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Sample records for resonance image segmentation

  1. Neural network segmentation of magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Frederick, Blaise

    1990-07-01

    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. A neural network classifier for image segmentation was implemented 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. The classifier''s performance was evaluated as a function of network size number of network layers and length of training. A single layer neural network performed quite well at

  2. Segmentation of neuroanatomy in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    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. MAP segmentation of magnetic resonance images using mean field annealing

    NASA Astrophysics Data System (ADS)

    Logenthiran, Ambalavaner; Snyder, Wesley E.; Santago, Peter, II; Link, Kerry M.

    1991-06-01

    An algorithm is described which segments magnetic resonance images while removing the noise from the images without blurring or other distortion of edges. The problem of segmentation and noise removal is posed as a restoration of an uncorrupted image, given additive white Gaussian noise and a segmentation cost. The problem is solved using a strategy called Mean Field Annealing. An a priori statistical model of the image, which includes the region classification, is chosen which drives the minimization toward solutions which are locally homogeneous and globally classified. Application of the algorithm to brain and knee images is presented.

  4. Novel technique in the segmentation of magnetic resonance image

    NASA Astrophysics Data System (ADS)

    Chan, Kwok-Leung

    1996-04-01

    In this investigation, automatic image segmentation is carried out on magnetic resonance image (MRI). A novel technique based on the maximum minimum measure is devised. The measure is improved by combining the smoothing and counting processes, and then normalizing the number of maximum and minimum positions over the region of interest (ROI). Two parameters (MM_H and MM_V) are generated and used for the segmentation. The technique is tested on some brain MRIs of a human male from the Visible Human Project of the National Library of Medicine, National Institutes of Health, USA. Preliminary results indicate that the maximum minimum measure can provide effective parameters for human tissue characterization and image segmentation with an added advantage of faster computation.

  5. Segmentation of magnetic resonance image using fractal dimension

    NASA Astrophysics Data System (ADS)

    Yau, Joseph K. K.; Wong, Sau-hoi; Chan, Kwok-Leung

    1997-04-01

    In recent years, much research has been conducted in the three-dimensional visualization of medical image. This requires a good segmentation technique. Many early works use first-order and second-order statistics. First-order statistical parameters can be calculated quickly but their effectiveness is influenced by many factors such as illumination, contrast and random noise of the image. Second-order statistical parameters, such as spatial gray level co-occurrence matrices statistics, take longer time to compute but can extract the textural information. In this investigating, two different parameters, namely the entropy and the fractal dimension, are employed to perform segmentation of the magnetic resonance images of the head of a male cadaver. The entropy is calculated from the spatial gray level co-occurrence matrices. The fractal dimension is calculated by the reticular cell counting method. Several regions of the human head are chosen for analysis. They are the bone, gyrus and lobe. Results show that the parameters are able to segment different types of tissue. The entropy gives very good result but it requires very long computation time and large amount of memory. The performance of the fractal dimension is comparable with the entropy. It is simple to estimate and demands lesser memory space.

  6. Applications of magnetic resonance image segmentation in neurology

    NASA Astrophysics Data System (ADS)

    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. Cerebral magnetic resonance image segmentation using data fusion

    SciTech Connect

    Rajapakse, J.C.; Giedd, J.N.; Krain, A.L.; Hamburger, S.D.; Rapoport, J.L.; DeCarli, C.

    1996-03-01

    A semiautomated method is described for segmenting dual echo MR head scans into gray and white matter and CSF. The method is applied to brain scans of 80 healthy children and adolescents. A probabilistic data fusion equation was used to combine simultaneously acquired T2-weighted and proton density head scans for tissue segmentation. The fusion equation optimizes the probability of a voxel being a particular tissue type, given the corresponding probabilities from both images. The algorithm accounts for the intensity inhomogeneities present in the images by fusion of local regions of the images. The method was validated using a phantom (agarose gel with iron oxide particles) and hand-segmented imager. Gray and white matter volumes for subjects aged 20-30 years were close to those previously published. White matter and CSF volume increased and gray matter volume decreased significantly across ages 4-18 years. White matter, gray matter, and CSF volumes were larger for males than for females. Males and females showed similar change of gray and white matter volumes with age. This simple, reliable, and valid method can be employed in clinical research for quantification of gray and white matter and CSF volumes in MR head scans. Increase in white matter volume may reflect ongoing axonal growth and myelination, and gray matter reductions may reflect synaptic pruning or cell death in the age span of 4-18 years. 41 refs., 5 figs., 3 tabs.

  8. Segmenting nonenhancing brain tumors from normal tissues in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Fletcher-Heath, Lynn M.; Hall, Lawrence O.; Goldgof, Dmitry B.

    1998-06-01

    Tumor segmentation from magnetic resonance (MR) images aids in tumor treatment by tracking the progress of tumor growth and/or shrinkage. In this paper we present an automatic segmentation method which separates non-enhancing brain tumors from healthy tissues in MR images. The MR feature images used for the segmentation consist of three weighted images (T1, T2 and proton density) for each axial slice through the head. An initial segmentation is computed using an unsupervised clustering algorithm. Then, integrated domain knowledge and image processing techniques contribute to the final tumor segmentation. The system was trained on two patient volumes and preliminary testing has shown successful tumor segmentations on four patient volumes.

  9. A confocal laser scanning microscope segmentation method applied to magnetic resonance images.

    PubMed

    Anderson, Jeffrey R; Barrett, Steven F

    2008-01-01

    Segmentation is the process of defining distinct objects in an image. A semi-automatic segmentation method has been developed for biological objects that have been recorded with a confocal laser scanning microscope (CLSM). The CLSM produces a sequence of thinly "sliced" images that represent cross-sectional views of the sample containing the object of interest. The cross-sectional representation, or "seed" is created of the object of interest within a single slice of the image stack. The segmentation method uses this "seed" to segment the same object in the adjacent image slice. The new "seed" is used for the next image slice and so on, until the object of interest is segmented in all images of the data set. The segmentation method is based on the idea that the object of interest does not change significantly from one image slice to the next. The segmented information is then used to create 3D renderings of the object. These renderings can be studied and analyzed on the computer screen. Previous work has demonstrated the usefulness of the algorithm as applied to the CLSM images. This paper explores the application of the segmentation method to a standard sequence of magnet resonance imaging (MRI) images. Typical MRI machines can produce impressive images of the human body. The resulting data set is often a sequence, or "stack" of cross-sectional slice images of a particular region of the body. The goal then, is to use the previously described segmentation method on a standard sequence of MRI images. This process will expose limitations with the segmentation method and areas where further work can be directed. This paper illustrates and discusses some of the differences between the data sets that make the current segmentation method inadequate for segmentation of MRI data set. Some of the differences can be corrected with modification of the segmentation algorithm, but other differences are beyond the capabilities of the segmentation method, and can possibly be

  10. Automated segmentation of in vivo and ex vivo mouse brain magnetic resonance images.

    PubMed

    Scheenstra, Alize E H; van de Ven, Rob C G; van der Weerd, Louise; van den Maagdenberg, Arn M J M; Dijkstra, Jouke; Reiber, Johan H C

    2009-01-01

    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. PMID:19344574

  11. Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research

    PubMed Central

    Prescott, Jeff W.; Gurcan, Metin N.

    2011-01-01

    In this paper, a new, fully automated, content-based system is proposed for knee bone segmentation from magnetic resonance images (MRI). The purpose of the bone segmentation is to support the discovery and characterization of imaging biomarkers for the incidence and progression of osteoarthritis, a debilitating joint disease, which affects a large portion of the aging population. The segmentation algorithm includes a novel content-based, two-pass disjoint block discovery mechanism, which is designed to support automation, segmentation initialization, and post processing. The block discovery is achieved by classifying the image content to bone and background blocks according to their similarity to the categories in the training data collected from typical bone structures. The classified blocks are then used to design an efficient graph-cut based segmentation algorithm. This algorithm requires constructing a graph using image pixel data followed by applying a maximum-flow algorithm which generates a minimum graph-cut that corresponds to an initial image segmentation. Content-based refinements and morphological operations are then applied to obtain the final segmentation. The proposed segmentation technique does not require any user interaction and can distinguish between bone and highly similar adjacent structures, such as fat tissues with high accuracy. The performance of the proposed system is evaluated by testing it on 376 MR images from the Osteoarthritis Initiative (OAI) database. This database included a selection of single images containing the femur and tibia from 200 subjects with varying levels of osteoarthritis severity. Additionally, a full three-dimensional segmentation of the bones from ten subjects with 14 slices each, and synthetic images with background having intensity and spatial characteristics similar to those of bone are used to assess the robustness and consistency of the developed algorithm. The results show an automatic bone detection rate of

  12. Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

    PubMed Central

    Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

    2013-01-01

    Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

  13. Robust kernelized local information fuzzy C-means clustering for brain magnetic resonance image segmentation.

    PubMed

    Elazab, Ahmed; AbdulAzeem, Yousry M; Wu, Shiqian; Hu, Qingmao

    2016-03-17

    Brain tissue segmentation from magnetic resonance (MR) images is an importance task for clinical use. The segmentation process becomes more challenging in the presence of noise, grayscale inhomogeneity, and other image artifacts. In this paper, we propose a robust kernelized local information fuzzy C-means clustering algorithm (RKLIFCM). It incorporates local information into the segmentation process (both grayscale and spatial) for more homogeneous segmentation. In addition, the Gaussian radial basis kernel function is adopted as a distance metric to replace the standard Euclidean distance. The main advantages of the new algorithm are: efficient utilization of local grayscale and spatial information, robustness to noise, ability to preserve image details, free from any parameter initialization, and with high speed as it runs on image histogram. We compared the proposed algorithm with 7 soft clustering algorithms that run on both image histogram and image pixels to segment brain MR images. Experimental results demonstrate that the proposed RKLIFCM algorithm is able to overcome the influence of noise and achieve higher segmentation accuracy with low computational complexity. PMID:27257884

  14. Model-driven, probabilistic level set based segmentation of magnetic resonance images of the brain.

    PubMed

    Verma, Nishant; Muralidhar, Gautam S; Bovik, Alan C; Cowperthwaite, Matthew C; Markey, Mia K

    2011-01-01

    Accurate segmentation of magnetic resonance (MR) images of the brain to differentiate features such as soft tissue, tumor, edema and necrosis is critical for both diagnosis and treatment purposes. Region-based formulations of geometric active contour models are popular choices for segmentation of MR and other medical images. Most of the traditional region-based formulations model local region intensity by assuming a piecewise constant approximation. However, the piecewise constant approximation rarely holds true for medical images such as MR images due to the presence of noise and bias field, which invariably results in a poor segmentation of the image. To overcome this problem, we have developed a probabilistic region-based active contour model for automatic segmentation of MR images of the brain. In our approach, a mixture of Gaussian distributions is used to accurately model the arbitrarily shaped local region intensity distribution. Prior spatial information derived from probabilistic atlases is also integrated into the level set evolution framework for guiding the segmentation process. Our experiments with a series of publicly available brain MR images show that the proposed active contour model gives stable and accurate segmentation results when compared to the traditional region based formulations. PMID:22254928

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

    SciTech Connect

    Korsager, Anne Sofie Østergaard, Lasse Riis; Fortunati, Valerio; Lijn, Fedde van der; Niessen, Wiro; Walsum, Theo van; Carl, Jesper

    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.

  16. Myocardial Infarct Segmentation from Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology

    PubMed Central

    Ukwatta, Eranga; Arevalo, Hermenegild; Li, Kristina; Yuan, Jing; Qiu, Wu; Malamas, Peter; Wu, Katherine C.

    2016-01-01

    Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology. PMID:26731693

  17. Myocardial Infarct Segmentation From Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology.

    PubMed

    Ukwatta, Eranga; Arevalo, Hermenegild; Li, Kristina; Yuan, Jing; Qiu, Wu; Malamas, Peter; Wu, Katherine C; Trayanova, Natalia A; Vadakkumpadan, Fijoy

    2016-06-01

    Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology. PMID:26731693

  18. Segmentation of tongue muscles from super-resolution magnetic resonance images.

    PubMed

    Ibragimov, Bulat; Prince, Jerry L; Murano, Emi Z; Woo, Jonghye; Stone, Maureen; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2015-02-01

    Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and

  19. Segmentation of tongue muscles from super-resolution magnetic resonance images.

    PubMed

    Ibragimov, Bulat; Prince, Jerry L; Murano, Emi Z; Woo, Jonghye; Stone, Maureen; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2015-02-01

    Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and

  20. [Graph-based interactive three-dimensional segmentation of magnetic resonance images of brain tumors].

    PubMed

    Li, Wei; Chen, Wu-fan

    2009-01-01

    We propose a graph-based three-dimensional (3D) algorithm to automatically segment brain tumors from magnetic resonance images (MRI). The algorithm uses minimum s/t cut criteria to obtain a global optimal result of objective function formed according to Markov Random Field Model and Maximum a posteriori (MAP-MRF) theory, and by combining the expectation-maximization (EM) algorithm to estimate the parameters of mixed Gaussian model for normal brain and tumor tissues. 3D segmentation results of brain tumors are fast achieved by our algorithm. The validation of the algorithm was tested and showed good accuracy and adaptation under simple interactions with the physicians. PMID:19218135

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

    PubMed

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

    2009-03-21

    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. PMID:19218737

  2. Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation.

    PubMed

    Roy, Snehashis; Carass, Aaron; Pacheco, Jennifer; Bilgel, Murat; Resnick, Susan M; Prince, Jerry L; Pham, Dzung L

    2016-01-01

    Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4-12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis. PMID:26958465

  3. Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation

    PubMed Central

    Roy, Snehashis; Carass, Aaron; Pacheco, Jennifer; Bilgel, Murat; Resnick, Susan M.; Prince, Jerry L.; Pham, Dzung L.

    2016-01-01

    Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4–12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis. PMID:26958465

  4. Generalized method for partial volume estimation and tissue segmentation in cerebral magnetic resonance images

    PubMed Central

    Khademi, April; Venetsanopoulos, Anastasios; Moody, Alan R.

    2014-01-01

    Abstract. An artifact found in magnetic resonance images (MRI) called partial volume averaging (PVA) has received much attention since accurate segmentation of cerebral anatomy and pathology is impeded by this artifact. Traditional neurological segmentation techniques rely on Gaussian mixture models to handle noise and PVA, or high-dimensional feature sets that exploit redundancy in multispectral datasets. Unfortunately, model-based techniques may not be optimal for images with non-Gaussian noise distributions and/or pathology, and multispectral techniques model probabilities instead of the partial volume (PV) fraction. For robust segmentation, a PV fraction estimation approach is developed for cerebral MRI that does not depend on predetermined intensity distribution models or multispectral scans. Instead, the PV fraction is estimated directly from each image using an adaptively defined global edge map constructed by exploiting a relationship between edge content and PVA. The final PVA map is used to segment anatomy and pathology with subvoxel accuracy. Validation on simulated and real, pathology-free T1 MRI (Gaussian noise), as well as pathological fluid attenuation inversion recovery MRI (non-Gaussian noise), demonstrate that the PV fraction is accurately estimated and the resultant segmentation is robust. Comparison to model-based methods further highlight the benefits of the current approach. PMID:26158022

  5. Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models.

    PubMed

    Piazzese, Concetta; Carminati, M Chiara; Colombo, Andrea; Krause, Rolf; Potse, Mark; Auricchio, Angelo; Weinert, Lynn; Tamborini, Gloria; Pepi, Mauro; Lang, Roberto M; Caiani, Enrico G

    2016-01-01

    We evaluate in this paper different strategies for the construction of a statistical shape model (SSM) of the left ventricle (LV) to be used for segmentation in cardiac magnetic resonance (CMR) images. From a large database of LV surfaces obtained throughout the cardiac cycle from 3D echocardiographic (3DE) LV images, different LV shape models were built by varying the considered phase in the cardiac cycle and the registration procedure employed for surface alignment. Principal component analysis was computed to describe the statistical variability of the SSMs, which were then deformed by applying an active shape model (ASM) approach to segment the LV endocardium in CMR images of 45 patients. Segmentation performance was evaluated by comparing LV volumes derived by ASM segmentation with different SSMs and those obtained by manual tracing, considered as a reference. A high correlation (r(2)>0.92) was found in all cases, with better results when using the SSM models comprising more than one frame of the cardiac cycle. PMID:27046100

  6. Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

    PubMed Central

    Chen, Yunjie; Zhan, Tianming; Zhang, Ji

    2016-01-01

    We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms. PMID:27648448

  7. Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images.

    PubMed

    Chen, Yunjie; Zhan, Tianming; Zhang, Ji; Wang, Hongyuan

    2016-01-01

    We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms. PMID:27648448

  8. Atlas-based segmentation of brainstem regions in neuromelanin-sensitive magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Puigvert, Marc; Castellanos, Gabriel; Uranga, Javier; Abad, Ricardo; Fernández-Seara, María. A.; Pastor, Pau; Pastor, María. A.; Muñoz-Barrutia, Arrate; Ortiz de Solórzano, Carlos

    2015-03-01

    We present a method for the automatic delineation of two neuromelanin rich brainstem structures -substantia nigra pars compacta (SN) and locus coeruleus (LC)- in neuromelanin sensitive magnetic resonance images of the brain. The segmentation method uses a dynamic multi-image reference atlas and a pre-registration atlas selection strategy. To create the atlas, a pool of 35 images of healthy subjects was pair-wise pre-registered and clustered in groups using an affinity propagation approach. Each group of the atlas is represented by a single exemplar image. Each new target image to be segmented is registered to the exemplars of each cluster. Then all the images of the highest performing clusters are enrolled into the final atlas, and the results of the registration with the target image are propagated using a majority voting approach. All registration processes used combined one two-stage affine and one elastic B-spline algorithm, to account for global positioning, region selection and local anatomic differences. In this paper, we present the algorithm, with emphasis in the atlas selection method and the registration scheme. We evaluate the performance of the atlas selection strategy using 35 healthy subjects and 5 Parkinson's disease patients. Then, we quantified the volume and contrast ratio of neuromelanin signal of these structures in 47 normal subjects and 40 Parkinson's disease patients to confirm that this method can detect neuromelanin-containing neurons loss in Parkinson's disease patients and could eventually be used for the early detection of SN and LC damage.

  9. Three-dimensional left ventricular segmentation from magnetic resonance imaging for patient-specific modelling purposes

    PubMed Central

    Caiani, Enrico G.; Colombo, Andrea; Pepi, Mauro; Piazzese, Concetta; Maffessanti, Francesco; Lang, Roberto M.; Carminati, Maria Chiara

    2014-01-01

    Aims To propose a nearly automated left ventricular (LV) three-dimensional (3D) surface segmentation procedure, based on active shape modelling (ASM) and built on a database of 3D echocardiographic (3DE) LV surfaces, for cardiac magnetic resonance (CMR) images, and to test its accuracy for LV volumes computation compared with ‘gold standard’ manual tracings and discs-summation method. Methods and results The ASM was created based on segmented LV surfaces (4D LV analysis, Tomtec) from 3DE datasets of 205 patients. Then, it was applied to the cardiac magnetic resonance imaging short-axis (SAX) images stack of 12 consecutive patients. After proper realignment using two- and four-chambers CMR long-axis views both as reference and for initializing LV apex and base (six points in total), the ASM was iteratively and automatically updated to match the information of all the SAX planes contemporaneously, resulting in an endocardial LV 3D mesh from which volume was directly derived. The same CMR images were analysed by an experienced cardiologist to derive end-diastolic and end-systolic volumes. Linear correlation and Bland–Altman analyses were applied vs. the manual ‘gold standard’. Active shape modelling results showed high correlations with manual values both for LV volumes (r2 > 0.98) and ejection fraction (EF) (r2 > 0.90), non-significant biases and narrow limits of agreement. Conclusion The proposed method resulted in accurate detection of 3D LV endocardial surfaces, which lead to fast and reliable measurements of LV volumes and EF when compared with manual tracing of CMR SAX images. The segmented 3D mesh, including a realistic LV apex and base, could constitute a novel starting point for more realistic patient-specific finite element modelling. PMID:25362176

  10. Segmentation and visualization of brain lesions in multispectral magnetic resonance images.

    PubMed

    Holden, M; Steen, E; Lundervold, A

    1995-01-01

    In this study we focus on the problem of segmentation and visualization of soft tissue structures in three-dimensional (3D) magnetic resonance (MR) imaging. We introduce a classification method which is a combination of a recently proposed contour detection algorithm and Haslett's contextual classification method extended to 3D. This classification method is used in the classification step of a rendering model suggested by Drebin et al. for visualizing normal and pathological tissue structures in the brain. We evaluate the combination of these two methodologies, and identify some problems which have to be solved in order to develop a clinical useful tool. PMID:7780944

  11. Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee.

    PubMed

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

    2010-01-01

    In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm.

  12. Automated segmentation of the corpus callosum in midsagittal brain magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Lee, Chulhee; Huh, Shin; Ketter, Terence A.; Unser, Michael A.

    2000-04-01

    We propose a new algorithm to find the corpus callosum automatically from midsagittal brain MR (magnetic resonance) images using the statistical characteristics and shape information of the corpus callosum. We first extract regions satisfying the statistical characteristics (gray level distributions) of the corpus callosum that have relatively high intensity values. Then we try to find a region matching the shape information of the corpus callosum. In order to match the shape information, we propose a new directed window region growing algorithm instead of using conventional contour matching. An innovative feature of the algorithm is that we adaptively relax the statistical requirement until we find a region matching the shape information. After the initial segmentation, a directed border path pruning algorithm is proposed in order to remove some undesired artifacts, especially on the top of the corpus callosum. The proposed algorithm was applied to over 120 images and provided promising results.

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

    PubMed Central

    A., Javadpour; A., Mohammadi

    2016-01-01

    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. PMID:27672629

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

    PubMed Central

    A., Javadpour; A., Mohammadi

    2016-01-01

    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.

  15. A new fuzzy C-means method for magnetic resonance image brain segmentation

    NASA Astrophysics Data System (ADS)

    Altameem, Torki; Zanaty, E. A.; Tolba, Amr

    2015-10-01

    In this paper, we introduce a new fuzzy c-means (FCM) method in order to improve the magnetic resonance images' (MRIs) segmentation. The proposed method combines the FCM and possiblistic c-means (PCM) functions using a weighted Gaussian function. The weighted Gaussian function is given to indicate the spatial influence of the neighbouring pixels on the central pixel. The parameters of weighting coefficients are automatically determined in the implementation using the Gaussian function for every pixel in the image. The proposed method is realised by modifying the objective function of the PCM algorithm to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centres for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, that is, the compatibilities of the points with the class prototypes to overcome the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments using MRIs and comparison with other state-of-the-art algorithms. In the proposed method, the effect of noise is controlled by incorporating the possibility (typicality) function in addition to the membership function. Consideration of these constraints can greatly control the noise in the image as shown in our experiments.

  16. Fuzzy neural-network-based segmentation of multispectral magnetic-resonance brain images

    NASA Astrophysics Data System (ADS)

    Blonda, Palma N.; Bennardo, A.; Satalino, Giuseppe; Pasquariello, Guido; De Blasi, Roberto A.; Milella, D.

    1996-06-01

    This study investigates the applicability of a multimodular neuro-fuzzy system in the multispectral analysis of magnetic resonance (MR) images of the human brain. The system consists of two components: an unsupervised neural module for image segmentation in tissue regions and a supervised module for tissue labeling. The former is the fuzzy Kohonen clustering network (FKCN). The latter is a feed-forward network based on the back-propagation learning rule. The results obtained with the FKCN have been compared with those extracted by a self organizing map (SOM). The system has been used to analyze the multispectral MR brain images of a healthy volunteer. The data set included the proton density (PD), T2, T1 weighted spin-echo (SE) bands and a new T1- weighted three dimensional sequence, i.e. the magnetization- prepared rapid gradient echo (MP-RAGE). One of the main objectives of this study has been to evaluate the usefulness of brain imaging with the MP-RAGE sequence in view of automatic tissue classification. To this purpose, a quantitative evaluation has been provided on the base of some labeled areas selected interactively by a neuro- radiologist from the input raw images. Quantitative results seem to indicate that the MP-RAGE sequence may provide higher tissue separability than the T1-weighted SE sequence.

  17. Identification of breast contour for nipple segmentation in breast magnetic resonance images

    SciTech Connect

    Gwo, Chih-Ying; Gwo, Allen; Wei, Chia-Hung; Huang, Pai Jung

    2014-02-15

    Purpose: The purpose of this study is to develop a method to simulate the breast contour and segment the nipple in breast magnetic resonance images. Methods: This study first identifies the chest wall and removes the chest part from the breast MR images. Subsequently, the cleavage and its motion artifacts are removed, distinguishing the separate breasts, where the edge points are sampled for curve fitting. Next, a region growing method is applied to find the potential nipple region. Finally, the potential nipple region above the simulated curve can be removed in order to retain the original smooth contour. Results: The simulation methods can achieve the least root mean square error (RMSE) for certain cases. The proposed YBnd and (Dmin+Dmax)/2 methods are significant due toP = 0.000. The breast contour curve detected by the two proposed methods is closer than that determined by the edge detection method. The (Dmin+Dmax)/2 method can achieve the lowest RMSE of 1.1029 on average, while the edge detection method results in the highest RMSE of 6.5655. This is only slighter better than the comparison methods, which implies that the performance of these methods depends upon the conditions of the cases themselves. Under this method, the maximal Dice coefficient is 0.881, and the centroid difference is 0.36 pixels. Conclusions: The contributions of this study are twofold. First, a method was proposed to identify and segment the nipple in breast MR images. Second, a curve-fitting method was used to simulate the breast contour, allowing the breast to retain its original smooth shape.

  18. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging.

    PubMed

    Peng, Peng; Lekadir, Karim; Gooya, Ali; Shao, Ling; Petersen, Steffen E; Frangi, Alejandro F

    2016-04-01

    Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.

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

    PubMed

    Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao

    2015-01-01

    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. PMID:26793269

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

    PubMed

    Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao

    2015-01-01

    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.

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

    NASA Astrophysics Data System (ADS)

    Shih, Tzu-Ching; Chen, Jeon-Hor; Liu, Dongxu; Nie, Ke; Sun, Lizhi; Lin, Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying

    2010-07-01

    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® 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® 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 different compression

  2. Automatic Segmentation of Pelvic Structures From Magnetic Resonance Images for Prostate Cancer Radiotherapy

    SciTech Connect

    Pasquier, David . E-mail: d-pasquier@o-lambret.fr; Lacornerie, Thomas; Vermandel, Maximilien; Rousseau, Jean; Lartigau, Eric; Betrouni, Nacim

    2007-06-01

    Purpose: Target-volume and organ-at-risk delineation is a time-consuming task in radiotherapy planning. The development of automated segmentation tools remains problematic, because of pelvic organ shape variability. We evaluate a three-dimensional (3D), deformable-model approach and a seeded region-growing algorithm for automatic delineation of the prostate and organs-at-risk on magnetic resonance images. Methods and Materials: Manual and automatic delineation were compared in 24 patients using a sagittal T2-weighted (T2-w) turbo spin echo (TSE) sequence and an axial T1-weighted (T1-w) 3D fast-field echo (FFE) or TSE sequence. For automatic prostate delineation, an organ model-based method was used. Prostates without seminal vesicles were delineated as the clinical target volume (CTV). For automatic bladder and rectum delineation, a seeded region-growing method was used. Manual contouring was considered the reference method. The following parameters were measured: volume ratio (Vr) (automatic/manual), volume overlap (Vo) (ratio of the volume of intersection to the volume of union; optimal value = 1), and correctly delineated volume (Vc) (percent ratio of the volume of intersection to the manually defined volume; optimal value 100). Results: For the CTV, the Vr, Vo, and Vc were 1.13 ({+-}0.1 SD), 0.78 ({+-}0.05 SD), and 94.75 ({+-}3.3 SD), respectively. For the rectum, the Vr, Vo, and Vc were 0.97 ({+-}0.1 SD), 0.78 ({+-}0.06 SD), and 86.52 ({+-}5 SD), respectively. For the bladder, the Vr, Vo, and Vc were 0.95 ({+-}0.03 SD), 0.88 ({+-}0.03 SD), and 91.29 ({+-}3.1 SD), respectively. Conclusions: Our results show that the organ-model method is robust, and results in reproducible prostate segmentation with minor interactive corrections. For automatic bladder and rectum delineation, magnetic resonance imaging soft-tissue contrast enables the use of region-growing methods.

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

    PubMed

    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

  4. Patellar segmentation from 3D magnetic resonance images using guided recursive ray-tracing for edge pattern detection

    NASA Astrophysics Data System (ADS)

    Cheng, Ruida; Jackson, Jennifer N.; McCreedy, Evan S.; Gandler, William; Eijkenboom, J. J. F. A.; van Middelkoop, M.; McAuliffe, Matthew J.; Sheehan, Frances T.

    2016-03-01

    The paper presents an automatic segmentation methodology for the patellar bone, based on 3D gradient recalled echo and gradient recalled echo with fat suppression magnetic resonance images. Constricted search space outlines are incorporated into recursive ray-tracing to segment the outer cortical bone. A statistical analysis based on the dependence of information in adjacent slices is used to limit the search in each image to between an outer and inner search region. A section based recursive ray-tracing mechanism is used to skip inner noise regions and detect the edge boundary. The proposed method achieves higher segmentation accuracy (0.23mm) than the current state-of-the-art methods with the average dice similarity coefficient of 96.0% (SD 1.3%) agreement between the auto-segmentation and ground truth surfaces.

  5. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

    NASA Technical Reports Server (NTRS)

    Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.

    1992-01-01

    Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

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

    NASA Astrophysics Data System (ADS)

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K.; Ourselin, Sébastien

    2007-03-01

    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.

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

    PubMed

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

    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.

  8. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Castro-Mateos, Isaac; Pozo, Jose M.; Eltes, Peter E.; Del Rio, Luis; Lazary, Aron; Frangi, Alejandro F.

    2014-12-01

    Computational medicine aims at employing personalised computational models in diagnosis and treatment planning. The use of such models to help physicians in finding the best treatment for low back pain (LBP) is becoming popular. One of the challenges of creating such models is to derive patient-specific anatomical and tissue models of the lumbar intervertebral discs (IVDs), as a prior step. This article presents a segmentation scheme that obtains accurate results irrespective of the degree of IVD degeneration, including pathological discs with protrusion or herniation. The segmentation algorithm, employing a novel feature selector, iteratively deforms an initial shape, which is projected into a statistical shape model space at first and then, into a B-Spline space to improve accuracy. The method was tested on a MR dataset of 59 patients suffering from LBP. The images follow a standard T2-weighted protocol in coronal and sagittal acquisitions. These two image volumes were fused in order to overcome large inter-slice spacing. The agreement between expert-delineated structures, used here as gold-standard, and our automatic segmentation was evaluated using Dice Similarity Index and surface-to-surface distances, obtaining a mean error of 0.68 mm in the annulus segmentation and 1.88 mm in the nucleus, which are the best results with respect to the image resolution in the current literature.

  9. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images.

    PubMed

    Castro-Mateos, Isaac; Pozo, Jose M; Eltes, Peter E; Rio, Luis Del; Lazary, Aron; Frangi, Alejandro F

    2014-12-21

    Computational medicine aims at employing personalised computational models in diagnosis and treatment planning. The use of such models to help physicians in finding the best treatment for low back pain (LBP) is becoming popular. One of the challenges of creating such models is to derive patient-specific anatomical and tissue models of the lumbar intervertebral discs (IVDs), as a prior step. This article presents a segmentation scheme that obtains accurate results irrespective of the degree of IVD degeneration, including pathological discs with protrusion or herniation. The segmentation algorithm, employing a novel feature selector, iteratively deforms an initial shape, which is projected into a statistical shape model space at first and then, into a B-Spline space to improve accuracy.The method was tested on a MR dataset of 59 patients suffering from LBP. The images follow a standard T2-weighted protocol in coronal and sagittal acquisitions. These two image volumes were fused in order to overcome large inter-slice spacing. The agreement between expert-delineated structures, used here as gold-standard, and our automatic segmentation was evaluated using Dice Similarity Index and surface-to-surface distances, obtaining a mean error of 0.68 mm in the annulus segmentation and 1.88 mm in the nucleus, which are the best results with respect to the image resolution in the current literature.

  10. Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Johnston, Brian; Atkins, M. Stella; Booth, Kellogg S.

    1994-05-01

    An important first step in diagnosis and treatment planning using tomographic imaging is differentiating and quantifying diseased as well as healthy tissue. One of the difficulties encountered in solving this problem to date has been distinguishing the partial volume constituents of each voxel in the image volume. Most proposed solutions to this problem involve analysis of planar images, in sequence, in two dimensions only. We have extended a model-based method of image segmentation which applies the technique of iterated conditional modes in three dimensions. A minimum of user intervention is required to train the algorithm. Partial volume estimates for each voxel in the image are obtained yielding fractional compositions of multiple tissue types for individual voxels. A multispectral approach is applied, where spatially registered data sets are available. The algorithm is simple and has been parallelized using a dataflow programming environment to reduce the computational burden. The algorithm has been used to segment dual echo MRI data sets of multiple sclerosis patients using lesions, gray matter, white matter, and cerebrospinal fluid as the partial volume constituents. The results of the application of the algorithm to these datasets is presented and compared to the manual lesion segmentation of the same data.

  11. Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel, Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data

    PubMed Central

    Cadotte, Adam; Cadotte, David W.; Livne, Micha; Cohen-Adad, Julien; Fleet, David; Mikulis, David; Fehlings, Michael G.

    2015-01-01

    Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects. PMID:26445367

  12. Diffusion maps clustering for magnetic resonance q-ball imaging segmentation.

    PubMed

    Wassermann, Demian; Descoteaux, Maxime; Deriche, Rachid

    2008-01-01

    White matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a diffusion maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles. It is well known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent high-angular resolution diffusion imaging (HARDI) such as Q-Ball imaging (QBI) has been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations. In this paper, we use a spherical harmonic ODF representation as input to the diffusion maps clustering method. We first show the advantage of using diffusion maps clustering over classical methods such as N-Cuts and Laplacian eigenmaps. In particular, our ODF diffusion maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation, and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptive scale-space parameter. We also show that our ODF diffusion maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters. Finally, we show results on a real-brain dataset where we segment well-known fiber bundles. PMID:18317506

  13. Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Yang, Zhengyi; Fripp, Jurgen; Chandra, Shekhar S.; Neubert, Aleš; Xia, Ying; Strudwick, Mark; Paproki, Anthony; Engstrom, Craig; Crozier, Stuart

    2015-02-01

    We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926  ±  0.050 and 0.837  ±  0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806  ±  0.133 for the humerus and 0.795  ±  0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.

  14. Segmentation-based method incorporating fractional volume analysis for quantification of brain atrophy on magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Wang, Deming; Doddrell, David M.

    2001-07-01

    Partial volume effect is a major problem in brain tissue segmentation on digital images such as magnetic resonance (MR) images. In this paper, special attention has been paid to partial volume effect when developing a method for quantifying brain atrophy. Specifically, partial volume effect is minimized in the process of parameter estimation prior to segmentation by identifying and excluding those voxels with possible partial volume effect. A quantitative measure for partial volume effect was also introduced through developing a model that calculates fractional volumes for voxels with mixtures of two different tissues. For quantifying cerebrospinal fluid (CSF) volumes, fractional volumes are calculated for two classes of mixture involving gray matter and CSF, and white matter and CSF. Tissue segmentation is carried out using 1D and 2D thresholding techniques after images are intensity- corrected. Threshold values are estimated using the minimum error method. Morphological processing and region identification analysis are used extensively in the algorithm. As an application, the method was employed for evaluating rates of brain atrophy based on serially acquired structural brain MR images. Consistent and accurate rates of brain atrophy have been obtained for patients with Alzheimer's disease as well as for elderly subjects due to normal aging process.

  15. Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis

    NASA Astrophysics Data System (ADS)

    Shahedi, Maysam; Fenster, Aaron; Cool, Derek W.; Romagnoli, Cesare; Ward, Aaron D.

    2013-03-01

    3D segmentation of the prostate in medical images is useful to prostate cancer diagnosis and therapy guidance, but is time-consuming to perform manually. Clinical translation of computer-assisted segmentation algorithms for this purpose requires a comprehensive and complementary set of evaluation metrics that are informative to the clinical end user. We have developed an interactive 3D prostate segmentation method for 1.5T and 3.0T T2-weighted magnetic resonance imaging (T2W MRI) acquired using an endorectal coil. We evaluated our method against manual segmentations of 36 3D images using complementary boundary-based (mean absolute distance; MAD), regional overlap (Dice similarity coefficient; DSC) and volume difference (ΔV) metrics. Our technique is based on inter-subject prostate shape and local boundary appearance similarity. In the training phase, we calculated a point distribution model (PDM) and a set of local mean intensity patches centered on the prostate border to capture shape and appearance variability. To segment an unseen image, we defined a set of rays - one corresponding to each of the mean intensity patches computed in training - emanating from the prostate centre. We used a radial-based search strategy and translated each mean intensity patch along its corresponding ray, selecting as a candidate the boundary point with the highest normalized cross correlation along each ray. These boundary points were then regularized using the PDM. For the whole gland, we measured a mean+/-std MAD of 2.5+/-0.7 mm, DSC of 80+/-4%, and ΔV of 1.1+/-8.8 cc. We also provided an anatomic breakdown of these metrics within the prostatic base, mid-gland, and apex.

  16. Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images.

    PubMed

    Moldovanu, Simona; Moraru, Luminița; Biswas, Anjan

    2015-12-01

    This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu's, Sauvola's, Niblack's, and Bernsen's binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.

  17. Automatic plaque characterization and vessel wall segmentation in magnetic resonance images of atherosclerotic carotid arteries

    NASA Astrophysics Data System (ADS)

    Adame, Isabel M.; van der Geest, Rob J.; Wasserman, Bruce A.; Mohamed, Mona; Reiber, Johan H. C.; Lelieveldt, Boudewijn P. F.

    2004-05-01

    Composition and structure of atherosclerotic plaque is a primary focus of cardiovascular research. In vivo MRI provides a meanse to non-invasively image and assess the morphological features of athersclerotic and normal human carotid arteries. To quantitatively assess the vulnerability and the type of plaque, the contours of the lumen, outer boundary of the vessel wall and plaque components, need to be traced. To achieve this goal, we have developed an automated contou detection technique, which consists of three consecutive steps: firstly, the outer boundary of the vessel wall is detected by means of an ellipse-fitting procedure in order to obtain smoothed shapes; secondly, the lumen is segnented using fuzzy clustering. Thre region to be classified is that within the outer vessel wall boundary obtained from the previous step; finally, for plaque detection we follow the same approach as for lumen segmentation: fuzzy clustering. However, plaque is more difficult to segment, as the pixel gray value can differ considerably from one region to another, even when it corresponds to the same type of tissue. That makes further processing necessary. All these three steps might be carried out combining information from different sequences (PD-, T2-, T1-weighted images, pre- and post-contrast), to improve the contour detection. The algorithm has been validated in vivo on 58 high-resolution PD and T1 weighted MR images (19 patients). The results demonstrate excellent correspondence between automatic and manual area measurements: lumen (r=0.94), outer (r=0.92), and acceptable for fibrous cap thickness (r=0.76).

  18. A novel segmentation method to identify left ventricular infarction in short-axis composite strain-encoded magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Algohary, Ahmad O.; Metwally, Muhammad K.; El-Bialy, Ahmed M.; Kandil, Ahmed H.; Osman, Nael F.

    2011-03-01

    Composite Strain Encoding (CSENC) is a new Magnetic Resonance Imaging (MRI) technique for simultaneously acquiring cardiac functional and viability images. It combines the use of Delayed Enhancement (DE) and the Strain Encoding (SENC) imaging techniques to identify the infracted (dead) tissue and to image the myocardial deformation inside the heart muscle. In this work, a new unsupervised segmentation method is proposed to identify infarcted left ventricular tissue in the images provided by CSENC MRI. The proposed method is based on the sequential application of Bayesian classifier, Otsu's thresholding, morphological opening, radial sweep boundary tracing and the fuzzy C-means (FCM) clustering algorithm. This method is tested on images of twelve patients with and without myocardial infarction (MI) and on simulated heart images with various levels of superimposed noise. The resulting clustered images are compared with those marked up by an expert cardiologist who assisted in validating results coming from the proposed method. Infarcted myocardium is correctly identified using the proposed method with high levels of accuracy and precision.

  19. Computational Simulation of Breast Compression Based on Segmented Breast and Fibroglandular Tissues on Magnetic Resonance Images

    PubMed Central

    Shih, Tzu-Ching; Chen, Jeon-Hor; Liu, Dongxu; Nie, Ke; Sun, Lizhi; Lin, Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying

    2010-01-01

    This study presents a finite element based computational model to simulate the three-dimensional deformation of the breast and the fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and the craniocaudal and mediolateral oblique compression as used in mammography was applied. The geometry of whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo® 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the non-linear elastic tissue deformation under compression, using the MSC.Marc® software package. The model was tested in 4 cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these 4 cases at 60% compression ratio was in the range of 5-7 cm, which is the typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at 60% compression ratio was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on 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 measurements 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 different compression conditions. PMID:20601773

  20. Development and Evaluation of an Algorithm for the Computer-Assisted Segmentation of the Human Hypothalamus on 7-Tesla Magnetic Resonance Images

    PubMed Central

    Schmidt, Laura; Anwander, Alfred; Strauß, Maria; Trampel, Robert; Bazin, Pierre-Louis; Möller, Harald E.; Hegerl, Ulrich; Turner, Robert; Geyer, Stefan

    2013-01-01

    Post mortem studies have shown volume changes of the hypothalamus in psychiatric patients. With 7T magnetic resonance imaging this effect can now be investigated in vivo in detail. To benefit from the sub-millimeter resolution requires an improved segmentation procedure. The traditional anatomical landmarks of the hypothalamus were refined using 7T T1-weighted magnetic resonance images. A detailed segmentation algorithm (unilateral hypothalamus) was developed for colour-coded, histogram-matched images, and evaluated in a sample of 10 subjects. Test-retest and inter-rater reliabilities were estimated in terms of intraclass-correlation coefficients (ICC) and Dice's coefficient (DC). The computer-assisted segmentation algorithm ensured test-retest reliabilities of ICC≥.97 (DC≥96.8) and inter-rater reliabilities of ICC≥.94 (DC = 95.2). There were no significant volume differences between the segmentation runs, raters, and hemispheres. The estimated volumes of the hypothalamus lie within the range of previous histological and neuroimaging results. We present a computer-assisted algorithm for the manual segmentation of the human hypothalamus using T1-weighted 7T magnetic resonance imaging. Providing very high test-retest and inter-rater reliabilities, it outperforms former procedures established at 1.5T and 3T magnetic resonance images and thus can serve as a gold standard for future automated procedures. PMID:23935821

  1. High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging.

    PubMed

    Winterburn, Julie; Pruessner, Jens C; Sofia, Chavez; Schira, Mark M; Lobaugh, Nancy J; Voineskos, Aristotle N; Chakravarty, M Mallar

    2015-01-01

    The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has been heavily studied. While many imaging studies treat the hippocampus as a single unitary neuroanatomical structure, it is, in fact, composed of several subfields that have a complex three-dimensional geometry. As such, it is known that these subfields perform specialized functions and are differentially affected through the course of different disease states. Magnetic resonance (MR) imaging can be used as a powerful tool to interrogate the morphology of the hippocampus and its subfields. Many groups use advanced imaging software and hardware (>3T) to image the subfields; however this type of technology may not be readily available in most research and clinical imaging centers. To address this need, this manuscript provides a detailed step-by-step protocol for segmenting the full anterior-posterior length of the hippocampus and its subfields: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus (DG), strata radiatum/lacunosum/moleculare (SR/SL/SM), and subiculum. This protocol has been applied to five subjects (3F, 2M; age 29-57, avg. 37). Protocol reliability is assessed by resegmenting either the right or left hippocampus of each subject and computing the overlap using the Dice's kappa metric. Mean Dice's kappa (range) across the five subjects are: whole hippocampus, 0.91 (0.90-0.92); CA1, 0.78 (0.77-0.79); CA2/CA3, 0.64 (0.56-0.73); CA4/dentate gyrus, 0.83 (0.81-0.85); strata radiatum/lacunosum/moleculare, 0.71 (0.68-0.73); and subiculum 0.75 (0.72-0.78). The segmentation protocol presented here provides other laboratories with a reliable method to study the hippocampus and hippocampal subfields in vivo using commonly available MR tools.

  2. High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging.

    PubMed

    Winterburn, Julie; Pruessner, Jens C; Sofia, Chavez; Schira, Mark M; Lobaugh, Nancy J; Voineskos, Aristotle N; Chakravarty, M Mallar

    2015-01-01

    The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has been heavily studied. While many imaging studies treat the hippocampus as a single unitary neuroanatomical structure, it is, in fact, composed of several subfields that have a complex three-dimensional geometry. As such, it is known that these subfields perform specialized functions and are differentially affected through the course of different disease states. Magnetic resonance (MR) imaging can be used as a powerful tool to interrogate the morphology of the hippocampus and its subfields. Many groups use advanced imaging software and hardware (>3T) to image the subfields; however this type of technology may not be readily available in most research and clinical imaging centers. To address this need, this manuscript provides a detailed step-by-step protocol for segmenting the full anterior-posterior length of the hippocampus and its subfields: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus (DG), strata radiatum/lacunosum/moleculare (SR/SL/SM), and subiculum. This protocol has been applied to five subjects (3F, 2M; age 29-57, avg. 37). Protocol reliability is assessed by resegmenting either the right or left hippocampus of each subject and computing the overlap using the Dice's kappa metric. Mean Dice's kappa (range) across the five subjects are: whole hippocampus, 0.91 (0.90-0.92); CA1, 0.78 (0.77-0.79); CA2/CA3, 0.64 (0.56-0.73); CA4/dentate gyrus, 0.83 (0.81-0.85); strata radiatum/lacunosum/moleculare, 0.71 (0.68-0.73); and subiculum 0.75 (0.72-0.78). The segmentation protocol presented here provides other laboratories with a reliable method to study the hippocampus and hippocampal subfields in vivo using commonly available MR tools. PMID:26575133

  3. Fusing Markov random fields with anatomical knowledge and shape-based analysis to segment multiple sclerosis white matter lesions in magnetic resonance images of the brain

    NASA Astrophysics Data System (ADS)

    AlZubi, Stephan; Toennies, Klaus D.; Bodammer, N.; Hinrichs, Herman

    2002-05-01

    This paper proposes an image analysis system to segment multiple sclerosis lesions of magnetic resonance (MR) brain volumes consisting of 3 mm thick slices using three channels (images showing T1-, T2- and PD -weighted contrast). The method uses the statistical model of Markov Random Fields (MRF) both at low and high levels. The neighborhood system used in this MRF is defined in three types: (1) Voxel to voxel: a low-level heterogeneous neighborhood system is used to restore noisy images. (2) Voxel to segment: a fuzzy atlas, which indicates the probability distribution of each tissue type in the brain, is registered elastically with the MRF. It is used by the MRF as a-priori knowledge to correct miss-classified voxels. (3) Segment to segment: Remaining lesion candidates are processed by a feature based classifier that looks at unary and neighborhood information to eliminate more false positives. An expert's manual segmentation was compared with the algorithm.

  4. Image segmentation using fuzzy LVQ clustering networks

    NASA Technical Reports Server (NTRS)

    Tsao, Eric Chen-Kuo; Bezdek, James C.; Pal, Nikhil R.

    1992-01-01

    In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of a Kohonen learning vector quantization (LVQ) which integrates the Fuzzy c-Means (FCM) model with the learning rate and updating strategies of the LVQ is used for this task. This network, which segments images in an unsupervised manner, is thus related to the FCM optimization problem. Numerical examples on photographic and magnetic resonance images are given to illustrate this approach to image segmentation.

  5. Image segmentation survey

    NASA Technical Reports Server (NTRS)

    Haralick, R. M.

    1982-01-01

    The methodologies and capabilities of image segmentation techniques are reviewed. Single linkage schemes, hybrid linkage schemes, centroid linkage schemes, histogram mode seeking, spatial clustering, and split and merge schemes are addressed.

  6. Magnetic resonance imaging on disc degeneration changes after implantation of an interspinous spacer and fusion of the adjacent segment

    PubMed Central

    Liu, Xiaokang; Liu, Yingjie; Lian, Xiaofeng; Xu, Jianguang

    2015-01-01

    The aim of the study was to investigate the changes of the lumbar intervertebral disc degeneration by magnetic resonance imaging (MRI) after the implantation of interspinous device and the fusion of the adjacent segment. A total of 62 consecutive patients suffering L5/S1 lumbar disc herniation (LDH) with concomitant disc space narrowing or low-grade instability up to 5 mm translational slip in L5/S1 level were treated with lumbar interbody fusion (LIF) via posterior approach. Thirty-four of these patients (Coflex group) received an additional implantation of the interspinous spacer device (Coflex™) in the level L4/L5, while the rest of 28 patients (fusion group) underwent the fusion surgery alone. Clinical and radiographic examinations were performed at pre- and postoperative visits to compare the clinical outcomes and the changes of the L4/L5 vertebral disc degeneration on MRI in both Coflex and fusion group. Although both Coflex and fusion group showed improvements of the clinical outcomes assessed by the Oswestry Disability Index (ODI) after surgery, patients in Coflex group had more significant amelioration (P < 0.05) compared to fusion group. During follow up, the postoperative disc degeneration changes in Coflex group assessed by the relative signal intensity (RSI) differed from those in fusion group (P < 0.05). The supplemental implantation of Coflex™ after the fusion surgery could delay the disc degeneration of the adjacent segment. PMID:26131210

  7. Magnetic resonance imaging on disc degeneration changes after implantation of an interspinous spacer and fusion of the adjacent segment.

    PubMed

    Liu, Xiaokang; Liu, Yingjie; Lian, Xiaofeng; Xu, Jianguang

    2015-01-01

    The aim of the study was to investigate the changes of the lumbar intervertebral disc degeneration by magnetic resonance imaging (MRI) after the implantation of interspinous device and the fusion of the adjacent segment. A total of 62 consecutive patients suffering L5/S1 lumbar disc herniation (LDH) with concomitant disc space narrowing or low-grade instability up to 5 mm translational slip in L5/S1 level were treated with lumbar interbody fusion (LIF) via posterior approach. Thirty-four of these patients (Coflex group) received an additional implantation of the interspinous spacer device (Coflex™) in the level L4/L5, while the rest of 28 patients (fusion group) underwent the fusion surgery alone. Clinical and radiographic examinations were performed at pre- and postoperative visits to compare the clinical outcomes and the changes of the L4/L5 vertebral disc degeneration on MRI in both Coflex and fusion group. Although both Coflex and fusion group showed improvements of the clinical outcomes assessed by the Oswestry Disability Index (ODI) after surgery, patients in Coflex group had more significant amelioration (P < 0.05) compared to fusion group. During follow up, the postoperative disc degeneration changes in Coflex group assessed by the relative signal intensity (RSI) differed from those in fusion group (P < 0.05). The supplemental implantation of Coflex™ after the fusion surgery could delay the disc degeneration of the adjacent segment.

  8. Nanofiber-segment ring resonator

    NASA Astrophysics Data System (ADS)

    Jones, D. E.; Hickman, G. T.; Franson, J. D.; Pittman, T. B.

    2016-08-01

    We describe a fiber ring resonator comprised of a relatively long loop of standard single-mode fiber with a short nanofiber segment. The evanescent mode of the nanofiber segment allows the cavity-enhanced field to interact with atoms in close proximity to the nanofiber surface. We report on an experiment using a warm atomic vapor and low-finesse cavity, and briefly discuss the potential for reaching the strong coupling regime of cavity QED by using trapped atoms and a high-finesse cavity of this kind.

  9. Scorpion image segmentation system

    NASA Astrophysics Data System (ADS)

    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.

  10. 2D segmentation of intervertebral discs and its degree of degeneration from T2-weighted magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Castro-Mateos, Isaac; Pozo, José Maria; Lazary, Aron; Frangi, Alejandro F.

    2014-03-01

    Low back pain (LBP) is a disorder suffered by a large population around the world. A key factor causing this illness is Intervertebral Disc (IVD) degeneration, whose early diagnosis could help in preventing this widespread condition. Clinicians base their diagnosis on visual inspection of 2D slices of Magnetic Resonance (MR) images, which is subject to large interobserver variability. In this work, an automatic classification method is presented, which provides the Pfirrmann degree of degeneration from a mid-sagittal MR slice. The proposed method utilizes Active Contour Models, with a new geometrical energy, to achieve an initial segmentation, which is further improved using fuzzy C-means. Then, IVDs are classified according to their degree of degeneration. This classification is attained by employing Adaboost on five specific features: the mean and the variance of the probability map of the nucleus using two different approaches and the eccentricity of the fitting ellipse to the contour of the IVD. The classification method was evaluated using a cohort of 150 intervertebral discs assessed by three experts, resulting in a mean specificity (93%) and sensitivity (83%) similar to the one provided by every expert with respect to the most voted value. The segmentation accuracy was evaluated using the Dice Similarity Index (DSI) and Root Mean Square Error (RMSE) of the point-to-contour distance. The mean DSI ± 2 standard deviation was 91:7% ±5:6%, the mean RMSE was 0:82mm and the 95 percentile was 1:36mm. These results were found accurate when compared to the state-of-the-art.

  11. Segmentation of knee cartilage by using a hierarchical active shape model based on multi-resolution transforms in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    León, Madeleine; Escalante-Ramirez, Boris

    2013-11-01

    Knee osteoarthritis (OA) is characterized by the morphological degeneration of cartilage. Efficient segmentation of cartilage is important for cartilage damage diagnosis and to support therapeutic responses. We present a method for knee cartilage segmentation in magnetic resonance images (MRI). Our method incorporates the Hermite Transform to obtain a hierarchical decomposition of contours which describe knee cartilage shapes. Then, we compute a statistical model of the contour of interest from a set of training images. Thereby, our Hierarchical Active Shape Model (HASM) captures a large range of shape variability even from a small group of training samples, improving segmentation accuracy. The method was trained with a training set of 16- MRI of knee and tested with leave-one-out method.

  12. Cooperative processes in image segmentation

    NASA Technical Reports Server (NTRS)

    Davis, L. S.

    1982-01-01

    Research into the role of cooperative, or relaxation, processes in image segmentation is surveyed. Cooperative processes can be employed at several levels of the segmentation process as a preprocessing enhancement step, during supervised or unsupervised pixel classification and, finally, for the interpretation of image segments based on segment properties and relations.

  13. Bone image segmentation.

    PubMed

    Liu, Z Q; Liew, H L; Clement, J G; Thomas, C D

    1999-05-01

    Characteristics of microscopic structures in bone cross sections carry essential clues in age determination in forensic science and in the study of age-related bone developments and bone diseases. Analysis of bone cross sections represents a major area of research in bone biology. However, traditional approaches in bone biology have relied primarily on manual processes with very limited number of bone samples. As a consequence, it is difficult to reach reliable and consistent conclusions. In this paper we present an image processing system that uses microstructural and relational knowledge present in the bone cross section for bone image segmentation. This system automates the bone image analysis process and is able to produce reliable results based on quantitative measurements from a large number of bone images. As a result, using large databases of bone images to study the correlation between bone structural features and age-related bone developments becomes feasible.

  14. Two and three-dimensional segmentation of hyperpolarized 3He magnetic resonance imaging of pulmonary gas distribution

    NASA Astrophysics Data System (ADS)

    Heydarian, Mohammadreza; Kirby, Miranda; Wheatley, Andrew; Fenster, Aaron; Parraga, Grace

    2012-03-01

    A semi-automated method for generating hyperpolarized helium-3 (3He) measurements of individual slice (2D) or whole lung (3D) gas distribution was developed. 3He MRI functional images were segmented using two-dimensional (2D) and three-dimensional (3D) hierarchical K-means clustering of the 3He MRI signal and in addition a seeded region-growing algorithm was employed for segmentation of the 1H MRI thoracic cavity volume. 3He MRI pulmonary function measurements were generated following two-dimensional landmark-based non-rigid registration of the 3He and 1H pulmonary images. We applied this method to MRI of healthy subjects and subjects with chronic obstructive lung disease (COPD). The results of hierarchical K-means 2D and 3D segmentation were compared to an expert observer's manual segmentation results using linear regression, Pearson correlations and the Dice similarity coefficient. 2D hierarchical K-means segmentation of ventilation volume (VV) and ventilation defect volume (VDV) was strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.97, p<.0001) and mean Dice coefficients were greater than 92% for all subjects. 3D hierarchical K-means segmentation of VV and VDV was also strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.64, p<.0001) and the mean Dice coefficients were greater than 91% for all subjects. Both 2D and 3D semi-automated segmentation of 3He MRI gas distribution provides a way to generate novel pulmonary function measurements.

  15. Resonant Anderson localization in segmented wires.

    PubMed

    Estarellas, Cristian; Serra, Llorenç

    2016-03-01

    We discuss a model of random segmented wire, with linear segments of two-dimensional wires joined by circular bends. The joining vertices act as scatterers on the propagating electron waves. The model leads to resonant Anderson localization when all segments are of similar length. The resonant behavior is present with one and also with several propagating modes. The probability distributions evolve from diffusive to localized regimes when increasing the number of segments in a similar way for long and short localization lengths. As a function of the energy, a finite segmented wire typically evolves from localized to diffusive to ballistic behavior in each conductance plateau.

  16. D-Dimer Levels Predict Myocardial Injury in ST-Segment Elevation Myocardial Infarction: A Cardiac Magnetic Resonance Imaging Study

    PubMed Central

    Song, Young Bin; Lima, Joao A. C.; Guallar, Eliseo; Choe, Yeon Hyeon; Hwang, Jin Kyung; Kim, Eun Kyoung; Yang, Jeong Hoon; Hahn, Joo-Yong; Choi, Seung-Hyuk; Lee, Sang-Chol; Lee, Sang Hoon; Gwon, Hyeon-Cheol

    2016-01-01

    Objectives Elevated D-dimer levels on admission predict prognosis in patients undergoing primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI), but the association of D-dimer levels with structural markers of myocardial injury in these patients is unknown. Methods We performed cardiac magnetic resonance (CMR) imaging in 208 patients treated with primary PCI for STEMI. CMR was performed a median of 3 days after the index procedure. Of the 208 patients studied, 75 patients had D-dimer levels above the normal range on admission (>0.5 μg/mL; high D-dimer group) while 133 had normal levels (≤0.5 μg/mL; low D-dimer group). The primary outcome was myocardial infarct size assessed by CMR. Secondary outcomes included area at risk (AAR), microvascular obstruction (MVO) area, and myocardial salvage index (MSI). Results In CMR analysis, myocardial infarct size was larger in the high D-dimer group than in the low D-dimer group (22.3% [16.2–30.5] versus 18.8% [10.7–26.7]; p = 0.02). Compared to the low D-dimer group, the high D-dimer group also had a larger AAR (38.1% [31.7–46.9] versus 35.8% [24.2–45.3]; p = 0.04) and a smaller MSI (37.7 [28.2–46.9] versus 47.1 [33.2–57.0]; p = 0.01). In multivariate analysis, high D-dimer levels were significantly associated with larger myocardial infarct (OR 2.59; 95% CI 1.37–4.87; p<0.01) and lower MSI (OR 2.62; 95% CI 1.44–4.78; p<0.01). Conclusions In STEMI patients undergoing primary PCI, high D-dimer levels on admission were associated with a larger myocardial infarct size, a greater extent of AAR, and lower MSI, as assessed by CMR data. Elevated initial D-dimer level may be a marker of advanced myocardial injury in patients treated with primary PCI for STEMI. PMID:27513758

  17. Semi-automatic segmentation and modeling of the cervical spinal cord for volume quantification in multiple sclerosis patients from magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Sonkova, Pavlina; Evangelou, Iordanis E.; Gallo, Antonio; Cantor, Fredric K.; Ohayon, Joan; McFarland, Henry F.; Bagnato, Francesca

    2008-03-01

    Spinal cord (SC) tissue loss is known to occur in some patients with multiple sclerosis (MS), resulting in SC atrophy. Currently, no measurement tools exist to determine the magnitude of SC atrophy from Magnetic Resonance Images (MRI). We have developed and implemented a novel semi-automatic method for quantifying the cervical SC volume (CSCV) from Magnetic Resonance Images (MRI) based on level sets. The image dataset consisted of SC MRI exams obtained at 1.5 Tesla from 12 MS patients (10 relapsing-remitting and 2 secondary progressive) and 12 age- and gender-matched healthy volunteers (HVs). 3D high resolution image data were acquired using an IR-FSPGR sequence acquired in the sagittal plane. The mid-sagittal slice (MSS) was automatically located based on the entropy calculation for each of the consecutive sagittal slices. The image data were then pre-processed by 3D anisotropic diffusion filtering for noise reduction and edge enhancement before segmentation with a level set formulation which did not require re-initialization. The developed method was tested against manual segmentation (considered ground truth) and intra-observer and inter-observer variability were evaluated.

  18. Segmentation of stereo terrain images

    NASA Astrophysics Data System (ADS)

    George, Debra A.; Privitera, Claudio M.; Blackmon, Theodore T.; Zbinden, Eric; Stark, Lawrence W.

    2000-06-01

    We have studied four approaches to segmentation of images: three automatic ones using image processing algorithms and a fourth approach, human manual segmentation. We were motivated toward helping with an important NASA Mars rover mission task -- replacing laborious manual path planning with automatic navigation of the rover on the Mars terrain. The goal of the automatic segmentations was to identify an obstacle map on the Mars terrain to enable automatic path planning for the rover. The automatic segmentation was first explored with two different segmentation methods: one based on pixel luminance, and the other based on pixel altitude generated through stereo image processing. The third automatic segmentation was achieved by combining these two types of image segmentation. Human manual segmentation of Martian terrain images was used for evaluating the effectiveness of the combined automatic segmentation as well as for determining how different humans segment the same images. Comparisons between two different segmentations, manual or automatic, were measured using a similarity metric, SAB. Based on this metric, the combined automatic segmentation did fairly well in agreeing with the manual segmentation. This was a demonstration of a positive step towards automatically creating the accurate obstacle maps necessary for automatic path planning and rover navigation.

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

    SciTech Connect

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

  20. Cardiac image segmentation using spatiotemporal clustering

    NASA Astrophysics Data System (ADS)

    Galic, Sasa; Loncaric, Sven

    2001-07-01

    Image segmentation is an important and challenging problem in image analysis. Segmentation of moving objects in image sequences is even more difficult and computationally expensive. In this work we propose a technique for spatio- temporal segmentation of medical sequences based on K-mean clustering in the feature vector space. The motivation for spatio-temporalsegmentation approach comes from the fact that motion is a useful clue for object segmentation. Two- dimensional feature vector has been used for clustering in the feature space. In this paper we apply the proposed technique to segmentation of cardiac images. The first feature used in this particular application is image brightness, which reveals the structure of interest in the image. The second feature is the Euclidean norm of the optical flow vector. The third feature is the three- dimensional optical flow vector, which consists of computed motion in all three dimensions. The optical flow itself is computed using Horn-Schunck algorithm. The fourth feature is the mean brightness of the input image in a local neighborhood. By applying the clustering algorithm it is possible to detect moving object in the image sequence. The experiment has been conducted using a sequence of ECG-gated magnetic resonance (MR) images of a beating heart taken as in time so in the space.

  1. Image segmentation using random features

    NASA Astrophysics Data System (ADS)

    Bull, Geoff; Gao, Junbin; Antolovich, Michael

    2014-01-01

    This paper presents a novel algorithm for selecting random features via compressed sensing to improve the performance of Normalized Cuts in image segmentation. Normalized Cuts is a clustering algorithm that has been widely applied to segmenting images, using features such as brightness, intervening contours and Gabor filter responses. Some drawbacks of Normalized Cuts are that computation times and memory usage can be excessive, and the obtained segmentations are often poor. This paper addresses the need to improve the processing time of Normalized Cuts while improving the segmentations. A significant proportion of the time in calculating Normalized Cuts is spent computing an affinity matrix. A new algorithm has been developed that selects random features using compressed sensing techniques to reduce the computation needed for the affinity matrix. The new algorithm, when compared to the standard implementation of Normalized Cuts for segmenting images from the BSDS500, produces better segmentations in significantly less time.

  2. Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging.

    PubMed

    Lebenberg, Jessica; Lalande, Alain; Clarysse, Patrick; Buvat, Irene; Casta, Christopher; Cochet, Alexandre; Constantinidès, Constantin; Cousty, Jean; de Cesare, Alain; Jehan-Besson, Stephanie; Lefort, Muriel; Najman, Laurent; Roullot, Elodie; Sarry, Laurent; Tilmant, Christophe; Frouin, Frederique; Garreau, Mireille

    2015-01-01

    This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert. PMID:26287691

  3. Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging

    PubMed Central

    Lebenberg, Jessica; Lalande, Alain; Clarysse, Patrick; Buvat, Irene; Casta, Christopher; Cochet, Alexandre; Constantinidès, Constantin; Cousty, Jean; de Cesare, Alain; Jehan-Besson, Stephanie; Lefort, Muriel; Najman, Laurent; Roullot, Elodie; Sarry, Laurent; Tilmant, Christophe

    2015-01-01

    This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert. PMID:26287691

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

    PubMed

    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.

  5. Bayesian segmentation of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Mohammadpour, Adel; Féron, Olivier; Mohammad-Djafari, Ali

    2004-11-01

    In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.

  6. Segmenting Images for a Better Diagnosis

    NASA Technical Reports Server (NTRS)

    2004-01-01

    NASA's Hierarchical Segmentation (HSEG) software has been adapted by Bartron Medical Imaging, LLC, for use in segmentation feature extraction, pattern recognition, and classification of medical images. Bartron acquired licenses from NASA Goddard Space Flight Center for application of the HSEG concept to medical imaging, from the California Institute of Technology/Jet Propulsion Laboratory to incorporate pattern-matching software, and from Kennedy Space Center for data-mining and edge-detection programs. The Med-Seg[TM] united developed by Bartron provides improved diagnoses for a wide range of medical images, including computed tomography scans, positron emission tomography scans, magnetic resonance imaging, ultrasound, digitized Z-ray, digitized mammography, dental X-ray, soft tissue analysis, and moving object analysis. It also can be used in analysis of soft-tissue slides. Bartron's future plans include the application of HSEG technology to drug development. NASA is advancing it's HSEG software to learn more about the Earth's magnetosphere.

  7. Automated segmentation of MR images of brain tumors.

    PubMed

    Kaus, M R; Warfield, S K; Nabavi, A; Black, P M; Jolesz, F A; Kikinis, R

    2001-02-01

    An automated brain tumor segmentation method was developed and validated against manual segmentation with three-dimensional magnetic resonance images in 20 patients with meningiomas and low-grade gliomas. The automated method (operator time, 5-10 minutes) allowed rapid identification of brain and tumor tissue with an accuracy and reproducibility comparable to those of manual segmentation (operator time, 3-5 hours), making automated segmentation practical for low-grade gliomas and meningiomas. PMID:11161183

  8. [Automatic segmentation of three dimension medical image series].

    PubMed

    Ding, Siyi; Yang, Jie; Yao, Lixiu; Xu, Qing

    2006-08-01

    We propose an improved version of regional competition algorithm in this paper, and apply it to the automatic segmentation of medical image series, particularly in the segmentation and recognition of brain tumor. The traditional regional competition is enhanced by combining the attractive aspects of fuzzy segmentation, and thus it provides an efficient approach to segment the fuzzy and heterogeneous medical images. In order to perform regional competition on medical image series, we utilize the segmentation result of a slice to initiate the next segmented slice, while the first slice is initialized using regional growing algorithm. Moreover, we develop an algorithm to recognize the tumors automatically, taking into account its characters. Experimental results show that our algorithm performs well on the segmentation of magnetic resonance imaging (MRI) image series with high speed and precision. PMID:17002088

  9. Image Information Mining Utilizing Hierarchical Segmentation

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

    2002-01-01

    The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

  10. Multiresolution segmentation technique for spine MRI images

    NASA Astrophysics Data System (ADS)

    Li, Haiyun; Yan, Chye H.; Ong, Sim Heng; Chui, Cheekong K.; Teoh, Swee H.

    2002-05-01

    In this paper, we describe a hybrid method for segmentation of spinal magnetic resonance imaging that has been developed based on the natural phenomenon of stones appearing as water recedes. The candidate segmentation region corresponds to the stones with characteristics similar to that of intensity extrema, edges, intensity ridge and grey-level blobs. The segmentation method is implemented based on a combination of wavelet multiresolution decomposition and fuzzy clustering. First thresholding is performed dynamically according to local characteristic to detect possible target areas, We then use fuzzy c-means clustering in concert with wavelet multiscale edge detection to identify the maximum likelihood anatomical and functional target areas. Fuzzy C-Means uses iterative optimization of an objective function based on a weighted similarity measure between the pixels in the image and each of c cluster centers. Local extrema of this objective function are indicative of an optimal clustering of the input data. The multiscale edges can be detected and characterized from local maxima of the modulus of the wavelet transform while the noise can be reduced to some extent by enacting thresholds. The method provides an efficient and robust algorithm for spinal image segmentation. Examples are presented to demonstrate the efficiency of the technique on some spinal MRI images.

  11. Adaptive image segmentation by quantization

    NASA Astrophysics Data System (ADS)

    Liu, Hui; Yun, David Y.

    1992-12-01

    Segmentation of images into textural homogeneous regions is a fundamental problem in an image understanding system. Most region-oriented segmentation approaches suffer from the problem of different thresholds selecting for different images. In this paper an adaptive image segmentation based on vector quantization is presented. It automatically segments images without preset thresholds. The approach contains a feature extraction module and a two-layer hierarchical clustering module, a vector quantizer (VQ) implemented by a competitive learning neural network in the first layer. A near-optimal competitive learning algorithm (NOLA) is employed to train the vector quantizer. NOLA combines the advantages of both Kohonen self- organizing feature map (KSFM) and K-means clustering algorithm. After the VQ is trained, the weights of the network and the number of input vectors clustered by each neuron form a 3- D topological feature map with separable hills aggregated by similar vectors. This overcomes the inability to visualize the geometric properties of data in a high-dimensional space for most other clustering algorithms. The second clustering algorithm operates in the feature map instead of the input set itself. Since the number of units in the feature map is much less than the number of feature vectors in the feature set, it is easy to check all peaks and find the `correct' number of clusters, also a key problem in current clustering techniques. In the experiments, we compare our algorithm with K-means clustering method on a variety of images. The results show that our algorithm achieves better performance.

  12. Colony image acquisition and segmentation

    NASA Astrophysics Data System (ADS)

    Wang, W. X.

    2007-12-01

    For counting of both colonies and plaques, there is a large number of applications including food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing, AMES testing, pharmaceuticals, paints, sterile fluids and fungal contamination. Recently, many researchers and developers have made efforts for this kind of systems. By investigation, some existing systems have some problems. The main problems are image acquisition and image segmentation. In order to acquire colony images with good quality, an illumination box was constructed as: the box includes front lightning and back lightning, which can be selected by users based on properties of colony dishes. With the illumination box, lightning can be uniform; colony dish can be put in the same place every time, which make image processing easy. The developed colony image segmentation algorithm consists of the sub-algorithms: (1) image classification; (2) image processing; and (3) colony delineation. The colony delineation algorithm main contain: the procedures based on grey level similarity, on boundary tracing, on shape information and colony excluding. In addition, a number of algorithms are developed for colony analysis. The system has been tested and satisfactory.

  13. Random walks for image segmentation.

    PubMed

    Grady, Leo

    2006-11-01

    A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with user-defined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a high-quality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs.

  14. Segmentation algorithms for ear image data towards biomechanical studies.

    PubMed

    Ferreira, Ana; Gentil, Fernanda; Tavares, João Manuel R S

    2014-01-01

    In recent years, the segmentation, i.e. the identification, of ear structures in video-otoscopy, computerised tomography (CT) and magnetic resonance (MR) image data, has gained significant importance in the medical imaging area, particularly those in CT and MR imaging. Segmentation is the fundamental step of any automated technique for supporting the medical diagnosis and, in particular, in biomechanics studies, for building realistic geometric models of ear structures. In this paper, a review of the algorithms used in ear segmentation is presented. The review includes an introduction to the usually biomechanical modelling approaches and also to the common imaging modalities. Afterwards, several segmentation algorithms for ear image data are described, and their specificities and difficulties as well as their advantages and disadvantages are identified and analysed using experimental examples. Finally, the conclusions are presented as well as a discussion about possible trends for future research concerning the ear segmentation.

  15. Metric Learning to Enhance Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

    Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

  16. Magnetic resonance imaging

    SciTech Connect

    Stark, D.D.; Bradley, W.G. Jr.

    1988-01-01

    The authors present a review of magnetic resonance imaging. Many topics are explored from instrumentation, spectroscopy, blood flow and sodium imaging to detailed clinical applications such as the differential diagnosis of multiple sclerosis or adrenal adenoma. The emphasis throughout is on descriptions of normal multiplanar anatomy and pathology as displayed by MRI.

  17. Functional Magnetic Resonance Imaging

    ERIC Educational Resources Information Center

    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…

  18. Image segmentation and lightness perception.

    PubMed

    Anderson, Barton L; Winawer, Jonathan

    2005-03-01

    The perception of surface albedo (lightness) is one of the most basic aspects of visual awareness. It is well known that the apparent lightness of a target depends on the context in which it is embedded, but there is extensive debate about the computations and representations underlying perceived lightness. One view asserts that the visual system explicitly separates surface reflectance from the prevailing illumination and atmospheric conditions in which it is embedded, generating layered image representations. Some recent theory has challenged this view and asserted that the human visual system derives surface lightness without explicitly segmenting images into multiple layers. Here we present new lightness illusions--the largest reported to date--that unequivocally demonstrate the effect that layered image representations can have in lightness perception. We show that the computations that underlie the decomposition of luminance into multiple layers under conditions of transparency can induce dramatic lightness illusions, causing identical texture patches to appear either black or white. These results indicate that mechanisms involved in decomposing images into layered representations can play a decisive role in the perception of surface lightness. PMID:15744303

  19. Hierarchical image segmentation for learning object priors

    SciTech Connect

    Prasad, Lakshman; Yang, Xingwei; Latecki, Longin J; Li, Nan

    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.

  20. Image Segmentation Using Hierarchical Merge Tree

    NASA Astrophysics Data System (ADS)

    Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga

    2016-10-01

    This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with over-segmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then be inferred by finding globally optimal solutions to the model efficiently. We also present an iterative training and testing algorithm that generates various tree structures and combines them to emphasize accurate boundaries by segmentation accumulation. Experiment results and comparisons with other very recent methods on six public data sets demonstrate that our approach achieves the state-of-the-art region accuracy and is very competitive in image segmentation without semantic priors.

  1. Improving the accuracy of volumetric segmentation using pre-processing boundary detection and image reconstruction.

    PubMed

    Archibald, Rick; Hu, Jiuxiang; Gelb, Anne; Farin, Gerald

    2004-04-01

    The concentration edge -detection and Gegenbauer image-reconstruction methods were previously shown to improve the quality of segmentation in magnetic resonance imaging. In this study, these methods are utilized as a pre-processing step to the Weibull E-SD field segmentation. It is demonstrated that the combination of the concentration edge detection and Gegenbauer reconstruction method improves the accuracy of segmentation for the simulated test data and real magnetic resonance images used in this study. PMID:15376580

  2. Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling.

    PubMed

    Öztürk, Ceyda Nur; Albayrak, Songül

    2016-05-01

    Anatomical structures that can deteriorate over time, such as cartilage, can be successfully delineated with voxel-classification approaches in magnetic resonance (MR) images. However, segmentation via voxel-classification is a computationally demanding process for high-field MR images with high spatial resolutions. In this study, the whole femoral, tibial, and patellar cartilage compartments in the knee joint were automatically segmented in high-field MR images obtained from Osteoarthritis Initiative using a voxel-classification-driven region-growing algorithm with sample-expand method. Computational complexity of the classification was alleviated via subsampling of the background voxels in the training MR images and selecting a small subset of significant features by taking into consideration systems with limited memory and processing power. Although subsampling of the voxels may lead to a loss of generality of the training models and a decrease in segmentation accuracies, effective subsampling strategies can overcome these problems. Therefore, different subsampling techniques, which involve uniform, Gaussian, vicinity-correlated (VC) sparse, and VC dense subsampling, were used to generate four training models. The segmentation system was experimented using 10 training and 23 testing MR images, and the effects of different training models on segmentation accuracies were investigated. Experimental results showed that the highest mean Dice similarity coefficient (DSC) values for all compartments were obtained when the training models of VC sparse subsampling technique were used. Mean DSC values optimized with this technique were 82.6%, 83.1%, and 72.6% for femoral, tibial, and patellar cartilage compartments, respectively, when mean sensitivities were 79.9%, 84.0%, and 71.5%, and mean specificities were 99.8%, 99.9%, and 99.9%.

  3. Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling.

    PubMed

    Öztürk, Ceyda Nur; Albayrak, Songül

    2016-05-01

    Anatomical structures that can deteriorate over time, such as cartilage, can be successfully delineated with voxel-classification approaches in magnetic resonance (MR) images. However, segmentation via voxel-classification is a computationally demanding process for high-field MR images with high spatial resolutions. In this study, the whole femoral, tibial, and patellar cartilage compartments in the knee joint were automatically segmented in high-field MR images obtained from Osteoarthritis Initiative using a voxel-classification-driven region-growing algorithm with sample-expand method. Computational complexity of the classification was alleviated via subsampling of the background voxels in the training MR images and selecting a small subset of significant features by taking into consideration systems with limited memory and processing power. Although subsampling of the voxels may lead to a loss of generality of the training models and a decrease in segmentation accuracies, effective subsampling strategies can overcome these problems. Therefore, different subsampling techniques, which involve uniform, Gaussian, vicinity-correlated (VC) sparse, and VC dense subsampling, were used to generate four training models. The segmentation system was experimented using 10 training and 23 testing MR images, and the effects of different training models on segmentation accuracies were investigated. Experimental results showed that the highest mean Dice similarity coefficient (DSC) values for all compartments were obtained when the training models of VC sparse subsampling technique were used. Mean DSC values optimized with this technique were 82.6%, 83.1%, and 72.6% for femoral, tibial, and patellar cartilage compartments, respectively, when mean sensitivities were 79.9%, 84.0%, and 71.5%, and mean specificities were 99.8%, 99.9%, and 99.9%. PMID:27017069

  4. Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles

    PubMed Central

    Tang, Xiaoying; Crocetti, Deana; Kutten, Kwame; Ceritoglu, Can; Albert, Marilyn S.; Mori, Susumu; Mostofsky, Stewart H.; Miller, Michael I.

    2015-01-01

    We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF). In MALF, estimation of the parameter of interest is performed via maximum a posteriori estimation using the expectation-maximization (EM) algorithm. The likelihoods of multiple atlases are fused in the E-step while the optimal estimator, a single maximizer of the fused likelihoods, is then obtained in the M-step. There are two stages in the proposed pipeline; first the input T1-weighted image is automatically skull-stripped via a fast MALF, then internal brain structures of interest are automatically extracted using a regular MALF. We assess the performance of each of the two modules in the pipeline based on two sets of images with markedly different anatomical and photometric contrasts; 3T MPRAGE scans of pediatric subjects with developmental disorders vs. 1.5T SPGR scans of elderly subjects with dementia. Evaluation is performed quantitatively using the Dice overlap as well as qualitatively via visual inspections. As a result, we demonstrate subject-level differences in the performance of the proposed pipeline, which may be accounted for by age, diagnosis, or the imaging parameters (particularly the field strength). For the subcortical and ventricular structures of the two datasets, the hierarchical pipeline is capable of producing automated segmentations with Dice overlaps ranging from 0.8 to 0.964 when compared with the gold standard. Comparisons with other representative segmentation algorithms are presented, relative to which the proposed hierarchical pipeline demonstrates comparative or superior accuracy. PMID:25784852

  5. Stepped Impedance Resonators for High Field Magnetic Resonance Imaging

    PubMed Central

    Akgun, Can E.; DelaBarre, Lance; Yoo, Hyoungsuk; Sohn, Sung-Min; Snyder, Carl J.; Adriany, Gregor; Ugurbil, Kamil; Gopinath, Anand; Vaughan, J. Thomas

    2014-01-01

    Multi-element volume radio-frequency (RF) coils are an integral aspect of the growing field of high field magnetic resonance imaging (MRI). In these systems, a popular volume coil of choice has become the transverse electromagnetic (TEM) multi-element transceiver coil consisting of microstrip resonators. In this paper, to further advance this design approach, a new microstrip resonator strategy in which the transmission line is segmented into alternating impedance sections referred to as stepped impedance resonators (SIRs) is investigated. Single element simulation results in free space and in a phantom at 7 tesla (298 MHz) demonstrate the rationale and feasibility of the SIR design strategy. Simulation and image results at 7 tesla in a phantom and human head illustrate the improvements in transmit magnetic field, as well as, RF efficiency (transmit magnetic field versus SAR) when two different SIR designs are incorporated in 8-element volume coil configurations and compared to a volume coil consisting of microstrip elements. PMID:23508243

  6. Metric Learning for Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

    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.

  7. Automatic segmentation of anterior segment optical coherence tomography images

    NASA Astrophysics Data System (ADS)

    Williams, Dominic; Zheng, Yalin; Bao, Fangjun; Elsheikh, Ahmed

    2013-05-01

    Optical coherence tomography (OCT) images can provide quantitative measurements of the eye's entire anterior segment. A new technique founded on a newly proposed level set-based shape prior segmentation model has been developed for automatic segmentation of the cornea's anterior and posterior boundaries. This technique comprises three major steps: removal of regions containing irrelevant structures and artifacts, estimation of the cornea's location using a thresholding technique, and application of the new level set-based shape prior segmentation model to improve segmentation. The performance of our technique is compared to previously developed methods for analysis of the cornea in 33 OCT images of normal eyes, whereby manual annotations are used as a reference standard. The new technique achieves much improved concordance than previous methods, with a mean Dice's similarity coefficient of >0.92. This demonstrates the technique's potential to provide accurate and reliable measurements of the anterior segment geometry, which is important for many applications, including the construction of representative numerical simulations of the eye's mechanical behavior.

  8. Automatic segmentation of anterior segment optical coherence tomography images.

    PubMed

    Williams, Dominic; Zheng, Yalin; Bao, Fangjun; Elsheikh, Ahmed

    2013-05-01

    Optical coherence tomography (OCT) images can provide quantitative measurements of the eye's entire anterior segment. A new technique founded on a newly proposed level set-based shape prior segmentation model has been developed for automatic segmentation of the cornea's anterior and posterior boundaries. This technique comprises three major steps: removal of regions containing irrelevant structures and artifacts, estimation of the cornea's location using a thresholding technique, and application of the new level set-based shape prior segmentation model to improve segmentation. The performance of our technique is compared to previously developed methods for analysis of the cornea in 33 OCT images of normal eyes, whereby manual annotations are used as a reference standard. The new technique achieves much improved concordance than previous methods, with a mean Dice's similarity coefficient of > 0.92. This demonstrates the technique's potential to provide accurate and reliable measurements of the anterior segment geometry, which is important for many applications, including the construction of representative numerical simulations of the eye's mechanical behavior. PMID:23640074

  9. Comparison of automated and manual segmentation of hippocampus MR images

    NASA Astrophysics Data System (ADS)

    Haller, John W.; Christensen, Gary E.; Miller, Michael I.; Joshi, Sarang C.; Gado, Mokhtar; Csernansky, John G.; Vannier, Michael W.

    1995-05-01

    The precision and accuracy of area estimates from magnetic resonance (MR) brain images and using manual and automated segmentation methods are determined. Areas of the human hippocampus were measured to compare a new automatic method of segmentation with regions of interest drawn by an expert. MR images of nine normal subjects and nine schizophrenic patients were acquired with a 1.5-T unit (Siemens Medical Systems, Inc., Iselin, New Jersey). From each individual MPRAGE 3D volume image a single comparable 2-D slice (matrix equals 256 X 256) was chosen which corresponds to the same coronal slice of the hippocampus. The hippocampus was first manually segmented, then segmented using high dimensional transformations of a digital brain atlas to individual brain MR images. The repeatability of a trained rater was assessed by comparing two measurements from each individual subject. Variability was also compared within and between subject groups of schizophrenics and normal subjects. Finally, the precision and accuracy of automated segmentation of hippocampal areas were determined by comparing automated measurements to manual segmentation measurements made by the trained rater on MR and brain slice images. The results demonstrate the high repeatability of area measurement from MR images of the human hippocampus. Automated segmentation using high dimensional transformations from a digital brain atlas provides repeatability superior to that of manual segmentation. Furthermore, the validity of automated measurements was demonstrated by a high correlation with manual segmentation measurements made by a trained rater. Quantitative morphometry of brain substructures (e.g. hippocampus) is feasible by use of a high dimensional transformation of a digital brain atlas to an individual MR image. This method automates the search for neuromorphological correlates of schizophrenia by a new mathematically robust method with unprecedented sensitivity to small local and regional differences.

  10. Fuzzy local Gaussian mixture model for brain MR image segmentation.

    PubMed

    Ji, Zexuan; Xia, Yong; Sun, Quansen; Chen, Qiang; Xia, Deshen; Feng, David Dagan

    2012-05-01

    Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.

  11. Improving image segmentation by learning region affinities

    SciTech Connect

    Prasad, Lakshman; Yang, Xingwei; Latecki, Longin J

    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.

  12. Feature encoding for color image segmentation

    NASA Astrophysics Data System (ADS)

    Li, Ning; Li, Youfu

    2001-09-01

    An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. It is different from the pervious methods where SOFM is used for construct the feature encoding so that the feature-encoding can self-organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well-suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. Our study shows that the feature encoding approach offers great promise in automating and optimizing color image segmentation.

  13. Automatic setae segmentation from Chaetoceros microscopic images.

    PubMed

    Zheng, Haiyong; Zhao, Hongmiao; Sun, Xue; Gao, Huihui; Ji, Guangrong

    2014-09-01

    A novel image processing model Grayscale Surface Direction Angle Model (GSDAM) is presented and the algorithm based on GSDAM is developed to segment setae from Chaetoceros microscopic images. The proposed model combines the setae characteristics of the microscopic images with the spatial analysis of image grayscale surface to detect and segment the direction thin and long setae from the low contrast background as well as noise which may make the commonly used segmentation methods invalid. The experimental results show that our algorithm based on GSDAM outperforms the boundary-based and region-based segmentation methods Canny edge detector, iterative threshold selection, Otsu's thresholding, minimum error thresholding, K-means clustering, and marker-controlled watershed on the setae segmentation more accurately and completely. PMID:24913015

  14. A Unified Framework for Brain Segmentation in MR Images

    PubMed Central

    Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

    2015-01-01

    Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

  15. Regression Segmentation for M³ Spinal Images.

    PubMed

    Wang, Zhijie; Zhen, Xiantong; Tay, KengYeow; Osman, Said; Romano, Walter; Li, Shuo

    2015-08-01

    Clinical routine often requires to analyze spinal images of multiple anatomic structures in multiple anatomic planes from multiple imaging modalities (M(3)). Unfortunately, existing methods for segmenting spinal images are still limited to one specific structure, in one specific plane or from one specific modality (S(3)). In this paper, we propose a novel approach, Regression Segmentation, that is for the first time able to segment M(3) spinal images in one single unified framework. This approach formulates the segmentation task innovatively as a boundary regression problem: modeling a highly nonlinear mapping function from substantially diverse M(3) images directly to desired object boundaries. Leveraging the advancement of sparse kernel machines, regression segmentation is fulfilled by a multi-dimensional support vector regressor (MSVR) which operates in an implicit, high dimensional feature space where M(3) diversity and specificity can be systematically categorized, extracted, and handled. The proposed regression segmentation approach was thoroughly tested on images from 113 clinical subjects including both disc and vertebral structures, in both sagittal and axial planes, and from both MRI and CT modalities. The overall result reaches a high dice similarity index (DSI) 0.912 and a low boundary distance (BD) 0.928 mm. With our unified and expendable framework, an efficient clinical tool for M(3) spinal image segmentation can be easily achieved, and will substantially benefit the diagnosis and treatment of spinal diseases.

  16. Hierarchical Image Segmentation Using Correlation Clustering.

    PubMed

    Alush, Amir; Goldberger, Jacob

    2016-06-01

    In this paper, we apply efficient implementations of integer linear programming to the problem of image segmentation. The image is first grouped into superpixels and then local information is extracted for each pair of spatially adjacent superpixels. Given local scores on a map of several hundred superpixels, we use correlation clustering to find the global segmentation that is most consistent with the local evidence. We show that, although correlation clustering is known to be NP-hard, finding the exact global solution is still feasible by breaking the segmentation problem down into subproblems. Each such sub-problem can be viewed as an automatically detected image part. We can further accelerate the process by using the cutting-plane method, which provides a hierarchical structure of the segmentations. The efficiency and improved performance of the proposed method is compared to several state-of-the-art methods and demonstrated on several standard segmentation data sets.

  17. Generalized nonconvex optimization for medical image segmentation

    NASA Astrophysics Data System (ADS)

    Mitra, Sunanda; Joshi, Sujit

    2000-06-01

    Design of a generalized technique for medical image segmentation is a challenging task. Currently a number of approaches are being investigated for 2-D and 3-D medical image segmentation for diagnostic and research applications. The methodology used in this work is aimed at obtaining a generalized solution of non-convex optimization problems by including a structural constraint of mass or density and the concept of additivity properties of entropy to a recently developed statistical approach to clustering and classification. The original computationally intensive procedure is made more efficient both in processing time and accuracy by employing a new similarity parameter for generating the initial clusters that are updated by minimizing an energy function relating the image entropy and expected distortion. The application of the computationally intensive yet generalized solution to nonconvex optimization to a limited set of medical images has resulted in excellent segmentation when compared to other clustering based segmentation approaches. The addition of the parametric approach to determine the initial number of clusters allows significant reduction in processing time and better design of automated segmentation procedure. This research work generalizes a deterministic annealing i.e. a specific statistical approach to solve nonconvex optimization problems by developing a more efficient technique applicable to nonconvex optimization problems (getting trapped in local minima). However, the DA approach is extremely computationally intensive for applications such as image segmentation. The new integrated approach developed in this work allows this optimization technique to be used for medical image segmentation.

  18. Cardiovascular Magnetic Resonance Imaging

    NASA Astrophysics Data System (ADS)

    Pelc, Norbert

    2000-03-01

    Cardiovascular diseases are a major source of morbidity and mortality in the United States. Early detection of disease can often be used to improved outcomes, either through direct interventions (e.g. surgical corrections) or by causing the patient to modify his or her behavior (e.g. smoking cessation or dietary changes). Ideally, the detection process should be noninvasive (i.e. it should not be associated with significant risk). Magnetic Resonance Imaging (MRI) refers to the formation of images by localizing NMR signals, typically from protons in the body. As in other applications of NMR, a homogeneous static magnetic field ( ~0.5 to 4 T) is used to create ``longitudinal" magnetization. A magnetic field rotating at the Larmor frequency (proportional to the static field) excites spins, converting longitudinal magnetization to ``transverse" magnetization and generating a signal. Localization is performed using pulsed gradients in the static field. MRI can produce images of 2-D slices, 3-D volumes, time-resolved images of pseudo-periodic phenomena such as heart function, and even real-time imaging. It is also possible to acquire spatially localized NMR spectra. MRI has a number of advantages, but perhaps the most fundamental is the richness of the contrast mechanisms. Tissues can be differentiated by differences in proton density, NMR properties, and even flow or motion. We also have the ability to introduce substances that alter NMR signals. These contrast agents can be used to enhance vascular structures and measure perfusion. Cardiovascular MRI allows the reliable diagnosis of important conditions. It is possible to image the blood vessel tree, quantitate flow and perfusion, and image cardiac contraction. Fundamentally, the power of MRI as a diagnostic tool stems from the richness of the contrast mechanisms and the flexibility in control of imaging parameters.

  19. FISICO: Fast Image SegmentatIon COrrection

    PubMed Central

    Valenzuela, Waldo; Ferguson, Stephen J.; Ignasiak, Dominika; Diserens, Gaëlle; Häni, Levin; Wiest, Roland; Vermathen, Peter; Boesch, Chris

    2016-01-01

    Background and Purpose In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis. Methods We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images. Results Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively. PMID:27224061

  20. Isoperimetric graph partitioning for image segmentation.

    PubMed

    Grady, Leo; Schwartz, Eric L

    2006-03-01

    Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability.

  1. Magnetic Resonance Imaging Duodenoscope.

    PubMed

    Syms, Richard R A; Young, Ian R; Wadsworth, Christopher A; Taylor-Robinson, Simon D; Rea, Marc

    2013-12-01

    A side-viewing duodenoscope capable of both optical and magnetic resonance imaging (MRI) is described. The instrument is constructed from MR-compatible materials and combines a coherent fiber bundle for optical imaging, an irrigation channel and a side-opening biopsy channel for the passage of catheter tools with a tip saddle coil for radio-frequency signal reception. The receiver coil is magnetically coupled to an internal pickup coil to provide intrinsic safety. Impedance matching is achieved using a mechanically variable mutual inductance, and active decoupling by PIN-diode switching. (1)H MRI of phantoms and ex vivo porcine liver specimens was carried out at 1.5 T. An MRI field-of-view appropriate for use during endoscopic retrograde cholangiopancreatography (ERCP) was obtained, with limited artefacts, and a signal-to-noise ratio advantage over a surface array coil was demonstrated. PMID:23807423

  2. Convergent Coarseness Regulation for Segmented Images

    SciTech Connect

    Paglieroni, D W

    2004-05-27

    In segmentation of remotely sensed images, the number of pixel classes and their spectral representations are often unknown a priori. Even with prior knowledge, pixels with spectral components from multiple classes lead to classification errors and undesired small region artifacts. Coarseness regulation for segmented images is proposed as an efficient novel technique for handling these problems. Beginning with an over-segmented image, perceptually similar connected regions are iteratively merged using a method reminiscent of region growing, except the primitives are regions, not pixels. Interactive coarseness regulation is achieved by specifying the area {alpha} of the largest region eligible for merging. A region with area less than {alpha} is merged with the most spectrally similar connected region, unless the regions are perceived as spectrally dissimilar. In convergent coarseness regulation, which requires no user interaction, {alpha} is specified as the total number of pixels in the image, and the coarseness regulation output converges to a steady-state segmentation that remains unchanged as {alpha} is further increased. By applying convergent coarseness regulation to AVIRIS, IKONOS and DigitalGlobe images, and quantitatively comparing computer-generated segmentations to segmentations generated manually by a human analyst, it was found that the quality of the input segmentations was consistently and dramatically improved.

  3. A segmentation algorithm for noisy images

    SciTech Connect

    Xu, Y.; Olman, V.; Uberbacher, E.C.

    1996-12-31

    This paper presents a 2-D image segmentation algorithm and addresses issues related to its performance on noisy images. The algorithm segments an image by first constructing a minimum spanning tree representation of the image and then partitioning the spanning tree into sub-trees representing different homogeneous regions. The spanning tree is partitioned in such a way that the sum of gray-level variations over all partitioned subtrees is minimized under the constraints that each subtree has at least a specified number of pixels and two adjacent subtrees have significantly different ``average`` gray-levels. Two types of noise, transmission errors and Gaussian additive noise. are considered and their effects on the segmentation algorithm are studied. Evaluation results have shown that the segmentation algorithm is robust in the presence of these two types of noise.

  4. A summary of image segmentation techniques

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly

    1993-01-01

    Machine vision systems are often considered to be composed of two subsystems: low-level vision and high-level vision. Low level vision consists primarily of image processing operations performed on the input image to produce another image with more favorable characteristics. These operations may yield images with reduced noise or cause certain features of the image to be emphasized (such as edges). High-level vision includes object recognition and, at the highest level, scene interpretation. The bridge between these two subsystems is the segmentation system. Through segmentation, the enhanced input image is mapped into a description involving regions with common features which can be used by the higher level vision tasks. There is no theory on image segmentation. Instead, image segmentation techniques are basically ad hoc and differ mostly in the way they emphasize one or more of the desired properties of an ideal segmenter and in the way they balance and compromise one desired property against another. These techniques can be categorized in a number of different groups including local vs. global, parallel vs. sequential, contextual vs. noncontextual, interactive vs. automatic. In this paper, we categorize the schemes into three main groups: pixel-based, edge-based, and region-based. Pixel-based segmentation schemes classify pixels based solely on their gray levels. Edge-based schemes first detect local discontinuities (edges) and then use that information to separate the image into regions. Finally, region-based schemes start with a seed pixel (or group of pixels) and then grow or split the seed until the original image is composed of only homogeneous regions. Because there are a number of survey papers available, we will not discuss all segmentation schemes. Rather than a survey, we take the approach of a detailed overview. We focus only on the more common approaches in order to give the reader a flavor for the variety of techniques available yet present enough

  5. A New Markov Random Field Segmentation Method for Breast Lesion Segmentation in MR images.

    PubMed

    Azmi, Reza; Norozi, Narges

    2011-07-01

    Breast cancer is a major public health problem for women in the Iran and many other parts of the world. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a pivotal role in breast cancer care, including detection, diagnosis, and treatment monitoring. But segmentation of these images which is seriously affected by intensity inhomogeneities created by radio-frequency coils is a challenging task. Markov Random Field (MRF) is used widely in medical image segmentation especially in MR images. It is because this method can model intensity inhomogeneities occurring in these images. But this method has two critical weaknesses: Computational complexity and sensitivity of the results to the models parameters. To overcome these problems, in this paper, we present Improved-Markov Random Field (I-MRF) method for breast lesion segmentation in MR images. Unlike the conventional MRF, in the proposed approach, we don't use the Iterative Conditional Mode (ICM) method or Simulated Annealing (SA) for class membership estimation of each pixel (lesion and non-lesion). The prior distribution of the class membership is modeled as a ratio of two conditional probability distributions in a neighborhood which is defined for each pixel: probability distribution of similar pixels and non-similar ones. Since our proposed approach don't use an iterative method for maximizing the posterior probability, above mentioned problems are solved. Experimental results show that performance of segmentation in this approach is higher than conventional MRF in terms of accuracy, precision, and Computational complexity.

  6. Toward a generic evaluation of image segmentation.

    PubMed

    Cardoso, Jaime S; Corte-Real, Luís

    2005-11-01

    Image segmentation plays a major role in a broad range of applications. Evaluating the adequacy of a segmentation algorithm for a given application is a requisite both to allow the appropriate selection of segmentation algorithms as well as to tune their parameters for optimal performance. However, objective segmentation quality evaluation is far from being a solved problem. In this paper, a generic framework for segmentation evaluation is introduced after a brief review of previous work. A metric based on the distance between segmentation partitions is proposed to overcome some of the limitations of existing approaches. Symmetric and asymmetric distance metric alternatives are presented to meet the specificities of a wide class of applications. Experimental results confirm the potential of the proposed measures. PMID:16279178

  7. [Magnetic resonance tomography as the basis for biomechanical analysis. The simulation of the birth process as an example of the expanded information potentials of segmental imaging procedures].

    PubMed

    Wischnik, A; Lehmann, K J; Nalepa, E; Wentz, K U; Georgi, M; Melchert, F

    1992-10-01

    A method is presented that enables the use of (static) informations from magnetic resonance imaging (MRI) for (dynamic) biomechanical analysis. Using a specially developed software MRI pixel matrices are colour-coded and--according to the principle of same density--line data are created. After sectional attribution of the resulting polygons a three-dimensional mesh of so-called finite elements is created which can then be used in deformation analysis. This method is exemplified by a project dealing with the simulation of birth mechanics, which is finally aimed at validating the results from radiologic pelvimetry. First analyses show that even under foetal head moulding conditions, being considered as normal, such sensitive structures as the cerebellum, brain stem as well as the ventricles with the plexus chorioidei are to be found within the maximum isobars within a range of 104-140 N(10.6-14.3 kp). PMID:1391834

  8. Identification of High-Risk Patients with Non-ST Segment Elevation Myocardial Infarction using Strain Doppler Echocardiography: Correlation with Cardiac Magnetic Resonance Imaging

    PubMed Central

    Loutfi, Mohamed; Ashour, Sanaa; El-Sharkawy, Eman; El-Fawal, Sara; El-Touny, Karim

    2016-01-01

    Assessment of left ventricular (LV) function is important for decision-making and risk stratification in patients with acute coronary syndrome. Many patients with non-ST segment elevation myocardial infarction (NSTEMI) have substantial infarction, but these patients often do not reveal clinical signs of instability, and they rarely fulfill criteria for acute revascularization therapy. AIM This study evaluated the potential of strain Doppler echocardiography analysis for the assessment of LV infarct size when compared with standard two-dimensional echo and cardiac magnetic resonance (CMR) data. METHODS Thirty patients with NSTEMI were examined using echocardiography after hospitalization for 1.8 ± 1.1 days for the assessment of left ventricular ejection fraction, wall motion score index (WMSI), and LV global longitudinal strain (GLS). Infarct size was assessed using delayed enhancement CMR 6.97 ± 3.2 days after admission as a percentage of total myocardial volume. RESULTS GLS was performed in 30 patients, and 82.9% of the LV segments were accepted for GLS analysis. Comparisons between patients with a complete set of GLS and standard echo, GLS and CMR were performed. The linear relationship demonstrated moderately strong and significant associations between GLS and ejection fraction (EF) as determined using standard echo (r = 0.452, P = 0.012), WMSI (r = 0.462, P = 0.010), and the gold standard CMR-determined EF (r = 0.57, P < 0.001). Receiver operating characteristic curves were used to analyze the ability of GLS to evaluate infarct size. GLS was the best predictor of infarct size in a multivariate linear regression analysis (β = 1.51, P = 0.027). WMSI >1.125 and a GLS cutoff value of −11.29% identified patients with substantial infarction (≥12% of total myocardial volume measured using CMR) with accuracies of 76.7% and 80%, respectively. However, GLS remained the only independent predictor in a multivariate logistic regression analysis to identify an infarct

  9. Review methods for image segmentation from computed tomography images

    SciTech Connect

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

    2014-12-04

    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.

  10. Segmentation of MR images for computer-assisted surgery of the lumbar spine

    NASA Astrophysics Data System (ADS)

    Hoad, C. L.; Martel, A. L.

    2002-10-01

    This paper describes a segmentation algorithm designed to separate bone from soft tissue in magnetic resonance (MR) images developed for computer-assisted surgery of the spine. The algorithm was applied to MR images of the spine of healthy volunteers. Registration experiments were carried out on a physical model of a spine generated from computed tomography (CT) data of a surgical patient. Segmented CT, manually segmented MR and MR images segmented using the developed algorithm were compared. The algorithm performed well at segmenting bone from soft tissue on images taken of healthy volunteers. Registration experiments showed similar results between the CT and MR data. The MR data, which were manually segmented, performed worse on visual verification experiments than both the CT and semi-automatic segmented data. The algorithm developed performs well at segmenting bone from soft tissue in MR images of the spine as measured using registration experiments.

  11. Magnetic resonance brain tissue segmentation based on sparse representations

    NASA Astrophysics Data System (ADS)

    Rueda, Andrea

    2015-12-01

    Segmentation or delineation of specific organs and structures in medical images is an important task in the clinical diagnosis and treatment, since it allows to characterize pathologies through imaging measures (biomarkers). In brain imaging, segmentation of main tissues or specific structures is challenging, due to the anatomic variability and complexity, and the presence of image artifacts (noise, intensity inhomogeneities, partial volume effect). In this paper, an automatic segmentation strategy is proposed, based on sparse representations and coupled dictionaries. Image intensity patterns are singly related to tissue labels at the level of small patches, gathering this information in coupled intensity/segmentation dictionaries. This dictionaries are used within a sparse representation framework to find the projection of a new intensity image onto the intensity dictionary, and the same projection can be used with the segmentation dictionary to estimate the corresponding segmentation. Preliminary results obtained with two publicly available datasets suggest that the proposal is capable of estimating adequate segmentations for gray matter (GM) and white matter (WM) tissues, with an average overlapping of 0:79 for GM and 0:71 for WM (with respect to original segmentations).

  12. Intraoperative magnetic resonance imaging.

    PubMed

    Hall, Walter A; Truwit, Charles L

    2011-01-01

    Neurosurgeons have become reliant on image-guidance to perform safe and successful surgery both time-efficiently and cost-effectively. Neuronavigation typically involves either rigid (frame-based) or skull-mounted (frameless) stereotactic guidance derived from computed tomography (CT) or magnetic resonance imaging (MRI) that is obtained days or immediately before the planned surgical procedure. These systems do not accommodate for brain shift that is unavoidable once the cranium is opened and cerebrospinal fluid is lost. Intraoperative MRI (ioMRI) systems ranging in strength from 0.12 to 3 Tesla (T) have been developed in part because they afford neurosurgeons the opportunity to accommodate for brain shift during surgery. Other distinct advantages of ioMRI include the excellent soft tissue discrimination, the ability to view the surgical site in three dimensions, and the ability to "see" tumor beyond the surface visualization of the surgeon's eye, either with or without a surgical microscope. The enhanced ability to view the tumor being biopsied or resected allows the surgeon to choose a safe surgical corridor that avoids critical structures, maximizes the extent of the tumor resection, and confirms that an intraoperative hemorrhage has not resulted from surgery. Although all ioMRI systems allow for basic T1- and T2-weighted imaging, only high-field (>1.5 T) MRI systems are capable of MR spectroscopy (MRS), MR angiography (MRA), MR venography (MRV), diffusion-weighted imaging (DWI), and brain activation studies. By identifying vascular structures with MRA and MRV, it may be possible to prevent their inadvertent injury during surgery. Biopsying those areas of elevated phosphocholine on MRS may improve the diagnostic yield for brain biopsy. Mapping out eloquent brain function may influence the surgical path to a tumor being resected or biopsied. The optimal field strength for an ioMRI-guided surgical system and the best configuration for that system are as yet

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

    SciTech Connect

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

    1996-12-31

    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.

  14. Simplified labeling process for medical image segmentation.

    PubMed

    Gao, Mingchen; Huang, Junzhou; Huang, Xiaolei; Zhang, Shaoting; Metaxas, Dimitris N

    2012-01-01

    Image segmentation plays a crucial role in many medical imaging applications by automatically locating the regions of interest. Typically supervised learning based segmentation methods require a large set of accurately labeled training data. However, thel labeling process is tedious, time consuming and sometimes not necessary. We propose a robust logistic regression algorithm to handle label outliers such that doctors do not need to waste time on precisely labeling images for training set. To validate its effectiveness and efficiency, we conduct carefully designed experiments on cervigram image segmentation while there exist label outliers. Experimental results show that the proposed robust logistic regression algorithms achieve superior performance compared to previous methods, which validates the benefits of the proposed algorithms. PMID:23286072

  15. [Medical image automatic adjusting window and segmentation].

    PubMed

    Zhou, Zhenhuan; Chen, Siping; Tao, Duchun; Chen, Xinhai

    2005-04-01

    Image guided surgical navigation system is the most advanced surgical apparatus, which develops most rapidly and has great application prospects in neurosurgery, orthopaedics, E.N.T. department etc. In current surgical navigation systems, windowing, segmenting and registration of medical images all depend on manual operation, and automation of image processing is urgently needed. This paper proposes the algorithm which realizes very well automatic windowing and segmentation of medical images: first, we analyze a lot of MRI and CT images and propose corresponding windowing algorithm according to their common features of intensity distribution. Experiments show that the effects of windowing of most MRI and CT images are optimized. Second, we propose the seed growing algorithm based on intensity connectivity,which can segment tumor and its boundary exactly by simply clicking the mouse, and control dynamically the results in real time. If computer memory permits, the algorithm can segment 3D images directly. Tests show that this function is able to shorten the time of surgical planning, lower the complexity, and improve the efficiency in navigation surgery. PMID:15884547

  16. MBIS: multivariate Bayesian image segmentation tool.

    PubMed

    Esteban, Oscar; Wollny, Gert; Gorthi, Subrahmanyam; Ledesma-Carbayo, María-J; Thiran, Jean-Philippe; Santos, Andrés; Bach-Cuadra, Meritxell

    2014-07-01

    We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

  17. Automated segmentation of three-dimensional MR brain images

    NASA Astrophysics Data System (ADS)

    Park, Jonggeun; Baek, Byungjun; Ahn, Choong-Il; Ku, Kyo Bum; Jeong, Dong Kyun; Lee, Chulhee

    2006-03-01

    Brain segmentation is a challenging problem due to the complexity of the brain. In this paper, we propose an automated brain segmentation method for 3D magnetic resonance (MR) brain images which are represented as a sequence of 2D brain images. The proposed method consists of three steps: pre-processing, removal of non-brain regions (e.g., the skull, meninges, other organs, etc), and spinal cord restoration. In pre-processing, we perform adaptive thresholding which takes into account variable intensities of MR brain images corresponding to various image acquisition conditions. In segmentation process, we iteratively apply 2D morphological operations and masking for the sequences of 2D sagittal, coronal, and axial planes in order to remove non-brain tissues. Next, final 3D brain regions are obtained by applying OR operation for segmentation results of three planes. Finally we reconstruct the spinal cord truncated during the previous processes. Experiments are performed with fifteen 3D MR brain image sets with 8-bit gray-scale. Experiment results show the proposed algorithm is fast, and provides robust and satisfactory results.

  18. OCT image segmentation of the prostate nerves

    NASA Astrophysics Data System (ADS)

    Chitchian, Shahab; Weldon, Thomas P.; Fried, Nathaniel M.

    2009-08-01

    The cavernous nerves course along the surface of the prostate and are responsible for erectile function. Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery may improve nerve preservation and postoperative sexual potency. In this study, 2-D OCT images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. Three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The features were segmented using a nearestneighbor classifier. N-ary morphological post-processing was used to remove small voids. The cavernous nerves were differentiated from the prostate gland with a segmentation error rate of only 0.058 +/- 0.019.

  19. Hierarchical Segmentation Enhances Diagnostic Imaging

    NASA Technical Reports Server (NTRS)

    2007-01-01

    Bartron Medical Imaging LLC (BMI), of New Haven, Connecticut, gained a nonexclusive license from Goddard Space Flight Center to use the RHSEG software in medical imaging. To manage image data, BMI then licensed two pattern-matching software programs from NASA's Jet Propulsion Laboratory that were used in image analysis and three data-mining and edge-detection programs from Kennedy Space Center. More recently, BMI made NASA history by being the first company to partner with the Space Agency through a Cooperative Research and Development Agreement to develop a 3-D version of RHSEG. With U.S. Food and Drug Administration clearance, BMI will sell its Med-Seg imaging system with the 2-D version of the RHSEG software to analyze medical imagery from CAT and PET scans, MRI, ultrasound, digitized X-rays, digitized mammographies, dental X-rays, soft tissue analyses, moving object analyses, and soft-tissue slides such as Pap smears for the diagnoses and management of diseases. Extending the software's capabilities to three dimensions will eventually enable production of pixel-level views of a tumor or lesion, early identification of plaque build-up in arteries, and identification of density levels of microcalcification in mammographies.

  20. A statistically based flow for image segmentation.

    PubMed

    Pichon, Eric; Tannenbaum, Allen; Kikinis, Ron

    2004-09-01

    In this paper we present a new algorithm for 3D medical image segmentation. The algorithm is versatile, fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from a learned non-parametric estimation of the statistics of the region to be segmented. Implementation details are discussed and source code is freely available as part of the 3D Slicer project. In addition, a new unified set of validation metrics is proposed. Results on artificial and real MRI images show that the algorithm performs well on large brain structures both in terms of accuracy and robustness to noise. PMID:15450221

  1. Functional magnetic resonance imaging.

    PubMed

    Buchbinder, Bradley R

    2016-01-01

    Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks. PMID:27432660

  2. Functional magnetic resonance imaging.

    PubMed

    Buchbinder, Bradley R

    2016-01-01

    Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks.

  3. Color Image Segmentation in a Quaternion Framework

    PubMed Central

    Subakan, Özlem N.; Vemuri, Baba C.

    2010-01-01

    In this paper, we present a feature/detail preserving color image segmentation framework using Hamiltonian quaternions. First, we introduce a novel Quaternionic Gabor Filter (QGF) which can combine the color channels and the orientations in the image plane. Using the QGFs, we extract the local orientation information in the color images. Second, in order to model this derived orientation information, we propose a continuous mixture of appropriate hypercomplex exponential basis functions. We derive a closed form solution for this continuous mixture model. This analytic solution is in the form of a spatially varying kernel which, when convolved with the signed distance function of an evolving contour (placed in the color image), yields a detail preserving segmentation. PMID:21243101

  4. From Acoustic Segmentation to Language Processing: Evidence from Optical Imaging

    PubMed Central

    Obrig, Hellmuth; Rossi, Sonja; Telkemeyer, Silke; Wartenburger, Isabell

    2010-01-01

    During language acquisition in infancy and when learning a foreign language, the segmentation of the auditory stream into words and phrases is a complex process. Intuitively, learners use “anchors” to segment the acoustic speech stream into meaningful units like words and phrases. Regularities on a segmental (e.g., phonological) or suprasegmental (e.g., prosodic) level can provide such anchors. Regarding the neuronal processing of these two kinds of linguistic cues a left-hemispheric dominance for segmental and a right-hemispheric bias for suprasegmental information has been reported in adults. Though lateralization is common in a number of higher cognitive functions, its prominence in language may also be a key to understanding the rapid emergence of the language network in infants and the ease at which we master our language in adulthood. One question here is whether the hemispheric lateralization is driven by linguistic input per se or whether non-linguistic, especially acoustic factors, “guide” the lateralization process. Methodologically, functional magnetic resonance imaging provides unsurpassed anatomical detail for such an enquiry. However, instrumental noise, experimental constraints and interference with EEG assessment limit its applicability, pointedly in infants and also when investigating the link between auditory and linguistic processing. Optical methods have the potential to fill this gap. Here we review a number of recent studies using optical imaging to investigate hemispheric differences during segmentation and basic auditory feature analysis in language development. PMID:20725516

  5. From acoustic segmentation to language processing: evidence from optical imaging.

    PubMed

    Obrig, Hellmuth; Rossi, Sonja; Telkemeyer, Silke; Wartenburger, Isabell

    2010-01-01

    During language acquisition in infancy and when learning a foreign language, the segmentation of the auditory stream into words and phrases is a complex process. Intuitively, learners use "anchors" to segment the acoustic speech stream into meaningful units like words and phrases. Regularities on a segmental (e.g., phonological) or suprasegmental (e.g., prosodic) level can provide such anchors. Regarding the neuronal processing of these two kinds of linguistic cues a left-hemispheric dominance for segmental and a right-hemispheric bias for suprasegmental information has been reported in adults. Though lateralization is common in a number of higher cognitive functions, its prominence in language may also be a key to understanding the rapid emergence of the language network in infants and the ease at which we master our language in adulthood. One question here is whether the hemispheric lateralization is driven by linguistic input per se or whether non-linguistic, especially acoustic factors, "guide" the lateralization process. Methodologically, functional magnetic resonance imaging provides unsurpassed anatomical detail for such an enquiry. However, instrumental noise, experimental constraints and interference with EEG assessment limit its applicability, pointedly in infants and also when investigating the link between auditory and linguistic processing. Optical methods have the potential to fill this gap. Here we review a number of recent studies using optical imaging to investigate hemispheric differences during segmentation and basic auditory feature analysis in language development.

  6. Segmentation of prostate cancer tissue microarray images

    NASA Astrophysics Data System (ADS)

    Cline, Harvey E.; Can, Ali; Padfield, Dirk

    2006-02-01

    Prostate cancer is diagnosed by histopathology interpretation of hematoxylin and eosin (H and E)-stained tissue sections. Gland and nuclei distributions vary with the disease grade. The morphological features vary with the advance of cancer where the epithelial regions grow into the stroma. An efficient pathology slide image analysis method involved using a tissue microarray with known disease stages. Digital 24-bit RGB images were acquired for each tissue element on the slide with both 10X and 40X objectives. Initial segmentation at low magnification was accomplished using prior spectral characteristics from a training tissue set composed of four tissue clusters; namely, glands, epithelia, stroma and nuclei. The segmentation method was automated by using the training RGB values as an initial guess and iterating the averaging process 10 times to find the four cluster centers. Labels were assigned to the nearest cluster center in red-blue spectral feature space. An automatic threshold algorithm separated the glands from the tissue. A visual pseudo color representation of 60 segmented tissue microarray image was generated where white, pink, red, blue colors represent glands, epithelia, stroma and nuclei, respectively. The higher magnification images provided refined nuclei morphology. The nuclei were detected with a RGB color space principle component analysis that resulted in a grey scale image. The shape metrics such as compactness, elongation, minimum and maximum diameters were calculated based on the eigenvalues of the best-fitting ellipses to the nuclei.

  7. Hepatic lesions segmentation in ultrasound nonlinear imaging

    NASA Astrophysics Data System (ADS)

    Kissi, Adelaide A.; Cormier, Stephane; Pourcelot, Leandre; Tranquart, Francois

    2005-04-01

    Doppler has been used for many years for cardiovascular exploration in order to visualize the vessels walls and anatomical or functional diseases. The use of ultrasound contrast agents makes it possible to improve ultrasonic information. Nonlinear ultrasound imaging highlights the detection of these agents within an organ and hence is a powerful technique to image perfusion of an organ in real-time. The visualization of flow and perfusion provides important information for the diagnosis of various diseases as well as for the detection of tumors. However, the images are buried in noise, the speckle, inherent in the image formation. Furthermore at portal phase, there is often an absence of clear contrast between lesions and surrounding tissues because the organ is filled with agents. In this context, we propose a new method of automatic liver lesions segmentation in nonlinear imaging sequences for the quantification of perfusion. Our method of segmentation is divided into two stages. Initially, we developed an anisotropic diffusion step which raised the structural characteristics to eliminate the speckle. Then, a fuzzy competitive clustering process allowed us to delineate liver lesions. This method has been used to detect focal hepatic lesions (metastasis, nodular hyperplasia, adenoma). Compared to medical expert"s report obtained on 15 varied lesions, the automatic segmentation allows us to identify and delineate focal liver lesions during the portal phase which high accuracy. Our results show that this method improves markedly the recognition of focal hepatic lesions and opens the way for future precise quantification of contrast enhancement.

  8. Neutron Resonance Spin Determination Using Multi-Segmented Detector DANCE

    SciTech Connect

    Baramsai, B.; Mitchell, G. E.; Chyzh, A.; Dashdorj, D.; Walker, C.; Agvaanluvsan, U.; Becvar, F.; Krticka, M.; Bredeweg, T. A.; Couture, A.; Haight, R. C.; Jandel, M.; Keksis, A. L.; O'Donnell, J. M.; Rundberg, R. S.; Ullmann, J. L.; Vieira, D. J.; Wouters, J. M.

    2011-06-01

    A sensitive method to determine the spin of neutron resonances is introduced based on the statistical pattern recognition technique. The new method was used to assign the spins of s-wave resonances in {sup 155}Gd. The experimental neutron capture data for these nuclei were measured with the DANCE (Detector for Advanced Neutron Capture Experiment) calorimeter at the Los Alamos Neutron Science Center. The highly segmented calorimeter provided detailed multiplicity distributions of the capture {gamma}-rays. Using this information, the spins of the neutron capture resonances were determined. With these new spin assignments, level spacings are determined separately for s-wave resonances with J{sup {pi}} = 1{sup -} and 2{sup -}.

  9. Optically detected magnetic resonance imaging

    SciTech Connect

    Blank, Aharon; Shapiro, Guy; Fischer, Ran; London, Paz; Gershoni, David

    2015-01-19

    Optically detected magnetic resonance provides ultrasensitive means to detect and image a small number of electron and nuclear spins, down to the single spin level with nanoscale resolution. Despite the significant recent progress in this field, it has never been combined with the power of pulsed magnetic resonance imaging techniques. Here, we demonstrate how these two methodologies can be integrated using short pulsed magnetic field gradients to spatially encode the sample. This result in what we denote as an 'optically detected magnetic resonance imaging' technique. It offers the advantage that the image is acquired in parallel from all parts of the sample, with well-defined three-dimensional point-spread function, and without any loss of spectroscopic information. In addition, this approach may be used in the future for parallel but yet spatially selective efficient addressing and manipulation of the spins in the sample. Such capabilities are of fundamental importance in the field of quantum spin-based devices and sensors.

  10. Automatic segmentation of mammogram and tomosynthesis images

    NASA Astrophysics Data System (ADS)

    Sargent, Dusty; Park, Sun Young

    2016-03-01

    Breast cancer is a one of the most common forms of cancer in terms of new cases and deaths both in the United States and worldwide. However, the survival rate with breast cancer is high if it is detected and treated before it spreads to other parts of the body. The most common screening methods for breast cancer are mammography and digital tomosynthesis, which involve acquiring X-ray images of the breasts that are interpreted by radiologists. The work described in this paper is aimed at optimizing the presentation of mammography and tomosynthesis images to the radiologist, thereby improving the early detection rate of breast cancer and the resulting patient outcomes. Breast cancer tissue has greater density than normal breast tissue, and appears as dense white image regions that are asymmetrical between the breasts. These irregularities are easily seen if the breast images are aligned and viewed side-by-side. However, since the breasts are imaged separately during mammography, the images may be poorly centered and aligned relative to each other, and may not properly focus on the tissue area. Similarly, although a full three dimensional reconstruction can be created from digital tomosynthesis images, the same centering and alignment issues can occur for digital tomosynthesis. Thus, a preprocessing algorithm that aligns the breasts for easy side-by-side comparison has the potential to greatly increase the speed and accuracy of mammogram reading. Likewise, the same preprocessing can improve the results of automatic tissue classification algorithms for mammography. In this paper, we present an automated segmentation algorithm for mammogram and tomosynthesis images that aims to improve the speed and accuracy of breast cancer screening by mitigating the above mentioned problems. Our algorithm uses information in the DICOM header to facilitate preprocessing, and incorporates anatomical region segmentation and contour analysis, along with a hidden Markov model (HMM) for

  11. Automatic scale selection for medical image segmentation

    NASA Astrophysics Data System (ADS)

    Bayram, Ersin; Wyatt, Christopher L.; Ge, Yaorong

    2001-07-01

    The scale of interesting structures in medical images is space variant because of partial volume effects, spatial dependence of resolution in many imaging modalities, and differences in tissue properties. Existing segmentation methods either apply a single scale to the entire image or try fine-to-coarse/coarse-to-fine tracking of structures over multiple scales. While single scale approaches fail to fully recover the perceptually important structures, multi-scale methods have problems in providing reliable means to select proper scales and integrating information over multiple scales. A recent approach proposed by Elder and Zucker addresses the scale selection problem by computing a minimal reliable scale for each image pixel. The basic premise of this approach is that, while the scale of structures within an image vary spatially, the imaging system is fixed. Hence, sensor noise statistics can be calculated. Based on a model of edges to be detected, and operators to be used for detection, one can locally compute a unique minimal reliable scale at which the likelihood of error due to sensor noise is less than or equal to a predetermined threshold. In this paper, we improve the segmentation method based on the minimal reliable scale selection and evaluate its effectiveness with both simulated and actual medical data.

  12. Image Segmentation, Registration, Compression, and Matching

    NASA Technical Reports Server (NTRS)

    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

  13. Fast planar segmentation of depth images

    NASA Astrophysics Data System (ADS)

    Javan Hemmat, Hani; Pourtaherian, Arash; Bondarev, Egor; de With, Peter H. N.

    2015-03-01

    One of the major challenges for applications dealing with the 3D concept is the real-time execution of the algorithms. Besides this, for the indoor environments, perceiving the geometry of surrounding structures plays a prominent role in terms of application performance. Since indoor structures mainly consist of planar surfaces, fast and accurate detection of such features has a crucial impact on quality and functionality of the 3D applications, e.g. decreasing model size (decimation), enhancing localization, mapping, and semantic reconstruction. The available planar-segmentation algorithms are mostly developed using surface normals and/or curvatures. Therefore, they are computationally expensive and challenging for real-time performance. In this paper, we introduce a fast planar-segmentation method for depth images avoiding surface normal calculations. Firstly, the proposed method searches for 3D edges in a depth image and finds the lines between identified edges. Secondly, it merges all the points on each pair of intersecting lines into a plane. Finally, various enhancements (e.g. filtering) are applied to improve the segmentation quality. The proposed algorithm is capable of handling VGA-resolution depth images at a 6 FPS frame-rate with a single-thread implementation. Furthermore, due to the multi-threaded design of the algorithm, we achieve a factor of 10 speedup by deploying a GPU implementation.

  14. Cerebral microbleed segmentation from susceptibility weighted images

    NASA Astrophysics Data System (ADS)

    Roy, Snehashis; Jog, Amod; Magrath, Elizabeth; Butman, John A.; Pham, Dzung L.

    2015-03-01

    Cerebral microbleeds (CMB) are a common marker of traumatic brain injury. Accurate detection and quantification of the CMBs are important for better understanding the progression and prognosis of the injury. Previous microbleed detection methods have suffered from a high rate of false positives, which is time consuming to manually correct. In this paper, we propose a fully automatic, example-based method to segment CMBs from susceptibility-weighted (SWI) scans, where examples from an already segmented template SWI image are used to detect CMBs in a new image. First, multiple radial symmetry transforms (RST) are performed on the template SWI to detect small ellipsoidal structures, which serve as potential microbleed candidates. Then 3D patches from the SWI and its RSTs are combined to form a feature vector at each voxel of the image. A random forest regression is trained using the feature vectors, where the dependent variable is the binary segmentation voxel of the template. Once the regression is learnt, it is applied to a new SWI scan, whose feature vectors contain patches from SWI and its RSTs. Experiments on 26 subjects with mild to severe brain injury show a CMB detection sensitivity of 85:7%, specificity 99:5%, and a false positive to true positive ratio of 1:73, which is competitive with published methods while providing a significant reduction in computation time.

  15. Image segmentation using association rule features.

    PubMed

    Rushing, John A; Ranganath, Heggere; Hinke, Thomas H; Graves, Sara J

    2002-01-01

    A new type of texture feature based on association rules is described. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. The frequency of occurrence of these structures can be used to characterize texture. Methods for segmentation of textured images based on association rule features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. Association rule features are used to detect cumulus cloud fields in GOES satellite images and are found to achieve higher accuracy than other statistical texture features for this problem.

  16. MRI (Magnetic Resonance Imaging)

    MedlinePlus

    ... some MRI exams, intravenous (IV) drugs, such as gadolinium-based contrast agents (GBCAs) are used to change the contrast of the MR image. Gadolinium-based contrast agents are rare earth metals that ...

  17. Magnetic resonance imaging of optic nerve

    PubMed Central

    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. PMID:26752822

  18. Optimal retinal cyst segmentation from OCT images

    NASA Astrophysics Data System (ADS)

    Oguz, Ipek; Zhang, Li; Abramoff, Michael D.; Sonka, Milan

    2016-03-01

    Accurate and reproducible segmentation of cysts and fluid-filled regions from retinal OCT images is an important step allowing quantification of the disease status, longitudinal disease progression, and response to therapy in wet-pathology retinal diseases. However, segmentation of fluid-filled regions from OCT images is a challenging task due to their inhomogeneous appearance, the unpredictability of their number, size and location, as well as the intensity profile similarity between such regions and certain healthy tissue types. While machine learning techniques can be beneficial for this task, they require large training datasets and are often over-fitted to the appearance models of specific scanner vendors. We propose a knowledge-based approach that leverages a carefully designed cost function and graph-based segmentation techniques to provide a vendor-independent solution to this problem. We illustrate the results of this approach on two publicly available datasets with a variety of scanner vendors and retinal disease status. Compared to a previous machine-learning based approach, the volume similarity error was dramatically reduced from 81:3+/-56:4% to 22:2+/-21:3% (paired t-test, p << 0:001).

  19. Magnetic Resonance Image Example Based Contrast Synthesis

    PubMed Central

    Roy, Snehashis; Carass, Aaron; Prince, Jerry L.

    2013-01-01

    The performance of image analysis algorithms applied to magnetic resonance images is strongly influenced by the pulse sequences used to acquire the images. Algorithms are typically optimized for a targeted tissue contrast obtained from a particular implementation of a pulse sequence on a specific scanner. There are many practical situations, including multi-institution trials, rapid emergency scans, and scientific use of historical data, where the images are not acquired according to an optimal protocol or the desired tissue contrast is entirely missing. This paper introduces an image restoration technique that recovers images with both the desired tissue contrast and a normalized intensity profile. This is done using patches in the acquired images and an atlas containing patches of the acquired and desired tissue contrasts. The method is an example-based approach relying on sparse reconstruction from image patches. Its performance in demonstrated using several examples, including image intensity normalization, missing tissue contrast recovery, automatic segmentation, and multimodal registration. These examples demonstrate potential practical uses and also illustrate limitations of our approach. PMID:24058022

  20. Embedded Implementation of VHR Satellite Image Segmentation.

    PubMed

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-05-27

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage.

  1. Embedded Implementation of VHR Satellite Image Segmentation.

    PubMed

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-01-01

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage. PMID:27240370

  2. Embedded Implementation of VHR Satellite Image Segmentation

    PubMed Central

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-01-01

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage. PMID:27240370

  3. Unsupervised texture image segmentation by improved neural network ART2

    NASA Technical Reports Server (NTRS)

    Wang, Zhiling; Labini, G. Sylos; Mugnuolo, R.; Desario, Marco

    1994-01-01

    We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

  4. Segmentation of polycystic kidneys from MR images

    NASA Astrophysics Data System (ADS)

    Racimora, Dimitri; Vivier, Pierre-Hugues; Chandarana, Hersh; Rusinek, Henry

    2010-03-01

    Polycystic kidney disease (PKD) is a disorder characterized by the growth of numerous fluid filled cysts in the kidneys. Measuring cystic kidney volume is thus crucial to monitoring the evolution of the disease. While T2-weighted MRI delineates the organ, automatic segmentation is very difficult due to highly variable shape and image contrast. The interactive stereology methods used currently involve a compromise between segmentation accuracy and time. We have investigated semi-automated methods: active contours and a sub-voxel morphology based algorithm. Coronal T2- weighted images of 17 patients were acquired in four breath-holds using the HASTE sequence on a 1.5 Tesla MRI unit. The segmentation results were compared to ground truth kidney masks obtained as a consensus of experts. Automatic active contour algorithm yielded an average 22% +/- 8.6% volume error. A recently developed method (Bridge Burner) based on thresholding and constrained morphology failed to separate PKD from the spleen, yielding 37.4% +/- 8.7% volume error. Manual post-editing reduced the volume error to 3.2% +/- 0.8% for active contours and 3.2% +/- 0.6% for Bridge Burner. The total time (automated algorithm plus editing) was 15 min +/- 5 min for active contours and 19 min +/- 11 min for Bridge Burner. The average volume errors for stereology method were 5.9%, 6.2%, 5.4% for mesh size 6.6, 11, 16.5 mm. The average processing times were 17, 7, 4 min. These results show that nearly two-fold improvement in PKD segmentation accuracy over stereology technique can be achieved with a combination of active contours and postediting.

  5. A general framework for image segmentation using ordered spatial dependency.

    PubMed

    Rousson, Mikaël; Xu, Chenyang

    2006-01-01

    The segmentation problem appears in most medical imaging applications. Many research groups are pushing toward a whole body segmentation based on atlases. With a similar objective, we propose a general framework to segment several structures. Rather than inventing yet another segmentation algorithm, we introduce inter-structure spatial dependencies to work with existing segmentation algorithms. Ranking the structures according to their dependencies, we end up with a hierarchical approach that improves each individual segmentation and provides automatic initializations. The best ordering of the structures can be learned off-line. We apply this framework to the segmentation of several structures in brain MR images.

  6. Cavity- and waveguide-resonators in electron paramagnetic resonance, nuclear magnetic resonance, and magnetic resonance imaging.

    PubMed

    Webb, Andrew

    2014-11-01

    Cavity resonators are widely used in electron paramagnetic resonance, very high field magnetic resonance microimaging and also in high field human imaging. The basic principles and designs of different forms of cavity resonators including rectangular, cylindrical, re-entrant, cavity magnetrons, toroidal cavities and dielectric resonators are reviewed. Applications in EPR and MRI are summarized, and finally the topic of traveling wave MRI using the magnet bore as a waveguide is discussed.

  7. Segmentation of moving images by the human visual system.

    PubMed

    Chantelau, K

    1997-08-01

    New segments appearing in an image sequence or spontaneously accelerated segments are band limited by the visual system due to a nonperfect tracking of these segments by eye movements. In spite of this band limitation and acceleration of segments, a coarse segmentation (initial segmentation phase) can be performed by the visual system. This is interesting for the development of purely automatic segmentation algorithms for multimedia applications. In this paper the segmentation of the visual system is modelled and used in an automatic coarse initial segmentation. A suitable model for motion processing based on a spectral representation is presented and applied to the segmentation of synthetic and real image sequences with band limited and accelerated moving foreground and background segments.

  8. Low field magnetic resonance imaging

    DOEpatents

    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.

  9. Intensity-based segmentation of microarray images.

    PubMed

    Nagarajan, Radhakrishnan

    2003-07-01

    The underlying principle in microarray image analysis is that the spot intensity is a measure of the gene expression. This implicitly assumes the gene expression of a spot to be governed entirely by the distribution of the pixel intensities. Thus, a segmentation technique based on the distribution of the pixel intensities is appropriate for the current problem. In this paper, clustering-based segmentation is described to extract the target intensity of the spots. The approximate boundaries of the spots in the microarray are determined by manual adjustment of rectilinear grids. The distribution of the pixel intensity in a grid containing a spot is assumed to be the superposition of the foreground and the local background. The k-means clustering technique and the partitioning around medoids (PAM) were used to generate a binary partition of the pixel intensity distribution. The median (k-means) and the medoid (PAM) of the cluster members are chosen as the cluster representatives. The effectiveness of the clustering-based segmentation techniques was tested on publicly available arrays generated in a lipid metabolism experiment (Callow et al., 2000). The results are compared against those obtained using the region-growing approach (SPOT) (Yang et al., 2001). The effect of additive white Gaussian noise is also investigated. PMID:12906242

  10. a Minimum Spanning Tree Based Method for Uav Image Segmentation

    NASA Astrophysics Data System (ADS)

    Wang, Ping; Wei, Zheng; Cui, Weihong; Lin, Zhiyong

    2016-06-01

    This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

  11. Magnetic resonance imaging in endourology.

    PubMed

    Chan, A J; Prasad, P V; Saltzman, B

    2001-02-01

    Historically, the utilization of magnetic resonance imaging (MRI) in endourology has been limited. The availability of faster and stronger gradient systems has given rise to a number of data acquisition strategies that have significantly broadened the scope of MRI applications. These methods have led to the evaluation of anatomy and function using a single modality, and we describe our experience with MRI for comprehensive evaluation of the obstructed ureteropelvic junction. We also utilize these new imaging sequences in the investigation of alterated renal hemodynamics after extracorporeal shockwave lithotripsy and present our preliminary data on the application of MR perfusion imaging as a noninvasive technique for the evaluation of renal blood flow.

  12. Segmentation Fusion Techniques with Application to Plenoptic Images: A Survey.

    NASA Astrophysics Data System (ADS)

    Evin, D.; Hadad, A.; Solano, A.; Drozdowicz, B.

    2016-04-01

    The segmentation of anatomical and pathological structures plays a key role in the characterization of clinically relevant evidence from digital images. Recently, plenoptic imaging has emerged as a new promise to enrich the diagnostic potential of conventional photography. Since the plenoptic images comprises a set of slightly different versions of the target scene, we propose to make use of those images to improve the segmentation quality in relation to the scenario of a single image segmentation. The problem of finding a segmentation solution from multiple images of a single scene, is called segmentation fusion. This paper reviews the issue of segmentation fusion in order to find solutions that can be applied to plenoptic images, particularly images from the ophthalmological domain.

  13. Towards Automatic Image Segmentation Using Optimised Region Growing Technique

    NASA Astrophysics Data System (ADS)

    Alazab, Mamoun; Islam, Mofakharul; Venkatraman, Sitalakshmi

    Image analysis is being adopted extensively in many applications such as digital forensics, medical treatment, industrial inspection, etc. primarily for diagnostic purposes. Hence, there is a growing interest among researches in developing new segmentation techniques to aid the diagnosis process. Manual segmentation of images is labour intensive, extremely time consuming and prone to human errors and hence an automated real-time technique is warranted in such applications. There is no universally applicable automated segmentation technique that will work for all images as the image segmentation is quite complex and unique depending upon the domain application. Hence, to fill the gap, this paper presents an efficient segmentation algorithm that can segment a digital image of interest into a more meaningful arrangement of regions and objects. Our algorithm combines region growing approach with optimised elimination of false boundaries to arrive at more meaningful segments automatically. We demonstrate this using X-ray teeth images that were taken for real-life dental diagnosis.

  14. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Lawrence, William T.

    2005-01-01

    NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.

  15. Efficient segmentation of skin epidermis in whole slide histopathological images.

    PubMed

    Xu, Hongming; Mandal, Mrinal

    2015-08-01

    Segmentation of epidermis areas is an important step towards automatic analysis of skin histopathological images. This paper presents a robust technique for epidermis segmentation in whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then estimated by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is suspected to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques. PMID:26737135

  16. Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour Methods

    NASA Astrophysics Data System (ADS)

    Mikulka, J.; Gescheidtova, E.; Bartusek, K.

    2012-01-01

    The paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It focuses primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR). It is easy to describe edges of the sought objects using segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown. Research in the area of image segmentation focuses on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. In the paper, results of the segmentation of medical images by the active contour method are compared with results of the segmentation by other existing methods. Experimental applications which demonstrate the very good properties of the active contour method are given.

  17. A Review on Segmentation of Positron Emission Tomography Images

    PubMed Central

    Foster, Brent; Bagci, Ulas; Mansoor, Awais; Xu, Ziyue; Mollura, Daniel J.

    2014-01-01

    Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images the distribution of biologically targeted radiotracers with high sensitivity. PET imaging provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, and cancer by detecting emitted photons from a radiotracer localized to abnormal cells. In order to differentiate abnormal tissue from surrounding areas in PET images, image segmentation methods play a vital role; therefore, accurate image segmentation is often necessary for proper disease detection, diagnosis, treatment planning, and follow-ups. In this review paper, we present state-of-the-art PET image segmentation methods, as well as the recent advances in image segmentation techniques. In order to make this manuscript self-contained, we also briefly explain the fundamentals of PET imaging, the challenges of diagnostic PET image analysis, and the effects of these challenges on the segmentation results. PMID:24845019

  18. Magnetic Resonance Imaging of Electrolysis.

    NASA Astrophysics Data System (ADS)

    Meir, Arie; Hjouj, Mohammad; Rubinsky, Liel; Rubinsky, Boris

    2015-02-01

    This study explores the hypothesis that Magnetic Resonance Imaging (MRI) can image the process of electrolysis by detecting pH fronts. The study has relevance to real time control of cell ablation with electrolysis. To investigate the hypothesis we compare the following MR imaging sequences: T1 weighted, T2 weighted and Proton Density (PD), with optical images acquired using pH-sensitive dyes embedded in a physiological saline agar solution phantom treated with electrolysis and discrete measurements with a pH microprobe. We further demonstrate the biological relevance of our work using a bacterial E. Coli model, grown on the phantom. The results demonstrate the ability of MRI to image electrolysis produced pH changes in a physiological saline phantom and show that these changes correlate with cell death in the E. Coli model grown on the phantom. The results are promising and invite further experimental research.

  19. Image enhancement and segmentation using weighted morphological connected slope filters

    NASA Astrophysics Data System (ADS)

    Mendiola-Santibañez, Jorge D.; Terol-Villalobos, Iván R.

    2013-04-01

    The morphological connected slope filters (MCSFs) are studied as gray level transformations, and two contributions are made on these operators with the purpose of modifying the gradient criterion performance. The proposals consist of: (a) the introduction of three weighting functions and (b) the application of a displacement parameter. The displacement parameter will permit the image segmentation in a certain intensity interval and the contrast improvement at the same time. This characteristic is an important difference among the MCSFs introduced previously, together with the other transformations defined in the current literature utilized uniquely to enhance contrast. Also, an application example of the weighted morphological slope filters is provided. In such an example, white matter is separated from brain magnetic resonance images T1.

  20. Microscopy image segmentation tool: Robust image data analysis

    SciTech Connect

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

    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.

  1. Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy in Dementias

    PubMed Central

    Hsu, Yuan-Yu; Du, An-Tao; Schuff, Norbert; Weiner, Michael W.

    2007-01-01

    This article reviews recent studies of magnetic resonance imaging and magnetic resonance spectroscopy in dementia, including Alzheimer's disease, frontotemporal dementia, dementia with Lewy bodies, idiopathic Parkinson's disease, Huntington's disease, and vascular dementia. Magnetic resonance imaging and magnetic resonance spectroscopy can detect structural alteration and biochemical abnormalities in the brain of demented subjects and may help in the differential diagnosis and early detection of affected individuals, monitoring disease progression, and evaluation of therapeutic effect. PMID:11563438

  2. Automatic three-dimensional segmentation of MR images applied to the rat uterus

    NASA Astrophysics Data System (ADS)

    Akselrod-Ballin, Ayelet; Eyal, Erez; Galun, Meirav; Furman-Haran, Edna; Gomori, John M.; Basri, Ronen; Degani, Hadassa; Brandt, Achi

    2006-03-01

    We introduce an automatic 3D multiscale automatic segmentation algorithm for delineating specific organs in Magnetic Resonance images (MRI). The algorithm can process several modalities simultaneously, and handle both isotropic and anisotropic data in only linear time complexity. It produces a hierarchical decomposition of MRI scans. During this segmentation process a rich set of features describing the segments in terms of intensity, shape and location are calculated, reflecting the formation of the hierarchical decomposition. We show that this method can delineate the entire uterus of the rat abdomen in 3D MR images utilizing a combination of scanning protocols that jointly achieve high contrast between the uterus and other abdominal organs and between inner structures of the rat uterus. Both single and multi-channel automatic segmentation demonstrate high correlation to a manual segmentation. While the focus here is on the rat uterus, the general approach can be applied to recognition in 2D, 3D and multi-channel medical images.

  3. Image Segmentation for Connectomics Using Machine Learning

    SciTech Connect

    Tasdizen, Tolga; Seyedhosseini, Mojtaba; Liu, TIng; Jones, Cory; Jurrus, Elizabeth R.

    2014-12-01

    Reconstruction of neural circuits at the microscopic scale of individual neurons and synapses, also known as connectomics, is an important challenge for neuroscience. While an important motivation of connectomics is providing anatomical ground truth for neural circuit models, the ability to decipher neural wiring maps at the individual cell level is also important in studies of many neurodegenerative diseases. Reconstruction of a neural circuit at the individual neuron level requires the use of electron microscopy images due to their extremely high resolution. Computational challenges include pixel-by-pixel annotation of these images into classes such as cell membrane, mitochondria and synaptic vesicles and the segmentation of individual neurons. State-of-the-art image analysis solutions are still far from the accuracy and robustness of human vision and biologists are still limited to studying small neural circuits using mostly manual analysis. In this chapter, we describe our image analysis pipeline that makes use of novel supervised machine learning techniques to tackle this problem.

  4. Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

    PubMed Central

    Attique, Muhammad; Gilanie, Ghulam; Hafeez-Ullah; Mehmood, Malik S.; Naweed, Muhammad S.; Ikram, Masroor; Kamran, Javed A.; Vitkin, Alex

    2012-01-01

    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. PMID:22479421

  5. A Clustering Algorithm for Liver Lesion Segmentation of Diffusion-Weighted MR Images

    PubMed Central

    Jha, Abhinav K.; Rodríguez, Jeffrey J.; Stephen, Renu M.; Stopeck, Alison T.

    2010-01-01

    In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms. PMID:21151837

  6. [An adaptive threshloding segmentation method for urinary sediment image].

    PubMed

    Li, Yongming; Zeng, Xiaoping; Qin, Jian; Han, Liang

    2009-02-01

    In this paper is proposed a new method to solve the segmentation of the complicated defocusing urinary sediment image. The main points of the method are: (1) using wavelet transforms and morphology to erase the effect of defocusing and realize the first segmentation, (2) using adaptive threshold processing in accordance to the subimages after wavelet processing, and (3) using 'peel off' algorithm to deal with the overlapped cells' segmentations. The experimental results showed that this method was not affected by the defocusing, and it made good use of many kinds of characteristics of the images. So this new mehtod can get very precise segmentation; it is effective for defocusing urinary sediment image segmentation.

  7. Diffusion-weighted Imaging Using Readout-segmented EPI Reveals Bony Metastases from Neuroblastoma.

    PubMed

    Hayes, Laura L; Alazraki, Adina; Wasilewski-Masker, Karen; Jones, Richard A; Porter, David A; Palasis, Susan

    2016-10-01

    Identifying neuroblastoma (NBL) metastases is crucial to treatment and prognosis. Metaiodobenzylguanidine and Tc99M bone scans are standard for identifying bony metastases but can underestimate disease. Diffusion-weighted imaging (DWI) of the spine has shown promise in evaluating bony metastases but has been limited by artifacts. Readout-segmented echo planar imaging is a technique for DWI that minimizes artifacts allowing for improved identification of spinal disease. This report illustrates the utility of DWI of the spine using readout-segmented echo planar imaging in the detection of bony NBL metastases in a child, lending support that DWI should be included in magnetic resonance imaging scans for NBL.

  8. Diffusion-weighted Imaging Using Readout-segmented EPI Reveals Bony Metastases from Neuroblastoma.

    PubMed

    Hayes, Laura L; Alazraki, Adina; Wasilewski-Masker, Karen; Jones, Richard A; Porter, David A; Palasis, Susan

    2016-10-01

    Identifying neuroblastoma (NBL) metastases is crucial to treatment and prognosis. Metaiodobenzylguanidine and Tc99M bone scans are standard for identifying bony metastases but can underestimate disease. Diffusion-weighted imaging (DWI) of the spine has shown promise in evaluating bony metastases but has been limited by artifacts. Readout-segmented echo planar imaging is a technique for DWI that minimizes artifacts allowing for improved identification of spinal disease. This report illustrates the utility of DWI of the spine using readout-segmented echo planar imaging in the detection of bony NBL metastases in a child, lending support that DWI should be included in magnetic resonance imaging scans for NBL. PMID:27571120

  9. Multi-atlas segmentation with particle-based group-wise image registration.

    PubMed

    Lee, Joohwi; Lyu, Ilwoo; Styner, Martin

    2014-03-21

    We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles. The particle-guided image registration method is robust with low signal-to-noise ratio images as well as differing sizes and shapes observed in the developing rodent brain. Also, the use of an implicit common reference frame can prevent potential bias induced by the use of a single template in the segmentation process. We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided template labels as an additional constraint. In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration. The comparison with previous group-wise registration method is provided as well.

  10. Basics of magnetic resonance imaging

    SciTech Connect

    Oldendorf, W.; Oldendorf, W. Jr.

    1988-01-01

    Beginning with the behavior of a compass needle in a magnetic field, this text uses analogies from everyday experience to explain the phenomenon of nuclear magnetic resonance and how it is used for imaging. Using a minimum of scientific abbreviations and symbols, the basics of tissue visualization and characterization are presented. A description of the various types of magnets and scanners is followed by the practical advantages and limitations of MRI relative to x-ray CT scanning.

  11. 3D segmentation of masses in DCE-MRI images using FCM and adaptive MRF

    NASA Astrophysics Data System (ADS)

    Zhang, Chengjie; Li, Lihua

    2014-03-01

    Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging modality for the detection of breast cancer. Automated segmentation of breast lesions in DCE-MRI images is challenging due to inherent signal-to-noise ratios and high inter-patient variability. A novel 3D segmentation method based on FCM and MRF is proposed in this study. In this method, a MRI image is segmented by spatial FCM, firstly. And then MRF segmentation is conducted to refine the result. We combined with the 3D information of lesion in the MRF segmentation process by using segmentation result of contiguous slices to constraint the slice segmentation. At the same time, a membership matrix of FCM segmentation result is used for adaptive adjustment of Markov parameters in MRF segmentation process. The proposed method was applied for lesion segmentation on 145 breast DCE-MRI examinations (86 malignant and 59 benign cases). An evaluation of segmentation was taken using the traditional overlap rate method between the segmented region and hand-drawing ground truth. The average overlap rates for benign and malignant lesions are 0.764 and 0.755 respectively. Then we extracted five features based on the segmentation region, and used an artificial neural network (ANN) to classify between malignant and benign cases. The ANN had a classification performance measured by the area under the ROC curve of AUC=0.73. The positive and negative predictive values were 0.86 and 0.58, respectively. The results demonstrate the proposed method not only achieves a better segmentation performance in accuracy also has a reasonable classification performance.

  12. Segmentation of thermographic images of hands using a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Ghosh, Payel; Mitchell, Melanie; Gold, Judith

    2010-01-01

    This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The dataset consists of thermographic images of hands of patients suffering from upper extremity musculo-skeletal disorders (UEMSD). Thermographic images are acquired to study the skin temperature as a surrogate for the amount of blood flow in the hands of these patients. Since entire hands are not visible on these images, segmentation of the outline of the hands on these images is typically performed by a human. In this paper several different methods have been tried for segmenting thermographic images: Gabor-wavelet-based texture segmentation method, the level set method of segmentation and our GA which we termed LSGA because it combines level sets with genetic algorithms. The results show a comparative evaluation of the segmentation performed by all the methods. We conclude that LSGA successfully segments entire hands on images in which hands are only partially visible.

  13. Image segmentation using an improved differential algorithm

    NASA Astrophysics Data System (ADS)

    Gao, Hao; Shi, Yujiao; Wu, Dongmei

    2014-10-01

    Among all the existing segmentation techniques, the thresholding technique is one of the most popular due to its simplicity, robustness, and accuracy (e.g. the maximum entropy method, Otsu's method, and K-means clustering). However, the computation time of these algorithms grows exponentially with the number of thresholds due to their exhaustive searching strategy. As a population-based optimization algorithm, differential algorithm (DE) uses a population of potential solutions and decision-making processes. It has shown considerable success in solving complex optimization problems within a reasonable time limit. Thus, applying this method into segmentation algorithm should be a good choice during to its fast computational ability. In this paper, we first propose a new differential algorithm with a balance strategy, which seeks a balance between the exploration of new regions and the exploitation of the already sampled regions. Then, we apply the new DE into the traditional Otsu's method to shorten the computation time. Experimental results of the new algorithm on a variety of images show that, compared with the EA-based thresholding methods, the proposed DE algorithm gets more effective and efficient results. It also shortens the computation time of the traditional Otsu method.

  14. Multidimensionally encoded magnetic resonance imaging.

    PubMed

    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.

  15. Building roof segmentation from aerial images using a lineand region-based watershed segmentation technique.

    PubMed

    El Merabet, Youssef; Meurie, Cyril; Ruichek, Yassine; Sbihi, Abderrahmane; Touahni, Raja

    2015-02-02

    In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG.

  16. Automatic bone segmentation in knee MR images using a coarse-to-fine strategy

    NASA Astrophysics Data System (ADS)

    Park, Sang Hyun; Lee, Soochahn; Yun, Il Dong; Lee, Sang Uk

    2012-02-01

    Segmentation of bone and cartilage from a three dimensional knee magnetic resonance (MR) image is a crucial element in monitoring and understanding of development and progress of osteoarthritis. Until now, various segmentation methods have been proposed to separate the bone from other tissues, but it still remains challenging problem due to different modality of MR images, low contrast between bone and tissues, and shape irregularity. In this paper, we present a new fully-automatic segmentation method of bone compartments using relevant bone atlases from a training set. To find the relevant bone atlases and obtain the segmentation, a coarse-to-fine strategy is proposed. In the coarse step, the best atlas among the training set and an initial segmentation are simultaneously detected using branch and bound tree search. Since the best atlas in the coarse step is not accurately aligned, all atlases from the training set are aligned to the initial segmentation, and the best aligned atlas is selected in the middle step. Finally, in the fine step, segmentation is conducted as adaptively integrating shape of the best aligned atlas and appearance prior based on characteristics of local regions. For experiment, femur and tibia bones of forty test MR images are segmented by the proposed method using sixty training MR images. Experimental results show that a performance of the segmentation and the registration becomes better as going near the fine step, and the proposed method obtain the comparable performance with the state-of-the-art methods.

  17. Nonparametric segmentation of multispectral MR images incorporating spatial and intensity information

    NASA Astrophysics Data System (ADS)

    Derganc, Joze; Likar, Bostjan; Pernus, Franjo

    2002-05-01

    Image segmentation is concerned with partitioning an image into non-overlapping, constituent regions, which are homogeneous with respect to certain features. In magnetic resonance imaging (MRI), the most discriminative and commonly used features are the image intensities themselves. However, due to noise, partial volume effects, natural and spurious intensity variations, intensity distributions of distinct tissues generally overlap, which makes segmentation difficult and less precise. Using multi-spectral MR images and mapping intensities into a multidimensional feature space may help in segmentation. To further facilitate segmentation, we map the intensities and second derivatives of multi-spectral images into a common multidimensional feature space. Integration of intensity and spatial information may yield complex clusters that cannot be described by Gaussian mixture models or by hyper-spherical shapes. For this reason we devise a novel segmentation method based on non-parametric valley-seeking clustering. The valleys are found by estimating feature density gradients. The proposed segmentation method, with and without spatial information, is tested on simulated and real, single- and multi-spectral, MR brain images. The segmentation results are highly consistent with the gold standard, especially when combined with a procedure for intensity non-uniformity correction, presented in MI 4684-177.

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

    PubMed Central

    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

  19. Segmentation of multispectral bladder MR images with inhomogeneity correction for virtual cystoscopy

    NASA Astrophysics Data System (ADS)

    Li, Lihong; Liang, Zhengrong; Wang, Su; Lu, Hongyu; Wei, Xinzhou; Wagshul, Mark; Zawin, Marlene; Posniak, Erica J.; Lee, Christopher S.

    2008-03-01

    Virtual cystoscopy (VC) is a developing noninvasive, safe, and low-cost technique for bladder cancer screening. Multispectral (T I- and T II-weighted) magnetic resonance (MR) images provide a better tissue contrast between bladder wall and bladder lumen comparing with computed tomography (CT) images. The intrinsic T I and T II contrast of the urine against the bladder wall eliminates the invasive air insufflation procedure which is often used in CT-based VC. We propose a new partial volume (PV) segmentation scheme with inhomogeneity correction to segment multispectral MR images for tumor screening by virtual cystoscopy. The proposed PV segmentation algorithm automatically estimates the bias field and segments tissue mixtures inside each voxel of MR images, thus preserving texture information. Experimental results indicate that the present scheme is promising towards mass screening by virtual cystoscopy means.

  20. Atlas-registration based image segmentation of MRI human thigh muscles in 3D space

    NASA Astrophysics Data System (ADS)

    Ahmad, Ezak; Yap, Moi Hoon; Degens, Hans; McPhee, Jamie S.

    2014-03-01

    Automatic segmentation of anatomic structures of magnetic resonance thigh scans can be a challenging task due to the potential lack of precisely defined muscle boundaries and issues related to intensity inhomogeneity or bias field across an image. In this paper, we demonstrate a combination framework of atlas construction and image registration methods to propagate the desired region of interest (ROI) between atlas image and the targeted MRI thigh scans for quadriceps muscles, femur cortical layer and bone marrow segmentations. The proposed system employs a semi-automatic segmentation method on an initial image in one dataset (from a series of images). The segmented initial image is then used as an atlas image to automate the segmentation of other images in the MRI scans (3-D space). The processes include: ROI labeling, atlas construction and registration, and morphological transform correspondence pixels (in terms of feature and intensity value) between the atlas (template) image and the targeted image based on the prior atlas information and non-rigid image registration methods.

  1. Hybrid segmentation framework for 3D medical image analysis

    NASA Astrophysics Data System (ADS)

    Chen, Ting; Metaxas, Dimitri N.

    2003-05-01

    Medical image segmentation is the process that defines the region of interest in the image volume. Classical segmentation methods such as region-based methods and boundary-based methods cannot make full use of the information provided by the image. In this paper we proposed a general hybrid framework for 3D medical image segmentation purposes. In our approach we combine the Gibbs Prior model, and the deformable model. First, Gibbs Prior models are applied onto each slice in a 3D medical image volume and the segmentation results are combined to a 3D binary masks of the object. Then we create a deformable mesh based on this 3D binary mask. The deformable model will be lead to the edge features in the volume with the help of image derived external forces. The deformable model segmentation result can be used to update the parameters for Gibbs Prior models. These methods will then work recursively to reach a global segmentation solution. The hybrid segmentation framework has been applied to images with the objective of lung, heart, colon, jaw, tumor, and brain. The experimental data includes MRI (T1, T2, PD), CT, X-ray, Ultra-Sound images. High quality results are achieved with relatively efficient time cost. We also did validation work using expert manual segmentation as the ground truth. The result shows that the hybrid segmentation may have further clinical use.

  2. [Segmentation Method for Liver Organ Based on Image Sequence Context].

    PubMed

    Zhang, Meiyun; Fang, Bin; Wang, Yi; Zhong, Nanchang

    2015-10-01

    In view of the problems of more artificial interventions and segmentation defects in existing two-dimensional segmentation methods and abnormal liver segmentation errors in three-dimensional segmentation methods, this paper presents a semi-automatic liver organ segmentation method based on the image sequence context. The method takes advantage of the existing similarity between the image sequence contexts of the prior knowledge of liver organs, and combines region growing and level set method to carry out semi-automatic segmentation of livers, along with the aid of a small amount of manual intervention to deal with liver mutation situations. The experiment results showed that the liver segmentation algorithm presented in this paper had a high precision, and a good segmentation effect on livers which have greater variability, and can meet clinical application demands quite well.

  3. Robust image segmentation using local robust statistics and correntropy-based K-means clustering

    NASA Astrophysics Data System (ADS)

    Huang, Chencheng; Zeng, Li

    2015-03-01

    It is an important work to segment the real world images with intensity inhomogeneity such as magnetic resonance (MR) and computer tomography (CT) images. In practice, such images are often polluted by noise which make them difficult to be segmented by traditional level set based segmentation models. In this paper, we propose a robust level set image segmentation model combining local with global fitting energies to segment noised images. In the proposed model, the local fitting energy is based on the local robust statistics (LRS) information of an input image, which can efficiently reduce the effects of the noise, and the global fitting energy utilizes the correntropy-based K-means (CK) method, which can adaptively emphasize the samples that are close to their corresponding cluster centers. By integrating the advantages of global information and local robust statistics characteristics, the proposed model can efficiently segment images with intensity inhomogeneity and noise. Then, a level set regularization term is used to avoid re-initialization procedures in the process of curve evolution. In addition, the Gaussian filter is utilized to keep the level set smoothing in the curve evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. Experimental results show the advantages of our model in terms of accuracy and robustness to the noise. In particular, our method has been applied on some synthetic and real images with desirable results.

  4. Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

    PubMed Central

    Maji, Pradipta; Roy, Shaswati

    2015-01-01

    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. PMID:25848961

  5. Semiautomatic tumor segmentation with multimodal images in a conditional random field framework.

    PubMed

    Hu, Yu-Chi; Grossberg, Michael; Mageras, Gikas

    2016-04-01

    Volumetric medical images of a single subject can be acquired using different imaging modalities, such as computed tomography, magnetic resonance imaging (MRI), and positron emission tomography. In this work, we present a semiautomatic segmentation algorithm that can leverage the synergies between different image modalities while integrating interactive human guidance. The algorithm provides a statistical segmentation framework partly automating the segmentation task while still maintaining critical human oversight. The statistical models presented are trained interactively using simple brush strokes to indicate tumor and nontumor tissues and using intermediate results within a patient's image study. To accomplish the segmentation, we construct the energy function in the conditional random field (CRF) framework. For each slice, the energy function is set using the estimated probabilities from both user brush stroke data and prior approved segmented slices within a patient study. The progressive segmentation is obtained using a graph-cut-based minimization. Although no similar semiautomated algorithm is currently available, we evaluated our method with an MRI data set from Medical Image Computing and Computer Assisted Intervention Society multimodal brain segmentation challenge (BRATS 2012 and 2013) against a similar fully automatic method based on CRF and a semiautomatic method based on grow-cut, and our method shows superior performance. PMID:27413768

  6. Validation of model-based pelvis bone segmentation from MR images for PET/MR attenuation correction

    NASA Astrophysics Data System (ADS)

    Renisch, S.; Blaffert, T.; Tang, J.; Hu, Z.

    2012-02-01

    With the recent introduction of combined Magnetic Resonance Imaging (MRI) / Positron Emission Tomography (PET) systems, the generation of attenuation maps for PET based on MR images gained substantial attention. One approach for this problem is the segmentation of structures on the MR images with subsequent filling of the segments with respective attenuation values. Structures of particular interest for the segmentation are the pelvis bones, since those are among the most heavily absorbing structures for many applications, and they can serve at the same time as valuable landmarks for further structure identification. In this work the model-based segmentation of the pelvis bones on gradient-echo MR images is investigated. A processing chain for the detection and segmentation of the pelvic bones is introduced, and the results are evaluated using CT-generated "ground truth" data. The results indicate that a model based segmentation of the pelvis bone is feasible with moderate requirements to the pre- and postprocessing steps of the segmentation.

  7. Multimodal Correlative Preclinical Whole Body Imaging and Segmentation

    PubMed Central

    Akselrod-Ballin, Ayelet; Dafni, Hagit; Addadi, Yoseph; Biton, Inbal; Avni, Reut; Brenner, Yafit; Neeman, Michal

    2016-01-01

    Segmentation of anatomical structures and particularly abdominal organs is a fundamental problem for quantitative image analysis in preclinical research. This paper presents a novel approach for whole body segmentation of small animals in a multimodal setting of MR, CT and optical imaging. The algorithm integrates multiple imaging sequences into a machine learning framework, which generates supervoxels by an efficient hierarchical agglomerative strategy and utilizes multiple SVM-kNN classifiers each constrained by a heatmap prior region to compose the segmentation. We demonstrate results showing segmentation of mice images into several structures including the heart, lungs, liver, kidneys, stomach, vena cava, bladder, tumor, and skeleton structures. Experimental validation on a large set of mice and organs, indicated that our system outperforms alternative state of the art approaches. The system proposed can be generalized to various tissues and imaging modalities to produce automatic atlas-free segmentation, thereby enabling a wide range of applications in preclinical studies of small animal imaging. PMID:27325178

  8. Multimodal Correlative Preclinical Whole Body Imaging and Segmentation.

    PubMed

    Akselrod-Ballin, Ayelet; Dafni, Hagit; Addadi, Yoseph; Biton, Inbal; Avni, Reut; Brenner, Yafit; Neeman, Michal

    2016-01-01

    Segmentation of anatomical structures and particularly abdominal organs is a fundamental problem for quantitative image analysis in preclinical research. This paper presents a novel approach for whole body segmentation of small animals in a multimodal setting of MR, CT and optical imaging. The algorithm integrates multiple imaging sequences into a machine learning framework, which generates supervoxels by an efficient hierarchical agglomerative strategy and utilizes multiple SVM-kNN classifiers each constrained by a heatmap prior region to compose the segmentation. We demonstrate results showing segmentation of mice images into several structures including the heart, lungs, liver, kidneys, stomach, vena cava, bladder, tumor, and skeleton structures. Experimental validation on a large set of mice and organs, indicated that our system outperforms alternative state of the art approaches. The system proposed can be generalized to various tissues and imaging modalities to produce automatic atlas-free segmentation, thereby enabling a wide range of applications in preclinical studies of small animal imaging. PMID:27325178

  9. Segmentation and learning in the quantitative analysis of microscopy images

    NASA Astrophysics Data System (ADS)

    Ruggiero, Christy; Ross, Amy; Porter, Reid

    2015-02-01

    In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

  10. Joint Lung CT Image Segmentation: A Hierarchical Bayesian Approach

    PubMed Central

    Cheng, Wenjun; Ma, Luyao; Yang, Tiejun; Liang, Jiali

    2016-01-01

    Accurate lung CT image segmentation is of great clinical value, especially when it comes to delineate pathological regions including lung tumor. In this paper, we present a novel framework that jointly segments multiple lung computed tomography (CT) images via hierarchical Dirichlet process (HDP). In specifics, based on the assumption that lung CT images from different patients share similar image structure (organ sets and relative positioning), we derive a mathematical model to segment them simultaneously so that shared information across patients could be utilized to regularize each individual segmentation. Moreover, compared to many conventional models, the algorithm requires little manual involvement due to the nonparametric nature of Dirichlet process (DP). We validated proposed model upon clinical data consisting of healthy and abnormal (lung cancer) patients. We demonstrate that, because of the joint segmentation fashion, more accurate and consistent segmentations could be obtained. PMID:27611188

  11. Joint Lung CT Image Segmentation: A Hierarchical Bayesian Approach.

    PubMed

    Cheng, Wenjun; Ma, Luyao; Yang, Tiejun; Liang, Jiali; Zhang, Yan

    2016-01-01

    Accurate lung CT image segmentation is of great clinical value, especially when it comes to delineate pathological regions including lung tumor. In this paper, we present a novel framework that jointly segments multiple lung computed tomography (CT) images via hierarchical Dirichlet process (HDP). In specifics, based on the assumption that lung CT images from different patients share similar image structure (organ sets and relative positioning), we derive a mathematical model to segment them simultaneously so that shared information across patients could be utilized to regularize each individual segmentation. Moreover, compared to many conventional models, the algorithm requires little manual involvement due to the nonparametric nature of Dirichlet process (DP). We validated proposed model upon clinical data consisting of healthy and abnormal (lung cancer) patients. We demonstrate that, because of the joint segmentation fashion, more accurate and consistent segmentations could be obtained. PMID:27611188

  12. Segmentation of knee injury swelling on infrared images

    NASA Astrophysics Data System (ADS)

    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.

  13. Automated image segmentation using support vector machines

    NASA Astrophysics Data System (ADS)

    Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

    2007-03-01

    Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

  14. A new level set model for cell image segmentation

    NASA Astrophysics Data System (ADS)

    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.

  15. Analysis of image thresholding segmentation algorithms based on swarm intelligence

    NASA Astrophysics Data System (ADS)

    Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

    2013-03-01

    Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

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

    NASA Astrophysics Data System (ADS)

    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.

  17. A learning-based automatic clinical organ segmentation in medical images

    NASA Astrophysics Data System (ADS)

    Liu, Xiaoqing; Samarabandu, Jagath; Li, Shuo; Ross, Ian; Garvin, Greg

    2007-03-01

    Image segmentation plays an important role in medical image analysis and visualization since it greatly enhances the clinical diagnosis. Although many algorithms have been proposed, it is challenging to achieve an automatic clinical organ segmentation which requires speed and robustness. Automatically segmenting cardiac Magnetic Resonance Imaging (MRI) image is extremely challenging due to the artifacts of cardiac motion and characteristics of MRI. Moreover many of the existing algorithms are specific to a particular view of cardiac MRI images. We proposed a generic view-independent, learning-based method to automatically segment cardiac MRI images, which uses machine learning techniques and the geometric shape information. A main feature of our contribution is the fact that the proposed algorithm can use a training set containing a mix of various views and is able to successfully segment any given views. The proposed method consists of four stages. First, we partition the input image into a number of image regions based on their intensity characteristics. Then, we calculate the pre-selected feature descriptions for each generated region and use a trained classi.er to learn the conditional probabilities for every pixel based on the calculated features. In this paper, we use the Support Vector Machine (SVM) to train our classifier. The learned conditional probabilities of every pixel are then fed into an energy function to segment the input image. We optimize our energy function with graph cuts. Finally, domain knowledge is applied to verify the segmentation. Experimental results show that this method is very efficient and robust with respect to image views, slices and motion phases. The method also has the potential to be imaging modality independent as the proposed algorithm is not specific to a particular imaging modality.

  18. Ferromagnetic Resonance Force Microscopy Imaging

    NASA Astrophysics Data System (ADS)

    Chen, Wei; Midzor, Melissa; Cross, Michael; Wigen, Philip; Hammel, Chris; Roukes, Michael

    2001-03-01

    Magnetic resonance force microscopy (MRFM) has been used to investigate magnetostatic waves on microscopic samples of YIG. This work elucidates the nature of scanned probe (local) imaging in ferromagnetically-coupled systems. Scanning was performed with a specially-designed ultrasharp tip with Permalloy (NiFe) deposited solely in the tip region, to yield a spatial sensitivity of <10um. This has provided the first direct imaging of fundamental and higher order magnetostatic modes in micromagnetic systems. The modal dependence upon applied field and sample size was measured and compares well with theoretical models. However, unlike traditional ferromagnetic resonance detection technique, MRFM not only serves as a non-perturbative detection tool of magnetostatic modes, but also can locally change their dispersion relations via the strong field gradients generated from the cantilever tip. As a result, when the tip is positioned closely to the YIG surface, certain modes of the magnetostatic waves are either enhanced or depressed, depending on their respective wavelengths. This corresponds to the fact when the tip is further away, the dispersion of the FMR modes is mainly determined by the sample size. As the tip moves closer to the surface, a new regime emerges where the FMR dispersion is dominated by the local magnetic field. A quantitative model based on DE theory is proposed, and it explains the main features of the observed tip influence on different magnetostatic modes.

  19. Image segmentation with genetic algorithms: a formulation and implementation

    NASA Astrophysics Data System (ADS)

    Seetharaman, Gunasekaran; Narasimhan, Amruthur; Sathe, Anand; Storc, Lisa

    1991-10-01

    Image segmentation is an important step in any computer vision system. Segmentation refers to the partitioning of the image plane into several regions, such that each region corresponds to a logical entity present in the scene. The problem is inherently NP, and the theory on the existence and uniqueness of the ideal segmentation is not yet established. Several methods have been proposed in literature for image segmentation. With the exception of the state-space approach to segmentation, other methods lack generality. The state-space approach, however, amounts to searching for the solution in a large search space of 22n(2) possibilities for a n X n image. In this paper, a classic approach based on state-space techniques for segmentation due to Brice and Fennema is reformulated using genetic algorithms. The state space representation of a partially segmented image lends itself to binary strings, in which the dominant substrings are easily explained in terms of chromosomes. Also the operations such as crossover and mutations are easily abstracted. In particular, when multiple images are segmented from an image sequence, fusion of constraints from one to the other becomes clear under this formulation.

  20. A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images.

    PubMed

    Gloger, Oliver; Kühn, Jens; Stanski, Adam; Völzke, Henry; Puls, Ralf

    2010-07-01

    Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties.

  1. Anterior segment imaging in glaucoma: An updated review

    PubMed Central

    Maslin, Jessica S; Barkana, Yaniv; Dorairaj, Syril K

    2015-01-01

    Anterior segment imaging allows for an objective method of visualizing the anterior segment angle. Two of the most commonly used devices for anterior segment imaging include the anterior segment optical coherence tomography (AS-OCT) and the ultrasound biomicroscopy (UBM). AS-OCT technology has several types, including time-domain, swept-source, and spectral-domain-based configurations. We performed a literature search on PubMed for articles containing the text “anterior segment OCT,” “ultrasound biomicroscopy,” and “anterior segment imaging” since 2004, with some pertinent references before 2004 included for completeness. This review compares the advantages and disadvantages of AS-OCT and UBM, and summarizes the most recent literature regarding the importance of these devices in glaucoma diagnosis and management. These devices not only aid in visualization of the angle, but also have important postsurgical applications in bleb and tube imaging. PMID:26576519

  2. Robust image modeling technique with a bioluminescence image segmentation application

    NASA Astrophysics Data System (ADS)

    Zhong, Jianghong; Wang, Ruiping; Tian, Jie

    2009-02-01

    A robust pattern classifier algorithm for the variable symmetric plane model, where the driving noise is a mixture of a Gaussian and an outlier process, is developed. The veracity and high-speed performance of the pattern recognition algorithm is proved. Bioluminescence tomography (BLT) has recently gained wide acceptance in the field of in vivo small animal molecular imaging. So that it is very important for BLT to how to acquire the highprecision region of interest in a bioluminescence image (BLI) in order to decrease loss of the customers because of inaccuracy in quantitative analysis. An algorithm in the mode is developed to improve operation speed, which estimates parameters and original image intensity simultaneously from the noise corrupted image derived from the BLT optical hardware system. The focus pixel value is obtained from the symmetric plane according to a more realistic assumption for the noise sequence in the restored image. The size of neighborhood is adaptive and small. What's more, the classifier function is base on the statistic features. If the qualifications for the classifier are satisfied, the focus pixel intensity is setup as the largest value in the neighborhood.Otherwise, it will be zeros.Finally,pseudo-color is added up to the result of the bioluminescence segmented image. The whole process has been implemented in our 2D BLT optical system platform and the model is proved.

  3. Outstanding-objects-oriented color image segmentation using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Hayasaka, Rina; Zhao, Jiying; Matsushita, Yutaka

    1997-10-01

    This paper presents a novel fuzzy-logic-based color image segmentation scheme focusing on outstanding objects to human eyes. The scheme first segments the image into rough fuzzy regions, chooses visually significant regions, and conducts fine segmentation on the chosen regions. It can not only reduce the computational load, but also make contour detection easy because the brief object externals has been previously determined. The scheme reflects human sense, and it can be sued efficiently in automatic extraction of image retrieval key, robot vision and region-adaptive image compression.

  4. Artifacts in magnetic resonance imaging.

    PubMed

    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. Artifacts in Magnetic Resonance Imaging

    PubMed Central

    Krupa, Katarzyna; Bekiesińska-Figatowska, Monika

    2015-01-01

    Summary 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. PMID:25745524

  6. Single 3D cell segmentation from optical CT microscope images

    NASA Astrophysics Data System (ADS)

    Xie, Yiting; Reeves, Anthony P.

    2014-03-01

    The automated segmentation of the nucleus and cytoplasm regions in 3D optical CT microscope images has been achieved with two methods, a global threshold gradient based approach and a graph-cut approach. For the first method, the first two peaks of a gradient figure of merit curve are selected as the thresholds for cytoplasm and nucleus segmentation. The second method applies a graph-cut segmentation twice: the first identifies the nucleus region and the second identifies the cytoplasm region. Image segmentation of single cells is important for automated disease diagnostic systems. The segmentation methods were evaluated with 200 3D images consisting of 40 samples of 5 different cell types. The cell types consisted of columnar, macrophage, metaplastic and squamous human cells and cultured A549 cancer cells. The segmented cells were compared with both 2D and 3D reference images and the quality of segmentation was determined by the Dice Similarity Coefficient (DSC). In general, the graph-cut method had a superior performance to the gradient-based method. The graph-cut method achieved an average DSC of 86% and 72% for nucleus and cytoplasm segmentations respectively for the 2D reference images and 83% and 75% for the 3D reference images. The gradient method achieved an average DSC of 72% and 51% for nucleus and cytoplasm segmentation for the 2D reference images and 71% and 51% for the 3D reference images. The DSC of cytoplasm segmentation was significantly lower than for the nucleus since the cytoplasm was not differentiated as well by image intensity from the background.

  7. Image analysis for neuroblastoma classification: segmentation of cell nuclei.

    PubMed

    Gurcan, Metin N; Pan, Tony; Shimada, Hiro; Saltz, Joel

    2006-01-01

    Neuroblastoma is a childhood cancer of the nervous system. Current prognostic classification of this disease partly relies on morphological characteristics of the cells from H&E-stained images. In this work, an automated cell nuclei segmentation method is developed. This method employs morphological top-hat by reconstruction algorithm coupled with hysteresis thresholding to both detect and segment the cell nuclei. Accuracy of the automated cell nuclei segmentation algorithm is measured by comparing its outputs to manual segmentation. The average segmentation accuracy is 90.24+/-5.14% PMID:17947119

  8. Improved document image segmentation algorithm using multiresolution morphology

    NASA Astrophysics Data System (ADS)

    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.

  9. On the Performance of Stochastic Model-Based Image Segmentation

    NASA Astrophysics Data System (ADS)

    Lei, Tianhu; Sewchand, Wilfred

    1989-11-01

    A new stochastic model-based image segmentation technique for X-ray CT image has been developed and has been extended to the more general nondiffraction CT images which include MRI, SPELT, and certain type of ultrasound images [1,2]. The nondiffraction CT image is modeled by a Finite Normal Mixture. The technique utilizes the information theoretic criterion to detect the number of the region images, uses the Expectation-Maximization algorithm to estimate the parameters of the image, and uses the Bayesian classifier to segment the observed image. How does this technique over/under-estimate the number of the region images? What is the probability of errors in the segmentation of this technique? This paper addresses these two problems and is a continuation of [1,2].

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

    PubMed

    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.

  11. Multiscale Segmentation of Polarimetric SAR Image Based on Srm Superpixels

    NASA Astrophysics Data System (ADS)

    Lang, F.; Yang, J.; Wu, L.; Li, D.

    2016-06-01

    Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR) image multi-scale segmentation algorithms are usually inefficient and impractical. In this paper, we proposed a superpixel-based binary partition tree (BPT) segmentation algorithm by combining the generalized statistical region merging (GSRM) algorithm and the BPT algorithm. First, superpixels are obtained by setting a maximum region number threshold to GSRM. Then, the region merging process of the BPT algorithm is implemented based on superpixels but not pixels. The proposed algorithm inherits the advantages of both GSRM and BPT. The operation efficiency is obviously improved compared to the pixel-based BPT segmentation. Experiments using the Lband ESAR image over the Oberpfaffenhofen test site proved the effectiveness of the proposed method.

  12. Segmenting images analytically in shape space

    NASA Astrophysics Data System (ADS)

    Rathi, Yogesh; Dambreville, Samuel; Niethammer, Marc; Malcolm, James; Levitt, James; Shenton, Martha E.; Tannenbaum, Allen

    2008-03-01

    This paper presents a novel analytic technique to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert a given test volume into a binary map representation, and a novel energy functional is proposed whose minimum can be analytically computed to obtain the desired segmentation in the shape space. We compare the proposed method with the log-likelihood based energy to elucidate some key differences. Our algorithm is applied to the segmentation of brain caudate nucleus and hippocampus from MRI data, which is of interest in the study of schizophrenia and Alzheimer's disease. Our validation (we compute the Hausdorff distance and the DICE coefficient between the automatic segmentation and ground-truth) shows that the proposed algorithm is very fast, requires no initialization and outperforms the log-likelihood based energy.

  13. Three-dimensional CT image segmentation by volume growing

    NASA Astrophysics Data System (ADS)

    Zhu, Dongping; Conners, Richard W.; Araman, Philip A.

    1991-11-01

    The research reported in this paper is aimed at locating, identifying, and quantifying internal (anatomical or physiological) structures, by 3-D image segmentation. Computerized tomography (CT) images of an object are first processed on a slice-by-slice basis, generating a stack of image slices that have been smoothed and pre-segmented. The image smoothing operation is executed by a spatially adaptive filter, and the 2-D pre-segmentation is achieved by a thresholding process whereby each individual pixel in the input image space is consistently assigned a label, according to its CT number, i.e., the gray-level value. Given a sequence of pre-segmented images as 3-D input scene (a stack of image slices), the spatial connectivity that exists among neighboring image pixels is utilized in a volume growing process which generates a number of well-defined volumetric regions or image solides, each representing an individual anatomical or physiological structure in the input scene. The 3-D segmentation is implemented using a volume growing process so that the aspect of pixel spatial connectivity is incorporated into the image segmentation procedure. To initialize the volume growing process for each volumetric region in the input 3-D scene, a seed location for a region is defined and loaded into a queue data structure called seed queue. The volume growing process consists of a set of procedures that perform different operations on the volumetric data of a CT image sequence. Examples of experiment of the described system with CT image data of several hardwood logs are given to demonstrate usefulness and flexibility of this approach. This allows solutions to industrial web inspection, as well as to several problems in medical image analysis where low-level image segmentation plays an important role toward successful image interpretation tasks.

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

    SciTech Connect

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

    1988-09-01

    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. Automated Tumor Volumetry Using Computer-Aided Image Segmentation

    PubMed Central

    Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A.; Ali, Zarina S.; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M.; Davatzikos, Christos

    2015-01-01

    Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. PMID:25770633

  16. High-resolution CISS MR imaging with and without contrast for evaluation of the upper cranial nerves: segmental anatomy and selected pathologic conditions of the cisternal through extraforaminal segments.

    PubMed

    Blitz, Ari M; Macedo, Leonardo L; Chonka, Zachary D; Ilica, Ahmet T; Choudhri, Asim F; Gallia, Gary L; Aygun, Nafi

    2014-02-01

    The authors review the course and appearance of the major segments of the upper cranial nerves from their apparent origin at the brainstem through the proximal extraforaminal region, focusing on the imaging and anatomic features of particular relevance to high-resolution magnetic resonance imaging evaluation. Selected pathologic entities are included in the discussion of the corresponding cranial nerve segments for illustrative purposes.

  17. Validation of image segmentation by estimating rater bias and variance.

    PubMed

    Warfield, Simon K; Zou, Kelly H; Wells, William M

    2006-01-01

    The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a "ground truth" or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare to segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically these segmentations differ due to intra-rater and inter-rater variability. The most appropriate way to compare such segmentations has been unclear. We present here a new algorithm to enable the estimation of performance characteristics, and a true labeling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, amongst others, surface, distance transform or level set representations of segmentations, and can be used to assess whether or not a rater consistently over-estimates or under-estimates the position of a boundary. PMID:17354851

  18. Validation of image segmentation by estimating rater bias and variance.

    PubMed

    Warfield, Simon K; Zou, Kelly H; Wells, William M

    2008-07-13

    The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a 'ground truth' or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare with segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically, these segmentations differ due to intra-rater and inter-rater variability. The most appropriate way to compare such segmentations has been unclear. We present here a new algorithm to enable the estimation of performance characteristics, and a true labelling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, among others, surface, distance transform or level-set representations of segmentations, and can be used to assess whether or not a rater consistently overestimates or underestimates the position of a boundary. PMID:18407896

  19. Pixel classification based color image segmentation using quaternion exponent moments.

    PubMed

    Wang, Xiang-Yang; Wu, Zhi-Fang; Chen, Liang; Zheng, Hong-Liang; Yang, Hong-Ying

    2016-02-01

    Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we propose a pixel classification based color image segmentation using quaternion exponent moments. Firstly, the pixel-level image feature is extracted based on quaternion exponent moments (QEMs), which can capture effectively the image pixel content by considering the correlation between different color channels. Then, the pixel-level image feature is used as input of twin support vector machines (TSVM) classifier, and the TSVM model is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained TSVM model. The proposed scheme has the following advantages: (1) the effective QEMs is introduced to describe color image pixel content, which considers the correlation between different color channels, (2) the excellent TSVM classifier is utilized, which has lower computation time and higher classification accuracy. Experimental results show that our proposed method has very promising segmentation performance compared with the state-of-the-art segmentation approaches recently proposed in the literature.

  20. Pixel classification based color image segmentation using quaternion exponent moments.

    PubMed

    Wang, Xiang-Yang; Wu, Zhi-Fang; Chen, Liang; Zheng, Hong-Liang; Yang, Hong-Ying

    2016-02-01

    Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we propose a pixel classification based color image segmentation using quaternion exponent moments. Firstly, the pixel-level image feature is extracted based on quaternion exponent moments (QEMs), which can capture effectively the image pixel content by considering the correlation between different color channels. Then, the pixel-level image feature is used as input of twin support vector machines (TSVM) classifier, and the TSVM model is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained TSVM model. The proposed scheme has the following advantages: (1) the effective QEMs is introduced to describe color image pixel content, which considers the correlation between different color channels, (2) the excellent TSVM classifier is utilized, which has lower computation time and higher classification accuracy. Experimental results show that our proposed method has very promising segmentation performance compared with the state-of-the-art segmentation approaches recently proposed in the literature. PMID:26618250

  1. A Novel Statistical Approach for Brain MR Images Segmentation Based on Relaxation Times

    PubMed Central

    Ferraioli, Giampaolo; Pascazio, Vito

    2015-01-01

    Brain tissue segmentation in Magnetic Resonance Imaging is useful for a wide range of applications. Classical approaches exploit the gray levels image and implement criteria for differentiating regions. Within this paper a novel approach for brain tissue joint segmentation and classification is presented. Starting from the estimation of proton density and relaxation times, we propose a novel method for identifying the optimal decision regions. The approach exploits the statistical distribution of the involved signals in the complex domain. The technique, compared to classical threshold based ones, is able to globally improve the classification rate. The effectiveness of the approach is evaluated on both simulated and real datasets. PMID:26798631

  2. Functional Magnetic Resonance Imaging Methods

    PubMed Central

    Chen, Jingyuan E.; Glover, Gary H.

    2015-01-01

    Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the “resting state”). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals. PMID:26248581

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

    PubMed

    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.

  4. Pocket atlas of cranial magnetic resonance imaging

    SciTech Connect

    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.

  5. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery.

    PubMed

    Gao, Yi; Sandhu, Romeil; Fichtinger, Gabor; Tannenbaum, Allen Robert

    2010-10-01

    Extracting the prostate from magnetic resonance (MR) imagery is a challenging and important task for medical image analysis and surgical planning. We present in this work a unified shape-based framework to extract the prostate from MR prostate imagery. In many cases, shape-based segmentation is a two-part problem. First, one must properly align a set of training shapes such that any variation in shape is not due to pose. Then segmentation can be performed under the constraint of the learnt shape. However, the general registration task of prostate shapes becomes increasingly difficult due to the large variations in pose and shape in the training sets, and is not readily handled through existing techniques. Thus, the contributions of this paper are twofold. We first explicitly address the registration problem by representing the shapes of a training set as point clouds. In doing so, we are able to exploit the more global aspects of registration via a certain particle filtering based scheme. In addition, once the shapes have been registered, a cost functional is designed to incorporate both the local image statistics as well as the learnt shape prior. We provide experimental results, which include several challenging clinical data sets, to highlight the algorithm's capability of robustly handling supine/prone prostate registration and the overall segmentation task.

  6. GPU-accelerated MRF segmentation algorithm for SAR images

    NASA Astrophysics Data System (ADS)

    Sui, Haigang; Peng, Feifei; Xu, Chuan; Sun, Kaimin; Gong, Jianya

    2012-06-01

    Markov Random Field (MRF) approaches have been widely studied for Synthetic Aperture Radar (SAR) image segmentation, but they have a large computational cost and hence are not widely used in practice. Fortunately parallel algorithms have been documented to enjoy significant speedups when ported to run on a graphics processing units (GPUs) instead of a standard CPU. Presented here is an implementation of graphics processing units in General Purpose Computation (GPGPU) for SAR image segmentation based on the MRF method, using the C-oriented Compute Unified Device Architecture (CUDA) developed by NVIDIA. This experiment with GPGPU shows that the speed of segmentation can be increased by a factor of 10 for large images.

  7. Objective measurements to evaluate glottal space segmentation from laryngeal images.

    PubMed

    Gutiérrez-Arriola, J M; Osma-Ruiz, V; Sáenz-Lechón, N; Godino-Llorente, J I; Fraile, R; Arias-Londoño, J D

    2012-01-01

    Objective evaluation of the results of medical image segmentation is a known problem. Applied to the task of automatically detecting the glottal area from laryngeal images, this paper proposes a new objective measurement to evaluate the quality of a segmentation algorithm by comparing with the results given by a human expert. The new figure of merit is called Area Index, and its effectiveness is compared with one of the most used figures of merit found in the literature: the Pratt Index. Results over 110 laryngeal images presented high correlations between both indexes, demonstrating that the proposed measure is comparable to the Pratt Index and it is a good indicator of the segmentation quality.

  8. 3D Prostate Segmentation of Ultrasound Images Combining Longitudinal Image Registration and Machine Learning

    PubMed Central

    Yang, Xiaofeng; Fei, Baowei

    2012-01-01

    We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 ± 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images. PMID:24027622

  9. Understanding the optics to aid microscopy image segmentation.

    PubMed

    Yin, Zhaozheng; Li, Kang; Kanade, Takeo; Chen, Mei

    2010-01-01

    Image segmentation is essential for many automated microscopy image analysis systems. Rather than treating microscopy images as general natural images and rushing into the image processing warehouse for solutions, we propose to study a microscope's optical properties to model its image formation process first using phase contrast microscopy as an exemplar. It turns out that the phase contrast imaging system can be relatively well explained by a linear imaging model. Using this model, we formulate a quadratic optimization function with sparseness and smoothness regularizations to restore the "authentic" phase contrast images that directly correspond to specimen's optical path length without phase contrast artifacts such as halo and shade-off. With artifacts removed, high quality segmentation can be achieved by simply thresholding the restored images. The imaging model and restoration method are quantitatively evaluated on two sequences with thousands of cells captured over several days. PMID:20879233

  10. Sonar image segmentation using an unsupervised hierarchical MRF model.

    PubMed

    Mignotte, M; Collet, C; Perez, P; Bouthemy, P

    2000-01-01

    This paper is concerned with hierarchical Markov random field (MRP) models and their application to sonar image segmentation. We present an original hierarchical segmentation procedure devoted to images given by a high-resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) and sea-bottom reverberation. The proposed unsupervised scheme takes into account the variety of the laws in the distribution mixture of a sonar image, and it estimates both the parameters of noise distributions and the parameters of the Markovian prior. For the estimation step, we use an iterative technique which combines a maximum likelihood approach (for noise model parameters) with a least-squares method (for MRF-based prior). In order to model more precisely the local and global characteristics of image content at different scales, we introduce a hierarchical model involving a pyramidal label field. It combines coarse-to-fine causal interactions with a spatial neighborhood structure. This new method of segmentation, called the scale causal multigrid (SCM) algorithm, has been successfully applied to real sonar images and seems to be well suited to the segmentation of very noisy images. The experiments reported in this paper demonstrate that the discussed method performs better than other hierarchical schemes for sonar image segmentation.

  11. Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique

    NASA Astrophysics Data System (ADS)

    Guan, Yana; Li, Qingli; Wang, Yiting; Liu, Hongying; Zhu, Ziqiang

    2012-05-01

    White blood cells (WBC) are comparatively significant components in the human blood system, and they have a pathological relationship with some blood-related diseases. To analyze the disease information accurately, the most essential work is to segment WBCs. We propose a new method for pathological WBC segmentation based on a hyperspectral imaging system. This imaging system is used to capture WBC images, which is characterized by acquiring 1-D spectral information and 2-D spatial information for each pixel. A spectral information divergence algorithm is presented to segment pathological WBCs into four parts. In order to evaluate the performance of the new approach, K-means and spectral angle mapper-based segmental methods are tested in contrast on six groups of blood smears. Experimental results show that the presented method can segment pathological WBCs more accurately, regardless of their irregular shapes, sizes, and gray-values.

  12. Adaptive automatic segmentation of Leishmaniasis parasite in Indirect Immunofluorescence images.

    PubMed

    Ouertani, F; Amiri, H; Bettaib, J; Yazidi, R; Ben Salah, A

    2014-01-01

    This paper describes the first steps for the automation of the serum titration process. In fact, this process requires an Indirect Immunofluorescence (IIF) diagnosis automation. We deal with the initial phase that represents the fluorescence images segmentation. Our approach consists of three principle stages: (1) a color based segmentation which aims at extracting the fluorescent foreground based on k-means clustering, (2) the segmentation of the fluorescent clustered image, and (3) a region-based feature segmentation, intended to remove the fluorescent noisy regions and to locate fluorescent parasites. We evaluated the proposed method on 40 IIF images. Experimental results show that such a method provides reliable and robust automatic segmentation of fluorescent Promastigote parasite. PMID:25571049

  13. A Bayesian Approach for Image Segmentation with Shape Priors

    SciTech Connect

    Chang, Hang; Yang, Qing; Parvin, Bahram

    2008-06-20

    Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missingparts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances quality of the segmentation where color and/or texture are not solely adequate. The novelties of our approach are in (i) formulating the segmentation problem in a well-de?ned Bayesian framework with multiple shape priors, (ii) ef?ciently estimating parameters of the Bayesian model, and (iii) multi-object segmentation through user-speci?ed priors. We demonstrate the effectiveness of our method on a set of natural and synthetic images.

  14. An improved level set method for vertebra CT image segmentation

    PubMed Central

    2013-01-01

    Background Clinical diagnosis and therapy for the lumbar disc herniation requires accurate vertebra segmentation. The complex anatomical structure and the degenerative deformations of the vertebrae makes its segmentation challenging. Methods An improved level set method, namely edge- and region-based level set method (ERBLS), is proposed for vertebra CT images segmentation. By considering the gradient information and local region characteristics of images, the proposed model can efficiently segment images with intensity inhomogeneity and blurry or discontinuous boundaries. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a simple initialization method for the level set function is built, which utilizes the Otsu threshold. In addition, the need of the costly re-initialization procedure is completely eliminated. Results Experimental results on both synthetic and real images demonstrated that the proposed ERBLS model is very robust and efficient. Compared with the well-known local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour. The proposed method has also applied to 56 patient data sets and produced very promising results. Conclusions An improved level set method suitable for vertebra CT images segmentation is proposed. It has the flexibility of segmenting the vertebra CT images with blurry or discontinuous edges, internal inhomogeneity and no need of re-initialization. PMID:23714300

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

    PubMed Central

    2010-01-01

    Background 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. Results 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

  16. Functional Magnetic Resonance Imaging and Pediatric Anxiety

    ERIC Educational Resources Information Center

    Pine, Daniel S.; Guyer, Amanda E.; Leibenluft, Ellen; Peterson, Bradley S.; Gerber, Andrew

    2008-01-01

    The use of functional magnetic resonance imaging in investigating pediatric anxiety disorders is studied. Functional magnetic resonance imaging can be utilized in demonstrating parallels between the neural architecture of difference in anxiety of humans and the neural architecture of attention-orienting behavior in nonhuman primates or rodents.…

  17. A translational registration system for LANDSAT image segments

    NASA Technical Reports Server (NTRS)

    Parada, N. D. J. (Principal Investigator); Erthal, G. J.; Velasco, F. R. D.; Mascarenhas, N. D. D.

    1983-01-01

    The use of satellite images obtained from various dates is essential for crop forecast systems. In order to make possible a multitemporal analysis, it is necessary that images belonging to each acquisition have pixel-wise correspondence. A system developed to obtain, register and record image segments from LANDSAT images in computer compatible tapes is described. The translational registration of the segments is performed by correlating image edges in different acquisitions. The system was constructed for the Burroughs B6800 computer in ALGOL language.

  18. Three-dimensional segmentation of bone structures in CT images

    NASA Astrophysics Data System (ADS)

    Boehm, Guenther; Knoll, Christian J.; Grau Colomer, Vincente; Alcaniz-Raya, Mariano L.; Albalat, Salvador E.

    1999-05-01

    This work is concerned with the implementation of a fully 3D-consistent, automatic segmentation of bone structures in CT images. The morphological watersheds algorithm has been chosen as the base of the low-level segmentation. The over- segmentation, a phenomenon normally involved with this transformation, has been sorted out successfully by inserting modifying modules that act already within the algorithm. When dealing with a maxillofacial image, this approach also includes the possibility to provide two different divisions of the image: a fine-grained tessellation geared to the following high-level segmentation and a more coarse-grained one for the segmentation of the teeth. In the knowledge-based high-level segmentation, probabilistic considerations make use of specific properties of the 3D low-level regions to find the most probable tissue for each region. Low-level regions that cannot be classified with the necessary certainty are passed to a second stage, where--embedded in their respective environment--they are compared with structural patterns deduced from anatomical knowledge. The tooth segmentation takes the coarse-grained tessellation as its starting point. The few regions making up each tooth are grouped to 3D envelopes--one envelope per tooth. Matched filtering detects the bases of these envelopes. After a refinement they are fitted into the fine- grained, high-level segmented image.

  19. Unified wavelet and gaussian filtering for segmentation of CT images; application in segmentation of bone in pelvic CT images

    PubMed Central

    Vasilache, Simina; Ward, Kevin; Cockrell, Charles; Ha, Jonathan; Najarian, Kayvan

    2009-01-01

    Background The analysis of pelvic CT scans is a crucial step for detecting and assessing the severity of Traumatic Pelvic Injuries. Automating the processing of pelvic CT scans could impact decision accuracy, decrease the time for decision making, and reduce health care cost. This paper discusses a method to automate the segmentation of bone from pelvic CT images. Accurate segmentation of bone is very important for developing an automated assisted-decision support system for Traumatic Pelvic Injury diagnosis and treatment. Methods The automated method for pelvic CT bone segmentation is a hierarchical approach that combines filtering and histogram equalization, for image enhancement, wavelet analysis and automated seeded region growing. Initial results of segmentation are used to identify the region where bone is present and to target histogram equalization towards the specific area. Speckle Reducing Anisotropic Didffusion (SRAD) filter is applied to accentuate the desired features in the region. Automated seeded region growing is performed to refine the initial bone segmentation results. Results The proposed method automatically processes pelvic CT images and produces accurate segmentation. Bone connectivity is achieved and the contours and sizes of bones are true to the actual contour and size displayed in the original image. Results are promising and show great potential for fracture detection and assessing hemorrhage presence and severity. Conclusion Preliminary experimental results of the automated method show accurate bone segmentation. The novelty of the method lies in the unique hierarchical combination of image enhancement and segmentation methods that aims at maximizing the advantages of the combined algorithms. The proposed method has the following advantages: it produces accurate bone segmentation with maintaining bone contour and size true to the original image and is suitable for automated bone segmentation from pelvic CT images. PMID:19891802

  20. Image segmentation evaluation for very-large datasets

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

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

    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 con-tours 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

  2. Mammographic images segmentation based on chaotic map clustering algorithm

    PubMed Central

    2014-01-01

    Background This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. Results The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. Conclusions We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI. PMID:24666766

  3. Comparison of algorithms for ultrasound image segmentation without ground truth

    NASA Astrophysics Data System (ADS)

    Sikka, Karan; Deserno, Thomas M.

    2010-02-01

    Image segmentation is a pre-requisite to medical image analysis. A variety of segmentation algorithms have been proposed, and most are evaluated on a small dataset or based on classification of a single feature. The lack of a gold standard (ground truth) further adds to the discrepancy in these comparisons. This work proposes a new methodology for comparing image segmentation algorithms without ground truth by building a matrix called region-correlation matrix. Subsequently, suitable distance measures are proposed for quantitative assessment of similarity. The first measure takes into account the degree of region overlap or identical match. The second considers the degree of splitting or misclassification by using an appropriate penalty term. These measures are shown to satisfy the axioms of a quasi-metric. They are applied for a comparative analysis of synthetic segmentation maps to show their direct correlation with human intuition of similar segmentation. Since ultrasound images are difficult to segment and usually lack a ground truth, the measures are further used to compare the recently proposed spectral clustering algorithm (encoding spatial and edge information) with standard k-means over abdominal ultrasound images. Improving the parameterization and enlarging the feature space for k-means steadily increased segmentation quality to that of spectral clustering.

  4. Real-time kidney ultrasound image segmentation: a prospective study

    NASA Astrophysics Data System (ADS)

    Dahdouh, S.; Frenoux, E.; Osorio, A.

    2009-02-01

    Segmentation of ultrasound kidney images represents a challenge due to low quality data. Speckle, shadows, signal dropout and low contrast make segmentation a harsh task. In addition, kidney ultrasound imaging presents a great variability concerning the organ's shape on the image. This characteristic makes learning methods hard to use. The aim of this study is to develop a real time kidney ultrasound image segmentation method usable during surgical operations such as punctures. To deal with real time constraints, we decided to focus on region based methods and particularly split and merge algorithm. In this prospective study, the selection of the interesting area in the initial image is made by the physician, drawing a coarse bounding box around the organ. A pre-processing phase is first performed to correct image's artefacts. This phase is composed of three major steps. First, an image specification is made between the image to segment and a reference one. Then, a Haar wavelet filtering method is applied on the resulting image and finally an anisotropic diffusion filter is applied to smooth the result. Then, a split and merge algorithm is applied on the resulting image. Both split and merge criteria are based on regions statistics. Our method has been successfully applied on a set of 22 clinical images coming from 10 different patients and presenting different points of view regarding kidney's shape. We obtained very good results, for an average computational time of 8.5 seconds per image.

  5. Skin lesion image segmentation using Delaunay Triangulation for melanoma detection.

    PubMed

    Pennisi, Andrea; Bloisi, Domenico D; Nardi, Daniele; Giampetruzzi, Anna Rita; Mondino, Chiara; Facchiano, Antonio

    2016-09-01

    Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.

  6. Skin lesion image segmentation using Delaunay Triangulation for melanoma detection.

    PubMed

    Pennisi, Andrea; Bloisi, Domenico D; Nardi, Daniele; Giampetruzzi, Anna Rita; Mondino, Chiara; Facchiano, Antonio

    2016-09-01

    Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection. PMID:27215953

  7. Neural cell image segmentation method based on support vector machine

    NASA Astrophysics Data System (ADS)

    Niu, Shiwei; Ren, Kan

    2015-10-01

    In the analysis of neural cell images gained by optical microscope, accurate and rapid segmentation is the foundation of nerve cell detection system. In this paper, a modified image segmentation method based on Support Vector Machine (SVM) is proposed to reduce the adverse impact caused by low contrast ratio between objects and background, adherent and clustered cells' interference etc. Firstly, Morphological Filtering and OTSU Method are applied to preprocess images for extracting the neural cells roughly. Secondly, the Stellate Vector, Circularity and Histogram of Oriented Gradient (HOG) features are computed to train SVM model. Finally, the incremental learning SVM classifier is used to classify the preprocessed images, and the initial recognition areas identified by the SVM classifier are added to the library as the positive samples for training SVM model. Experiment results show that the proposed algorithm can achieve much better segmented results than the classic segmentation algorithms.

  8. Spectral segmentation of polygonized images with normalized cuts

    SciTech Connect

    Matsekh, Anna; Skurikhin, Alexei; Rosten, Edward

    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.

  9. Automatic segmentation of MR images using self-organizing feature mapping and neural networks

    NASA Astrophysics Data System (ADS)

    Alirezaie, Javad; Jernigan, M. Ed; Nahmias, Claude

    1997-04-01

    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 (ANN) for feature mapping and generates a set of codebook vectors for each tissue class. Features are selected from three image spectra: T1, T2 and proton density (PD) weighted images. An algorithm has been developed for isolating the cerebrum from the head scan prior to the segmentation. To classify the map, we extend the network by adding an associative layer. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. Any unclassified tissues were remained as unknown tissue class.

  10. Implementation and assessment of diffusion-weighted partial Fourier readout-segmented echo-planar imaging.

    PubMed

    Frost, Robert; Porter, David A; Miller, Karla L; Jezzard, Peter

    2012-08-01

    Single-shot echo-planar imaging has been used widely in diffusion magnetic resonance imaging due to the difficulties in correcting motion-induced phase corruption in multishot data. Readout-segmented EPI has addressed the multishot problem by introducing a two-dimensional nonlinear navigator correction with online reacquisition of uncorrectable data to enable acquisition of high-resolution diffusion data with reduced susceptibility artifact and T*(2) blurring. The primary shortcoming of readout-segmented EPI in its current form is its long acquisition time (longer than similar resolution single-shot echo-planar imaging protocols by approximately the number of readout segments), which limits the number of diffusion directions. By omitting readout segments at one side of k-space and using partial Fourier reconstruction, readout-segmented EPI imaging times could be reduced. In this study, the effects of homodyne and projection onto convex sets reconstructions on estimates of the fractional anisotropy, mean diffusivity, and diffusion orientation in fiber tracts and raw T(2)- and trace-weighted signal are compared, along with signal-to-noise ratio results. It is found that projections onto convex sets reconstruction with 3/5 segments in a 2 mm isotropic diffusion tensor image acquisition and 9/13 segments in a 0.9 × 0.9 × 4.0 mm(3) diffusion-weighted image acquisition provide good fidelity relative to the full k-space parameters. This allows application of readout-segmented EPI to tractography studies, and clinical stroke and oncology protocols.

  11. An enhanced fast scanning algorithm for image segmentation

    NASA Astrophysics Data System (ADS)

    Ismael, Ahmed Naser; Yusof, Yuhanis binti

    2015-12-01

    Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical images. It scans all pixels in the image and cluster each pixel according to the upper and left neighbor pixels. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold. Such an approach will lead to a weak reliability and shape matching of the produced segments. This paper proposes an adaptive threshold function to be used in the clustering process of the Fast Scanning algorithm. This function used the gray'value in the image's pixels and variance Also, the level of the image that is more the threshold are converted into intensity values between 0 and 1, and other values are converted into intensity values zero. The proposed enhanced Fast Scanning algorithm is realized on images of the public and private transportation in Iraq. Evaluation is later made by comparing the produced images of proposed algorithm and the standard Fast Scanning algorithm. The results showed that proposed algorithm is faster in terms the time from standard fast scanning.

  12. Object segmentation based on guided layering from video image

    NASA Astrophysics Data System (ADS)

    Lin, Guangfeng; Zhu, Hong; Fan, Caixia; Zhang, Erhu

    2011-09-01

    When the object is similar to the background, it is difficult to segment the completed human body object from video images. To solve the problem, this paper proposes an object segmentation algorithm based on guided layering from video images. This algorithm adopts the structure of advance by degrees, including three parts altogether. Each part constructs the different energy function in terms of the spatiotemporal information to maximize the posterior probability of segmentation label. In part one, the energy functions are established, respectively, with the frame difference information in the first layer and second layer. By optimization, the initial segmentation is solved in the first layer, and then the amended segmentation is obtained in the second layer. In part two, the energy function is built in the interframe with the shape feature as the prior guiding to eliminate the interframe difference of the segmentation result. In art three, the segmentation results in the previous two parts are fused to suppress or inhibit the over-repairing segmentation and the object shape variations in the adjacent two-frame. The results from the compared experiment indicate that this algorithm can obtain the completed human body object in the case of the video image with similarity between object and background.

  13. Image segmentation and 3D visualization for MRI mammography

    NASA Astrophysics Data System (ADS)

    Li, Lihua; Chu, Yong; Salem, Angela F.; Clark, Robert A.

    2002-05-01

    MRI mammography has a number of advantages, including the tomographic, and therefore three-dimensional (3-D) nature, of the images. It allows the application of MRI mammography to breasts with dense tissue, post operative scarring, and silicon implants. However, due to the vast quantity of images and subtlety of difference in MR sequence, there is a need for reliable computer diagnosis to reduce the radiologist's workload. The purpose of this work was to develop automatic breast/tissue segmentation and visualization algorithms to aid physicians in detecting and observing abnormalities in breast. Two segmentation algorithms were developed: one for breast segmentation, the other for glandular tissue segmentation. In breast segmentation, the MRI image is first segmented using an adaptive growing clustering method. Two tracing algorithms were then developed to refine the breast air and chest wall boundaries of breast. The glandular tissue segmentation was performed using an adaptive thresholding method, in which the threshold value was spatially adaptive using a sliding window. The 3D visualization of the segmented 2D slices of MRI mammography was implemented under IDL environment. The breast and glandular tissue rendering, slicing and animation were displayed.

  14. Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method

    NASA Astrophysics Data System (ADS)

    Gao, Yi; Tannenbaum, Allen

    2010-03-01

    In this paper, we present a shape based segmentation methodology for magnetic resonance prostate images. We first propose a new way to represent shapes via the hyperbolic tangent of the signed distance function. This effectively corrects the drawbacks of the signed distance function and yields very reasonable results for the shape registration and learning. Secondly, under a Bayesian statistical framework, instead of computing the posterior using a uniform prior, a directional distance map is introduced in order to incorporate a priori knowledge of image content as well as the estimated center of target object. Essentially, the image is modeled as a Finsler manifold and the metric is computed out of the directional derivative of the image. Then the directional distance map is computed to suppress the posterior remote from the object center. Thirdly, in the posterior image, a localized region based cost functional is designed to drive the shape based segmentation. Such cost functional utilizes the local regional information and is robust to both image noise and remote/irrelevant disturbances. With these three major components, the entire shape based segmentation procedure is provided as a complete open source pipeline and is applied to magnetic resonance image (MRI) prostate data.

  15. Optical image segmentation using neural-based wavelet filtering techniques

    NASA Astrophysics Data System (ADS)

    Veronin, Christopher P.; Priddy, Kevin L.; Rogers, Steven K.; Ayer, Kevin W.; Kabrisky, Matthew; Welsh, Byron M.

    1992-02-01

    This paper presents a neural based optical image segmentation scheme for locating potential targets in cluttered FLIR images. The advantage of such a scheme is speed, i.e., the speed of light. Such a design is critical to achieve real-time segmentation and classification for machine vision applications. The segmentation scheme used was based on texture discrimination and employed biologically based orientation specific filters (wavelet filters) as its main component. These filters are well understood impulse response functions of mammalian vision systems from input to striate cortex. By using the proper choice of aperture pair separation, dilation, and orientation, targets in FLIR imagery were optically segmented. Wavelet filtering is illustrated for glass template slides, as well as segmentation for static and real-time FLIR imagery displayed on a liquid crystal television.

  16. Watershed Merge Tree Classification for Electron Microscopy Image Segmentation

    SciTech Connect

    Liu, TIng; Jurrus, Elizabeth R.; Seyedhosseini, Mojtaba; Ellisman, Mark; Tasdizen, Tolga

    2012-11-11

    Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which merge decisions are made with consistency constraints in the sense of optimization to acquire the final segmentation. Independent of classifiers and decision strategies, our approach proposes a general framework for efficient hierarchical segmentation with statistical learning. We demonstrate that our method leads to a substantial improvement in segmentation accuracy.

  17. Fuzzy fusion of results of medical image segmentation

    NASA Astrophysics Data System (ADS)

    Guliato, Denise; Rangayyan, Rangaraj M.; Carnielli, Walter A.; Zuffo, Joao A.; Desautels, J. E. Leo

    1999-05-01

    We propose an abstract concept of data fusion based on finite automata and fuzzy sets to integrate and evaluate different sources of information, in particular results of multiple image segmentation procedures. We give an example of how the method may be applied to the problem of mammographic image segmentation to combine results of region growing and closed- contour detection techniques. We further propose a measure of fuzziness to assess the agreement between a segmented region and a reference contour. Results of application to breast tumor detection in mammograms indicate that the fusion results agree with reference contours provided by a radiologist to a higher extent than the results of the individual methods.

  18. A segmentation algorithm of intracranial hemorrhage CT image

    NASA Astrophysics Data System (ADS)

    Wang, Haibo; Chen, Zhiguo; Wang, Jianzhi

    2011-10-01

    To develop a computer aided detection (CAD) system that improves diagnostic accuracy of intracranial hemorrhage on cerebral CT. A method for CT image segmentation of brain is proposed, with which, several regions that are suspicious of hemorrhage can be segmented rapidly and effectively. Extracting intracranial area algorithm is introduced firstly to extract intracranial area. Secondly, FCM is employed twice, we named it with TFCM. FCM is first employed to identify areas of intracranial hemorrhage. Finally, FCM is employed to segment the lesions. Experimental results on real medical images demonstrate the efficiency and effectiveness.

  19. Segmentation and image navigation in digitized spine x rays

    NASA Astrophysics Data System (ADS)

    Long, L. Rodney; Thoma, George R.

    2000-06-01

    The National Library of Medicine has archived a collection of 17,000 digitized x-rays of the cervical and lumbar spines. Extensive health information has been collected on the subjects of these x-rays, but no information has been derived from the image contents themselves. We are researching algorithms to segment anatomy in these images and to derive from the segmented data measurements useful for indexing this image set for characteristics important to researchers in rheumatology, bone morphometry, and related areas. Active Shape Modeling is currently being investigated for use in location and boundary definition for the vertebrae in these images.

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

    PubMed

    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

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

    PubMed

    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

  2. Segmentation of vertebral bodies in CT and MR images based on 3D deterministic models

    NASA Astrophysics Data System (ADS)

    Štern, Darko; Vrtovec, Tomaž; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

    The evaluation of vertebral deformations is of great importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is oriented towards the computed tomography (CT) and magnetic resonance (MR) imaging techniques, as they can provide a detailed 3D representation of vertebrae, the established methods for the evaluation of vertebral deformations still provide only a two-dimensional (2D) geometrical description. Segmentation of vertebrae in 3D may therefore not only improve their visualization, but also provide reliable and accurate 3D measurements of vertebral deformations. In this paper we propose a method for 3D segmentation of individual vertebral bodies that can be performed in CT and MR images. Initialized with a single point inside the vertebral body, the segmentation is performed by optimizing the parameters of a 3D deterministic model of the vertebral body to achieve the best match of the model to the vertebral body in the image. The performance of the proposed method was evaluated on five CT (40 vertebrae) and five T2-weighted MR (40 vertebrae) spine images, among them five are normal and five are pathological. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images and that the proposed model can describe a variety of vertebral body shapes. The method may be therefore used for initializing whole vertebra segmentation or reliably describing vertebral body deformations.

  3. A Generative Model for Image Segmentation Based on Label Fusion

    PubMed Central

    Thomas Yeo, B. T.; Van Leemput, Koen; Fischl, Bruce; Golland, Polina

    2012-01-01

    We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans—with manually segmented white matter, cerebral cortex, ventricles and subcortical structures—to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer’s Disease. PMID:20562040

  4. White matter lesion segmentation using machine learning and weakly labeled MR images

    NASA Astrophysics Data System (ADS)

    Xie, Yuchen; Tao, Xiaodong

    2011-03-01

    We propose a fast, learning-based algorithm for segmenting white matter (WM) lesions for magnetic resonance (MR) brain images. The inputs to the algorithm are T1, T2, and FLAIR images. Unlike most of the previously reported learning-based algorithms, which treat expert labeled lesion map as ground truth in the training step, the proposed algorithm only requires the user to provide a few regions of interest (ROI's) containing lesions. An unsupervised clustering algorithm is applied to segment these ROI's into areas. Based on the assumption that lesion voxels have higher intensity on FLAIR image, areas corresponding to lesions are identified and their probability distributions in T1, T2, and FLAIR images are computed. The lesion segmentation in 3D is done by using the probability distributions to generate a confidence map of lesion and applying a graph based segmentation algorithm to label lesion voxels. The initial lesion label is used to further refine the probability distribution estimation for the final lesion segmentation. The advantages of the proposed algorithm are: 1. By using the weak labels, we reduced the dependency of the segmentation performance on the expert discrimination of lesion voxels in the training samples; 2. The training can be done using labels generated by users with only general knowledge of brain anatomy and image characteristics of WM lesion, instead of these carefully labeled by experienced radiologists; 3. The algorithm is fast enough to make interactive segmentation possible. We test the algorithm on nine ACCORD-MIND MRI datasets. Experimental results show that our algorithm agrees well with expert labels and outperforms a support vector machine based WM lesion segmentation algorithm.

  5. A variational approach to bone segmentation in CT images

    NASA Astrophysics Data System (ADS)

    Calder, Jeff; Tahmasebi, Amir M.; Mansouri, Abdol-Reza

    2011-03-01

    We present a variational approach for segmenting bone structures in Computed Tomography (CT) images. We introduce a novel functional on the space of image segmentations, and subsequently minimize this functional through a gradient descent partial differential equation. The functional we propose provides a measure of similarity of the intensity characteristics of the bone and tissue regions through a comparison of their cumulative distribution functions; minimizing this similarity measure therefore yields the maximal separation between the two regions. We perform the minimization of our proposed functional using level set partial differential equations; in addition to numerical stability, this yields topology independence, which is especially useful in the context of CT bone segmentation where a bone region may consist of several disjoint pieces. Finally, we present an extensive validation of our method against expert manual segmentation on CT images of the wrist, ankle, foot, and pelvis.

  6. Leaf image segmentation method based on multifractal detrended fluctuation analysis

    NASA Astrophysics Data System (ADS)

    Wang, Fang; Li, Jin-Wei; Shi, Wen; Liao, Gui-Ping

    2013-12-01

    To identify singular regions of crop leaf affected by diseases, based on multifractal detrended fluctuation analysis (MF-DFA), an image segmentation method is proposed. In the proposed method, first, we defend a new texture descriptor: local generalized Hurst exponent, recorded as LHq based on MF-DFA. And then, box-counting dimension f(LHq) is calculated for sub-images constituted by the LHq of some pixels, which come from a specific region. Consequently, series of f(LHq) of the different regions can be obtained. Finally, the singular regions are segmented according to the corresponding f(LHq). Six kinds of corn diseases leaf's images are tested in our experiments. Both the proposed method and other two segmentation methods—multifractal spectrum based and fuzzy C-means clustering have been compared in the experiments. The comparison results demonstrate that the proposed method can recognize the lesion regions more effectively and provide more robust segmentations.

  7. Segmentation method for in vivo meibomian gland OCT image

    NASA Astrophysics Data System (ADS)

    Shin, Jun Geun; Lee, Byeong Ha; Eom, Tae Joong

    2014-02-01

    We demonstrate segmentation of human MGs based on several image processing technic. 3D volumetric data of upper eyelid was acquired from real-time FD-OCT, and its acini area of MGs was segmented. Three dimensional volume informations of meibomian glands should be helpful to diagnose meibomian gland related disease. In order to reveal boundary between tarsal plate and acini, each B-scan images were obtained before averaged three times. Imaging area was 10x10mm and 700x1000x500 voxels. The acquisition time was 60ms for B-scan and 30sec for C-scan. The 3D data was flattened to remove curvature and axial vibration, and resized to reduce computational costs, and filtered to minimize speckles, and segmented. Marker based watershed transform was employed to segment each acini area of meibomian gland.

  8. Automatic image segmentation by dynamic region growth and multiresolution merging.

    PubMed

    Ugarriza, Luis Garcia; Saber, Eli; Vantaram, Sreenath Rao; Amuso, Vincent; Shaw, Mark; Bhaskar, Ranjit

    2009-10-01

    Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L *a *b * color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method. PMID:19535323

  9. Segmentation and tracking of facial regions in color image sequences

    NASA Astrophysics Data System (ADS)

    Menser, Bernd; Wien, Mathias

    2000-05-01

    In this paper a new algorithm for joint detection and segmentation of human faces in color images sequence is presented. A skin probability image is generated using a model for skin color. Instead of a binary segmentation to detect skin regions, connected operators are used to analyze the skin probability image at different threshold levels. A hierarchical scheme of operators using shape and texture simplifies the skin probability image. For the remaining connected components, the likelihood of being a face is estimated using principal components analysis. To track a detected face region through the sequence, the connected component that represent the face in the previous frame is projected into the current frame. Using the projected segment as a marker, connected operators extract the actual face region from the skin probability image.

  10. Performance benchmarking of liver CT image segmentation and volume estimation

    NASA Astrophysics Data System (ADS)

    Xiong, Wei; Zhou, Jiayin; Tian, Qi; Liu, Jimmy J.; Qi, Yingyi; Leow, Wee Kheng; Han, Thazin; Wang, Shih-chang

    2008-03-01

    In recent years more and more computer aided diagnosis (CAD) systems are being used routinely in hospitals. Image-based knowledge discovery plays important roles in many CAD applications, which have great potential to be integrated into the next-generation picture archiving and communication systems (PACS). Robust medical image segmentation tools are essentials for such discovery in many CAD applications. In this paper we present a platform with necessary tools for performance benchmarking for algorithms of liver segmentation and volume estimation used for liver transplantation planning. It includes an abdominal computer tomography (CT) image database (DB), annotation tools, a ground truth DB, and performance measure protocols. The proposed architecture is generic and can be used for other organs and imaging modalities. In the current study, approximately 70 sets of abdominal CT images with normal livers have been collected and a user-friendly annotation tool is developed to generate ground truth data for a variety of organs, including 2D contours of liver, two kidneys, spleen, aorta and spinal canal. Abdominal organ segmentation algorithms using 2D atlases and 3D probabilistic atlases can be evaluated on the platform. Preliminary benchmark results from the liver segmentation algorithms which make use of statistical knowledge extracted from the abdominal CT image DB are also reported. We target to increase the CT scans to about 300 sets in the near future and plan to make the DBs built available to medical imaging research community for performance benchmarking of liver segmentation algorithms.

  11. Vectorized image segmentation via trixel agglomeration

    DOEpatents

    Prasad, Lakshman; Skourikhine, Alexei N.

    2006-10-24

    A computer implemented method transforms an image comprised of pixels into a vectorized image specified by a plurality of polygons that can be subsequently used to aid in image processing and understanding. The pixelated image is processed to extract edge pixels that separate different colors and a constrained Delaunay triangulation of the edge pixels forms a plurality of triangles having edges that cover the pixelated image. A color for each one of the plurality of triangles is determined from the color pixels within each triangle. A filter is formed with a set of grouping rules related to features of the pixelated image and applied to the plurality of triangle edges to merge adjacent triangles consistent with the filter into polygons having a plurality of vertices. The pixelated image may be then reformed into an array of the polygons, that can be represented collectively and efficiently by standard vector image.

  12. Automatic labeling and segmentation of vertebrae in CT images

    NASA Astrophysics Data System (ADS)

    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.

  13. Imaging agents for in vivo magnetic resonance and scintigraphic imaging

    DOEpatents

    Engelstad, Barry L.; Raymond, Kenneth N.; Huberty, John P.; White, David L.

    1991-01-01

    Methods are provided for in vivo magnetic resonance imaging and/or scintigraphic imaging of a subject using chelated transition metal and lanthanide metal complexes. Novel ligands for these complexes are provided.

  14. Imaging agents for in vivo magnetic resonance and scintigraphic imaging

    DOEpatents

    Engelstad, B.L.; Raymond, K.N.; Huberty, J.P.; White, D.L.

    1991-04-23

    Methods are provided for in vivo magnetic resonance imaging and/or scintigraphic imaging of a subject using chelated transition metal and lanthanide metal complexes. Novel ligands for these complexes are provided. No Drawings

  15. Automatic segmentation of the striatum and globus pallidus using MIST: Multimodal Image Segmentation Tool.

    PubMed

    Visser, Eelke; Keuken, Max C; Douaud, Gwenaëlle; Gaura, Veronique; Bachoud-Levi, Anne-Catherine; Remy, Philippe; Forstmann, Birte U; Jenkinson, Mark

    2016-01-15

    Accurate segmentation of the subcortical structures is frequently required in neuroimaging studies. Most existing methods use only a T1-weighted MRI volume to segment all supported structures and usually rely on a database of training data. We propose a new method that can use multiple image modalities simultaneously and a single reference segmentation for initialisation, without the need for a manually labelled training set. The method models intensity profiles in multiple images around the boundaries of the structure after nonlinear registration. It is trained using a set of unlabelled training data, which may be the same images that are to be segmented, and it can automatically infer the location of the physical boundary using user-specified priors. We show that the method produces high-quality segmentations of the striatum, which is clearly visible on T1-weighted scans, and the globus pallidus, which has poor contrast on such scans. The method compares favourably to existing methods, showing greater overlap with manual segmentations and better consistency. PMID:26477650

  16. Image segmentation for automated dental identification

    NASA Astrophysics Data System (ADS)

    Haj Said, Eyad; Nassar, Diaa Eldin M.; Ammar, Hany H.

    2006-02-01

    Dental features are one of few biometric identifiers that qualify for postmortem identification; therefore, creation of an Automated Dental Identification System (ADIS) with goals and objectives similar to the Automated Fingerprint Identification System (AFIS) has received increased attention. As a part of ADIS, teeth segmentation from dental radiographs films is an essential step in the identification process. In this paper, we introduce a fully automated approach for teeth segmentation with goal to extract at least one tooth from the dental radiograph film. We evaluate our approach based on theoretical and empirical basis, and we compare its performance with the performance of other approaches introduced in the literature. The results show that our approach exhibits the lowest failure rate and the highest optimality among all full automated approaches introduced in the literature.

  17. Colony image acquisition and genetic segmentation algorithm and colony analyses

    NASA Astrophysics Data System (ADS)

    Wang, W. X.

    2012-01-01

    Colony anaysis is used in a large number of engineerings such as food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing. In order to reduce laboring and increase analysis acuracy, many researchers and developers have made efforts for image analysis systems. The main problems in the systems are image acquisition, image segmentation and image analysis. In this paper, to acquire colony images with good quality, an illumination box was constructed. In the box, the distances between lights and dishe, camra lens and lights, and camera lens and dishe are adjusted optimally. In image segmentation, It is based on a genetic approach that allow one to consider the segmentation problem as a global optimization,. After image pre-processing and image segmentation, the colony analyses are perfomed. The colony image analysis consists of (1) basic colony parameter measurements; (2) colony size analysis; (3) colony shape analysis; and (4) colony surface measurements. All the above visual colony parameters can be selected and combined together, used to make a new engineeing parameters. The colony analysis can be applied into different applications.

  18. Learning evaluation of ultrasound image segmentation using combined measures

    NASA Astrophysics Data System (ADS)

    Fang, Mengjie; Luo, Yongkang; Ding, Mingyue

    2016-03-01

    Objective evaluation of medical image segmentation is one of the important steps for proving its validity and clinical applicability. Although there are many researches presenting segmentation methods on medical image, while with few studying the evaluation methods on their results, this paper presents a learning evaluation method with combined measures to make it as close as possible to the clinicians' judgment. This evaluation method is more quantitative and precise for the clinical diagnose. In our experiment, the same data sets include 120 segmentation results of lumen-intima boundary (LIB) and media-adventitia boundary (MAB) of carotid ultrasound images respectively. And the 15 measures of goodness method and discrepancy method are used to evaluate the different segmentation results alone. Furthermore, the experimental results showed that compared with the discrepancy method, the accuracy with the measures of goodness method is poor. Then, by combining with the measures of two methods, the average accuracy and the area under the receiver operating characteristic (ROC) curve of 2 segmentation groups are higher than 93% and 0.9 respectively. And the results of MAB are better than LIB, which proved that this novel method can effectively evaluate the segmentation results. Moreover, it lays the foundation for the non-supervised segmentation evaluation system.

  19. Automatic segmentation of breast MR images through a Markov random field statistical model.

    PubMed

    Ribes, S; Didierlaurent, D; Decoster, N; Gonneau, E; Risser, L; Feillel, V; Caselles, O

    2014-10-01

    An algorithm dedicated to automatic segmentation of breast magnetic resonance images is presented in this paper. Our approach is based on a pipeline that includes a denoising step and statistical segmentation. The noise removal preprocessing relies on an anisotropic diffusion scheme, whereas the statistical segmentation is conducted through a Markov random field model. The continuous updating of all parameters governing the diffusion process enables automatic denoising, and the partial volume effect is also addressed during the labeling step. To assess the relevance, the Jaccard similarity coefficient was computed. Experiments were conducted on synthetic data and breast magnetic resonance images extracted from a high-risk population. The relevance of the approach for the dataset is highlighted, and we demonstrate accuracy superior to that of traditional clustering algorithms. The results emphasize the benefits of both denoising guided by input data and the inclusion of spatial dependency through a Markov random field. For example, the Jaccard coefficient for the clinical data was increased by 114%, 109%, and 140% with respect to a K-means algorithm and, respectively, for the adipose, glandular and muscle and skin components. Moreover, the agreement between the manual segmentations provided by an experienced radiologist and the automatic segmentations performed with this algorithm was good, with Jaccard coefficients equal to 0.769, 0.756, and 0.694 for the above-mentioned classes.

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

    DOEpatents

    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.

  1. Segmentation of confocal microscopic image of insect brain

    NASA Astrophysics Data System (ADS)

    Wu, Ming-Jin; Lin, Chih-Yang; Ching, Yu-Tai

    2002-05-01

    Accurate analysis of insect brain structures in digital confocal microscopic images is valuable and important to biology research needs. The first step is to segment meaningful structures from images. Active contour model, known as snakes, is widely used for segmentation of medical images. A new class of active contour model called gradient vector flow snake has been introduced in 1998 to overcome some critical problems encountered in the traditional snake. In this paper, we use gradient vector flow snake to segment the mushroom body and the central body from the confocal microscopic insect brain images. First, an edge map is created from images by some edge filters. Second, a gradient vector flow field is calculated from the edge map using a computational diffusion process. Finally, a traditional snake deformation process starts until it reaches a stable configuration. User interface is also provided here, allowing users to edit the snake during deformation process, if desired. Using the gradient vector flow snake as the main segmentation method and assist with user interface, we can properly segment the confocal microscopic insect brain image for most of the cases. The identified mushroom and central body can then be used as the preliminary results toward a 3-D reconstruction process for further biology researches.

  2. Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images.

    PubMed

    Wu, Pengfei; Liu, Yiguang; Li, Yongzhong; Liu, Bingbing

    2015-06-01

    Accurate segmentation is usually crucial in transrectal ultrasound (TRUS) image based prostate diagnosis; however, it is always hampered by heavy speckles. Contrary to the traditional view that speckles are adverse to segmentation, we exploit intrinsic properties induced by speckles to facilitate the task, based on the observations that sizes and orientations of speckles provide salient cues to determine the prostate boundary. Since the speckle orientation changes in accordance with a statistical prior rule, rotation-invariant texture feature is extracted along the orientations revealed by the rule. To address the problem of feature changes due to different speckle sizes, TRUS images are split into several arc-like strips. In each strip, every individual feature vector is sparsely represented, and representation residuals are obtained. The residuals, along with the spatial coherence inherited from biological tissues, are combined to segment the prostate preliminarily via graph cuts. After that, the segmentation is fine-tuned by a novel level sets model, which integrates (1) the prostate shape prior, (2) dark-to-light intensity transition near the prostate boundary, and (3) the texture feature just obtained. The proposed method is validated on two 2-D image datasets obtained from two different sonographic imaging systems, with the mean absolute distance on the mid gland images only 1.06±0.53 mm and 1.25±0.77 mm, respectively. The method is also extended to segment apex and base images, producing competitive results over the state of the art.

  3. Supervised segmentation methods for the hippocampus in MR images

    NASA Astrophysics Data System (ADS)

    van Stralen, Marijn; Geerlings, Mirjam I.; Vincken, Koen L.; Pluim, Josien P. W.

    2011-03-01

    This study compares three different types of fully automated supervised methods for segmentation of the hippocampus in MR images. Many of such methods, trained using example data, have been presented for various medical imaging applications, but comparison of the methods is obscured because of optimization for, and evaluation on, different data. We compare three methods based on different methodological bases: atlas-based segmentation (ABS), active appearance model segmentation (AAM) and k-nearest neighbor voxel classification (KNN). All three methods are trained on 100 T1-weighted images with manual segmentations of the right hippocampus, and applied to 103 different images from the same study. Straightforward implementation of each of the three methods resulted in competitive segmentations, both mutually, as compared with methods currently reported in literature. AAM and KNN are favorable in terms of computational costs, requiring only a fraction of the time needed for ABS. The high accuracy and low computational cost make KNN the most favorable method based on this study. AAM achieves similar results as ABS in significantly less computation time. Further improvements might be achieved by fusion of the presented techniques, either methodologically or by direct fusion of the segmentation results.

  4. On the convergence of EM-like algorithms for image segmentation using Markov random fields.

    PubMed

    Roche, Alexis; Ribes, Delphine; Bach-Cuadra, Meritxell; Krüger, Gunnar

    2011-12-01

    Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

  5. Compton imager based on a single planar segmented HPGe detector

    NASA Astrophysics Data System (ADS)

    Khaplanov, A.; Pettersson, J.; Cederwall, B.

    2007-10-01

    A collimator-free Compton imaging system has been developed based on a single high-purity germanium detector and used to generate images of radioactive sources emitting γ rays. The detector has a planar crystal with one pixellated contact with a total of 25 segments. Pulse shape analysis has been applied to achieve a 3D-position sensitivity of the detector. The first imaging results from this detector are presented, based on the reconstruction of events where a γ ray is fully absorbed after scattering between adjacent segments.

  6. Volumetric segmentation of range images for printed circuit board inspection

    NASA Astrophysics Data System (ADS)

    Van Dop, Erik R.; Regtien, Paul P. L.

    1996-10-01

    Conventional computer vision approaches towards object recognition and pose estimation employ 2D grey-value or color imaging. As a consequence these images contain information about projections of a 3D scene only. The subsequent image processing will then be difficult, because the object coordinates are represented with just image coordinates. Only complicated low-level vision modules like depth from stereo or depth from shading can recover some of the surface geometry of the scene. Recent advances in fast range imaging have however paved the way towards 3D computer vision, since range data of the scene can now be obtained with sufficient accuracy and speed for object recognition and pose estimation purposes. This article proposes the coded-light range-imaging method together with superquadric segmentation to approach this task. Superquadric segments are volumetric primitives that describe global object properties with 5 parameters, which provide the main features for object recognition. Besides, the principle axes of a superquadric segment determine the phase of an object in the scene. The volumetric segmentation of a range image can be used to detect missing, false or badly placed components on assembled printed circuit boards. Furthermore, this approach will be useful to recognize and extract valuable or toxic electronic components on printed circuit boards scrap that currently burden the environment during electronic waste processing. Results on synthetic range images with errors constructed according to a verified noise model illustrate the capabilities of this approach.

  7. Fuzzy object models for newborn brain MR image segmentation

    NASA Astrophysics Data System (ADS)

    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.

  8. Weakly supervised histopathology cancer image segmentation and classification.

    PubMed

    Xu, Yan; Zhu, Jun-Yan; Chang, Eric I-Chao; Lai, Maode; Tu, Zhuowen

    2014-04-01

    Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed manual annotations for the cancer pixels, which are time-consuming to obtain. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL) (along the line of weakly supervised learning) for histopathology image segmentation. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), medical image segmentation (cancer vs. non-cancer tissue), and patch-level clustering (different classes). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework. In addition, we introduce contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL. Experimental results on histopathology colon cancer images and cytology images demonstrate the great advantage of MCIL over the competing methods.

  9. Segmentation of bionano images for understanding cell dynamics.

    PubMed

    Mukewar, Pushkar; Wang, Geoffrey; Henning, Peter; Bao, Gang; Wang, May

    2004-01-01

    The use of quantum dots (QDs) and molecular beacons (MBs) is a recent advance in the field of nanotechnology. These techniques have enabled us to detect a single molecule in a cell, which helps in understanding the dynamics of a cell. The success of these techniques depends on the accurate and efficient analysis of the imaging data these techniques produce. The processing involves--segmentation of the particles, colocalisation and their tracking over multiple frames in 2D and 3D space. In this paper we have used the active contour models: snakes and their variation--GVF (gradient vector field) snakes for segmentation of nano(QD) and cell(MB) images. The results of segmentation have been used to measure the degree of colocalisation for quantum dot images and the gene expression values for molecular beacon images.

  10. Bacterial foraging based edge detection for cell image segmentation.

    PubMed

    Pan, Yongsheng; Zhou, Tao; Xia, Yong

    2015-01-01

    Edge detection is the most popular and common choices for cell image segmentation, in which local searching strategies are commonly used. In spite of their computational efficiency, traditional edge detectors, however, may either produce discontinued edges or rely heavily on initializations. In this paper, we propose a bacterial foraging based edge detection (BFED) algorithm for cell image segmentation. We model the gradients of intensities as the nutrient concentration and propel bacteria to forage along nutrient-rich locations via mimicking the behavior of Escherichia coli, including the chemotaxis, swarming, reproduction, elimination and dispersal. As a nature-inspired evolutionary technique, this algorithm can identify the desired edges and mark them as the tracks of bacteria. We have evaluated the proposed algorithm against the Canny, SUSAN, Verma's and an active contour model (ACM) based edge detectors on both synthetic and real cell images. Our results suggest that the BFED algorithm can identify boundaries more effectively and provide more accurate cell image segmentation. PMID:26737139

  11. Prostate segmentation in MRI using fused T2-weighted and elastography images

    NASA Astrophysics Data System (ADS)

    Nir, Guy; Sahebjavaher, Ramin S.; Baghani, Ali; Sinkus, Ralph; Salcudean, Septimiu E.

    2014-03-01

    Segmentation of the prostate in medical imaging is a challenging and important task for surgical planning and delivery of prostate cancer treatment. Automatic prostate segmentation can improve speed, reproducibility and consistency of the process. In this work, we propose a method for automatic segmentation of the prostate in magnetic resonance elastography (MRE) images. The method utilizes the complementary property of the elastogram and the corresponding T2-weighted image, which are obtained from the phase and magnitude components of the imaging signal, respectively. It follows a variational approach to propagate an active contour model based on the combination of region statistics in the elastogram and the edge map of the T2-weighted image. The method is fast and does not require prior shape information. The proposed algorithm is tested on 35 clinical image pairs from five MRE data sets, and is evaluated in comparison with manual contouring. The mean absolute distance between the automatic and manual contours is 1.8mm, with a maximum distance of 5.6mm. The relative area error is 7.6%, and the duration of the segmentation process is 2s per slice.

  12. An active contour framework based on the Hermite transform for shape segmentation of cardiac MR images

    NASA Astrophysics Data System (ADS)

    Barba-J, Leiner; Escalante-Ramírez, Boris

    2016-04-01

    Early detection of cardiac affections is fundamental to address a correct treatment that allows preserving the patient's life. Since heart disease is one of the main causes of death in most countries, analysis of cardiac images is of great value for cardiac assessment. Cardiac MR has become essential for heart evaluation. In this work we present a segmentation framework for shape analysis in cardiac magnetic resonance (MR) images. The method consists of an active contour model which is guided by the spectral coefficients obtained from the Hermite transform (HT) of the data. The HT is used as model to code image features of the analyzed images. Region and boundary based energies are coded using the zero and first order coefficients. An additional shape constraint based on an elliptical function is used for controlling the active contour deformations. The proposed framework is applied to the segmentation of the endocardial and epicardial boundaries of the left ventricle using MR images with short axis view. The segmentation is sequential for both regions: the endocardium is segmented followed by the epicardium. The algorithm is evaluated with several MR images at different phases of the cardiac cycle demonstrating the effectiveness of the proposed method. Several metrics are used for performance evaluation.

  13. MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images.

    PubMed

    Chu, Chengwen; Bai, Junjie; Wu, Xiaodong; Zheng, Guoyan

    2015-12-01

    This paper addresses the issue of fully automatic segmentation of a hip CT image with the goal to preserve the joint structure for clinical applications in hip disease diagnosis and treatment. For this purpose, we propose a Multi-Atlas Segmentation Constrained Graph (MASCG) method. The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. We then introduce a graph cut constrained graph search algorithm to further improve the segmentation accuracy around the bilateral hip joint regions. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 15-fold cross validation. When the present approach was compared to manual segmentation, an average surface distance error of 0.30 mm, 0.29 mm, and 0.30 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. A further look at the bilateral hip joint regions demonstrated an average surface distance error of 0.16 mm, 0.21 mm and 0.20 mm for the acetabulum, the left femoral head, and the right femoral head, respectively.

  14. Semiautomatic segmentation of liver metastases on volumetric CT images

    SciTech Connect

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

    2015-11-15

    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

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

    NASA Astrophysics Data System (ADS)

    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.

  16. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI.

    PubMed

    Li, Chunming; Huang, Rui; Ding, Zhaohua; Gatenby, J Chris; Metaxas, Dimitris N; Gore, John C

    2011-07-01

    Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In a level set formulation, this criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results. PMID:21518662

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

    PubMed Central

    Lee, Kyungmoo; Buitendijk, Gabriëlle H.S.; Bogunovic, Hrvoje; Springelkamp, Henriët; Hofman, Albert; Wahle, Andreas; Sonka, Milan; Vingerling, Johannes R.; Klaver, Caroline C.W.; Abràmoff, Michael 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 × 6.0 × 2.3 mm3) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate the reliability of the layer segmentations, we have developed a new metric, segmentability index SI, which is obtained from a random forest regressor based on 12 features using OCT voxel intensities, edge-based costs, and on-surface costs. The SI was compared with well-known quality indices, quality index (QI), and maximum tissue contrast index (mTCI), using receiver operating characteristic (ROC) analysis. Results The 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC (P < 0.01). Conclusions The segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected. Translational Relevance Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies. PMID:27066311

  18. Segment fusion of ToF-SIMS images.

    PubMed

    Milillo, Tammy M; Miller, Mary E; Fischione, Remo; Montes, Angelina; Gardella, Joseph A

    2016-06-08

    The imaging capabilities of time-of-flight secondary ion mass spectrometry (ToF-SIMS) have not been used to their full potential in the analysis of polymer and biological samples. Imaging has been limited by the size of the dataset and the chemical complexity of the sample being imaged. Pixel and segment based image fusion algorithms commonly used in remote sensing, ecology, geography, and geology provide a way to improve spatial resolution and classification of biological images. In this study, a sample of Arabidopsis thaliana was treated with silver nanoparticles and imaged with ToF-SIMS. These images provide insight into the uptake mechanism for the silver nanoparticles into the plant tissue, giving new understanding to the mechanism of uptake of heavy metals in the environment. The Munechika algorithm was programmed in-house and applied to achieve pixel based fusion, which improved the spatial resolution of the image obtained. Multispectral and quadtree segment or region based fusion algorithms were performed using ecognition software, a commercially available remote sensing software suite, and used to classify the images. The Munechika fusion improved the spatial resolution for the images containing silver nanoparticles, while the segment fusion allowed classification and fusion based on the tissue types in the sample, suggesting potential pathways for the uptake of the silver nanoparticles.

  19. Breast image pre-processing for mammographic tissue segmentation.

    PubMed

    He, Wenda; Hogg, Peter; Juette, Arne; Denton, Erika R E; Zwiggelaar, Reyer

    2015-12-01

    During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image interpretation and may not be desirable for computer aided mammography, leading to incorrect diagnosis and/or detection which can have a negative impact on sensitivity and specificity of screening mammography. This paper describes a novel mammographic image pre-processing method to improve image quality for analysis. An image selection process is incorporated to better target problematic images. The processed images show improved mammographic appearances not only in the breast periphery but also across the mammograms. Mammographic segmentation and risk/density classification were performed to facilitate a quantitative and qualitative evaluation. When using the processed images, the results indicated more anatomically correct segmentation in tissue specific areas, and subsequently better classification accuracies were achieved. Visual assessments were conducted in a clinical environment to determine the quality of the processed images and the resultant segmentation. The developed method has shown promising results. It is expected to be useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

  20. Segment fusion of ToF-SIMS images.

    PubMed

    Milillo, Tammy M; Miller, Mary E; Fischione, Remo; Montes, Angelina; Gardella, Joseph A

    2016-06-01

    The imaging capabilities of time-of-flight secondary ion mass spectrometry (ToF-SIMS) have not been used to their full potential in the analysis of polymer and biological samples. Imaging has been limited by the size of the dataset and the chemical complexity of the sample being imaged. Pixel and segment based image fusion algorithms commonly used in remote sensing, ecology, geography, and geology provide a way to improve spatial resolution and classification of biological images. In this study, a sample of Arabidopsis thaliana was treated with silver nanoparticles and imaged with ToF-SIMS. These images provide insight into the uptake mechanism for the silver nanoparticles into the plant tissue, giving new understanding to the mechanism of uptake of heavy metals in the environment. The Munechika algorithm was programmed in-house and applied to achieve pixel based fusion, which improved the spatial resolution of the image obtained. Multispectral and quadtree segment or region based fusion algorithms were performed using ecognition software, a commercially available remote sensing software suite, and used to classify the images. The Munechika fusion improved the spatial resolution for the images containing silver nanoparticles, while the segment fusion allowed classification and fusion based on the tissue types in the sample, suggesting potential pathways for the uptake of the silver nanoparticles. PMID:26746167

  1. Interactive image segmentation framework based on control theory

    NASA Astrophysics Data System (ADS)

    Zhu, Liangjia; Kolesov, Ivan; Ratner, Vadim; Karasev, Peter; Tannenbaum, Allen

    2015-03-01

    Segmentation of anatomical structures in medical imagery is a key step in a variety of clinical applications. Designing a generic, automated method that works for various structures and imaging modalities is a daunting task. Instead of proposing a new specific segmentation algorithm, in this paper, we present a general design principle on how to integrate user interactions from the perspective of control theory. In this formulation, Lyapunov stability analysis is employed to design an interactive segmentation system. The effectiveness and robustness of the proposed method are demonstrated.

  2. Automatic watershed segmentation of randomly textured color images.

    PubMed

    Shafarenko, L; Petrou, M; Kittler, J

    1997-01-01

    A new method is proposed for processing randomly textured color images. The method is based on a bottom-up segmentation algorithm that takes into consideration both color and texture properties of the image. An LUV gradient is introduced, which provides both a color similarity measure and a basis for applying the watershed transform. The patches of watershed mosaic are merged according to their color contrast until a termination criterion is met. This criterion is based on the topology of the typical processed image. The resulting algorithm does not require any additional information, be it various thresholds, marker extraction rules, and suchlike, thus being suitable for automatic processing of color images. The algorithm is demonstrated within the framework of the problem of automatic granite inspection. The segmentation procedure has been found to be very robust, producing good results not only on granite images, but on the wide range of other noisy color images as well, subject to the termination criterion.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    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. PMID:26577158

  5. Apparatus for investigating resonance with application to magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Murphy, Sytil; Jones, Dyan L.; Gross, Josh; Zollman, Dean

    2015-11-01

    Resonance is typically studied in the context of either a pendulum or a mass on a spring. We have developed an apparatus that enables beginning students to investigate resonant behavior of changing magnetic fields, in addition to the properties of the magnetic field due to a wire and the superposition of magnetic fields. In this resonant system, a compass oscillates at a frequency determined by the compass's physical properties and an external magnetic field. While the analysis is mathematically similar to that of the pendulum, this apparatus has an advantage that the magnetic field is easily controlled, while it is difficult to control the strength of gravity. This apparatus has been incorporated into a teaching module on magnetic resonance imaging.

  6. Phase contrast image segmentation using a Laue analyser crystal

    NASA Astrophysics Data System (ADS)

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

    2011-02-01

    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.

  7. Classifying and segmenting microscopy images with deep multiple instance learning

    PubMed Central

    Kraus, Oren Z.; Ba, Jimmy Lei; Frey, Brendan J.

    2016-01-01

    Motivation: High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on automated image analysis. Recently, deep learning approaches that learn feature representations directly from pixel intensity values have dominated object recognition challenges. These tasks typically have a single centered object per image and existing models are not directly applicable to microscopy datasets. Here we develop an approach that combines deep convolutional neural networks (CNNs) with multiple instance learning (MIL) in order to classify and segment microscopy images using only whole image level annotations. Results: We introduce a new neural network architecture that uses MIL to simultaneously classify and segment microscopy images with populations of cells. We base our approach on the similarity between the aggregation function used in MIL and pooling layers used in CNNs. To facilitate aggregating across large numbers of instances in CNN feature maps we present the Noisy-AND pooling function, a new MIL operator that is robust to outliers. Combining CNNs with MIL enables training CNNs using whole microscopy images with image level labels. We show that training end-to-end MIL CNNs outperforms several previous methods on both mammalian and yeast datasets without requiring any segmentation steps. Availability and implementation: Torch7 implementation available upon request. Contact: oren.kraus@mail.utoronto.ca PMID:27307644

  8. Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts

    NASA Astrophysics Data System (ADS)

    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.

  9. Sub-Markov Random Walk for Image Segmentation.

    PubMed

    Dong, Xingping; Shen, Jianbing; Shao, Ling; Van Gool, Luc

    2016-02-01

    A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded image segmentation, which can be interpreted as a traditional random walker on a graph with added auxiliary nodes. Under this explanation, we unify the proposed subRW and other popular random walk (RW) algorithms. This unifying view will make it possible for transferring intrinsic findings between different RW algorithms, and offer new ideas for designing novel RW algorithms by adding or changing auxiliary nodes. To verify the second benefit, we design a new subRW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts. The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous RW algorithms for seeded image segmentation.

  10. Anterior Segment Imaging in Ocular Surface Squamous Neoplasia

    PubMed Central

    Vora, Gargi K.

    2016-01-01

    Recent advances in anterior segment imaging have transformed the way ocular surface squamous neoplasia (OSSN) is diagnosed and monitored. Ultrasound biomicroscopy (UBM) has been reported to be useful primarily in the assessment of intraocular invasion and metastasis. In vivo confocal microscopy (IVCM) shows enlarged and irregular nuclei with hyperreflective cells in OSSN lesions and this has been found to correlate with histopathology findings. Anterior segment optical coherence tomography (AS-OCT) demonstrates thickened hyperreflective epithelium with an abrupt transition between abnormal and normal epithelium in OSSN lesions and this has also been shown to mimic histopathology findings. Although there are limitations to each of these imaging modalities, they can be useful adjunctive tools in the diagnosis of OSSN and could greatly assist the clinician in the management of OSSN patients. Nevertheless, anterior segment imaging has not replaced histopathology's role as the gold standard in confirming diagnosis. PMID:27800176

  11. Computer Based Melanocytic and Nevus Image Enhancement and Segmentation

    PubMed Central

    Jamil, Uzma; Khalid, Shehzad; Abbas, Sarmad; Saleem, Kashif

    2016-01-01

    Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion's images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach. PMID:27774454

  12. Convex-relaxed kernel mapping for image segmentation.

    PubMed

    Ben Salah, Mohamed; Ben Ayed, Ismail; Jing Yuan; Hong Zhang

    2014-03-01

    This paper investigates a convex-relaxed kernel mapping formulation of image segmentation. We optimize, under some partition constraints, a functional containing two characteristic terms: 1) a data term, which maps the observation space to a higher (possibly infinite) dimensional feature space via a kernel function, thereby evaluating nonlinear distances between the observations and segments parameters and 2) a total-variation term, which favors smooth segment surfaces (or boundaries). The algorithm iterates two steps: 1) a convex-relaxation optimization with respect to the segments by solving an equivalent constrained problem via the augmented Lagrange multiplier method and 2) a convergent fixed-point optimization with respect to the segments parameters. The proposed algorithm can bear with a variety of image types without the need for complex and application-specific statistical modeling, while having the computational benefits of convex relaxation. Our solution is amenable to parallelized implementations on graphics processing units (GPUs) and extends easily to high dimensions. We evaluated the proposed algorithm with several sets of comprehensive experiments and comparisons, including: 1) computational evaluations over 3D medical-imaging examples and high-resolution large-size color photographs, which demonstrate that a parallelized implementation of the proposed method run on a GPU can bring a significant speed-up and 2) accuracy evaluations against five state-of-the-art methods over the Berkeley color-image database and a multimodel synthetic data set, which demonstrates competitive performances of the algorithm. PMID:24723519

  13. Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation.

    PubMed

    Liu, Guoying; Zhang, Yun; Wang, Aimin

    2015-11-01

    Fuzzy c-means (FCM) clustering with spatial constraints has attracted great attention in the field of image segmentation. However, most of the popular techniques fail to resolve misclassification problems due to the inaccuracy of their spatial models. This paper presents a new unsupervised FCM-based image segmentation method by paying closer attention to the selection of local information. In this method, region-level local information is incorporated into the fuzzy clustering procedure to adaptively control the range and strength of interactive pixels. First, a novel dissimilarity function is established by combining region-based and pixel-based distance functions together, in order to enhance the relationship between pixels which have similar local characteristics. Second, a novel prior probability function is developed by integrating the differences between neighboring regions into the mean template of the fuzzy membership function, which adaptively selects local spatial constraints by a tradeoff weight depending upon whether a pixel belongs to a homogeneous region or not. Through incorporating region-based information into the spatial constraints, the proposed method strengthens the interactions between pixels within the same region and prevents over smoothing across region boundaries. Experimental results over synthetic noise images, natural color images, and synthetic aperture radar images show that the proposed method achieves more accurate segmentation results, compared with five state-of-the-art image segmentation methods.

  14. Appearance of the canine meninges in subtraction magnetic resonance images.

    PubMed

    Lamb, Christopher R; Lam, Richard; Keenihan, Erin K; Frean, Stephen

    2014-01-01

    The canine meninges are not visible as discrete structures in noncontrast magnetic resonance (MR) images, and are incompletely visualized in T1-weighted, postgadolinium images, reportedly appearing as short, thin curvilinear segments with minimal enhancement. Subtraction imaging facilitates detection of enhancement of tissues, hence may increase the conspicuity of meninges. The aim of the present study was to describe qualitatively the appearance of canine meninges in subtraction MR images obtained using a dynamic technique. Images were reviewed of 10 consecutive dogs that had dynamic pre- and postgadolinium T1W imaging of the brain that was interpreted as normal, and had normal cerebrospinal fluid. Image-anatomic correlation was facilitated by dissection and histologic examination of two canine cadavers. Meningeal enhancement was relatively inconspicuous in postgadolinium T1-weighted images, but was clearly visible in subtraction images of all dogs. Enhancement was visible as faint, small-rounded foci compatible with vessels seen end on within the sulci, a series of larger rounded foci compatible with vessels of variable caliber on the dorsal aspect of the cerebral cortex, and a continuous thin zone of moderate enhancement around the brain. Superimposition of color-encoded subtraction images on pregadolinium T1- and T2-weighted images facilitated localization of the origin of enhancement, which appeared to be predominantly dural, with relatively few leptomeningeal structures visible. Dynamic subtraction MR imaging should be considered for inclusion in clinical brain MR protocols because of the possibility that its use may increase sensitivity for lesions affecting the meninges.

  15. Automatic tissue segmentation of neonate brain MR Images with subject-specific atlases

    NASA Astrophysics Data System (ADS)

    Cherel, Marie; Budin, Francois; Prastawa, Marcel; Gerig, Guido; Lee, Kevin; Buss, Claudia; Lyall, Amanda; Zaldarriaga Consing, Kirsten; Styner, Martin

    2015-03-01

    Automatic tissue segmentation of the neonate brain using Magnetic Resonance Images (MRI) is extremely important to study brain development and perform early diagnostics but is challenging due to high variability and inhomogeneity in contrast throughout the image due to incomplete myelination of the white matter tracts. For these reasons, current methods often totally fail or give unsatisfying results. Furthermore, most of the subcortical midbrain structures are misclassified due to a lack of contrast in these regions. We have developed a novel method that creates a probabilistic subject-specific atlas based on a population atlas currently containing a number of manually segmented cases. The generated subject-specific atlas is sharp and adapted to the subject that is being processed. We then segment brain tissue classes using the newly created atlas with a single-atlas expectation maximization based method. Our proposed method leads to a much lower failure rate in our experiments. The overall segmentation results are considerably improved when compared to using a non-subject-specific, population average atlas. Additionally, we have incorporated diffusion information obtained from Diffusion Tensor Images (DTI) to improve the detection of white matter that is not visible at this early age in structural MRI (sMRI) due to a lack of myelination. Although this necessitates the acquisition of an additional sequence, the diffusion information improves the white matter segmentation throughout the brain, especially for the mid-brain structures such as the corpus callosum and the internal capsule.

  16. Automatic Tissue Segmentation of Neonate Brain MR Images with Subject-specific Atlases

    PubMed Central

    Cherel, Marie; Budin, Francois; Prastawa, Marcel; Gerig, Guido; Lee, Kevin; Buss, Claudia; Lyall, Amanda; Consing, Kirsten Zaldarriaga; Styner, Martin

    2015-01-01

    Automatic tissue segmentation of the neonate brain using Magnetic Resonance Images (MRI) is extremely important to study brain development and perform early diagnostics but is challenging due to high variability and inhomogeneity in contrast throughout the image due to incomplete myelination of the white matter tracts. For these reasons, current methods often totally fail or give unsatisfying results. Furthermore, most of the subcortical midbrain structures are misclassified due to a lack of contrast in these regions. We have developed a novel method that creates a probabilistic subject-specific atlas based on a population atlas currently containing a number of manually segmented cases. The generated subject-specific atlas is sharp and adapted to the subject that is being processed. We then segment brain tissue classes using the newly created atlas with a single-atlas expectation maximization based method. Our proposed method leads to a much lower failure rate in our experiments. The overall segmentation results are considerably improved when compared to using a non-subject-specific, population average atlas. Additionally, we have incorporated diffusion information obtained from Diffusion Tensor Images (DTI) to improve the detection of white matter that is not visible at this early age in structural MRI (sMRI) due to a lack of myelination. Although this necessitates the acquisition of an additional sequence, the diffusion information improves the white matter segmentation throughout the brain, especially for the mid-brain structures such as the corpus callosum and the internal capsule. PMID:26089584

  17. Robust Intensity Standardization in Brain Magnetic Resonance Images.

    PubMed

    De Nunzio, Giorgio; Cataldo, Rosella; Carlà, Alessandra

    2015-12-01

    The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient obtained on the same scanner in different moments. This affects postprocessing tasks such as automatic segmentation or unsupervised/supervised classification methods, which strictly depend on the observed image intensities, compromising the accuracy and efficiency of many image analyses algorithms. A large number of MR images from public databases, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, were employed together with synthetic MRIs. Combining both histogram and tissue-specific intensity information, a correspondence is obtained for each tissue across images. The novelty consists of computing three standardizing transformations for the three main brain tissues, for each tissue class separately. In order to create a continuous intensity mapping, spline smoothing of the overall slightly discontinuous piecewise-linear intensity transformation is performed. The robustness of the technique is assessed in a post hoc manner, by verifying that automatic segmentation of images before and after standardization gives a high overlapping (Dice index >0.9) for each tissue class, even across images coming from different sources. Furthermore, SBST efficacy is tested by evaluating if and how much it increases intertissue discrimination and by assessing gaussianity of tissue gray-level distributions before and after standardization. Some quantitative comparisons to already existing different approaches available in the literature are performed.

  18. Variable-rate colour image quantization based on quadtree segmentation

    NASA Astrophysics Data System (ADS)

    Hu, Y. C.; Li, C. Y.; Chuang, J. C.; Lo, C. C.

    2011-09-01

    A novel variable-sized block encoding with threshold control for colour image quantization (CIQ) is presented in this paper. In CIQ, the colour palette used has a great influence on the reconstructed image quality. Typically, a higher image quality and a larger storage cost are obtained when a larger-sized palette is used in CIQ. To cut down the storage cost while preserving quality of the reconstructed images, the threshold control policy for quadtree segmentation is used in this paper. Experimental results show that the proposed method adaptively provides desired bit rates while having better image qualities comparing to CIQ with the usage of multiple palettes of different sizes.

  19. Hyperspectral image segmentation using a cooperative nonparametric approach

    NASA Astrophysics Data System (ADS)

    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%.

  20. Automatic segmentation applied to obstetric images

    NASA Astrophysics Data System (ADS)

    Vuwong, Vanee; Hiller, John B.; Jin, Jesse S.

    1998-06-01

    This paper presents a shape-based approach for searching and extracting fetal skull boundaries from an obstetric image. The proposed method relies on two major steps. Firstly, we apply the reference axes to scan the image for all potential skull boundaries. The possible skull boundaries are determined whether they are candidates. The candidate with the highest confident value will be selected as the expected head boundary. Then, the position of the expected head boundary is initialized. Secondly, we refine the initial skull boundary using the fuzzy contour model modified from the active contour basis. This results the continuous and smooth fetal skull boundary that we can use for the medical parameter measurement.

  1. Efficient Fuzzy C-Means Architecture for Image Segmentation

    PubMed Central

    Li, Hui-Ya; Hwang, Wen-Jyi; Chang, Chia-Yen

    2011-01-01

    This paper presents a novel VLSI architecture for image segmentation. The architecture is based on the fuzzy c-means algorithm with spatial constraint for reducing the misclassification rate. In the architecture, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. In addition, an efficient pipelined circuit is used for the updating process for accelerating the computational speed. Experimental results show that the the proposed circuit is an effective alternative for real-time image segmentation with low area cost and low misclassification rate. PMID:22163980

  2. Segmentation of virus particle candidates in transmission electron microscopy images.

    PubMed

    Kylberg, G; Uppström, M; Hedlund, K-O; Borgefors, G; Sintorn, I-M

    2012-02-01

    In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.

  3. Segmented infrared image analysis for rotating machinery fault diagnosis

    NASA Astrophysics Data System (ADS)

    Duan, Lixiang; Yao, Mingchao; Wang, Jinjiang; Bai, Tangbo; Zhang, Laibin

    2016-07-01

    As a noncontact and non-intrusive technique, infrared image analysis becomes promising for machinery defect diagnosis. However, the insignificant information and strong noise in infrared image limit its performance. To address this issue, this paper presents an image segmentation approach to enhance the feature extraction in infrared image analysis. A region selection criterion named dispersion degree is also formulated to discriminate fault representative regions from unrelated background information. Feature extraction and fusion methods are then applied to obtain features from selected regions for further diagnosis. Experimental studies on a rotor fault simulator demonstrate that the presented segmented feature enhancement approach outperforms the one from the original image using both Naïve Bayes classifier and support vector machine.

  4. Simple and Inexpensive Classroom Demonstrations of Nuclear Magnetic Resonance and Magnetic Resonance Imaging.

    ERIC Educational Resources Information Center

    Olson, Joel A.; Nordell, Karen J.; Chesnik, Marla A.; Landis, Clark R.; Ellis, Arthur B.; Rzchowski, M. S.; Condren, S. Michael; Lisensky, George C.

    2000-01-01

    Describes a set of simple, inexpensive, classical demonstrations of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) principles that illustrate the resonance condition associated with magnetic dipoles and the dependence of the resonance frequency on environment. (WRM)

  5. Automated Segmentation of Nuclei in Breast Cancer Histopathology Images

    PubMed Central

    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. PMID:27649496

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

    PubMed

    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. PMID:27649496

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

    PubMed

    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. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

    PubMed Central

    Yang, Zhang; Li, Guo; Weifeng, Ding

    2016-01-01

    The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

  9. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

    PubMed

    Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

    2016-01-01

    The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

  10. Image Segmentation Analysis for NASA Earth Science Applications

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    2010-01-01

    NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.

  11. Magnetic Resonance Imaging in Biomedical Engineering

    NASA Astrophysics Data System (ADS)

    Kaśpar, Jan; Hána, Karel; Smrčka, Pavel; Brada, Jiří; Beneš, Jiří; Šunka, Pavel

    2007-11-01

    The basic principles of magnetic resonance imaging covering physical principles and basic imaging techniques will be presented as a strong tool in biomedical engineering. Several applications of MRI in biomedical research practiced at the MRI laboratory of the FBMI CTU including other laboratory instruments and activities are introduced.

  12. Brain Morphometry Using Anatomical Magnetic Resonance Imaging

    ERIC Educational Resources Information Center

    Bansal, Ravi; Gerber, Andrew J.; Peterson, Bradley S.

    2008-01-01

    The efficacy of anatomical magnetic resonance imaging (MRI) in studying the morphological features of various regions of the brain is described, also providing the steps used in the processing and studying of the images. The ability to correlate these features with several clinical and psychological measures can help in using anatomical MRI to…

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

    NASA Astrophysics Data System (ADS)

    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.

  14. Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images.

    PubMed

    Shahvaran, Zahra; Kazemi, Kamran; Helfroush, Mohammad Sadegh; Jafarian, Nassim; Noorizadeh, Negar

    2012-08-15

    Noise and intensity non-uniformity are causing major difficulties in magnetic resonance (MR) image segmentation. This paper introduces a variational level set approach for simultaneous MR image segmentation and intensity non-uniformity correction. The proposed energy functional is based on local Gaussian intensity fitting with local means and variances. Furthermore, the proposed model utilizes Markov random fields to model the spatial correlation between neighboring pixels/voxels. The improvements achieved with our method are demonstrated by brain segmentation experiments with simulated and real magnetic resonance images with different noise and bias level. In particular, it is superior in term of accuracy as compared to LGDF and FSL-FAST methods.

  15. Image Segmentation Using Higher-Order Correlation Clustering.

    PubMed

    Kim, Sungwoong; Yoo, Chang D; Nowozin, Sebastian; Kohli, Pushmeet

    2014-09-01

    In this paper, a hypergraph-based image segmentation framework is formulated in a supervised manner for many high-level computer vision tasks. To consider short- and long-range dependency among various regions of an image and also to incorporate wider selection of features, a higher-order correlation clustering (HO-CC) is incorporated in the framework. Correlation clustering (CC), which is a graph-partitioning algorithm, was recently shown to be effective in a number of applications such as natural language processing, document clustering, and image segmentation. It derives its partitioning result from a pairwise graph by optimizing a global objective function such that it simultaneously maximizes both intra-cluster similarity and inter-cluster dissimilarity. In the HO-CC, the pairwise graph which is used in the CC is generalized to a hypergraph which can alleviate local boundary ambiguities that can occur in the CC. Fast inference is possible by linear programming relaxation, and effective parameter learning by structured support vector machine is also possible by incorporating a decomposable structured loss function. Experimental results on various data sets show that the proposed HO-CC outperforms other state-of-the-art image segmentation algorithms. The HO-CC framework is therefore an efficient and flexible image segmentation framework. PMID:26352230

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

    NASA Astrophysics Data System (ADS)

    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.

  17. An approach toward fast gradient-based image segmentation.

    PubMed

    Hell, Benjamin; Kassubeck, Marc; Bauszat, Pablo; Eisemann, Martin; Magnor, Marcus

    2015-09-01

    In this paper, we present and investigate an approach to fast multilabel color image segmentation using convex optimization techniques. The presented model is in some ways related to the well-known Mumford-Shah model, but deviates in certain important aspects. The optimization problem has been designed with two goals in mind. The objective function should represent fundamental concepts of image segmentation, such as incorporation of weighted curve length and variation of intensity in the segmented regions, while allowing transformation into a convex concave saddle point problem that is computationally inexpensive to solve. This paper introduces such a model, the nontrivial transformation of this model into a convex-concave saddle point problem, and the numerical treatment of the problem. We evaluate our approach by applying our algorithm to various images and show that our results are competitive in terms of quality at unprecedentedly low computation times. Our algorithm allows high-quality segmentation of megapixel images in a few seconds and achieves interactive performance for low resolution images.

  18. Automatic segmentation of MR brain images in multiple sclerosis patients

    NASA Astrophysics Data System (ADS)

    Avula, Ramesh T. V.; Erickson, Bradley J.

    1996-04-01

    A totally automatic scheme for segmenting brain from extracranial tissues and to classify all intracranial voxels as CSF, gray matter (GM), white matter (WM), or abnormality such as multiple sclerosis (MS) lesions is presented in this paper. It is observed that in MR head images, if a tissue's intensity values are normalized, its relationship to the other tissues is essentially constant for a given type of image. Based on this approach, the subcutaneous fat surrounding the head is normalized to classify other tissues. Spatially registered 3 mm MR head image slices of T1 weighted, fast spin echo [dual echo T2 weighted and proton density (PD) weighted images] and fast fluid attenuated inversion recovery (FLAIR) sequences are used for segmentation. Subcutaneous fat surrounding the skull was identified based on intensity thresholding from T1 weighted images. A multiparametric space map was developed for CSF, GM and WM by normalizing each tissue with respect to the mean value of corresponding subcutaneous fat on each pulse sequence. To reduce the low frequency noise without blurring the fine morphological high frequency details an anisotropic diffusion filter was applied to all images before segmentation. An initial slice by slice classification was followed by morphological operations to delete any brides connecting extracranial segments. Finally 3-dimensional region growing of the segmented brain extracts GM, WM and pathology. The algorithm was tested on sequential scans of 10 patients with MS lesions. For well registered sequences, tissues and pathology have been accurately classified. This procedure does not require user input or image training data sets, and shows promise for automatic classification of brain and pathology.

  19. Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

    PubMed Central

    2013-01-01

    Background Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. Method An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. Results The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. Conclusions In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation. PMID:23937790

  20. Tutte polynomial in functional magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    García-Castillón, Marlly V.

    2015-09-01

    Methods of graph theory are applied to the processing of functional magnetic resonance images. Specifically the Tutte polynomial is used to analyze such kind of images. Functional Magnetic Resonance Imaging provide us connectivity networks in the brain which are represented by graphs and the Tutte polynomial will be applied. The problem of computing the Tutte polynomial for a given graph is #P-hard even for planar graphs. For a practical application the maple packages "GraphTheory" and "SpecialGraphs" will be used. We will consider certain diagram which is depicting functional connectivity, specifically between frontal and posterior areas, in autism during an inferential text comprehension task. The Tutte polynomial for the resulting neural networks will be computed and some numerical invariants for such network will be obtained. Our results show that the Tutte polynomial is a powerful tool to analyze and characterize the networks obtained from functional magnetic resonance imaging.

  1. Magnetic resonance imaging of the body

    SciTech Connect

    Higgins, C.B.; Hricak, H.

    1987-01-01

    This text provides reference to magnetic resonance imaging (MRI) of the body. Beginning with explanatory chapters on the physics, instrumentation, and interpretation of MRI, it proceeds to the normal anatomy of the neck, thorax, abdomen, and pelvis. Other chapters cover magnetic resonance imaging of blood flow, the larynx, the lymph nodes, and the spine, as well as MRI in obstetrics. The text features detailed coverage of magnetic resonance imaging of numerous disorders and disease states, including neck disease, thoracic disease; breast disease; congenital and acquired heart disease; vascular disease; diseases of the liver, pancreas, and spleen; diseases of the kidney, adrenals, and retroperitoneum; diseases of the male and female pelvis; and musculoskeletal diseases. Chapters on the biological and environmental hazards of MRI, the current clinical status of MRI in comparison to other imaging modalities, and economic considerations are also included.

  2. Automatic segmentation and classification of seven-segment display digits on auroral images

    NASA Astrophysics Data System (ADS)

    Savolainen, Tuomas; Whiter, Daniel Keith; Partamies, Noora

    2016-07-01

    In this paper we describe a new and fully automatic method for segmenting and classifying digits in seven-segment displays. The method is applied to a dataset consisting of about 7 million auroral all-sky images taken during the time period of 1973-1997 at camera stations centred around Sodankylä observatory in northern Finland. In each image there is a clock display for the date and time together with the reflection of the whole night sky through a spherical mirror. The digitised film images of the night sky contain valuable scientific information but are impractical to use without an automatic method for extracting the date-time from the display. We describe the implementation and the results of such a method in detail in this paper.

  3. Interactive Medical Image Segmentation using PDE Control of Active Contours

    PubMed Central

    Karasev, Peter; Kolesov, Ivan; Fritscher, Karl; Vela, Patricio; Mitchell, Phillip; Tannenbaum, Allen

    2014-01-01

    Segmentation of injured or unusual anatomic structures in medical imagery is a problem that has continued to elude fully automated solutions. In this paper, the goal of easy-to-use and consistent interactive segmentation is transformed into a control synthesis problem. A nominal level set PDE is assumed to be given; this open-loop system achieves correct segmentation under ideal conditions, but does not agree with a human expert's ideal boundary for real image data. Perturbing the state and dynamics of a level set PDE via the accumulated user input and an observer-like system leads to desirable closed-loop behavior. The input structure is designed such that a user can stabilize the boundary in some desired state without needing to understand any mathematical parameters. Effectiveness of the technique is illustrated with applications to the challenging segmentations of a patellar tendon in MR and a shattered femur in CT. PMID:23893712

  4. Surface plasmon resonance of two-segmented Au Cu nanorods

    NASA Astrophysics Data System (ADS)

    Azarian, A.; Iraji zad, A.; Dolati, A.; Ghorbani, M.

    2008-10-01

    Two-segmented gold-copper nanorods were electrodeposited inside the pores of polycarbonate track-etched membranes from two separate solutions. The PCT membranes were dissolved in dichloromethane (CH2Cl2) and the solvent was replaced by methanol solution. Optical absorption spectra of two-segmented Au-Cu nanorods dispersed in methanol showed two peaks which were related to the transverse mode of copper and the longitudinal mode of gold. By increasing the length of the gold segment, when the total length of both metals was fixed at 1 µm, the copper and gold peaks shifted to the blue and red wavelengths, respectively. We observed that the wavelengths of the extinction peaks are not in good agreement with the expected value obtained from calculations using the nominal dielectric constant of the medium. Therefore, we suggest the effective medium dielectric constant (ɛmeff) for Cu and Au segments by comparing the experimental data and calculation results. The values of ɛmeff decrease by increasing the gold length.

  5. Multimodality medical image fusion: probabilistic quantification, segmentation, and registration

    NASA Astrophysics Data System (ADS)

    Wang, Yue J.; Freedman, Matthew T.; Xuan, Jian Hua; Zheng, Qinfen; Mun, Seong K.

    1998-06-01

    Multimodality medical image fusion is becoming increasingly important in clinical applications, which involves information processing, registration and visualization of interventional and/or diagnostic images obtained from different modalities. This work is to develop a multimodality medical image fusion technique through probabilistic quantification, segmentation, and registration, based on statistical data mapping, multiple feature correlation, and probabilistic mean ergodic theorems. The goal of image fusion is to geometrically align two or more image areas/volumes so that pixels/voxels representing the same underlying anatomical structure can be superimposed meaningfully. Three steps are involved. To accurately extract the regions of interest, we developed the model supported Bayesian relaxation labeling, and edge detection and region growing integrated algorithms to segment the images into objects. After identifying the shift-invariant features (i.e., edge and region information), we provided an accurate and robust registration technique which is based on matching multiple binary feature images through a site model based image re-projection. The image was initially segmented into specified number of regions. A rough contour can be obtained by delineating and merging some of the segmented regions. We applied region growing and morphological filtering to extract the contour and get rid of some disconnected residual pixels after segmentation. The matching algorithm is implemented as follows: (1) the centroids of PET/CT and MR images are computed and then translated to the center of both images. (2) preliminary registration is performed first to determine an initial range of scaling factors and rotations, and the MR image is then resampled according to the specified parameters. (3) the total binary difference of the corresponding binary maps in both images is calculated for the selected registration parameters, and the final registration is achieved when the

  6. Breast segmentation in MR images using three-dimensional spiral scanning and dynamic programming

    NASA Astrophysics Data System (ADS)

    Jiang, Luan; Lian, Yanyun; Gu, Yajia; Li, Qiang

    2013-03-01

    Magnetic resonance (MR) imaging has been widely used for risk assessment and diagnosis of breast cancer in clinic. To develop a computer-aided diagnosis (CAD) system, breast segmentation is the first important and challenging task. The accuracy of subsequent quantitative measurement of breast density and abnormalities depends on accurate definition of the breast area in the images. The purpose of this study is to develop and evaluate a fully automated method for accurate segmentation of breast in three-dimensional (3-D) MR images. A fast method was developed to identify bounding box, i.e., the volume of interest (VOI), for breasts. A 3-D spiral scanning method was used to transform the VOI of each breast into a single two-dimensional (2-D) generalized polar-coordinate image. Dynamic programming technique was applied to the transformed 2-D image for delineating the "optimal" contour of the breast. The contour of the breast in the transformed 2-D image was utilized to reconstruct the segmentation results in the 3-D MR images using interpolation and lookup table. The preliminary results on 17 cases show that the proposed method can obtain accurate segmentation of the breast based on subjective observation. By comparing with the manually delineated region of 16 breasts in 8 cases, an overlap index of 87.6% +/- 3.8% (mean +/- SD), and a volume agreement of 93.4% +/- 4.5% (mean +/- SD) were achieved, respectively. It took approximately 3 minutes for our method to segment the breast in an MR scan of 256 slices.

  7. Filter Design and Performance Evaluation for Fingerprint Image Segmentation.

    PubMed

    Thai, Duy Hoang; Huckemann, Stephan; Gottschlich, Carsten

    2016-01-01

    Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: 'true' foreground can be labeled as background and features like minutiae can be lost, or conversely 'true' background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.

  8. Filter Design and Performance Evaluation for Fingerprint Image Segmentation.

    PubMed

    Thai, Duy Hoang; Huckemann, Stephan; Gottschlich, Carsten

    2016-01-01

    Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: 'true' foreground can be labeled as background and features like minutiae can be lost, or conversely 'true' background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available. PMID:27171150

  9. Multiple LREK active contours for knee meniscus ultrasound image segmentation.

    PubMed

    Faisal, Amir; Ng, Siew-Cheok; Goh, Siew-Li; George, John; Supriyanto, Eko; Lai, Khin W

    2015-10-01

    Quantification of knee meniscus degeneration and displacement in an ultrasound image requires simultaneous segmentation of femoral condyle, meniscus, and tibial plateau in order to determine the area and the position of the meniscus. In this paper, we present an active contour for image segmentation that uses scalable local regional information on expandable kernel (LREK). It includes using a strategy to adapt the size of a local window in order to avoid being confined locally in a homogeneous region during the segmentation process. We also provide a multiple active contours framework called multiple LREK (MLREK) to deal with multiple object segmentation without merging and overlapping between the neighboring contours in the shared boundaries of separate regions. We compare its performance to other existing active contour models and show an improvement offered by our model. We then investigate the choice of various parameters in the proposed framework in response to the segmentation outcome. Dice coefficient and Hausdorff distance measures over a set of real knee meniscus ultrasound images indicate a potential application of MLREK for assessment of knee meniscus degeneration and displacement. PMID:25910057

  10. Filter Design and Performance Evaluation for Fingerprint Image Segmentation

    PubMed Central

    Thai, Duy Hoang; Huckemann, Stephan; Gottschlich, Carsten

    2016-01-01

    Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available. PMID:27171150

  11. Three-dimensional model-guided segmentation and analysis of medical images

    NASA Astrophysics Data System (ADS)

    Arata, Louis K.; Dhawan, Atam P.; Broderick, Joseph; Gaskill, Mary

    1992-06-01

    Automated or semi-automated analysis and labeling of structural brain images, such as magnetic resonance (MR) and computed tomography, is desirable for a number of reasons. Quantification of brain volumes can aid in the study of various diseases and the affect of various drug regimes. A labeled structural image, when registered with a functional image such as positron emission tomography or single photon emission computed tomography, allows the quantification of activity in various brain subvolumes such as the major lobes. Because even low resolution scans (7.5 to 8.0 mm slices) have 15 to 17 slices in order to image the entire head of the subject hand segmentation of these slices is a very laborious process. However, because of the spatial complexity of many of the brain structures notably the ventricles, automatic segmentation is not a simple undertaking. In order to accurately segment a structure such as the ventricles we must have a model of equal complexity to guide the segmentation. Also, we must have a model which can incorporate the variability among different subjects from a pre-specified group. Analysis of MR brain scans is accomplished by utilizing the data from T2 weighted and proton density images to isolate the regions of interest. Identification is then done automatically with the aid of a composite model formed from the operator assisted segmentation of MR scans of subjects from the same group. We describe the construction of the model and demonstrate its use in the segmentation and labeling of the ventricles in the brain.

  12. User-agent cooperation in multiagent IVUS image segmentation.

    PubMed

    Bovenkamp, E G P; Dijkstra, J; Bosch, J G; Reiber, J H C

    2009-01-01

    Automated interpretation of complex images requires elaborate knowledge and model-based image analysis, but often needs interaction with an expert as well. This research describes expert interaction with a multiagent image interpretation system using only a restricted vocabulary of high-level user interactions. The aim is to minimize inter- and intra-observer variability by keeping the total number of interactions as low and simple as possible. The multiagent image interpretation system has elaborate high-level knowledge-based control over low-level image segmentation algorithms. Agents use contextual knowledge to keep the number of interactions low but, when in doubt, present the user with the most likely interpretation of the situation. The user, in turn, can correct, supplement, and/or confirm the results of image-processing agents. This is done at a very high level of abstraction such that no knowledge of the underlying segmentation methods, parameters or agent functioning is needed. High-level interaction thereby replaces more traditional contour correction methods like inserting points and/or (re)drawing contours. This makes it easier for the user to obtain good results, while inter- and intra-observer variability are kept minimal, since the image segmentation itself remains under control of image-processing agents. The system has been applied to intravascular ultrasound (IVUS) images. Experiments show that with an average of 2-3 high-level user interactions per correction, segmentation results substantially improve while the variation is greatly reduced. The achieved level of accuracy and repeatability is equivalent to that of manual drawing by an expert. PMID:19116192

  13. A dual RF resonator system for high-field functional magnetic resonance imaging of small animals.

    PubMed

    Ludwig, R; Bodgdanov, G; King, J; Allard, A; Ferris, C F

    2004-01-30

    A new apparatus has been developed that integrates an animal restrainer arrangement for small animals with an actively tunable/detunable dual radio-frequency (RF) coil system for in vivo anatomical and functional magnetic resonance imaging of small animals at 4.7 T. The radio-frequency coil features an eight-element microstrip line configuration that, in conjunction with a segmented outer copper shield, forms a transversal electromagnetic (TEM) resonator structure. Matching and active tuning/detuning is achieved through fixed/variable capacitors and a PIN diode for each resonator element. These components along with radio-frequency chokes (RFCs) and blocking capacitors are placed on two printed circuit boards (PCBs) whose copper coated ground planes form the front and back of the volume coil and are therefore an integral part of the resonator structure. The magnetic resonance signal response is received with a dome-shaped single-loop surface coil that can be height-adjustable with respect to the animal's head. The conscious animal is immobilized through a mechanical arrangement that consists of a Plexiglas body tube and a head restrainer. This restrainer has a cylindrical holder with a mouthpiece and position screws to receive and restrain the head of the animal. The apparatus is intended to perform anatomical and functional magnetic resonance imaging in conscious animals such as mice, rats, hamsters, and marmosets. Cranial images acquired from fully conscious rats in a 4.7 T Bruker 40 cm bore animal scanner underscore the feasibility of this approach and bode well to extend this system to the imaging of other animals. PMID:14706710

  14. Quantitative measure in image segmentation for skin lesion images: A preliminary study

    NASA Astrophysics Data System (ADS)

    Azmi, Nurulhuda Firdaus Mohd; Ibrahim, Mohd Hakimi Aiman; Keng, Lau Hui; Ibrahim, Nuzulha Khilwani; Sarkan, Haslina Md

    2014-12-01

    Automatic Skin Lesion Diagnosis (ASLD) allows skin lesion diagnosis by using a computer or mobile devices. The idea of using a computer to assist in diagnosis of skin lesions was first proposed in the literature around 1985. Images of skin lesions are analyzed by the computer to capture certain features thought to be characteristic of skin diseases. These features (expressed as numeric values) are then used to classify the image and report a diagnosis. Image segmentation is often a critical step in image analysis and it may use statistical classification, thresholding, edge detection, region detection, or any combination of these techniques. Nevertheless, image segmentation of skin lesion images is yet limited to superficial evaluations which merely display images of the segmentation results and appeal to the reader's intuition for evaluation. There is a consistent lack of quantitative measure, thus, it is difficult to know which segmentation present useful results and in which situations they do so. If segmentation is done well, then, all other stages in image analysis are made simpler. If significant features (that are crucial for diagnosis) are not extracted from images, it will affect the accuracy of the automated diagnosis. This paper explore the existing quantitative measure in image segmentation ranging in the application of pattern recognition for example hand writing, plat number, and colour. Selecting the most suitable segmentation measure is highly important so that as much relevant features can be identified and extracted.

  15. Segmented images and 3D images for studying the anatomical structures in MRIs

    NASA Astrophysics Data System (ADS)

    Lee, Yong Sook; Chung, Min Suk; Cho, Jae Hyun

    2004-05-01

    For identifying the pathological findings in MRIs, the anatomical structures in MRIs should be identified in advance. For studying the anatomical structures in MRIs, an education al tool that includes the horizontal, coronal, sagittal MRIs of entire body, corresponding segmented images, 3D images, and browsing software is necessary. Such an educational tool, however, is hard to obtain. Therefore, in this research, such an educational tool which helps medical students and doctors study the anatomical structures in MRIs was made as follows. A healthy, young Korean male adult with standard body shape was selected. Six hundred thirteen horizontal MRIs of the entire body were scanned and inputted to the personal computer. Sixty anatomical structures in the horizontal MRIs were segmented to make horizontal segmented images. Coronal, sagittal MRIs and coronal, sagittal segmented images were made. 3D images of anatomical structures in the segmented images were reconstructed by surface rendering method. Browsing software of the MRIs, segmented images, and 3D images was composed. This educational tool that includes horizontal, coronal, sagittal MRIs of entire body, corresponding segmented images, 3D images, and browsing software is expected to help medical students and doctors study anatomical structures in MRIs.

  16. Supervised segmentation of MRI brain images using combination of multiple classifiers.

    PubMed

    Ahmadvand, Ali; Sharififar, Mohammad; Daliri, Mohammad Reza

    2015-06-01

    Segmentation of different tissues is one of the initial and most critical tasks in different aspects of medical image processing. Manual segmentation of brain images resulted from magnetic resonance imaging is time consuming, so automatic image segmentation is widely used in this area. Ensemble based algorithms are very reliable and generalized methods for classification. In this paper, a supervised method named dynamic classifier selection-dynamic local training local tanimoto index, which is a member of combination of multiple classifiers (CMCs) methods is proposed. The proposed method uses dynamic local training sets instead of a full statics one and also it change the classifier rank criterion properly for brain tissue classification. Selection policy for combining the different decisions is implemented here and the K-nearest neighbor algorithm is used to find the best local classifier. Experimental results show that the proposed method can classify the real datasets of the internet brain segmentation repository better than all single classifiers in ensemble and produces significantly improvement on other CMCs methods. PMID:26130310

  17. Segmentation and separation of venous vasculatures in liver CT images

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Hansen, Christian; Zidowitz, Stephan; Hahn, Horst K.

    2014-03-01

    Computer-aided analysis of venous vasculatures including hepatic veins and portal veins is important in liver surgery planning. The analysis normally consists of two important pre-processing tasks: segmenting both vasculatures and separating them from each other by assigning different labels. During the acquisition of multi-phase CT images, both of the venous vessels are enhanced by injected contrast agent and acquired either in a common phase or in two individual phases. The enhanced signals established by contrast agent are often not stably acquired due to non-optimal acquisition time. Inadequate contrast and the presence of large lesions in oncological patients, make the segmentation task quite challenging. To overcome these diffculties, we propose a framework with minimal user interactions to analyze venous vasculatures in multi-phase CT images. Firstly, presented vasculatures are automatically segmented adopting an efficient multi-scale Hessian-based vesselness filter. The initially segmented vessel trees are then converted to a graph representation, on which a series of graph filters are applied in post-processing steps to rule out irrelevant structures. Eventually, we develop a semi-automatic workow to refine the segmentation in the areas of inferior vena cava and entrance of portal veins, and to simultaneously separate hepatic veins from portal veins. Segmentation quality was evaluated with intensive tests enclosing 60 CT images from both healthy liver donors and oncological patients. To quantitatively measure the similarities between segmented and reference vessel trees, we propose three additional metrics: skeleton distance, branch coverage, and boundary surface distance, which are dedicated to quantifying the misalignment induced by both branching patterns and radii of two vessel trees.

  18. Performance evaluation of an automatic segmentation method of cerebral arteries in MRA images by use of a large image database

    NASA Astrophysics Data System (ADS)

    Uchiyama, Yoshikazu; Asano, Tatsunori; Hara, Takeshi; Fujita, Hiroshi; Kinosada, Yasutomi; Asano, Takahiko; Kato, Hiroki; Kanematsu, Masayuki; Hoshi, Hiroaki; Iwama, Toru

    2009-02-01

    The detection of cerebrovascular diseases such as unruptured aneurysm, stenosis, and occlusion is a major application of magnetic resonance angiography (MRA). However, their accurate detection is often difficult for radiologists. Therefore, several computer-aided diagnosis (CAD) schemes have been developed in order to assist radiologists with image interpretation. The purpose of this study was to develop a computerized method for segmenting cerebral arteries, which is an essential component of CAD schemes. For the segmentation of vessel regions, we first used a gray level transformation to calibrate voxel values. To adjust for variations in the positioning of patients, registration was subsequently employed to maximize the overlapping of the vessel regions in the target image and reference image. The vessel regions were then segmented from the background using gray-level thresholding and region growing techniques. Finally, rule-based schemes with features such as size, shape, and anatomical location were employed to distinguish between vessel regions and false positives. Our method was applied to 854 clinical cases obtained from two different hospitals. The segmentation of cerebral arteries in 97.1%(829/854) of the MRA studies was attained as an acceptable result. Therefore, our computerized method would be useful in CAD schemes for the detection of cerebrovascular diseases in MRA images.

  19. Medical image segmentation using object atlas versus object cloud models

    NASA Astrophysics Data System (ADS)

    Phellan, Renzo; Falcão, Alexandre X.; Udupa, Jayaram K.

    2015-03-01

    Medical image segmentation is crucial for quantitative organ analysis and surgical planning. Since interactive segmentation is not practical in a production-mode clinical setting, automatic methods based on 3D object appearance models have been proposed. Among them, approaches based on object atlas are the most actively investigated. A key drawback of these approaches is that they require a time-costly image registration process to build and deploy the atlas. Object cloud models (OCM) have been introduced to avoid registration, considerably speeding up the whole process, but they have not been compared to object atlas models (OAM). The present paper fills this gap by presenting a comparative analysis of the two approaches in the task of individually segmenting nine anatomical structures of the human body. Our results indicate that OCM achieve a statistically significant better accuracy for seven anatomical structures, in terms of Dice Similarity Coefficient and Average Symmetric Surface Distance.

  20. Automatic sputum color image segmentation for tuberculosis diagnosis

    NASA Astrophysics Data System (ADS)

    Forero-Vargas, Manuel G.; Sierra-Ballen, Eduard L.; Alvarez-Borrego, Josue; Pech-Pacheco, Jose L.; Cristobal-Perez, Gabriel; Alcala, Luis; Desco, Manuel

    2001-11-01

    Tuberculosis (TB) and other mycobacteriosis are serious illnesses which control is mainly based on presumptive diagnosis. Besides of clinical suspicion, the diagnosis of mycobacteriosis must be done through genus specific smears of clinical specimens. However, these techniques lack of sensitivity and consequently clinicians must wait culture results as much as two months. Computer analysis of digital images from these smears could improve sensitivity of the test and, moreover, decrease workload of the micobacteriologist. Bacteria segmentation of particular species entails a complex process. Bacteria shape is not enough as a discriminant feature, because there are many species that share the same shape. Therefore the segmentation procedure requires to be improved using the color image information. In this paper we present two segmentation procedures based on fuzzy rules and phase-only correlation techniques respectively that will provide the basis of a future automatic particle' screening.

  1. Automatic segmentation of chromosomes in Q-band images.

    PubMed

    Grisan, Enrico; Poletti, Enea; Tomelleri, Christopher; Ruggeri, Alfredo

    2007-01-01

    Karyotype analysis is a widespread procedure in cytogenetics to assess the possible presence of genetics defects. The procedure is lengthy and repetitive, so that an automatic analysis would greatly help the cytogeneticist routine work. Still, automatic segmentation and full disentangling of chromosomes are open issues. We propose an automatic procedure to obtain the separated chromosomes, which are then ready for a subsequent classification step. The segmentation is carried out by means of a space variant thresholding scheme, which proved to be successful even in presence of hyper- or hypo-fluorescent regions in the image. Then a greedy approach is used to identify and resolve touching and overlapping chromosomes, based on geometric evidence and image information. We show the effectiveness of the proposed method on routine data: 90% of the overlaps and 92% of the adjacencies are resolved, resulting in a correct segmentation of 96% of the chromosomes.

  2. Simultaneous registration and segmentation of images in wavelet domain

    NASA Astrophysics Data System (ADS)

    Yoshida, Hiroyuki

    1999-10-01

    A novel method for simultaneous registration and segmentation is developed. The method is designed to register two similar images while a region with significant difference is adaptively segmented. This is achieved by minimization of a non-linear functional that models the statistical properties of the subtraction of the two images. Minimization is performed in the wavelet domain by a coarse- to-fine approach to yield a mapping that yields the registration and the boundary that yields the segmentation. The new method was applied to the registration of the left and the right lung regions in chest radiographs for extraction of lung nodules while the normal anatomic structures such as ribs are removed. A preliminary result shows that our method is very effective in reducing the number of false detections obtained with our computer-aided diagnosis scheme for detection of lung nodules in chest radiographs.

  3. Hierarchical nucleus segmentation in digital pathology images

    NASA Astrophysics Data System (ADS)

    Gao, Yi; Ratner, Vadim; Zhu, Liangjia; Diprima, Tammy; Kurc, Tahsin; Tannenbaum, Allen; Saltz, Joel

    2016-03-01

    Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set.

  4. Hierarchical nucleus segmentation in digital pathology images

    PubMed Central

    Gao, Yi; Ratner, Vadim; Zhu, Liangjia; Diprima, Tammy; Kurc, Tahsin; Tannenbaum, Allen; Saltz, Joel

    2016-01-01

    Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set. PMID:27375315

  5. Deformable M-Reps for 3D Medical Image Segmentation.

    PubMed

    Pizer, Stephen M; Fletcher, P Thomas; Joshi, Sarang; Thall, Andrew; Chen, James Z; Fridman, Yonatan; Fritsch, Daniel S; Gash, Graham; Glotzer, John M; Jiroutek, Michael R; Lu, Conglin; Muller, Keith E; Tracton, Gregg; Yushkevich, Paul; Chaney, Edward L

    2003-11-01

    M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures - each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure. A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects. The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.

  6. Magnetic resonance imaging for image-guided implantology

    NASA Astrophysics Data System (ADS)

    Eggers, Georg; Kress, Bodo; Fiebach, Jochen; Rieker, Marcus; Spitzenberg, Doreen; Marmulla, Rüdiger; Dickhaus, Hartmut; Mühling, Joachim

    2006-03-01

    Image guided implantology using navigation systems is more accurate than manual dental implant insertion. The underlying image data are usually derived from computer tomography. The suitability of MR imaging for dental implant planning is a marginal issue so far. MRI data from cadaver heads were acquired using various MRI sequences. The data were assessed for the quality of anatomical imaging, geometric accuracy and susceptibility to dental metal artefacts. For dental implant planning, 3D models of the jaws were created. A software system for segmentation of the mandible and maxilla MRI data was implemented using c++, mitk, and qt. With the VIBE_15 sequence, image data with high geometric accuracy were acquired. Dental metal artefacts were lower than in CT data of the same heads. The segmentation of the jaws was feasible, in contrast to the segmentation of the dentition, since there is a lack of contrast to the intraoral soft tissue structures. MRI is a suitable method for imaging of the region of mouth and jaws. The geometric accuracy is excellent and the susceptibility to artefacts is low. However, there are yet two limitations: Firstly, the imaging of the dentition needs further improvement to allow accurate segmentation of these regions. Secondly, the sequence used in this study takes several minutes and hence is susceptible to motion artefacts.

  7. Magnetic resonance imaging of iliotibial band syndrome.

    PubMed

    Ekman, E F; Pope, T; Martin, D F; Curl, W W

    1994-01-01

    Seven cases of iliotibial band syndrome and the pathoanatomic findings of each, as demonstrated by magnetic resonance imaging, are presented. These findings were compared with magnetic resonance imaging scans of 10 age- and sex-matched control knees without evidence of lateral knee pain. Magnetic resonance imaging signal consistent with fluid was seen deep to the iliotibial band in the region of the lateral femoral epicondyle in five of the seven cases. Additionally, when compared with the control group, patients with iliotibial band syndrome demonstrated a significantly thicker iliotibial band over the lateral femoral epicondyle (P < 0.05). Thickness of the iliotibial band in the disease group was 5.49 +/- 2.12 mm, as opposed to 2.52 +/- 1.56 mm in the control group. Cadaveric dissections were performed on 10 normal knees to further elucidate the exact nature of the area under the iliotibial band. A potential space, i.e., a bursa, was found between the iliotibial band and the knee capsule. This series suggests that magnetic resonance imaging demonstrates objective evidence of iliotibial band syndrome and can be helpful when a definitive diagnosis is essential. Furthermore, correlated with anatomic dissection, magnetic resonance imaging identifies this as a problem within a bursa beneath the iliotibial band and not a problem within the knee joint.

  8. An Investigation of Implicit Active Contours for Scientific Image Segmentation

    SciTech Connect

    Weeratunga, S K; Kamath, C

    2003-10-29

    The use of partial differential equations in image processing has become an active area of research in the last few years. In particular, active contours are being used for image segmentation, either explicitly as snakes, or implicitly through the level set approach. In this paper, we consider the use of the implicit active contour approach for segmenting scientific images of pollen grains obtained using a scanning electron microscope. Our goal is to better understand the pros and cons of these techniques and to compare them with the traditional approaches such as the Canny and SUSAN edge detectors. The preliminary results of our study show that the level set method is computationally expensive and requires the setting of several different parameters. However, it results in closed contours, which may be useful in separating objects from the background in an image.

  9. Statistical Characterization and Segmentation of Drusen in Fundus Images

    SciTech Connect

    Santos-Villalobos, Hector J; Karnowski, Thomas Paul; Aykac, Deniz; Giancardo, Luca; Li, Yaquin; Nichols, Trent L; Tobin Jr, Kenneth William; Chaum, Edward

    2011-01-01

    Age related Macular Degeneration (AMD) is a disease of the retina associated with aging. AMD progression in patients is characterized by drusen, pigmentation changes, and geographic atrophy, which can be seen using fundus imagery. The level of AMD is characterized by standard scaling methods, which can be somewhat subjective in practice. In this work we propose a statistical image processing approach to segment drusen with the ultimate goal of characterizing the AMD progression in a data set of longitudinal images. The method characterizes retinal structures with a statistical model of the colors in the retina image. When comparing the segmentation results of the method between longitudinal images with known AMD progression and those without, the method detects progression in our longitudinal data set with an area under the receiver operating characteristics curve of 0.99.

  10. Cervigram image segmentation based on reconstructive sparse representations

    NASA Astrophysics Data System (ADS)

    Zhang, Shaoting; Huang, Junzhou; Wang, Wei; Huang, Xiaolei; Metaxas, Dimitris

    2010-03-01

    We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination and specular reflection, the color and texture features in optical images often overlap with each other and are not linearly separable. By leveraging sparse representations the data can be transformed to higher dimensions with sparse constraints and become more separated. K-SVD algorithm is employed to find sparse representations and corresponding dictionaries. The data can be reconstructed from its sparse representations and positive and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In the experiments we applied our method to automatically segment the biomarker AcetoWhite (AW) regions in an archive of 60,000 images of the uterine cervix. Compared with other general methods, our approach showed lower space and time complexity and higher sensitivity.

  11. Segmentation of mosaicism in cervicographic images using support vector machines

    NASA Astrophysics Data System (ADS)

    Xue, Zhiyun; Long, L. Rodney; Antani, Sameer; Jeronimo, Jose; Thoma, George R.

    2009-02-01

    The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating a large digital repository of cervicographic images for the study of uterine cervix cancer prevention. One of the research goals is to automatically detect diagnostic bio-markers in these images. Reliable bio-marker segmentation in large biomedical image collections is a challenging task due to the large variation in image appearance. Methods described in this paper focus on segmenting mosaicism, which is an important vascular feature used to visually assess the degree of cervical intraepithelial neoplasia. The proposed approach uses support vector machines (SVM) trained on a ground truth dataset annotated by medical experts (which circumvents the need for vascular structure extraction). We have evaluated the performance of the proposed algorithm and experimentally demonstrated its feasibility.

  12. Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images

    NASA Astrophysics Data System (ADS)

    Suzani, Amin; Rasoulian, Abtin; Seitel, Alexander; Fels, Sidney; Rohling, Robert N.; Abolmaesumi, Purang

    2015-03-01

    This paper proposes an automatic method for vertebra localization, labeling, and segmentation in multi-slice Magnetic Resonance (MR) images. Prior work in this area on MR images mostly requires user interaction while our method is fully automatic. Cubic intensity-based features are extracted from image voxels. A deep learning approach is used for simultaneous localization and identification of vertebrae. The localized points are refined by local thresholding in the region of the detected vertebral column. Thereafter, a statistical multi-vertebrae model is initialized on the localized vertebrae. An iterative Expectation Maximization technique is used to register the vertebral body of the model to the image edges and obtain a segmentation of the lumbar vertebral bodies. The method is evaluated by applying to nine volumetric MR images of the spine. The results demonstrate 100% vertebra identification and a mean surface error of below 2.8 mm for 3D segmentation. Computation time is less than three minutes per high-resolution volumetric image.

  13. Automatic tissue segmentation of breast biopsies imaged by QPI

    NASA Astrophysics Data System (ADS)

    Majeed, Hassaan; Nguyen, Tan; Kandel, Mikhail; Marcias, Virgilia; Do, Minh; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel

    2016-03-01

    The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel's label and the histogram of these textons in that pixel's neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel's neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.

  14. Magnetic resonance imaging of anorectal malformations.

    PubMed

    Podberesky, Daniel J; Towbin, Alexander J; Eltomey, Mohamed A; Levitt, Marc A

    2013-11-01

    Anorectal malformation (ARM) occurs in approximately 1 in 5000 newborns and is frequently accompanied by anomalies of the genitalia, gynecologic system, urinary tract, spine, and skeletal system. Diagnostic imaging plays a central role in ARM evaluation. Because of the lack of ionizing radiation, excellent intrinsic contrast resolution, multiplanar imaging capabilities, technical advances in hardware, and innovative imaging protocols, magnetic resonance (MR) imaging is increasingly important in assessment of ARM patients in utero, postnatally before definitive surgical correction, and in the postoperative period. This article discusses the role of MR imaging in evaluating ARM patients. PMID:24183526

  15. A geometric deformable model for echocardiographic image segmentation

    NASA Technical Reports Server (NTRS)

    Hang, X.; Greenberg, N. L.; Thomas, J. D.

    2002-01-01

    Gradient vector flow (GVF), an elegant external force for parametric deformable models, can capture object boundaries from both sides. A new geometric deformable model is proposed that combines GVF and the geodesic active contour model. The level set method is used as the numerical method of this model. The model is applied for echocardiographic image segmentation.

  16. Liver segmentation for CT images using GVF snake

    SciTech Connect

    Liu Fan; Zhao Binsheng; Kijewski, Peter K.; Wang Liang; Schwartz, Lawrence H.

    2005-12-15

    Accurate liver segmentation on computed tomography (CT) images is a challenging task especially at sites where surrounding tissues (e.g., stomach, kidney) have densities similar to that of the liver and lesions reside at the liver edges. We have developed a method for semiautomatic delineation of the liver contours on contrast-enhanced CT images. The method utilizes a snake algorithm with a gradient vector flow (GVF) field as its external force. To improve the performance of the GVF snake in the segmentation of the liver contour, an edge map was obtained with a Canny edge detector, followed by modifications using a liver template and a concavity removal algorithm. With the modified edge map, for which unwanted edges inside the liver were eliminated, the GVF field was computed and an initial liver contour was formed. The snake algorithm was then applied to obtain the actual liver contour. This algorithm was extended to segment the liver volume in a slice-by-slice fashion, where the result of the preceding slice constrained the segmentation of the adjacent slice. 551 two-dimensional liver images from 20 volumetric images with colorectal metastases spreading throughout the livers were delineated using this method, and also manually by a radiologist for evaluation. The difference ratio, which is defined as the percentage ratio of mismatching volume between the computer and the radiologist's results, ranged from 2.9% to 7.6% with a median value of 5.3%.

  17. Registration, segmentation, and visualization of multimodal brain images.

    PubMed

    Viergever, M A; Maintz, J B; Niessen, W J; Noordmans, H J; Pluim, J P; Stokking, R; Vincken, K L

    2001-01-01

    This paper gives an overview of the studies performed at our institute over the last decade on the processing and visualization of brain images, in the context of international developments in the field. The focus is on multimodal image registration and multimodal visualization, while segmentation is touched upon as a preprocessing step for visualization. The state-of-the-art in these areas is discussed and suggestions for future research are given. PMID:11137791

  18. Community detection for fluorescent lifetime microscopy image segmentation

    NASA Astrophysics Data System (ADS)

    Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Achilefu, Samuel; Nussinov, Zohar

    2014-03-01

    Multiresolution community detection (CD) method has been suggested in a recent work as an efficient method for performing unsupervised segmentation of fluorescence lifetime (FLT) images of live cell images containing fluorescent molecular probes.1 In the current paper, we further explore this method in FLT images of ex vivo tissue slices. The image processing problem is framed as identifying clusters with respective average FLTs against a background or "solvent" in FLT imaging microscopy (FLIM) images derived using NIR fluorescent dyes. We have identified significant multiresolution structures using replica correlations in these images, where such correlations are manifested by information theoretic overlaps of the independent solutions ("replicas") attained using the multiresolution CD method from different starting points. In this paper, our method is found to be more efficient than a current state-of-the-art image segmentation method based on mixture of Gaussian distributions. It offers more than 1:25 times diversity based on Shannon index than the latter method, in selecting clusters with distinct average FLTs in NIR FLIM images.

  19. Automatic 3D lesion segmentation on breast ultrasound images

    NASA Astrophysics Data System (ADS)

    Kuo, Hsien-Chi; Giger, Maryellen L.; Reiser, Ingrid; Drukker, Karen; Edwards, Alexandra; Sennett, Charlene A.

    2013-02-01

    Automatically acquired and reconstructed 3D breast ultrasound images allow radiologists to detect and evaluate breast lesions in 3D. However, assessing potential cancers in 3D ultrasound can be difficult and time consuming. In this study, we evaluate a 3D lesion segmentation method, which we had previously developed for breast CT, and investigate its robustness on lesions on 3D breast ultrasound images. Our dataset includes 98 3D breast ultrasound images obtained on an ABUS system from 55 patients containing 64 cancers. Cancers depicted on 54 US images had been clinically interpreted as negative on screening mammography and 44 had been clinically visible on mammography. All were from women with breast density BI-RADS 3 or 4. Tumor centers and margins were indicated and outlined by radiologists. Initial RGI-eroded contours were automatically calculated and served as input to the active contour segmentation algorithm yielding the final lesion contour. Tumor segmentation was evaluated by determining the overlap ratio (OR) between computer-determined and manually-drawn outlines. Resulting average overlap ratios on coronal, transverse, and sagittal views were 0.60 +/- 0.17, 0.57 +/- 0.18, and 0.58 +/- 0.17, respectively. All OR values were significantly higher the 0.4, which is deemed "acceptable". Within the groups of mammogram-negative and mammogram-positive cancers, the overlap ratios were 0.63 +/- 0.17 and 0.56 +/- 0.16, respectively, on the coronal views; with similar results on the other views. The segmentation performance was not found to be correlated to tumor size. Results indicate robustness of the 3D lesion segmentation technique in multi-modality 3D breast imaging.

  20. Automated Drusen Segmentation and Quantification in SD-OCT Images

    PubMed Central

    Chen, Qiang; Leng, Theodore; Zheng, Luoluo; Kutzscher, Lauren; Ma, Jeffrey; de Sisternes, Luis; Rubin, Daniel L.

    2013-01-01

    Spectral domain optical coherence tomography (SD-OCT) is a useful tool for the visualization of drusen, a retinal abnormality seen in patients with age-related macular degeneration (AMD); however, objective assessment of drusen is thwarted by the lack of a method to robustly quantify these lesions on serial OCT images. Here, we describe an automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features. The highly reflective and locally connected pixels located below the retinal nerve fiber layer (RNFL) are used to generate a segmentation of the retinal pigment epithelium (RPE) layer. The observed and expected contours of the RPE layer are obtained by interpolating and fitting the shape of the segmented RPE layer, respectively. The areas located between the interpolated and fitted RPE shapes (which have nonzero area when drusen occurs) are marked as drusen. To enhance drusen quantification, we also developed a novel method of retinal projection to generate an en face retinal image based on the RPE extraction, which improves the quality of drusen visualization over the current approach to producing retinal projections from SD-OCT images based on a summed-voxel projection (SVP), and it provides a means of obtaining quantitative features of drusen in the en face projection. Visualization of the segmented drusen is refined through several post-processing steps, drusen detection to eliminate false positive detections on consecutive slices, drusen refinement on a projection view of drusen, and drusen smoothing. Experimental evaluation results demonstrate that our method is effective for drusen segmentation. In a preliminary analysis of the potential clinical utility of our methods, quantitative drusen measurements, such as area and volume, can be correlated with the drusen progression in non-exudative AMD, suggesting that our approach may produce useful quantitative imaging biomarkers

  1. Deep convolutional networks for pancreas segmentation in CT imaging

    NASA Astrophysics Data System (ADS)

    Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

    2015-03-01

    Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

  2. A general approach to liver lesion segmentation in CT images

    NASA Astrophysics Data System (ADS)

    Cao, Li; Udupa, Jayaram K.; Odhner, Dewey; Huang, Lidong; Tong, Yubing; Torigian, Drew A.

    2016-03-01

    Lesion segmentation has remained a challenge in different body regions. Generalizability is lacking in published methods as variability in results is common, even for a given organ and modality, such that it becomes difficult to establish standardized methods of disease quantification and reporting. This paper makes an attempt at a generalizable method based on classifying lesions along with their background into groups using clinically used visual attributes. Using an Iterative Relative Fuzzy Connectedness (IRFC) delineation engine, the ideas are implemented for the task of liver lesion segmentation in computed tomography (CT) images. For lesion groups with the same background properties, a few subjects are chosen as the training set to obtain the optimal IRFC parameters for the background tissue components. For lesion groups with similar foreground properties, optimal foreground parameters for IRFC are set as the median intensity value of the training lesion subset. To segment liver lesions belonging to a certain group, the devised method requires manual loading of the corresponding parameters, and correct setting of the foreground and background seeds. The segmentation is then completed in seconds. Segmentation accuracy and repeatability with respect to seed specification are evaluated. Accuracy is assessed by the assignment of a delineation quality score (DQS) to each case. Inter-operator repeatability is assessed by the difference between segmentations carried out independently by two operators. Experiments on 80 liver lesion cases show that the proposed method achieves a mean DQS score of 4.03 and inter-operator repeatability of 92.3%.

  3. Automated 3D renal segmentation based on image partitioning

    NASA Astrophysics Data System (ADS)

    Yeghiazaryan, Varduhi; Voiculescu, Irina D.

    2016-03-01

    Despite several decades of research into segmentation techniques, automated medical image segmentation is barely usable in a clinical context, and still at vast user time expense. This paper illustrates unsupervised organ segmentation through the use of a novel automated labelling approximation algorithm followed by a hypersurface front propagation method. The approximation stage relies on a pre-computed image partition forest obtained directly from CT scan data. We have implemented all procedures to operate directly on 3D volumes, rather than slice-by-slice, because our algorithms are dimensionality-independent. The results picture segmentations which identify kidneys, but can easily be extrapolated to other body parts. Quantitative analysis of our automated segmentation compared against hand-segmented gold standards indicates an average Dice similarity coefficient of 90%. Results were obtained over volumes of CT data with 9 kidneys, computing both volume-based similarity measures (such as the Dice and Jaccard coefficients, true positive volume fraction) and size-based measures (such as the relative volume difference). The analysis considered both healthy and diseased kidneys, although extreme pathological cases were excluded from the overall count. Such cases are difficult to segment both manually and automatically due to the large amplitude of Hounsfield unit distribution in the scan, and the wide spread of the tumorous tissue inside the abdomen. In the case of kidneys that have maintained their shape, the similarity range lies around the values obtained for inter-operator variability. Whilst the procedure is fully automated, our tools also provide a light level of manual editing.

  4. A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information

    PubMed Central

    Cong, Wang; Luan, Kuan; Liang, Hong; Ma, Xingcheng

    2016-01-01

    Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected. PMID:27660649

  5. A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information

    PubMed Central

    Cong, Wang; Luan, Kuan; Liang, Hong; Ma, Xingcheng

    2016-01-01

    Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected.

  6. Surface plasmon resonance of two-segmented Au—Cu nanowires in polycarbonate template

    NASA Astrophysics Data System (ADS)

    Babaei, F.; Azarian, A.

    2013-10-01

    Two segmented gold—copper nanowires were grown inside the pores of polycarbonate track (PCT) etched membranes from two separate solutions by the electrodeposition method. Optical absorption spectra of two segmented Au—Cu nanowires in PCT template showed a surface plasmon resonance peak at about 900 nm for incident angle θ=65° but for θ=0 there are no peaks in spectra. This work is possibly useful as labels in biological assays or as embedded identification tags.

  7. Magnetic Resonance Imaging of Perirenal Pathology.

    PubMed

    Glockner, James F; Lee, Christine U

    2016-05-01

    The perirenal space can be involved by a variety of neoplastic, inflammatory, infectious, and proliferative disorders. Magnetic resonance imaging is often an ideal technique for identification and staging of lesions arising within the perirenal space, with its superior soft tissue characterization as well as its ability to visualize extension into blood vessels and adjacent organs. This pictorial essay describes the magnetic resonance imaging appearance of a variety of pathologies which can arise from or involve the perirenal space, and provides a framework for categorization and differential diagnosis of these lesions.

  8. Granular convection observed by magnetic resonance imaging

    SciTech Connect

    Ehrichs, E.E.; Jaeger, H.M.; Knight, J.B.; Nagel, S.R.; Karczmar, G.S.; Kuperman, V.Yu.

    1995-03-17

    Vibrations in a granular material can spontaneously produce convection rolls reminiscent of those seen in fluids. Magnetic resonance imaging provides a sensitive and noninvasive probe for the detection of these convection currents, which have otherwise been difficult to observe. A magnetic resonance imaging study of convection in a column of poppy seeds yielded data about the detailed shape of the convection rolls and the depth dependence of the convection velocity. The velocity was found to decrease exponentially with depth; a simple model for this behavior is presented here. 31 refs., 4 figs.

  9. Granular convection observed by magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Ehrichs, E. E.; Jaeger, H. M.; Karczmar, Greg S.; Knight, James B.; Kuperman, Vadim Yu.; Nagel, Sidney R.

    1995-03-01

    Vibrations in a granular material can spontaneously produce convection rolls reminiscent of those seen in fluids. Magnetic resonance imaging provides a sensitive and noninvasive probe for the detection of these convection currents, which have otherwise been difficult to observe. A magnetic resonance imaging study of convection in a column of poppy seeds yielded data about the detailed shape of the convection rolls and the depth dependence of the convection velocity. The velocity was found to decrease exponentially with depth; a simple model for this behavior is presented here.

  10. Building Roof Segmentation from Aerial Images Using a Line-and Region-Based Watershed Segmentation Technique

    PubMed Central

    Merabet, Youssef El; Meurie, Cyril; Ruichek, Yassine; Sbihi, Abderrahmane; Touahni, Raja

    2015-01-01

    In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG. PMID:25648706

  11. Superpixel Cut for Figure-Ground Image Segmentation

    NASA Astrophysics Data System (ADS)

    Yang, Michael Ying; Rosenhahn, Bodo

    2016-06-01

    Figure-ground image segmentation has been a challenging problem in computer vision. Apart from the difficulties in establishing an effective framework to divide the image pixels into meaningful groups, the notions of figure and ground often need to be properly defined by providing either user inputs or object models. In this paper, we propose a novel graph-based segmentation framework, called superpixel cut. The key idea is to formulate foreground segmentation as finding a subset of superpixels that partitions a graph over superpixels. The problem is formulated as Min-Cut. Therefore, we propose a novel cost function that simultaneously minimizes the inter-class similarity while maximizing the intra-class similarity. This cost function is optimized using parametric programming. After a small learning step, our approach is fully automatic and fully bottom-up, which requires no high-level knowledge such as shape priors and scene content. It recovers coherent components of images, providing a set of multiscale hypotheses for high-level reasoning. We evaluate our proposed framework by comparing it to other generic figure-ground segmentation approaches. Our method achieves improved performance on state-of-the-art benchmark databases.

  12. Segmentation of intensity inhomogeneous brain MR images using active contours.

    PubMed

    Akram, Farhan; Kim, Jeong Heon; Lim, Han Ul; Choi, Kwang Nam

    2014-01-01

    Segmentation of intensity inhomogeneous regions is a well-known problem in image analysis applications. This paper presents a region-based active contour method for image segmentation, which properly works in the context of intensity inhomogeneity problem. The proposed region-based active contour method embeds both region and gradient information unlike traditional methods. It contains mainly two terms, area and length, in which the area term practices a new region-based signed pressure force (SPF) function, which utilizes mean values from a certain neighborhood using the local binary fitted (LBF) energy model. In turn, the length term uses gradient information. The novelty of our method is to locally compute new SPF function, which uses local mean values and is able to detect boundaries of the homogenous regions. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed method targets the segmentation problem of intensity inhomogeneous images and reduces the time complexity among locally computed active contour methods. The experimental results show that the proposed method yields better segmentation result as well as less time complexity compared with the state-of-the-art active contour methods. PMID:25143780

  13. Unsupervised segmentation of MRI knees using image partition forests

    NASA Astrophysics Data System (ADS)

    Marčan, Marija; Voiculescu, Irina

    2016-03-01

    Nowadays many people are affected by arthritis, a condition of the joints with limited prevention measures, but with various options of treatment the most radical of which is surgical. In order for surgery to be successful, it can make use of careful analysis of patient-based models generated from medical images, usually by manual segmentation. In this work we show how to automate the segmentation of a crucial and complex joint -- the knee. To achieve this goal we rely on our novel way of representing a 3D voxel volume as a hierarchical structure of partitions which we have named Image Partition Forest (IPF). The IPF contains several partition layers of increasing coarseness, with partitions nested across layers in the form of adjacency graphs. On the basis of a set of properties (size, mean intensity, coordinates) of each node in the IPF we classify nodes into different features. Values indicating whether or not any particular node belongs to the femur or tibia are assigned through node filtering and node-based region growing. So far we have evaluated our method on 15 MRI knee images. Our unsupervised segmentation compared against a hand-segmented gold standard has achieved an average Dice similarity coefficient of 0.95 for femur and 0.93 for tibia, and an average symmetric surface distance of 0.98 mm for femur and 0.73 mm for tibia. The paper also discusses ways to introduce stricter morphological and spatial conditioning in the bone labelling process.

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

    PubMed Central

    Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B.; Ayache, Nicholas; Buendia, Patricia; Collins, D. Louis; Cordier, Nicolas; Corso, Jason J.; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R.; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M.; Jena, Raj; John, Nigel M.; Konukoglu, Ender; Lashkari, Danial; Mariz, José António; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J.; Raviv, Tammy Riklin; Reza, Syed M. S.; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A.; Sousa, Nuno; Subbanna, Nagesh K.; Szekely, Gabor; Taylor, Thomas J.; Thomas, Owen M.; Tustison, Nicholas J.; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

    2016-01-01

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using 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 benchmarking resource. PMID:25494501

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

    PubMed

    Menze, Bjoern H; Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B; Ayache, Nicholas; Buendia, Patricia; Collins, D Louis; Cordier, Nicolas; Corso, Jason J; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M; Jena, Raj; John, Nigel M; Konukoglu, Ender; Lashkari, Danial; Mariz, José Antonió; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J; Raviv, Tammy Riklin; Reza, Syed M S; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A; Sousa, Nuno; Subbanna, Nagesh K; Szekely, Gabor; Taylor, Thomas J; Thomas, Owen M; Tustison, Nicholas J; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

    2015-10-01

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using 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 benchmarking resource.

  16. Cryo-EM Structure Determination Using Segmented Helical Image Reconstruction.

    PubMed

    Fromm, S A; Sachse, C

    2016-01-01

    Treating helices as single-particle-like segments followed by helical image reconstruction has become the method of choice for high-resolution structure determination of well-ordered helical viruses as well as flexible filaments. In this review, we will illustrate how the combination of latest hardware developments with optimized image processing routines have led to a series of near-atomic resolution structures of helical assemblies. Originally, the treatment of helices as a sequence of segments followed by Fourier-Bessel reconstruction revealed the potential to determine near-atomic resolution structures from helical specimens. In the meantime, real-space image processing of helices in a stack of single particles was developed and enabled the structure determination of specimens that resisted classical Fourier helical reconstruction and also facilitated high-resolution structure determination. Despite the progress in real-space analysis, the combination of Fourier and real-space processing is still commonly used to better estimate the symmetry parameters as the imposition of the correct helical symmetry is essential for high-resolution structure determination. Recent hardware advancement by the introduction of direct electron detectors has significantly enhanced the image quality and together with improved image processing procedures has made segmented helical reconstruction a very productive cryo-EM structure determination method.

  17. Object Segmentation and Ground Truth in 3D Embryonic Imaging.

    PubMed

    Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C

    2016-01-01

    Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets. PMID:27332860

  18. Object Segmentation and Ground Truth in 3D Embryonic Imaging

    PubMed Central

    Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C.

    2016-01-01

    Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets. PMID:27332860

  19. Cryo-EM Structure Determination Using Segmented Helical Image Reconstruction.

    PubMed

    Fromm, S A; Sachse, C

    2016-01-01

    Treating helices as single-particle-like segments followed by helical image reconstruction has become the method of choice for high-resolution structure determination of well-ordered helical viruses as well as flexible filaments. In this review, we will illustrate how the combination of latest hardware developments with optimized image processing routines have led to a series of near-atomic resolution structures of helical assemblies. Originally, the treatment of helices as a sequence of segments followed by Fourier-Bessel reconstruction revealed the potential to determine near-atomic resolution structures from helical specimens. In the meantime, real-space image processing of helices in a stack of single particles was developed and enabled the structure determination of specimens that resisted classical Fourier helical reconstruction and also facilitated high-resolution structure determination. Despite the progress in real-space analysis, the combination of Fourier and real-space processing is still commonly used to better estimate the symmetry parameters as the imposition of the correct helical symmetry is essential for high-resolution structure determination. Recent hardware advancement by the introduction of direct electron detectors has significantly enhanced the image quality and together with improved image processing procedures has made segmented helical reconstruction a very productive cryo-EM structure determination method. PMID:27572732

  20. Segmentation of ultrasound breast images based on a neutrosophic method

    NASA Astrophysics Data System (ADS)

    Zhang, Ming; Zhang, Ling; Cheng, Heng-Da

    2010-11-01

    Breast cancer is one of the leading cancers of women. Ultrasound is often used for breast cancer diagnosis because it is harmless, portable, and low-cost. However, the segmentation of breast ultrasound (BUS) images is a difficult task due to their low contrast and speckle noise. Neutrosophy studies the origin, nature, and scope of neutralities and their interactions with different ideational spectra. It is a new philosophy to extend fuzzy logic and is the basis of neutrosophic logic, neutrosophic probability theory, neutrosophic set theory, and neutrosophic statistics. In this paper, we employ neutrosophy and develop a fully automatic algorithm for BUS image segmentation. By using neutrosophy, we integrate two conflicting opinions about speckle in ultrasound image: speckle is noise and speckle includes pattern information. The experiments demonstrate that the proposed approach is accurate, effective, and robust.

  1. Minimum description length synthetic aperture radar image segmentation.

    PubMed

    Galland, Frédéric; Bertaux, Nicolas; Réfrégier, Philippe

    2003-01-01

    We present a new minimum description length (MDL) approach based on a deformable partition--a polygonal grid--for automatic segmentation of a speckled image composed of several homogeneous regions. The image segmentation thus consists in the estimation of the polygonal grid, or, more precisely, its number of regions, its number of nodes and the location of its nodes. These estimations are performed by minimizing a unique MDL criterion which takes into account the probabilistic properties of speckle fluctuations and a measure of the stochastic complexity of the polygonal grid. This approach then leads to a global MDL criterion without an undetermined parameter since no other regularization term than the stochastic complexity of the polygonal grid is necessary and noise parameters can be estimated with maximum likelihood-like approaches. The performance of this technique is illustrated on synthetic and real synthetic aperture radar images of agricultural regions and the influence of different terms of the model is analyzed.

  2. Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.

    PubMed

    Dera, Dimah; Bouaynaya, Nidhal; Fathallah-Shaykh, Hassan M

    2016-07-01

    We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions, which is incorporated into the energy functional of the LSM. The results demonstrate that our NMF-LSM method is superior to other approaches when applied to synthetic binary and gray-scale images and to clinical magnetic resonance images (MRI) of the human brain with and without a malignant brain tumor, glioblastoma multiforme. In particular, the NMF-LSM method is fully automated, highly accurate, less sensitive to the initial selection of the contour(s) or initial conditions, more robust to noise and model parameters, and able to detect as small distinct regions as desired. These advantages stem from the fact that the proposed method relies on histogram information instead of intensity values and does not introduce nuisance model parameters. These properties provide a general approach for automated robust region discovery and segmentation in heterogeneous images. Compared with the retrospective radiological diagnoses of two patients with non-enhancing grade 2 and 3 oligodendroglioma, the NMF-LSM detects earlier progression times and appears suitable for monitoring tumor response. The NMF-LSM method fills an important need of automated segmentation of clinical MRI. PMID:27417984

  3. A unifying framework for partial volume segmentation of brain MR images.

    PubMed

    Van Leemput, Koen; Maes, Frederik; Vandermeulen, Dirk; Suetens, Paul

    2003-01-01

    Accurate brain tissue segmentation by intensity-based voxel classification of magnetic resonance (MR) images is complicated by partial volume (PV) voxels that contain a mixture of two or more tissue types. In this paper, we present a statistical framework for PV segmentation that encompasses and extends existing techniques. We start from a commonly used parametric statistical image model in which each voxel belongs to one single tissue type, and introduce an additional downsampling step that causes partial voluming along the borders between tissues. An expectation-maximization approach is used to simultaneously estimate the parameters of the resulting model and perform a PV classification. We present results on well-chosen simulated images and on real MR images of the brain, and demonstrate that the use of appropriate spatial prior knowledge not only improves the classifications, but is often indispensable for robust parameter estimation as well. We conclude that general robust PV segmentation of MR brain images requires statistical models that describe the spatial distribution of brain tissues more accurately than currently available models.

  4. A modified probabilistic neural network for partial volume segmentation in brain MR image.

    PubMed

    Song, Tao; Jamshidi, Mo M; Lee, Roland R; Huang, Mingxiong

    2007-09-01

    A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN's kernel function, and weighting factors are added in the pattern of summation layer. This weighted probabilistic neural network (WPNN) classifier can account for partial volume effects, which exist commonly in MRI, not only in the final result stage, but also in the modeling process. It adopts the self-organizing map (SOM) neural network to overly segment the input MR image, and yield reference vectors necessary for probabilistic density function (pdf) estimation. A supervised "soft" labeling mechanism based on Bayesian rule is developed, so that weighting factors can be generated along with corresponding SOM reference vectors. Tissue classification results from various algorithms are compared, and the effectiveness and robustness of the proposed approach are demonstrated. PMID:18220190

  5. Possibilistic-clustering-based MR brain image segmentation with accurate initialization

    NASA Astrophysics Data System (ADS)

    Liao, Qingmin; Deng, Yingying; Dou, Weibei; Ruan, Su; Bloyet, Daniel

    2004-01-01

    Magnetic resonance image analysis by computer is useful to aid diagnosis of malady. We present in this paper a automatic segmentation method for principal brain tissues. It is based on the possibilistic clustering approach, which is an improved fuzzy c-means clustering method. In order to improve the efficiency of clustering process, the initial value problem is discussed and solved by combining with a histogram analysis method. Our method can automatically determine number of classes to cluster and the initial values for each class. It has been tested on a set of forty MR brain images with or without the presence of tumor. The experimental results showed that it is simple, rapid and robust to segment the principal brain tissues.

  6. Magnetic resonance imaging in Mexico

    NASA Astrophysics Data System (ADS)

    Rodriguez, A. O.; Rojas, R.; Barrios, F. A.

    2001-10-01

    MR imaging has experienced an important growth worldwide and in particular in the USA and Japan. This imaging technique has also shown an important rise in the number of MR imagers in Mexico. However, the development of MRI has followed a typical way of Latin American countries, which is very different from the path shown in the industrialised countries. Despite the fact that Mexico was one the very first countries to install and operate MR imagers in the world, it still lacks of qualified clinical and technical personnel. Since the first MR scanner started to operate, the number of units has grown at a moderate space that now sums up approximately 60 system installed nationwide. Nevertheless, there are no official records of the number of MR units operating, physicians and technicians involved in this imaging modality. The MRI market is dominated by two important companies: General Electric (approximately 51%) and Siemens (approximately 17.5%), the rest is shared by other five companies. According to the field intensity, medium-field systems (0.5 Tesla) represent 60% while a further 35% are 1.0 T or higher. Almost all of these units are in private hospitals and clinics: there is no high-field MR imagers in any public hospital. Because the political changes in the country, a new public plan for health care is still in the process and will be published soon this year. This plan will be determined by the new Congress. North American Free Trade Agreement (NAFTA) and president Fox. Experience acquired in the past shows that the demand for qualified professionals will grow in the new future. Therefore, systematic training of clinical and technical professionals will be in high demand to meet the needs of this technique. The National University (UNAM) and the Metropolitan University (UAM-Iztapalapa) are collaborating with diverse clinical groups in private facilities to create a systematic training program and carry out research and development in MRI

  7. Image enhancement and segmentation of fluid-filled structures in 3D ultrasound images

    NASA Astrophysics Data System (ADS)

    Chalana, Vikram; Dudycha, Stephen; McMorrow, Gerald

    2003-05-01

    Segmentation of fluid-filled structures, such as the urinary bladder, from three-dimensional ultrasound images is necessary for measuring their volume. This paper describes a system for image enhancement, segmentation and volume measurement of fluid-filled structures on 3D ultrasound images. The system was applied for the measurement of urinary bladder volume. Results show an average error of less than 10% in the estimation of the total bladder volume.

  8. Defining the human hippocampus in cerebral magnetic resonance images—An overview of current segmentation protocols

    PubMed Central

    Konrad, C.; Ukas, T.; Nebel, C.; Arolt, V.; Toga, A.W.; Narr, K.L.

    2011-01-01

    Due to its crucial role for memory processes and its relevance in neurological and psychiatric disorders, the hippocampus has been the focus of neuroimaging research for several decades. In vivo measurement of human hippocampal volume and shape with magnetic resonance imaging has become an important element of neuroimaging research. Nevertheless, volumetric findings are still inconsistent and controversial for many psychiatric conditions including affective disorders. Here we review the wealth of anatomical protocols for the delineation of the hippocampus in MR images, taking into consideration 71 different published protocols from the neuroimaging literature, with an emphasis on studies of affective disorders. We identified large variations between protocols in five major areas. 1) The inclusion/exclusion of hippocampal white matter (alveus and fimbria), 2) the definition of the anterior hippocampal–amygdala border, 3) the definition of the posterior border and the extent to which the hippocampal tail is included, 4) the definition of the inferior medial border of the hippocampus, and 5) the use of varying arbitrary lines. These are major sources of variance between different protocols. In contrast, the definitions of the lateral, superior, and inferior borders are less disputed. Directing resources to replication studies that incorporate characteristics of the segmentation protocols presented herein may help resolve seemingly contradictory volumetric results between prior neuroimaging studies and facilitate the appropriate selection of protocols for manual or automated delineation of the hippocampus for future research purposes. PMID:19447182

  9. Review: Magnetic resonance imaging techniques in ophthalmology

    PubMed Central

    Fagan, Andrew J.

    2012-01-01

    Imaging the eye with magnetic resonance imaging (MRI) has proved difficult due to the eye’s propensity to move involuntarily over typical imaging timescales, obscuring the fine structure in the eye due to the resulting motion artifacts. However, advances in MRI technology help to mitigate such drawbacks, enabling the acquisition of high spatiotemporal resolution images with a variety of contrast mechanisms. This review aims to classify the MRI techniques used to date in clinical and preclinical ophthalmologic studies, describing the qualitative and quantitative information that may be extracted and how this may inform on ocular pathophysiology. PMID:23112569

  10. Magnetic Resonance (MR) Metabolic Imaging in Glioma.

    PubMed

    Chaumeil, Myriam M; Lupo, Janine M; Ronen, Sabrina M

    2015-11-01

    This review is focused on describing the use of magnetic resonance (MR) spectroscopy for metabolic imaging of brain tumors. We will first review the MR metabolic imaging findings generated from preclinical models, focusing primarily on in vivo studies, and will then describe the use of metabolic imaging in the clinical setting. We will address relatively well-established (1) H MRS approaches, as well as (31) P MRS, (13) C MRS and emerging hyperpolarized (13) C MRS methodologies, and will describe the use of metabolic imaging for understanding the basic biology of glioma as well as for improving the characterization and monitoring of brain tumors in the clinic.

  11. Magnetic Resonance Imaging of Spinal Emergencies.

    PubMed

    Kawakyu-O'Connor, Daniel; Bordia, Ritu; Nicola, Refky

    2016-05-01

    Magnetic resonance (MR) imaging of the spine is increasingly being used in the evaluation of spinal emergencies because it is highly sensitive and specific in the diagnosis of acute conditions of the spine. The prompt and accurate recognition allows for appropriate medical and surgical intervention. This article reviews the MR imaging features of common emergent conditions, such as spinal trauma, acute disc herniation, infection, and tumors. In addition, we describe common MR imaging sequences, discuss challenges encountered in emergency imaging of the spine, and illustrate multiple mimics of acute conditions. PMID:27150322

  12. Magnetic resonance imaging in rheumatology. An overview.

    PubMed

    Nissenbaum, M A; Adamis, M K

    1994-05-01

    Magnetic resonance (MR) imaging has revolutionized the assessment of pathology involving the musculoskeletal system. The soft tissue contrast, superb resolution, multiplanar acquisition potential, and the ability to monitor physiologic processes combine the best features of other imaging modalities. The sensitivity and specificity of MR imaging for a wide range of disease processes matches or supersedes conventional radiology, nuclear medicine, and clinical examination. This article provides a brief overview of the use of MR imaging for some of the more common clinical situations confronting the rheumatologist.

  13. Survey of contemporary trends in color image segmentation

    NASA Astrophysics Data System (ADS)

    Vantaram, Sreenath Rao; Saber, Eli

    2012-10-01

    In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.

  14. Fusion of color Doppler and magnetic resonance images of the heart.

    PubMed

    Wang, Chao; Chen, Ming; Zhao, Jiang-Min; Liu, Yi

    2011-12-01

    This study was designed to establish and analyze color Doppler and magnetic resonance fusion images of the heart, an approach for simultaneous testing of cardiac pathological alterations, performance, and hemodynamics. Ten volunteers were tested in this study. The echocardiographic images were produced by Philips IE33 system and the magnetic resonance images were generated from Philips 3.0-T system. The fusion application was implemented on MATLAB platform utilizing image processing technology. The fusion image was generated from the following steps: (1) color Doppler blood flow segmentation, (2) image registration of color Doppler and magnetic resonance imaging, and (3) image fusion of different image types. The fusion images of color Doppler blood flow and magnetic resonance images were implemented by MATLAB programming in our laboratory. Images and videos were displayed and saved as AVI and JPG. The present study shows that the method we have developed can be used to fuse color flow Doppler and magnetic resonance images of the heart. We believe that the method has the potential to: fill in information missing from the ultrasound or MRI alone, show structures outside the field of view of the ultrasound through MR imaging, and obtain complementary information through the fusion of the two imaging methods (structure from MRI and function from ultrasound). PMID:21656081

  15. An image segmentation method for apple sorting and grading using support vector machine and Otsu's method

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Segmentation is the first step in image analysis to subdivide an image into meaningful regions. The segmentation result directly affects the subsequent image analysis. The objective of the research was to develop an automatic adjustable algorithm for segmentation of color images, using linear suppor...

  16. Control of multiple excited image states around segmented carbon nanotubes

    SciTech Connect

    Knörzer, J. Fey, C.; Sadeghpour, H. R.; Schmelcher, P.

    2015-11-28

    Electronic image states around segmented carbon nanotubes can be confined and shaped along the nanotube axis by engineering the image potential. We show how several such image states can be prepared simultaneously along the same nanotube. The inter-electronic distance can be controlled a priori by engineering tubes of specific geometries. High sensitivity to external electric and magnetic fields can be exploited to manipulate these states and their mutual long-range interactions. These building blocks provide access to a new kind of tailored interacting quantum systems.

  17. Colour image segmentation using unsupervised clustering technique for acute leukemia images

    NASA Astrophysics Data System (ADS)

    Halim, N. H. Abd; Mashor, M. Y.; Nasir, A. S. Abdul; Mustafa, N.; Hassan, R.

    2015-05-01

    Colour image segmentation has becoming more popular for computer vision due to its important process in most medical analysis tasks. This paper proposes comparison between different colour components of RGB(red, green, blue) and HSI (hue, saturation, intensity) colour models that will be used in order to segment the acute leukemia images. First, partial contrast stretching is applied on leukemia images to increase the visual aspect of the blast cells. Then, an unsupervised moving k-means clustering algorithm is applied on the various colour components of RGB and HSI colour models for the purpose of segmentation of blast cells from the red blood cells and background regions in leukemia image. Different colour components of RGB and HSI colour models have been analyzed in order to identify the colour component that can give the good segmentation performance. The segmented images are then processed using median filter and region growing technique to reduce noise and smooth the images. The results show that segmentation using saturation component of HSI colour model has proven to be the best in segmenting nucleus of the blast cells in acute leukemia image as compared to the other colour components of RGB and HSI colour models.

  18. An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images.

    PubMed

    Guo, Yanen; Xu, Xiaoyin; Wang, Yuanyuan; Wang, Yaming; Xia, Shunren; Yang, Zhong

    2014-08-01

    Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images.

  19. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images.

    PubMed

    Jain, Saurabh; Sima, Diana M; Ribbens, Annemie; Cambron, Melissa; Maertens, Anke; Van Hecke, Wim; De Mey, Johan; Barkhof, Frederik; Steenwijk, Martijn D; Daams, Marita; Maes, Frederik; Van Huffel, Sabine; Vrenken, Hugo; Smeets, Dirk

    2015-01-01

    The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter

  20. Fetal Cerebral Magnetic Resonance Imaging Beyond Morphology.

    PubMed

    Jakab, András; Pogledic, Ivana; Schwartz, Ernst; Gruber, Gerlinde; Mitter, Christian; Brugger, Peter C; Langs, Georg; Schöpf, Veronika; Kasprian, Gregor; Prayer, Daniela

    2015-12-01

    The recent technological advancement of fast magnetic resonance imaging (MRI) sequences allowed the inclusion of diffusion tensor imaging, functional MRI, and proton MR spectroscopy in prenatal imaging protocols. These methods provide information beyond morphology and hold the key to improving several fields of human neuroscience and clinical diagnostics. Our review introduces the fundamental works that enabled these imaging techniques, and also highlights the most recent contributions to this emerging field of prenatal diagnostics, such as the structural and functional connectomic approach. We introduce the advanced image processing approaches that are extensively used to tackle fetal or maternal movement-related image artifacts, and which are necessary for the optimal interpretation of such imaging data. PMID:26614130

  1. Fingerprint image segmentation based on multi-features histogram analysis

    NASA Astrophysics Data System (ADS)

    Wang, Peng; Zhang, Youguang

    2007-11-01

    An effective fingerprint image segmentation based on multi-features histogram analysis is presented. We extract a new feature, together with three other features to segment fingerprints. Two of these four features, each of which is related to one of the other two, are reciprocals with each other, so features are divided into two groups. These two features' histograms are calculated respectively to determine which feature group is introduced to segment the aim-fingerprint. The features could also divide fingerprints into two classes with high and low quality. Experimental results show that our algorithm could classify foreground and background effectively with lower computational cost, and it can also reduce pseudo-minutiae detected and improve the performance of AFIS.

  2. Automatic segmentation method of striatum regions in quantitative susceptibility mapping images

    NASA Astrophysics Data System (ADS)

    Murakawa, Saki; Uchiyama, Yoshikazu; Hirai, Toshinori

    2015-03-01

    Abnormal accumulation of brain iron has been detected in various neurodegenerative diseases. Quantitative susceptibility mapping (QSM) is a novel contrast mechanism in magnetic resonance (MR) imaging and enables the quantitative analysis of local tissue susceptibility property. Therefore, automatic segmentation tools of brain regions on QSM images would be helpful for radiologists' quantitative analysis in various neurodegenerative diseases. The purpose of this study was to develop an automatic segmentation and classification method of striatum regions on QSM images. Our image database consisted of 22 QSM images obtained from healthy volunteers. These images were acquired on a 3.0 T MR scanner. The voxel size was 0.9×0.9×2 mm. The matrix size of each slice image was 256×256 pixels. In our computerized method, a template mating technique was first used for the detection of a slice image containing striatum regions. An image registration technique was subsequently employed for the classification of striatum regions in consideration of the anatomical knowledge. After the image registration, the voxels in the target image which correspond with striatum regions in the reference image were classified into three striatum regions, i.e., head of the caudate nucleus, putamen, and globus pallidus. The experimental results indicated that 100% (21/21) of the slice images containing striatum regions were detected accurately. The subjective evaluation of the classification results indicated that 20 (95.2%) of 21 showed good or adequate quality. Our computerized method would be useful for the quantitative analysis of Parkinson diseases in QSM images.

  3. Cardiovascular magnetic resonance phase contrast imaging.

    PubMed

    Nayak, Krishna S; Nielsen, Jon-Fredrik; Bernstein, Matt A; Markl, Michael; D Gatehouse, Peter; M Botnar, Rene; Saloner, David; Lorenz, Christine; Wen, Han; S Hu, Bob; Epstein, Frederick H; N Oshinski, John; Raman, Subha V

    2015-01-01

    Cardiovascular magnetic resonance (CMR) phase contrast imaging has undergone a wide range of changes with the development and availability of improved calibration procedures, visualization tools, and analysis methods. This article provides a comprehensive review of the current state-of-the-art in CMR phase contrast imaging methodology, clinical applications including summaries of past clinical performance, and emerging research and clinical applications that utilize today's latest technology. PMID:26254979

  4. An efficient MRF embedded level set method for image segmentation.

    PubMed

    Yang, Xi; Gao, Xinbo; Tao, Dacheng; Li, Xuelong; Li, Jie

    2015-01-01

    This paper presents a fast and robust level set method for image segmentation. To enhance the robustness against noise, we embed a Markov random field (MRF) energy function to the conventional level set energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them to fall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraic multigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain, respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of our method for big image databases. By comparing the proposed fast and robust level set method with the standard level set method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medical images, and natural images, we comprehensively demonstrate the new method is robust against various kinds of noises. In particular, the new level set method can segment an image of size 500 × 500 within 3 s on MATLAB R2010b installed in a computer with 3.30-GHz CPU and 4-GB memory.

  5. Joint graph cut and relative fuzzy connectedness image segmentation algorithm.

    PubMed

    Ciesielski, Krzysztof Chris; Miranda, Paulo A V; Falcão, Alexandre X; Udupa, Jayaram K

    2013-12-01

    We introduce an image segmentation algorithm, called GC(sum)(max), which combines, in novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding "shrinking problem" of GC), while keeping GC's stronger control over the problem of "leaking though poorly defined boundary segments." The analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC can be described within the framework of Generalized GC (GGC) segmentation algorithms. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Forest Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear. Experimental comparison of GC(sum)(max) to GC, an iterative version of RFC (IRFC), and power watershed (PW), based on a variety medical and non-medical images, indicates superior accuracy performance of GC(sum)(max) over these other methods, resulting in a rank ordering of GC(sum)(max)>PW∼IRFC>GC.

  6. Automated segmentation of the melanocytes in skin histopathological images.

    PubMed

    Lu, Cheng; Mahmood, Muhammad; Jha, Naresh; Mandal, Mrinal

    2013-03-01

    In the diagnosis of skin melanoma by analyzing histopathological images, the detection of the melanocytes in the epidermis area is an important step. However, the detection of melanocytes in the epidermis area is dicult because other keratinocytes that are very similar to the melanocytes are also present. This paper proposes a novel computer-aided technique for segmentation of the melanocytes in the skin histopathological images. In order to reduce the local intensity variant, a mean-shift algorithm is applied for the initial segmentation of the image. A local region recursive segmentation algorithm is then proposed to filter out the candidate nuclei regions based on the domain prior knowledge. To distinguish the melanocytes from other keratinocytes in the epidermis area, a novel descriptor, named local double ellipse descriptor (LDED), is proposed to measure the local features of the candidate regions. The LDED uses two parameters: region ellipticity and local pattern characteristics to distinguish the melanocytes from the candidate nuclei regions. Experimental results on 28 dierent histopathological images of skin tissue with dierent zooming factors show that the proposed technique provides a superior performance.

  7. Crowdsourcing the creation of image segmentation algorithms for connectomics.

    PubMed

    Arganda-Carreras, Ignacio; Turaga, Srinivas C; Berger, Daniel R; Cireşan, Dan; Giusti, Alessandro; Gambardella, Luca M; Schmidhuber, Jürgen; Laptev, Dmitry; Dwivedi, Sarvesh; Buhmann, Joachim M; Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga; Kamentsky, Lee; Burget, Radim; Uher, Vaclav; Tan, Xiao; Sun, Changming; Pham, Tuan D; Bas, Erhan; Uzunbas, Mustafa G; Cardona, Albert; Schindelin, Johannes; Seung, H Sebastian

    2015-01-01

    To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge. PMID:26594156

  8. Crowdsourcing the creation of image segmentation algorithms for connectomics

    PubMed Central

    Arganda-Carreras, Ignacio; Turaga, Srinivas C.; Berger, Daniel R.; Cireşan, Dan; Giusti, Alessandro; Gambardella, Luca M.; Schmidhuber, Jürgen; Laptev, Dmitry; Dwivedi, Sarvesh; Buhmann, Joachim M.; Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga; Kamentsky, Lee; Burget, Radim; Uher, Vaclav; Tan, Xiao; Sun, Changming; Pham, Tuan D.; Bas, Erhan; Uzunbas, Mustafa G.; Cardona, Albert; Schindelin, Johannes; Seung, H. Sebastian

    2015-01-01

    To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This “deep learning” approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge. PMID:26594156

  9. GPU-based relative fuzzy connectedness image segmentation

    SciTech Connect

    Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.

    2013-01-15

    Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an Script-Small-L {sub {infinity}}-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8 Multiplication-Sign , 22.9 Multiplication-Sign , 20.9 Multiplication-Sign , and 17.5 Multiplication-Sign , correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

  10. Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+pose model

    NASA Astrophysics Data System (ADS)

    Suzani, Amin; Rasoulian, Abtin; Fels, Sidney; Rohling, Robert N.; Abolmaesumi, Purang

    2014-03-01

    Segmentation of vertebral structures in magnetic resonance (MR) images is challenging because of poor con­trast between bone surfaces and surrounding soft tissue. This paper describes a semi-automatic method for segmenting vertebral bodies in multi-slice MR images. In order to achieve a fast and reliable segmentation, the method takes advantage of the correlation between shape and pose of different vertebrae in the same patient by using a statistical multi-vertebrae anatomical shape+pose model. Given a set of MR images of the spine, we initially reduce the intensity inhomogeneity in the images by using an intensity-correction algorithm. Then a 3D anisotropic diffusion filter smooths the images. Afterwards, we extract edges from a relatively small region of the pre-processed image with a simple user interaction. Subsequently, an iterative Expectation Maximization tech­nique is used to register the statistical multi-vertebrae anatomical model to the extracted edge points in order to achieve a fast and reliable segmentation for lumbar vertebral bodies. We evaluate our method in terms of speed and accuracy by applying it to volumetric MR images of the spine acquired from nine patients. Quantitative and visual results demonstrate that the method is promising for segmentation of vertebral bodies in volumetric MR images.

  11. Reducing Field Distortion in Magnetic Resonance Imaging

    NASA Technical Reports Server (NTRS)

    Eom, Byeong Ho; Penanen, Konstantin; Hahn, Inseob

    2010-01-01

    A concept for a magnetic resonance imaging (MRI) system that would utilize a relatively weak magnetic field provides for several design features that differ significantly from the corresponding features of conventional MRI systems. Notable among these features are a magnetic-field configuration that reduces (relative to the conventional configuration) distortion and blurring of the image, the use of a superconducting quantum interference device (SQUID) magnetometer as the detector, and an imaging procedure suited for the unconventional field configuration and sensor. In a typical application of MRI, a radio-frequency pulse is used to excite precession of the magnetic moments of protons in an applied magnetic field, and the decaying precession is detected for a short time following the pulse. The precession occurs at a resonance frequency proportional to the strengths of the magnetic field and the proton magnetic moment. The magnetic field is configured to vary with position in a known way; hence, by virtue of the aforesaid proportionality, the resonance frequency varies with position in a known way. In other words, position is encoded as resonance frequency. MRI using magnetic fields weaker than those of conventional MRI offers several advantages, including cheaper and smaller equipment, greater compatibility with metallic objects, and higher image quality because of low susceptibility distortion and enhanced spin-lattice-relaxation- time contrast. SQUID MRI is being developed into a practical MRI method for applied magnetic flux densities of the order of only 100 T

  12. Sports health magnetic resonance imaging challenge.

    PubMed

    Howell, Gary A; Stadnick, Michael E; Awh, Mark H

    2010-11-01

    Injuries to the Lisfranc ligament complex are often suspected, particularly in the setting of midfoot pain without radiographic abnormality. Knowledge of the anatomy and magnetic resonance imaging findings of injuries to this region is helpful for the diagnosing and treating physicians.

  13. Estimation of generalized mixtures and its application in image segmentation.

    PubMed

    Delignon, Y; Marzouki, A; Pieczynski, W

    1997-01-01

    We introduce the notion of a generalized mixture and propose some methods for estimating it, along with applications to unsupervised statistical image segmentation. A distribution mixture is said to be "generalized" when the exact nature of the components is not known, but each belongs to a finite known set of families of distributions. For instance, we can consider a mixture of three distributions, each being exponential or Gaussian. The problem of estimating such a mixture contains thus a new difficulty: we have to label each of three components (there are eight possibilities). We show that the classical mixture estimation algorithms-expectation-maximization (EM), stochastic EM (SEM), and iterative conditional estimation (ICE)-can be adapted to such situations once as we dispose of a method of recognition of each component separately. That is, when we know that a sample proceeds from one family of the set considered, we have a decision rule for what family it belongs to. Considering the Pearson system, which is a set of eight families, the decision rule above is defined by the use of "skewness" and "kurtosis". The different algorithms so obtained are then applied to the problem of unsupervised Bayesian image segmentation, We propose the adaptive versions of SEM, EM, and ICE in the case of "blind", i.e., "pixel by pixel", segmentation. "Global" segmentation methods require modeling by hidden random Markov fields, and we propose adaptations of two traditional parameter estimation algorithms: Gibbsian EM (GEM) and ICE allowing the estimation of generalized mixtures corresponding to Pearson's system. The efficiency of different methods is compared via numerical studies, and the results of unsupervised segmentation of three real radar images by different methods are presented.

  14. Segmentation and visualization of anatomical structures from volumetric medical images

    NASA Astrophysics Data System (ADS)

    Park, Jonghyun; Park, Soonyoung; Cho, Wanhyun; Kim, Sunworl; Kim, Gisoo; Ahn, Gukdong; Lee, Myungeun; Lim, Junsik

    2011-03-01

    This paper presents a method that can extract and visualize anatomical structures from volumetric medical images by using a 3D level set segmentation method and a hybrid volume rendering technique. First, the segmentation using the level set method was conducted through a surface evolution framework based on the geometric variation principle. This approach addresses the topological changes in the deformable surface by using the geometric integral measures and level set theory. These integral measures contain a robust alignment term, an active region term, and a mean curvature term. By using the level set method with a new hybrid speed function derived from the geometric integral measures, the accurate deformable surface can be extracted from a volumetric medical data set. Second, we employed a hybrid volume rendering approach to visualize the extracted deformable structures. Our method combines indirect and direct volume rendering techniques. Segmented objects within the data set are rendered locally by surface rendering on an object-by-object basis. Globally, all the results of subsequent object rendering are obtained by direct volume rendering (DVR). Then the two rendered results are finally combined in a merging step. This is especially useful when inner structures should be visualized together with semi-transparent outer parts. This merging step is similar to the focus-plus-context approach known from information visualization. Finally, we verified the accuracy and robustness of the proposed segmentation method for various medical volume images. The volume rendering results of segmented 3D objects show that our proposed method can accurately extract and visualize human organs from various multimodality medical volume images.

  15. Vascular Tree Segmentation in Medical Images Using Hessian-Based Multiscale Filtering and Level Set Method

    PubMed Central

    Jin, Jiaoying; Yang, Linjun; Zhang, Xuming

    2013-01-01

    Vascular segmentation plays an important role in medical image analysis. A novel technique for the automatic extraction of vascular trees from 2D medical images is presented, which combines Hessian-based multiscale filtering and a modified level set method. In the proposed algorithm, the morphological top-hat transformation is firstly adopted to attenuate background. Then Hessian-based multiscale filtering is used to enhance vascular structures by combining Hessian matrix with Gaussian convolution to tune the filtering response to the specific scales. Because Gaussian convolution tends to blur vessel boundaries, which makes scale selection inaccurate, an improved level set method is finally proposed to extract vascular structures by introducing an external constrained term related to the standard deviation of Gaussian function into the traditional level set. Our approach was tested on synthetic images with vascular-like structures and 2D slices extracted from real 3D abdomen magnetic resonance angiography (MRA) images along the coronal plane. The segmentation rates for synthetic images are above 95%. The results for MRA images demonstrate that the proposed method can extract most of the vascular structures successfully and accurately in visualization. Therefore, the proposed method is effective for the vascular tree extraction in medical images. PMID:24348738

  16. Knowledge-based 3D segmentation of the brain in MR images for quantitative multiple sclerosis lesion tracking

    NASA Astrophysics Data System (ADS)

    Fisher, Elizabeth; Cothren, Robert M., Jr.; Tkach, Jean A.; Masaryk, Thomas J.; Cornhill, J. Fredrick

    1997-04-01

    Brain segmentation in magnetic resonance (MR) images is an important step in quantitative analysis applications, including the characterization of multiple sclerosis (MS) lesions over time. Our approach is based on a priori knowledge of the intensity and three-dimensional (3D) spatial relationships of structures in MR images of the head. Optimal thresholding and connected-components analysis are used to generate a starting point for segmentation. A 3D radial search is then performed to locate probable locations of the intra-cranial cavity (ICC). Missing portions of the ICC surface are interpolated in order to exclude connected structures. Partial volume effects and inter-slice intensity variations in the image are accounted for automatically. Several studies were conducted to validate the segmentation. Accuracy was tested by calculating the segmented volume and comparing to known volumes of a standard MR phantom. Reliability was tested by comparing calculated volumes of individual segmentation results from multiple images of the same subject. The segmentation results were also compared to manual tracings. The average error in volume measurements for the phantom was 1.5% and the average coefficient of variation of brain volume measurements of the same subject was 1.2%. Since the new algorithm requires minimal user interaction, variability introduced by manual tracing and interactive threshold or region selection was eliminated. Overall, the new algorithm was shown to produce a more accurate and reliable brain segmentation than existing manual and semi-automated techniques.

  17. Automated bone segmentation from large field of view 3D MR images of the hip joint

    NASA Astrophysics Data System (ADS)

    Xia, Ying; Fripp, Jurgen; Chandra, Shekhar S.; Schwarz, Raphael; Engstrom, Craig; Crozier, Stuart

    2013-10-01

    Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation.

  18. Automated bone segmentation from large field of view 3D MR images of the hip joint.

    PubMed

    Xia, Ying; Fripp, Jurgen; Chandra, Shekhar S; Schwarz, Raphael; Engstrom, Craig; Crozier, Stuart

    2013-10-21

    Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation.

  19. Iterative normalization method for improved prostate cancer localization with multispectral magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Liu, Xin; Samil Yetik, Imam

    2012-04-01

    Use of multispectral magnetic resonance imaging has received a great interest for prostate cancer localization in research and clinical studies. Manual extraction of prostate tumors from multispectral magnetic resonance imaging is inefficient and subjective, while automated segmentation is objective and reproducible. For supervised, automated segmentation approaches, learning is essential to obtain the information from training dataset. However, in this procedure, all patients are assumed to have similar properties for the tumor and normal tissues, and the segmentation performance suffers since the variations across patients are ignored. To conquer this difficulty, we propose a new iterative normalization method based on relative intensity values of tumor and normal tissues to normalize multispectral magnetic resonance images and improve segmentation performance. The idea of relative intensity mimics the manual segmentation performed by human readers, who compare the contrast between regions without knowing the actual intensity values. We compare the segmentation performance of the proposed method with that of z-score normalization followed by support vector machine, local active contours, and fuzzy Markov random field. Our experimental results demonstrate that our method outperforms the three other state-of-the-art algorithms, and was found to have specificity of 0.73, sensitivity of 0.69, and accuracy of 0.79, significantly better than alternative methods.

  20. Image segmentation for integrated multiphoton microscopy and reflectance confocal microscopy imaging of human skin in vivo

    PubMed Central

    Chen, Guannan; Lui, Harvey

    2015-01-01

    Background Non-invasive cellular imaging of the skin in vivo can be achieved in reflectance confocal microscopy (RCM) and multiphoton microscopy (MPM) modalities to yield complementary images of the skin based on different optical properties. One of the challenges of in vivo microscopy is the delineation (i.e., segmentation) of cellular and subcellular architectural features. Methods In this work we present a method for combining watershed and level-set models for segmentation of multimodality images obtained by an integrated MPM and RCM imaging system from human skin in vivo. Results Firstly, a segmentation model based on watershed is introduced for obtaining the accurate structure of cell borders from the RCM image. Secondly,, a global region based energy level-set model is constructed for extracting the nucleus of each cell from the MPM image. Thirdly, a local region-based Lagrange Continuous level-set approach is used for segmenting cytoplasm from the MPM image. Conclusions Experimental results demonstrated that cell borders from RCM image and boundaries of cytoplasm and nucleus from MPM image can be obtained by our segmentation method with better accuracy and effectiveness. We are planning to use this method to perform quantitative analysis of MPM and RCM images of in vivo human skin to study the variations of cellular parameters such as cell size, nucleus size and other mophormetric features with skin pathologies. PMID:25694949

  1. Poisson and Multinomial Mixture Models for Multivariate SIMS Image Segmentation

    SciTech Connect

    Willse, Alan R.; Tyler, Bonnie

    2002-11-08

    Multivariate statistical methods have been advocated for analysis of spectral images, such as those obtained with imaging time-of-flight secondary ion mass spectrometry (TOF-SIMS). TOF-SIMS images using total secondary ion counts or secondary ion counts at individual masses often fail to reveal all salient chemical patterns on the surface. Multivariate methods simultaneously analyze peak intensities at all masses. We propose multivariate methods based on Poisson and multinomial mixture models to segment SIMS images into chemically homogeneous regions. The Poisson mixture model is derived from the assumption that secondary ion counts at any mass in a chemically homogeneous region vary according to the Poisson distribution. The multinomial model is derived as a standardized Poisson mixture model, which is analogous to standardizing the data by dividing by total secondary ion counts. The methods are adapted for contextual image segmentation, allowing for spatial correlation of neighboring pixels. The methods are applied to 52 mass units of a SIMS image with known chemical components. The spectral profile and relative prevalence for each chemical phase are obtained from estimates of model parameters.

  2. Hyperspectral image segmentation using spatial-spectral graphs

    NASA Astrophysics Data System (ADS)

    Gillis, David B.; Bowles, Jeffrey H.

    2012-06-01

    Spectral graph theory has proven to be a useful tool in the analysis of high-dimensional data sets. Recall that, mathematically, a graph is a collection of objects (nodes) and connections between them (edges); a weighted graph additionally assigns numerical values (weights) to the edges. Graphs are represented by their adjacency whose elements are the weights between the nodes. Spectral graph theory uses the eigendecomposition of the adjacency matrix (or, more generally, the Laplacian of the graph) to derive information about the underlying graph. In this paper, we develop a spectral method based on the 'normalized cuts' algorithm to segment hyperspectral image data (HSI). In particular, we model an image as a weighted graph whose nodes are the image pixels, and edges defined as connecting spatial neighbors; the edge weights are given by a weighted combination of the spatial and spectral distances between nodes. We then use the Laplacian of the graph to recursively segment the image. The advantages of our approach are that, first, the graph structure naturally incorporates both the spatial and spectral information present in HSI; also, by using only spatial neighbors, the adjacency matrix is highly sparse; as a result, it is possible to apply our technique to much larger images than previous techniques. In the paper, we present the details of our algorithm, and include experimental results from a variety of hyperspectral images.

  3. Segmentation and classification of cell cycle phases in fluorescence imaging.

    PubMed

    Ersoy, Ilker; Bunyak, Filiz; Chagin, Vadim; Cardoso, M Christina; Palaniappan, Kannappan

    2009-01-01

    Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

  4. Segmentation of scarred and non-scarred myocardium in LG enhanced CMR images using intensity-based textural analysis.

    PubMed

    Kotu, Lasya Priya; Engan, Kjersti; Eftestøl, Trygve; Ørn, Stein; Woie, Leik

    2011-01-01

    The Late Gadolinium (LG) enhancement in Cardiac Magnetic Resonance (CMR) imaging is used to increase the intensity of scarred area in myocardium for thorough examination. Automatic segmentation of scar is important because scar size is largely responsible in changing the size, shape and functioning of left ventricle and it is a preliminary step required in exploring the information present in scar. We have proposed a new technique to segment scar (infarct region) from non-scarred myocardium using intensity-based texture analysis. Our new technique uses dictionary-based texture features and dc-values to segment scarred and non-scarred myocardium using Maximum Likelihood Estimator (MLE) based Bayes classification. Texture analysis aided with intensity values gives better segmentation of scar from myocardium with high sensitivity and specificity values in comparison to manual segmentation by expert cardiologists.

  5. Multi-atlas segmentation of biomedical images: A survey.

    PubMed

    Iglesias, Juan Eugenio; Sabuncu, Mert R

    2015-08-01

    Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation. PMID:26201875

  6. Multi-Atlas Segmentation of Biomedical Images: A Survey

    PubMed Central

    Iglesias, Juan Eugenio; Sabuncu, Mert R.

    2015-01-01

    Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation. PMID:26201875

  7. Magnetic Resonance Imaging in ADNI

    PubMed Central

    Jack, Clifford R.; Barnes, Josephine; Bernstein, Matt A.; Borowski, Bret J.; Brewer, James; Clegg, Shona; Dale, Anders M.; Carmichael, Owen; Ching, Christopher; DeCarli, Charles; Desikan, Rahul S.; Fennema-Notestine, Christine; Fjell, Anders M.; Fletcher, Evan; Fox, Nick C.; Gunter, Jeff; Gutman, Boris A.; Holland, Dominic; Hua, Xue; Insel, Philip; Kantarci, Kejal; Killiany, Ron J.; Krueger, Gunnar; Leung, Kelvin K.; Mackin, Scott; Maillard, Pauline; Molone, Ian; Mattsson, Niklas; McEvoy, Linda; Modat, Marc; Mueller, Susanne; Nosheny, Rachel; Ourselin, Sebastien; Schuff, Norbert; Senjem, Matthew L.; Simonson, Alix; Thompson, Paul M.; Rettmann, Dan; Vemuri, Prashanthi; Walhovd, Kristine; Zhao, Yansong; Zuk, Samantha; Weiner, Michael

    2015-01-01

    INTRODUCTION ADNI is now in its 10th year. The primary objective of the MRI core of ADNI has been to improve methods for clinical trials in Alzheimer’s disease and related disorders. METHODS We review the contributions of the MRI core from present and past cycles of ADNI (ADNI 1, GO and 2). We also review plans for the future – ADNI 3. RESULTS Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. DISCUSSION Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in Alzheimer’s disease and will continue to do so in the future. PMID:26194310

  8. Deformable M-Reps for 3D Medical Image Segmentation

    PubMed Central

    Pizer, Stephen M.; Fletcher, P. Thomas; Joshi, Sarang; Thall, Andrew; Chen, James Z.; Fridman, Yonatan; Fritsch, Daniel S.; Gash, Graham; Glotzer, John M.; Jiroutek, Michael R.; Lu, Conglin; Muller, Keith E.; Tracton, Gregg; Yushkevich, Paul; Chaney, Edward L.

    2013-01-01

    M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures – each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure. A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects. The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported. PMID

  9. Nucleus segmentation in histology images with hierarchical multilevel thresholding

    NASA Astrophysics Data System (ADS)

    Ahmady Phoulady, Hady; Goldgof, Dmitry B.; Hall, Lawrence O.; Mouton, Peter R.

    2016-03-01

    Automatic segmentation of histological images is an important step for increasing throughput while maintaining high accuracy, avoiding variation from subjective bias, and reducing the costs for diagnosing human illnesses such as cancer and Alzheimer's disease. In this paper, we present a novel method for unsupervised segmentation of cell nuclei in stained histology tissue. Following an initial preprocessing step involving color deconvolution and image reconstruction, the segmentation step consists of multilevel thresholding and a series of morphological operations. The only parameter required for the method is the minimum region size, which is set according to the resolution of the image. Hence, the proposed method requires no training sets or parameter learning. Because the algorithm requires no assumptions or a priori information with regard to cell morphology, the automatic approach is generalizable across a wide range of tissues. Evaluation across a dataset consisting of diverse tissues, including breast, liver, gastric mucosa and bone marrow, shows superior performance over four other recent methods on the same dataset in terms of F-measure with precision and recall of 0.929 and 0.886, respectively.

  10. A holistic image segmentation framework for cloud detection and extraction

    NASA Astrophysics Data System (ADS)

    Shen, Dan; Xu, Haotian; Blasch, Erik; Horvath, Gregory; Pham, Khanh; Zheng, Yufeng; Ling, Haibin; Chen, Genshe

    2013-05-01

    Atmospheric clouds are commonly encountered phenomena affecting visual tracking from air-borne or space-borne sensors. Generally clouds are difficult to detect and extract because they are complex in shape and interact with sunlight in a complex fashion. In this paper, we propose a clustering game theoretic image segmentation based approach to identify, extract, and patch clouds. In our framework, the first step is to decompose a given image containing clouds. The problem of image segmentation is considered as a "clustering game". Within this context, the notion of a cluster is equivalent to a classical equilibrium concept from game theory, as the game equilibrium reflects both the internal and external (e.g., two-player) cluster conditions. To obtain the evolutionary stable strategies, we explore three evolutionary dynamics: fictitious play, replicator dynamics, and infection and immunization dynamics (InImDyn). Secondly, we use the boundary and shape features to refine the cloud segments. This step can lower the false alarm rate. In the third step, we remove the detected clouds and patch the empty spots by performing background recovery. We demonstrate our cloud detection framework on a video clip provides supportive results.

  11. A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery

    NASA Astrophysics Data System (ADS)

    Viswanath, Satish; Rosen, Mark; Madabhushi, Anant

    2008-03-01

    Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multi-site ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect

  12. Fully automatic segmentation of complex organ systems: example of trachea, esophagus and heart segmentation in CT images

    NASA Astrophysics Data System (ADS)

    Meyer, Carsten; Peters, Jochen; Weese, Jürgen

    2011-03-01

    Automatic segmentation is a prerequisite to efficiently analyze the large amount of image data produced by modern imaging modalities. Many algorithms exist to segment individual organs or organ systems. However, new clinical applications and the progress in imaging technology will require the segmentation of more and more complex organ systems composed of a number of substructures, e.g., the heart, the trachea, and the esophagus. The goal of this work is to demonstrate that such complex organ systems can be successfully segmented by integrating the individual organs into a general model-based segmentation framework, without tailoring the core adaptation engine to the individual organs. As an example, we address the fully automatic segmentation of the trachea (around its main bifurcation, including the proximal part of the two main bronchi) and the esophagus in addition to the heart with all chambers and attached major vessels. To this end, we integrate the trachea and the esophagus into a model-based cardiac segmentation framework. Specifically, in a first parametric adaptation step of the segmentation workflow, the trachea and the esophagus share global model transformations with adjacent heart structures. This allows to obtain a robust, approximate segmentation for the trachea even if it is only partly inside the field-of-view, and for the esophagus in spite of limited contrast. The segmentation is then refined in a subsequent deformable adaptation step. We obtained a mean segmentation error of about 0.6mm for the trachea and 2.3mm for the esophagus on a database of 23 volumetric cardiovascular CT images. Furthermore, we show by quantitative evaluation that our integrated framework outperforms individual esophagus segmentation, and individual trachea segmentation if the trachea is only partly inside the field-of-view.

  13. Kinetic modeling based probabilistic segmentation for molecular images.

    PubMed

    Saad, Ahmed; Hamarneh, Ghassan; Möller, Torsten; Smith, Ben

    2008-01-01

    We propose a semi-supervised, kinetic modeling based segmentation technique for molecular imaging applications. It is an iterative, self-learning algorithm based on uncertainty principles, designed to alleviate low signal-to-noise ratio (SNR) and partial volume effect (PVE) problems. Synthetic fluorodeoxyglucose (FDG) and simulated Raclopride dynamic positron emission tomography (dPET) brain images with excessive noise levels are used to validate our algorithm. We show, qualitatively and quantitatively, that our algorithm outperforms state-of-the-art techniques in identifying different functional regions and recovering the kinetic parameters.

  14. Automatic optic disc segmentation based on image brightness and contrast

    NASA Astrophysics Data System (ADS)

    Lu, Shijian; Liu, Jiang; Lim, Joo Hwee; Zhang, Zhuo; Tan, Ngan Meng; Wong, Wing Kee; Li, Huiqi; Wong, Tien Yin

    2010-03-01

    Untreated glaucoma leads to permanent damage of the optic nerve and resultant visual field loss, which can progress to blindness. As glaucoma often produces additional pathological cupping of the optic disc (OD), cupdisc- ratio is one measure that is widely used for glaucoma diagnosis. This paper presents an OD localization method that automatically segments the OD and so can be applied for the cup-disc-ratio based glaucoma diagnosis. The proposed OD segmentation method is based on the observations that the OD is normally much brighter and at the same time have a smoother texture characteristics compared with other regions within retinal images. Given a retinal image we first capture the ODs smooth texture characteristic by a contrast image that is constructed based on the local maximum and minimum pixel lightness within a small neighborhood window. The centre of the OD can then be determined according to the density of the candidate OD pixels that are detected by retinal image pixels of the lowest contrast. After that, an OD region is approximately determined by a pair of morphological operations and the OD boundary is finally determined by an ellipse that is fitted by the convex hull of the detected OD region. Experiments over 71 retinal images of different qualities show that the OD region overlapping reaches up to 90.37% according to the OD boundary ellipses determined by our proposed method and the one manually plotted by an ophthalmologist.

  15. Terahertz imaging system with resonant tunneling diodes

    NASA Astrophysics Data System (ADS)

    Miyamoto, Tomoyuki; Yamaguchi, Atsushi; Mukai, Toshikazu

    2016-03-01

    We report a feasibility study of a terahertz imaging system with resonant tunneling diodes (RTDs) that oscillate at 0.30 THz. A pair of RTDs acted as an emitter and a detector in the system. Terahertz reflection images of opaque samples were acquired with our RTD imaging system. A spatial resolution of 1 mm, which is equal to the wavelength of the RTD emitter, was achieved. The signal-to-noise ratio (SNR) of the reflection image was improved by 6 dB by using polarization optics that reduced interference effects. Additionally, the coherence of the RTD enabled a depth resolution of less than 3 µm to be achieved by an interferometric technique. Thus, RTDs are an attractive candidate for use in small THz imaging systems.

  16. Fully Automated Segmentation of the Pons and Midbrain Using Human T1 MR Brain Images

    PubMed Central

    Nigro, Salvatore; Cerasa, Antonio; Zito, Giancarlo; Perrotta, Paolo; Chiaravalloti, Francesco; Donzuso, Giulia; Fera, Franceso; Bilotta, Eleonora; Pantano, Pietro; Quattrone, Aldo

    2014-01-01

    Purpose This paper describes a novel method to automatically segment the human brainstem into midbrain and pons, called LABS: Landmark-based Automated Brainstem Segmentation. LABS processes high-resolution structural magnetic resonance images (MRIs) according to a revised landmark-based approach integrated with a thresholding method, without manual interaction. Methods This method was first tested on morphological T1-weighted MRIs of 30 healthy subjects. Its reliability was further confirmed by including neurological patients (with Alzheimer's Disease) from the ADNI repository, in whom the presence of volumetric loss within the brainstem had been previously described. Segmentation accuracies were evaluated against expert-drawn manual delineation. To evaluate the quality of LABS segmentation we used volumetric, spatial overlap and distance-based metrics. Results The comparison between the quantitative measurements provided by LABS against manual segmentations revealed excellent results in healthy controls when considering either the midbrain (DICE measures higher that 0.9; Volume ratio around 1 and Hausdorff distance around 3) or the pons (DICE measures around 0.93; Volume ratio ranging 1.024–1.05 and Hausdorff distance around 2). Similar performances were detected for AD patients considering segmentation of the pons (DICE measures higher that 0.93; Volume ratio ranging from 0.97–0.98 and Hausdorff distance ranging 1.07–1.33), while LABS performed lower for the midbrain (DICE measures ranging 0.86–0.88; Volume ratio around 0.95 and Hausdorff distance ranging 1.71–2.15). Conclusions Our study represents the first attempt to validate a new fully automated method for in vivo segmentation of two anatomically complex brainstem subregions. We retain that our method might represent a useful tool for future applications in clinical practice. PMID:24489664

  17. Volume coil based on hybridized resonators for magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Jouvaud, C.; Abdeddaim, R.; Larrat, B.; de Rosny, J.

    2016-01-01

    We present an electromagnetic device based on hybridization of four half-wavelength dipoles which increases the uniformity and the strength of the radio-frequency (RF) field of a Magnetic Resonant Imaging (MRI) apparatus. Numerical results show that this Hybridized Coil (HC) excited with a classical loop coil takes advantage of the magnetic hybrid modes. The distribution of the RF magnetic field is experimentally confirmed on a 7-T MRI with a gelatin phantom. Finally, the HC is validated in vivo by imaging the head of an anesthetized rat. We measure an overall increase of the signal to noise ratio with up to 2.4 fold increase in regions of interest far from the active loop coil.

  18. Automatic segmentation of brain images: selection of region extraction methods

    NASA Astrophysics Data System (ADS)

    Gong, Leiguang; Kulikowski, Casimir A.; Mezrich, Reuben S.

    1991-07-01

    In automatically analyzing brain structures from a MR image, the choice of low level region extraction methods depends on the characteristics of both the target object and the surrounding anatomical structures in the image. The authors have experimented with local thresholding, global thresholding, and other techniques, using various types of MR images for extracting the major brian landmarks and different types of lesions. This paper describes specifically a local- binary thresholding method and a new global-multiple thresholding technique developed for MR image segmentation and analysis. The initial testing results on their segmentation performance are presented, followed by a comparative analysis of the two methods and their ability to extract different types of normal and abnormal brain structures -- the brain matter itself, tumors, regions of edema surrounding lesions, multiple sclerosis lesions, and the ventricles of the brain. The analysis and experimental results show that the global multiple thresholding techniques are more than adequate for extracting regions that correspond to the major brian structures, while local binary thresholding is helpful for more accurate delineation of small lesions such as those produced by MS, and for the precise refinement of lesion boundaries. The detection of other landmarks, such as the interhemispheric fissure, may require other techniques, such as line-fitting. These experiments have led to the formulation of a set of generic computer-based rules for selecting the appropriate segmentation packages for particular types of problems, based on which further development of an innovative knowledge- based, goal directed biomedical image analysis framework is being made. The system will carry out the selection automatically for a given specific analysis task.

  19. Computerized segmentation and measurement of chronic wound images.

    PubMed

    Ahmad Fauzi, Mohammad Faizal; Khansa, Ibrahim; Catignani, Karen; Gordillo, Gayle; Sen, Chandan K; Gurcan, Metin N

    2015-05-01

    An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.

  20. A new iterative triclass thresholding technique in image segmentation.

    PubMed

    Cai, Hongmin; Yang, Zhong; Cao, Xinhua; Xia, Weiming; Xu, Xiaoyin

    2014-03-01

    We present a new method in image segmentation that is based on Otsu's method but iteratively searches for subregions of the image for segmentation, instead of treating the full image as a whole region for processing. The iterative method starts with Otsu's threshold and computes the mean values of the two classes as separated by the threshold. Based on the Otsu's threshold and the two mean values, the method separates the image into three