking of cine magnetic resonance images identify left ventricular segments with myocardial scar

    Directory of Open Access Journals (Sweden)

    Nylander Eva

    2009-11-01

    Full Text Available Abstract Background The aim of the study was to perform a feature tracking analysis on cine magnetic resonance (MR images to elucidate if functional measurements of the motion of the left ventricular wall may detect scar defined with gadolinium enhanced MR. Myocardial contraction can be measured in terms of the velocity, displacement and local deformation (strain of a particular myocardial segment. Contraction of the myocardial wall will be reduced in the presence of scar and as a consequence of reduced myocardial blood flow. Methods Thirty patients (3 women and 27 men were selected based on the presence or absence of extensive scar in the anteroseptal area of the left ventricle. The patients were investigated in stable clinical condition, 4-8 weeks post ST-elevation myocardial infarction treated with percutaneous coronary intervention. Seventeen had a scar area >75% in at least one anteroseptal segment (scar and thirteen had scar area Results In the scar patients, segments with scar showed lower functional measurements than remote segments. Radial measurements of velocity, displacement and strain performed better in terms of receiver-operator-characteristic curves (ROC than the corresponding longitudinal measurements. The best area-under-curve was for radial strain, 0.89, where a cut-off value of 38.8% had 80% sensitivity and 86% specificity for the detection of a segment with scar area >50%. As a percentage of the mean, intraobserver variability was 16-14-26% for radial measurements of displacement-velocity-strain and corresponding interobserver variability was 13-12-18%. Conclusion Feature tracking analysis of cine-MR displays velocity, displacement and strain in the radial and longitudinal direction and may be used for the detection of transmural scar. The accuracy and repeatability of the radial functional measurements is satisfactory and global measures agree.

  1. Understanding how axial loads on the spine influence segmental biomechanics for idiopathic scoliosis patients: A magnetic resonance imaging study.

    Science.gov (United States)

    Little, J P; Pearcy, M J; Izatt, M T; Boom, K; Labrom, R D; Askin, G N; Adam, C J

    2016-02-01

    Segmental biomechanics of the scoliotic spine are important since the overall spinal deformity is comprised of the cumulative coronal and axial rotations of individual joints. This study investigates the coronal plane segmental biomechanics for adolescent idiopathic scoliosis patients in response to physiologically relevant axial compression. Individual spinal joint compliance in the coronal plane was measured for a series of 15 idiopathic scoliosis patients using axially loaded magnetic resonance imaging. Each patient was first imaged in the supine position with no axial load, and then again following application of an axial compressive load. Coronal plane disc wedge angles in the unloaded and loaded configurations were measured. Joint moments exerted by the axial compressive load were used to derive estimates of individual joint compliance. The mean standing major Cobb angle for this patient series was 46°. Mean intra-observer measurement error for endplate inclination was 1.6°. Following loading, initially highly wedged discs demonstrated a smaller change in wedge angle, than less wedged discs for certain spinal levels (+2,+1,-2 relative to the apex, (pbiomechanical data on in vivo spinal biomechanics of the scoliotic spine, for analysis of deformity progression and surgical planning. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Fast and robust multi-atlas segmentation of brain magnetic resonance images

    DEFF Research Database (Denmark)

    Lötjönen, Jyrki Mp; Wolz, Robin; Koikkalainen, Juha R

    2010-01-01

    of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity...

  3. Different approaches to synovial membrane volume determination by magnetic resonance imaging: manual versus automated segmentation

    DEFF Research Database (Denmark)

    Østergaard, Mikkel

    1997-01-01

    Automated fast (5-20 min) synovial membrane volume determination by MRI, based on pre-set post-gadolinium-DTPA enhancement thresholds, was evaluated as a substitute for a time-consuming (45-120 min), previously validated, manual segmentation method. Twenty-nine knees [rheumatoid arthritis (RA) 13...

  4. Dictionary Based Image Segmentation

    DEFF Research Database (Denmark)

    Dahl, Anders Bjorholm; Dahl, Vedrana Andersen

    2015-01-01

    We propose a method for weakly supervised segmentation of natural images, which may contain both textured or non-textured regions. Our texture representation is based on a dictionary of image patches. To divide an image into separated regions with similar texture we use an implicit level sets...

  5. Scorpion image segmentation system

    Science.gov (United States)

    Joseph, E.; Aibinu, A. M.; Sadiq, B. A.; Bello Salau, H.; Salami, M. J. E.

    2013-12-01

    Death as a result of scorpion sting has been a major public health problem in developing countries. Despite the high rate of death as a result of scorpion sting, little report exists in literature of intelligent device and system for automatic detection of scorpion. This paper proposed a digital image processing approach based on the floresencing characteristics of Scorpion under Ultra-violet (UV) light for automatic detection and identification of scorpion. The acquired UV-based images undergo pre-processing to equalize uneven illumination and colour space channel separation. The extracted channels are then segmented into two non-overlapping classes. It has been observed that simple thresholding of the green channel of the acquired RGB UV-based image is sufficient for segmenting Scorpion from other background components in the acquired image. Two approaches to image segmentation have also been proposed in this work, namely, the simple average segmentation technique and K-means image segmentation. The proposed algorithm has been tested on over 40 UV scorpion images obtained from different part of the world and results obtained show an average accuracy of 97.7% in correctly classifying the pixel into two non-overlapping clusters. The proposed 1system will eliminate the problem associated with some of the existing manual approaches presently in use for scorpion detection.

  6. Atlas-based segmentation and classification of magnetic resonance brain images

    OpenAIRE

    Bach Cuadra, Meritxell; Thiran, Jean-Philippe

    2005-01-01

    A wide range of different image modalities can be found today in medical imaging. These modalities allow the physician to obtain a non-invasive view of the internal organs of the human body, such as the brain. All these three dimensional images are of extreme importance in several domains of medicine, for example, to detect pathologies, follow the evolution of these pathologies, prepare and realize surgical planning with, or without, the help of robot systems or for statistical studies. Among...

  7. MRI (Magnetic Resonance Imaging)

    Science.gov (United States)

    ... Procedures Medical Imaging MRI (Magnetic Resonance Imaging) MRI (Magnetic Resonance Imaging) Share Tweet Linkedin Pin it More sharing options Linkedin Pin it Email Print Magnetic Resonance Imaging (MRI) is a medical imaging procedure for ...

  8. A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy.

    Science.gov (United States)

    Guo, Lu; Wang, Ping; Sun, Ranran; Yang, Chengwen; Zhang, Ning; Guo, Yu; Feng, Yuanming

    2018-02-19

    The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice's similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

  9. Unsupervised Image Segmentation

    Czech Academy of Sciences Publication Activity Database

    Haindl, Michal; Mikeš, Stanislav

    2014-01-01

    Roč. 36, č. 4 (2014), s. 23-23 R&D Projects: GA ČR(CZ) GA14-10911S Institutional support: RVO:67985556 Keywords : unsupervised image segmentation Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2014/RO/haindl-0434412.pdf

  10. Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM.

    Directory of Open Access Journals (Sweden)

    Sarah Schlaeger

    Full Text Available Magnetic resonance imaging (MRI can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD. Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA and volume. Proton density fat fraction (PDFF of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.

  11. A Hybrid Technique for Medical Image Segmentation

    Directory of Open Access Journals (Sweden)

    Alamgir Nyma

    2012-01-01

    Full Text Available Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.

  12. Metrics for image segmentation

    Science.gov (United States)

    Rees, Gareth; Greenway, Phil; Morray, Denise

    1998-07-01

    An important challenge in mapping image-processing techniques onto applications is the lack of quantitative performance measures. From a systems engineering perspective these are essential if system level requirements are to be decomposed into sub-system requirements which can be understood in terms of algorithm selection and performance optimization. Nowhere in computer vision is this more evident than in the area of image segmentation. This is a vigorous and innovative research activity, but even after nearly two decades of progress, it remains almost impossible to answer the question 'what would the performance of this segmentation algorithm be under these new conditions?' To begin to address this shortcoming, we have devised a well-principled metric for assessing the relative performance of two segmentation algorithms. This allows meaningful objective comparisons to be made between their outputs. It also estimates the absolute performance of an algorithm given ground truth. Our approach is an information theoretic one. In this paper, we describe the theory and motivation of our method, and present practical results obtained from a range of state of the art segmentation methods. We demonstrate that it is possible to measure the objective performance of these algorithms, and to use the information so gained to provide clues about how their performance might be improved.

  13. Transmission Line Resonator Segmented with Series Capacitors

    DEFF Research Database (Denmark)

    Zhurbenko, Vitaliy; Boer, Vincent; Petersen, Esben Thade

    2016-01-01

    Transmission line resonators are often used as coils in high field MRI. Due to distributed nature of such resonators, coils based on them produce inhomogeneous field. This work investigates application of series capacitors to improve field homogeneity along the resonator. The equations for optimal...... values of evenly distributed capacitors are presented. The performances of the segmented resonator and a regular transmission line resonator are compared....

  14. Region segmentation along image sequence

    International Nuclear Information System (INIS)

    Monchal, L.; Aubry, P.

    1995-01-01

    A method to extract regions in sequence of images is proposed. Regions are not matched from one image to the following one. The result of a region segmentation is used as an initialization to segment the following and image to track the region along the sequence. The image sequence is exploited as a spatio-temporal event. (authors). 12 refs., 8 figs

  15. Magnetic resonance image segmentation using semi-automated software for quantification of knee articular cartilage - initial evaluation of a technique for paired scans

    International Nuclear Information System (INIS)

    Brem, M.H.; Lang, P.K.; Neumann, G.; Schlechtweg, P.M.; Yoshioka, H.; Pappas, G.; Duryea, J.; Schneider, E.; Jackson, R.; Yu, J.; Eaton, C.B.; Hennig, F.F.

    2009-01-01

    Software-based image analysis is important for studies of cartilage changes in knee osteoarthritis (OA). This study describes an evaluation of a semi-automated cartilage segmentation software tool capable of quantifying paired images for potential use in longitudinal studies of knee OA. We describe the methodology behind the analysis and demonstrate its use by determination of test-retest analysis precision of duplicate knee magnetic resonance imaging (MRI) data sets. Test-retest knee MR images of 12 subjects with a range of knee health were evaluated from the Osteoarthritis Initiative (OAI) pilot MR study. Each subject was removed from the magnet between the two scans. The 3D DESS (sagittal, 0.456 mm x 0.365 mm, 0.7 mm slice thickness, TR 16.5 ms, TE 4.7 ms) images were obtained on a 3-T Siemens Trio MR system with a USA Instruments quadrature transmit-receive extremity coil. Segmentation of one 3D-image series was first performed and then the corresponding retest series was segmented by viewing both image series concurrently in two adjacent windows. After manual registration of the series, the first segmentation cartilage outline served as an initial estimate for the second segmentation. We evaluated morphometric measures of the bone and cartilage surface area (tAB and AC), cartilage volume (VC), and mean thickness (ThC.me) for medial/lateral tibia (MT/LT), total femur (F) and patella (P). Test-retest reproducibility was assessed using the root-mean square coefficient of variation (RMS CV%). For the paired analyses, RMS CV % ranged from 0.9% to 1.2% for VC, from 0.3% to 0.7% for AC, from 0.6% to 2.7% for tAB and 0.8% to 1.5% for ThC.me. Paired image analysis improved the measurement precision of cartilage segmentation. Our results are in agreement with other publications supporting the use of paired analysis for longitudinal studies of knee OA. (orig.)

  16. Magnetic resonance image segmentation using semi-automated software for quantification of knee articular cartilage - initial evaluation of a technique for paired scans

    Energy Technology Data Exchange (ETDEWEB)

    Brem, M.H. [Brigham and Women' s Hospital, Department of Radiology, Boston, MA (United States); Friedrich-Alexander-University Erlangen Nurenberg, Division of Orthopaedic and Trauma Surgery, Department of Surgery, Erlangen (Germany); Lang, P.K.; Neumann, G.; Schlechtweg, P.M.; Yoshioka, H.; Pappas, G.; Duryea, J. [Brigham and Women' s Hospital, Department of Radiology, Boston, MA (United States); Schneider, E. [LLC, SciTrials, Rocky River, OH (United States); Cleveland Clinic, Imaging Institute, Cleveland, OH (United States); Jackson, R.; Yu, J. [Ohio State University, Diabetes and Metabolism and Radiology, Department of Endocrinology, Columbus, OH (United States); Eaton, C.B. [Center for Primary Care and Prevention and the Warren Alpert Medical School of Brown University, Memorial Hospital of Rhode Island, Providence, RI (United States); Hennig, F.F. [Friedrich-Alexander-University Erlangen Nurenberg, Division of Orthopaedic and Trauma Surgery, Department of Surgery, Erlangen (Germany)

    2009-05-15

    Software-based image analysis is important for studies of cartilage changes in knee osteoarthritis (OA). This study describes an evaluation of a semi-automated cartilage segmentation software tool capable of quantifying paired images for potential use in longitudinal studies of knee OA. We describe the methodology behind the analysis and demonstrate its use by determination of test-retest analysis precision of duplicate knee magnetic resonance imaging (MRI) data sets. Test-retest knee MR images of 12 subjects with a range of knee health were evaluated from the Osteoarthritis Initiative (OAI) pilot MR study. Each subject was removed from the magnet between the two scans. The 3D DESS (sagittal, 0.456 mm x 0.365 mm, 0.7 mm slice thickness, TR 16.5 ms, TE 4.7 ms) images were obtained on a 3-T Siemens Trio MR system with a USA Instruments quadrature transmit-receive extremity coil. Segmentation of one 3D-image series was first performed and then the corresponding retest series was segmented by viewing both image series concurrently in two adjacent windows. After manual registration of the series, the first segmentation cartilage outline served as an initial estimate for the second segmentation. We evaluated morphometric measures of the bone and cartilage surface area (tAB and AC), cartilage volume (VC), and mean thickness (ThC.me) for medial/lateral tibia (MT/LT), total femur (F) and patella (P). Test-retest reproducibility was assessed using the root-mean square coefficient of variation (RMS CV%). For the paired analyses, RMS CV % ranged from 0.9% to 1.2% for VC, from 0.3% to 0.7% for AC, from 0.6% to 2.7% for tAB and 0.8% to 1.5% for ThC.me. Paired image analysis improved the measurement precision of cartilage segmentation. Our results are in agreement with other publications supporting the use of paired analysis for longitudinal studies of knee OA. (orig.)

  17. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

    Energy Technology Data Exchange (ETDEWEB)

    Ciller, Carlos, E-mail: carlos.cillerruiz@unil.ch [Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne (Switzerland); Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Centre d’Imagerie BioMédicale, University of Lausanne, Lausanne (Switzerland); De Zanet, Sandro I.; Rüegsegger, Michael B. [Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Department of Ophthalmology, Inselspital, Bern University Hospital, Bern (Switzerland); Pica, Alessia [Department of Radiation Oncology, Inselspital, Bern University Hospital, Bern (Switzerland); Sznitman, Raphael [Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Department of Ophthalmology, Inselspital, Bern University Hospital, Bern (Switzerland); Thiran, Jean-Philippe [Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne (Switzerland); Signal Processing Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne (Switzerland); Maeder, Philippe [Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne (Switzerland); Munier, Francis L. [Unit of Pediatric Ocular Oncology, Jules Gonin Eye Hospital, Lausanne (Switzerland); Kowal, Jens H. [Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Department of Ophthalmology, Inselspital, Bern University Hospital, Bern (Switzerland); and others

    2015-07-15

    Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.

  18. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma.

    Science.gov (United States)

    Ciller, Carlos; De Zanet, Sandro I; Rüegsegger, Michael B; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L; Kowal, Jens H; Cuadra, Meritxell Bach

    2015-07-15

    Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

    International Nuclear Information System (INIS)

    Ciller, Carlos; De Zanet, Sandro I.; Rüegsegger, Michael B.; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L.; Kowal, Jens H.

    2015-01-01

    Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor

  20. ADVANCED CLUSTER BASED IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    D. Kesavaraja

    2011-11-01

    Full Text Available This paper presents efficient and portable implementations of a useful image segmentation technique which makes use of the faster and a variant of the conventional connected components algorithm which we call parallel Components. In the Modern world majority of the doctors are need image segmentation as the service for various purposes and also they expect this system is run faster and secure. Usually Image segmentation Algorithms are not working faster. In spite of several ongoing researches in Conventional Segmentation and its Algorithms might not be able to run faster. So we propose a cluster computing environment for parallel image Segmentation to provide faster result. This paper is the real time implementation of Distributed Image Segmentation in Clustering of Nodes. We demonstrate the effectiveness and feasibility of our method on a set of Medical CT Scan Images. Our general framework is a single address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient cluster process which uses a novel approach for parallel merging. Our experimental results are consistent with the theoretical analysis and practical results. It provides the faster execution time for segmentation, when compared with Conventional method. Our test data is different CT scan images from the Medical database. More efficient implementations of Image Segmentation will likely result in even faster execution times.

  1. Pediatric magnetic resonance imaging

    International Nuclear Information System (INIS)

    Cohen, M.D.

    1986-01-01

    This book defines the current clinical potential of magnetic resonance imaging and focuses on direct clinical work with pediatric patients. A section dealing with the physics of magnetic resonance imaging provides an introduction to enable clinicians to utilize the machine and interpret the images. Magnetic resonance imaging is presented as an appropriate imaging modality for pediatric patients utilizing no radiation

  2. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas.

    Science.gov (United States)

    Saygin, Z M; Kliemann, D; Iglesias, J E; van der Kouwe, A J W; Boyd, E; Reuter, M; Stevens, A; Van Leemput, K; McKee, A; Frosch, M P; Fischl, B; Augustinack, J C

    2017-07-15

    The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high resolution (100-150µm) at 7T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE) with standard resolution T1 data, used individual volumetric data of the amygdala nuclei as the measure and found that our atlas i) discriminates between Alzheimer's disease participants and age-matched control participants with 84% accuracy (AUC=0.915), and ii) discriminates between individuals with autism and age-, sex- and IQ-matched neurotypically developed control participants with 59.5% accuracy (AUC=0.59). For both datasets, the new ex vivo atlas significantly outperformed (all p amygdala derived from the segmentation in FreeSurfer 5.1 (ADNI: 75%, ABIDE: 54% accuracy), as well as classification based on whole amygdala volume (using the sum of all amygdala nuclei volumes; ADNI: 81%, ABIDE: 55% accuracy). This new atlas and the segmentation tools that utilize it will provide neuroimaging researchers with the ability to explore the function and connectivity of the human amygdala nuclei with unprecedented detail in healthy adults as well as those with neurodevelopmental and neurodegenerative disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas

    DEFF Research Database (Denmark)

    Saygin, Z M; Kliemann, D; Iglesias, J. E.

    2017-01-01

    The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high...... resolution (100-150µm) at 7T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently...... developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE...

  4. Effects of segmental traction therapy on lumbar disc herniation in patients with acute low back pain measured by magnetic resonance imaging: A single arm clinical trial.

    Science.gov (United States)

    Karimi, Noureddin; Akbarov, Parvin; Rahnama, Leila

    2017-01-01

    Low Back Pain (LBP) is considered as one of the most frequent disorders, which about 80% of adults experience in their lives. Lumbar disc herniation (LDH) is a cause for acute LBP. Among conservative treatments, traction is frequently used by clinicians to manage LBP resulting from LDH. However, there is still a lack of consensus about its efficacy. The purpose of this study was to evaluate the effects of segmental traction therapy on lumbar discs herniation, pain, lumbar range of motion (ROM), and back extensor muscles endurance in patients with acute LBP induced by LDH. Fifteen patients with acute LBP diagnosed by LDH participated in the present study. Participants undertook 15 sessions of segmental traction therapy along with conventional physiotherapy, 5 times a week for 3 weeks. Lumbar herniated mass size was measured before and after the treatment protocol using magnetic resonance imaging. Furthermore, pain, lumbar ROM and back muscle endurance were evaluated before and after the procedure using clinical outcome measures. Following the treatment protocol, herniated mass size and patients' pain were reduced significantly. In addition, lumbar flexion ROM showed a significant improvement. However, no significant change was observed for back extensor muscle endurance after the treatment procedure. The result of the present study showed segmental traction therapy might play an important role in the treatment of acute LBP stimulated by LDH.

  5. Lumbo-pelvic joint protection against antigravity forces: motor control and segmental stiffness assessed with magnetic resonance imaging.

    Science.gov (United States)

    Richardson, C A; Hides, J A; Wilson, S; Stanton, W; Snijders, C J

    2004-07-01

    The antigravity muscles of the lumbo-pelvic region, especially transversus abdominis (TrA), are important for the protection and support of the weightbearing joints. Measures of TrA function (the response to the postural cue of drawing in the abdominal wall) have been developed and quantified using magnetic resonance imaging (MRI). Cross-sections through the trunk allowed muscle contraction as well as the large fascial attachments of the TrA to be visualized. The cross sectional area (CSA) of the deep musculo-fascial system was measured at rest and in the contracted state, using static images as well as a cine sequence. In this developmental study, MRI measures were undertaken on a small sample of low back pain (LBP) and non LBP subjects. Results demonstrated that, in non LBP subjects, the draw in action produced a symmetrical deep musculo-fascial "corset" which encircles the abdomen. This study demonstrated a difference in this "corset" measure between subjects with and without LBP. These measures may also prove useful to quantify the effect of unloading in bedrest and microgravity exposure.

  6. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.

    Science.gov (United States)

    Lavdas, Ioannis; Glocker, Ben; Kamnitsas, Konstantinos; Rueckert, Daniel; Mair, Henrietta; Sandhu, Amandeep; Taylor, Stuart A; Aboagye, Eric O; Rockall, Andrea G

    2017-10-01

    As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean surface distance

  7. Tumor segmentation of whole-body magnetic resonance imaging in neurofibromatosis type 1 patients: tumor burden correlates

    Energy Technology Data Exchange (ETDEWEB)

    Heffler, Michael A.; Xi, Yin; Chhabra, Avneesh [University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX (United States); Le, Lu Q. [University of Texas Southwestern Medical Center, Department of Dermatology, Dallas, TX (United States)

    2017-01-15

    Segmentation of whole-body MRI (WBMRI) to assess the feasibility, quantitate the total tumor volume (tumor burden) in patients with neurofibromatosis type 1 (NF1) and examine associations with demographic, disease-related and anthropomorphic features. A consecutive series of patients with NF1 underwent WBMRI and were reviewed for tumors. Tumors were segmented using a semiautomated software-based tool. Tumors were classified as superficial or deep and discrete or plexiform. Segmentation times were recorded. Segmentation yielded the quantity and tumor burden of superficial, internal and plexiform tumors. Correlations between segmentation data and demographic, disease-related and anthropomorphic features were examined. Fifteen patients were evaluated (42.3 ± 13.6 years, 10 female, 5 male). Segmentation times were a median of 30 min and yielded 2,328 tumors (1,582 superficial, 746 internal and 23 plexiform). One tumor was malignant. Tumor counts ranged from 14 to 397. Tumor burden ranged from 6.95 cm3 to 571 cm3. Individual tumor volume ranged from 0.0120 cm3 to 298 cm3. Significant correlation was found between the total volume of superficial tumors and height (ρ = 0.5966, p < 0.02). Male patients had higher overall tumor burdens (p < 0.05) and higher superficial tumor burden (p < 0.03). Patients with negative family history had more tumors (p < 0.05). Segmentation of WBMRI in patients with NF1 is feasible and elucidates meaningful relationships among disease phenotype, anthropomorphic and demographic features. (orig.)

  8. Tumor segmentation of whole-body magnetic resonance imaging in neurofibromatosis type 1 patients: tumor burden correlates

    International Nuclear Information System (INIS)

    Heffler, Michael A.; Xi, Yin; Chhabra, Avneesh; Le, Lu Q.

    2017-01-01

    Segmentation of whole-body MRI (WBMRI) to assess the feasibility, quantitate the total tumor volume (tumor burden) in patients with neurofibromatosis type 1 (NF1) and examine associations with demographic, disease-related and anthropomorphic features. A consecutive series of patients with NF1 underwent WBMRI and were reviewed for tumors. Tumors were segmented using a semiautomated software-based tool. Tumors were classified as superficial or deep and discrete or plexiform. Segmentation times were recorded. Segmentation yielded the quantity and tumor burden of superficial, internal and plexiform tumors. Correlations between segmentation data and demographic, disease-related and anthropomorphic features were examined. Fifteen patients were evaluated (42.3 ± 13.6 years, 10 female, 5 male). Segmentation times were a median of 30 min and yielded 2,328 tumors (1,582 superficial, 746 internal and 23 plexiform). One tumor was malignant. Tumor counts ranged from 14 to 397. Tumor burden ranged from 6.95 cm3 to 571 cm3. Individual tumor volume ranged from 0.0120 cm3 to 298 cm3. Significant correlation was found between the total volume of superficial tumors and height (ρ = 0.5966, p < 0.02). Male patients had higher overall tumor burdens (p < 0.05) and higher superficial tumor burden (p < 0.03). Patients with negative family history had more tumors (p < 0.05). Segmentation of WBMRI in patients with NF1 is feasible and elucidates meaningful relationships among disease phenotype, anthropomorphic and demographic features. (orig.)

  9. Cardiac magnetic resonance imaging

    African Journals Online (AJOL)

    2011-03-06

    Mar 6, 2011 ... Cardiac magnetic resonance imaging. Cardiovascular magnetic resonance imaging is becoming a routine diagnostic technique. BRUCE s sPOTTiswOOdE, PhD. MRC/UCT Medical Imaging Research Unit, University of Cape Town, and Division of Radiology, Stellenbosch University. Bruce Spottiswoode ...

  10. Multiple Segmentation of Image Stacks

    DEFF Research Database (Denmark)

    Smets, Jonathan; Jaeger, Manfred

    2014-01-01

    We propose a method for the simultaneous construction of multiple image segmentations by combining a recently proposed “convolution of mixtures of Gaussians” model with a multi-layer hidden Markov random field structure. The resulting method constructs for a single image several, alternative...

  11. Brain Tumor Image Segmentation in MRI Image

    Science.gov (United States)

    Peni Agustin Tjahyaningtijas, Hapsari

    2018-04-01

    Brain tumor segmentation plays an important role in medical image processing. Treatment of patients with brain tumors is highly dependent on early detection of these tumors. Early detection of brain tumors will improve the patient’s life chances. Diagnosis of brain tumors by experts usually use a manual segmentation that is difficult and time consuming because of the necessary automatic segmentation. Nowadays automatic segmentation is very populer and can be a solution to the problem of tumor brain segmentation with better performance. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. this paper, we focus on the recent trend of automatic segmentation in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of full automatic segmentaion are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

  12. Magnetic resonance imaging apparatus

    International Nuclear Information System (INIS)

    Ehnholm, G.J.

    1991-01-01

    This patent describes an electron spin resonance enhanced magnetic resonance (MR) imaging (ESREMRI) apparatus able to generate a primary magnetic field during periods of nuclear spin transition excitation and magnetic resonance signal detection. This allows the generation of ESREMRI images of a subject. A primary magnetic field of a second and higher value generated during periods of nuclear spin transition excitation and magnetic resonance signal detection can be used to generate conventional MR images of a subject. The ESREMRI and native MR images so generated may be combined, (or superimposed). (author)

  13. CLG for Automatic Image Segmentation

    OpenAIRE

    Christo Ananth; S.Santhana Priya; S.Manisha; T.Ezhil Jothi; M.S.Ramasubhaeswari

    2017-01-01

    This paper proposes an automatic segmentation method which effectively combines Active Contour Model, Live Wire method and Graph Cut approach (CLG). The aim of Live wire method is to provide control to the user on segmentation process during execution. Active Contour Model provides a statistical model of object shape and appearance to a new image which are built during a training phase. In the graph cut technique, each pixel is represented as a node and the distance between those nodes is rep...

  14. Total resection of any segment of the lateral meniscus may cause early cartilage degeneration: Evaluation by magnetic resonance imaging using T2 mapping.

    Science.gov (United States)

    Murakami, Koji; Arai, Yuji; Ikoma, Kazuya; Kato, Kammei; Inoue, Hiroaki; Nakagawa, Shuji; Fujii, Yuta; Ueshima, Keiichiro; Fujiwara, Hiroyoshi; Kubo, Toshikazu

    2018-06-01

    The aim of this study was to perform quantitative evaluation of degeneration of joint cartilage using T2 mapping in magnetic resonance imaging (MRI) after arthroscopic partial resection of the lateral meniscus.The subjects were 21 patients (23 knees) treated with arthroscopic partial resection of the lateral meniscus. MRI was performed for all knees before surgery and 6 months after surgery to evaluate the center of the lateral condyle of the femur in sagittal images for T2 mapping. Ten regions of interest (ROIs) on the articular cartilage were established at 10-degree intervals, from the point at which the femur shaft crossed the lateral femoral condyle joint to the articular cartilage 90° relative to the femur shaft. Preoperative and postoperative T2 values were evaluated at each ROI. Age, sex, body mass index, femorotibial angle, Tegner score, and amount of meniscal resection were evaluated when the T2 value increased more than 6% at 30°.T2 values at approximately 10 °, 20 °, 30 °, 40 °, 50 °, and 60 ° degrees relative to the anatomical axis of the femur were significantly greater postoperatively (3.1, 3.6, 5.5, 4.4, 5.0, 6.4%, respectively) than preoperatively. A >6% increase at 30° was associated with total resection of any segment of the meniscus.Degeneration of the articular cartilage, as shown by the disorganization of collagen arrays at positions approximately 10 °, 20 °, 30 °, 40 °, 50 °, and 60 ° relative to the anatomical axis of the femur, may start soon after arthroscopic lateral meniscectomy. Total resection of any segment of the lateral meniscus may cause T2 elevation of articular cartilage of lateral femoral condyle.

  15. Transfer learning improves supervised image segmentation across imaging protocols.

    Science.gov (United States)

    van Opbroek, Annegreet; Ikram, M Arfan; Vernooij, Meike W; de Bruijne, Marleen

    2015-05-01

    The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

  16. Brain MR image segmentation using NAMS in pseudo-color.

    Science.gov (United States)

    Li, Hua; Chen, Chuanbo; Fang, Shaohong; Zhao, Shengrong

    2017-12-01

    Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

  17. Cardiac index after acute ST-segment elevation myocardial infarction measured with phase-contrast cardiac magnetic resonance imaging

    International Nuclear Information System (INIS)

    Klug, Gert; Reinstadler, Sebastian Johannes; Feistritzer, Hans-Josef; Schwaiger, Johannes P.; Reindl, Martin; Mair, Johannes; Mueller, Silvana; Franz, Wolfgang-Michael; Metzler, Bernhard; Kremser, Christian; Mayr, Agnes

    2016-01-01

    Phase-contrast CMR (PC-CMR) might provide a fast and robust non-invasive determination of left ventricular function in patients after ST-segment elevation myocardial infarction (STEMI). Cine sequences in the left-ventricular (LV) short-axis and free-breathing, retrospectively gated PC-CMR were performed in 90 patients with first acute STEMI and 15 healthy volunteers. Inter- and intra-observer agreement was determined. The correlations of clinical variables age, gender, ejection fraction, NT pro-brain natriuretic peptide [NT-proBNP] with cardiac index (CI) were calculated. For CI, there was a strong agreement of cine CMR with PC-CMR in healthy volunteers (r: 0.82, mean difference: -0.14 l/min/m 2 , error ± 23 %). Agreement was lower in STEMI patients (r: 0.61, mean difference: -0.17 l/min/m 2 , error ± 32 %). In STEMI patients, CI measured with PC-CMR showed lower intra-observer (1 % vs. 9 %) and similar inter-observer variability (9 % vs. 12 %) compared to cine CMR. CI was significantly correlated with age, ejection fraction and NT-proBNP values in STEMI patients. The agreement of PC-CMR and cine CMR for the determination of CI is lower in STEMI patients than in healthy volunteers. After acute STEMI, CI measured with PC-CMR decreases with age, LV ejection fraction and higher NT-proBNP. (orig.)

  18. Magnetic resonance imaging (MRI

    Directory of Open Access Journals (Sweden)

    Takavar A

    1993-04-01

    Full Text Available Basic physical principles of nuclear magnetic resonance imaging (N.M.R.I, a nonionizing medical imaging technique, are described. Principles of NMRI with other conventional imaging methods, ie, isotope scanning, ultrasonography and radiography have been compared. T1 and T2 and spin density (S.D. factors and different image construction techniques based on their different combinations is discussed and at the end physical properties of some N.M.R images is mentioned.

  19. Magnetic resonance imaging (MRI)

    OpenAIRE

    Takavar A

    1993-01-01

    Basic physical principles of nuclear magnetic resonance imaging (N.M.R.I), a nonionizing medical imaging technique, are described. Principles of NMRI with other conventional imaging methods, ie, isotope scanning, ultrasonography and radiography have been compared. T1 and T2 and spin density (S.D.) factors and different image construction techniques based on their different combinations is discussed and at the end physical properties of some N.M.R images is mentioned.

  20. Magnetic resonance imaging

    International Nuclear Information System (INIS)

    Robertson, Angus

    1990-01-01

    An assessment is made of the clinical benefits of expensive diagnostic technology, such as the magnetic resonance imaging. It is concluded that to most radiologists, magnetic resonance imaging has a definite place in the diagnostic scenario, especially for demonstrating central nervous system lesions in multiple sclerosis. While it is recognized that medical and financial resources are limited, it is emphasised that the cost to society must be balanced against the patient benefit. 17 refs

  1. A semi-automated measuring system of brain diffusion and perfusion magnetic resonance imaging abnormalities in patients with multiple sclerosis based on the integration of coregistration and tissue segmentation procedures

    International Nuclear Information System (INIS)

    Revenaz, Alfredo; Ruggeri, Massimiliano; Laganà, Marcella; Bergsland, Niels; Groppo, Elisabetta; Rovaris, Marco; Fainardi, Enrico

    2016-01-01

    Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) abnormalities in patients with multiple sclerosis (MS) are currently measured by a complex combination of separate procedures. Therefore, the purpose of this study was to provide a reliable method for reducing analysis complexity and obtaining reproducible results. We implemented a semi-automated measuring system in which different well-known software components for magnetic resonance imaging (MRI) analysis are integrated to obtain reliable measurements of DWI and PWI disturbances in MS. We generated the Diffusion/Perfusion Project (DPP) Suite, in which a series of external software programs are managed and harmonically and hierarchically incorporated by in-house developed Matlab software to perform the following processes: 1) image pre-processing, including imaging data anonymization and conversion from DICOM to Nifti format; 2) co-registration of 2D and 3D non-enhanced and Gd-enhanced T1-weighted images in fluid-attenuated inversion recovery (FLAIR) space; 3) lesion segmentation and classification, in which FLAIR lesions are at first segmented and then categorized according to their presumed evolution; 4) co-registration of segmented FLAIR lesion in T1 space to obtain the FLAIR lesion mask in the T1 space; 5) normal appearing tissue segmentation, in which T1 lesion mask is used to segment basal ganglia/thalami, normal appearing grey matter (NAGM) and normal appearing white matter (NAWM); 6) DWI and PWI map generation; 7) co-registration of basal ganglia/thalami, NAGM, NAWM, DWI and PWI maps in previously segmented FLAIR space; 8) data analysis. All these steps are automatic, except for lesion segmentation and classification. We developed a promising method to limit misclassifications and user errors, providing clinical researchers with a practical and reproducible tool to measure DWI and PWI changes in MS

  2. Parallel fuzzy connected image segmentation on GPU

    OpenAIRE

    Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.

    2011-01-01

    Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm impleme...

  3. Imaging by magnetic resonance

    International Nuclear Information System (INIS)

    Duroure, J.F.; Serpolay, H.; Vallens, D.

    1995-01-01

    Here are described the advanced technology for nuclear magnetic resonance imaging: reduction of acquisition times, and rebuilding times, images quality improvement. The tendency is to open the machines at low and middle field, on a market being at 10% of NMR I sales, with economical, scientifical and ergonomic reasons broadly developed by constructors

  4. Functional Magnetic Resonance Imaging

    Science.gov (United States)

    Voos, Avery; Pelphrey, Kevin

    2013-01-01

    Functional magnetic resonance imaging (fMRI), with its excellent spatial resolution and ability to visualize networks of neuroanatomical structures involved in complex information processing, has become the dominant technique for the study of brain function and its development. The accessibility of in-vivo pediatric brain-imaging techniques…

  5. Nuclear magnetic resonance imaging

    International Nuclear Information System (INIS)

    Young, I.R.

    1984-01-01

    In a method of imaging a body in which nuclear magnetic resonance is excited in a region including part of the body, and the free induction decay signal is measured, a known quantity of a material of known nuclear magnetic resonance properties, for example a bag of water, is included in the region so as to enhance the measured free induction decay signal. This then reduces the generation of noise during subsequent processing of the signal. (author)

  6. Magnetic resonance imaging

    International Nuclear Information System (INIS)

    Anon.

    1988-01-01

    Magnetic resonance imaging (MRI) is a new and innovative technique that affords anatomic images in multiple planes and that may provide information about tissue characterization. The magnetic resonance images are obtained by placing the patient or the area of interest within a powerful, highly uniform, static magnetic field. Magnetized protons (hydrogen nuclei) within the patient align like small magnets in this field. Radiofrequency pulses are then used to create an oscillating magnetic field perpendicular to the main field. Magnetic resonance images differ from those produced by x-rays: the latter are associated with absorption of x-ray energy while magnetic resonance images are based on proton density and proton relaxation dynamics. Proton characteristics vary according to the tissue under examination and reflect its physical and chemical properties. To resolve issues regarding safety and efficacy, the Warren Grant Magnuson Clinical Center and the Office of Medical Applications of Research of the National Institutes of Health (NIH) convened a consensus conference about MRI Oct 26 through 28, 1987. At the NIH, the Consensus Development Conference brings together investigators in the biomedical sciences, clinical investigators, practicing physicians, and consumer and special interest groups to make a scientific assessment of technologies, including drugs, devices, and procedures, and to seek agreement on their safety and effectiveness

  7. FCM Clustering Algorithms for Segmentation of Brain MR Images

    Directory of Open Access Journals (Sweden)

    Yogita K. Dubey

    2016-01-01

    Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

  8. Image Segmentation Using Minimum Spanning Tree

    Science.gov (United States)

    Dewi, M. P.; Armiati, A.; Alvini, S.

    2018-04-01

    This research aim to segmented the digital image. The process of segmentation is to separate the object from the background. So the main object can be processed for the other purposes. Along with the development of technology in digital image processing application, the segmentation process becomes increasingly necessary. The segmented image which is the result of the segmentation process should accurate due to the next process need the interpretation of the information on the image. This article discussed the application of minimum spanning tree on graph in segmentation process of digital image. This method is able to separate an object from the background and the image will change to be the binary images. In this case, the object that being the focus is set in white, while the background is black or otherwise.

  9. Magnetic resonance imaging

    International Nuclear Information System (INIS)

    Sigal, R.

    1988-01-01

    This book is an introduction to magnetic resonance imaging (MRI). The basic principles for the interpretation of MR images are developed. The book is divided into five chapters: introduction, tissue, parameters, acquisition parameters, contribution to diagnosis, and practical management of an MR examination. Eight exercises allow the reader to test the knowledge he has acquired. Signal localization and MR artefacts are reviewed in an appendix

  10. Electron Paramagnetic Resonance Imaging

    Indian Academy of Sciences (India)

    Twentieth century bore witness to remarkable scientists whohave advanced our understanding of the brain. Among them,EPR (Electron Paramagnetic Resonance) imaging is particularlyuseful in monitoring hypoxic zones in tumors which arehighly resistant to radiation and chemotherapeutic treatment.This first part of the ...

  11. Nuclear magnetic resonance imaging

    International Nuclear Information System (INIS)

    1983-06-01

    This report summarises the aspects of nuclear magnetic resonance imaging (NMRI) considered by the National Health Technology Advisory Panel and makes recommendations on its introduction in Australia with particular regard to the need for thorough evaluation of its cost effectiveness. Topics covered are: principles of the technique, equipment required, installation, costs, reliability, performance parameters, clinical indications, training and staff requirements, and safety considerations

  12. Color image Segmentation using automatic thresholding techniques

    International Nuclear Information System (INIS)

    Harrabi, R.; Ben Braiek, E.

    2011-01-01

    In this paper, entropy and between-class variance based thresholding methods for color images segmentation are studied. The maximization of the between-class variance (MVI) and the entropy (ME) have been used as a criterion functions to determine an optimal threshold to segment images into nearly homogenous regions. Segmentation results from the two methods are validated and the segmentation sensitivity for the test data available is evaluated, and a comparative study between these methods in different color spaces is presented. The experimental results demonstrate the superiority of the MVI method for color image segmentation.

  13. Hierarchical image segmentation for learning object priors

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.; Li, Nan [TEMPLE UNIV.

    2010-11-10

    The proposed segmentation approach naturally combines experience based and image based information. The experience based information is obtained by training a classifier for each object class. For a given test image, the result of each classifier is represented as a probability map. The final segmentation is obtained with a hierarchial image segmentation algorithm that considers both the probability maps and the image features such as color and edge strength. We also utilize image region hierarchy to obtain not only local but also semi-global features as input to the classifiers. Moreover, to get robust probability maps, we take into account the region context information by averaging the probability maps over different levels of the hierarchical segmentation algorithm. The obtained segmentation results are superior to the state-of-the-art supervised image segmentation algorithms.

  14. Cranial magnetic resonance imaging

    International Nuclear Information System (INIS)

    Elster, A.D.

    1988-01-01

    Cranial Magnetic Resonance Imaging is comprehensive, well structured, and well written. The material is current and well referenced. The illustrations are good and complement the text well. The overall quality of publication is above average. The greatest attribute of the book is its readability. The author demonstrates ample skill in making complex subjects, such as MR physics and imaging of cerebral hemorrhage, easy to understand. The book closes with a detailed atlas on the anatomic appearance of the brain on MR images in the axial, coronal, and sagittal planes

  15. Dental magnetic resonance imaging

    International Nuclear Information System (INIS)

    Hilgenfeld, Tim; Bendszus, Martin; Haehnel, Stefan

    2016-01-01

    Growing distribution and utilization of digital volume tomography (DVT) extend the spectrum of clinical dental imaging. Additional diagnostic value, however, comes along with an increasing amount of radiation. In contrast, magnetic resonance imaging is a radiation free imaging technique. Furthermore, it offers a high soft tissue contrast. Morphological and numerical dental anomalies, differentiation of periapical lesions and exclusion of complications of dental diseases are field of applications for dental MRI. In addition, detection of caries and periodontal lesions and injury of inferior alveolar nerve are promising application areas in the future.

  16. Convolutional Neural Networks for SAR Image Segmentation

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Nobel-Jørgensen, Morten

    2015-01-01

    Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...

  17. Unsupervised Performance Evaluation of Image Segmentation

    Directory of Open Access Journals (Sweden)

    Chabrier Sebastien

    2006-01-01

    Full Text Available We present in this paper a study of unsupervised evaluation criteria that enable the quantification of the quality of an image segmentation result. These evaluation criteria compute some statistics for each region or class in a segmentation result. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of unsupervised evaluation, and then, we compare six unsupervised evaluation criteria. For this comparative study, we use a database composed of 8400 synthetic gray-level images segmented in four different ways. Vinet's measure (correct classification rate is used as an objective criterion to compare the behavior of the different criteria. Finally, we present the experimental results on the segmentation evaluation of a few gray-level natural images.

  18. Distance measures for image segmentation evaluation

    OpenAIRE

    Monteiro, Fernando C.; Campilho, Aurélio

    2012-01-01

    In this paper we present a study of evaluation measures that enable the quantification of the quality of an image segmentation result. Despite significant advances in image segmentation techniques, evaluation of these techniques thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images and is otherwise left to subjective evaluation by the reader. Such an evaluation criterion can be useful for differ...

  19. Magnetic Resonance Imaging (MRI) Safety

    Science.gov (United States)

    ... News Physician Resources Professions Site Index A-Z Magnetic Resonance Imaging (MRI) Safety What is MRI and how ... What is MRI and how does it work? Magnetic resonance imaging, or MRI, is a way of obtaining ...

  20. Magnetic Resonance Imaging of Stroke

    NARCIS (Netherlands)

    Bouts, Mark. J. R. J.; Wu, O.; Dijkhuizen, R. M.

    2017-01-01

    Magnetic resonance imaging (MRI) provides a powerful (neuro)imaging modality for the diagnosis and outcome prediction after (acute) stroke. Since MRI allows noninvasive, longitudinal, and three-dimensional assessment of vessel occlusion (with magnetic resonance angiography (MRA)), tissue injury

  1. Task-oriented lossy compression of magnetic resonance images

    Science.gov (United States)

    Anderson, Mark C.; Atkins, M. Stella; Vaisey, Jacques

    1996-04-01

    A new task-oriented image quality metric is used to quantify the effects of distortion introduced into magnetic resonance images by lossy compression. This metric measures the similarity between a radiologist's manual segmentation of pathological features in the original images and the automated segmentations performed on the original and compressed images. The images are compressed using a general wavelet-based lossy image compression technique, embedded zerotree coding, and segmented using a three-dimensional stochastic model-based tissue segmentation algorithm. The performance of the compression system is then enhanced by compressing different regions of the image volume at different bit rates, guided by prior knowledge about the location of important anatomical regions in the image. Application of the new system to magnetic resonance images is shown to produce compression results superior to the conventional methods, both subjectively and with respect to the segmentation similarity metric.

  2. Fast iterative segmentation of high resolution medical images

    International Nuclear Information System (INIS)

    Hebert, T.J.

    1996-01-01

    Various applications in positron emission tomography (PET), single photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI) require segmentation of 20 to 60 high resolution images of size 256x256 pixels in 3-9 seconds per image. This places particular constraints on the design of image segmentation algorithms. This paper examines the trade-offs in segmenting images based on fitting a density function to the pixel intensities using curve-fitting versus the maximum likelihood method. A quantized data representation is proposed and the EM algorithm for fitting a finite mixture density function to the quantized representation for an image is derived. A Monte Carlo evaluation of mean estimation error and classification error showed that the resulting quantized EM algorithm dramatically reduces the required computation time without loss of accuracy

  3. Children's (Pediatric) Magnetic Resonance Imaging

    Science.gov (United States)

    ... Physician Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance Imaging Children’s magnetic resonance imaging (MRI) ... limitations of Children’s (Pediatric) MRI? What is Children’s (Pediatric) MRI? Magnetic resonance imaging (MRI) is a noninvasive ...

  4. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... Physician Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance Imaging Children’s magnetic resonance imaging (MRI) ... limitations of Children’s (Pediatric) MRI? What is Children’s (Pediatric) MRI? Magnetic resonance imaging (MRI) is a noninvasive ...

  5. Magnetic Resonance Imaging (MRI) -- Head

    Science.gov (United States)

    ... News Physician Resources Professions Site Index A-Z Magnetic Resonance Imaging (MRI) - Head Magnetic resonance imaging (MRI) of the head uses a powerful ... the Head? What is MRI of the Head? Magnetic resonance imaging (MRI) is a noninvasive medical test that ...

  6. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... News Physician Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance Imaging Children’s magnetic resonance imaging ( ... the limitations of Children’s (Pediatric) MRI? What is Children’s (Pediatric) MRI? Magnetic resonance imaging (MRI) is a ...

  7. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available ... News Physician Resources Professions Site Index A-Z Magnetic Resonance Imaging (MRI) - Head Magnetic resonance imaging (MRI) of the head uses a powerful ... the Head? What is MRI of the Head? Magnetic resonance imaging (MRI) is a noninvasive medical test that ...

  8. Performance Analysis of Segmentation of Hyperspectral Images Based on Color Image Segmentation

    Directory of Open Access Journals (Sweden)

    Praveen Agarwal

    2017-06-01

    Full Text Available Image segmentation is a fundamental approach in the field of image processing and based on user’s application .This paper propose an original and simple segmentation strategy based on the EM approach that resolves many informatics problems about hyperspectral images which are observed by airborne sensors. In a first step, to simplify the input color textured image into a color image without texture. The final segmentation is simply achieved by a spatially color segmentation using feature vector with the set of color values contained around the pixel to be classified with some mathematical equations. The spatial constraint allows taking into account the inherent spatial relationships of any image and its color. This approach provides effective PSNR for the segmented image. These results have the better performance as the segmented images are compared with Watershed & Region Growing Algorithm and provide effective segmentation for the Spectral Images & Medical Images.

  9. Parallel magnetic resonance imaging

    International Nuclear Information System (INIS)

    Larkman, David J; Nunes, Rita G

    2007-01-01

    Parallel imaging has been the single biggest innovation in magnetic resonance imaging in the last decade. The use of multiple receiver coils to augment the time consuming Fourier encoding has reduced acquisition times significantly. This increase in speed comes at a time when other approaches to acquisition time reduction were reaching engineering and human limits. A brief summary of spatial encoding in MRI is followed by an introduction to the problem parallel imaging is designed to solve. There are a large number of parallel reconstruction algorithms; this article reviews a cross-section, SENSE, SMASH, g-SMASH and GRAPPA, selected to demonstrate the different approaches. Theoretical (the g-factor) and practical (coil design) limits to acquisition speed are reviewed. The practical implementation of parallel imaging is also discussed, in particular coil calibration. How to recognize potential failure modes and their associated artefacts are shown. Well-established applications including angiography, cardiac imaging and applications using echo planar imaging are reviewed and we discuss what makes a good application for parallel imaging. Finally, active research areas where parallel imaging is being used to improve data quality by repairing artefacted images are also reviewed. (invited topical review)

  10. Colour application on mammography image segmentation

    Science.gov (United States)

    Embong, R.; Aziz, N. M. Nik Ab.; Karim, A. H. Abd; Ibrahim, M. R.

    2017-09-01

    The segmentation process is one of the most important steps in image processing and computer vision since it is vital in the initial stage of image analysis. Segmentation of medical images involves complex structures and it requires precise segmentation result which is necessary for clinical diagnosis such as the detection of tumour, oedema, and necrotic tissues. Since mammography images are grayscale, researchers are looking at the effect of colour in the segmentation process of medical images. Colour is known to play a significant role in the perception of object boundaries in non-medical colour images. Processing colour images require handling more data, hence providing a richer description of objects in the scene. Colour images contain ten percent (10%) additional edge information as compared to their grayscale counterparts. Nevertheless, edge detection in colour image is more challenging than grayscale image as colour space is considered as a vector space. In this study, we implemented red, green, yellow, and blue colour maps to grayscale mammography images with the purpose of testing the effect of colours on the segmentation of abnormality regions in the mammography images. We applied the segmentation process using the Fuzzy C-means algorithm and evaluated the percentage of average relative error of area for each colour type. The results showed that all segmentation with the colour map can be done successfully even for blurred and noisy images. Also the size of the area of the abnormality region is reduced when compare to the segmentation area without the colour map. The green colour map segmentation produced the smallest percentage of average relative error (10.009%) while yellow colour map segmentation gave the largest percentage of relative error (11.367%).

  11. Probabilistic segmentation of remotely sensed images

    NARCIS (Netherlands)

    Gorte, B.

    1998-01-01

    For information extraction from image data to create or update geographic information systems, objects are identified and labeled using an integration of segmentation and classification. This yields geometric and thematic information, respectively.

    Bayesian image

  12. A new framework for interactive images segmentation

    International Nuclear Information System (INIS)

    Ashraf, M.; Sarim, M.; Shaikh, A.B.

    2017-01-01

    Image segmentation has become a widely studied research problem in image processing. There exist different graph based solutions for interactive image segmentation but the domain of image segmentation still needs persistent improvements. The segmentation quality of existing techniques generally depends on the manual input provided in beginning, therefore, these algorithms may not produce quality segmentation with initial seed labels provided by a novice user. In this work we investigated the use of cellular automata in image segmentation and proposed a new algorithm that follows a cellular automaton in label propagation. It incorporates both the pixel's local and global information in the segmentation process. We introduced the novel global constraints in automata evolution rules; hence proposed scheme of automata evolution is more effective than the automata based earlier evolution schemes. Global constraints are also effective in deceasing the sensitivity towards small changes made in manual input; therefore proposed approach is less dependent on label seed marks. It can produce the quality segmentation with modest user efforts. Segmentation results indicate that the proposed algorithm performs better than the earlier segmentation techniques. (author)

  13. Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

    Directory of Open Access Journals (Sweden)

    I. Cruz-Aceves

    2013-01-01

    Full Text Available This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

  14. SALIENCY BASED SEGMENTATION OF SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    A. Sharma

    2015-03-01

    Full Text Available Saliency gives the way as humans see any image and saliency based segmentation can be eventually helpful in Psychovisual image interpretation. Keeping this in view few saliency models are used along with segmentation algorithm and only the salient segments from image have been extracted. The work is carried out for terrestrial images as well as for satellite images. The methodology used in this work extracts those segments from segmented image which are having higher or equal saliency value than a threshold value. Salient and non salient regions of image become foreground and background respectively and thus image gets separated. For carrying out this work a dataset of terrestrial images and Worldview 2 satellite images (sample data are used. Results show that those saliency models which works better for terrestrial images are not good enough for satellite image in terms of foreground and background separation. Foreground and background separation in terrestrial images is based on salient objects visible on the images whereas in satellite images this separation is based on salient area rather than salient objects.

  15. Metric Learning for Hyperspectral Image Segmentation

    Science.gov (United States)

    Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

    2011-01-01

    We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

  16. SVM Pixel Classification on Colour Image Segmentation

    Science.gov (United States)

    Barui, Subhrajit; Latha, S.; Samiappan, Dhanalakshmi; Muthu, P.

    2018-04-01

    The aim of image segmentation is to simplify the representation of an image with the help of cluster pixels into something meaningful to analyze. Segmentation is typically used to locate boundaries and curves in an image, precisely to label every pixel in an image to give each pixel an independent identity. SVM pixel classification on colour image segmentation is the topic highlighted in this paper. It holds useful application in the field of concept based image retrieval, machine vision, medical imaging and object detection. The process is accomplished step by step. At first we need to recognize the type of colour and the texture used as an input to the SVM classifier. These inputs are extracted via local spatial similarity measure model and Steerable filter also known as Gabon Filter. It is then trained by using FCM (Fuzzy C-Means). Both the pixel level information of the image and the ability of the SVM Classifier undergoes some sophisticated algorithm to form the final image. The method has a well developed segmented image and efficiency with respect to increased quality and faster processing of the segmented image compared with the other segmentation methods proposed earlier. One of the latest application result is the Light L16 camera.

  17. Reflection symmetry-integrated image segmentation.

    Science.gov (United States)

    Sun, Yu; Bhanu, Bir

    2012-09-01

    This paper presents a new symmetry-integrated region-based image segmentation method. The method is developed to obtain improved image segmentation by exploiting image symmetry. It is realized by constructing a symmetry token that can be flexibly embedded into segmentation cues. Interesting points are initially extracted from an image by the SIFT operator and they are further refined for detecting the global bilateral symmetry. A symmetry affinity matrix is then computed using the symmetry axis and it is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of the segmented regions. A multi-objective genetic search finds the segmentation result with the highest performance for both segmentation and symmetry, which is close to the global optimum. The method has been investigated experimentally in challenging natural images and images containing man-made objects. It is shown that the proposed method outperforms current segmentation methods both with and without exploiting symmetry. A thorough experimental analysis indicates that symmetry plays an important role as a segmentation cue, in conjunction with other attributes like color and texture.

  18. Improving image segmentation by learning region affinities

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.

    2010-11-03

    We utilize the context information of other regions in hierarchical image segmentation to learn new regions affinities. It is well known that a single choice of quantization of an image space is highly unlikely to be a common optimal quantization level for all categories. Each level of quantization has its own benefits. Therefore, we utilize the hierarchical information among different quantizations as well as spatial proximity of their regions. The proposed affinity learning takes into account higher order relations among image regions, both local and long range relations, making it robust to instabilities and errors of the original, pairwise region affinities. Once the learnt affinities are obtained, we use a standard image segmentation algorithm to get the final segmentation. Moreover, the learnt affinities can be naturally unutilized in interactive segmentation. Experimental results on Berkeley Segmentation Dataset and MSRC Object Recognition Dataset are comparable and in some aspects better than the state-of-art methods.

  19. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... As the hydrogen atoms return to their usual alignment, they emit different amounts of energy that vary ... story about radiology? Share your patient story here Images × Image Gallery Magnetic Resonance Imaging (MRI) procedure View ...

  20. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... it is useful to bring that to the attention of the scheduler before the exam and bring ... Image Gallery Magnetic Resonance Imaging (MRI) procedure View full size with caption Pediatric Content Some imaging tests ...

  1. Magnetic resonance image enhancement using V-filter

    International Nuclear Information System (INIS)

    Yamamoto, H.; Sugita, K.; Kanzaki, N.; Johja, I.; Hiraki, Y.

    1990-01-01

    The purpose of this study is to present a method of boundary enhancement algorithms for magnetic resonance images using a V-filter. The boundary of the brain tumor was precisely extracted by the region segmentation techniques

  2. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... for Brain Tumors Radiation Therapy for Head and Neck Cancer Others : American Stroke Association National Stroke Association ... MRA) Magnetic Resonance, Functional (fMRI) - Brain Head and Neck Cancer Treatment Brain Tumor Treatment Magnetic Resonance Imaging ( ...

  3. Segmentation of liver tumors on CT images

    International Nuclear Information System (INIS)

    Pescia, D.

    2011-01-01

    This thesis is dedicated to 3D segmentation of liver tumors in CT images. This is a task of great clinical interest since it allows physicians benefiting from reproducible and reliable methods for segmenting such lesions. Accurate segmentation would indeed help them during the evaluation of the lesions, the choice of treatment and treatment planning. Such a complex segmentation task should cope with three main scientific challenges: (i) the highly variable shape of the structures being sought, (ii) their similarity of appearance compared with their surrounding medium and finally (iii) the low signal to noise ratio being observed in these images. This problem is addressed in a clinical context through a two step approach, consisting of the segmentation of the entire liver envelope, before segmenting the tumors which are present within the envelope. We begin by proposing an atlas-based approach for computing pathological liver envelopes. Initially images are pre-processed to compute the envelopes that wrap around binary masks in an attempt to obtain liver envelopes from estimated segmentation of healthy liver parenchyma. A new statistical atlas is then introduced and used to segmentation through its diffeomorphic registration to the new image. This segmentation is achieved through the combination of image matching costs as well as spatial and appearance prior using a multi-scale approach with MRF. The second step of our approach is dedicated to lesions segmentation contained within the envelopes using a combination of machine learning techniques and graph based methods. First, an appropriate feature space is considered that involves texture descriptors being determined through filtering using various scales and orientations. Then, state of the art machine learning techniques are used to determine the most relevant features, as well as the hyper plane that separates the feature space of tumoral voxels to the ones corresponding to healthy tissues. Segmentation is then

  4. Cluster Ensemble-Based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Xiaoru Wang

    2013-07-01

    Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

  5. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance Imaging Children’s magnetic resonance imaging (MRI) uses ... identify and accurately characterize diseases than other imaging methods. This detail makes MRI an invaluable tool in ...

  6. Medical image segmentation using genetic algorithms.

    Science.gov (United States)

    Maulik, Ujjwal

    2009-03-01

    Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.

  7. Field Sampling from a Segmented Image

    CSIR Research Space (South Africa)

    Debba, Pravesh

    2008-06-01

    Full Text Available This paper presents a statistical method for deriving the optimal prospective field sampling scheme on a remote sensing image to represent different categories in the field. The iterated conditional modes algorithm (ICM) is used for segmentation...

  8. Muscles of mastication model-based MR image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Ng, H.P. [NUS Graduate School for Integrative Sciences and Engineering, Singapore (Singapore); Agency for Science Technology and Research, Singapore (Singapore). Biomedical Imaging Lab.; Ong, S.H. [National Univ. of Singapore (Singapore). Dept. of Electrical and Computer Engineering; National Univ. of Singapore (Singapore). Div. of Bioengineering; Hu, Q.; Nowinski, W.L. [Agency for Science Technology and Research, Singapore (Singapore). Biomedical Imaging Lab.; Foong, K.W.C. [NUS Graduate School for Integrative Sciences and Engineering, Singapore (Singapore); National Univ. of Singapore (Singapore). Dept. of Preventive Dentistry; Goh, P.S. [National Univ. of Singapore (Singapore). Dept. of Diagnostic Radiology

    2006-11-15

    Objective: The muscles of mastication play a major role in the orodigestive system as the principal motive force for the mandible. An algorithm for segmenting these muscles from magnetic resonance (MR) images was developed and tested. Materials and methods: Anatomical information about the muscles of mastication in MR images is used to obtain the spatial relationships relating the muscle region of interest (ROI) and head ROI. A model-based technique that involves the spatial relationships between head and muscle ROIs as well as muscle templates is developed. In the segmentation stage, the muscle ROI is derived from the model. Within the muscle ROI, anisotropic diffusion is applied to smooth the texture, followed by thresholding to exclude bone and fat. The muscle template and morphological operators are employed to obtain an initial estimate of the muscle boundary, which then serves as the input contour to the gradient vector flow snake that iterates to the final segmentation. Results: The method was applied to segmentation of the masseter, lateral pterygoid and medial pterygoid in 75 images. The overlap indices (K) achieved are 91.4, 92.1 and 91.2%, respectively. Conclusion: A model-based method for segmenting the muscles of mastication from MR images was developed and tested. The results show good agreement between manual and automatic segmentations. (orig.)

  9. Muscles of mastication model-based MR image segmentation

    International Nuclear Information System (INIS)

    Ng, H.P.; Agency for Science Technology and Research, Singapore; Ong, S.H.; National Univ. of Singapore; Hu, Q.; Nowinski, W.L.; Foong, K.W.C.; National Univ. of Singapore; Goh, P.S.

    2006-01-01

    Objective: The muscles of mastication play a major role in the orodigestive system as the principal motive force for the mandible. An algorithm for segmenting these muscles from magnetic resonance (MR) images was developed and tested. Materials and methods: Anatomical information about the muscles of mastication in MR images is used to obtain the spatial relationships relating the muscle region of interest (ROI) and head ROI. A model-based technique that involves the spatial relationships between head and muscle ROIs as well as muscle templates is developed. In the segmentation stage, the muscle ROI is derived from the model. Within the muscle ROI, anisotropic diffusion is applied to smooth the texture, followed by thresholding to exclude bone and fat. The muscle template and morphological operators are employed to obtain an initial estimate of the muscle boundary, which then serves as the input contour to the gradient vector flow snake that iterates to the final segmentation. Results: The method was applied to segmentation of the masseter, lateral pterygoid and medial pterygoid in 75 images. The overlap indices (K) achieved are 91.4, 92.1 and 91.2%, respectively. Conclusion: A model-based method for segmenting the muscles of mastication from MR images was developed and tested. The results show good agreement between manual and automatic segmentations. (orig.)

  10. Magnetic resonance imaging. 1

    International Nuclear Information System (INIS)

    Wall, E.E. van der; Roos, A.A. de; Doornbos, J.; Dijkman, P.R.M. van; Matheijssen, N.A.A.; Laarse, A. van der; Krauss, X.H.; Blokland, J.A.k.; Manger Cats, V.; Voorthuisen, A.E. van; Bruschke, A.V.G.

    1991-01-01

    The cardiovascular applications of MRI in coronary artery disease have considerably increased in recent years. Although many applications overlap those of other more cost-effective techniques, such as echocardiography, radionuclide angiography, and CT, MRI offers unique features not shared by the conventional techniques. Technical advantages are the excellent spatial resolution, the characterization of myocardial tissue, and the potential for three-dimensional imaging. This allows the accurate assessment of left ventricular mass and volume, the differentiation of infarcted tissue from normal myocardial tissue, and the determination of systolic wall thickening and regional wall motion abnormalities. Also inducible myocardial ischemia using pharmacological stress (dipyramidole or dobutamine) may be assessed by magnetic resonance imaging. Future technical developments include real-time imaging and noninvasive visualization of the coronary arteries. These advantages will have a major impact on the application of MRI in coronary artery disease, potentially unsurpassed by other techniques and certainly justifying the expenses. Consequently, the clinical use of MRI for the detection of coronary artery disease largely depends on the progress of technical developments. (author). 134 refs.; 10 figs.; 2 tabs

  11. Compound image segmentation of published biomedical figures.

    Science.gov (United States)

    Li, Pengyuan; Jiang, Xiangying; Kambhamettu, Chandra; Shatkay, Hagit

    2018-04-01

    Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images. In this article, we develop a new compound image segmentation system, FigSplit, which is based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentation is inaccurate. Experimental results show the effectiveness of our method compared with other methods. The system is publicly available for use at: https://www.eecis.udel.edu/~compbio/FigSplit. The code is available upon request. shatkay@udel.edu. Supplementary data are available online at Bioinformatics.

  12. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... As the hydrogen atoms return to their usual alignment, they emit different amounts of energy that vary ... story about radiology? Share your patient story here Images × Image Gallery Radiologist prepping patient for magnetic resonance ...

  13. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... by the interpreting radiologist. Frequently, the differentiation of abnormal (diseased) tissue from normal tissues is better with ... Tumor Treatment Magnetic Resonance Imaging (MRI) Safety Alzheimer's Disease Head Injury Brain Tumors Images related to Magnetic ...

  14. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... it is useful to bring that to the attention of the technologist or scheduler before the exam. ... patient for magnetic resonance imaging (MRI) exam. View full size with caption Pediatric Content Some imaging tests ...

  15. Review methods for image segmentation from computed tomography images

    International Nuclear Information System (INIS)

    Mamat, Nurwahidah; Rahman, Wan Eny Zarina Wan Abdul; Soh, Shaharuddin Cik; Mahmud, Rozi

    2014-01-01

    Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affect the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan

  16. Growing Region Segmentation Software (GRES) for quantitative magnetic resonance imaging of multiple sclerosis: intra- and inter-observer agreement variability: a comparison with manual contouring method

    International Nuclear Information System (INIS)

    Parodi, Roberto C.; Sardanelli, Francesco; Renzetti, Paolo; Rosso, Elisabetta; Losacco, Caterina; Ferrari, Alessandra; Levrero, Fabrizio; Pilot, Alberto; Inglese, Matilde; Mancardi, Giovanni L.

    2002-01-01

    Lesion area measurement in multiple sclerosis (MS) is one of the key points in evaluating the natural history and in monitoring the efficacy of treatments. This study was performed to check the intra- and inter-observer agreement variability of a locally developed Growing Region Segmentation Software (GRES), comparing them to those obtained using manual contouring (MC). From routine 1.5-T MRI study of clinically definite multiple sclerosis patients, 36 lesions seen on proton-density-weighted images (PDWI) and 36 enhancing lesion on Gd-DTPA-BMA-enhanced T1-weighted images (Gd-T1WI) were randomly chosen and were evaluated by three observers. The mean range of lesion size was 9.9-536.0 mm 2 on PDWI and 3.6-57.2 mm 2 on Gd-T1WI. The median intra- and inter-observer agreement were, respectively, 97.1 and 90.0% using GRES on PDWI, 81.0 and 70.0% using MC on PDWI, 88.8 and 80.0% using GRES on Gd-T1WI, and 85.8 and 70.0% using MC on Gd-T1WI. The intra- and inter-observer agreements were significantly greater for GRES compared with MC (P<0.0001 and P=0.0023, respectively) for PDWI, while no difference was found between GRES an MC for Gd-T1WI. The intra-observer variability for GRES was significantly lower on both PDWI (P=0.0001) and Gd-T1WI (P=0.0067), whereas for MC the same result was found only for PDWI (P=0.0147). These data indicate that GRES reduces both the intra- and the inter-observer variability in assessing the area of MS lesions on PDWI and may prove useful in multicentre studies. (orig.)

  17. Magnetic Resonance Imaging. Chapter 15

    Energy Technology Data Exchange (ETDEWEB)

    Leach, M. O. [The Institute of Cancer Research and The Royal Marsden Hospital, London (United Kingdom)

    2014-09-15

    In Chapter 14, the principles of nuclear magnetic resonance were presented, along with an introduction to image forming processes. In this chapter, magnetic resonance imaging (MRI) will be reviewed, beginning with the hardware needed and its impact on image quality. The acquisition processes and image reconstruction will be discussed, as well as the artefacts that are possible, with discussion of the important area of safety and bioeffects completing the chapter.

  18. Magnetic resonance imaging of optic nerve

    International Nuclear Information System (INIS)

    Gala, Foram

    2015-01-01

    Optic nerves are the second pair of cranial nerves and are unique as they represent an extension of the central nervous system. Apart from clinical and ophthalmoscopic evaluation, imaging, especially magnetic resonance imaging (MRI), plays an important role in the complete evaluation of optic nerve and the entire visual pathway. In this pictorial essay, the authors describe segmental anatomy of the optic nerve and review the imaging findings of various conditions affecting the optic nerves. MRI allows excellent depiction of the intricate anatomy of optic nerves due to its excellent soft tissue contrast without exposure to ionizing radiation, better delineation of the entire visual pathway, and accurate evaluation of associated intracranial pathologies

  19. Natural color image segmentation using integrated mechanism

    Institute of Scientific and Technical Information of China (English)

    Jie Xu (徐杰); Pengfei Shi (施鹏飞)

    2003-01-01

    A new method for natural color image segmentation using integrated mechanism is proposed in this paper.Edges are first detected in term of the high phase congruency in the gray-level image. K-mean cluster is used to label long edge lines based on the global color information to estimate roughly the distribution of objects in the image, while short ones are merged based on their positions and local color differences to eliminate the negative affection caused by texture or other trivial features in image. Region growing technique is employed to achieve final segmentation results. The proposed method unifies edges, whole and local color distributions, as well as spatial information to solve the natural image segmentation problem.The feasibility and effectiveness of this method have been demonstrated by various experiments.

  20. Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation

    Directory of Open Access Journals (Sweden)

    Kazemi K

    2014-03-01

    Full Text Available Background: Accurate brain tissue segmentation from magnetic resonance (MR images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM, white matter (WM and cerebrospinal fluid (CSF is needed for the neuroimaging applications. Methods: In this paper, performance evaluation of three widely used brain segmentation software packages SPM8, FSL and Brainsuite is presented. Segmentation with SPM8 has been performed in three frameworks: i default segmentation, ii SPM8 New-segmentation and iii modified version using hidden Markov random field as implemented in SPM8-VBM toolbox. Results: The accuracy of the segmented GM, WM and CSF and the robustness of the tools against changes of image quality has been assessed using Brainweb simulated MR images and IBSR real MR images. The calculated similarity between the segmented tissues using different tools and corresponding ground truth shows variations in segmentation results. Conclusion: A few studies has investigated GM, WM and CSF segmentation. In these studies, the skull stripping and bias correction are performed separately and they just evaluated the segmentation. Thus, in this study, assessment of complete segmentation framework consisting of pre-processing and segmentation of these packages is performed. The obtained results can assist the users in choosing an appropriate segmentation software package for the neuroimaging application of interest.

  1. Magnetic resonance imaging methodology

    International Nuclear Information System (INIS)

    Moser, Ewald; Stadlbauer, Andreas; Windischberger, Christian; Quick, Harald H.; Ladd, Mark E.

    2009-01-01

    Magnetic resonance (MR) methods are non-invasive techniques to provide detailed, multi-parametric information on human anatomy, function and metabolism. Sensitivity, specificity, spatial and temporal resolution may, however, vary depending on hardware (e.g., field strength, gradient strength and speed) and software (optimised measurement protocols and parameters for the various techniques). Furthermore, multi-modality imaging may enhance specificity to better characterise complex disease patterns. Positron emission tomography (PET) is an interesting, largely complementary modality, which might be combined with MR. Despite obvious advantages, combining these rather different physical methods may also pose challenging problems. At this early stage, it seems that PET quality may be preserved in the magnetic field and, if an adequate detector material is used for the PET, MR sensitivity should not be significantly degraded. Again, this may vary for the different MR techniques, whereby functional and metabolic MR is more susceptible than standard anatomical imaging. Here we provide a short introduction to MR basics and MR techniques, also discussing advantages, artefacts and problems when MR hardware and PET detectors are combined. In addition to references for more detailed descriptions of MR fundamentals and applications, we provide an early outlook on this novel and exciting multi-modality approach to PET/MR. (orig.)

  2. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance Imaging Children’s magnetic resonance imaging (MRI) uses a powerful ... for an MRI exam contains a metal called gadolinium . Gadolinium can be used in patients with iodine ...

  3. Automatic segmentation of cerebral MR images using artificial neural networks

    International Nuclear Information System (INIS)

    Alirezaie, J.; Jernigan, M.E.; Nahmias, C.

    1996-01-01

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem

  4. Retinal Image Preprocessing: Background and Noise Segmentation

    Directory of Open Access Journals (Sweden)

    Usman Akram

    2012-09-01

    Full Text Available Retinal images are used for the automated screening and diagnosis of diabetic retinopathy. The retinal image quality must be improved for the detection of features and abnormalities and for this purpose preprocessing of retinal images is vital. In this paper, we present a novel automated approach for preprocessing of colored retinal images. The proposed technique improves the quality of input retinal image by separating the background and noisy area from the overall image. It contains coarse segmentation and fine segmentation. Standard retinal images databases Diaretdb0, Diaretdb1, DRIVE and STARE are used to test the validation of our preprocessing technique. The experimental results show the validity of proposed preprocessing technique.

  5. Segmentation of elongated structures in medical images

    NARCIS (Netherlands)

    Staal, Jozef Johannes

    2004-01-01

    The research described in this thesis concerns the automatic detection, recognition and segmentation of elongated structures in medical images. For this purpose techniques have been developed to detect subdimensional pointsets (e.g. ridges, edges) in images of arbitrary dimension. These

  6. A competition in unsupervised color image segmentation

    Czech Academy of Sciences Publication Activity Database

    Haindl, Michal; Mikeš, Stanislav

    2016-01-01

    Roč. 57, č. 9 (2016), s. 136-151 ISSN 0031-3203 R&D Projects: GA ČR(CZ) GA14-10911S Institutional support: RVO:67985556 Keywords : Unsupervised image segmentation * Segmentation contest * Texture analysis Subject RIV: BD - Theory of Information Impact factor: 4.582, year: 2016 http://library.utia.cas.cz/separaty/2016/RO/haindl-0459179.pdf

  7. Active Mask Segmentation of Fluorescence Microscope Images

    OpenAIRE

    Srinivasa, Gowri; Fickus, Matthew C.; Guo, Yusong; Linstedt, Adam D.; Kovačević, Jelena

    2009-01-01

    We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the “contour” to that of “inside and outside”, or, masks, allowing for easy mul...

  8. An unsupervised strategy for biomedical image segmentation

    Directory of Open Access Journals (Sweden)

    Roberto Rodríguez

    2010-09-01

    Full Text Available Roberto Rodríguez1, Rubén Hernández21Digital Signal Processing Group, Institute of Cybernetics, Mathematics, and Physics, Havana, Cuba; 2Interdisciplinary Professional Unit of Engineering and Advanced Technology, IPN, MexicoAbstract: Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained.Keywords: segmentation, mean shift, unsupervised segmentation, entropy

  9. Image-guided regularization level set evolution for MR image segmentation and bias field correction.

    Science.gov (United States)

    Wang, Lingfeng; Pan, Chunhong

    2014-01-01

    Magnetic resonance (MR) image segmentation is a crucial step in surgical and treatment planning. In this paper, we propose a level-set-based segmentation method for MR images with intensity inhomogeneous problem. To tackle the initialization sensitivity problem, we propose a new image-guided regularization to restrict the level set function. The maximum a posteriori inference is adopted to unify segmentation and bias field correction within a single framework. Under this framework, both the contour prior and the bias field prior are fully used. As a result, the image intensity inhomogeneity can be well solved. Extensive experiments are provided to evaluate the proposed method, showing significant improvements in both segmentation and bias field correction accuracies as compared with other state-of-the-art approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Robust generative asymmetric GMM for brain MR image segmentation.

    Science.gov (United States)

    Ji, Zexuan; Xia, Yong; Zheng, Yuhui

    2017-11-01

    Accurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction. First, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. To demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity inhomogeneity. In this paper, the RGAGMM

  11. Video-based noncooperative iris image segmentation.

    Science.gov (United States)

    Du, Yingzi; Arslanturk, Emrah; Zhou, Zhi; Belcher, Craig

    2011-02-01

    In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.

  12. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

    2015-01-01

    with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two MRI brain-segmentation tasks with multi-site data: white matter, gray matter, and CSF segmentation; and white-matter- /MS-lesion segmentation......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore...

  13. Flood Water Segmentation from Crowdsourced Images

    Science.gov (United States)

    Nguyen, J. K.; Minsker, B. S.

    2017-12-01

    In the United States, 176 people were killed by flooding in 2015. Along with the loss of human lives is the economic cost which is estimated to be $4.5 billion per flood event. Urban flooding has become a recent concern due to the increase in population, urbanization, and global warming. As more and more people are moving into towns and cities with infrastructure incapable of coping with floods, there is a need for more scalable solutions for urban flood management.The proliferation of camera-equipped mobile devices have led to a new source of information for flood research. In-situ photographs captured by people provide information at the local level that remotely sensed images fail to capture. Applications of crowdsourced images to flood research required understanding the content of the image without the need for user input. This paper addresses the problem of how to automatically segment a flooded and non-flooded region in crowdsourced images. Previous works require two images taken at similar angle and perspective of the location when it is flooded and when it is not flooded. We examine three different algorithms from the computer vision literature that are able to perform segmentation using a single flood image without these assumptions. The performance of each algorithm is evaluated on a collection of labeled crowdsourced flood images. We show that it is possible to achieve a segmentation accuracy of 80% using just a single image.

  14. Coupled dictionary learning for joint MR image restoration and segmentation

    Science.gov (United States)

    Yang, Xuesong; Fan, Yong

    2018-03-01

    To achieve better segmentation of MR images, image restoration is typically used as a preprocessing step, especially for low-quality MR images. Recent studies have demonstrated that dictionary learning methods could achieve promising performance for both image restoration and image segmentation. These methods typically learn paired dictionaries of image patches from different sources and use a common sparse representation to characterize paired image patches, such as low-quality image patches and their corresponding high quality counterparts for the image restoration, and image patches and their corresponding segmentation labels for the image segmentation. Since learning these dictionaries jointly in a unified framework may improve the image restoration and segmentation simultaneously, we propose a coupled dictionary learning method to concurrently learn dictionaries for joint image restoration and image segmentation based on sparse representations in a multi-atlas image segmentation framework. Particularly, three dictionaries, including a dictionary of low quality image patches, a dictionary of high quality image patches, and a dictionary of segmentation label patches, are learned in a unified framework so that the learned dictionaries of image restoration and segmentation can benefit each other. Our method has been evaluated for segmenting the hippocampus in MR T1 images collected with scanners of different magnetic field strengths. The experimental results have demonstrated that our method achieved better image restoration and segmentation performance than state of the art dictionary learning and sparse representation based image restoration and image segmentation methods.

  15. Development of representative magnetic resonance imaging-based atlases of the canine brain and evaluation of three methods for atlas-based segmentation.

    Science.gov (United States)

    Milne, Marjorie E; Steward, Christopher; Firestone, Simon M; Long, Sam N; O'Brien, Terrence J; Moffat, Bradford A

    2016-04-01

    To develop representative MRI atlases of the canine brain and to evaluate 3 methods of atlas-based segmentation (ABS). 62 dogs without clinical signs of epilepsy and without MRI evidence of structural brain disease. The MRI scans from 44 dogs were used to develop 4 templates on the basis of brain shape (brachycephalic, mesaticephalic, dolichocephalic, and combined mesaticephalic and dolichocephalic). Atlas labels were generated by segmenting the brain, ventricular system, hippocampal formation, and caudate nuclei. The MRI scans from the remaining 18 dogs were used to evaluate 3 methods of ABS (manual brain extraction and application of a brain shape-specific template [A], automatic brain extraction and application of a brain shape-specific template [B], and manual brain extraction and application of a combined template [C]). The performance of each ABS method was compared by calculation of the Dice and Jaccard coefficients, with manual segmentation used as the gold standard. Method A had the highest mean Jaccard coefficient and was the most accurate ABS method assessed. Measures of overlap for ABS methods that used manual brain extraction (A and C) ranged from 0.75 to 0.95 and compared favorably with repeated measures of overlap for manual extraction, which ranged from 0.88 to 0.97. Atlas-based segmentation was an accurate and repeatable method for segmentation of canine brain structures. It could be performed more rapidly than manual segmentation, which should allow the application of computer-assisted volumetry to large data sets and clinical cases and facilitate neuroimaging research and disease diagnosis.

  16. Parallel fuzzy connected image segmentation on GPU.

    Science.gov (United States)

    Zhuge, Ying; Cao, Yong; Udupa, Jayaram K; Miller, Robert W

    2011-07-01

    Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as CUDA kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set.

  17. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... work? Unlike conventional x-ray examinations and computed tomography (CT) scans, MRI does not utilize ionizing radiation. Instead, ... Angiography Magnetic Resonance, Functional (fMRI) - Brain Children's (Pediatric) CT (Computed Tomography) Magnetic Resonance Imaging (MRI) Safety Contrast Materials Children ...

  18. Fluorescence Image Segmentation by using Digitally Reconstructed Fluorescence Images

    OpenAIRE

    Blumer, Clemens; Vivien, Cyprien; Oertner, Thomas G; Vetter, Thomas

    2011-01-01

    In biological experiments fluorescence imaging is used to image living and stimulated neurons. But the analysis of fluorescence images is a difficult task. It is not possible to conclude the shape of an object from fluorescence images alone. Therefore, it is not feasible to get good manual segmented nor ground truth data from fluorescence images. Supervised learning approaches are not possible without training data. To overcome this issues we propose to synthesize fluorescence images and call...

  19. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available ... is not harmful, but it may cause some medical devices to malfunction. Most orthopedic implants pose no ... Head? Magnetic resonance imaging (MRI) is a noninvasive medical test that physicians use to diagnose medical conditions. ...

  20. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... are the limitations of MRI of the Head? What is MRI of the Head? Magnetic resonance imaging ( ... brain) in routine clinical practice. top of page What are some common uses of the procedure? MR ...

  1. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... magnetic resonance imaging (MRI) uses a powerful magnetic field, radio waves and a computer to produce detailed ... problems, medications, recent surgeries and allergies. The magnetic field is not harmful, but it may cause some ...

  2. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... MRI) exam. View full size with caption Pediatric Content Some imaging tests and treatments have special pediatric considerations. The teddy bear denotes child-specific content. Related Articles and Media Catheter Angiography Magnetic Resonance, ...

  3. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available ... be necessary. Your doctor will explain the exact reason why another exam is requested. Sometimes a follow- ... necessary in trauma situations. Although there is no reason to believe that magnetic resonance imaging harms the ...

  4. Magnetic resonance imaging the basics

    CERN Document Server

    Constantinides, Christakis

    2014-01-01

    Magnetic resonance imaging (MRI) is a rapidly developing field in basic applied science and clinical practice. Research efforts in this area have already been recognized with five Nobel prizes awarded to seven Nobel laureates in the past 70 years. Based on courses taught at The Johns Hopkins University, Magnetic Resonance Imaging: The Basics provides a solid introduction to this powerful technology. The book begins with a general description of the phenomenon of magnetic resonance and a brief summary of Fourier transformations in two dimensions. It examines the fundamental principles of physics for nuclear magnetic resonance (NMR) signal formation and image construction and provides a detailed explanation of the mathematical formulation of MRI. Numerous image quantitative indices are discussed, including (among others) signal, noise, signal-to-noise, contrast, and resolution. The second part of the book examines the hardware and electronics of an MRI scanner and the typical measurements and simulations of m...

  5. Active mask segmentation of fluorescence microscope images.

    Science.gov (United States)

    Srinivasa, Gowri; Fickus, Matthew C; Guo, Yusong; Linstedt, Adam D; Kovacević, Jelena

    2009-08-01

    We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.

  6. Magnetic resonance imaging of facial nerve schwannoma.

    Science.gov (United States)

    Thompson, Andrew L; Aviv, Richard I; Chen, Joseph M; Nedzelski, Julian M; Yuen, Heng-Wai; Fox, Allan J; Bharatha, Aditya; Bartlett, Eric S; Symons, Sean P

    2009-12-01

    This study characterizes the magnetic resonance (MR) appearances of facial nerve schwannoma (FNS). We hypothesize that the extent of FNS demonstrated on MR will be greater compared to prior computed tomography studies, that geniculate involvement will be most common, and that cerebellar pontine angle (CPA) and internal auditory canal (IAC) involvement will more frequently result in sensorineural hearing loss (SNHL). Retrospective study. Clinical, pathologic, and enhanced MR imaging records of 30 patients with FNS were analyzed. Morphologic characteristics and extent of segmental facial nerve involvement were documented. Median age at initial imaging was 51 years (range, 28-76 years). Pathologic confirmation was obtained in 14 patients (47%), and the diagnosis reached in the remainder by identification of a mass, thickening, and enhancement along the course of the facial nerve. All 30 lesions involved two or more contiguous segments of the facial nerve, with 28 (93%) involving three or more segments. The median segments involved per lesion was 4, mean of 3.83. Geniculate involvement was most common, in 29 patients (97%). CPA (P = .001) and IAC (P = .02) involvement was significantly related to SNHL. Seventeen patients (57%) presented with facial nerve dysfunction, manifesting in 12 patients as facial nerve weakness or paralysis, and/or in eight with involuntary movements of the facial musculature. This study highlights the morphologic heterogeneity and typical multisegment involvement of FNS. Enhanced MR is the imaging modality of choice for FNS. The neuroradiologist must accurately diagnose and characterize this lesion, and thus facilitate optimal preoperative planning and counseling.

  7. Segmentation and Classification of Burn Color Images

    Science.gov (United States)

    2001-10-25

    SEGMENTATION AND CLASSIFICATION OF BURN COLOR IMAGES Begoña Acha1, Carmen Serrano1, Laura Roa2 1Área de Teoría de la Señal y Comunicaciones ...2000, Las Vegas (USA), pp. 411-415. [21] G. Wyszecki and W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (New

  8. The semiotics of medical image Segmentation.

    Science.gov (United States)

    Baxter, John S H; Gibson, Eli; Eagleson, Roy; Peters, Terry M

    2018-02-01

    As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical image segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive interpretation of the computer as a symbol processing machine. Using the perspective of the computer as dialogue partner, we can motivate the semiotic understanding of medical image segmentation. Taking advantage of Peircean semiotic traditions and new philosophical inquiry into the structure and quality of metaphors, we can construct a unified framework for the interpretation of medical image segmentation as a sign exchange in which each sign acts as an interface metaphor. This allows for a notion of finite semiosis, described through a schematic medium, that can rigorously describe how clinicians and computers interpret the signs mediating their interaction. Altogether, this framework provides a unified approach to the understanding and development of medical image segmentation interfaces. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Segmentation and Classification of Burn Color Images

    National Research Council Canada - National Science Library

    Acha, Begonya

    2001-01-01

    .... In the classification part, we take advantage of color information by clustering, with a vector quantization algorithm, the color centroids of small squares, taken from the burnt segmented part of the image, in the (V1, V2) plane into two possible groups, where V1 and V2 are the two chrominance components of the CIE Lab representation.

  10. Superresolution Imaging Using Resonant Multiples

    KAUST Repository

    Guo, Bowen

    2017-12-22

    A resonant multiple is defined as a multiple reflection that revisits the same subsurface location along coincident reflection raypaths. We show that resonant first-order multiples can be migrated with either Kirchhoff or wave-equation migration methods to give images with approximately twice the spatial resolution compared to post-stack primary-reflection images. A moveout-correction stacking method is proposed to enhance the signal-to-noise ratios (SNRs) of the resonant multiples before superresolution migration. The effectiveness of this procedure is validated by synthetic and field data tests.

  11. Superresolution Imaging Using Resonant Multiples

    KAUST Repository

    Guo, Bowen; Schuster, Gerard T.

    2017-01-01

    A resonant multiple is defined as a multiple reflection that revisits the same subsurface location along coincident reflection raypaths. We show that resonant first-order multiples can be migrated with either Kirchhoff or wave-equation migration methods to give images with approximately twice the spatial resolution compared to post-stack primary-reflection images. A moveout-correction stacking method is proposed to enhance the signal-to-noise ratios (SNRs) of the resonant multiples before superresolution migration. The effectiveness of this procedure is validated by synthetic and field data tests.

  12. Segmentation of multiple sclerosis lesions in MR images: a review

    Energy Technology Data Exchange (ETDEWEB)

    Mortazavi, Daryoush; Kouzani, Abbas Z. [Deakin University, School of Engineering, Geelong, Victoria (Australia); Soltanian-Zadeh, Hamid [Henry Ford Health System, Image Analysis Laboratory, Radiology Department, Detroit, MI (United States); University of Tehran, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, Tehran (Iran, Islamic Republic of); School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran (Iran, Islamic Republic of)

    2012-04-15

    Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease. For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past. This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques. Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels. (orig.)

  13. Segmentation of multiple sclerosis lesions in MR images: a review

    International Nuclear Information System (INIS)

    Mortazavi, Daryoush; Kouzani, Abbas Z.; Soltanian-Zadeh, Hamid

    2012-01-01

    Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease. For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past. This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques. Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels. (orig.)

  14. Medical image segmentation using improved FCM

    Institute of Scientific and Technical Information of China (English)

    ZHANG XiaoFeng; ZHANG CaiMing; TANG WenJing; WEI ZhenWen

    2012-01-01

    Image segmentation is one of the most important problems in medical image processing,and the existence of partial volume effect and other phenomena makes the problem much more complex. Fuzzy Cmeans,as an effective tool to deal with PVE,however,is faced with great challenges in efficiency.Aiming at this,this paper proposes one improved FCM algorithm based on the histogram of the given image,which will be denoted as HisFCM and divided into two phases.The first phase will retrieve several intervals on which to compute cluster centroids,and the second one will perform image segmentation based on improved FCM algorithm.Compared with FCM and other improved algorithms,HisFCM is of much higher efficiency with satisfying results.Experiments on medical images show that HisFCM can achieve good segmentation results in less than 0.1 second,and can satisfy real-time requirements of medical image processing.

  15. Extraction of Overt Verbal Response from the Acoustic Noise in a Functional Magnetic Resonance Imaging Scan by Use of Segmented Active Noise Cancellation

    Science.gov (United States)

    Jung, Kwan-Jin; Prasad, Parikshit; Qin, Yulin; Anderson, John R.

    2013-01-01

    A method to extract the subject's overt verbal response from the obscuring acoustic noise in an fMRI scan is developed by applying active noise cancellation with a conventional MRI microphone. Since the EPI scanning and its accompanying acoustic noise in fMRI are repetitive, the acoustic noise in one time segment was used as a reference noise in suppressing the acoustic noise in subsequent segments. However, the acoustic noise from the scanner was affected by the subject's movements, so the reference noise was adaptively adjusted as the scanner's acoustic properties varied in time. This method was successfully applied to a cognitive fMRI experiment with overt verbal responses. PMID:15723385

  16. Magnetic resonance imaging

    International Nuclear Information System (INIS)

    Murphy, W.A.

    1988-01-01

    After only a few years, MR imaging has proved to be an important method for imaging disorders of the musculoskeletal tissues. The images are characterized by great inherent contrast, excellent spatial resolution, and exquisite anatomic display - major reasons why MR imaging compares favorably with other imaging methods, such as radionuclide bone scanning and CT. MR imaging is particularly sensitive to bone marrow alterations and is very effective for detection and characterization of a wide variety of soft tissue conditions. Advances in surface coil technology will increase the usefulness of MR imaging in the evaluation of articular disease. In addition, chemical shift imaging and spectroscopy will add physiologic information to the anatomic features demonstrated by proton imaging

  17. Magnetic resonance vascular imaging

    International Nuclear Information System (INIS)

    Axel, L

    1989-01-01

    The basis principles of MRI are reviewed in order to understand how blood flow effects arise in conventional imaging. Then some of the ways these effects have ben used in MRI techniques specifically designed for vascular imaging, are considered. (author)

  18. Sensory analysis for magnetic resonance-image analysis: Using human perception and cognition to segment and assess the interior of potatoes

    DEFF Research Database (Denmark)

    Martens, Harald; Thybo, A.K.; Andersen, H.J.

    2002-01-01

    were developed by the panel during preliminary training sessions, and consisted in definitions of various biological compartments inside the tubers. The results from the sensory and the computer-assisted image analyses of the shape and interior structure of the tubers were related to the experimental...... able to detect differences between varieties as well as storage times. The sensory image analysis gave better discrimination between varieties than the computer-assisted image analysis presently employed, and was easier to interpret. Some sensory descriptors could be predicted from the computer......-assisted image analysis. The present results offer new information about using sensory analysis of MR-images not only for food science but also for medical applications for analysing MR and X-ray images and for training of personnel, such as radiologists and radiographers. (C) 2002 Elsevier Science Ltd....

  19. A novel algorithm for segmentation of brain MR images

    International Nuclear Information System (INIS)

    Sial, M.Y.; Yu, L.; Chowdhry, B.S.; Rajput, A.Q.K.; Bhatti, M.I.

    2006-01-01

    Accurate and fully automatic segmentation of brain from magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of . attention lately. Many researchers have applied various techniques however a standard fuzzy c-means algorithm has produced better results compared to other methods. In this paper, we present a modified fuzzy c-means (FCM) based algorithm for segmentation of brain MR images. Our algorithm is formulated by modifying the objective function of the standard FCM and uses a special spread method to get a smooth and slow varying bias field This method has the advantage that it can be applied at an early stage in an automated data analysis before a tissue model is available. The results on MRI images show that this method provides better results compared to standard FCM algorithms. (author)

  20. Adjacent segment degeneration after lumbar spinal fusion: the impact of anterior column support: a randomized clinical trial with an eight- to thirteen-year magnetic resonance imaging follow-up.

    Science.gov (United States)

    Videbaek, Tina S; Egund, Niels; Christensen, Finn B; Grethe Jurik, Anne; Bünger, Cody E

    2010-10-15

    Randomized controlled trial. To analyze long-term adjacent segment degeneration (ASD) after lumbar fusion on magnetic resonance imaging and compare randomization groups with and without anterior column support. ASD can be a long-term complication after fusion. The prevalence and the cause of ASD are not well documented, but ASD are one of the main arguments for introducing the use of motion-preserving techniques as an alternative to fusion. Anterior lumbar interbody fusion combined with posterolateral lumbar fusion (ALIF+PLF) has been proved superior to posterolateral fusion alone regarding outcome and cost-effectiveness. Between 1996 and 1999, 148 patients with severe chronic low back pain were randomly selected for ALIF+PLF or for PLF alone. Ninety-five patients participated. ASD was examined on magnetic resonance imaging with regard to disc degeneration, disc herniation, stenosis, and endplate changes. Disc heights on radiographs taken at index surgery and at long-term follow-up were compared. Outcome was assessed by validated questionnaires. The follow-up rate was 76%. ASD was similar between randomization groups. In the total cohort, endplate changes were seen in 26% of the participants and correlated significantly with the presence of disc degeneration and disc herniation. Disc degeneration and dorsal disc herniation were the parameters registered most frequently and were significantly more pronounced at the first adjacent level than at the second and the third adjacent levels. Patients without disc height reduction over time were significantly younger than patients with disc height reduction. Disc degeneration and stenosis correlated significantly with outcome at the first adjacent level. The cause of the superior outcome in the group with anterior support is still unclear. Compared with the findings reported in the literature, the prevalence of ASD is likely to be in concordance with the expected changes in a nonoperated symptomatic population and therefore

  1. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available Toggle navigation Test/Treatment Patient Type Screening/Wellness Disease/Condition Safety En Español More Info Images/Videos About Us News Physician Resources Professions Site Index A-Z Magnetic Resonance Imaging (MRI) - ...

  2. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available Toggle navigation Test/Treatment Patient Type Screening/Wellness Disease/Condition Safety En Español More Info Images/Videos About Us News Physician ... Magnetic resonance imaging (MRI) is a noninvasive medical test that physicians use to diagnose medical conditions. MRI ...

  3. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available Toggle navigation Test/Treatment Patient Type Screening/Wellness Disease/Condition Safety En Español More Info Images/Videos About Us News Physician ... Magnetic resonance imaging (MRI) is a noninvasive medical test that physicians use to diagnose medical conditions. MRI ...

  4. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... of which shows a thin slice of the body. The images can then be studied from different angles by ... about radiology? Share your patient story here Images ... Articles and Media Catheter Angiography Magnetic Resonance, Functional (fMRI) - Brain Children's ( ...

  5. NUCLEAR SEGMENTATION IN MICROSCOPE CELL IMAGES: A HAND-SEGMENTED DATASET AND COMPARISON OF ALGORITHMS

    OpenAIRE

    Coelho, Luís Pedro; Shariff, Aabid; Murphy, Robert F.

    2009-01-01

    Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images (a total of 4009 cells) and objectively evaluated some previously proposed segmentation algorithms.

  6. Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST

    Directory of Open Access Journals (Sweden)

    Lijia Wang

    2015-01-01

    Full Text Available CMR quantification of LV chamber volumes typically and manually defines the basal-most LV, which adds processing time and user-dependence. This study developed an LV segmentation method that is fully automated based on the spatiotemporal continuity of the LV (LV-FAST. An iteratively decreasing threshold region growing approach was used first from the midventricle to the apex, until the LV area and shape discontinued, and then from midventricle to the base, until less than 50% of the myocardium circumference was observable. Region growth was constrained by LV spatiotemporal continuity to improve robustness of apical and basal segmentations. The LV-FAST method was compared with manual tracing on cardiac cine MRI data of 45 consecutive patients. Of the 45 patients, LV-FAST and manual selection identified the same apical slices at both ED and ES and the same basal slices at both ED and ES in 38, 38, 38, and 41 cases, respectively, and their measurements agreed within -1.6±8.7 mL, -1.4±7.8 mL, and 1.0±5.8% for EDV, ESV, and EF, respectively. LV-FAST allowed LV volume-time course quantitatively measured within 3 seconds on a standard desktop computer, which is fast and accurate for processing the cine volumetric cardiac MRI data, and enables LV filling course quantification over the cardiac cycle.

  7. Image Denoising And Segmentation Approchto Detect Tumor From BRAINMRI Images

    Directory of Open Access Journals (Sweden)

    Shanta Rangaswamy

    2018-04-01

    Full Text Available The detection of the Brain Tumor is a challenging problem, due to the structure of the Tumor cells in the brain. This project presents a systematic method that enhances the detection of brain tumor cells and to analyze functional structures by training and classification of the samples in SVM and tumor cell segmentation of the sample using DWT algorithm. From the input MRI Images collected, first noise is removed from MRI images by applying wiener filtering technique. In image enhancement phase, all the color components of MRI Images will be converted into gray scale image and make the edges clear in the image to get better identification and improvised quality of the image. In the segmentation phase, DWT on MRI Image to segment the grey-scale image is performed. During the post-processing, classification of tumor is performed by using SVM classifier. Wiener Filter, DWT, SVM Segmentation strategies were used to find and group the tumor position in the MRI filtered picture respectively. An essential perception in this work is that multi arrange approach utilizes various leveled classification strategy which supports execution altogether. This technique diminishes the computational complexity quality in time and memory. This classification strategy works accurately on all images and have achieved the accuracy of 93%.

  8. Microstrip Resonator for High Field MRI with Capacitor-Segmented Strip and Ground Plane

    DEFF Research Database (Denmark)

    Zhurbenko, Vitaliy; Boer, Vincent; Petersen, Esben Thade

    2017-01-01

    ) segmenting stripe and ground plane of the resonator with series capacitors. The design equations for capacitors providing symmetric current distribution are derived. The performance of two types of segmented resonators are investigated experimentally. To authors’ knowledge, a microstrip resonator, where both......, strip and ground plane are capacitor-segmented, is shown here for the first time....

  9. Impact of early, late, and no ST-segment resolution measured by continuous ST Holter monitoring on left ventricular ejection fraction and infarct size as determined by cardiovascular magnetic resonance imaging

    NARCIS (Netherlands)

    Haeck, Joost D. E.; Verouden, Niels J. W.; Kuijt, Wichert J.; Koch, Karel T.; Majidi, Mohamed; Hirsch, Alexander; Tijssen, Jan G. P.; Krucoff, Mitchell W.; de Winter, Robbert J.

    2011-01-01

    Background: The goal of this study is to determine the predictive value of ST-segment resolution (STR) early after percutaneous coronary intervention (PCI), late STR, and no STR for left ventricular ejection fraction (LVEF) and infarct size (IS) by cardiovascular magnetic resonance (CMR) at

  10. Graphical user interface to optimize image contrast parameters used in object segmentation - biomed 2009.

    Science.gov (United States)

    Anderson, Jeffrey R; Barrett, Steven F

    2009-01-01

    Image segmentation is the process of isolating distinct objects within an image. Computer algorithms have been developed to aid in the process of object segmentation, but a completely autonomous segmentation algorithm has yet to be developed [1]. This is because computers do not have the capability to understand images and recognize complex objects within the image. However, computer segmentation methods [2], requiring user input, have been developed to quickly segment objects in serial sectioned images, such as magnetic resonance images (MRI) and confocal laser scanning microscope (CLSM) images. In these cases, the segmentation process becomes a powerful tool in visualizing the 3D nature of an object. The user input is an important part of improving the performance of many segmentation methods. A double threshold segmentation method has been investigated [3] to separate objects in gray scaled images, where the gray level of the object is among the gray levels of the background. In order to best determine the threshold values for this segmentation method the image must be manipulated for optimal contrast. The same is true of other segmentation and edge detection methods as well. Typically, the better the image contrast, the better the segmentation results. This paper describes a graphical user interface (GUI) that allows the user to easily change image contrast parameters that will optimize the performance of subsequent object segmentation. This approach makes use of the fact that the human brain is extremely effective in object recognition and understanding. The GUI provides the user with the ability to define the gray scale range of the object of interest. These lower and upper bounds of this range are used in a histogram stretching process to improve image contrast. Also, the user can interactively modify the gamma correction factor that provides a non-linear distribution of gray scale values, while observing the corresponding changes to the image. This

  11. Image Segmentation, Registration, Compression, and Matching

    Science.gov (United States)

    Yadegar, Jacob; Wei, Hai; Yadegar, Joseph; Ray, Nilanjan; Zabuawala, Sakina

    2011-01-01

    A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity

  12. Optical coherence tomography in anterior segment imaging

    Science.gov (United States)

    Kalev-Landoy, Maya; Day, Alexander C.; Cordeiro, M. Francesca; Migdal, Clive

    2008-01-01

    Purpose To evaluate the ability of optical coherence tomography (OCT), designed primarily to image the posterior segment, to visualize the anterior chamber angle (ACA) in patients with different angle configurations. Methods In a prospective observational study, the anterior segments of 26 eyes of 26 patients were imaged using the Zeiss Stratus OCT, model 3000. Imaging of the anterior segment was achieved by adjusting the focusing control on the Stratus OCT. A total of 16 patients had abnormal angle configurations including narrow or closed angles and plateau irides, and 10 had normal angle configurations as determined by prior full ophthalmic examination, including slit-lamp biomicroscopy and gonioscopy. Results In all cases, OCT provided high-resolution information regarding iris configuration. The ACA itself was clearly visualized in patients with narrow or closed angles, but not in patients with open angles. Conclusions Stratus OCT offers a non-contact, convenient and rapid method of assessing the configuration of the anterior chamber. Despite its limitations, it may be of help during the routine clinical assessment and treatment of patients with glaucoma, particularly when gonioscopy is not possible or difficult to interpret. PMID:17355288

  13. Short- and long-term changes in myocardial function, morphology, edema, and infarct mass after ST-segment elevation myocardial infarction evaluated by serial magnetic resonance imaging

    DEFF Research Database (Denmark)

    Ripa, Rasmus Sejersten; Nilsson, Jens Christian; Wang, Yongzhong

    2007-01-01

    undertaken. The aim of this study was to evaluate effects of therapy for STEMI on left ventricular function and perfusion and to test the hypothesis that myocardial perfusion by MRI predicts recovery of left ventricular function. METHODS: Cine MRI, edema, first-pass perfusion, and late enhancement imaging...

  14. COMPARISON AND EVALUATION OF CLUSTER BASED IMAGE SEGMENTATION TECHNIQUES

    OpenAIRE

    Hetangi D. Mehta*, Daxa Vekariya, Pratixa Badelia

    2017-01-01

    Image segmentation is the classification of an image into different groups. Numerous algorithms using different approaches have been proposed for image segmentation. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity. A review is done on different types of clustering methods used for image segmentation. Also a methodology is proposed to classify and quantify different clustering algorithms based on their consistency in different...

  15. Intelligent Image Segment for Material Composition Detection

    Directory of Open Access Journals (Sweden)

    Liang Xiaodan

    2017-01-01

    Full Text Available In the process of material composition detection, the image analysis is an inevitable problem. Multilevel thresholding based OTSU method is one of the most popular image segmentation techniques. How, with the increase of the number of thresholds, the computing time increases exponentially. To overcome this problem, this paper proposed an artificial bee colony algorithm with a two-level topology. This improved artificial bee colony algorithm can quickly find out the suitable thresholds and nearly no trap into local optimal. The test results confirm it good performance.

  16. Magnetic resonance imaging

    International Nuclear Information System (INIS)

    Beer, A.; Bielke, G.; Bockenheimer, S.; Brenner, G.; Dieringer, H.; Esswein, H.; Hopf, H.; Koch, H.; Meves, M.; Nagel, F.; Oberstein, A.; Ostheimer, E.; Pfaff, M.; Schlaps, D.; Schopka, H.J.; Seiderer, M.

    1990-01-01

    The study investigates three points of main interest: (1) The clinical efficacy of MR imaging as a routine method, if possible to be assessed in comparison to comparable imaging methods, and referring to a broad spectrum of available types of equipment and modes of operation, to be expressed in terms of diagnostic value and indication of therapy. (2) Specific economic aspects, considering different sites of operation and application conditions. (3) Results of clinical application with regard to individual cases (patient careers), in order to establish a nationwide basis for economic cost-benefit assessment of this diagnostic tool. Another aspect taken into account whenever available data allow so, is substitutional or additional application of MR imaging. The survey is performed on the basis of data accumulated by more than 21.000 MR examinations, and of data describing the application environment, furnished by 25 users from university hospitals, general hospitals, or private practice. (orig./HP) [de

  17. Interventional magnetic resonance imaging

    International Nuclear Information System (INIS)

    Debatin, J.F.; Adam, G.

    1998-01-01

    With the advent of open configuration MR imaging systems, the vision of MRI-based guidance, control, and monitoring of minimally invasive interventions has evolved from a hypothetical concept to a practical possibility. This book provides a comprehensive overview of the very exciting emerging field of interventional MRI. The international authorship provides firsthand experience of all relevant topics. This book will familiarize the reader with the basic principles underlying currently available hardware and software configurations. In addition, technical aspects of thermosensitive imaging, techniques for instrument visualization, and safety aspects are covered. Finally, the book emphasizes both existing and future clinical applications. (orig.)

  18. Ant Colony Clustering Algorithm and Improved Markov Random Fusion Algorithm in Image Segmentation of Brain Images

    Directory of Open Access Journals (Sweden)

    Guohua Zou

    2016-12-01

    Full Text Available New medical imaging technology, such as Computed Tomography and Magnetic Resonance Imaging (MRI, has been widely used in all aspects of medical diagnosis. The purpose of these imaging techniques is to obtain various qualitative and quantitative data of the patient comprehensively and accurately, and provide correct digital information for diagnosis, treatment planning and evaluation after surgery. MR has a good imaging diagnostic advantage for brain diseases. However, as the requirements of the brain image definition and quantitative analysis are always increasing, it is necessary to have better segmentation of MR brain images. The FCM (Fuzzy C-means algorithm is widely applied in image segmentation, but it has some shortcomings, such as long computation time and poor anti-noise capability. In this paper, firstly, the Ant Colony algorithm is used to determine the cluster centers and the number of FCM algorithm so as to improve its running speed. Then an improved Markov random field model is used to improve the algorithm, so that its antinoise ability can be improved. Experimental results show that the algorithm put forward in this paper has obvious advantages in image segmentation speed and segmentation effect.

  19. Remote sensing image segmentation based on Hadoop cloud platform

    Science.gov (United States)

    Li, Jie; Zhu, Lingling; Cao, Fubin

    2018-01-01

    To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

  20. A comparative study on medical image segmentation methods

    Directory of Open Access Journals (Sweden)

    Praylin Selva Blessy SELVARAJ ASSLEY

    2014-03-01

    Full Text Available Image segmentation plays an important role in medical images. It has been a relevant research area in computer vision and image analysis. Many segmentation algorithms have been proposed for medical images. This paper makes a review on segmentation methods for medical images. In this survey, segmentation methods are divided into five categories: region based, boundary based, model based, hybrid based and atlas based. The five different categories with their principle ideas, advantages and disadvantages in segmenting different medical images are discussed.

  1. Segmentation Toolbox for Tomographic Image Data

    DEFF Research Database (Denmark)

    Einarsdottir, Hildur

    , techniques to automatically analyze such data becomes ever more important. Most segmentation methods for large datasets, such as CT images, deal with simple thresholding techniques, where intensity values cut offs are predetermined and hard coded. For data where the intensity difference is not sufficient......Motivation: Image acquisition has vastly improved over the past years, introducing techniques such as X-ray computed tomography (CT). CT images provide the means to probe a sample non-invasively to investigate its inner structure. Given the wide usage of this technique and massive data amounts......, and partial volume voxels occur frequently, thresholding methods do not suffice and more advanced methods are required. Contribution: To meet these requirements a toolbox has been developed, combining well known methods within the image analysis field. The toolbox includes cluster-based methods...

  2. Automatic Vessel Segmentation on Retinal Images

    Institute of Scientific and Technical Information of China (English)

    Chun-Yuan Yu; Chia-Jen Chang; Yen-Ju Yao; Shyr-Shen Yu

    2014-01-01

    Several features of retinal vessels can be used to monitor the progression of diseases. Changes in vascular structures, for example, vessel caliber, branching angle, and tortuosity, are portents of many diseases such as diabetic retinopathy and arterial hyper-tension. This paper proposes an automatic retinal vessel segmentation method based on morphological closing and multi-scale line detection. First, an illumination correction is performed on the green band retinal image. Next, the morphological closing and subtraction processing are applied to obtain the crude retinal vessel image. Then, the multi-scale line detection is used to fine the vessel image. Finally, the binary vasculature is extracted by the Otsu algorithm. In this paper, for improving the drawbacks of multi-scale line detection, only the line detectors at 4 scales are used. The experimental results show that the accuracy is 0.939 for DRIVE (digital retinal images for vessel extraction) retinal database, which is much better than other methods.

  3. Electron Paramagnetic Resonance Imaging

    Indian Academy of Sciences (India)

    IAS Admin

    is Professor of Chemistry at. IIT Madras. ... speeding up the CW imaging by special novel methods. How- ever, the ... presence of gradients which are applied in two or three dimen- sions and ... optics and mechanical engineer- ing stands for ...

  4. A general system for automatic biomedical image segmentation using intensity neighborhoods.

    Science.gov (United States)

    Chen, Cheng; Ozolek, John A; Wang, Wei; Rohde, Gustavo K

    2011-01-01

    Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

  5. A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods

    Directory of Open Access Journals (Sweden)

    Cheng Chen

    2011-01-01

    Full Text Available Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

  6. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available Toggle navigation Test/Treatment Patient Type Screening/Wellness Disease/Condition Safety En Español More Info Images/Videos About Us News Physician Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance ...

  7. Low field magnetic resonance imaging

    Science.gov (United States)

    Pines, Alexander; Sakellariou, Dimitrios; Meriles, Carlos A.; Trabesinger, Andreas H.

    2010-07-13

    A method and system of magnetic resonance imaging does not need a large homogenous field to truncate a gradient field. Spatial information is encoded into the spin magnetization by allowing the magnetization to evolve in a non-truncated gradient field and inducing a set of 180 degree rotations prior to signal acquisition.

  8. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... Magnetic Resonance Imaging Sponsored by Please note RadiologyInfo.org is not a medical facility. Please contact your ... links: For the convenience of our users, RadiologyInfo .org provides links to relevant websites. RadiologyInfo.org , ACR ...

  9. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available ... structures of the brain and can also provide functional information (fMRI) in selected cases. MR images of ... Articles and Media MR Angiography (MRA) Magnetic Resonance, Functional (fMRI) - Brain Head and Neck Cancer Treatment Brain ...

  10. Text segmentation in degraded historical document images

    Directory of Open Access Journals (Sweden)

    A.S. Kavitha

    2016-07-01

    Full Text Available Text segmentation from degraded Historical Indus script images helps Optical Character Recognizer (OCR to achieve good recognition rates for Hindus scripts; however, it is challenging due to complex background in such images. In this paper, we present a new method for segmenting text and non-text in Indus documents based on the fact that text components are less cursive compared to non-text ones. To achieve this, we propose a new combination of Sobel and Laplacian for enhancing degraded low contrast pixels. Then the proposed method generates skeletons for text components in enhanced images to reduce computational burdens, which in turn helps in studying component structures efficiently. We propose to study the cursiveness of components based on branch information to remove false text components. The proposed method introduces the nearest neighbor criterion for grouping components in the same line, which results in clusters. Furthermore, the proposed method classifies these clusters into text and non-text cluster based on characteristics of text components. We evaluate the proposed method on a large dataset containing varieties of images. The results are compared with the existing methods to show that the proposed method is effective in terms of recall and precision.

  11. Association between proximal internal carotid artery steno-occlusive disease and diffuse wall thickening in its petrous segment: a magnetic resonance vessel wall imaging study

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Xiaoyi; Li, Dongye [Capital Medical University and Beijing Institute for Brain Disorders, Center for Brain Disorders Research, Beijing (China); Tsinghua University School of Medicine, Center for Biomedical Imaging Research, Department of Biomedical Engineering, Beijing (China); Zhao, Huilin [Shanghai Jiao Tong University, Department of Radiology, Renji Hospital, School of Medicine, Shanghai (China); Chen, Zhensen; Qiao, Huiyu; He, Le; Li, Rui [Tsinghua University School of Medicine, Center for Biomedical Imaging Research, Department of Biomedical Engineering, Beijing (China); Cui, Yuanyuan [PLA General Hospital, Department of Radiology, Beijing (China); Zhou, Zechen [Philips Research China, Healthcare Department, Beijing (China); Yuan, Chun [Tsinghua University School of Medicine, Center for Biomedical Imaging Research, Department of Biomedical Engineering, Beijing (China); University of Washington, Department of Radiology, Seattle, WA (United States); Zhao, Xihai [Tsinghua University School of Medicine, Center for Biomedical Imaging Research, Department of Biomedical Engineering, Beijing (China); Beijing Institute for Brain Disorders, Center for Stroke, Beijing (China)

    2017-05-15

    Significant stenosis or occlusion in carotid arteries may lead to diffuse wall thickening (DWT) in the arterial wall of downstream. This study aimed to investigate the correlation between proximal internal carotid artery (ICA) steno-occlusive disease and DWT in ipsilateral petrous ICA. Symptomatic patients with atherosclerotic stenosis (>0%) in proximal ICA were recruited and underwent carotid MR vessel wall imaging. The 3D motion sensitized-driven equilibrium prepared rapid gradient-echo (3D-MERGE) was acquired for characterizing the wall thickness and longitudinal extent of the lesions in petrous ICA and the distance from proximal lesion to the petrous ICA. The stenosis degree in proximal ICA was measured on the time-of-flight (TOF) images. In total, 166 carotid arteries from 125 patients (mean age 61.0 ± 10.5 years, 99 males) were eligible for final analysis and 64 showed DWT in petrous ICAs. The prevalence of severe DWT in petrous ICA was 1.4%, 5.3%, 5.9%, and 80.4% in ipsilateral proximal ICAs with stenosis category of 1%-49%, 50%-69%, 70%-99%, and total occlusion, respectively. Proximal ICA stenosis was significantly correlated with the wall thickness in petrous ICA (r = 0.767, P < 0.001). Logistic regression analysis showed that proximal ICA stenosis was independently associated with DWT in ipsilateral petrous ICA (odds ratio (OR) = 2.459, 95% confidence interval (CI) 1.896-3.189, P < 0.001). Proximal ICA steno-occlusive disease is independently associated with DWT in ipsilateral petrous ICA. (orig.)

  12. Association between proximal internal carotid artery steno-occlusive disease and diffuse wall thickening in its petrous segment: a magnetic resonance vessel wall imaging study

    International Nuclear Information System (INIS)

    Chen, Xiaoyi; Li, Dongye; Zhao, Huilin; Chen, Zhensen; Qiao, Huiyu; He, Le; Li, Rui; Cui, Yuanyuan; Zhou, Zechen; Yuan, Chun; Zhao, Xihai

    2017-01-01

    Significant stenosis or occlusion in carotid arteries may lead to diffuse wall thickening (DWT) in the arterial wall of downstream. This study aimed to investigate the correlation between proximal internal carotid artery (ICA) steno-occlusive disease and DWT in ipsilateral petrous ICA. Symptomatic patients with atherosclerotic stenosis (>0%) in proximal ICA were recruited and underwent carotid MR vessel wall imaging. The 3D motion sensitized-driven equilibrium prepared rapid gradient-echo (3D-MERGE) was acquired for characterizing the wall thickness and longitudinal extent of the lesions in petrous ICA and the distance from proximal lesion to the petrous ICA. The stenosis degree in proximal ICA was measured on the time-of-flight (TOF) images. In total, 166 carotid arteries from 125 patients (mean age 61.0 ± 10.5 years, 99 males) were eligible for final analysis and 64 showed DWT in petrous ICAs. The prevalence of severe DWT in petrous ICA was 1.4%, 5.3%, 5.9%, and 80.4% in ipsilateral proximal ICAs with stenosis category of 1%-49%, 50%-69%, 70%-99%, and total occlusion, respectively. Proximal ICA stenosis was significantly correlated with the wall thickness in petrous ICA (r = 0.767, P < 0.001). Logistic regression analysis showed that proximal ICA stenosis was independently associated with DWT in ipsilateral petrous ICA (odds ratio (OR) = 2.459, 95% confidence interval (CI) 1.896-3.189, P < 0.001). Proximal ICA steno-occlusive disease is independently associated with DWT in ipsilateral petrous ICA. (orig.)

  13. Automated image segmentation using information theory

    International Nuclear Information System (INIS)

    Hibbard, L.S.

    2001-01-01

    Full text: Our development of automated contouring of CT images for RT planning is based on maximum a posteriori (MAP) analyses of region textures, edges, and prior shapes, and assumes stationary Gaussian distributions for voxel textures and contour shapes. Since models may not accurately represent image data, it would be advantageous to compute inferences without relying on models. The relative entropy (RE) from information theory can generate inferences based solely on the similarity of probability distributions. The entropy of a distribution of a random variable X is defined as -Σ x p(x)log 2 p(x) for all the values x which X may assume. The RE (Kullback-Liebler divergence) of two distributions p(X), q(X), over X is Σ x p(x)log 2 {p(x)/q(x)}. The RE is a kind of 'distance' between p,q, equaling zero when p=q and increasing as p,q are more different. Minimum-error MAP and likelihood ratio decision rules have RE equivalents: minimum error decisions obtain with functions of the differences between REs of compared distributions. One applied result is the contour ideally separating two regions is that which maximizes the relative entropy of the two regions' intensities. A program was developed that automatically contours the outlines of patients in stereotactic headframes, a situation most often requiring manual drawing. The relative entropy of intensities inside the contour (patient) versus outside (background) was maximized by conjugate gradient descent over the space of parameters of a deformable contour. shows the computed segmentation of a patient from headframe backgrounds. This program is particularly useful for preparing images for multimodal image fusion. Relative entropy and allied measures of distribution similarity provide automated contouring criteria that do not depend on statistical models of image data. This approach should have wide utility in medical image segmentation applications. Copyright (2001) Australasian College of Physical Scientists and

  14. Principles of magnetic resonance imaging

    International Nuclear Information System (INIS)

    Mlynarik, V.; Tkac, I.; Srbecky, M.

    1995-01-01

    The aim of this review is to describe and explain the basic principles of magnetic resonance imaging. The first part of the text is devoted to the phenomenon of magnetic resonance (the interaction of RF magnetic field with the set of magnetic moments in the homogeneous magnetic field) and to relaxation processes. Then, the creation of MR image is described (slice selection, phase and frequency encoding of spatial information). The basic and the most frequently used techniques are explained (spin echo, gradient echo). The way the repetition and echo times influence the image quality and contrast (T1 or T2 weighing) is described. The part with the technical description of the MR equipment is included in the review. The MR imagination examination are compared with X-ray computer tomography technique

  15. A Semi-automated Approach to Improve the Efficiency of Medical Imaging Segmentation for Haptic Rendering.

    Science.gov (United States)

    Banerjee, Pat; Hu, Mengqi; Kannan, Rahul; Krishnaswamy, Srinivasan

    2017-08-01

    The Sensimmer platform represents our ongoing research on simultaneous haptics and graphics rendering of 3D models. For simulation of medical and surgical procedures using Sensimmer, 3D models must be obtained from medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography (CT). Image segmentation techniques are used to determine the anatomies of interest from the images. 3D models are obtained from segmentation and their triangle reduction is required for graphics and haptics rendering. This paper focuses on creating 3D models by automating the segmentation of CT images based on the pixel contrast for integrating the interface between Sensimmer and medical imaging devices, using the volumetric approach, Hough transform method, and manual centering method. Hence, automating the process has reduced the segmentation time by 56.35% while maintaining the same accuracy of the output at ±2 voxels.

  16. Magnetic Resonance Imaging (MRI): Brain (For Parents)

    Science.gov (United States)

    ... Staying Safe Videos for Educators Search English Español Magnetic Resonance Imaging (MRI): Brain KidsHealth / For Parents / Magnetic Resonance Imaging (MRI): Brain What's in this article? What ...

  17. Magnetic resonance imaging in neuroradiology

    International Nuclear Information System (INIS)

    Voigt, K.; Lotx, J.W.

    1990-01-01

    Magnetic resonance imaging (MRI) is now accepted as an effective method of investigating a wide range of disorders, especially of the brain and spine. A short introduction on image contrast in MRI is given and the advantages and disadvantages for the different diseases of the brain is discussed. Excellent soft-tissue contrast, multiplanar imaging capabilities and lack of ionising radiation are conspicuous advantages, and it is now established as the investigation of choice in a large number of clinical conditions, especially when the central nervous system is involved. However, it remains only one of a series of imaging modalities. A confident provisional clinical diagnosis is essential for establishing an imaging protocol and the intention should always be to reach a definitive diagnosis in the least invasive and most cost-effective way. 7 figs., 19 refs

  18. Performance evaluation of image segmentation algorithms on microscopic image data

    Czech Academy of Sciences Publication Activity Database

    Beneš, Miroslav; Zitová, Barbara

    2015-01-01

    Roč. 275, č. 1 (2015), s. 65-85 ISSN 0022-2720 R&D Projects: GA ČR GAP103/12/2211 Institutional support: RVO:67985556 Keywords : image segmentation * performance evaluation * microscopic images Subject RIV: JC - Computer Hardware ; Software Impact factor: 2.136, year: 2015 http://library.utia.cas.cz/separaty/2014/ZOI/zitova-0434809-DOI.pdf

  19. DETECTION OF CANCEROUS LESION BY UTERINE CERVIX IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    P. Priya

    2014-02-01

    Full Text Available This paper works at segmentation of lesion observed in cervical cancer, which is the second most common cancer among women worldwide. The purpose of segmentation is to determine the location for a biopsy to be taken for diagnosis. Cervix cancer is a disease in which cancer cells are found in the tissues of the cervix. The acetowhite region is a major indicator of abnormality in the cervix image. This project addresses the problem of segmenting uterine cervix image into different regions. We analyze two algorithms namely Watershed, K-means clustering algorithm, Expectation Maximization (EM Image Segmentation algorithm. These segmentations methods are carried over for the colposcopic uterine cervix image.

  20. Heuristically improved Bayesian segmentation of brain MR images ...

    African Journals Online (AJOL)

    Heuristically improved Bayesian segmentation of brain MR images. ... or even the most prevalent task in medical image processing is image segmentation. Among them, brain MR images suffer ... show that our algorithm performs well in comparison with the one implemented in SPM. It can be concluded that incorporating ...

  1. A NDVI assisted remote sensing image adaptive scale segmentation method

    Science.gov (United States)

    Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

    2018-03-01

    Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.

  2. FUSION SEGMENTATION METHOD BASED ON FUZZY THEORY FOR COLOR IMAGES

    Directory of Open Access Journals (Sweden)

    J. Zhao

    2017-09-01

    Full Text Available The image segmentation method based on two-dimensional histogram segments the image according to the thresholds of the intensity of the target pixel and the average intensity of its neighborhood. This method is essentially a hard-decision method. Due to the uncertainties when labeling the pixels around the threshold, the hard-decision method can easily get the wrong segmentation result. Therefore, a fusion segmentation method based on fuzzy theory is proposed in this paper. We use membership function to model the uncertainties on each color channel of the color image. Then, we segment the color image according to the fuzzy reasoning. The experiment results show that our proposed method can get better segmentation results both on the natural scene images and optical remote sensing images compared with the traditional thresholding method. The fusion method in this paper can provide new ideas for the information extraction of optical remote sensing images and polarization SAR images.

  3. An LG-graph-based early evaluation of segmented images

    International Nuclear Information System (INIS)

    Tsitsoulis, Athanasios; Bourbakis, Nikolaos

    2012-01-01

    Image segmentation is one of the first important parts of image analysis and understanding. Evaluation of image segmentation, however, is a very difficult task, mainly because it requires human intervention and interpretation. In this work, we propose a blind reference evaluation scheme based on regional local–global (RLG) graphs, which aims at measuring the amount and distribution of detail in images produced by segmentation algorithms. The main idea derives from the field of image understanding, where image segmentation is often used as a tool for scene interpretation and object recognition. Evaluation here derives from summarization of the structural information content and not from the assessment of performance after comparisons with a golden standard. Results show measurements for segmented images acquired from three segmentation algorithms, applied on different types of images (human faces/bodies, natural environments and structures (buildings)). (paper)

  4. Magnetic Resonance Imaging Features of Neuromyelitis Optica

    Energy Technology Data Exchange (ETDEWEB)

    You, Sun Kyung; Song, Chang June; Park, Woon Ju; Lee, In Ho; Son, Eun Hee [Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon (Korea, Republic of)

    2013-03-15

    To report the magnetic resonance (MR) imaging features of the spinal cord and brain in patients of neuromyelitis optica (NMO). Between January 2001 and March 2010, the MR images (spinal cord, brain, and orbit) and the clinical and serologic findings of 11 NMO patients were retrospectively reviewed. The contrast-enhancement of the spinal cord was performed (20/23). The presence and pattern of the contrast-enhancement in the spinal cord were classified into 5 types. Acute myelitis was monophasic in 8 patients (8/11, 72.7%); and optic neuritis preceded acute myelitis in most patients. Longitudinally extensive cord lesion (average, 7.3 vertebral segments) was involved. The most common type was the diffuse and subtle enhancement of the spinal cord with a multifocal nodular, linear or segmental intense enhancement (45%). Most of the brain lesions (5/11, 10 lesions) were located in the brain stem, thalamus and callososeptal interphase. Anti-Ro autoantibody was positive in 2 patients, and they showed a high relapse rate of acute myelitis. Anti-NMO IgG was positive in 4 patients (4/7, 66.7%). The imaging findings of acute myelitis in NMO may helpful in making an early diagnosis of NMO which can result in a severe damage to the spinal cord, and to make a differential diagnosis of multiple sclerosis and inflammatory diseases of the spinal cord such as toxocariasis.

  5. Magnetic Resonance Imaging Features of Neuromyelitis Optica

    International Nuclear Information System (INIS)

    You, Sun Kyung; Song, Chang June; Park, Woon Ju; Lee, In Ho; Son, Eun Hee

    2013-01-01

    To report the magnetic resonance (MR) imaging features of the spinal cord and brain in patients of neuromyelitis optica (NMO). Between January 2001 and March 2010, the MR images (spinal cord, brain, and orbit) and the clinical and serologic findings of 11 NMO patients were retrospectively reviewed. The contrast-enhancement of the spinal cord was performed (20/23). The presence and pattern of the contrast-enhancement in the spinal cord were classified into 5 types. Acute myelitis was monophasic in 8 patients (8/11, 72.7%); and optic neuritis preceded acute myelitis in most patients. Longitudinally extensive cord lesion (average, 7.3 vertebral segments) was involved. The most common type was the diffuse and subtle enhancement of the spinal cord with a multifocal nodular, linear or segmental intense enhancement (45%). Most of the brain lesions (5/11, 10 lesions) were located in the brain stem, thalamus and callososeptal interphase. Anti-Ro autoantibody was positive in 2 patients, and they showed a high relapse rate of acute myelitis. Anti-NMO IgG was positive in 4 patients (4/7, 66.7%). The imaging findings of acute myelitis in NMO may helpful in making an early diagnosis of NMO which can result in a severe damage to the spinal cord, and to make a differential diagnosis of multiple sclerosis and inflammatory diseases of the spinal cord such as toxocariasis.

  6. Breast Cancer Image Segmentation Using K-Means Clustering Based on GPU Cuda Parallel Computing

    Directory of Open Access Journals (Sweden)

    Andika Elok Amalia

    2018-02-01

    Full Text Available Image processing technology is now widely used in the health area, one example is to help the radiologist to analyze the result of MRI (Magnetic Resonance Imaging, CT Scan and Mammography. Image segmentation is a process which is intended to obtain the objects contained in the image by dividing the image into several areas that have similarity attributes on an object with the aim of facilitating the analysis process. The increasing amount  of patient data and larger image size are new challenges in segmentation process to use time efficiently while still keeping the process quality. Research on the segmentation of medical images have been done but still few that combine with parallel computing. In this research, K-Means clustering on the image of mammography result is implemented using two-way computation which are serial and parallel. The result shows that parallel computing  gives faster average performance execution up to twofold.

  7. Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

    Science.gov (United States)

    Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

    2017-12-01

    Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

  8. Endometrial cancer: magnetic resonance imaging.

    Science.gov (United States)

    Manfredi, R; Gui, B; Maresca, G; Fanfani, F; Bonomo, L

    2005-01-01

    Carcinoma of the endometrium is the most common invasive gynecologic malignancy of the female genital tract. Clinically, patients with endometrial carcinoma present with abnormal uterine bleeding. The role of magnetic resonance imaging (MRI) in endometrial carcinoma is disease staging and treatment planning. MRI has been shown to be the most valuable imaging mod-ality in this task, compared with endovaginal ultrasound and computed tomography, because of its intrinsic contrast resolution and multiplanar capability. MRI protocol includes axial T1-weighted images; axial, sagittal, and coronal T2-weighted images; and dynamic gadolinium-enhanced T1-weighted imaging. MR examination is usually performed in the supine position with a phased array multicoil using a four-coil configuration. Endometrial carcinoma is isointense with the normal endometrium and myometrium on noncontrast T1-weighted images and has a variable appearance on T2-weighted images demonstrating heterogeneous signal intensity. The appearance of noninvasive endometrial carcinoma on MRI is characterized by a normal or thickened endometrium, with an intact junctional zone and a sharp tumor-myometrium interface. Invasive endometrial carcinoma is characterized disruption or irregularity of the junctional zone by intermediate signal intensity mass on T2-weighted images. Invasion of the cervical stroma is diagnosed when the low signal intensity cervical stroma is disrupted by the higher signal intensity endometrial carcinoma. MRI in endometrial carcinoma performs better than other imaging modalities in disease staging and treatment planning. Further, the accuracy and the cost of MRI are equivalent to those of surgical staging.

  9. Delphi definition of the EADC-ADNI Harmonized Protocol for hippocampal segmentation on magnetic resonance

    NARCIS (Netherlands)

    Boccardi, M.; Bocchetta, M.; Apostolova, L.G.; Barnes, J.; Bartzokis, G.; Corbetta, G.; deCarli, C.; DeToledo-Morrell, L.; Firbank, M.; Ganzola, R.; Gerritsen, L.; Henneman, W.; Killiany, R.J.; Malykhin, N.; Pasqualetti, P.; Pruessner, J.C.; Redolfi, A.; Robitaille, N.; Soininen, H.; Tolomeo, D.; Wang, L.; Watson, C.; Wolf, H; Duvernoy, H.; Duchesne, S.; Jack, C.R.; Frisoni, G. B.

    2015-01-01

    Background: This study aimed to have international experts converge on a harmonized definition of whole hippocampus boundaries and segmentation procedures, to define standard operating procedures for magnetic resonance (MR)-based manual hippocampal segmentation. Methods: The panel received a

  10. An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

    Directory of Open Access Journals (Sweden)

    Ningning Zhou

    2014-01-01

    Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

  11. Microscopy image segmentation tool: Robust image data analysis

    Energy Technology Data Exchange (ETDEWEB)

    Valmianski, Ilya, E-mail: ivalmian@ucsd.edu; Monton, Carlos; Schuller, Ivan K. [Department of Physics and Center for Advanced Nanoscience, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093 (United States)

    2014-03-15

    We present a software package called Microscopy Image Segmentation Tool (MIST). MIST is designed for analysis of microscopy images which contain large collections of small regions of interest (ROIs). Originally developed for analysis of porous anodic alumina scanning electron images, MIST capabilities have been expanded to allow use in a large variety of problems including analysis of biological tissue, inorganic and organic film grain structure, as well as nano- and meso-scopic structures. MIST provides a robust segmentation algorithm for the ROIs, includes many useful analysis capabilities, and is highly flexible allowing incorporation of specialized user developed analysis. We describe the unique advantages MIST has over existing analysis software. In addition, we present a number of diverse applications to scanning electron microscopy, atomic force microscopy, magnetic force microscopy, scanning tunneling microscopy, and fluorescent confocal laser scanning microscopy.

  12. Microscopy image segmentation tool: Robust image data analysis

    Science.gov (United States)

    Valmianski, Ilya; Monton, Carlos; Schuller, Ivan K.

    2014-03-01

    We present a software package called Microscopy Image Segmentation Tool (MIST). MIST is designed for analysis of microscopy images which contain large collections of small regions of interest (ROIs). Originally developed for analysis of porous anodic alumina scanning electron images, MIST capabilities have been expanded to allow use in a large variety of problems including analysis of biological tissue, inorganic and organic film grain structure, as well as nano- and meso-scopic structures. MIST provides a robust segmentation algorithm for the ROIs, includes many useful analysis capabilities, and is highly flexible allowing incorporation of specialized user developed analysis. We describe the unique advantages MIST has over existing analysis software. In addition, we present a number of diverse applications to scanning electron microscopy, atomic force microscopy, magnetic force microscopy, scanning tunneling microscopy, and fluorescent confocal laser scanning microscopy.

  13. Microscopy image segmentation tool: Robust image data analysis

    International Nuclear Information System (INIS)

    Valmianski, Ilya; Monton, Carlos; Schuller, Ivan K.

    2014-01-01

    We present a software package called Microscopy Image Segmentation Tool (MIST). MIST is designed for analysis of microscopy images which contain large collections of small regions of interest (ROIs). Originally developed for analysis of porous anodic alumina scanning electron images, MIST capabilities have been expanded to allow use in a large variety of problems including analysis of biological tissue, inorganic and organic film grain structure, as well as nano- and meso-scopic structures. MIST provides a robust segmentation algorithm for the ROIs, includes many useful analysis capabilities, and is highly flexible allowing incorporation of specialized user developed analysis. We describe the unique advantages MIST has over existing analysis software. In addition, we present a number of diverse applications to scanning electron microscopy, atomic force microscopy, magnetic force microscopy, scanning tunneling microscopy, and fluorescent confocal laser scanning microscopy

  14. Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

    Directory of Open Access Journals (Sweden)

    Sharma Rakesh

    2004-05-01

    Full Text Available Abstract Functional magnetic resonance imaging (fMRI is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities.

  15. Multidimensionally encoded magnetic resonance imaging.

    Science.gov (United States)

    Lin, Fa-Hsuan

    2013-07-01

    Magnetic resonance imaging (MRI) typically achieves spatial encoding by measuring the projection of a q-dimensional object over q-dimensional spatial bases created by linear spatial encoding magnetic fields (SEMs). Recently, imaging strategies using nonlinear SEMs have demonstrated potential advantages for reconstructing images with higher spatiotemporal resolution and reducing peripheral nerve stimulation. In practice, nonlinear SEMs and linear SEMs can be used jointly to further improve the image reconstruction performance. Here, we propose the multidimensionally encoded (MDE) MRI to map a q-dimensional object onto a p-dimensional encoding space where p > q. MDE MRI is a theoretical framework linking imaging strategies using linear and nonlinear SEMs. Using a system of eight surface SEM coils with an eight-channel radiofrequency coil array, we demonstrate the five-dimensional MDE MRI for a two-dimensional object as a further generalization of PatLoc imaging and O-space imaging. We also present a method of optimizing spatial bases in MDE MRI. Results show that MDE MRI with a higher dimensional encoding space can reconstruct images more efficiently and with a smaller reconstruction error when the k-space sampling distribution and the number of samples are controlled. Copyright © 2012 Wiley Periodicals, Inc.

  16. Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues.

    Directory of Open Access Journals (Sweden)

    Muhammad Attique

    Full Text Available Characterization of tissues like brain by using magnetic resonance (MR images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii a segmentation method (both hard and soft segmentation to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM, white matter (WM, and cerebrospinal fluid (CSF using prior anatomical knowledge. Results have been successfully validated on human T2-weighted (T2 brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.

  17. Color Segmentation of Homogeneous Areas on Colposcopical Images

    Directory of Open Access Journals (Sweden)

    Kosteley Yana

    2016-01-01

    Full Text Available The article provides an analysis of image processing and color segmentation applied to the problem of selection of homogeneous regions in the parameters of the color model. Methods of image processing such as Gaussian filter, median filter, histogram equalization and mathematical morphology are considered. The segmentation algorithm with the parameters of color components is presented, followed by isolation of the resulting connected component of a binary segmentation mask. Analysis of methods performed on images colposcopic research.

  18. Energy functionals for medical image segmentation: choices and consequences

    OpenAIRE

    McIntosh, Christopher

    2011-01-01

    Medical imaging continues to permeate the practice of medicine, but automated yet accurate segmentation and labeling of anatomical structures continues to be a major obstacle to computerized medical image analysis. Though there exists numerous approaches for medical image segmentation, one in particular has gained increasing popularity: energy minimization-based techniques, and the large set of methods encompassed therein. With these techniques an energy function must be chosen, segmentations...

  19. Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

    Science.gov (United States)

    Cruz-Aceves, I.; Avina-Cervantes, J. G.; Lopez-Hernandez, J. M.; Rostro-Gonzalez, H.; Garcia-Capulin, C. H.; Torres-Cisneros, M.; Guzman-Cabrera, R.

    2013-01-01

    This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation. PMID:23983809

  20. Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

    Directory of Open Access Journals (Sweden)

    I. Cruz-Aceves

    2013-01-01

    Full Text Available This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.

  1. Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation

    Science.gov (United States)

    Cruz-Aceves, I.; Aviña-Cervantes, J. G.; López-Hernández, J. M.; González-Reyna, S. E.

    2013-01-01

    This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability. PMID:23762177

  2. Quantitative perfusion imaging in magnetic resonance imaging

    International Nuclear Information System (INIS)

    Zoellner, F.G.; Gaa, T.; Zimmer, F.; Ong, M.M.; Riffel, P.; Hausmann, D.; Schoenberg, S.O.; Weis, M.

    2016-01-01

    Magnetic resonance imaging (MRI) is recognized for its superior tissue contrast while being non-invasive and free of ionizing radiation. Due to the development of new scanner hardware and fast imaging techniques during the last decades, access to tissue and organ functions became possible. One of these functional imaging techniques is perfusion imaging with which tissue perfusion and capillary permeability can be determined from dynamic imaging data. Perfusion imaging by MRI can be performed by two approaches, arterial spin labeling (ASL) and dynamic contrast-enhanced (DCE) MRI. While the first method uses magnetically labelled water protons in arterial blood as an endogenous tracer, the latter involves the injection of a contrast agent, usually gadolinium (Gd), as a tracer for calculating hemodynamic parameters. Studies have demonstrated the potential of perfusion MRI for diagnostics and also for therapy monitoring. The utilization and application of perfusion MRI are still restricted to specialized centers, such as university hospitals. A broad application of the technique has not yet been implemented. The MRI perfusion technique is a valuable tool that might come broadly available after implementation of standards on European and international levels. Such efforts are being promoted by the respective professional bodies. (orig.) [de

  3. Magnetic resonance imaging of Parkinsonism

    International Nuclear Information System (INIS)

    Mukai, Eiichiro; Makino, Naoki; Fujishiro, Kenichiro.

    1989-01-01

    We have analyzed magnetic resonance images in 33 patients; 18 patients with Parkinson's disease, 1 patient with diurnally fluctuating progressive dystonia, 1 patient with pure akinesia, 6 patients with multiple system atrophy, 1 patient with flunarizine induced parkinsonism, and 4 patients with unclassified parkinsonism. The MR images were obtained using a 1.5-T GE MR System. A spin-echo pulse sequence was used with a TE of 30 msec and 80 msec and a TR of 2000 msec. No signal abnormalities were seen in any patient with Parkinson's disease but 3 showed slightly decreased signal intensity of the putamen on T2-weighted sequences. Patients with diurnally fluctuating progressive dystonia and pure akinesia evidensed no abnormal findings. All six patients with multiple system atrophy demonstrated decreased signal intensity of the putamen, particularly along their lateral and posterior portions, and an enlarged substantia nigra. Atrophy of the pons and cerebellum was detected in all cases with multiple system atrophy. One case of flunarizine induced parkinsonism showed slightly decreased signal intensity of the putamen. Four cases of unclassified parkinsonism showed decreased signal in the putamen on T2-weighted sequences. Magnetic resonance imaging has the potential to become a useful diagnostic tool in the management of parkinsonism. (author)

  4. Magnetic resonance imaging of Parkinsonism

    Energy Technology Data Exchange (ETDEWEB)

    Mukai, Eiichiro [National Hospital of Nagoya (Japan); Makino, Naoki; Fujishiro, Kenichiro

    1989-06-01

    We have analyzed magnetic resonance images in 33 patients; 18 patients with Parkinson's disease, 1 patient with diurnally fluctuating progressive dystonia, 1 patient with pure akinesia, 6 patients with multiple system atrophy, 1 patient with flunarizine induced parkinsonism, and 4 patients with unclassified parkinsonism. The MR images were obtained using a 1.5-T GE MR System. A spin-echo pulse sequence was used with a TE of 30 msec and 80 msec and a TR of 2000 msec. No signal abnormalities were seen in any patient with Parkinson's disease but 3 showed slightly decreased signal intensity of the putamen on T2-weighted sequences. Patients with diurnally fluctuating progressive dystonia and pure akinesia evidensed no abnormal findings. All six patients with multiple system atrophy demonstrated decreased signal intensity of the putamen, particularly along their lateral and posterior portions, and an enlarged substantia nigra. Atrophy of the pons and cerebellum was detected in all cases with multiple system atrophy. One case of flunarizine induced parkinsonism showed slightly decreased signal intensity of the putamen. Four cases of unclassified parkinsonism showed decreased signal in the putamen on T2-weighted sequences. Magnetic resonance imaging has the potential to become a useful diagnostic tool in the management of parkinsonism. (author).

  5. Magnetic resonance imaging of chemistry.

    Science.gov (United States)

    Britton, Melanie M

    2010-11-01

    Magnetic resonance imaging (MRI) has long been recognized as one of the most important tools in medical diagnosis and research. However, MRI is also well placed to image chemical reactions and processes, determine the concentration of chemical species, and look at how chemistry couples with environmental factors, such as flow and heterogeneous media. This tutorial review will explain how magnetic resonance imaging works, reviewing its application in chemistry and its ability to directly visualise chemical processes. It will give information on what resolution and contrast are possible, and what chemical and physical parameters can be measured. It will provide examples of the use of MRI to study chemical systems, its application in chemical engineering and the identification of contrast agents for non-clinical applications. A number of studies are presented including investigation of chemical conversion and selectivity in fixed-bed reactors, temperature probes for catalyst pellets, ion mobility during tablet dissolution, solvent dynamics and ion transport in Nafion polymers and the formation of chemical waves and patterns.

  6. SEGMENTATION AND QUALITY ANALYSIS OF LONG RANGE CAPTURED IRIS IMAGE

    Directory of Open Access Journals (Sweden)

    Anand Deshpande

    2016-05-01

    Full Text Available The iris segmentation plays a major role in an iris recognition system to increase the performance of the system. This paper proposes a novel method for segmentation of iris images to extract the iris part of long range captured eye image and an approach to select best iris frame from the iris polar image sequences by analyzing the quality of iris polar images. The quality of iris image is determined by the frequency components present in the iris polar images. The experiments are carried out on CASIA-long range captured iris image sequences. The proposed segmentation method is compared with Hough transform based segmentation and it has been determined that the proposed method gives higher accuracy for segmentation than Hough transform.

  7. Automatic segmentation of blood vessels from retinal fundus images ...

    Indian Academy of Sciences (India)

    The retinal blood vessels were segmented through color space conversion and color channel .... Retinal blood vessel segmentation was also attempted through multi-scale operators. A few works in this ... fundus camera at 35 degrees field of view. The image ... vessel segmentation is available from two human observers.

  8. Automatic segmentation and disease classification using cardiac cine MR images

    NARCIS (Netherlands)

    Wolterink, Jelmer M.; Leiner, Tim; Viergever, Max A.; Išgum, Ivana

    2018-01-01

    Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images. A convolutional neural network (CNN) was designed to simultaneously segment the left ventricle

  9. Tissues segmentation based on multi spectral medical images

    Science.gov (United States)

    Li, Ya; Wang, Ying

    2017-11-01

    Each band image contains the most obvious tissue feature according to the optical characteristics of different tissues in different specific bands for multispectral medical images. In this paper, the tissues were segmented by their spectral information at each multispectral medical images. Four Local Binary Patter descriptors were constructed to extract blood vessels based on the gray difference between the blood vessels and their neighbors. The segmented tissue in each band image was merged to a clear image.

  10. Magnetic resonance imaging in psychiatry

    International Nuclear Information System (INIS)

    Mann, K.

    1993-01-01

    Diagnosis and research in psychiatry are increasingly availing themselves of magnetic resonance imaging (MRI). In comparison to computed tomography (CT), this offers the combined benefits of no exposure to radiation, high resolution, artefact-free display of structures near bone, and a sharp contrast between the grey and white brain matter, with freedom to select the section. With the exception of very anxious patients, MRI will gradually replace CT scans for a wide range of differential diagnostic investigations. Its superiority in systematic studies of psychiatric patients with discrete cerebral parenchyma lesions is already considered proven. This is illustrated on the basis of research into schizophrenia and alcoholism. (orig.) [de

  11. Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

    Directory of Open Access Journals (Sweden)

    Pradipta Maji

    Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

  12. Magnetic resonance imaging of hypophysis

    International Nuclear Information System (INIS)

    Malla Huesh, I. V.

    2016-01-01

    Hypothalamic-pituitary diseases represent with wide variety of symptoms in regard with changes in the endocrine function. Magnetic resonance imaging has a crucial role in detecting the morphologic appearance in physiologic conditions, malformative diseases and acquired pathologies. The MR-imaging is established as the method of choice in assessing the changes in the hypothalamic-pituitary axis. The pituitary gland is a complex structure with an important role in the homeostasis of the organism even though it is so small? It is surrounded by bony structures, vessels, nerves and the brain parenchyma. It consists of three parts - anterior called - adenohypophysis, posterior - neurohypophysis and pituitary stalk. The anterior part comprises about 75% of the gland. Computed tomography (CT) has a limited role in detecting the pituitary gland. It is mainly used in cases of elevated intracranial pressure due to suspected apoplexy. The gland's small size, relation to other structures and its soft tissue characteristic make it an accessible region of interest for detecting with MR-imaging. The lack of ionizing energy and the technical advances in the MR-methods are responsible for the creating images with better spatial resolution and signal to noise ratio. The examination is carried out on a standard protocol. It is important that thin slices are executed in sagittal and coronal planes. Performing a sequence, regarding the brain parenchyma is essential, since many malformations of the pituitary gland are associated with other congenital conditions. The examination starts with a T1W sequence to assess the normal anatomic condition of the gland. The intensity of the adenohypophysis is compared to the one in the pons. It is hypointense, whereas the neurohypophysis is hyperintense, due to the lipid neurosecretory granules transported along the hypothalamic-pituitary axis. T2W-images in coronal plane are used to evaluate the hypothalamus, pituitary stalk, optic chiasm, olfactory

  13. Fetal abdominal magnetic resonance imaging

    International Nuclear Information System (INIS)

    Brugger, Peter C.; Prayer, Daniela

    2006-01-01

    This review deals with the in vivo magnetic resonance imaging (MRI) appearance of the human fetal abdomen. Imaging findings are correlated with current knowledge of human fetal anatomy and physiology, which are crucial to understand and interpret fetal abdominal MRI scans. As fetal MRI covers a period of more than 20 weeks, which is characterized not only by organ growth, but also by changes and maturation of organ function, a different MR appearance of the fetal abdomen results. This not only applies to the fetal intestines, but also to the fetal liver, spleen, and adrenal glands. Choosing the appropriate sequences, various aspects of age-related and organ-specific function can be visualized with fetal MRI, as these are mirrored by changes in signal intensities. Knowledge of normal development is essential to delineate normal from pathological findings in the respective developmental stages

  14. Fetal abdominal magnetic resonance imaging

    Energy Technology Data Exchange (ETDEWEB)

    Brugger, Peter C. [Center of Anatomy and Cell Biology, Integrative Morphology Group, Medical University of Vienna, Waehringerstrasse 13, 1090 Vienna (Austria)]. E-mail: peter.brugger@meduniwien.ac.at; Prayer, Daniela [Department of Radiology, Medical University of Vienna, Waehringerguertel 18-20, 1090 Vienna (Austria)

    2006-02-15

    This review deals with the in vivo magnetic resonance imaging (MRI) appearance of the human fetal abdomen. Imaging findings are correlated with current knowledge of human fetal anatomy and physiology, which are crucial to understand and interpret fetal abdominal MRI scans. As fetal MRI covers a period of more than 20 weeks, which is characterized not only by organ growth, but also by changes and maturation of organ function, a different MR appearance of the fetal abdomen results. This not only applies to the fetal intestines, but also to the fetal liver, spleen, and adrenal glands. Choosing the appropriate sequences, various aspects of age-related and organ-specific function can be visualized with fetal MRI, as these are mirrored by changes in signal intensities. Knowledge of normal development is essential to delineate normal from pathological findings in the respective developmental stages.

  15. Deformable meshes for medical image segmentation accurate automatic segmentation of anatomical structures

    CERN Document Server

    Kainmueller, Dagmar

    2014-01-01

    ? Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatom

  16. Segmentation of knee injury swelling on infrared images

    Science.gov (United States)

    Puentes, John; Langet, Hélène; Herry, Christophe; Frize, Monique

    2011-03-01

    Interpretation of medical infrared images is complex due to thermal noise, absence of texture, and small temperature differences in pathological zones. Acute inflammatory response is a characteristic symptom of some knee injuries like anterior cruciate ligament sprains, muscle or tendons strains, and meniscus tear. Whereas artificial coloring of the original grey level images may allow to visually assess the extent inflammation in the area, their automated segmentation remains a challenging problem. This paper presents a hybrid segmentation algorithm to evaluate the extent of inflammation after knee injury, in terms of temperature variations and surface shape. It is based on the intersection of rapid color segmentation and homogeneous region segmentation, to which a Laplacian of a Gaussian filter is applied. While rapid color segmentation enables to properly detect the observed core of swollen area, homogeneous region segmentation identifies possible inflammation zones, combining homogeneous grey level and hue area segmentation. The hybrid segmentation algorithm compares the potential inflammation regions partially detected by each method to identify overlapping areas. Noise filtering and edge segmentation are then applied to common zones in order to segment the swelling surfaces of the injury. Experimental results on images of a patient with anterior cruciate ligament sprain show the improved performance of the hybrid algorithm with respect to its separated components. The main contribution of this work is a meaningful automatic segmentation of abnormal skin temperature variations on infrared thermography images of knee injury swelling.

  17. Magnetic resonance imaging of the prostate

    DEFF Research Database (Denmark)

    Iversen, P; Kjaer, L; Thomsen, C

    1988-01-01

    Magnetic resonance imaging offers new possibilities in investigation of the prostate gland. Current results of imaging and tissue discrimination in the evaluation of prostatic disease are reviewed. Magnetic resonance imaging may be useful in the staging of carcinoma of the prostate....

  18. Magnetic resonance imaging of the prostate

    DEFF Research Database (Denmark)

    Iversen, P; Kjaer, L; Thomsen, C

    1987-01-01

    Magnetic resonance imaging offers new possibilities in the investigation of the prostate. The current results of imaging and tissue discrimination in the evaluation of prostatic disease are reviewed. Magnetic resonance imaging may be of value in the staging of carcinoma of the prostate....

  19. Open-source software platform for medical image segmentation applications

    Science.gov (United States)

    Namías, R.; D'Amato, J. P.; del Fresno, M.

    2017-11-01

    Segmenting 2D and 3D images is a crucial and challenging problem in medical image analysis. Although several image segmentation algorithms have been proposed for different applications, no universal method currently exists. Moreover, their use is usually limited when detection of complex and multiple adjacent objects of interest is needed. In addition, the continually increasing volumes of medical imaging scans require more efficient segmentation software design and highly usable applications. In this context, we present an extension of our previous segmentation framework which allows the combination of existing explicit deformable models in an efficient and transparent way, handling simultaneously different segmentation strategies and interacting with a graphic user interface (GUI). We present the object-oriented design and the general architecture which consist of two layers: the GUI at the top layer, and the processing core filters at the bottom layer. We apply the framework for segmenting different real-case medical image scenarios on public available datasets including bladder and prostate segmentation from 2D MRI, and heart segmentation in 3D CT. Our experiments on these concrete problems show that this framework facilitates complex and multi-object segmentation goals while providing a fast prototyping open-source segmentation tool.

  20. Presurgical functional magnetic resonance imaging

    International Nuclear Information System (INIS)

    Stippich, C.

    2010-01-01

    Functional magnetic resonance imaging (fMRI) is an important and novel neuroimaging modality for patients with brain tumors. By non-invasive measurement, localization and lateralization of brain activiation, most importantly of motor and speech function, fMRI facilitates the selection of the most appropriate and sparing treatment and function-preserving surgery. Prerequisites for the diagnostic use of fMRI are the application of dedicated clinical imaging protocols and standardization of the respective imaging procedures. The combination with diffusion tensor imaging (DTI) also enables tracking and visualization of important fiber bundles such as the pyramidal tract and the arcuate fascicle. These multimodal MR data can be implemented in computer systems for functional neuronavigation or radiation treatment. The practicability, accuracy and reliability of presurgical fMRI have been validated by large numbers of published data. However, fMRI cannot be considered as a fully established modality of diagnostic neuroimaging due to the lack of guidelines of the responsible medical associations as well as the lack of medical certification of important hardware and software components. This article reviews the current research in the field and provides practical information relevant for presurgical fMRI. (orig.) [de

  1. Optimization of Segmentation Quality of Integrated Circuit Images

    Directory of Open Access Journals (Sweden)

    Gintautas Mušketas

    2012-04-01

    Full Text Available The paper presents investigation into the application of genetic algorithms for the segmentation of the active regions of integrated circuit images. This article is dedicated to a theoretical examination of the applied methods (morphological dilation, erosion, hit-and-miss, threshold and describes genetic algorithms, image segmentation as optimization problem. The genetic optimization of the predefined filter sequence parameters is carried out. Improvement to segmentation accuracy using a non optimized filter sequence makes 6%.Artcile in Lithuanian

  2. Integration of speckle de-noising and image segmentation using ...

    Indian Academy of Sciences (India)

    2Department of Electronics and Communication Engineering, National Institute of Technology Karnataka,. Surathkal, Mangalore 575 025, India. ... cal images obtained from the satellites are often prone to bad climatic conditions and hence ... (2009) for satellite image segmentation. Mean shift segmentation (MSS) is a non-.

  3. An interactive medical image segmentation framework using iterative refinement.

    Science.gov (United States)

    Kalshetti, Pratik; Bundele, Manas; Rahangdale, Parag; Jangra, Dinesh; Chattopadhyay, Chiranjoy; Harit, Gaurav; Elhence, Abhay

    2017-04-01

    Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Artifacts in Magnetic Resonance Imaging

    International Nuclear Information System (INIS)

    Krupa, Katarzyna; Bekiesińska-Figatowska, Monika

    2015-01-01

    Artifacts in magnetic resonance imaging and foreign bodies within the patient’s body may be confused with a pathology or may reduce the quality of examinations. Radiologists are frequently not informed about the medical history of patients and face postoperative/other images they are not familiar with. A gallery of such images was presented in this manuscript. A truncation artifact in the spinal cord could be misinterpreted as a syrinx. Motion artifacts caused by breathing, cardiac movement, CSF pulsation/blood flow create a ghost artifact which can be reduced by patient immobilization, or cardiac/respiratory gating. Aliasing artifacts can be eliminated by increasing the field of view. An artificially hyperintense signal on FLAIR images can result from magnetic susceptibility artifacts, CSF/vascular pulsation, motion, but can also be found in patients undergoing MRI examinations while receiving supplemental oxygen. Metallic and other foreign bodies which may be found on and in patients’ bodies are the main group of artifacts and these are the focus of this study: e.g. make-up, tattoos, hairbands, clothes, endovascular embolization, prostheses, surgical clips, intraorbital and other medical implants, etc. Knowledge of different types of artifacts and their origin, and of possible foreign bodies is necessary to eliminate them or to reduce their negative influence on MR images by adjusting acquisition parameters. It is also necessary to take them into consideration when interpreting the images. Some proposals of reducing artifacts have been mentioned. Describing in detail the procedures to avoid or limit the artifacts would go beyond the scope of this paper but technical ways to reduce them can be found in the cited literature

  5. A new level set model for cell image segmentation

    Science.gov (United States)

    Ma, Jing-Feng; Hou, Kai; Bao, Shang-Lian; Chen, Chun

    2011-02-01

    In this paper we first determine three phases of cell images: background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.

  6. Segmentation of medical images using explicit anatomical knowledge

    Science.gov (United States)

    Wilson, Laurie S.; Brown, Stephen; Brown, Matthew S.; Young, Jeanne; Li, Rongxin; Luo, Suhuai; Brandt, Lee

    1999-07-01

    Knowledge-based image segmentation is defined in terms of the separation of image analysis procedures and representation of knowledge. Such architecture is particularly suitable for medical image segmentation, because of the large amount of structured domain knowledge. A general methodology for the application of knowledge-based methods to medical image segmentation is described. This includes frames for knowledge representation, fuzzy logic for anatomical variations, and a strategy for determining the order of segmentation from the modal specification. This method has been applied to three separate problems, 3D thoracic CT, chest X-rays and CT angiography. The application of the same methodology to such a range of applications suggests a major role in medical imaging for segmentation methods incorporating representation of anatomical knowledge.

  7. GPU accelerated fuzzy connected image segmentation by using CUDA.

    Science.gov (United States)

    Zhuge, Ying; Cao, Yong; Miller, Robert W

    2009-01-01

    Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.

  8. Cellular image segmentation using n-agent cooperative game theory

    Science.gov (United States)

    Dimock, Ian B.; Wan, Justin W. L.

    2016-03-01

    Image segmentation is an important problem in computer vision and has significant applications in the segmentation of cellular images. Many different imaging techniques exist and produce a variety of image properties which pose difficulties to image segmentation routines. Bright-field images are particularly challenging because of the non-uniform shape of the cells, the low contrast between cells and background, and imaging artifacts such as halos and broken edges. Classical segmentation techniques often produce poor results on these challenging images. Previous attempts at bright-field imaging are often limited in scope to the images that they segment. In this paper, we introduce a new algorithm for automatically segmenting cellular images. The algorithm incorporates two game theoretic models which allow each pixel to act as an independent agent with the goal of selecting their best labelling strategy. In the non-cooperative model, the pixels choose strategies greedily based only on local information. In the cooperative model, the pixels can form coalitions, which select labelling strategies that benefit the entire group. Combining these two models produces a method which allows the pixels to balance both local and global information when selecting their label. With the addition of k-means and active contour techniques for initialization and post-processing purposes, we achieve a robust segmentation routine. The algorithm is applied to several cell image datasets including bright-field images, fluorescent images and simulated images. Experiments show that the algorithm produces good segmentation results across the variety of datasets which differ in cell density, cell shape, contrast, and noise levels.

  9. Dental x-ray image segmentation

    Science.gov (United States)

    Said, Eyad; Fahmy, Gamal F.; Nassar, Diaa; Ammar, Hany

    2004-08-01

    Law enforcement agencies have been exploiting biometric identifiers for decades as key tools in forensic identification. With the evolution in information technology and the huge volume of cases that need to be investigated by forensic specialists, it has become important to automate forensic identification systems. While, ante mortem (AM) identification, that is identification prior to death, is usually possible through comparison of many biometric identifiers, postmortem (PM) identification, that is identification after death, is impossible using behavioral biometrics (e.g. speech, gait). Moreover, under severe circumstances, such as those encountered in mass disasters (e.g. airplane crashers) or if identification is being attempted more than a couple of weeks postmortem, under such circumstances, most physiological biometrics may not be employed for identification, because of the decay of soft tissues of the body to unidentifiable states. Therefore, a postmortem biometric identifier has to resist the early decay that affects body tissues. Because of their survivability and diversity, the best candidates for postmortem biometric identification are the dental features. In this paper we present an over view about an automated dental identification system for Missing and Unidentified Persons. This dental identification system can be used by both law enforcement and security agencies in both forensic and biometric identification. We will also present techniques for dental segmentation of X-ray images. These techniques address the problem of identifying each individual tooth and how the contours of each tooth are extracted.

  10. Multifractal-based nuclei segmentation in fish images.

    Science.gov (United States)

    Reljin, Nikola; Slavkovic-Ilic, Marijeta; Tapia, Coya; Cihoric, Nikola; Stankovic, Srdjan

    2017-09-01

    The method for nuclei segmentation in fluorescence in-situ hybridization (FISH) images, based on the inverse multifractal analysis (IMFA) is proposed. From the blue channel of the FISH image in RGB format, the matrix of Holder exponents, with one-by-one correspondence with the image pixels, is determined first. The following semi-automatic procedure is proposed: initial nuclei segmentation is performed automatically from the matrix of Holder exponents by applying predefined hard thresholding; then the user evaluates the result and is able to refine the segmentation by changing the threshold, if necessary. After successful nuclei segmentation, the HER2 (human epidermal growth factor receptor 2) scoring can be determined in usual way: by counting red and green dots within segmented nuclei, and finding their ratio. The IMFA segmentation method is tested over 100 clinical cases, evaluated by skilled pathologist. Testing results show that the new method has advantages compared to already reported methods.

  11. Endovascular interventional magnetic resonance imaging

    International Nuclear Information System (INIS)

    Bartels, L W; Bakker, C J G

    2003-01-01

    Minimally invasive interventional radiological procedures, such as balloon angioplasty, stent placement or coiling of aneurysms, play an increasingly important role in the treatment of patients suffering from vascular disease. The non-destructive nature of magnetic resonance imaging (MRI), its ability to combine the acquisition of high quality anatomical images and functional information, such as blood flow velocities, perfusion and diffusion, together with its inherent three dimensionality and tomographic imaging capacities, have been advocated as advantages of using the MRI technique for guidance of endovascular radiological interventions. Within this light, endovascular interventional MRI has emerged as an interesting and promising new branch of interventional radiology. In this review article, the authors will give an overview of the most important issues related to this field. In this context, we will focus on the prerequisites for endovascular interventional MRI to come to maturity. In particular, the various approaches for device tracking that were proposed will be discussed and categorized. Furthermore, dedicated MRI systems, safety and compatibility issues and promising applications that could become clinical practice in the future will be discussed. (topical review)

  12. Myositis ossificans: magnetic resonance images

    International Nuclear Information System (INIS)

    Dosda, R.; Marti-Bonmati, L.; Concepcion, L.; Galant, J.

    1999-01-01

    Myositis ossificans is characterized by a benign, self-limiting, ossifying mass of the white tissue. In the present report, we describe the magnetic resonance (MR) images in three cases of myositis ossificans in pediatric patients, correlating the MR findings with those obtained with other radiological studies. The lesions were detected in three patients, two boys and one girl, ranging in age between 10 and 14 years. The nature of the lesion was confirmed histologically in all three cases. The MR images were obtained using superconductive units at 0.5 Teslas, with T1 and T2-weighted spin-echo and STIR sequences. In two patients, gadolinium-enhanced T1-weighted images were also obtained. As in any process of maturation, the proliferation/maturation ratio depends on the moment in the course of the lesion, which affects its MR features,. In acute phases, the soft tissue mass with an intraosseous, perilesional adematous reaction predominates, while annular calcification and lesser edema are characteristic of subacute episode. Myositis ossificans is very rare in children. The inflammatory response may present a radiological pattern difficult to distinguish from that of aggressive tumor or infection, especially in the acute phase. (Author) 7 refs

  13. Olfactometer for functional resonance imaging

    International Nuclear Information System (INIS)

    Andrieu, Patrice

    2013-01-01

    The Magnetic Resonance Imaging (fMRI) has been developing for twenty years. Indeed, the marketing of high-resolution MRI (5 Tesla and 7 Tesla recently) allowed the study of brain mechanisms. The research work of this PHD was to develop instrumentation for objective studies of brain behavior during a sensory stimulation. We are interested in the study of olfaction. We have designed and built a six-channel olfactometer, synchronized with breathing and controlled by computer. The originality of our work lies in the modularity of our device, which makes it adaptable to a wide range of studies. We also propose a new method to change the intensity of stimulation delivered: the Pulse Width Modulation (PWM). This device has been used in several studies in fMRI. The effectiveness of the PWM is highlighted in a psychophysical study described in this manuscript. (author)

  14. Magnetic resonance imaging in patients with unstable angina: comparison with acute myocardial infarction and normals

    International Nuclear Information System (INIS)

    Ahmad, M.; Johnson, R.F. Jr.; Fawcett, H.D.; Schreiber, M.H.

    1988-01-01

    The role of magnetic resonance imaging in characterizing normal, ischemic and infarcted segments of myocardium was examined in 8 patients with unstable angina, 11 patients with acute myocardial infarction, and 7 patients with stable angina. Eleven normal volunteers were imaged for comparison. Myocardial segments in short axis magnetic resonance images were classified as normal or abnormal on the basis of perfusion changes observed in thallium-201 images in 22 patients and according to the electrocariographic localization of infarction in 4 patients. T2 relaxation time was measured in 57 myocardial segments with abnormal perfusion (24 with reversible and 33 with irreversible perfusion changes) and in 25 normally perfused segments. T2 measurements in normally perfused segments of patients with acute myocardial infarction, unstable angina and stable angina were within normal range derived from T2 measurements in 48 myocardial segments of 11 normal volunteers (42 +/- 10 ms). T2 in abnormal myocardial segments of patients with stable angina also was not significantly different from normal. T2 of abnormal segments in patients with unstable angina (64 +/- 14 in reversibly ischemic and 67 +/- 21 in the irreversibly ischemic segments) was prolonged when compared to normal (p less than 0.0001) and was not significantly different from T2 in abnormal segments of patients with acute myocardial infarction (62 +/- 18 for reversibly and 66 +/- 11 for irreversibly ischemic segments). The data indicate that T2 prolongation is not specific for acute myocardial infarction and may be observed in abnormally perfused segments of patients with unstable angina

  15. Improved document image segmentation algorithm using multiresolution morphology

    Science.gov (United States)

    Bukhari, Syed Saqib; Shafait, Faisal; Breuel, Thomas M.

    2011-01-01

    Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,1 which is also available in his open-source Leptonica library.2The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.

  16. Scale selection for supervised image segmentation

    DEFF Research Database (Denmark)

    Li, Yan; Tax, David M J; Loog, Marco

    2012-01-01

    schemes are usually unsupervised, as they do not take into account the actual segmentation problem at hand. In this paper, we consider the problem of selecting scales, which aims at an optimal discrimination between user-defined classes in the segmentation. We show the deficiency of the classical...

  17. Fast globally optimal segmentation of cells in fluorescence microscopy images.

    Science.gov (United States)

    Bergeest, Jan-Philip; Rohr, Karl

    2011-01-01

    Accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression in high-throughput screening applications. We propose a new approach for segmenting cell nuclei which is based on active contours and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images of different cell types. We have also performed a quantitative comparison with previous segmentation approaches.

  18. AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING

    Directory of Open Access Journals (Sweden)

    Liang Tang

    2011-05-01

    Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

  19. Pocket atlas of cranial magnetic resonance imaging

    International Nuclear Information System (INIS)

    Haughton, V.M.; Daniels, D.L.

    1986-01-01

    This atlas illustrates normal cerebral anatomy in magnetic resonance images. From their studies in cerebral anatomy utilizing cryomicrotome and other techniques, the authors selected more than 100 high-resolution images that represent the most clinically useful scans

  20. Functional magnetic resonance imaging by visual stimulation

    International Nuclear Information System (INIS)

    Nishimura, Yukiko; Negoro, Kiyoshi; Morimatsu, Mitsunori; Hashida, Masahiro

    1996-01-01

    We evaluated functional magnetic resonance images obtained in 8 healthy subjects in response to visual stimulation using a conventional clinical magnetic resonance imaging system with multi-slice spin-echo echo planar imaging. Activation in the visual cortex was clearly demonstrated by the multi-slice experiment with a task-related change in signal intensity. In addition to the primary visual cortex, other areas were also activated by a complicated visual task. Multi-slice spin-echo echo planar imaging offers high temporal resolution and allows the three-dimensional analysis of brain function. Functional magnetic resonance imaging provides a useful noninvasive method of mapping brain function. (author)

  1. Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.

    Science.gov (United States)

    Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui

    2014-09-01

    Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

    OpenAIRE

    Fu, Chichen; Lee, Soonam; Ho, David Joon; Han, Shuo; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

    2018-01-01

    Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D d...

  3. IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES

    Institute of Scientific and Technical Information of China (English)

    纳瑟; 刘重庆

    2002-01-01

    This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K-means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.

  4. Magnetic resonance imaging of radiation optic neuropathy

    International Nuclear Information System (INIS)

    Zimmerman, C.F.; Schatz, N.J.; Glaser, J.S.

    1990-01-01

    Three patients with delayed radiation optic neuropathy after radiation therapy for parasellar neoplasms underwent magnetic resonance imaging. The affected optic nerves and chiasms showed enlargement and focal gadopentetate dimeglumine enhancement. The magnetic resonance imaging technique effectively detected and defined anterior visual pathway changes of radionecrosis and excluded the clinical possibility of visual loss because of tumor recurrence

  5. Graph run-length matrices for histopathological image segmentation.

    Science.gov (United States)

    Tosun, Akif Burak; Gunduz-Demir, Cigdem

    2011-03-01

    The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from "graph run-length matrices" lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation.

  6. Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

    Directory of Open Access Journals (Sweden)

    Ward Kevin R

    2009-11-01

    novelty of the proposed method lies in its incorporation of anatomical features for ideal midline detection and the two-step ventricle segmentation method. Our method offers the following improvements over existing approaches: accurate detection of the ideal midline and accurate recognition of ventricles using both anatomical features and spatial templates derived from Magnetic Resonance Images.

  7. An Efficient Evolutionary Based Method For Image Segmentation

    OpenAIRE

    Aslanzadeh, Roohollah; Qazanfari, Kazem; Rahmati, Mohammad

    2017-01-01

    The goal of this paper is to present a new efficient image segmentation method based on evolutionary computation which is a model inspired from human behavior. Based on this model, a four layer process for image segmentation is proposed using the split/merge approach. In the first layer, an image is split into numerous regions using the watershed algorithm. In the second layer, a co-evolutionary process is applied to form centers of finals segments by merging similar primary regions. In the t...

  8. Multilevel segmentation of intracranial aneurysms in CT angiography images

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yan [Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94122 and University of Lyon, CREATIS, CNRS UMR 5220, INSERM U1206, UCB Lyon1, INSA Lyon, Lyon 69100 (France); Zhang, Yue, E-mail: y.zhang525@gmail.com [Veterans Affairs Medical Center, San Francisco, California 94121 and University of Lyon, CREATIS, CNRS UMR 5220, INSERM U1206, UCB Lyon1, INSA Lyon, Lyon 69100 (France); Navarro, Laurent [Ecole Nationale Superieure des Mines de Saint-Etienne, Saint-Etienne 42015 (France); Eker, Omer Faruk [CHU Montpellier, Neuroradiologie, Montpellier 34000 (France); Corredor Jerez, Ricardo A. [Ecole Polytechnique Federale de Lausanne, Lausanne 1015 (Switzerland); Chen, Yu; Zhu, Yuemin; Courbebaisse, Guy [University of Lyon, CREATIS, CNRS UMR 5220, INSERM U1206, UCB Lyon1, INSA Lyon, Lyon 69100 (France)

    2016-04-15

    Purpose: Segmentation of aneurysms plays an important role in interventional planning. Yet, the segmentation of both the lumen and the thrombus of an intracranial aneurysm in computed tomography angiography (CTA) remains a challenge. This paper proposes a multilevel segmentation methodology for efficiently segmenting intracranial aneurysms in CTA images. Methods: The proposed methodology first uses the lattice Boltzmann method (LBM) to extract the lumen part directly from the original image. Then, the LBM is applied again on an intermediate image whose lumen part is filled by the mean gray-level value outside the lumen, to yield an image region containing part of the aneurysm boundary. After that, an expanding disk is introduced to estimate the complete contour of the aneurysm. Finally, the contour detected is used as the initial contour of the level set with ellipse to refine the aneurysm. Results: The results obtained on 11 patients from different hospitals showed that the proposed segmentation was comparable with manual segmentation, and that quantitatively, the average segmentation matching factor (SMF) reached 86.99%, demonstrating good segmentation accuracy. Chan–Vese method, Sen’s model, and Luca’s model were used to compare the proposed method and their average SMF values were 39.98%, 40.76%, and 77.11%, respectively. Conclusions: The authors have presented a multilevel segmentation method based on the LBM and level set with ellipse for accurate segmentation of intracranial aneurysms. Compared to three existing methods, for all eleven patients, the proposed method can successfully segment the lumen with the highest SMF values for nine patients and second highest SMF values for the two. It also segments the entire aneurysm with the highest SMF values for ten patients and second highest SMF value for the one. This makes it potential for clinical assessment of the volume and aspect ratio of the intracranial aneurysms.

  9. COMPARISON OF DIFFERENT SEGMENTATION ALGORITHMS FOR DERMOSCOPIC IMAGES

    Directory of Open Access Journals (Sweden)

    A.A. Haseena Thasneem

    2015-05-01

    Full Text Available This paper compares different algorithms for the segmentation of skin lesions in dermoscopic images. The basic segmentation algorithms compared are Thresholding techniques (Global and Adaptive, Region based techniques (K-means, Fuzzy C means, Expectation Maximization and Statistical Region Merging, Contour models (Active Contour Model and Chan - Vese Model and Spectral Clustering. Accuracy, sensitivity, specificity, Border error, Hammoude distance, Hausdorff distance, MSE, PSNR and elapsed time metrices were used to evaluate various segmentation techniques.

  10. Exterior Decay of Wood-Plastic Composite Boards: Characterization and Magnetic Resonance Imaging

    Science.gov (United States)

    Rebecca Ibach; Grace Sun; Marek Gnatowski; Jessie Glaeser; Mathew Leung; John Haight

    2016-01-01

    Magnetic resonance imaging (MRI) was used to evaluate free water content and distribution in wood-plastic composite (WPC) materials decayed during exterior exposure near Hilo, Hawaii. Two segments of the same board blend were selected from 6 commercial decking boards that had fungal fruiting bodies. One of the two board segments was exposed in sun, the other in shadow...

  11. Content Based Retrieval System for Magnetic Resonance Images

    International Nuclear Information System (INIS)

    Trojachanets, Katarina

    2010-01-01

    The amount of medical images is continuously increasing as a consequence of the constant growth and development of techniques for digital image acquisition. Manual annotation and description of each image is impractical, expensive and time consuming approach. Moreover, it is an imprecise and insufficient way for describing all information stored in medical images. This induces the necessity for developing efficient image storage, annotation and retrieval systems. Content based image retrieval (CBIR) emerges as an efficient approach for digital image retrieval from large databases. It includes two phases. In the first phase, the visual content of the image is analyzed and the feature extraction process is performed. An appropriate descriptor, namely, feature vector is then associated with each image. These descriptors are used in the second phase, i.e. the retrieval process. With the aim to improve the efficiency and precision of the content based image retrieval systems, feature extraction and automatic image annotation techniques are subject of continuous researches and development. Including the classification techniques in the retrieval process enables automatic image annotation in an existing CBIR system. It contributes to more efficient and easier image organization in the system.Applying content based retrieval in the field of magnetic resonance is a big challenge. Magnetic resonance imaging is an image based diagnostic technique which is widely used in medical environment. According to this, the number of magnetic resonance images is enormously growing. Magnetic resonance images provide plentiful medical information, high resolution and specific nature. Thus, the capability of CBIR systems for image retrieval from large database is of great importance for efficient analysis of this kind of images. The aim of this thesis is to propose content based retrieval system architecture for magnetic resonance images. To provide the system efficiency, feature

  12. Label fusion based brain MR image segmentation via a latent selective model

    Science.gov (United States)

    Liu, Gang; Guo, Xiantang; Zhu, Kai; Liao, Hengxu

    2018-04-01

    Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.

  13. Design and validation of Segment - freely available software for cardiovascular image analysis

    International Nuclear Information System (INIS)

    Heiberg, Einar; Sjögren, Jane; Ugander, Martin; Carlsson, Marcus; Engblom, Henrik; Arheden, Håkan

    2010-01-01

    Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format. Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page (http://segment.heiberg.se). Segment

  14. Remote Sensing Image Registration with Line Segments and Their Intersections

    Directory of Open Access Journals (Sweden)

    Chengjin Lyu

    2017-05-01

    Full Text Available Image registration is a basic but essential step for remote sensing image processing, and finding stable features in multitemporal images is one of the most considerable challenges in the field. The main shape contours of artificial objects (e.g., roads, buildings, farmlands, and airports can be generally described as a group of line segments, which are stable features, even in images with evident background changes (e.g., images taken before and after a disaster. In this study, a registration method that uses line segments and their intersections is proposed for multitemporal remote sensing images. First, line segments are extracted in image pyramids to unify the scales of the reference image and the test image. Then, a line descriptor based on the gradient distribution of local areas is constructed, and the segments are matched in image pyramids. Lastly, triplets of intersections of matching lines are selected to estimate affine transformation between two images. Additional corresponding intersections are provided based on the estimated transformation, and an iterative process is adopted to remove outliers. The performance of the proposed method is tested on a variety of optical remote sensing image pairs, including synthetic and real data. Compared with existing methods, our method can provide more accurate registration results, even in images with significant background changes.

  15. Image segmentation evaluation for very-large datasets

    Science.gov (United States)

    Reeves, Anthony P.; Liu, Shuang; Xie, Yiting

    2016-03-01

    With the advent of modern machine learning methods and fully automated image analysis there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. Current approaches of visual inspection and manual markings do not scale well to big data. We present a new approach that depends on fully automated algorithm outcomes for segmentation documentation, requires no manual marking, and provides quantitative evaluation for computer algorithms. The documentation of new image segmentations and new algorithm outcomes are achieved by visual inspection. The burden of visual inspection on large datasets is minimized by (a) customized visualizations for rapid review and (b) reducing the number of cases to be reviewed through analysis of quantitative segmentation evaluation. This method has been applied to a dataset of 7,440 whole-lung CT images for 6 different segmentation algorithms designed to fully automatically facilitate the measurement of a number of very important quantitative image biomarkers. The results indicate that we could achieve 93% to 99% successful segmentation for these algorithms on this relatively large image database. The presented evaluation method may be scaled to much larger image databases.

  16. A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

    Science.gov (United States)

    Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

    2016-03-01

    On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different

  17. Magnetic resonance imaging of Parkinsonism

    International Nuclear Information System (INIS)

    Nakamura, Yusaku; Takahashi, Mitsuo; Kitaguchi, Masataka; Akaneya, Yukio; Mitui, Yoshiyuki; Tanaka, Hisashi

    1991-01-01

    We studied eighteen patients affected by Parkinsonism with symptoms of tremor, bradykinesia, or rigidity using magnetic resonance imaging (MRI). Patients ranged in age from 34 to 80 years (mean 62.8±11.6 years), and the duration of their disease had been 3.8±3.2 years. MRI examinations were performed with Shimazu and Siemens superconducting magnets, operating at 0.5 and 1.5 T magnetic fields, respectively. Both T 1 - and T 2 -weighted spin echo (SE) pulse sequences were used. In eight patients (44.4%), MRI demonstrated bilateral multiple lacunar infarction of the basal ganglia. The most common abnormality identified was multiple, bilateral lacunar infarcts in the lateral portion of the putamen. The average size of the lacunar infarction of the putamen was less than half that of the entire putamen. Patients with multiple lacunar infarction were significantly older than the other patients and had lower Yahr's scores. The clinical symptoms of patients with bilateral multiple lacunar infarction of the basal ganglia were compatible with the diagnosis of arteriosclerotic Parkinsonism of akinetic rigid type. It has been suggested that multiple lacunar infarction of the basal ganglia may have led to Parkinsonism in these patients. (author)

  18. Magnetic resonance imaging and neurolupus

    International Nuclear Information System (INIS)

    Schott, A.M.; Colson, F.; Tebib, J.; Noel, E.; Bouvier, M.

    1990-01-01

    Magnetic resonance imaging (MRI) was assessed in the management of neuropsychiatric manifestations occurring in 6 SLE patients. The MRI scans were normal in 3 cases and was associated with remission of the symptoms except for a patient who experienced a chorea at the time of the examination. Abnormal MRI scans always revealed more lesions than CT scan. 2 different patterns of abnormalities seem to correspond to 2 specific disorders. In 2 patients with clinical presentation suggesting a cortical ischemia by vascular thrombosis, both MRI scans showed areas of abnormal high signal intensities located in the subcortical white matter. In one last patient, MRI scan revealed multiple focal areas of high signal intensities (on T 1 weighter scans) disseminated not only in the deep white matter but also in the gray one. These lesions could be depend upon demyelinisation which may occur by a local vascular process. This serie confirms the interest of MRI in the management of SLE brain involvement as well as it points out some problem of interpretation. This suggest further comparative studies especially at the real onset and during the course of neuro-psychiatric manifestations. At last, the coronal sections may be more informative for the diagnosis and patholophysiology than the horizontal ones [fr

  19. Nuclear magnetic resonance imaging method

    International Nuclear Information System (INIS)

    Johnson, G.; MacDonald, J.; Hutchison, S.; Eastwood, L.M.; Redpath, T.W.T.; Mallard, J.R.

    1984-01-01

    A method of deriving three dimensional image information from an object using nuclear magnetic resonance signals comprises subjecting the object to a continuous, static magnetic field and carrying out the following set of sequential steps: 1) exciting nuclear spins in a selected volume (90deg pulse); 2) applying non-aligned first, second and third gradients of the magnetic field; 3) causing the spins to rephase periodically by reversal of the first gradient to produce spin echoes, and applying pulses of the second gradient prior to every read-out of an echo signal from the object, to differently encode the spin in the second gradient direction for each read-out signal. The above steps 1-3 are then successively repeated with different values of gradient of the third gradient, there being a recovery interval between the repetition of successive sets of steps. Alternate echoes only are read out, the other echoes being time-reversed and ignored for convenience. The resulting signals are appropriately sampled, set out in an array and subjected to three dimensional Fourier transformation. (author)

  20. Channeler Ant Model: 3 D segmentation of medical images through ant colonies

    International Nuclear Information System (INIS)

    Fiorina, E.; Valzano, S.; Arteche Diaz, R.; Bosco, P.; Gargano, G.; Megna, R.; Oppedisano, C.; Massafra, A.

    2011-01-01

    In this paper the Channeler Ant Model (CAM) and some results of its application to the analysis of medical images are described. The CAM is an algorithm able to segment 3 D structures with different shapes, intensity and background. It makes use of virtual and colonies and exploits their natural capabilities to modify the environment and communicate with each other by pheromone deposition. Its performance has been validated with the segmentation of 3 D artificial objects and it has been already used successfully in lung nodules detection on Computer Tomography images. This work tries to evaluate the CAM as a candidate to solve the quantitative segmentation problem in Magnetic Resonance brain images: to evaluate the percentage of white matter, gray matter and cerebrospinal fluid in each voxel.

  1. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    DEFF Research Database (Denmark)

    Menze, Bjoern H.; Jakab, Andras; Bauer, Stefan

    2015-01-01

    a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing...

  2. Segmentation techniques for extracting humans from thermal images

    CSIR Research Space (South Africa)

    Dickens, JS

    2011-11-01

    Full Text Available A pedestrian detection system for underground mine vehicles is being developed that requires the segmentation of people from thermal images in underground mine tunnels. A number of thresholding techniques are outlined and their performance on a...

  3. Image segmentation algorithm based on T-junctions cues

    Science.gov (United States)

    Qian, Yanyu; Cao, Fengyun; Wang, Lu; Yang, Xuejie

    2016-03-01

    To improve the over-segmentation and over-merge phenomenon of single image segmentation algorithm,a novel approach of combing Graph-Based algorithm and T-junctions cues is proposed in this paper. First, a method by L0 gradient minimization is applied to the smoothing of the target image eliminate artifacts caused by noise and texture detail; Then, the initial over-segmentation result of the smoothing image using the graph-based algorithm; Finally, the final results via a region fusion strategy by t-junction cues. Experimental results on a variety of images verify the new approach's efficiency in eliminating artifacts caused by noise,segmentation accuracy and time complexity has been significantly improved.

  4. Spectral segmentation of polygonized images with normalized cuts

    Energy Technology Data Exchange (ETDEWEB)

    Matsekh, Anna [Los Alamos National Laboratory; Skurikhin, Alexei [Los Alamos National Laboratory; Rosten, Edward [UNIV OF CAMBRIDGE

    2009-01-01

    We analyze numerical behavior of the eigenvectors corresponding to the lowest eigenvalues of the generalized graph Laplacians arising in the Normalized Cuts formulations of the image segmentation problem on coarse polygonal grids.

  5. A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

    Science.gov (United States)

    Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J.; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa

    2016-01-01

    On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and

  6. Learning normalized inputs for iterative estimation in medical image segmentation.

    Science.gov (United States)

    Drozdzal, Michal; Chartrand, Gabriel; Vorontsov, Eugene; Shakeri, Mahsa; Di Jorio, Lisa; Tang, An; Romero, Adriana; Bengio, Yoshua; Pal, Chris; Kadoury, Samuel

    2018-02-01

    In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data. In our image segmentation pipeline, we use FCNs to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction. As in other fully convolutional approaches, our pipeline can be used off-the-shelf on different image modalities. We show that using this pipeline, we exhibit state-of-the-art performance on the challenging Electron Microscopy benchmark, when compared to other 2D methods. We improve segmentation results on CT images of liver lesions, when contrasting with standard FCN methods. Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods. The obtained results illustrate the strong potential and versatility of the pipeline by achieving accurate segmentations on a variety of image modalities and different anatomical regions. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Variational segmentation problems using prior knowledge in imaging and vision

    DEFF Research Database (Denmark)

    Fundana, Ketut

    This dissertation addresses variational formulation of segmentation problems using prior knowledge. Variational models are among the most successful approaches for solving many Computer Vision and Image Processing problems. The models aim at finding the solution to a given energy functional defined......, prior knowledge is needed to obtain the desired solution. The introduction of shape priors in particular, has proven to be an effective way to segment objects of interests. Firstly, we propose a prior-based variational segmentation model to segment objects of interest in image sequences, that can deal....... Many objects have high variability in shape and orientation. This often leads to unsatisfactory results, when using a segmentation model with single shape template. One way to solve this is by using more sophisticated shape models. We propose to incorporate shape priors from a shape sub...

  8. A new level set model for cell image segmentation

    International Nuclear Information System (INIS)

    Ma Jing-Feng; Chen Chun; Hou Kai; Bao Shang-Lian

    2011-01-01

    In this paper we first determine three phases of cell images: background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing. (cross-disciplinary physics and related areas of science and technology)

  9. Multi-object segmentation framework using deformable models for medical imaging analysis.

    Science.gov (United States)

    Namías, Rafael; D'Amato, Juan Pablo; Del Fresno, Mariana; Vénere, Marcelo; Pirró, Nicola; Bellemare, Marc-Emmanuel

    2016-08-01

    Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed

  10. Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect

    Directory of Open Access Journals (Sweden)

    Qikui Zhu

    2018-01-01

    Full Text Available Segmentation of the prostate from Magnetic Resonance Imaging (MRI plays an important role in prostate cancer diagnosis. However, the lack of clear boundary and significant variation of prostate shapes and appearances make the automatic segmentation very challenging. In the past several years, approaches based on deep learning technology have made significant progress on prostate segmentation. However, those approaches mainly paid attention to features and contexts within each single slice of a 3D volume. As a result, this kind of approaches faces many difficulties when segmenting the base and apex of the prostate due to the limited slice boundary information. To tackle this problem, in this paper, we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation. In addition to utilizing the intraslice contexts and features, the proposed model also treats prostate slices as a data sequence and utilizes the interslice contexts to assist segmentation. The experimental results show that the proposed approach achieved significant segmentation improvement compared to other reported methods.

  11. SEGMENTATION OF MITOCHONDRIA IN ELECTRON MICROSCOPY IMAGES USING ALGEBRAIC CURVES.

    Science.gov (United States)

    Seyedhosseini, Mojtaba; Ellisman, Mark H; Tasdizen, Tolga

    2013-01-01

    High-resolution microscopy techniques have been used to generate large volumes of data with enough details for understanding the complex structure of the nervous system. However, automatic techniques are required to segment cells and intracellular structures in these multi-terabyte datasets and make anatomical analysis possible on a large scale. We propose a fully automated method that exploits both shape information and regional statistics to segment irregularly shaped intracellular structures such as mitochondria in electron microscopy (EM) images. The main idea is to use algebraic curves to extract shape features together with texture features from image patches. Then, these powerful features are used to learn a random forest classifier, which can predict mitochondria locations precisely. Finally, the algebraic curves together with regional information are used to segment the mitochondria at the predicted locations. We demonstrate that our method outperforms the state-of-the-art algorithms in segmentation of mitochondria in EM images.

  12. Image Mosaic Method Based on SIFT Features of Line Segment

    Directory of Open Access Journals (Sweden)

    Jun Zhu

    2014-01-01

    Full Text Available This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

  13. Automatic labeling and segmentation of vertebrae in CT images

    Science.gov (United States)

    Rasoulian, Abtin; Rohling, Robert N.; Abolmaesumi, Purang

    2014-03-01

    Labeling and segmentation of the spinal column from CT images is a pre-processing step for a range of image- guided interventions. State-of-the art techniques have focused either on image feature extraction or template matching for labeling of the vertebrae followed by segmentation of each vertebra. Recently, statistical multi- object models have been introduced to extract common statistical characteristics among several anatomies. In particular, we have created models for segmentation of the lumbar spine which are robust, accurate, and computationally tractable. In this paper, we reconstruct a statistical multi-vertebrae pose+shape model and utilize it in a novel framework for labeling and segmentation of the vertebra in a CT image. We validate our technique in terms of accuracy of the labeling and segmentation of CT images acquired from 56 subjects. The method correctly labels all vertebrae in 70% of patients and is only one level off for the remaining 30%. The mean distance error achieved for the segmentation is 2.1 +/- 0.7 mm.

  14. Evaluating the impact of image preprocessing on iris segmentation

    Directory of Open Access Journals (Sweden)

    José F. Valencia-Murillo

    2014-08-01

    Full Text Available Segmentation is one of the most important stages in iris recognition systems. In this paper, image preprocessing algorithms are applied in order to evaluate their impact on successful iris segmentation. The preprocessing algorithms are based on histogram adjustment, Gaussian filters and suppression of specular reflections in human eye images. The segmentation method introduced by Masek is applied on 199 images acquired under unconstrained conditions, belonging to the CASIA-irisV3 database, before and after applying the preprocessing algorithms. Then, the impact of image preprocessing algorithms on the percentage of successful iris segmentation is evaluated by means of a visual inspection of images in order to determine if circumferences of iris and pupil were detected correctly. An increase from 59% to 73% in percentage of successful iris segmentation is obtained with an algorithm that combine elimination of specular reflections, followed by the implementation of a Gaussian filter having a 5x5 kernel. The results highlight the importance of a preprocessing stage as a previous step in order to improve the performance during the edge detection and iris segmentation processes.

  15. Automated breast segmentation in ultrasound computer tomography SAFT images

    Science.gov (United States)

    Hopp, T.; You, W.; Zapf, M.; Tan, W. Y.; Gemmeke, H.; Ruiter, N. V.

    2017-03-01

    Ultrasound Computer Tomography (USCT) is a promising new imaging system for breast cancer diagnosis. An essential step before further processing is to remove the water background from the reconstructed images. In this paper we present a fully-automated image segmentation method based on three-dimensional active contours. The active contour method is extended by applying gradient vector flow and encoding the USCT aperture characteristics as additional weighting terms. A surface detection algorithm based on a ray model is developed to initialize the active contour, which is iteratively deformed to capture the breast outline in USCT reflection images. The evaluation with synthetic data showed that the method is able to cope with noisy images, and is not influenced by the position of the breast and the presence of scattering objects within the breast. The proposed method was applied to 14 in-vivo images resulting in an average surface deviation from a manual segmentation of 2.7 mm. We conclude that automated segmentation of USCT reflection images is feasible and produces results comparable to a manual segmentation. By applying the proposed method, reproducible segmentation results can be obtained without manual interaction by an expert.

  16. Magnetic Resonance Imaging and conformal radiotherapy: Characterization of MRI alone simulation for conformal radiotherapy. Development and evaluation of an automatic volumes of interest segmentation tool for prostate cancer radiotherapy

    International Nuclear Information System (INIS)

    Pasquier, David

    2006-01-01

    Radiotherapy is a curative treatment of malignant tumours. Radiotherapy techniques considerably evolved last years with the increasing integration of medical images in conformal radiotherapy. This technique makes it possible to elaborate a complex ballistics conforming to target volume and sparing healthy tissues. The examination currently used to delineate volumes of interest is Computed Tomography (CT), on account of its geometrical precision and the information that it provides on electronic densities needed to dose calculation. Magnetic Resonance Imaging (MRI) ensures a more precise delineation of target volumes in many locations, such as pelvis and brain. For pelvic tumours, the use of MRI needs image registration, which complicates treatment planning and poses the problem of the lack of in vivo standard method of validation. The obstacles in the use of MRI alone in treatment planning were evaluated. Neither geometrical distortion linked with the system and the patient nor the lack of information on electronic densities represent stumbling obstacles. Distortion remained low even in edge of large field of view on modern machines. The assignment of electronic densities to bone structures and soft tissues in MR images permitted to obtain equivalent dosimetry to that carried out on the original CT, with a good reproducibility and homogeneous distribution within target volume. The assignment of electronic densities could not be carried out using 20 MV photons and suitable ballistics. The development of Image Guided Radiotherapy could facilitate the use of MRI alone in treatment planning. Target volumes and organ at risk delineation is a time consuming task in radiotherapy planning. We took part in the development and evaluated a method of automatic and semi automatic delineation of volumes of interest from MRI images for prostate cancer radiotherapy. For prostate and organ at risk automatic delineation an organ model-based method and a seeded region growing method

  17. Contrast agents in magnetic resonance imaging

    International Nuclear Information System (INIS)

    Karadjian, V.

    1987-01-01

    The origine of nuclear magnetic resonance signal is reminded and different ways for contrast enhancement in magnetic resonance imaging are presented, especially, modifications of tissus relaxation times. Investigations have focused on development of agents incorporating either paramagnetic ions or stable free radicals. Pharmacological and toxicological aspects are developed. The diagnostic potential of these substances is illustrated by the example of gadolinium complexes [fr

  18. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... clear images. Patient movement can have the same effect. A very irregular heartbeat may affect the quality of images obtained using techniques that time the imaging based on the electrical activity of ...

  19. Magnetic resonance imaging in multiple sclerosis

    International Nuclear Information System (INIS)

    Kojima, Shigeyuki; Hirayama, Keizo

    1989-01-01

    Magnetic resonance imaging (MRI) of the brain was performed in a total of 45 patients with multiple sclerosis (MS), comprising 27 with brain symptoms and 18 without it. The results were compared with X-ray computed tomography (CT). Some of the 45 MS patients were also examined by neurophysiological studies for comparison. MRI showed demyelinating plaques of the brain in a total of 31 patients (69%) - 20 symptomatic and 11 asymptomatic patients. For symptomatic patients, MRI was capable of detecting brain lesions in 6 (86%) of 7 acute stage patients and 14 (70%) of 20 non-acute stage patients. It was also capable of detecting brain lesions in 21 (70%) of 30 clinically definite MR patients and 10 (67%) of 15 clinically probable MS patients. Concurrently available X-ray CT revealed brain lesions in 9 symptomatic patients (33%) and one asymptomatic patient (6%). Visual evoked potentials examined in 31 patients showed abnormality in one (11%) of 9 patients without symptoms of optic neuritis and all (100%) of the other 22 patients with symptoms. In 19 evaluable patients, auditory brainstem responses were abnormal in one (11%) of 9 patients without brainstem symptoms and 3 (30%) of 10 patients with symptoms. MRI of the brain was far superior to X-ray CT, visual evoked potentials and auditory brainstem responses in detecting clinically unsuspected lesions. We proposed new diagnostic criteria including MRI findings of the brain in the Japanese MS diagnostic criteria. MRI of the spinal cord was performed in 12 MS patients with spinal cord symptoms by sagittal and coronal images. It demonstrated demyelinating lesions within the cervical and superior thoracic cord in 8 MS acute stage patients. Spinal cord lesions were longitudinally continuous as long as many spinal segments, with swelling in 6 patients and atrophy in 2 patients. MRI of spinal cord was useful in deciding superior and inferior limits of cord lesions and in visualizing cord swelling or atrophy. (Namekawa, K)

  20. High intensity region segmentation in MR imaging of multiple sclerosis

    International Nuclear Information System (INIS)

    Rodrigo, F; Filipuzzi, M; Graffigna, J P; Isoardi, R; Noceti, M

    2013-01-01

    Numerous pathologies are often manifest in Magnetic Resonance Imaging (MRI) as hyperintense or bright regions as compared to normal tissue. It is of particular interest to develop an algorithm to detect, identify and define those Regions of Interest (ROI) when analyzing MRI studies, particularly for lesions of Multiple Sclerosis (MS). The objective of this study is to analyze those parameters which optimize segmentation of the areas of interest. To establish which areas should be considered as hyperintense regions, we developed a database (DB), with studies of patients diagnosed with MS. This disease causes axonal demyelination and it is expressed as bright regions in PD, T2 and FLAIR MRI sequences. Thus, with more than 4300 hyperintense regions validated by an expert physician, an algorithm was developed to detect such spots, approximating the results the expert obtained. Alongside these hyperintense lesion regions, it also detected bone regions with high intensity levels, similar to the intensity of the lesions, but with other features that allow a good differentiation.The algorithm will then detect ROIs with similar intensity levels and performs classification through data mining techniques

  1. Hitchhiker'S Guide to Voxel Segmentation for Partial Volume Correction of in Vivo Magnetic Resonance Spectroscopy

    Directory of Open Access Journals (Sweden)

    Scott Quadrelli

    2016-01-01

    Full Text Available Partial volume effects have the potential to cause inaccuracies when quantifying metabolites using proton magnetic resonance spectroscopy (MRS. In order to correct for cerebrospinal fluid content, a spectroscopic voxel needs to be segmented according to different tissue contents. This article aims to detail how automated partial volume segmentation can be undertaken and provides a software framework for researchers to develop their own tools. While many studies have detailed the impact of partial volume correction on proton magnetic resonance spectroscopy quantification, there is a paucity of literature explaining how voxel segmentation can be achieved using freely available neuroimaging packages.

  2. Can magnetic resonance imaging differentiate undifferentiated arthritis?

    DEFF Research Database (Denmark)

    Østergaard, Mikkel; Duer, Anne; Hørslev-Petersen, K

    2005-01-01

    A high sensitivity for the detection of inflammatory and destructive changes in inflammatory joint diseases makes magnetic resonance imaging potentially useful for assigning specific diagnoses, such as rheumatoid arthritis and psoriatic arthritis in arthritides, that remain undifferentiated after...... conventional clinical, biochemical and radiographic examinations. With recent data as the starting point, the present paper describes the current knowledge on magnetic resonance imaging in the differential diagnosis of undifferentiated arthritis....

  3. Image segmentation by hierarchial agglomeration of polygons using ecological statistics

    Science.gov (United States)

    Prasad, Lakshman; Swaminarayan, Sriram

    2013-04-23

    A method for rapid hierarchical image segmentation based on perceptually driven contour completion and scene statistics is disclosed. The method begins with an initial fine-scale segmentation of an image, such as obtained by perceptual completion of partial contours into polygonal regions using region-contour correspondences established by Delaunay triangulation of edge pixels as implemented in VISTA. The resulting polygons are analyzed with respect to their size and color/intensity distributions and the structural properties of their boundaries. Statistical estimates of granularity of size, similarity of color, texture, and saliency of intervening boundaries are computed and formulated into logical (Boolean) predicates. The combined satisfiability of these Boolean predicates by a pair of adjacent polygons at a given segmentation level qualifies them for merging into a larger polygon representing a coarser, larger-scale feature of the pixel image and collectively obtains the next level of polygonal segments in a hierarchy of fine-to-coarse segmentations. The iterative application of this process precipitates textured regions as polygons with highly convolved boundaries and helps distinguish them from objects which typically have more regular boundaries. The method yields a multiscale decomposition of an image into constituent features that enjoy a hierarchical relationship with features at finer and coarser scales. This provides a traversable graph structure from which feature content and context in terms of other features can be derived, aiding in automated image understanding tasks. The method disclosed is highly efficient and can be used to decompose and analyze large images.

  4. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... bear denotes child-specific content. Related Articles and Media MR Angiography (MRA) Magnetic Resonance, Functional (fMRI) - Brain ... the web pages found at these links. About Us | Contact Us | FAQ | Privacy | Terms of Use | Links | ...

  5. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... bear denotes child-specific content. Related Articles and Media Catheter Angiography Magnetic Resonance, Functional (fMRI) - Brain Children's ( ... the web pages found at these links. About Us | Contact Us | FAQ | Privacy | Terms of Use | Links | ...

  6. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... immediately after the exam. A few patients experience side effects from the contrast material, including nausea and local ... Related Articles and Media Catheter Angiography Magnetic Resonance, Functional (fMRI) - Brain Children's (Pediatric) CT (Computed Tomography) Magnetic ...

  7. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... or thyroid problems. Any of these conditions may influence the decision on whether contrast material will be ... bear denotes child-specific content. Related Articles and Media Catheter Angiography Magnetic Resonance, Functional (fMRI) - Brain Children's ( ...

  8. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... Related Articles and Media Catheter Angiography Magnetic Resonance, Functional (fMRI) - Brain Children's (Pediatric) CT (Computed Tomography) Magnetic ... the possible charges you will incur. Web page review process: This Web page is reviewed regularly by ...

  9. Comparison of segmentation algorithms for fluorescence microscopy images of cells.

    Science.gov (United States)

    Dima, Alden A; Elliott, John T; Filliben, James J; Halter, Michael; Peskin, Adele; Bernal, Javier; Kociolek, Marcin; Brady, Mary C; Tang, Hai C; Plant, Anne L

    2011-07-01

    The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc.

  10. Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images

    International Nuclear Information System (INIS)

    Hou, Zujun; Huang, Su

    2008-01-01

    Quantitative analysis of gray matter and white matter in brain magnetic resonance imaging (MRI) is valuable for neuroradiology and clinical practice. Submission of large collections of MRI scans to pipeline processing is increasingly important. We characterized this process and suggest several improvements. To investigate tissue segmentation from brain MR images through a sequential approach, a pipeline that consecutively executes denoising, skull/scalp removal, intensity inhomogeneity correction and intensity-based classification was developed. The denoising phase employs a 3D-extension of the Bayes-Shrink method. The inhomogeneity is corrected by an improvement of the Dawant et al.'s method with automatic generation of reference points. The N3 method has also been evaluated. Subsequently the brain tissue is segmented into cerebrospinal fluid, gray matter and white matter by a generalized Otsu thresholding technique. Intensive comparisons with other sequential or iterative methods have been carried out using simulated and real images. The sequential approach with judicious selection on the algorithm selection in each stage is not only advantageous in speed, but also can attain at least as accurate segmentation as iterative methods under a variety of noise or inhomogeneity levels. A sequential approach to tissue segmentation, which consecutively executes the wavelet shrinkage denoising, scalp/skull removal, inhomogeneity correction and intensity-based classification was developed to automatically segment the brain tissue into CSF, GM and WM from brain MR images. This approach is advantageous in several common applications, compared with other pipeline methods. (orig.)

  11. Automatic segmentation of blood vessels from retinal fundus images ...

    Indian Academy of Sciences (India)

    The retinal blood vessels were segmented through color space conversion and color channel extraction, image pre-processing, Gabor filtering, image postprocessing, feature construction through application of principal component analysis, k-means clustering and first level classification using Naïve–Bayes classification ...

  12. Fuzzy object models for newborn brain MR image segmentation

    Science.gov (United States)

    Kobashi, Syoji; Udupa, Jayaram K.

    2013-03-01

    Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

  13. Combining Constraint Types From Public Data in Aerial Image Segmentation

    DEFF Research Database (Denmark)

    Jacobsen, Thomas Stig; Jensen, Jacob Jon; Jensen, Daniel Rune

    2013-01-01

    We introduce a method for image segmentation that constraints the clustering with map and point data. The method is showcased by applying the spectral clustering algorithm on aerial images for building detection with constraints built from a height map and address point data. We automatically det...

  14. High-dynamic-range imaging for cloud segmentation

    Science.gov (United States)

    Dev, Soumyabrata; Savoy, Florian M.; Lee, Yee Hui; Winkler, Stefan

    2018-04-01

    Sky-cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg - an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.

  15. Multi scales based sparse matrix spectral clustering image segmentation

    Science.gov (United States)

    Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

    2018-04-01

    In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

  16. Objectness Supervised Merging Algorithm for Color Image Segmentation

    Directory of Open Access Journals (Sweden)

    Haifeng Sima

    2016-01-01

    Full Text Available Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

  17. Magnetic resonance imaging: hazard, risk and safety

    International Nuclear Information System (INIS)

    Narayan, Pradeep; Suri, S.; Singh, P.

    2001-01-01

    The hazard and risk associated with magnetic resonance imaging is a matter of concern. In 1982, the Food and Drug Administration (FDA), USA issued guidelines to Hospital's Investigational Review Board (IRBs) in 'Guidelines for Evaluating Electromagnetic Exposure Risks for Trials of Clinical Nuclear Magnetic Resonance (NMR)'. In 1997, the Berufsgenossenschaft (BG), professional association for precision engineering and electronics of Germany, in their preliminary proposal for safety limits extended their concerns on static magnetic field. Owing to both time varying and static magnetic fields applied in Magnetic Resonance Imaging (MRI) this became of immediate concern to user community to assess the potential hazard and risk associated with the NMR system

  18. Classifying magnetic resonance image modalities with convolutional neural networks

    Science.gov (United States)

    Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

    2018-02-01

    Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

  19. Semiautomatic segmentation of liver metastases on volumetric CT images

    International Nuclear Information System (INIS)

    Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng

    2015-01-01

    Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accurately delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation

  20. Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network.

    Science.gov (United States)

    Pal, Anabik; Garain, Utpal; Chandra, Aditi; Chatterjee, Raghunath; Senapati, Swapan

    2018-06-01

    Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images. Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers. An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches. The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease. Copyright © 2018 Elsevier B.V. All rights reserved.

  1. FBIH financial market segmentation on the basis of image factors

    Directory of Open Access Journals (Sweden)

    Arnela Bevanda

    2008-12-01

    Full Text Available The aim of the study is to recognize, single out and define market segments useful for future marketing strategies, using certain statistical techniques on the basis of influence of various image factors of financial institutions. The survey included a total of 500 interviewees: 250 bank clients and 250 clients of insurance companies. Starting from the problem area and research goal, the following hypothesis has been formulated: Basic preferences of clients in regard of image factors while selecting financial institutions are different enough to be used as such for differentiating significant market segments of clients. Two segments have been singled out by cluster analysis and named, respectively, traditionalists and visualists. Results of the research confirmed the established hypothesis and pointed to the fact that managers in the financial institutions of the Federation of Bosnia and Herzegovina (FBIH must undertake certain corrective actions, especially when planning and implementing communication strategies, if they wish to maintain their competitiveness in serving both selected segments.

  2. Active contour based segmentation of resected livers in CT images

    Science.gov (United States)

    Oelmann, Simon; Oyarzun Laura, Cristina; Drechsler, Klaus; Wesarg, Stefan

    2015-03-01

    The majority of state of the art segmentation algorithms are able to give proper results in healthy organs but not in pathological ones. However, many clinical applications require an accurate segmentation of pathological organs. The determination of the target boundaries for radiotherapy or liver volumetry calculations are examples of this. Volumetry measurements are of special interest after tumor resection for follow up of liver regrow. The segmentation of resected livers presents additional challenges that were not addressed by state of the art algorithms. This paper presents a snakes based algorithm specially developed for the segmentation of resected livers. The algorithm is enhanced with a novel dynamic smoothing technique that allows the active contour to propagate with different speeds depending on the intensities visible in its neighborhood. The algorithm is evaluated in 6 clinical CT images as well as 18 artificial datasets generated from additional clinical CT images.

  3. Segmentation of images for gingival growth measurement

    Science.gov (United States)

    Kim, Dong-Il; Wilson, Joseph N.

    1992-12-01

    The ability to measure gingival volume growth from dental casts would provide a valuable resource for periodontists. This problem is attractive from a computer vision standpoint due to the complexities of data acquisition, segmentation of gingival and tooth surfaces and boundaries, and extraction of features (such as tooth axes) to help solve the correspondence problem for multiple casts. In this paper, a structured light 3-D range finder is used to collect raw data. The most complicated subtask is that of detecting discontinuities such as the gingival margin. Discontinuity detection is hindered both by cast anomalies (such as bubbles and holes generated during the process of dental impression) and by the subtle nature of the discontinuities themselves. First, we discuss an approach to segmenting a dental cast into tooth and gingival units using depth and orientation discontinuities. The visible cast surface is reconstructed by obtaining the minimum of a parameterized functional. The first derivative of the energy functional (which corresponds to the Euler-Lagrange equation) is solved using the multigrid methods. both orientation and depth discontinuities are detected by adding a discrete discontinuity functional to the energy functional. The principal axes and boundaries of the teeth provide the information necessary to determine the region to be measured in estimating gingival growth. Finally, voxels corresponding to growth regions are counted to measure the target volume.

  4. Polarization image segmentation of radiofrequency ablated porcine myocardial tissue.

    Directory of Open Access Journals (Sweden)

    Iftikhar Ahmad

    Full Text Available Optical polarimetry has previously imaged the spatial extent of a typical radiofrequency ablated (RFA lesion in myocardial tissue, exhibiting significantly lower total depolarization at the necrotic core compared to healthy tissue, and intermediate values at the RFA rim region. Here, total depolarization in ablated myocardium was used to segment the total depolarization image into three (core, rim and healthy zones. A local fuzzy thresholding algorithm was used for this multi-region segmentation, and then compared with a ground truth segmentation obtained from manual demarcation of RFA core and rim regions on the histopathology image. Quantitative comparison of the algorithm segmentation results was performed with evaluation metrics such as dice similarity coefficient (DSC = 0.78 ± 0.02 and 0.80 ± 0.02, sensitivity (Sn = 0.83 ± 0.10 and 0.91 ± 0.08, specificity (Sp = 0.76 ± 0.17 and 0.72 ± 0.17 and accuracy (Acc = 0.81 ± 0.09 and 0.71 ± 0.10 for RFA core and rim regions, respectively. This automatic segmentation of parametric depolarization images suggests a novel application of optical polarimetry, namely its use in objective RFA image quantification.

  5. AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES.

    Science.gov (United States)

    Kong, Jun; Wang, Fusheng; Teodoro, George; Liang, Yanhui; Zhu, Yangyang; Tucker-Burden, Carol; Brat, Daniel J

    2015-04-01

    A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional (3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow (GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with (1) small cell false detection and missing rates for individual cells; and (2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.

  6. Magnetic resonance imaging of the fetal brain.

    Science.gov (United States)

    Tee, L Mf; Kan, E Yl; Cheung, J Cy; Leung, W C

    2016-06-01

    This review covers the recent literature on fetal brain magnetic resonance imaging, with emphasis on techniques, advances, common indications, and safety. We conducted a search of MEDLINE for articles published after 2010. The search terms used were "(fetal OR foetal OR fetus OR foetus) AND (MR OR MRI OR [magnetic resonance]) AND (brain OR cerebral)". Consensus statements from major authorities were also included. As a result, 44 relevant articles were included and formed the basis of this review. One major challenge is fetal motion that is largely overcome by ultra-fast sequences. Currently, single-shot fast spin-echo T2-weighted imaging remains the mainstay for motion resistance and anatomical delineation. Recently, a snap-shot inversion recovery sequence has enabled robust T1-weighted images to be obtained, which is previously a challenge for standard gradient-echo acquisitions. Fetal diffusion-weighted imaging, diffusion tensor imaging, and magnetic resonance spectroscopy are also being developed. With multiplanar capabilities, superior contrast resolution and field of view, magnetic resonance imaging does not have the limitations of sonography, and can provide additional important information. Common indications include ventriculomegaly, callosum and posterior fossa abnormalities, and twin complications. There are safety concerns about magnetic resonance-induced heating and acoustic damage but current literature showed no conclusive evidence of deleterious fetal effects. The American College of Radiology guideline states that pregnant patients can be accepted to undergo magnetic resonance imaging at any stage of pregnancy if risk-benefit ratio to patients warrants that the study be performed. Magnetic resonance imaging of the fetal brain is a safe and powerful adjunct to sonography in prenatal diagnosis. It can provide additional information that aids clinical management, prognostication, and counselling.

  7. A deep level set method for image segmentation

    OpenAIRE

    Tang, Min; Valipour, Sepehr; Zhang, Zichen Vincent; Cobzas, Dana; MartinJagersand

    2017-01-01

    This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can incorporatesmoothing and prior information to achieve an accurate segmentation.Furthermore, different than using the level set model as a post-processingtool, we integrate it into the training phase to fine-tune the FCN. Thisallows the use of unlabeled data during training in a semi-supervisedsetting. Using two types o...

  8. Continuously live image processor for drift chamber track segment triggering

    International Nuclear Information System (INIS)

    Berenyi, A.; Chen, H.K.; Dao, K.

    1999-01-01

    The first portion of the BaBar experiment Level 1 Drift Chamber Trigger pipeline is the Track Segment Finder (TSF). Using a novel method incorporating both occupancy and drift-time information, the TSF system continually searches for segments in the supercells of the full 7104-wire Drift Chamber hit image at 3.7 MHz. The TSF was constructed to operate in a potentially high beam-background environment while achieving high segment-finding efficiency, deadtime-free operation, a spatial resolution of 5 simulated physics events

  9. Evaluation of congenital heart disease by magnetic resonance imaging

    International Nuclear Information System (INIS)

    Roos, A. de; Roest, A.A.W.

    2000-01-01

    Magnetic resonance imaging has proven to be useful in the assessment of patients with complex congenital heart disease and in the post-surgical follow-up of patients with corrected congenital heart disease. A thorough understanding of the congenital cardiac malformations that can be encountered is needed and the use of the sequential segmental analysis helps to standardize the evaluation and diagnosis of (complex) congenital heart disease. After surgical correction of congenital heart defects, patients must be followed over extended periods of time, because morphological and functional abnormalities may still be present or may develop. The use of echocardiography may be hampered in these patients as scar tissue and thorax deformities limit the acoustic window. Magnetic resonance imaging has proven to be advantageous in the follow-up of these post-surgical patients and with the use of several different techniques the morphological as well as functional abnormalities can be evaluated and followed over time. (orig.)

  10. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... you! Do you have a personal story about radiology? Share your patient story here Images × Image Gallery ... reviewed by committees from the American College of Radiology (ACR) and the Radiological Society of North America ( ...

  11. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... you! Do you have a personal story about radiology? Share your patient story here Images × Image Gallery ... reviewed by committees from the American College of Radiology (ACR) and the Radiological Society of North America ( ...

  12. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... Imaging (MRI) procedure View full size with caption Pediatric Content Some imaging tests and treatments have special pediatric considerations. The teddy bear denotes child-specific content. ...

  13. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... six weeks) before being safe for MRI examinations. Examples include but are not limited to: artificial heart ... the area to be imaged. Furthermore, the examination takes longer than other imaging modalities (typically x-ray ...

  14. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... to a CD or uploaded to a digital cloud server. Currently, MRI is the most sensitive imaging ... over time. top of page What are the benefits vs. risks? Benefits MRI is a noninvasive imaging ...

  15. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... computer then processes the signals and generates a series of images, each of which shows a thin ... into the intravenous line (IV) after an initial series of scans. Additional series of images will be ...

  16. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... imaging modalities. top of page Additional Information and Resources RTAnswers.org : Radiation Therapy for Brain Tumors Radiation ... To locate a medical imaging or radiation oncology provider in your community, you can search the ACR- ...

  17. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... practice. top of page What are some common uses of the procedure? MR imaging of the head ... is done because a potential abnormality needs further evaluation with additional views or a special imaging technique. ...

  18. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... Most MRI exams are painless. However, some patients find it uncomfortable to remain still during MR imaging. ... anxious, confused or in severe pain, you may find it difficult to lie still during imaging. A ...

  19. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... determine the presence of certain diseases. The images can then be examined on a computer monitor, transmitted ... for imaging the joints and bones, where it can help: diagnose sports-related injuries detect the presence ...

  20. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... identify and accurately characterize diseases than other imaging methods. This detail makes MRI an invaluable tool in ... might be obscured by bone with other imaging methods. The contrast material used in MRI exams is ...

  1. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... are clearer and more detailed than other imaging methods. This exam does not use ionizing radiation and ... clearer and more detailed than with other imaging methods. This detail makes MRI an invaluable tool in ...

  2. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... other imaging methods. This exam does not use ionizing radiation and may require an injection of a contrast ... other internal body structures. MRI does not use ionizing radiation (x-rays). Detailed MR images allow physicians to ...

  3. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... claustrophobia. Newer open MRI units provide very high quality images for many types of exams. Older open MRI units may not provide this same image quality. Certain types of exams cannot be performed using ...

  4. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... claustrophobia. Newer open MRI units provide very high quality images for many types of exams; however, older ... MRI units may not provide this same image quality. Certain types of exams cannot be performed using ...

  5. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... a radiologist or other physician. To locate a medical imaging or radiation oncology provider in your community, you ... not provide cost information. The costs for specific medical imaging tests, treatments and procedures may vary by geographic ...

  6. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... a radiologist or other physician. To locate a medical imaging or radiation oncology provider in your community, you ... not provide cost information. The costs for specific medical imaging tests, treatments and procedures may vary by geographic ...

  7. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... Detailed MR images allow physicians to evaluate various parts of the body and determine the presence of ... machine and in some cases, placed around the part of the body being imaged, send and receive ...

  8. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... metallic objects. Patient movement can have the same effect. A very irregular heartbeat may affect the quality of images obtained using techniques that time the imaging based on the electrical activity of ...

  9. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... based on clinical judgment. This is because traction devices and many types of life support equipment may distort the MR images and as a result, must be kept away from the area to be imaged. Furthermore, the examination takes longer than other imaging modalities (typically x-ray ...

  10. ImageSURF: An ImageJ Plugin for Batch Pixel-Based Image Segmentation Using Random Forests

    Directory of Open Access Journals (Sweden)

    Aidan O'Mara

    2017-11-01

    Full Text Available Image segmentation is a necessary step in automated quantitative imaging. ImageSURF is a macro-compatible ImageJ2/FIJI plugin for pixel-based image segmentation that considers a range of image derivatives to train pixel classifiers which are then applied to image sets of any size to produce segmentations without bias in a consistent, transparent and reproducible manner. The plugin is available from ImageJ update site http://sites.imagej.net/ImageSURF/ and source code from https://github.com/omaraa/ImageSURF. Funding statement: This research was supported by an Australian Government Research Training Program Scholarship.

  11. Bladder segmentation in MR images with watershed segmentation and graph cut algorithm

    Science.gov (United States)

    Blaffert, Thomas; Renisch, Steffen; Schadewaldt, Nicole; Schulz, Heinrich; Wiemker, Rafael

    2014-03-01

    Prostate and cervix cancer diagnosis and treatment planning that is based on MR images benefit from superior soft tissue contrast compared to CT images. For these images an automatic delineation of the prostate or cervix and the organs at risk such as the bladder is highly desirable. This paper describes a method for bladder segmentation that is based on a watershed transform on high image gradient values and gray value valleys together with the classification of watershed regions into bladder contents and tissue by a graph cut algorithm. The obtained results are superior if compared to a simple region-after-region classification.

  12. Image Segmentation and Processing for Efficient Parking Space Analysis

    OpenAIRE

    Tutika, Chetan Sai; Vallapaneni, Charan; R, Karthik; KP, Bharath; Muthu, N Ruban Rajesh Kumar

    2018-01-01

    In this paper, we develop a method to detect vacant parking spaces in an environment with unclear segments and contours with the help of MATLAB image processing capabilities. Due to the anomalies present in the parking spaces, such as uneven illumination, distorted slot lines and overlapping of cars. The present-day conventional algorithms have difficulties processing the image for accurate results. The algorithm proposed uses a combination of image pre-processing and false contour detection ...

  13. Magnetic resonance imaging in radiotherapy treatment planning

    NARCIS (Netherlands)

    Moerland, Marinus Adriaan

    1996-01-01

    From its inception in the early 1970's up to the present, magnetic resonance imaging (MRI) has evolved into a sophisticated technique, which has aroused considerable interest in var- ious subelds of medicine including radiotherapy. MRI is capable of imaging in any plane and does not use ionizing

  14. Magnetic resonance imaging in obstetric diagnosis.

    Science.gov (United States)

    Weinreb, J C; Lowe, T W; Santos-Ramos, R; Cunningham, F G; Parkey, R

    1985-01-01

    Five patients with abnormal pregnancies were examined with ultrasound (US) and magnetic resonance imaging (MR). Three had a malformed fetus, 1 had a molar pregnancy, and 1 had an ovarian mass. Both maternal and fetal structures were clearly shown, although fetal motion may have resulted in image degradation in some cases. The authors suggest that MR may be useful in obstetric diagnosis.

  15. Magnetic resonance imaging of semicircular canals.

    Science.gov (United States)

    Sbarbati, A; Leclercq, F; Zancanaro, C; Antonakis, K

    1992-01-01

    The present paper reports the results of the first investigation of the semicircular canals in a living, small animal by means of high spatial resolution magnetic resonance imaging. This procedure is noninvasive and allows identification of the endolymphatic and perilymphatic spaces yielding a morphology quite consistent with direct anatomical examination. Images Fig. 1 Fig. 2 Fig. 3 Fig. 4 PMID:1506290

  16. Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images

    NARCIS (Netherlands)

    Lee, K.; Buitendijk, G.H.; Bogunovic, H.; Springelkamp, H.; Hofman, A.; Wahle, A.; Sonka, M.; Vingerling, J.R.; Klaver, C.C.W.; Abramoff, M.D.

    2016-01-01

    PURPOSE: To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. METHODS: Six hundred ninety macular SD-OCT image volumes (6.0 x 6.0 x 2.3 mm3)

  17. Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation.

    Science.gov (United States)

    al-Rifaie, Mohammad Majid; Aber, Ahmed; Hemanth, Duraiswamy Jude

    2015-12-01

    This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence-learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration-dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

  18. Magnetic resonance imaging - first human images in Australia

    International Nuclear Information System (INIS)

    Baddeley, H.; Doddrell, D.M.; Brooks, W.M.; Field, J.; Irving, M.; Williams, J.E.

    1986-01-01

    The use of magnetic resonance imaging, in the demonstration of internal human anatomy and in the diagnosis of disease, has the major advantages that the technique is non-invasive, does not require the use of ionizing radiation and that it can demonstrate neurological and cardiovascular lesions that cannot be diagnosed easily by other imaging methods. The first magnetic resonance images of humans were obtained in Australia in October 1985 on the research instrument of the Queensland Medical Magnetic Resonance Research Centre, which is based at the Mater Hospital in Brisbane

  19. Multilevel Image Segmentation Based on an Improved Firefly Algorithm

    Directory of Open Access Journals (Sweden)

    Kai Chen

    2016-01-01

    Full Text Available Multilevel image segmentation is time-consuming and involves large computation. The firefly algorithm has been applied to enhancing the efficiency of multilevel image segmentation. However, in some cases, firefly algorithm is easily trapped into local optima. In this paper, an improved firefly algorithm (IFA is proposed to search multilevel thresholds. In IFA, in order to help fireflies escape from local optima and accelerate the convergence, two strategies (i.e., diversity enhancing strategy with Cauchy mutation and neighborhood strategy are proposed and adaptively chosen according to different stagnation stations. The proposed IFA is compared with three benchmark optimal algorithms, that is, Darwinian particle swarm optimization, hybrid differential evolution optimization, and firefly algorithm. The experimental results show that the proposed method can efficiently segment multilevel images and obtain better performance than the other three methods.

  20. Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts

    Science.gov (United States)

    Surinta, Olarik; Chamchong, Rapeeporn

    Palm leaf manuscripts were one of the earliest forms of written media and were used in Southeast Asia to store early written knowledge about subjects such as medicine, Buddhist doctrine and astrology. Therefore, historical handwritten palm leaf manuscripts are important for people who like to learn about historical documents, because we can learn more experience from them. This paper presents an image segmentation of historical handwriting from palm leaf manuscripts. The process is composed of three steps: 1) background elimination to separate text and background by Otsu's algorithm 2) line segmentation and 3) character segmentation by histogram of image. The end result is the character's image. The results from this research may be applied to optical character recognition (OCR) in the future.

  1. Automatic Segmentation of Dermoscopic Images by Iterative Classification

    Directory of Open Access Journals (Sweden)

    Maciel Zortea

    2011-01-01

    Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

  2. Phase contrast image segmentation using a Laue analyser crystal

    International Nuclear Information System (INIS)

    Kitchen, Marcus J; Paganin, David M; Lewis, Robert A; Pavlov, Konstantin M; Uesugi, Kentaro; Allison, Beth J; Hooper, Stuart B

    2011-01-01

    Dual-energy x-ray imaging is a powerful tool enabling two-component samples to be separated into their constituent objects from two-dimensional images. Phase contrast x-ray imaging can render the boundaries between media of differing refractive indices visible, despite them having similar attenuation properties; this is important for imaging biological soft tissues. We have used a Laue analyser crystal and a monochromatic x-ray source to combine the benefits of both techniques. The Laue analyser creates two distinct phase contrast images that can be simultaneously acquired on a high-resolution detector. These images can be combined to separate the effects of x-ray phase, absorption and scattering and, using the known complex refractive indices of the sample, to quantitatively segment its component materials. We have successfully validated this phase contrast image segmentation (PCIS) using a two-component phantom, containing an iodinated contrast agent, and have also separated the lungs and ribcage in images of a mouse thorax. Simultaneous image acquisition has enabled us to perform functional segmentation of the mouse thorax throughout the respiratory cycle during mechanical ventilation.

  3. In vivo determination of body composition of rats using magnetic resonance imaging.

    Science.gov (United States)

    Tang, H; Vasselli, J; Wu, E; Gallagher, D

    2000-05-01

    Magnetic resonance imaging (MRI) has potential as an instrument to measure body composition because it can discriminate various soft tissues in vivo. These soft tissues include adipose tissue, muscle, organs, and brain. We report on preliminary studies using a 4.2-tesla MRI for measuring body composition in the mouse and rat. We employed image segmentation methods that include an image correction method, a necessary requirement when the images are taken in the presence of nonuniform radio-frequency (RF) coil response. The software for 3-D data segmentation, quantification, correction, image manipulation, and visualization has been developed as a research tool. This method currently is being validated.

  4. Hyperspectral image segmentation using a cooperative nonparametric approach

    Science.gov (United States)

    Taher, Akar; Chehdi, Kacem; Cariou, Claude

    2013-10-01

    In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.

  5. Medical image segmentation by means of constraint satisfaction neural network

    International Nuclear Information System (INIS)

    Chen, C.T.; Tsao, C.K.; Lin, W.C.

    1990-01-01

    This paper applies the concept of constraint satisfaction neural network (CSNN) to the problem of medical image segmentation. Constraint satisfaction (or constraint propagation), the procedure to achieve global consistency through local computation, is an important paradigm in artificial intelligence. CSNN can be viewed as a three-dimensional neural network, with the two-dimensional image matrix as its base, augmented by various constraint labels for each pixel. These constraint labels can be interpreted as the connections and the topology of the neural network. Through parallel and iterative processes, the CSNN will approach a solution that satisfies the given constraints thus providing segmented regions with global consistency

  6. Basic principles of nuclear magnetic resonance imaging

    International Nuclear Information System (INIS)

    Valk, J.; MacLean, C.; Algra, P.R.

    1985-01-01

    The intent of this book is to help clinicians understand the basic physical principles of magnetic resonance (MR) imaging. The book consists of the following: a discussion of elementary considerations; pulse sequencing; localization of MR signals in space; MR equipment; MR contrast agents; clinical applications; MR spectroscopy; and biological effects of MR imaging; a set of appendixes; and a bibliography. Illustrations and images are included

  7. Automated Segmentation of Nuclei in Breast Cancer Histopathology Images.

    Science.gov (United States)

    Paramanandam, Maqlin; O'Byrne, Michael; Ghosh, Bidisha; Mammen, Joy John; Manipadam, Marie Therese; Thamburaj, Robinson; Pakrashi, Vikram

    2016-01-01

    The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods-Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets.

  8. Automated Segmentation of Nuclei in Breast Cancer Histopathology Images.

    Directory of Open Access Journals (Sweden)

    Maqlin Paramanandam

    Full Text Available The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP algorithm on a Markov Random Field (MRF. The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods-Wienert et al. (2012 and Veta et al. (2013, which were tested using their own datasets.

  9. Proton magnetic resonance spectroscopic imaging in neurodegenerative diseases

    International Nuclear Information System (INIS)

    Schuff, Norbert; Vermathen, Peter; Maudsley, Andrew A.; Weiner, Michael W.

    1999-01-01

    Proton magnetic resonance spectroscopic imaging ( 1 H MRSI) was used to investigate changes in brain metabolites in Alzheimer's disease, epilepsy, and amyotrophic lateral sclerosis. Examples of results from several ongoing clinical studies are provided. Multislice 1 H MRSI of the human brain, without volume pre selection offers considerable advantage over previously available techniques. Furthermore, MRI tissue segmentation and completely automated spectral curve fitting greatly facilitate quantitative data analysis. Future efforts will be devoted to obtain full volumetric brain coverage and data acquisition at short spin-echo times (TE<30 ms) for the detection of metabolites. (author)

  10. Whiplash Injuries Can be Visible by Functional Magnetic Resonance Imaging

    Directory of Open Access Journals (Sweden)

    Bengt H Johansson

    2006-01-01

    Full Text Available Whiplash trauma can result in injuries that are difficult to diagnose. Diagnosis is particularly difficult in injuries to the upper segments of the cervical spine (craniocervical joint [CCJ] complex. Studies indicate that injuries in that region may be responsible for the cervicoencephalic syndrome, as evidenced by headache, balance problems, vertigo, dizziness, eye problems, tinnitus, poor concentration, sensitivity to light and pronounced fatigue. Consequently, diagnosis of lesions in the CCJ region is important. Functional magnetic resonance imaging is a radiological technique that can visualize injuries of the ligaments and the joint capsules, and accompanying pathological movement patterns.

  11. Segmentation of fluorescence microscopy cell images using unsupervised mining.

    Science.gov (United States)

    Du, Xian; Dua, Sumeet

    2010-05-28

    The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

  12. Adaptive geodesic transform for segmentation of vertebrae on CT images

    Science.gov (United States)

    Gaonkar, Bilwaj; Shu, Liao; Hermosillo, Gerardo; Zhan, Yiqiang

    2014-03-01

    Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.

  13. Real-time cardiac magnetic resonance cine imaging with sparse sampling and iterative reconstruction for left-ventricular measures: Comparison with gold-standard segmented steady-state free precession.

    Science.gov (United States)

    Camargo, Gabriel C; Erthal, Fernanda; Sabioni, Leticia; Penna, Filipe; Strecker, Ralph; Schmidt, Michaela; Zenge, Michael O; Lima, Ronaldo de S L; Gottlieb, Ilan

    2017-05-01

    Segmented cine imaging with a steady-state free-precession sequence (Cine-SSFP) is currently the gold standard technique for measuring ventricular volumes and mass, but due to multi breath-hold (BH) requirements, it is prone to misalignment of consecutive slices, time consuming and dependent on respiratory capacity. Real-time cine avoids those limitations, but poor spatial and temporal resolution of conventional sequences has prevented its routine application. We sought to examine the accuracy and feasibility of a newly developed real-time sequence with aggressive under-sampling of k-space using sparse sampling and iterative reconstruction (Cine-RT). Stacks of short-axis cines were acquired covering both ventricles in a 1.5T system using gold standard Cine-SSFP and Cine-RT. Acquisition parameters for Cine-SSFP were: acquisition matrix of 224×196, temporal resolution of 39ms, retrospective gating, with an average of 8 heartbeats per slice and 1-2 slices/BH. For Cine-RT: acquisition matrix of 224×196, sparse sampling net acceleration factor of 11.3, temporal resolution of 41ms, prospective gating, real-time acquisition of 1 heart-beat/slice and all slices in one BH. LV contours were drawn at end diastole and systole to derive LV volumes and mass. Forty-one consecutive patients (15 male; 41±17years) in sinus rhythm were successfully included. All images from Cine-SSFP and Cine-RT were considered to have excellent quality. Cine-RT-derived LV volumes and mass were slightly underestimated but strongly correlated with gold standard Cine-SSFP. Inter- and intra-observer analysis presented similar results between both sequences. Cine-RT featuring sparse sampling and iterative reconstruction can achieve spatial and temporal resolution equivalent to Cine-SSFP, providing excellent image quality, with similar precision measurements and highly correlated and only slightly underestimated volume and mass values. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available ... you, notify the radiologist or technologist. It is important that you remain perfectly still while the images are being obtained, which is typically only a few seconds to a few minutes at a time. You will know when images are being recorded ...

  15. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... technologist through the two-way intercom. It is important that your child remain perfectly still while the images are being obtained, which is typically only a few seconds to a few minutes at a time. Your child will know when images are being ...

  16. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available ... examination poses almost no risk to the average patient when appropriate safety guidelines are followed. If sedation is used, there ... have a personal story about radiology? Share your patient story here Images ... Disease Head Injury Brain Tumors Images related ...

  17. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available Toggle navigation Test/Treatment Patient Type Screening/Wellness Disease/Condition Safety En Español More Info Images/Videos ... the body and determine the presence of certain diseases. The images can then be examined on a ...

  18. Children's (Pediatric) Magnetic Resonance Imaging

    Medline Plus

    Full Text Available ... exposure to ionizing radiation. MR imaging of the soft-tissue structures of the body—such as the heart, liver and many other organs—is more likely in some instances to identify and accurately characterize diseases than other imaging methods. This detail makes MRI an invaluable tool in ...

  19. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available ... of which shows a thin slice of the body. The images can then be studied from different angles by ... information please consult the ACR Manual on Contrast Media and its references. top of page What are the limitations of MRI of the Head? High-quality images are assured only if you are able to ...

  20. Magnetic Resonance Imaging (MRI) -- Head

    Medline Plus

    Full Text Available ... can also provide functional information (fMRI) in selected cases. MR images of the brain and other cranial structures are clearer and more detailed than with other imaging methods. This detail makes MRI an invaluable tool in early diagnosis and evaluation of many conditions, ...

  1. Magnetic resonance imaging of infectious myositis

    Energy Technology Data Exchange (ETDEWEB)

    Yun, Ji Young; Kim, Jee Young; Kim, Sang Heum; Jung, Youn Ju; Cha, Eun Suk; Park, Joung Mi; Park, Young Ha [The Catholic Univ., College of Medicine, Suwon (Korea, Republic of)

    1998-09-01

    To describe the findings of magnetic resonance imaging in infectious myositis and to determine their value for differentiation between ruberculous and bacterial myositis. Magnetic resonance images of ten proven cases of infectious myositis (five tuberculous and five bacterial) were retrospectively reviewed in the light of clinical and laboratory findings. On the basis of magnetic resonance images, signal intensity of the mass, the presence or absence of an abscess, signal intensity of the peripheral wall, patterns of contrast enhancement, and associated findings were evaluated. Compared with those of bacterial myositis, the symptoms of tuberculous myositis lasted longer but there were no difinite local inflammatory signs. In three of five cases of bacterial myositis there were specific medical records;trauma in two cases and systemic lupus erythematosus in one. All tuberculous myositis cases involved a single muscle, but bacterial myositis affected multipe muscles in three cases(60%). All but one case showed a mass in the involved muscles. In one bacterial case, there was diffuse swelling in the involved muscle. On T1-weighted images, eight infectious cases showed low signal intensity;two, of the bactrerial type, showed subtle increased signal intensity. all cases demonstrated high signal intensity on t2-weighted images. The signal intensity of peripheral wall was slightly increased on T1-weighted images, but low on T2-weighted. In four cases there was associated cellulitis, and in one case each, adjacent joint effusion and deep vein thrombosis were seen. After gadolinium infusion, peripheral rim enhancement was noted in nine cases and heterogeneous enhancement in one. After magnetic resonance imaging of infectious myositis, the characteristic finding was an abscessed lesion, with the peripheral wall showing high signal intensity on T1-weighted images and low signal intensity on T2 weighted. Although we found it difficult to differentiate bacterial from tuberculous

  2. Magnetic resonance imaging of infectious myositis

    International Nuclear Information System (INIS)

    Yun, Ji Young; Kim, Jee Young; Kim, Sang Heum; Jung, Youn Ju; Cha, Eun Suk; Park, Joung Mi; Park, Young Ha

    1998-01-01

    To describe the findings of magnetic resonance imaging in infectious myositis and to determine their value for differentiation between ruberculous and bacterial myositis. Magnetic resonance images of ten proven cases of infectious myositis (five tuberculous and five bacterial) were retrospectively reviewed in the light of clinical and laboratory findings. On the basis of magnetic resonance images, signal intensity of the mass, the presence or absence of an abscess, signal intensity of the peripheral wall, patterns of contrast enhancement, and associated findings were evaluated. Compared with those of bacterial myositis, the symptoms of tuberculous myositis lasted longer but there were no difinite local inflammatory signs. In three of five cases of bacterial myositis there were specific medical records;trauma in two cases and systemic lupus erythematosus in one. All tuberculous myositis cases involved a single muscle, but bacterial myositis affected multipe muscles in three cases(60%). All but one case showed a mass in the involved muscles. In one bacterial case, there was diffuse swelling in the involved muscle. On T1-weighted images, eight infectious cases showed low signal intensity;two, of the bactrerial type, showed subtle increased signal intensity. all cases demonstrated high signal intensity on t2-weighted images. The signal intensity of peripheral wall was slightly increased on T1-weighted images, but low on T2-weighted. In four cases there was associated cellulitis, and in one case each, adjacent joint effusion and deep vein thrombosis were seen. After gadolinium infusion, peripheral rim enhancement was noted in nine cases and heterogeneous enhancement in one. After magnetic resonance imaging of infectious myositis, the characteristic finding was an abscessed lesion, with the peripheral wall showing high signal intensity on T1-weighted images and low signal intensity on T2 weighted. Although we found it difficult to differentiate bacterial from tuberculous

  3. Contemporary imaging: Magnetic resonance imaging, computed tomography, and interventional radiology

    International Nuclear Information System (INIS)

    Goldberg, H.I.; Higgins, C.; Ring, E.J.

    1985-01-01

    In addition to discussing the most recent advances in magnetic resonance imaging (MRI), computerized tomography (CT), and the vast array of interventional procedures, this book explores the appropriate clinical applications of each of these important modalities

  4. Automatic comic page image understanding based on edge segment analysis

    Science.gov (United States)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

    2013-12-01

    Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

  5. Structural magnetic resonance imaging in epilepsy

    International Nuclear Information System (INIS)

    Deblaere, Karel; Achten, Eric

    2008-01-01

    Because of its sensitivity and high tissue contrast, magnetic resonance imaging (MRI) is the technique of choice for structural imaging in epilepsy. In this review the effect of using optimised scanning protocols and the use of high field MR systems on detection sensitivity is discussed. Also, the clinical relevance of adequate imaging in patients with focal epilepsy is highlighted. The most frequently encountered MRI findings in epilepsy are reported and their imaging characteristics depicted. Imaging focus will be on the diagnosis of hippocampal sclerosis and malformations of cortical development, two major causes of medically intractable focal epilepsy. (orig.)

  6. Structural magnetic resonance imaging in epilepsy

    Energy Technology Data Exchange (ETDEWEB)

    Deblaere, Karel [Ghent University Hospital, Department of Neuroradiology, Ghent (Belgium); Ghent University Hospital, MR Department - 1K12, Ghent (Belgium); Achten, Eric [Ghent University Hospital, Department of Neuroradiology, Ghent (Belgium)

    2008-01-15

    Because of its sensitivity and high tissue contrast, magnetic resonance imaging (MRI) is the technique of choice for structural imaging in epilepsy. In this review the effect of using optimised scanning protocols and the use of high field MR systems on detection sensitivity is discussed. Also, the clinical relevance of adequate imaging in patients with focal epilepsy is highlighted. The most frequently encountered MRI findings in epilepsy are reported and their imaging characteristics depicted. Imaging focus will be on the diagnosis of hippocampal sclerosis and malformations of cortical development, two major causes of medically intractable focal epilepsy. (orig.)

  7. Automatic segmentation of lumbar vertebrae in CT images

    Science.gov (United States)

    Kulkarni, Amruta; Raina, Akshita; Sharifi Sarabi, Mona; Ahn, Christine S.; Babayan, Diana; Gaonkar, Bilwaj; Macyszyn, Luke; Raghavendra, Cauligi

    2017-03-01

    Lower back pain is one of the most prevalent disorders in the developed/developing world. However, its etiology is poorly understood and treatment is often determined subjectively. In order to quantitatively study the emergence and evolution of back pain, it is necessary to develop consistently measurable markers for pathology. Imaging based measures offer one solution to this problem. The development of imaging based on quantitative biomarkers for the lower back necessitates automated techniques to acquire this data. While the problem of segmenting lumbar vertebrae has been addressed repeatedly in literature, the associated problem of computing relevant biomarkers on the basis of the segmentation has not been addressed thoroughly. In this paper, we propose a Random-Forest based approach that learns to segment vertebral bodies in CT images followed by a biomarker evaluation framework that extracts vertebral heights and widths from the segmentations obtained. Our dataset consists of 15 CT sagittal scans obtained from General Electric Healthcare. Our main approach is divided into three parts: the first stage is image pre-processing which is used to correct for variations in illumination across all the images followed by preparing the foreground and background objects from images; the next stage is Machine Learning using Random-Forests, which distinguishes the interest-point vectors between foreground or background; and the last step is image post-processing, which is crucial to refine the results of classifier. The Dice coefficient was used as a statistical validation metric to evaluate the performance of our segmentations with an average value of 0.725 for our dataset.

  8. Nuclear magnetic resonance spectroscopy and imaging

    International Nuclear Information System (INIS)

    Jiang Weiping; Wang Qi; Zhou Xin

    2013-01-01

    This paper briefly introduces the basic principle of nuclear magnetic resonance (NMR). Protein's structures and functions and dynamics studied by liquid NMR are elaborated; methods for enhancing the resolution of solid state NMR and its applications are discussed; the principle of magnetic resonance imaging (MRI) is interpreted, and applications in different aspects are reviewed. Finally, the progress of NMR is commented. (authors)

  9. Clinical magnetic resonance: imaging and spectroscopy

    International Nuclear Information System (INIS)

    Andrew, E.R.; Bydder, Graeme; Griffiths, John; Iles, Richard; Styles, Peter

    1990-01-01

    This book begins with a readable, comprehensive but non-mathematical introduction to the basic underlying principles of magnetic resonance. Further chapters include information on the theory and principles of MRI and MRS, the interpretation of MR images, the clinical applications and scope of MRI and MRS, practical aspects of spectroscopy and magnetic resonance, and also the practical problems associated with the siting, safety and operation of large MRI and MRS equipment. (author)

  10. DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

    Science.gov (United States)

    Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

    2017-11-27

    In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

  11. Effect of image scaling and segmentation in digital rock characterisation

    Science.gov (United States)

    Jones, B. D.; Feng, Y. T.

    2016-04-01

    Digital material characterisation from microstructural geometry is an emerging field in computer simulation. For permeability characterisation, a variety of studies exist where the lattice Boltzmann method (LBM) has been used in conjunction with computed tomography (CT) imaging to simulate fluid flow through microscopic rock pores. While these previous works show that the technique is applicable, the use of binary image segmentation and the bounceback boundary condition results in a loss of grain surface definition when the modelled geometry is compared to the original CT image. We apply the immersed moving boundary (IMB) condition of Noble and Torczynski as a partial bounceback boundary condition which may be used to better represent the geometric definition provided by a CT image. The IMB condition is validated against published work on idealised porous geometries in both 2D and 3D. Following this, greyscale image segmentation is applied to a CT image of Diemelstadt sandstone. By varying the mapping of CT voxel densities to lattice sites, it is shown that binary image segmentation may underestimate the true permeability of the sample. A CUDA-C-based code, LBM-C, was developed specifically for this work and leverages GPU hardware in order to carry out computations.

  12. Magnetic resonance imaging of breast implants.

    Science.gov (United States)

    Shah, Mala; Tanna, Neil; Margolies, Laurie

    2014-12-01

    Silicone breast implants have significantly evolved since their introduction half a century ago, yet implant rupture remains a common and expected complication, especially in patients with earlier-generation implants. Magnetic resonance imaging is the primary modality for assessing the integrity of silicone implants and has excellent sensitivity and specificity, and the Food and Drug Administration currently recommends periodic magnetic resonance imaging screening for silent silicone breast implant rupture. Familiarity with the types of silicone implants and potential complications is essential for the radiologist. Signs of intracapsular rupture include the noose, droplet, subcapsular line, and linguine signs. Signs of extracapsular rupture include herniation of silicone with a capsular defect and extruded silicone material. Specific sequences including water and silicone suppression are essential for distinguishing rupture from other pathologies and artifacts. Magnetic resonance imaging provides valuable information about the integrity of silicone implants and associated complications.

  13. Eliciting Perceptual Ground Truth for Image Segmentation

    OpenAIRE

    Hodge, Victoria Jane; Eakins, John; Austin, Jim

    2006-01-01

    In this paper, we investigate human visual perception and establish a body of ground truth data elicited from human visual studies. We aim to build on the formative work of Ren, Eakins and Briggs who produced an initial ground truth database. Human subjects were asked to draw and rank their perceptions of the parts of a series of figurative images. These rankings were then used to score the perceptions, identify the preferred human breakdowns and thus allow us to induce perceptual rules for h...

  14. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available Toggle navigation Test/Treatment Patient Type Screening/Wellness Disease/Condition Safety En Español More Info Images/Videos About Us News Physician Resources Professions Site ...

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    Full Text Available ... images removable dental work pens, pocket knives and eyeglasses body piercings In most cases, an MRI exam ... and treatments have special pediatric considerations. The teddy bear denotes child-specific content. Related Articles and Media ...

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    Full Text Available ... the exam. MRI scanners are air-conditioned and well-lit. Music may be played through the headphones ... full size with caption Pediatric Content Some imaging tests and treatments have special pediatric considerations. The teddy ...

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    Full Text Available ... a risk, depending on their nature and the strength of the MRI magnet. Many implanted devices will ... full size with caption Pediatric Content Some imaging tests and treatments have special pediatric considerations. The teddy ...

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    Full Text Available ... a risk, depending on their nature and the strength of the MRI magnet. Many implanted devices will ... full size with caption Pediatric Content Some imaging tests and treatments have special pediatric considerations. The teddy ...

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    Full Text Available ... examination poses almost no risk to the average patient when appropriate safety guidelines are followed. If sedation is used, there ... patient story here Images × ... and Radiation Safety Videos related ...

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    Full Text Available ... tissue from which they come. The MR scanner captures this energy and creates a picture of the ... imaging based on the electrical activity of the heart, such as electrocardiography (EKG). MRI generally is not ...

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    Full Text Available ... it may cause some medical devices to malfunction. Most orthopedic implants pose no risk, but you should ... a digital cloud server. Currently, MRI is the most sensitive imaging test of the head (particularly the ...

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    Full Text Available ... six weeks) before being safe for MRI examinations. Examples include but are not limited to: artificial heart ... electrocardiography (ECG). MRI typically costs more and may take more time to perform than other imaging modalities. ...

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    Full Text Available ... to a CD or uploaded to a digital cloud server. top of page What are some common ... over time. top of page What are the benefits vs. risks? Benefits MRI is a noninvasive imaging ...

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    Full Text Available ... or patients with claustrophobia. Other MRI machines are open on the sides (open MRI). Open units are especially helpful for examining larger patients or those with claustrophobia. Newer open MRI units provide very high quality images for ...

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    Full Text Available ... or patients with claustrophobia. Other MRI machines are open on the sides (open MRI). Open units are especially helpful for examining larger patients or those with claustrophobia. Newer open MRI units provide very high quality images for ...

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    Full Text Available ... metallic items, which can distort MRI images removable dental work pens, pocket knives and eyeglasses body piercings In most cases, an MRI exam is safe for patients with metal implants, except for a few types. ...

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    Full Text Available ... and may add approximately 15 minutes to the total exam time. top of page What will my ... are the limitations of Children’s (Pediatric) MRI? High-quality images are assured only if your child is ...

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    Full Text Available ... and may add approximately 15 minutes to the total exam time. top of page What will I ... the limitations of MRI of the Head? High-quality images are assured only if you are able ...

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    Full Text Available ... distort images of the facial area or brain, so you should let the radiologist know about them. ... MRI units, called short-bore systems , are designed so that the magnet does not completely surround you. ...

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    Full Text Available ... distort images of the facial area or brain, so the radiologist should be aware of them. Parents ... MRI units, called short-bore systems , are designed so that the magnet does not completely surround you. ...

  19. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... wide range of conditions in children due to injury, illness or congenital abnormalities. When imaging of a child’s brain and spinal cord is needed, MRI is useful because of ...

  20. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... to a CD or uploaded to a digital cloud server. Currently, MRI is the most sensitive imaging ... community, you can search the ACR-accredited facilities database . This website does not provide cost information. The ...

  1. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... experience some bruising. There is also a very small chance of skin irritation at the site of ... a result, must be kept away from the area to be imaged. Furthermore, the examination takes longer ...

  2. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... experience some bruising. There is also a very small chance of irritation of your skin at the ... a result, must be kept away from the area to be imaged. Furthermore, the examination takes longer ...

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    Full Text Available ... items, which can distort MRI images removable dental work pens, pocket knives and eyeglasses body piercings In most cases, an MRI exam is safe for patients with metal implants, except for a ...

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    Full Text Available ... items, which can distort MRI images removable dental work pens, pocket knives and eyeglasses body piercings In most cases, an MRI exam is safe for patients with metal implants, except for a ...

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    Full Text Available ... to cancer treatment MRI is often the best choice for imaging the joints and bones, where it ... regular daily routine and have him/her take food and medications as usual. Some MRI examinations may ...

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    Full Text Available ... processes the imaging information is located in a separate room from the scanner. top of page How does the procedure work? Unlike conventional x-ray examinations and computed tomography ( ...

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    Full Text Available ... processes the imaging information is located in a separate room from the scanner. top of page How does the procedure work? Unlike conventional x-ray examinations and computed tomography ( ...

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    Full Text Available ... images can then be examined on a computer monitor, transmitted electronically, printed or copied to a CD ... excessive sedation. However, the technologist or nurse will monitor your vital signs to minimize this risk. Although ...

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    Full Text Available ... an MRI scan, but this is rare. Tooth fillings and braces usually are not affected by the magnetic field, but they may distort images of the facial area or brain, so you should let the ...

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    Full Text Available ... lodged in the eyes are particularly important. Tooth fillings and braces usually are not affected by the magnetic field, but they may distort images of the facial area or brain, so the radiologist should be ...

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    Full Text Available ... metallic items, which can distort MRI images removable dental work pens, pocket knives and eyeglasses body piercings ... tomography (CT) scans, MRI does not utilize ionizing radiation. Instead, radiofrequency pulses re-align hydrogen atoms that ...

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    Full Text Available ... metallic items, which can distort MRI images removable dental work pens, pocket knives and eyeglasses body piercings ... tomography (CT) scans, MRI does not utilize ionizing radiation. Instead, radiofrequency pulses re-align hydrogen atoms that ...

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    Full Text Available ... eating and drinking before your exam vary between facilities. Unless you are told otherwise, take your regular ... with the specific exam and with the imaging facility. Unless you are told otherwise, you may follow ...

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    Full Text Available ... a CD or uploaded to a digital cloud server. Currently, MRI is the most sensitive imaging test ... understanding of the possible charges you will incur. Web page review process: This Web page is reviewed ...

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    Full Text Available ... room. In addition to affecting the MRI images, these objects can become projectiles within the MRI scanner ... may cause you and/or others nearby harm. These items include: jewelry, watches, credit cards and hearing ...

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    Full Text Available ... examinations may require your child to receive an injection of contrast material into the bloodstream. The radiologist , ... images will be taken during or following the injection. When the examination is complete, you and your ...

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    Full Text Available ... be asked to maintain his/her position without movement as much as possible. In some cases, intravenous ... makes it difficult to obtain clear images. Patient movement can have the same effect. A very irregular ...

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    Full Text Available ... the exam room. These items include: jewelry, watches, credit cards and hearing aids, all of which can ... top of page What are the benefits vs. risks? Benefits MRI is a noninvasive imaging technique that ...

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    Full Text Available ... others nearby harm. These items include: jewelry, watches, credit cards and hearing aids, all of which can ... top of page What are the benefits vs. risks? Benefits MRI is a noninvasive imaging technique that ...

  1. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... This website does not provide cost information. The costs for specific medical imaging tests, treatments and procedures may vary by geographic region. Discuss the fees associated with your prescribed procedure with your doctor, ...

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    Full Text Available ... This website does not provide cost information. The costs for specific medical imaging tests, treatments and procedures may vary by geographic region. Discuss the fees associated with your prescribed procedure with your doctor, ...

  3. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... to produce detailed pictures of organs, soft tissues, bone and virtually all other internal body structures. MRI ... discovery of abnormalities that might be obscured by bone with other imaging methods. The contrast material used ...

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    Full Text Available ... To locate a medical imaging or radiation oncology provider in your community, you can search the ACR- ... the medical facility staff and/or your insurance provider to get a better understanding of the possible ...

  5. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... contrast for an MRI. If you have a history of kidney disease or liver transplant, it will ... claustrophobia. Newer open MRI units provide very high quality images for many types of exams. Older open ...

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    Full Text Available ... radiation oncology provider in your community, you can search the ACR-accredited facilities database . This website does not provide cost information. The costs for specific medical imaging tests, treatments ...

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    Full Text Available ... with metal implants, except for a few types. People with the following implants cannot be scanned and ... it difficult to lie still during imaging. A person who is very large may not fit into ...

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    Full Text Available ... with metal implants, except for a few types. People with the following implants cannot be scanned and ... it difficult to lie still during imaging. A person who is very large may not fit into ...

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    Full Text Available ... is done because a potential abnormality needs further evaluation with additional views or a special imaging technique. ... MRI an invaluable tool in early diagnosis and evaluation of many conditions, including tumors. MRI enables the ...

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    Full Text Available ... or headphones during the exam. MRI scanners are air-conditioned and well-lit. Music may be played ... the limitations of MRI of the Head? High-quality images are assured only if you are able ...

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    Full Text Available ... type your comment or suggestion into the following text box: Comment: E-mail: Area code: Phone no: Thank ... View full size with caption Pediatric Content Some imaging tests ...

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    Full Text Available ... wide range of conditions in children due to injury, illness or congenital abnormalities. When imaging of a child’s brain and spinal cord is needed, MRI is useful because of its ...

  1. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... confused or in severe pain, he/she may find it difficult to lie still during imaging. A ... 25, 2016 Send us your feedback Did you find the information you were looking for? Yes No ...

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    Full Text Available ... determine the presence of certain diseases. The images can then be examined on a computer monitor, transmitted ... of abrupt onset or long-standing symptoms. It can help diagnose conditions such as: brain tumors stroke ...

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    Full Text Available ... for scanning patients since the 1980s with no reports of any ill effects on pregnant women or ... will analyze the images and send a signed report to your primary care or referring physician, who ...

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    Full Text Available ... used in tattoos may contain iron and could heat up during an MRI scan, but this is ... called MR angiography (MRA) provides detailed images of blood vessels in the brain—often without the need ...

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    Full Text Available ... you should let the radiologist know about them. Parents or family members who accompany patients into the ... part of the body being imaged, send and receive radio waves, producing signals that are detected by ...

  7. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... On very rare occasions, a few patients experience side effects from the contrast material, including nausea, headache and ... structures of the brain and can also provide functional information (fMRI) in selected cases. MR images of ...

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    Full Text Available ... practice. top of page What are some common uses of the procedure? MR imaging of the head ... for immediate assistance. Manufacturers of intravenous contrast indicate mothers should not breastfeed their babies for 24-48 ...

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    Full Text Available ... the area of your body being imaged to feel slightly warm, but if it bothers you, notify ... are being recorded because you will hear and feel loud tapping or thumping sounds when the coils ...

  10. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... area of your child’s body being imaged to feel slightly warm, but if your child is bothered ... being recorded because he/she will hear and feel loud tapping or thumping sounds when the coils ...

  11. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... scanner. top of page How does the procedure work? Unlike conventional x-ray examinations and computed tomography ( ... is because traction devices and many types of life support equipment may distort the MR images and ...

  12. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... scanner. top of page How does the procedure work? Unlike conventional x-ray examinations and computed tomography ( ... is because traction devices and many types of life support equipment may distort the MR images and ...

  13. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available Toggle navigation Test/Treatment Patient Type Screening/Wellness Disease/Condition Safety En Español More Info Images/Videos About Us News Physician Resources Professions Site Index ...

  14. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available Toggle navigation Test/Treatment Patient Type Screening/Wellness Disease/Condition Safety En Español More Info Images/Videos About Us News Physician Resources Professions Site Index ...

  15. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... images and send a signed report to your primary care or referring physician, who will share the ... Society of Urogenital Radiology note that the available data suggest that it is safe to continue breastfeeding ...

  16. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... a computer to produce detailed pictures of the brain and other cranial structures that are clearer and ... sensitive imaging test of the head (particularly the brain) in routine clinical practice. top of page What ...

  17. Children's (Pediatric) Magnetic Resonance Imaging

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    Full Text Available ... treatment for a variety of conditions within the brain, chest, abdomen, pelvis and extremities. Tell your doctor ... or congenital abnormalities. When imaging of a child’s brain and spinal cord is needed, MRI is useful ...

  18. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... Examples include but are not limited to: artificial heart valves implanted drug infusion ports artificial limbs or ... imaging based on the electrical activity of the heart, such as electrocardiography (EKG). MRI generally is not ...

  19. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... practice. top of page What are some common uses of the procedure? MR imaging of the head ... community, you can search the ACR-accredited facilities database . This website does not provide cost information. The ...

  20. Magnetic Resonance Imaging (MRI) -- Head

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    Full Text Available ... MRI) procedure View full size with caption Pediatric Content Some imaging tests and treatments have special pediatric considerations. The teddy bear denotes child-specific content. Related Articles and Media MR Angiography (MRA) Magnetic ...