WorldWideScience

Sample records for adaptive 3-d segmentation

  1. AN ADAPTIVE APPROACH FOR SEGMENTATION OF 3D LASER POINT CLOUD

    Directory of Open Access Journals (Sweden)

    Z. Lari

    2012-09-01

    Full Text Available Automatic processing and object extraction from 3D laser point cloud is one of the major research topics in the field of photogrammetry. Segmentation is an essential step in the processing of laser point cloud, and the quality of extracted objects from laser data is highly dependent on the validity of the segmentation results. This paper presents a new approach for reliable and efficient segmentation of planar patches from a 3D laser point cloud. In this method, the neighbourhood of each point is firstly established using an adaptive cylinder while considering the local point density and surface trend. This neighbourhood definition has a major effect on the computational accuracy of the segmentation attributes. In order to efficiently cluster planar surfaces and prevent introducing ambiguities, the coordinates of the origin's projection on each point's best fitted plane are used as the clustering attributes. Then, an octree space partitioning method is utilized to detect and extract peaks from the attribute space. Each detected peak represents a specific cluster of points which are located on a distinct planar surface in the object space. Experimental results show the potential and feasibility of applying this method for segmentation of both airborne and terrestrial laser data.

  2. Spatio-Temporal Video Object Segmentation via Scale-Adaptive 3D Structure Tensor

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    Hai-Yun Wang

    2004-06-01

    Full Text Available To address multiple motions and deformable objects' motions encountered in existing region-based approaches, an automatic video object (VO segmentation methodology is proposed in this paper by exploiting the duality of image segmentation and motion estimation such that spatial and temporal information could assist each other to jointly yield much improved segmentation results. The key novelties of our method are (1 scale-adaptive tensor computation, (2 spatial-constrained motion mask generation without invoking dense motion-field computation, (3 rigidity analysis, (4 motion mask generation and selection, and (5 motion-constrained spatial region merging. Experimental results demonstrate that these novelties jointly contribute much more accurate VO segmentation both in spatial and temporal domains.

  3. Adapting Active Shape Models for 3D segmentation of tubular structures in medical images.

    Science.gov (United States)

    de Bruijne, Marleen; van Ginneken, Bram; Viergever, Max A; Niessen, Wiro J

    2003-07-01

    Active Shape Models (ASM) have proven to be an effective approach for image segmentation. In some applications, however, the linear model of gray level appearance around a contour that is used in ASM is not sufficient for accurate boundary localization. Furthermore, the statistical shape model may be too restricted if the training set is limited. This paper describes modifications to both the shape and the appearance model of the original ASM formulation. Shape model flexibility is increased, for tubular objects, by modeling the axis deformation independent of the cross-sectional deformation, and by adding supplementary cylindrical deformation modes. Furthermore, a novel appearance modeling scheme that effectively deals with a highly varying background is developed. In contrast with the conventional ASM approach, the new appearance model is trained on both boundary and non-boundary points, and the probability that a given point belongs to the boundary is estimated non-parametrically. The methods are evaluated on the complex task of segmenting thrombus in abdominal aortic aneurysms (AAA). Shape approximation errors were successfully reduced using the two shape model extensions. Segmentation using the new appearance model significantly outperformed the original ASM scheme; average volume errors are 5.1% and 45% respectively.

  4. Segmentation of 3-D High-Frequency Ultrasound Images of Human Lymph Nodes Using Graph Cut With Energy Functional Adapted to Local Intensity Distribution.

    Science.gov (United States)

    Kuo, Jen-Wei; Mamou, Jonathan; Wang, Yao; Saegusa-Beecroft, Emi; Machi, Junji; Feleppa, Ernest J

    2017-10-01

    Previous studies by our group have shown that 3-D high-frequency quantitative ultrasound (QUS) methods have the potential to differentiate metastatic lymph nodes (LNs) from cancer-free LNs dissected from human cancer patients. To successfully perform these methods inside the LN parenchyma (LNP), an automatic segmentation method is highly desired to exclude the surrounding thin layer of fat from QUS processing and accurately correct for ultrasound attenuation. In high-frequency ultrasound images of LNs, the intensity distribution of LNP and fat varies spatially because of acoustic attenuation and focusing effects. Thus, the intensity contrast between two object regions (e.g., LNP and fat) is also spatially varying. In our previous work, nested graph cut (GC) demonstrated its ability to simultaneously segment LNP, fat, and the outer phosphate-buffered saline bath even when some boundaries are lost because of acoustic attenuation and focusing effects. This paper describes a novel approach called GC with locally adaptive energy to further deal with spatially varying distributions of LNP and fat caused by inhomogeneous acoustic attenuation. The proposed method achieved Dice similarity coefficients of 0.937±0.035 when compared with expert manual segmentation on a representative data set consisting of 115 3-D LN images obtained from colorectal cancer patients.

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

    Science.gov (United States)

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

    2015-04-01

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

  6. Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform

    Directory of Open Access Journals (Sweden)

    Shuo-Tsung Chen

    2015-01-01

    Full Text Available Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Results. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Conclusion. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

  7. Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform.

    Science.gov (United States)

    Chen, Shuo-Tsung; Wang, Tzung-Dau; Lee, Wen-Jeng; Huang, Tsai-Wei; Hung, Pei-Kai; Wei, Cheng-Yu; Chen, Chung-Ming; Kung, Woon-Man

    2015-01-01

    Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

  8. Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform

    OpenAIRE

    Shuo-Tsung Chen; Tzung-Dau Wang; Wen-Jeng Lee; Tsai-Wei Huang; Pei-Kai Hung; Cheng-Yu Wei; Chung-Ming Chen; Woon-Man Kung

    2015-01-01

    Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband c...

  9. A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data

    International Nuclear Information System (INIS)

    Spiegel, M; Hornegger, J; Redel, T; Struffert, T; Doerfler, A

    2011-01-01

    Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling.

  10. Interactive 3D segmentation using connected orthogonal contours

    NARCIS (Netherlands)

    de Bruin, P. W.; Dercksen, V. J.; Post, F. H.; Vossepoel, A. M.; Streekstra, G. J.; Vos, F. M.

    2005-01-01

    This paper describes a new method for interactive segmentation that is based on cross-sectional design and 3D modelling. The method represents a 3D model by a set of connected contours that are planar and orthogonal. Planar contours overlayed on image data are easily manipulated and linked contours

  11. Needle segmentation using 3D Hough transform in 3D TRUS guided prostate transperineal therapy

    International Nuclear Information System (INIS)

    Qiu Wu; Yuchi Ming; Ding Mingyue; Tessier, David; Fenster, Aaron

    2013-01-01

    Purpose: Prostate adenocarcinoma is the most common noncutaneous malignancy in American men with over 200 000 new cases diagnosed each year. Prostate interventional therapy, such as cryotherapy and brachytherapy, is an effective treatment for prostate cancer. Its success relies on the correct needle implant position. This paper proposes a robust and efficient needle segmentation method, which acts as an aid to localize the needle in three-dimensional (3D) transrectal ultrasound (TRUS) guided prostate therapy. Methods: The procedure of locating the needle in a 3D TRUS image is a three-step process. First, the original 3D ultrasound image containing a needle is cropped; the cropped image is then converted to a binary format based on its histogram. Second, a 3D Hough transform based needle segmentation method is applied to the 3D binary image in order to locate the needle axis. The position of the needle endpoint is finally determined by an optimal threshold based analysis of the intensity probability distribution. The overall efficiency is improved through implementing a coarse-fine searching strategy. The proposed method was validated in tissue-mimicking agar phantoms, chicken breast phantoms, and 3D TRUS patient images from prostate brachytherapy and cryotherapy procedures by comparison to the manual segmentation. The robustness of the proposed approach was tested by means of varying parameters such as needle insertion angle, needle insertion length, binarization threshold level, and cropping size. Results: The validation results indicate that the proposed Hough transform based method is accurate and robust, with an achieved endpoint localization accuracy of 0.5 mm for agar phantom images, 0.7 mm for chicken breast phantom images, and 1 mm for in vivo patient cryotherapy and brachytherapy images. The mean execution time of needle segmentation algorithm was 2 s for a 3D TRUS image with size of 264 × 376 × 630 voxels. Conclusions: The proposed needle segmentation

  12. Adaptive interrogation for 3D-PIV

    International Nuclear Information System (INIS)

    Novara, Matteo; Scarano, Fulvio; Ianiro, Andrea

    2013-01-01

    A method to adapt the shape and orientation of interrogation volumes for 3D-PIV motion analysis is introduced, aimed to increase the local spatial resolution. The main application of this approach is the detailed analysis of complex 3D and vortex-dominated flows that exhibit high vorticity in confined regions like shear layers and vortex filaments. The adaptive criterion is based on the analysis of the components of the local velocity gradient tensor, which returns the level of anisotropy of velocity spatial fluctuations. The principle to increase the local spatial resolution is based on the deformation of spherical isotropic interrogation regions, obtained by means of Gaussian weighting, into ellipsoids, with free choice of the principal axes and their directions. The interrogation region is contracted in the direction of the maximum velocity variation and elongated in the minimum one in order to maintain a constant interrogation volume. The adaptivity technique for three-dimensional PIV data takes advantage of the 3D topology of the flow, allowing increasing the spatial resolution not only in the case of shear layers, but also for vortex filaments, which is not possible for two-dimensional measurement in the plane normal to the vortex axis. The definition of the ellipsoidal interrogation region semi-axes is based on the singular values and singular directions of the local velocity gradient tensor as obtained by the singular values decomposition technique (SVD). The working principle is verified making use of numerical simulations of a shear layer and of a vortex filament. The application of the technique to data from a Tomo-PIV experiment conducted on a round jet, shows that the resolution of the shear layer at the jet exit can be considerably improved and an increase of about 25% in the vorticity peak is attained when the adaptive approach is applied. On the other hand, the peak vorticity description in the core of vortex rings is only slightly improved with

  13. Needle segmentation using 3D Hough transform in 3D TRUS guided prostate transperineal therapy

    Energy Technology Data Exchange (ETDEWEB)

    Qiu Wu [Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074 (China); Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario N6A 5K8 (Canada); Yuchi Ming; Ding Mingyue [Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074 (China); Tessier, David; Fenster, Aaron [Imaging Research Laboratories, Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5K8 (Canada)

    2013-04-15

    Purpose: Prostate adenocarcinoma is the most common noncutaneous malignancy in American men with over 200 000 new cases diagnosed each year. Prostate interventional therapy, such as cryotherapy and brachytherapy, is an effective treatment for prostate cancer. Its success relies on the correct needle implant position. This paper proposes a robust and efficient needle segmentation method, which acts as an aid to localize the needle in three-dimensional (3D) transrectal ultrasound (TRUS) guided prostate therapy. Methods: The procedure of locating the needle in a 3D TRUS image is a three-step process. First, the original 3D ultrasound image containing a needle is cropped; the cropped image is then converted to a binary format based on its histogram. Second, a 3D Hough transform based needle segmentation method is applied to the 3D binary image in order to locate the needle axis. The position of the needle endpoint is finally determined by an optimal threshold based analysis of the intensity probability distribution. The overall efficiency is improved through implementing a coarse-fine searching strategy. The proposed method was validated in tissue-mimicking agar phantoms, chicken breast phantoms, and 3D TRUS patient images from prostate brachytherapy and cryotherapy procedures by comparison to the manual segmentation. The robustness of the proposed approach was tested by means of varying parameters such as needle insertion angle, needle insertion length, binarization threshold level, and cropping size. Results: The validation results indicate that the proposed Hough transform based method is accurate and robust, with an achieved endpoint localization accuracy of 0.5 mm for agar phantom images, 0.7 mm for chicken breast phantom images, and 1 mm for in vivo patient cryotherapy and brachytherapy images. The mean execution time of needle segmentation algorithm was 2 s for a 3D TRUS image with size of 264 Multiplication-Sign 376 Multiplication-Sign 630 voxels. Conclusions

  14. Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

    Directory of Open Access Journals (Sweden)

    M. Arfan Jaffar

    2012-06-01

    Full Text Available In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally time consuming and error-prone task. Proposed system detects and presents a 3D visualization model of the brain and tumor inside which greatly helps the radiologist to effectively diagnose and analyze the brain tumor. We proposed a multi-phase segmentation and visualization technique which overcomes the many problems of 3D volume segmentation methods like lake of fine details. In this system segmentation is done in three different phases which reduces the error chances. The system finds contours for skull, brain and tumor. These contours are stacked over and two novel methods are used to find the 3D visualization models. The results of these techniques, particularly of interpolation based, are impressive. Proposed system is tested against publically available data set [41] and MRI datasets available from MRI aamp; CT center Rawalpindi, Pakistan [42].

  15. Adaptive regridding in 3D reflection tomography

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    A. Vesnaver

    1997-06-01

    Full Text Available 3D reflection tomography allows the macro-model of complex geological structures to be reconstructed. In the usual approach, the spatial distribution of the velocity field is discretized by regular grids. This choice simplifies the development of the related software, but introduces two serious drawbacks: various domains of the model may be poorly covered, and a relevant mismatch between the grid and a complex velocity field may occur. So the tomographic inversion becomes unstable, unreliable and necessarily blurred. In this paper we introduce an algorithm to adapt the grid to the available ray paths and to the velocity field in sequence: so we get irregular grids with a locally variable resolution. We can guide the grid fitting procedure interactively, if we are going to introduce some geological a priori information; otherwise, we define a fully automatic approach, which exploits the Delauny triangles and Voronoi polygons.

  16. Chest wall segmentation in automated 3D breast ultrasound scans.

    Science.gov (United States)

    Tan, Tao; Platel, Bram; Mann, Ritse M; Huisman, Henkjan; Karssemeijer, Nico

    2013-12-01

    In this paper, we present an automatic method to segment the chest wall in automated 3D breast ultrasound images. Determining the location of the chest wall in automated 3D breast ultrasound images is necessary in computer-aided detection systems to remove automatically detected cancer candidates beyond the chest wall and it can be of great help for inter- and intra-modal image registration. We show that the visible part of the chest wall in an automated 3D breast ultrasound image can be accurately modeled by a cylinder. We fit the surface of our cylinder model to a set of automatically detected rib-surface points. The detection of the rib-surface points is done by a classifier using features representing local image intensity patterns and presence of rib shadows. Due to attenuation of the ultrasound signal, a clear shadow is visible behind the ribs. Evaluation of our segmentation method is done by computing the distance of manually annotated rib points to the surface of the automatically detected chest wall. We examined the performance on images obtained with the two most common 3D breast ultrasound devices in the market. In a dataset of 142 images, the average mean distance of the annotated points to the segmented chest wall was 5.59 ± 3.08 mm. Copyright © 2012 Elsevier B.V. All rights reserved.

  17. Fast Segmentation From Blurred Data in 3D Fluorescence Microscopy.

    Science.gov (United States)

    Storath, Martin; Rickert, Dennis; Unser, Michael; Weinmann, Andreas

    2017-10-01

    We develop a fast algorithm for segmenting 3D images from linear measurements based on the Potts model (or piecewise constant Mumford-Shah model). To that end, we first derive suitable space discretizations of the 3D Potts model, which are capable of dealing with 3D images defined on non-cubic grids. Our discretization allows us to utilize a specific splitting approach, which results in decoupled subproblems of moderate size. The crucial point in the 3D setup is that the number of independent subproblems is so large that we can reasonably exploit the parallel processing capabilities of the graphics processing units (GPUs). Our GPU implementation is up to 18 times faster than the sequential CPU version. This allows to process even large volumes in acceptable runtimes. As a further contribution, we extend the algorithm in order to deal with non-negativity constraints. We demonstrate the efficiency of our method for combined image deconvolution and segmentation on simulated data and on real 3D wide field fluorescence microscopy data.

  18. 3D geomarketing segmentation: A higher spatial dimension planning perspective

    DEFF Research Database (Denmark)

    Suhaibah, A.; Uznir, U.; Rahman, A. A.

    2016-01-01

    Geomarketing is a discipline which uses geographic information in the process of planning and implementation of marketing activities. It can be used in any aspect of the marketing such as price, promotion or geo targeting. The analysis of geomarketing data use a huge data pool such as location...... residential areas, topography, it also analyzes demographic information such as age, genre, annual income and lifestyle. This information can help users to develop successful promotional campaigns in order to achieve marketing goals. One of the common activities in geomarketing is market segmentation...... a combination of market segmentation based on geographic criteria and clustering algorithm for 3D geomarketing data management. The proposed approach is capable in minimizing the overlap region during market segmentation. In this paper, geomarketing in urban area is used as a case study. Based on the case study...

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

    Science.gov (United States)

    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

  20. Ultrafast superpixel segmentation of large 3D medical datasets

    Science.gov (United States)

    Leblond, Antoine; Kauffmann, Claude

    2016-03-01

    Even with recent hardware improvements, superpixel segmentation of large 3D medical images at interactive speed (Gauss-Seidel like acceleration. The work unit partitioning scheme will however vary on odd- and even-numbered iterations to reduce convergence barriers. Synchronization will be ensured by an 8-step 3D variant of the traditional Red Black Ordering scheme. An attack model and early termination will also be described and implemented as additional acceleration techniques. Using our hybrid framework and typical operating parameters, we were able to compute the superpixels of a high-resolution 512x512x512 aortic angioCT scan in 283 ms using a AMD R9 290X GPU. We achieved a 22.3X speed-up factor compared to the published reference GPU implementation.

  1. Computer-aided segmentation system for 3D chest CT

    International Nuclear Information System (INIS)

    Iwasawa, Tae; Komagata, Takanobu; Ogura, Takashi; Iwao, Yuma; Goto, Toshiyuki; Asakura, Akira; Inoue, Tomio

    2012-01-01

    We will introduce the quantitative analysis of the chest CT images using computer-assisted segmentation system (Gaussian Histogram Normalized Correlation; GHNC). This system can divide the lung into several patterns, for example, normal, emphysema and fibrous lesion, and measure each lesion volume quantitatively. We analyzed 3D-CT images of 20 patients with lung cancer. GHNC could measure the volumes of emphysema and fibrosis lesions, respectively. GHNC analysis will be feasible for preoperative CT evaluation, especially in the patients with combined pulmonary fibrosis and emphysema. (author)

  2. Automatic airline baggage counting using 3D image segmentation

    Science.gov (United States)

    Yin, Deyu; Gao, Qingji; Luo, Qijun

    2017-06-01

    The baggage number needs to be checked automatically during baggage self-check-in. A fast airline baggage counting method is proposed in this paper using image segmentation based on height map which is projected by scanned baggage 3D point cloud. There is height drop in actual edge of baggage so that it can be detected by the edge detection operator. And then closed edge chains are formed from edge lines that is linked by morphological processing. Finally, the number of connected regions segmented by closed chains is taken as the baggage number. Multi-bag experiment that is performed on the condition of different placement modes proves the validity of the method.

  3. 3D GEOMARKETING SEGMENTATION: A HIGHER SPATIAL DIMENSION PLANNING PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    A. Suhaibah

    2016-09-01

    Full Text Available Geomarketing is a discipline which uses geographic information in the process of planning and implementation of marketing activities. It can be used in any aspect of the marketing such as price, promotion or geo targeting. The analysis of geomarketing data use a huge data pool such as location residential areas, topography, it also analyzes demographic information such as age, genre, annual income and lifestyle. This information can help users to develop successful promotional campaigns in order to achieve marketing goals. One of the common activities in geomarketing is market segmentation. The segmentation clusters the data into several groups based on its geographic criteria. To refine the search operation during analysis, we proposed an approach to cluster the data using a clustering algorithm. However, with the huge data pool, overlap among clusters may happen and leads to inefficient analysis. Moreover, geomarketing is usually active in urban areas and requires clusters to be organized in a three-dimensional (3D way (i.e. multi-level shop lots, residential apartments. This is a constraint with the current Geographic Information System (GIS framework. To avoid this issue, we proposed a combination of market segmentation based on geographic criteria and clustering algorithm for 3D geomarketing data management. The proposed approach is capable in minimizing the overlap region during market segmentation. In this paper, geomarketing in urban area is used as a case study. Based on the case study, several locations of customers and stores in 3D are used in the test. The experiments demonstrated in this paper substantiated that the proposed approach is capable of minimizing overlapping segmentation and reducing repetitive data entries. The structure is also tested for retrieving the spatial records from the database. For marketing purposes, certain radius of point is used to analyzing marketing targets. Based on the presented tests in this paper

  4. A spherical harmonics intensity model for 3D segmentation and 3D shape analysis of heterochromatin foci.

    Science.gov (United States)

    Eck, Simon; Wörz, Stefan; Müller-Ott, Katharina; Hahn, Matthias; Biesdorf, Andreas; Schotta, Gunnar; Rippe, Karsten; Rohr, Karl

    2016-08-01

    The genome is partitioned into regions of euchromatin and heterochromatin. The organization of heterochromatin is important for the regulation of cellular processes such as chromosome segregation and gene silencing, and their misregulation is linked to cancer and other diseases. We present a model-based approach for automatic 3D segmentation and 3D shape analysis of heterochromatin foci from 3D confocal light microscopy images. Our approach employs a novel 3D intensity model based on spherical harmonics, which analytically describes the shape and intensities of the foci. The model parameters are determined by fitting the model to the image intensities using least-squares minimization. To characterize the 3D shape of the foci, we exploit the computed spherical harmonics coefficients and determine a shape descriptor. We applied our approach to 3D synthetic image data as well as real 3D static and real 3D time-lapse microscopy images, and compared the performance with that of previous approaches. It turned out that our approach yields accurate 3D segmentation results and performs better than previous approaches. We also show that our approach can be used for quantifying 3D shape differences of heterochromatin foci. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. 3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

    Science.gov (United States)

    Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

    2015-03-01

    During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

  6. A Unified 3D Mesh Segmentation Framework Based on Markov Random Field

    OpenAIRE

    Z.F. Shi; L.Y. Lu; D. Le; X.M. Niu

    2012-01-01

    3D Mesh segmentation has become an important research field in computer graphics during the past decades. Many geometry based and semantic oriented approaches for 3D mesh segmentation has been presented. In this paper, we present a definition of mesh segmentation according to labeling problem. Inspired by the Markov Random Field (MRF) based image segmentation, we propose a new framework of 3D mesh segmentation based on MRF and use graph cuts to solve it. Any features of 3D mesh can be integra...

  7. Improving Semantic Updating Method on 3d City Models Using Hybrid Semantic-Geometric 3d Segmentation Technique

    Science.gov (United States)

    Sharkawi, K.-H.; Abdul-Rahman, A.

    2013-09-01

    to LoD4. The accuracy and structural complexity of the 3D objects increases with the LoD level where LoD0 is the simplest LoD (2.5D; Digital Terrain Model (DTM) + building or roof print) while LoD4 is the most complex LoD (architectural details with interior structures). Semantic information is one of the main components in CityGML and 3D City Models, and provides important information for any analyses. However, more often than not, the semantic information is not available for the 3D city model due to the unstandardized modelling process. One of the examples is where a building is normally generated as one object (without specific feature layers such as Roof, Ground floor, Level 1, Level 2, Block A, Block B, etc). This research attempts to develop a method to improve the semantic data updating process by segmenting the 3D building into simpler parts which will make it easier for the users to select and update the semantic information. The methodology is implemented for 3D buildings in LoD2 where the buildings are generated without architectural details but with distinct roof structures. This paper also introduces hybrid semantic-geometric 3D segmentation method that deals with hierarchical segmentation of a 3D building based on its semantic value and surface characteristics, fitted by one of the predefined primitives. For future work, the segmentation method will be implemented as part of the change detection module that can detect any changes on the 3D buildings, store and retrieve semantic information of the changed structure, automatically updates the 3D models and visualize the results in a userfriendly graphical user interface (GUI).

  8. Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

    OpenAIRE

    M. Arfan Jaffar; Sultan Zia; Ghaznafar Latif; AnwarM. Mirza; Irfan Mehmood; Naveed Ejaz; Sung Wook Baik

    2012-01-01

    In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally time consuming and error-prone task. Proposed system detects and presents a 3D visualization model of the brain and tumor inside which greatly helps the radiologist to effectively diagnose and ...

  9. Isotropic 3D cardiac cine MRI allows efficient sparse segmentation strategies based on 3D surface reconstruction.

    Science.gov (United States)

    Odille, Freddy; Bustin, Aurélien; Liu, Shufang; Chen, Bailiang; Vuissoz, Pierre-André; Felblinger, Jacques; Bonnemains, Laurent

    2018-05-01

    Segmentation of cardiac cine MRI data is routinely used for the volumetric analysis of cardiac function. Conventionally, 2D contours are drawn on short-axis (SAX) image stacks with relatively thick slices (typically 8 mm). Here, an acquisition/reconstruction strategy is used for obtaining isotropic 3D cine datasets; reformatted slices are then used to optimize the manual segmentation workflow. Isotropic 3D cine datasets were obtained from multiple 2D cine stacks (acquired during free-breathing in SAX and long-axis (LAX) orientations) using nonrigid motion correction (cine-GRICS method) and super-resolution. Several manual segmentation strategies were then compared, including conventional SAX segmentation, LAX segmentation in three views only, and combinations of SAX and LAX slices. An implicit B-spline surface reconstruction algorithm is proposed to reconstruct the left ventricular cavity surface from the sparse set of 2D contours. All tested sparse segmentation strategies were in good agreement, with Dice scores above 0.9 despite using fewer slices (3-6 sparse slices instead of 8-10 contiguous SAX slices). When compared to independent phase-contrast flow measurements, stroke volumes computed from four or six sparse slices had slightly higher precision than conventional SAX segmentation (error standard deviation of 5.4 mL against 6.1 mL) at the cost of slightly lower accuracy (bias of -1.2 mL against 0.2 mL). Functional parameters also showed a trend to improved precision, including end-diastolic volumes, end-systolic volumes, and ejection fractions). The postprocessing workflow of 3D isotropic cardiac imaging strategies can be optimized using sparse segmentation and 3D surface reconstruction. Magn Reson Med 79:2665-2675, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  10. 3D pulmonary nodules detection using fast marching segmentation ...

    African Journals Online (AJOL)

    This paper proposes an automated computer aided diagnosis system for detection of pulmonary nodules based on three dimensional (3D) structures. Lung ... The proposed detection methodology can give the accuracy of 92%. Keywords: lung cancer; pulmonary nodule; fast marching; 3D features; random forest classifier.

  11. 3D Printing device adaptable to Computer Numerical Control (CNC)

    OpenAIRE

    GARDAN , Julien; Danesi , F.; Roucoules , Lionel; Schneider , A.

    2014-01-01

    This article presents the development of a 3D printing device for the additive manufacturing adapted to a CNC machining. The application involves the integration of a specific printing head. Additive manufacturing technology is most commonly used for modeling, prototyping, tooling through an exclusive machine or 3D printer. A global review and analysis of technologies show the additive manufacturing presents little independent solutions [6][9]. The problem studied especially the additive manu...

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

    International Nuclear Information System (INIS)

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

    1987-01-01

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

  13. Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation

    Directory of Open Access Journals (Sweden)

    Shen Ying

    2015-08-01

    Full Text Available Three-dimensional (3D point analysis and visualization is one of the most effective methods of point cluster detection and segmentation in geospatial datasets. However, serious scattering and clotting characteristics interfere with the visual detection of 3D point clusters. To overcome this problem, this study proposes the use of 3D Voronoi diagrams to analyze and visualize 3D points instead of the original data item. The proposed algorithm computes the cluster of 3D points by applying a set of 3D Voronoi cells to describe and quantify 3D points. The decompositions of point cloud of 3D models are guided by the 3D Voronoi cell parameters. The parameter values are mapped from the Voronoi cells to 3D points to show the spatial pattern and relationships; thus, a 3D point cluster pattern can be highlighted and easily recognized. To capture different cluster patterns, continuous progressive clusters and segmentations are tested. The 3D spatial relationship is shown to facilitate cluster detection. Furthermore, the generated segmentations of real 3D data cases are exploited to demonstrate the feasibility of our approach in detecting different spatial clusters for continuous point cloud segmentation.

  14. Adaptive kernel regression for freehand 3D ultrasound reconstruction

    Science.gov (United States)

    Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

    2017-03-01

    Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

  15. 3D Building Models Segmentation Based on K-Means++ Cluster Analysis

    Science.gov (United States)

    Zhang, C.; Mao, B.

    2016-10-01

    3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model) 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid) and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

  16. 3D BUILDING MODELS SEGMENTATION BASED ON K-MEANS++ CLUSTER ANALYSIS

    Directory of Open Access Journals (Sweden)

    C. Zhang

    2016-10-01

    Full Text Available 3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

  17. Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images.

    Science.gov (United States)

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

    2016-05-01

    A fully automated segmentation algorithm, progressive surface resolution (PSR), is presented in this paper to determine the closed surface of approximately convex blob-like structures that are common in biomedical imaging. The PSR algorithm was applied to the cortical surface segmentation of 460 vertebral bodies on 46 low-dose chest CT images, which can be potentially used for automated bone mineral density measurement and compression fracture detection. The target surface is realized by a closed triangular mesh, which thereby guarantees the enclosure. The surface vertices of the triangular mesh representation are constrained along radial trajectories that are uniformly distributed in 3D angle space. The segmentation is accomplished by determining for each radial trajectory the location of its intersection with the target surface. The surface is first initialized based on an input high confidence boundary image and then resolved progressively based on a dynamic attraction map in an order of decreasing degree of evidence regarding the target surface location. For the visual evaluation, the algorithm achieved acceptable segmentation for 99.35 % vertebral bodies. Quantitative evaluation was performed on 46 vertebral bodies and achieved overall mean Dice coefficient of 0.939 (with max [Formula: see text] 0.957, min [Formula: see text] 0.906 and standard deviation [Formula: see text] 0.011) using manual annotations as the ground truth. Both visual and quantitative evaluations demonstrate encouraging performance of the PSR algorithm. This novel surface resolution strategy provides uniform angular resolution for the segmented surface with computation complexity and runtime that are linearly constrained by the total number of vertices of the triangular mesh representation.

  18. Nearest neighbor 3D segmentation with context features

    Science.gov (United States)

    Hristova, Evelin; Schulz, Heinrich; Brosch, Tom; Heinrich, Mattias P.; Nickisch, Hannes

    2018-03-01

    Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are - for a nearest neighbor method - surprisingly accurate, robust as well as data and time efficient.

  19. 3D Filament Network Segmentation with Multiple Active Contours

    Science.gov (United States)

    Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

    2014-03-01

    Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.

  20. Content-adaptive pyramid representation for 3D object classification

    DEFF Research Database (Denmark)

    Kounalakis, Tsampikos; Boulgouris, Nikolaos; Triantafyllidis, Georgios

    2016-01-01

    In this paper we introduce a novel representation for the classification of 3D images. Unlike most current approaches, our representation is not based on a fixed pyramid but adapts to image content and uses image regions instead of rectangular pyramid scales. Image characteristics, such as depth...... and color, are used for defining regions within images. Multiple region scales are formed in order to construct the proposed pyramid image representation. The proposed method achieves excellent results in comparison to conventional representations....

  1. Parallel 3D Mortar Element Method for Adaptive Nonconforming Meshes

    Science.gov (United States)

    Feng, Huiyu; Mavriplis, Catherine; VanderWijngaart, Rob; Biswas, Rupak

    2004-01-01

    High order methods are frequently used in computational simulation for their high accuracy. An efficient way to avoid unnecessary computation in smooth regions of the solution is to use adaptive meshes which employ fine grids only in areas where they are needed. Nonconforming spectral elements allow the grid to be flexibly adjusted to satisfy the computational accuracy requirements. The method is suitable for computational simulations of unsteady problems with very disparate length scales or unsteady moving features, such as heat transfer, fluid dynamics or flame combustion. In this work, we select the Mark Element Method (MEM) to handle the non-conforming interfaces between elements. A new technique is introduced to efficiently implement MEM in 3-D nonconforming meshes. By introducing an "intermediate mortar", the proposed method decomposes the projection between 3-D elements and mortars into two steps. In each step, projection matrices derived in 2-D are used. The two-step method avoids explicitly forming/deriving large projection matrices for 3-D meshes, and also helps to simplify the implementation. This new technique can be used for both h- and p-type adaptation. This method is applied to an unsteady 3-D moving heat source problem. With our new MEM implementation, mesh adaptation is able to efficiently refine the grid near the heat source and coarsen the grid once the heat source passes. The savings in computational work resulting from the dynamic mesh adaptation is demonstrated by the reduction of the the number of elements used and CPU time spent. MEM and mesh adaptation, respectively, bring irregularity and dynamics to the computer memory access pattern. Hence, they provide a good way to gauge the performance of computer systems when running scientific applications whose memory access patterns are irregular and unpredictable. We select a 3-D moving heat source problem as the Unstructured Adaptive (UA) grid benchmark, a new component of the NAS Parallel

  2. H-Ransac a Hybrid Point Cloud Segmentation Combining 2d and 3d Data

    Science.gov (United States)

    Adam, A.; Chatzilari, E.; Nikolopoulos, S.; Kompatsiaris, I.

    2018-05-01

    In this paper, we present a novel 3D segmentation approach operating on point clouds generated from overlapping images. The aim of the proposed hybrid approach is to effectively segment co-planar objects, by leveraging the structural information originating from the 3D point cloud and the visual information from the 2D images, without resorting to learning based procedures. More specifically, the proposed hybrid approach, H-RANSAC, is an extension of the well-known RANSAC plane-fitting algorithm, incorporating an additional consistency criterion based on the results of 2D segmentation. Our expectation that the integration of 2D data into 3D segmentation will achieve more accurate results, is validated experimentally in the domain of 3D city models. Results show that HRANSAC can successfully delineate building components like main facades and windows, and provide more accurate segmentation results compared to the typical RANSAC plane-fitting algorithm.

  3. Extended 3D Line Segments from RGB-D Data for Pose Estimation

    DEFF Research Database (Denmark)

    Buch, Anders Glent; Jessen, Jeppe Barsøe; Kraft, Dirk

    2013-01-01

    We propose a method for the extraction of complete and rich symbolic line segments in 3D based on RGB-D data. Edges are detected by combining cues from the RGB image and the aligned depth map. 3D line segments are then reconstructed by back-projecting 2D line segments and intersecting this with l...... this with local surface patches computed from the 3D point cloud. Different edge types are classified using the new enriched representation and the potential of this representation for the task of pose estimation is demonstrated....

  4. 3D Segmentation of Mammospheres for Localization Studies

    International Nuclear Information System (INIS)

    Han, Ju; Chang, Hang; Yang, Qing; Barcellos-Hoff, Mary Helen; Parvin, Bahram

    2006-01-01

    Three dimensional cell culture assays have emerged as the basis of an improved model system for evaluating therapeutic agents. molecular probes, and exogenous stimuli. However, there is a gap in robust computational techniques for segmentation of image data that are collected through confocal or deconvolution microscopy. The main issue is the volume of data, overlapping subcellular compartments, and variation in scale and size of subcompartments of interest. A geometric technique has been developed to bound the solution of the problem by first localizing centers of mass for each cell and then partitioning clump of cells along minimal intersecting surfaces. An approximate solution to the center of mass is realized through iterative spatial voting, which is tolerant to variation in shape morphologies and overlapping compartments and is shown to have an excellent noise immunity. These centers of mass are then used to partition a clump of cells along minimal intersecting surfaces that are estimated by Radon transform. Examples on real data and performance of the system over a large population of data are evaluated. Although proposed strategies have been developed and tested on data collected through fluorescence microscopy, they are applicable to other problems in low level vision and medical imaging

  5. 3D Game Content Distributed Adaptation in Heterogeneous Environments

    Directory of Open Access Journals (Sweden)

    Berretty Robert-Paul

    2007-01-01

    Full Text Available Most current multiplayer 3D games can only be played on a single dedicated platform (a particular computer, console, or cell phone, requiring specifically designed content and communication over a predefined network. Below we show how, by using signal processing techniques such as multiresolution representation and scalable coding for all the components of a 3D graphics object (geometry, texture, and animation, we enable online dynamic content adaptation, and thus delivery of the same content over heterogeneous networks to terminals with very different profiles, and its rendering on them. We present quantitative results demonstrating how the best displayed quality versus computational complexity versus bandwidth tradeoffs have been achieved, given the distributed resources available over the end-to-end content delivery chain. Additionally, we use state-of-the-art, standardised content representation and compression formats (MPEG-4 AFX, JPEG 2000, XML, enabling deployment over existing infrastructure, while keeping hooks to well-established practices in the game industry.

  6. 3D liver segmentation using multiple region appearances and graph cuts

    Energy Technology Data Exchange (ETDEWEB)

    Peng, Jialin, E-mail: 2004pjl@163.com; Zhang, Hongbo [College of Computer Science and Technology, Huaqiao University, Xiamen 361021 (China); Hu, Peijun; Lu, Fang; Kong, Dexing [College of Mathematics, Zhejiang University, Hangzhou 310027 (China); Peng, Zhiyi [Department of Radiology, First Affiliated Hospital, Zhejiang University, Hangzhou 310027 (China)

    2015-12-15

    Purpose: Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. Methods: The authors propose a novel multiregion-appearance based approach with graph cuts to delineate the liver surface. For livers with multiple subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. Results: The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4 ± 3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7% ± 0.5% and 97.5% ± 0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets

  7. Segmentation of multiple heart cavities in 3-D transesophageal ultrasound images

    NARCIS (Netherlands)

    Haak, A.; Vegas-Sanchez-Ferrero, G.; Mulder, H.W.; Ren, B.; Kirisli, H.A.; Metz, C.; van Burken, G.; van Stralen, M.; Pluim, J.P.W.; Steen, van der A.F.W.; Walsum, van T.; Bosch, J.G.

    Three-dimensional transesophageal echocardiography (TEE) is an excellent modality for real-time visualization of the heart and monitoring of interventions. To improve the usability of 3-D TEE for intervention monitoring and catheter guidance, automated segmentation is desired. However, 3-D TEE

  8. New approach for validating the segmentation of 3D data applied to individual fibre extraction

    DEFF Research Database (Denmark)

    Emerson, Monica Jane; Dahl, Anders Bjorholm; Dahl, Vedrana Andersen

    2017-01-01

    We present two approaches for validating the segmentation of 3D data. The first approach consists on comparing the amount of estimated material to a value provided by the manufacturer. The second approach consists on comparing the segmented results to those obtained from imaging modalities...

  9. Robust automatic high resolution segmentation of SOFC anode porosity in 3D

    DEFF Research Database (Denmark)

    Jørgensen, Peter Stanley; Bowen, Jacob R.

    2008-01-01

    Routine use of 3D characterization of SOFCs by focused ion beam (FIB) serial sectioning is generally restricted by the time consuming task of manually delineating structures within each image slice. We apply advanced image analysis algorithms to automatically segment the porosity phase of an SOFC...... anode in 3D. The technique is based on numerical approximations to partial differential equations to evolve a 3D surface to the desired phase boundary. Vector fields derived from the experimentally acquired data are used as the driving force. The automatic segmentation compared to manual delineation...... reveals and good correspondence and the two approaches are quantitatively compared. It is concluded that the. automatic approach is more robust, more reproduceable and orders of magnitude quicker than manual segmentation of SOFC anode porosity for subsequent quantitative 3D analysis. Lastly...

  10. 3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

    Science.gov (United States)

    Aloni, Doron; Jung, Jae-Hyun; Yitzhaky, Yitzhak

    2017-10-01

    Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.

  11. 3D segmentation of scintigraphic images with validation on realistic GATE simulations

    International Nuclear Information System (INIS)

    Burg, Samuel

    2011-01-01

    The objective of this thesis was to propose a new 3D segmentation method for scintigraphic imaging. The first part of the work was to simulate 3D volumes with known ground truth in order to validate a segmentation method over other. Monte-Carlo simulations were performed using the GATE software (Geant4 Application for Emission Tomography). For this, we characterized and modeled the gamma camera 'γ Imager' Biospace"T"M by comparing each measurement from a simulated acquisition to his real equivalent. The 'low level' segmentation tool that we have developed is based on a modeling of the levels of the image by probabilistic mixtures. Parameters estimation is done by an SEM algorithm (Stochastic Expectation Maximization). The 3D volume segmentation is achieved by an ICM algorithm (Iterative Conditional Mode). We compared the segmentation based on Gaussian and Poisson mixtures to segmentation by thresholding on the simulated volumes. This showed the relevance of the segmentations obtained using probabilistic mixtures, especially those obtained with Poisson mixtures. Those one has been used to segment real "1"8FDG PET images of the brain and to compute descriptive statistics of the different tissues. In order to obtain a 'high level' segmentation method and find anatomical structures (necrotic part or active part of a tumor, for example), we proposed a process based on the point processes formalism. A feasibility study has yielded very encouraging results. (author) [fr

  12. Reconstruction of incomplete cell paths through a 3D-2D level set segmentation

    Science.gov (United States)

    Hariri, Maia; Wan, Justin W. L.

    2012-02-01

    Segmentation of fluorescent cell images has been a popular technique for tracking live cells. One challenge of segmenting cells from fluorescence microscopy is that cells in fluorescent images frequently disappear. When the images are stacked together to form a 3D image volume, the disappearance of the cells leads to broken cell paths. In this paper, we present a segmentation method that can reconstruct incomplete cell paths. The key idea of this model is to perform 2D segmentation in a 3D framework. The 2D segmentation captures the cells that appear in the image slices while the 3D segmentation connects the broken cell paths. The formulation is similar to the Chan-Vese level set segmentation which detects edges by comparing the intensity value at each voxel with the mean intensity values inside and outside of the level set surface. Our model, however, performs the comparison on each 2D slice with the means calculated by the 2D projected contour. The resulting effect is to segment the cells on each image slice. Unlike segmentation on each image frame individually, these 2D contours together form the 3D level set function. By enforcing minimum mean curvature on the level set surface, our segmentation model is able to extend the cell contours right before (and after) the cell disappears (and reappears) into the gaps, eventually connecting the broken paths. We will present segmentation results of C2C12 cells in fluorescent images to illustrate the effectiveness of our model qualitatively and quantitatively by different numerical examples.

  13. 3D TEM reconstruction and segmentation process of laminar bio-nanocomposites

    International Nuclear Information System (INIS)

    Iturrondobeitia, M.; Okariz, A.; Fernandez-Martinez, R.; Jimbert, P.; Guraya, T.; Ibarretxe, J.

    2015-01-01

    The microstructure of laminar bio-nanocomposites (Poly (lactic acid)(PLA)/clay) depends on the amount of clay platelet opening after integration with the polymer matrix and determines the final properties of the material. Transmission electron microscopy (TEM) technique is the only one that can provide a direct observation of the layer dispersion and the degree of exfoliation. However, the orientation of the clay platelets, which affects the final properties, is practically immeasurable from a single 2D TEM image. This issue can be overcome using transmission electron tomography (ET), a technique that allows the complete 3D characterization of the structure, including the measurement of the orientation of clay platelets, their morphology and their 3D distribution. ET involves a 3D reconstruction of the study volume and a subsequent segmentation of the study object. Currently, accurate segmentation is performed manually, which is inefficient and tedious. The aim of this work is to propose an objective/automated segmentation methodology process of a 3D TEM tomography reconstruction. In this method the segmentation threshold is optimized by minimizing the variation of the dimensions of the segmented objects and matching the segmented V clay (%) and the actual one. The method is first validated using a fictitious set of objects, and then applied on a nanocomposite

  14. Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

    Science.gov (United States)

    Roth, Holger; Oda, Masahiro; Shimizu, Natsuki; Oda, Hirohisa; Hayashi, Yuichiro; Kitasaka, Takayuki; Fujiwara, Michitaka; Misawa, Kazunari; Mori, Kensaku

    2018-03-01

    Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that can process a 3D image including the whole pancreas and produce an automatic segmentation. We investigate two variations of the 3D FCN architecture; one with concatenation and one with summation skip connections to the decoder part of the network. We evaluate our methods on a dataset from a clinical trial with gastric cancer patients, including 147 contrast enhanced abdominal CT scans acquired in the portal venous phase. Using the summation architecture, we achieve an average Dice score of 89.7 +/- 3.8 (range [79.8, 94.8])% in testing, achieving the new state-of-the-art performance in pancreas segmentation on this dataset.

  15. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    International Nuclear Information System (INIS)

    Guo, Yanrong; Shao, Yeqin; Gao, Yaozong; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-01-01

    different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images

  16. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    Science.gov (United States)

    Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-01-01

    patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images. PMID:24989402

  17. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

    Science.gov (United States)

    Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-07-01

    prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

  18. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yanrong; Shao, Yeqin [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Price, True [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Oto, Aytekin [Department of Radiology, Section of Urology, University of Chicago, Illinois 60637 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-07-15

    different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

  19. 3D Segmentations of Neuronal Nuclei from Confocal Microscope Image Stacks

    Directory of Open Access Journals (Sweden)

    Antonio eLaTorre

    2013-12-01

    Full Text Available In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells. We have tested our algorithm in a real scenario --- the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei.

  20. 3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images

    Energy Technology Data Exchange (ETDEWEB)

    Pei, Yuru, E-mail: peiyuru@cis.pku.edu.cn; Ai, Xingsheng; Zha, Hongbin [Department of Machine Intelligence, School of EECS, Peking University, Beijing 100871 (China); Xu, Tianmin [School of Stomatology, Stomatology Hospital, Peking University, Beijing 100081 (China); Ma, Gengyu [uSens, Inc., San Jose, California 95110 (United States)

    2016-09-15

    Purpose: Tooth segmentation is an essential step in acquiring patient-specific dental geometries from cone-beam computed tomography (CBCT) images. Tooth segmentation from CBCT images is still a challenging task considering the comparatively low image quality caused by the limited radiation dose, as well as structural ambiguities from intercuspation and nearby alveolar bones. The goal of this paper is to present and discuss the latest accomplishments in semisupervised tooth segmentation with adaptive 3D shape constraints. Methods: The authors propose a 3D exemplar-based random walk method of tooth segmentation from CBCT images. The proposed method integrates semisupervised label propagation and regularization by 3D exemplar registration. To begin with, the pure random walk method is to get an initial segmentation of the teeth, which tends to be erroneous because of the structural ambiguity of CBCT images. And then, as an iterative refinement, the authors conduct a regularization by using 3D exemplar registration, as well as label propagation by random walks with soft constraints, to improve the tooth segmentation. In the first stage of the iteration, 3D exemplars with well-defined topologies are adapted to fit the tooth contours, which are obtained from the random walks based segmentation. The soft constraints on voxel labeling are defined by shape-based foreground dentine probability acquired by the exemplar registration, as well as the appearance-based probability from a support vector machine (SVM) classifier. In the second stage, the labels of the volume-of-interest (VOI) are updated by the random walks with soft constraints. The two stages are optimized iteratively. Instead of the one-shot label propagation in the VOI, an iterative refinement process can achieve a reliable tooth segmentation by virtue of exemplar-based random walks with adaptive soft constraints. Results: The proposed method was applied for tooth segmentation of twenty clinically captured CBCT

  1. 3D segmentation of kidney tumors from freehand 2D ultrasound

    Science.gov (United States)

    Ahmad, Anis; Cool, Derek; Chew, Ben H.; Pautler, Stephen E.; Peters, Terry M.

    2006-03-01

    To completely remove a tumor from a diseased kidney, while minimizing the resection of healthy tissue, the surgeon must be able to accurately determine its location, size and shape. Currently, the surgeon mentally estimates these parameters by examining pre-operative Computed Tomography (CT) images of the patient's anatomy. However, these images do not reflect the state of the abdomen or organ during surgery. Furthermore, these images can be difficult to place in proper clinical context. We propose using Ultrasound (US) to acquire images of the tumor and the surrounding tissues in real-time, then segmenting these US images to present the tumor as a three dimensional (3D) surface. Given the common use of laparoscopic procedures that inhibit the range of motion of the operator, we propose segmenting arbitrarily placed and oriented US slices individually using a tracked US probe. Given the known location and orientation of the US probe, we can assign 3D coordinates to the segmented slices and use them as input to a 3D surface reconstruction algorithm. We have implemented two approaches for 3D segmentation from freehand 2D ultrasound. Each approach was evaluated on a tissue-mimicking phantom of a kidney tumor. The performance of our approach was determined by measuring RMS surface error between the segmentation and the known gold standard and was found to be below 0.8 mm.

  2. 3D-SIFT-Flow for atlas-based CT liver image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Yan, E-mail: xuyan04@gmail.com [State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing 100191, China and Research Institute of Beihang University in Shenzhen and Microsoft Research, Beijing 100080 (China); Xu, Chenchao, E-mail: chenchaoxu33@gmail.com; Kuang, Xiao, E-mail: kuangxiao.ace@gmail.com [School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 (China); Wang, Hongkai, E-mail: wang.hongkai@gmail.com [Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024 (China); Chang, Eric I-Chao, E-mail: eric.chang@microsoft.com [Microsoft Research, Beijing 100080 (China); Huang, Weimin, E-mail: wmhuang@i2r.a-star.edu.sg [Institute for Infocomm Research (I2R), Singapore 138632 (Singapore); Fan, Yubo, E-mail: yubofan@buaa.edu.cn [Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing 100191 (China)

    2016-05-15

    Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

  3. 3D-SIFT-Flow for atlas-based CT liver image segmentation.

    Science.gov (United States)

    Xu, Yan; Xu, Chenchao; Kuang, Xiao; Wang, Hongkai; Chang, Eric I-Chao; Huang, Weimin; Fan, Yubo

    2016-05-01

    In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

  4. 3D-SIFT-Flow for atlas-based CT liver image segmentation

    International Nuclear Information System (INIS)

    Xu, Yan; Xu, Chenchao; Kuang, Xiao; Wang, Hongkai; Chang, Eric I-Chao; Huang, Weimin; Fan, Yubo

    2016-01-01

    Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

  5. Intensity-based segmentation and visualization of cells in 3D microscopic images using the GPU

    Science.gov (United States)

    Kang, Mi-Sun; Lee, Jeong-Eom; Jeon, Woong-ki; Choi, Heung-Kook; Kim, Myoung-Hee

    2013-02-01

    3D microscopy images contain abundant astronomical data, rendering 3D microscopy image processing time-consuming and laborious on a central processing unit (CPU). To solve these problems, many people crop a region of interest (ROI) of the input image to a small size. Although this reduces cost and time, there are drawbacks at the image processing level, e.g., the selected ROI strongly depends on the user and there is a loss in original image information. To mitigate these problems, we developed a 3D microscopy image processing tool on a graphics processing unit (GPU). Our tool provides efficient and various automatic thresholding methods to achieve intensity-based segmentation of 3D microscopy images. Users can select the algorithm to be applied. Further, the image processing tool provides visualization of segmented volume data and can set the scale, transportation, etc. using a keyboard and mouse. However, the 3D objects visualized fast still need to be analyzed to obtain information for biologists. To analyze 3D microscopic images, we need quantitative data of the images. Therefore, we label the segmented 3D objects within all 3D microscopic images and obtain quantitative information on each labeled object. This information can use the classification feature. A user can select the object to be analyzed. Our tool allows the selected object to be displayed on a new window, and hence, more details of the object can be observed. Finally, we validate the effectiveness of our tool by comparing the CPU and GPU processing times by matching the specification and configuration.

  6. Automatic segmentation of MRI head images by 3-D region growing method which utilizes edge information

    International Nuclear Information System (INIS)

    Jiang, Hao; Suzuki, Hidetomo; Toriwaki, Jun-ichiro

    1991-01-01

    This paper presents a 3-D segmentation method that automatically extracts soft tissue from multi-sliced MRI head images. MRI produces a sequence of two-dimensional (2-D) images which contains three-dimensional (3-D) information of organs. To utilize such information we need effective algorithms to treat 3-D digital images and to extract organs and tissues of interest. We developed a method to extract the brain from MRI images which uses a region growing procedure and integrates information of uniformity of gray levels and information of the presence of edge segments in the local area around the pixel of interest. First we generate a kernel region which is a part of brain tissue by simple thresholding. Then we grow the region by means of a region growing algorithm under the control of 3-D edge existence to obtain the region of the brain. Our method is rather simple because it uses basic 3-D image processing techniques like spatial difference. It is robust for variation of gray levels inside a tissue since it also refers to the edge information in the process of region growing. Therefore, the method is flexible enough to be applicable to the segmentation of other images including soft tissues which have complicated shapes and fluctuation in gray levels. (author)

  7. 3D prostate TRUS segmentation using globally optimized volume-preserving prior.

    Science.gov (United States)

    Qiu, Wu; Rajchl, Martin; Guo, Fumin; Sun, Yue; Ukwatta, Eranga; Fenster, Aaron; Yuan, Jing

    2014-01-01

    An efficient and accurate segmentation of 3D transrectal ultrasound (TRUS) images plays an important role in the planning and treatment of the practical 3D TRUS guided prostate biopsy. However, a meaningful segmentation of 3D TRUS images tends to suffer from US speckles, shadowing and missing edges etc, which make it a challenging task to delineate the correct prostate boundaries. In this paper, we propose a novel convex optimization based approach to extracting the prostate surface from the given 3D TRUS image, while preserving a new global volume-size prior. We, especially, study the proposed combinatorial optimization problem by convex relaxation and introduce its dual continuous max-flow formulation with the new bounded flow conservation constraint, which results in an efficient numerical solver implemented on GPUs. Experimental results using 12 patient 3D TRUS images show that the proposed approach while preserving the volume-size prior yielded a mean DSC of 89.5% +/- 2.4%, a MAD of 1.4 +/- 0.6 mm, a MAXD of 5.2 +/- 3.2 mm, and a VD of 7.5% +/- 6.2% in - 1 minute, deomonstrating the advantages of both accuracy and efficiency. In addition, the low standard deviation of the segmentation accuracy shows a good reliability of the proposed approach.

  8. Analysis of 3-D effects in segmented cylindrical quasi-Halbach magnet arrays

    NARCIS (Netherlands)

    Meessen, K.J.; Paulides, J.J.H.; Lomonova, E.

    2011-01-01

    To improve the performance of permanent magnet (PM) machines, quasi-Halbach PM arrays are used to increase the magnetic loading in these machines. In tubular PM actuators, these arrays are often approximated using segmented magnets resulting in a 3-D magnetic field effect. This paper describes the

  9. A wavelet-based Bayesian framework for 3D object segmentation in microscopy

    Science.gov (United States)

    Pan, Kangyu; Corrigan, David; Hillebrand, Jens; Ramaswami, Mani; Kokaram, Anil

    2012-03-01

    In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.

  10. A Hierarchical Building Segmentation in Digital Surface Models for 3D Reconstruction

    Directory of Open Access Journals (Sweden)

    Yiming Yan

    2017-01-01

    Full Text Available In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM, which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed ‘occlusions of random textures model’ are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images.

  11. A 3D Image Filter for Parameter-Free Segmentation of Macromolecular Structures from Electron Tomograms

    Science.gov (United States)

    Ali, Rubbiya A.; Landsberg, Michael J.; Knauth, Emily; Morgan, Garry P.; Marsh, Brad J.; Hankamer, Ben

    2012-01-01

    3D image reconstruction of large cellular volumes by electron tomography (ET) at high (≤5 nm) resolution can now routinely resolve organellar and compartmental membrane structures, protein coats, cytoskeletal filaments, and macromolecules. However, current image analysis methods for identifying in situ macromolecular structures within the crowded 3D ultrastructural landscape of a cell remain labor-intensive, time-consuming, and prone to user-bias and/or error. This paper demonstrates the development and application of a parameter-free, 3D implementation of the bilateral edge-detection (BLE) algorithm for the rapid and accurate segmentation of cellular tomograms. The performance of the 3D BLE filter has been tested on a range of synthetic and real biological data sets and validated against current leading filters—the pseudo 3D recursive and Canny filters. The performance of the 3D BLE filter was found to be comparable to or better than that of both the 3D recursive and Canny filters while offering the significant advantage that it requires no parameter input or optimisation. Edge widths as little as 2 pixels are reproducibly detected with signal intensity and grey scale values as low as 0.72% above the mean of the background noise. The 3D BLE thus provides an efficient method for the automated segmentation of complex cellular structures across multiple scales for further downstream processing, such as cellular annotation and sub-tomogram averaging, and provides a valuable tool for the accurate and high-throughput identification and annotation of 3D structural complexity at the subcellular level, as well as for mapping the spatial and temporal rearrangement of macromolecular assemblies in situ within cellular tomograms. PMID:22479430

  12. Investigations on the quality of manual image segmentation in 3D radiotherapy planning

    International Nuclear Information System (INIS)

    Perelmouter, J.; Tuebingen Univ.; Bohsung, J.; Nuesslin, F.; Becker, G.; Kortmann, R.D.; Bamberg, M.

    1998-01-01

    In 3D radiotherapy planning image segmentation plays an important role in the definition process of target volume and organs at risk. Here, we present a method to quantify the technical precision of the manual image segmentation process. To validate our method we developed a virtual phantom consisting of several geometrical objects of changing form and contrast, which should be contoured by volunteers using the TOMAS tool for manual segmentation of the Heidelberg VOXELPLAN system. The results of this examination are presented. (orig.) [de

  13. FROM2D to 3d Supervised Segmentation and Classification for Cultural Heritage Applications

    Science.gov (United States)

    Grilli, E.; Dininno, D.; Petrucci, G.; Remondino, F.

    2018-05-01

    The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials.

  14. An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

    Science.gov (United States)

    Dong, Zhen; Yang, Bisheng; Hu, Pingbo; Scherer, Sebastian

    2018-03-01

    Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., http://SEMANTIC3D.NET)

  15. Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud

    Directory of Open Access Journals (Sweden)

    Seoungjae Cho

    2014-01-01

    Full Text Available A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.

  16. Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images

    International Nuclear Information System (INIS)

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

    2011-01-01

    Accurate and objective evaluation of vertebral deformations is of significant importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is focused on three-dimensional (3D) computed tomography (CT) and magnetic resonance (MR) imaging techniques, the established methods for evaluation of vertebral deformations are limited to measuring deformations in two-dimensional (2D) x-ray images. In this paper, we propose a method for quantitative description of vertebral body deformations by efficient modelling and segmentation of vertebral bodies in 3D. The deformations are evaluated from the parameters of a 3D superquadric model, which is initialized as an elliptical cylinder and then gradually deformed by introducing transformations that yield a more detailed representation of the vertebral body shape. After modelling the vertebral body shape with 25 clinically meaningful parameters and the vertebral body pose with six rigid body parameters, the 3D model is aligned to the observed vertebral body in the 3D image. The performance of the method was evaluated on 75 vertebrae from CT and 75 vertebrae from T 2 -weighted MR spine images, extracted from the thoracolumbar part of normal and pathological spines. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images, as the proposed 3D model is able to describe both normal and pathological vertebral body deformations. The method may therefore be used for initialization of whole vertebra segmentation or for quantitative measurement of vertebral body deformations.

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  18. Segmentation of multiple heart cavities in 3-D transesophageal ultrasound images.

    Science.gov (United States)

    Haak, Alexander; Vegas-Sánchez-Ferrero, Gonzalo; Mulder, Harriët W; Ren, Ben; Kirişli, Hortense A; Metz, Coert; van Burken, Gerard; van Stralen, Marijn; Pluim, Josien P W; van der Steen, Antonius F W; van Walsum, Theo; Bosch, Johannes G

    2015-06-01

    Three-dimensional transesophageal echocardiography (TEE) is an excellent modality for real-time visualization of the heart and monitoring of interventions. To improve the usability of 3-D TEE for intervention monitoring and catheter guidance, automated segmentation is desired. However, 3-D TEE segmentation is still a challenging task due to the complex anatomy with multiple cavities, the limited TEE field of view, and typical ultrasound artifacts. We propose to segment all cavities within the TEE view with a multi-cavity active shape model (ASM) in conjunction with a tissue/blood classification based on a gamma mixture model (GMM). 3-D TEE image data of twenty patients were acquired with a Philips X7-2t matrix TEE probe. Tissue probability maps were estimated by a two-class (blood/tissue) GMM. A statistical shape model containing the left ventricle, right ventricle, left atrium, right atrium, and aorta was derived from computed tomography angiography (CTA) segmentations by principal component analysis. ASMs of the whole heart and individual cavities were generated and consecutively fitted to tissue probability maps. First, an average whole-heart model was aligned with the 3-D TEE based on three manually indicated anatomical landmarks. Second, pose and shape of the whole-heart ASM were fitted by a weighted update scheme excluding parts outside of the image sector. Third, pose and shape of ASM for individual heart cavities were initialized by the previous whole heart ASM and updated in a regularized manner to fit the tissue probability maps. The ASM segmentations were validated against manual outlines by two observers and CTA derived segmentations. Dice coefficients and point-to-surface distances were used to determine segmentation accuracy. ASM segmentations were successful in 19 of 20 cases. The median Dice coefficient for all successful segmentations versus the average observer ranged from 90% to 71% compared with an inter-observer range of 95% to 84%. The

  19. 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

    Science.gov (United States)

    Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

    2012-07-01

    Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational

  20. Denoising imaging polarimetry by adapted BM3D method.

    Science.gov (United States)

    Tibbs, Alexander B; Daly, Ilse M; Roberts, Nicholas W; Bull, David R

    2018-04-01

    In addition to the visual information contained in intensity and color, imaging polarimetry allows visual information to be extracted from the polarization of light. However, a major challenge of imaging polarimetry is image degradation due to noise. This paper investigates the mitigation of noise through denoising algorithms and compares existing denoising algorithms with a new method, based on BM3D (Block Matching 3D). This algorithm, Polarization-BM3D (PBM3D), gives visual quality superior to the state of the art across all images and noise standard deviations tested. We show that denoising polarization images using PBM3D allows the degree of polarization to be more accurately calculated by comparing it with spectral polarimetry measurements.

  1. Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods

    International Nuclear Information System (INIS)

    Beichel, Reinhard; Bornik, Alexander; Bauer, Christian; Sorantin, Erich

    2012-01-01

    Purpose: Liver segmentation is an important prerequisite for the assessment of liver cancer treatment options like tumor resection, image-guided radiation therapy (IGRT), radiofrequency ablation, etc. The purpose of this work was to evaluate a new approach for liver segmentation. Methods: A graph cuts segmentation method was combined with a three-dimensional virtual reality based segmentation refinement approach. The developed interactive segmentation system allowed the user to manipulate volume chunks and/or surfaces instead of 2D contours in cross-sectional images (i.e, slice-by-slice). The method was evaluated on twenty routinely acquired portal-phase contrast enhanced multislice computed tomography (CT) data sets. An independent reference was generated by utilizing a currently clinically utilized slice-by-slice segmentation method. After 1 h of introduction to the developed segmentation system, three experts were asked to segment all twenty data sets with the proposed method. Results: Compared to the independent standard, the relative volumetric segmentation overlap error averaged over all three experts and all twenty data sets was 3.74%. Liver segmentation required on average 16 min of user interaction per case. The calculated relative volumetric overlap errors were not found to be significantly different [analysis of variance (ANOVA) test, p = 0.82] between experts who utilized the proposed 3D system. In contrast, the time required by each expert for segmentation was found to be significantly different (ANOVA test, p = 0.0009). Major differences between generated segmentations and independent references were observed in areas were vessels enter or leave the liver and no accepted criteria for defining liver boundaries exist. In comparison, slice-by-slice based generation of the independent standard utilizing a live wire tool took 70.1 min on average. A standard 2D segmentation refinement approach applied to all twenty data sets required on average 38.2 min of

  2. Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods

    Energy Technology Data Exchange (ETDEWEB)

    Beichel, Reinhard; Bornik, Alexander; Bauer, Christian; Sorantin, Erich [Departments of Electrical and Computer Engineering and Internal Medicine, Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa 52242 (United States); Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, A-8010 Graz (Austria); Department of Electrical and Computer Engineering, Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa 52242 (United States); Department of Radiology, Medical University Graz, Auenbruggerplatz 34, A-8010 Graz (Austria)

    2012-03-15

    Purpose: Liver segmentation is an important prerequisite for the assessment of liver cancer treatment options like tumor resection, image-guided radiation therapy (IGRT), radiofrequency ablation, etc. The purpose of this work was to evaluate a new approach for liver segmentation. Methods: A graph cuts segmentation method was combined with a three-dimensional virtual reality based segmentation refinement approach. The developed interactive segmentation system allowed the user to manipulate volume chunks and/or surfaces instead of 2D contours in cross-sectional images (i.e, slice-by-slice). The method was evaluated on twenty routinely acquired portal-phase contrast enhanced multislice computed tomography (CT) data sets. An independent reference was generated by utilizing a currently clinically utilized slice-by-slice segmentation method. After 1 h of introduction to the developed segmentation system, three experts were asked to segment all twenty data sets with the proposed method. Results: Compared to the independent standard, the relative volumetric segmentation overlap error averaged over all three experts and all twenty data sets was 3.74%. Liver segmentation required on average 16 min of user interaction per case. The calculated relative volumetric overlap errors were not found to be significantly different [analysis of variance (ANOVA) test, p = 0.82] between experts who utilized the proposed 3D system. In contrast, the time required by each expert for segmentation was found to be significantly different (ANOVA test, p = 0.0009). Major differences between generated segmentations and independent references were observed in areas were vessels enter or leave the liver and no accepted criteria for defining liver boundaries exist. In comparison, slice-by-slice based generation of the independent standard utilizing a live wire tool took 70.1 min on average. A standard 2D segmentation refinement approach applied to all twenty data sets required on average 38.2 min of

  3. Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods.

    Science.gov (United States)

    Beichel, Reinhard; Bornik, Alexander; Bauer, Christian; Sorantin, Erich

    2012-03-01

    Liver segmentation is an important prerequisite for the assessment of liver cancer treatment options like tumor resection, image-guided radiation therapy (IGRT), radiofrequency ablation, etc. The purpose of this work was to evaluate a new approach for liver segmentation. A graph cuts segmentation method was combined with a three-dimensional virtual reality based segmentation refinement approach. The developed interactive segmentation system allowed the user to manipulate volume chunks and∕or surfaces instead of 2D contours in cross-sectional images (i.e, slice-by-slice). The method was evaluated on twenty routinely acquired portal-phase contrast enhanced multislice computed tomography (CT) data sets. An independent reference was generated by utilizing a currently clinically utilized slice-by-slice segmentation method. After 1 h of introduction to the developed segmentation system, three experts were asked to segment all twenty data sets with the proposed method. Compared to the independent standard, the relative volumetric segmentation overlap error averaged over all three experts and all twenty data sets was 3.74%. Liver segmentation required on average 16 min of user interaction per case. The calculated relative volumetric overlap errors were not found to be significantly different [analysis of variance (ANOVA) test, p = 0.82] between experts who utilized the proposed 3D system. In contrast, the time required by each expert for segmentation was found to be significantly different (ANOVA test, p = 0.0009). Major differences between generated segmentations and independent references were observed in areas were vessels enter or leave the liver and no accepted criteria for defining liver boundaries exist. In comparison, slice-by-slice based generation of the independent standard utilizing a live wire tool took 70.1 min on average. A standard 2D segmentation refinement approach applied to all twenty data sets required on average 38.2 min of user interaction

  4. SEGMENTATION OF 3D MODELS FOR CULTURAL HERITAGE STRUCTURAL ANALYSIS – SOME CRITICAL ISSUES

    Directory of Open Access Journals (Sweden)

    S. Gonizzi Barsanti

    2017-08-01

    Full Text Available Cultural Heritage documentation and preservation has become a fundamental concern in this historical period. 3D modelling offers a perfect aid to record ancient buildings and artefacts and can be used as a valid starting point for restoration, conservation and structural analysis, which can be performed by using Finite Element Methods (FEA. The models derived from reality-based techniques, made up of the exterior surfaces of the objects captured at high resolution, are - for this reason - made of millions of polygons. Such meshes are not directly usable in structural analysis packages and need to be properly pre-processed in order to be transformed in volumetric meshes suitable for FEA. In addition, dealing with ancient objects, a proper segmentation of 3D volumetric models is needed to analyse the behaviour of the structure with the most suitable level of detail for the different sections of the structure under analysis. Segmentation of 3D models is still an open issue, especially when dealing with ancient, complicated and geometrically complex objects that imply the presence of anomalies and gaps, due to environmental agents such as earthquakes, pollution, wind and rain, or human factors. The aims of this paper is to critically analyse some of the different methodologies and algorithms available to segment a 3D point cloud or a mesh, identifying difficulties and problems by showing examples on different structures.

  5. Segmentation of 3d Models for Cultural Heritage Structural Analysis - Some Critical Issues

    Science.gov (United States)

    Gonizzi Barsanti, S.; Guidi, G.; De Luca, L.

    2017-08-01

    Cultural Heritage documentation and preservation has become a fundamental concern in this historical period. 3D modelling offers a perfect aid to record ancient buildings and artefacts and can be used as a valid starting point for restoration, conservation and structural analysis, which can be performed by using Finite Element Methods (FEA). The models derived from reality-based techniques, made up of the exterior surfaces of the objects captured at high resolution, are - for this reason - made of millions of polygons. Such meshes are not directly usable in structural analysis packages and need to be properly pre-processed in order to be transformed in volumetric meshes suitable for FEA. In addition, dealing with ancient objects, a proper segmentation of 3D volumetric models is needed to analyse the behaviour of the structure with the most suitable level of detail for the different sections of the structure under analysis. Segmentation of 3D models is still an open issue, especially when dealing with ancient, complicated and geometrically complex objects that imply the presence of anomalies and gaps, due to environmental agents such as earthquakes, pollution, wind and rain, or human factors. The aims of this paper is to critically analyse some of the different methodologies and algorithms available to segment a 3D point cloud or a mesh, identifying difficulties and problems by showing examples on different structures.

  6. Fast CSF MRI for brain segmentation; Cross-validation by comparison with 3D T1-based brain segmentation methods.

    Science.gov (United States)

    van der Kleij, Lisa A; de Bresser, Jeroen; Hendrikse, Jeroen; Siero, Jeroen C W; Petersen, Esben T; De Vis, Jill B

    2018-01-01

    In previous work we have developed a fast sequence that focusses on cerebrospinal fluid (CSF) based on the long T2 of CSF. By processing the data obtained with this CSF MRI sequence, brain parenchymal volume (BPV) and intracranial volume (ICV) can be automatically obtained. The aim of this study was to assess the precision of the BPV and ICV measurements of the CSF MRI sequence and to validate the CSF MRI sequence by comparison with 3D T1-based brain segmentation methods. Ten healthy volunteers (2 females; median age 28 years) were scanned (3T MRI) twice with repositioning in between. The scan protocol consisted of a low resolution (LR) CSF sequence (0:57min), a high resolution (HR) CSF sequence (3:21min) and a 3D T1-weighted sequence (6:47min). Data of the HR 3D-T1-weighted images were downsampled to obtain LR T1-weighted images (reconstructed imaging time: 1:59 min). Data of the CSF MRI sequences was automatically segmented using in-house software. The 3D T1-weighted images were segmented using FSL (5.0), SPM12 and FreeSurfer (5.3.0). The mean absolute differences for BPV and ICV between the first and second scan for CSF LR (BPV/ICV: 12±9/7±4cc) and CSF HR (5±5/4±2cc) were comparable to FSL HR (9±11/19±23cc), FSL LR (7±4, 6±5cc), FreeSurfer HR (5±3/14±8cc), FreeSurfer LR (9±8, 12±10cc), and SPM HR (5±3/4±7cc), and SPM LR (5±4, 5±3cc). The correlation between the measured volumes of the CSF sequences and that measured by FSL, FreeSurfer and SPM HR and LR was very good (all Pearson's correlation coefficients >0.83, R2 .67-.97). The results from the downsampled data and the high-resolution data were similar. Both CSF MRI sequences have a precision comparable to, and a very good correlation with established 3D T1-based automated segmentations methods for the segmentation of BPV and ICV. However, the short imaging time of the fast CSF MRI sequence is superior to the 3D T1 sequence on which segmentation with established methods is performed.

  7. Super-Segments Based Classification of 3D Urban Street Scenes

    Directory of Open Access Journals (Sweden)

    Yu Zhou

    2012-12-01

    Full Text Available We address the problem of classifying 3D point clouds: given 3D urban street scenes gathered by a lidar sensor, we wish to assign a class label to every point. This work is a key step toward realizing applications in robots and cars, for example. In this paper, we present a novel approach to the classification of 3D urban scenes based on super-segments, which are generated from point clouds by two stages of segmentation: a clustering stage and a grouping stage. Then, six effective normal and dimension features that vary with object class are extracted at the super-segment level for training some general classifiers. We evaluate our method both quantitatively and qualitatively using the challenging Velodyne lidar data set. The results show that by only using normal and dimension features we can achieve better recognition than can be achieved with high-dimensional shape descriptors. We also evaluate the adopting of the MRF framework in our approach, but the experimental results indicate that thisbarely improved the accuracy of the classified results due to the sparse property of the super-segments.

  8. 3D automatic segmentation method for retinal optical coherence tomography volume data using boundary surface enhancement

    Directory of Open Access Journals (Sweden)

    Yankui Sun

    2016-03-01

    Full Text Available With the introduction of spectral-domain optical coherence tomography (SD-OCT, much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of three-dimensional (3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of datasets contains 97 OCT B-Scan images with a resolution of 496×512 (each B-Scan comprising 512 A-Scans containing 496 pixels; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.

  9. Segmental and regional quantification of 3D cellular density of human meniscus from osteoarthritic knee.

    Science.gov (United States)

    Cengiz, Ibrahim Fatih; Pereira, Hélder; Pêgo, José Miguel; Sousa, Nuno; Espregueira-Mendes, João; Oliveira, Joaquim Miguel; Reis, Rui Luís

    2017-06-01

    The knee menisci have important roles in the knee joint. Complete healing of the meniscus remains a challenge in the clinics. Cellularity is one of the most important biological parameters that must be taken into account in regenerative strategies. However, knowledge on the 3D cellularity of the human meniscus is lacking in the literature. The aim of this study was to quantify the 3D cellular density of human meniscus from the osteoarthritic knee in a segmental and regional manner with respect to laterality. Human lateral menisci were histologically processed and stained with Giemsa for histomorphometric analysis. The cells were counted in an in-depth fashion. 3D cellular density in the vascular region (27 199 cells/mm 3 ) was significantly higher than in the avascular region (12 820 cells/mm 3 ). The cells were observed to possess two distinct morphologies, roundish or flattened. The 3D density of cells with fibrochondrocyte morphology (14 705 cells/mm 3 ) was significantly greater than the 3D density of the cells with fibroblast-like cell morphology (5539 cells/mm 3 ). The best-fit equation for prediction of the 3D density of cells with fibrochondrocyte morphology was found to be: Density of cells with fibrochondrocyte morphology = 1.22 × density of cells withfibroblast-like cell morphology + 7750. The present study revealed the segmental and regional 3D cellular density of human lateral meniscus from osteoarthritic knee with respect to laterality. This crucial but so far missing information will empower cellular strategies aiming at meniscus tissue regeneration. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  10. Segmentation of 3D ultrasound computer tomography reflection images using edge detection and surface fitting

    Science.gov (United States)

    Hopp, T.; Zapf, M.; Ruiter, N. V.

    2014-03-01

    An essential processing step for comparison of Ultrasound Computer Tomography images to other modalities, as well as for the use in further image processing, is to segment the breast from the background. In this work we present a (semi-) automated 3D segmentation method which is based on the detection of the breast boundary in coronal slice images and a subsequent surface fitting. The method was evaluated using a software phantom and in-vivo data. The fully automatically processed phantom results showed that a segmentation of approx. 10% of the slices of a dataset is sufficient to recover the overall breast shape. Application to 16 in-vivo datasets was performed successfully using semi-automated processing, i.e. using a graphical user interface for manual corrections of the automated breast boundary detection. The processing time for the segmentation of an in-vivo dataset could be significantly reduced by a factor of four compared to a fully manual segmentation. Comparison to manually segmented images identified a smoother surface for the semi-automated segmentation with an average of 11% of differing voxels and an average surface deviation of 2mm. Limitations of the edge detection may be overcome by future updates of the KIT USCT system, allowing a fully-automated usage of our segmentation approach.

  11. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.

    Science.gov (United States)

    Zhou, Xiangrong; Takayama, Ryosuke; Wang, Song; Hara, Takeshi; Fujita, Hiroshi

    2017-10-01

    We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image. We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of multiple organ segmentations in CT cases of different sizes that cover arbitrary scan regions without any adjustment. The proposed network was trained and validated using the simultaneous segmentation of 19 anatomical structures in the human torso, including 17 major organs and two special regions (lumen and content inside of stomach). Some of these structures have never been reported in previous research on CT segmentation. A database consisting of 240 (95% for training and 5% for testing) 3D CT scans, together with their manually annotated ground-truth segmentations, was used in our experiments. The results show that the 19 structures of interest were segmented with acceptable accuracy (88.1% and 87.9% voxels in the training and testing datasets, respectively, were labeled correctly) against the ground truth. We propose a single network based on pixel-to-label deep learning to address the challenging

  12. Fast Streaming 3D Level set Segmentation on the GPU for Smooth Multi-phase Segmentation

    DEFF Research Database (Denmark)

    Sharma, Ojaswa; Zhang, Qin; Anton, François

    2011-01-01

    Level set method based segmentation provides an efficient tool for topological and geometrical shape handling, but it is slow due to high computational burden. In this work, we provide a framework for streaming computations on large volumetric images on the GPU. A streaming computational model...

  13. Spatiotemporal Segmentation and Modeling of the Mitral Valve in Real-Time 3D Echocardiographic Images.

    Science.gov (United States)

    Pouch, Alison M; Aly, Ahmed H; Lai, Eric K; Yushkevich, Natalie; Stoffers, Rutger H; Gorman, Joseph H; Cheung, Albert T; Gorman, Joseph H; Gorman, Robert C; Yushkevich, Paul A

    2017-09-01

    Transesophageal echocardiography is the primary imaging modality for preoperative assessment of mitral valves with ischemic mitral regurgitation (IMR). While there are well known echocardiographic insights into the 3D morphology of mitral valves with IMR, such as annular dilation and leaflet tethering, less is understood about how quantification of valve dynamics can inform surgical treatment of IMR or predict short-term recurrence of the disease. As a step towards filling this knowledge gap, we present a novel framework for 4D segmentation and geometric modeling of the mitral valve in real-time 3D echocardiography (rt-3DE). The framework integrates multi-atlas label fusion and template-based medial modeling to generate quantitatively descriptive models of valve dynamics. The novelty of this work is that temporal consistency in the rt-3DE segmentations is enforced during both the segmentation and modeling stages with the use of groupwise label fusion and Kalman filtering. The algorithm is evaluated on rt-3DE data series from 10 patients: five with normal mitral valve morphology and five with severe IMR. In these 10 data series that total 207 individual 3DE images, each 3DE segmentation is validated against manual tracing and temporal consistency between segmentations is demonstrated. The ultimate goal is to generate accurate and consistent representations of valve dynamics that can both visually and quantitatively provide insight into normal and pathological valve function.

  14. Fast globally optimal segmentation of 3D prostate MRI with axial symmetry prior.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron

    2013-01-01

    We propose a novel global optimization approach to segmenting a given 3D prostate T2w magnetic resonance (MR) image, which enforces the inherent axial symmetry of the prostate shape and simultaneously performs a sequence of 2D axial slice-wise segmentations with a global 3D coherence prior. We show that the proposed challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. With this regard, we introduce a novel coupled continuous max-flow model, which is dual to the studied convex relaxed optimization formulation and leads to an efficient multiplier augmented algorithm based on the modern convex optimization theory. Moreover, the new continuous max-flow based algorithm was implemented on GPUs to achieve a substantial improvement in computation. Experimental results using public and in-house datasets demonstrate great advantages of the proposed method in terms of both accuracy and efficiency.

  15. An accurate segmentation method for volumetry of brain tumor in 3D MRI

    Science.gov (United States)

    Wang, Jiahui; Li, Qiang; Hirai, Toshinori; Katsuragawa, Shigehiko; Li, Feng; Doi, Kunio

    2008-03-01

    Accurate volumetry of brain tumors in magnetic resonance imaging (MRI) is important for evaluating the interval changes in tumor volumes during and after treatment, and also for planning of radiation therapy. In this study, an automated volumetry method for brain tumors in MRI was developed by use of a new three-dimensional (3-D) image segmentation technique. First, the central location of a tumor was identified by a radiologist, and then a volume of interest (VOI) was determined automatically. To substantially simplify tumor segmentation, we transformed the 3-D image of the tumor into a two-dimensional (2-D) image by use of a "spiral-scanning" technique, in which a radial line originating from the center of the tumor scanned the 3-D image spirally from the "north pole" to the "south pole". The voxels scanned by the radial line provided a transformed 2-D image. We employed dynamic programming to delineate an "optimal" outline of the tumor in the transformed 2-D image. We then transformed the optimal outline back into 3-D image space to determine the volume of the tumor. The volumetry method was trained and evaluated by use of 16 cases with 35 brain tumors. The agreement between tumor volumes provided by computer and a radiologist was employed as a performance metric. Our method provided relatively accurate results with a mean agreement value of 88%.

  16. Dual optimization based prostate zonal segmentation in 3D MR images.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron

    2014-05-01

    Efficient and accurate segmentation of the prostate and two of its clinically meaningful sub-regions: the central gland (CG) and peripheral zone (PZ), from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, a novel multi-region segmentation approach is proposed to simultaneously segment the prostate and its two major sub-regions from only a single 3D T2-weighted (T2w) MR image, which makes use of the prior spatial region consistency and incorporates a customized prostate appearance model into the segmentation task. The formulated challenging combinatorial optimization problem is solved by means of convex relaxation, for which a novel spatially continuous max-flow model is introduced as the dual optimization formulation to the studied convex relaxed optimization problem with region consistency constraints. The proposed continuous max-flow model derives an efficient duality-based algorithm that enjoys numerical advantages and can be easily implemented on GPUs. The proposed approach was validated using 18 3D prostate T2w MR images with a body-coil and 25 images with an endo-rectal coil. Experimental results demonstrate that the proposed method is capable of efficiently and accurately extracting both the prostate zones: CG and PZ, and the whole prostate gland from the input 3D prostate MR images, with a mean Dice similarity coefficient (DSC) of 89.3±3.2% for the whole gland (WG), 82.2±3.0% for the CG, and 69.1±6.9% for the PZ in 3D body-coil MR images; 89.2±3.3% for the WG, 83.0±2.4% for the CG, and 70.0±6.5% for the PZ in 3D endo-rectal coil MR images. In addition, the experiments of intra- and inter-observer variability introduced by user initialization indicate a good reproducibility of the proposed approach in terms of volume difference (VD) and coefficient-of-variation (CV) of DSC. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Quantitative analysis of retina layer elasticity based on automatic 3D segmentation (Conference Presentation)

    Science.gov (United States)

    He, Youmin; Qu, Yueqiao; Zhang, Yi; Ma, Teng; Zhu, Jiang; Miao, Yusi; Humayun, Mark; Zhou, Qifa; Chen, Zhongping

    2017-02-01

    Age-related macular degeneration (AMD) is an eye condition that is considered to be one of the leading causes of blindness among people over 50. Recent studies suggest that the mechanical properties in retina layers are affected during the early onset of disease. Therefore, it is necessary to identify such changes in the individual layers of the retina so as to provide useful information for disease diagnosis. In this study, we propose using an acoustic radiation force optical coherence elastography (ARF-OCE) system to dynamically excite the porcine retina and detect the vibrational displacement with phase resolved Doppler optical coherence tomography. Due to the vibrational mechanism of the tissue response, the image quality is compromised during elastogram acquisition. In order to properly analyze the images, all signals, including the trigger and control signals for excitation, as well as detection and scanning signals, are synchronized within the OCE software and are kept consistent between frames, making it possible for easy phase unwrapping and elasticity analysis. In addition, a combination of segmentation algorithms is used to accommodate the compromised image quality. An automatic 3D segmentation method has been developed to isolate and measure the relative elasticity of every individual retinal layer. Two different segmentation schemes based on random walker and dynamic programming are implemented. The algorithm has been validated using a 3D region of the porcine retina, where individual layers have been isolated and analyzed using statistical methods. The errors compared to manual segmentation will be calculated.

  18. Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors

    Directory of Open Access Journals (Sweden)

    Andrés Ortiz

    2013-01-01

    Full Text Available Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE and has been assessed using real brain images from the Internet Brain Image Repository (IBSR.

  19. Oblique needle segmentation and tracking for 3D TRUS guided prostate brachytherapy

    International Nuclear Information System (INIS)

    Wei Zhouping; Gardi, Lori; Downey, Donal B.; Fenster, Aaron

    2005-01-01

    An algorithm was developed in order to segment and track brachytherapy needles inserted along oblique trajectories. Three-dimensional (3D) transrectal ultrasound (TRUS) images of the rigid rod simulating the needle inserted into the tissue-mimicking agar and chicken breast phantoms were obtained to test the accuracy of the algorithm under ideal conditions. Because the robot possesses high positioning and angulation accuracies, we used the robot as a ''gold standard,'' and compared the results of algorithm segmentation to the values measured by the robot. Our testing results showed that the accuracy of the needle segmentation algorithm depends on the needle insertion distance into the 3D TRUS image and the angulations with respect to the TRUS transducer, e.g., at a 10 deg. insertion anglulation in agar phantoms, the error of the algorithm in determining the needle tip position was less than 1 mm when the insertion distance was greater than 15 mm. Near real-time needle tracking was achieved by scanning a small volume containing the needle. Our tests also showed that, the segmentation time was less than 60 ms, and the scanning time was less than 1.2 s, when the insertion distance into the 3D TRUS image was less than 55 mm. In our needle tracking tests in chicken breast phantoms, the errors in determining the needle orientation were less than 2 deg. in robot yaw and 0.7 deg. in robot pitch orientations, for up to 20 deg. needle insertion angles with the TRUS transducer in the horizontal plane when the needle insertion distance was greater than 15 mm

  20. Computerized liver volumetry on MRI by using 3D geodesic active contour segmentation.

    Science.gov (United States)

    Huynh, Hieu Trung; Karademir, Ibrahim; Oto, Aytekin; Suzuki, Kenji

    2014-01-01

    Our purpose was to develop an accurate automated 3D liver segmentation scheme for measuring liver volumes on MRI. Our scheme for MRI liver volumetry consisted of three main stages. First, the preprocessing stage was applied to T1-weighted MRI of the liver in the portal venous phase to reduce noise and produce the boundary-enhanced image. This boundary-enhanced image was used as a speed function for a 3D fast-marching algorithm to generate an initial surface that roughly approximated the shape of the liver. A 3D geodesic-active-contour segmentation algorithm refined the initial surface to precisely determine the liver boundaries. The liver volumes determined by our scheme were compared with those manually traced by a radiologist, used as the reference standard. The two volumetric methods reached excellent agreement (intraclass correlation coefficient, 0.98) without statistical significance (p = 0.42). The average (± SD) accuracy was 99.4% ± 0.14%, and the average Dice overlap coefficient was 93.6% ± 1.7%. The mean processing time for our automated scheme was 1.03 ± 0.13 minutes, whereas that for manual volumetry was 24.0 ± 4.4 minutes (p volumetry based on our automated scheme agreed excellently with reference-standard volumetry, and it required substantially less completion time.

  1. Automated segmentation and geometrical modeling of the tricuspid aortic valve in 3D echocardiographic images.

    Science.gov (United States)

    Pouch, Alison M; Wang, Hongzhi; Takabe, Manabu; Jackson, Benjamin M; Sehgal, Chandra M; Gorman, Joseph H; Gorman, Robert C; Yushkevich, Paul A

    2013-01-01

    The aortic valve has been described with variable anatomical definitions, and the consistency of 2D manual measurement of valve dimensions in medical image data has been questionable. Given the importance of image-based morphological assessment in the diagnosis and surgical treatment of aortic valve disease, there is considerable need to develop a standardized framework for 3D valve segmentation and shape representation. Towards this goal, this work integrates template-based medial modeling and multi-atlas label fusion techniques to automatically delineate and quantitatively describe aortic leaflet geometry in 3D echocardiographic (3DE) images, a challenging task that has been explored only to a limited extent. The method makes use of expert knowledge of aortic leaflet image appearance, generates segmentations with consistent topology, and establishes a shape-based coordinate system on the aortic leaflets that enables standardized automated measurements. In this study, the algorithm is evaluated on 11 3DE images of normal human aortic leaflets acquired at mid systole. The clinical relevance of the method is its ability to capture leaflet geometry in 3DE image data with minimal user interaction while producing consistent measurements of 3D aortic leaflet geometry.

  2. Automated bone segmentation from large field of view 3D MR images of the hip joint

    International Nuclear Information System (INIS)

    Xia, Ying; Fripp, Jurgen; Chandra, Shekhar S; Schwarz, Raphael; Engstrom, Craig; Crozier, Stuart

    2013-01-01

    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. (paper)

  3. Automated bone segmentation from large field of view 3D MR images of the hip joint

    Science.gov (United States)

    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.

  4. Outdoor Illegal Construction Identification Algorithm Based on 3D Point Cloud Segmentation

    Science.gov (United States)

    An, Lu; Guo, Baolong

    2018-03-01

    Recently, various illegal constructions occur significantly in our surroundings, which seriously restrict the orderly development of urban modernization. The 3D point cloud data technology is used to identify the illegal buildings, which could address the problem above effectively. This paper proposes an outdoor illegal construction identification algorithm based on 3D point cloud segmentation. Initially, in order to save memory space and reduce processing time, a lossless point cloud compression method based on minimum spanning tree is proposed. Then, a ground point removing method based on the multi-scale filtering is introduced to increase accuracy. Finally, building clusters on the ground can be obtained using a region growing method, as a result, the illegal construction can be marked. The effectiveness of the proposed algorithm is verified using a publicly data set collected from the International Society for Photogrammetry and Remote Sensing (ISPRS).

  5. 3D segmentation of liver, kidneys and spleen from CT images

    International Nuclear Information System (INIS)

    Bekes, G.; Fidrich, M.; Nyul, L.G.; Mate, E.; Kuba, A.

    2007-01-01

    The clinicians often need to segment the abdominal organs for radiotherapy planning. Manual segmentation of these organs is very time-consuming, therefore automated methods are desired. We developed a semi-automatic segmentation method to outline liver, spleen and kidneys. It works on CT images without contrast intake that are acquired with a routine clinical protocol. From an initial surface around a user defined seed point, the segmentation of the organ is obtained by an active surface algorithm. Pre- and post-processing steps are used to adapt the general method for specific organs. The evaluation results show that the accuracy of our method is about 90%, which can be further improved with little manual editing, and that the precision is slightly higher than that of manual contouring. Our method is accurate, precise and fast enough to use in the clinical practice. (orig.)

  6. Adaptation of a 3D prostate cancer atlas for transrectal ultrasound guided target-specific biopsy

    International Nuclear Information System (INIS)

    Narayanan, R; Suri, J S; Werahera, P N; Barqawi, A; Crawford, E D; Shinohara, K; Simoneau, A R

    2008-01-01

    Due to lack of imaging modalities to identify prostate cancer in vivo, current TRUS guided prostate biopsies are taken randomly. Consequently, many important cancers are missed during initial biopsies. The purpose of this study was to determine the potential clinical utility of a high-speed registration algorithm for a 3D prostate cancer atlas. This 3D prostate cancer atlas provides voxel-level likelihood of cancer and optimized biopsy locations on a template space (Zhan et al 2007). The atlas was constructed from 158 expert annotated, 3D reconstructed radical prostatectomy specimens outlined for cancers (Shen et al 2004). For successful clinical implementation, the prostate atlas needs to be registered to each patient's TRUS image with high registration accuracy in a time-efficient manner. This is implemented in a two-step procedure, the segmentation of the prostate gland from a patient's TRUS image followed by the registration of the prostate atlas. We have developed a fast registration algorithm suitable for clinical applications of this prostate cancer atlas. The registration algorithm was implemented on a graphical processing unit (GPU) to meet the critical processing speed requirements for atlas guided biopsy. A color overlay of the atlas superposed on the TRUS image was presented to help pick statistically likely regions known to harbor cancer. We validated our fast registration algorithm using computer simulations of two optimized 7- and 12-core biopsy protocols to maximize the overall detection rate. Using a GPU, patient's TRUS image segmentation and atlas registration took less than 12 s. The prostate cancer atlas guided 7- and 12-core biopsy protocols had cancer detection rates of 84.81% and 89.87% respectively when validated on the same set of data. Whereas the sextant biopsy approach without the utility of 3D cancer atlas detected only 70.5% of the cancers using the same histology data. We estimate 10-20% increase in prostate cancer detection rates

  7. 3D medical image segmentation based on a continuous modelling of the volume

    International Nuclear Information System (INIS)

    Marque, I.

    1990-12-01

    Several medical imaging/techniques, including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) provide 3D information of the human body by means of a stack of parallel cross-sectional images. But a more sophisticated edge detection step has to be performed when the object under study is not well defined by its characteristic density or when an analytical knowledge of the surface of the object is useful for later processings. A new method for medical image segmentation has been developed: it uses the stability and differentiability properties of a continuous modelling of the 3D data. The idea is to build a system of Ordinary Differential Equations which the stable manifold is the surface of the object we are looking for. This technique has been applied to classical edge detection operators: threshold following, laplacian, gradient maximum in its direction. It can be used in 2D as well as in 3D and has been extended to seek particular points of the surface, such as local extrema. The major advantages of this method are as follows: the segmentation and boundary following steps are performed simultaneously, an analytical representation of the surface is obtained straightforwardly and complex objects in which branching problems may occur can be described automatically. Simulations on noisy synthetic images have induced a quantization step to test the sensitiveness to noise of our method with respect to each operator, and to study the influence of all the parameters. Last, this method has been applied to numerous real clinical exams: skull or femur images provided by CT, MR images of a cerebral tumor and of the ventricular system. These results show the reliability and the efficiency of this new method of segmentation [fr

  8. A 3-D Computational Study of a Variable Camber Continuous Trailing Edge Flap (VCCTEF) Spanwise Segment

    Science.gov (United States)

    Kaul, Upender K.; Nguyen, Nhan T.

    2015-01-01

    Results of a computational study carried out to explore the effects of various elastomer configurations joining spanwise contiguous Variable Camber Continuous Trailing Edge Flap (VCCTEF) segments are reported here. This research is carried out as a proof-of-concept study that will seek to push the flight envelope in cruise with drag optimization as the objective. The cruise conditions can be well off design such as caused by environmental conditions, maneuvering, etc. To handle these off-design conditions, flap deflection is used so when the flap is deflected in a given direction, the aircraft angle of attack changes accordingly to maintain a given lift. The angle of attack is also a design parameter along with the flap deflection. In a previous 2D study,1 the effect of camber was investigated and the results revealed some insight into the relative merit of various camber settings of the VCCTEF. The present state of the art has not advanced sufficiently to do a full 3-D viscous analysis of the whole NASA Generic Transport Model (GTM) wing with VCCTEF deployed with elastomers. Therefore, this study seeks to explore the local effects of three contiguous flap segments on lift and drag of a model devised here to determine possible trades among various flap deflections to achieve desired lift and drag results. Although this approach is an approximation, it provides new insights into the "local" effects of the relative deflections of the contiguous spanwise flap systems and various elastomer segment configurations. The present study is a natural extension of the 2-D study to assess these local 3-D effects. Design cruise condition at 36,000 feet at free stream Mach number of 0.797 and a mean aerodynamic chord (MAC) based Reynolds number of 30.734x10(exp 6) is simulated for an angle of attack (AoA) range of 0 to 6 deg. In the previous 2-D study, the calculations revealed that the parabolic arc camber (1x2x3) and circular arc camber (VCCTEF222) offered the best L

  9. A software tool for automatic classification and segmentation of 2D/3D medical images

    International Nuclear Information System (INIS)

    Strzelecki, Michal; Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur

    2013-01-01

    Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided

  10. A software tool for automatic classification and segmentation of 2D/3D medical images

    Energy Technology Data Exchange (ETDEWEB)

    Strzelecki, Michal, E-mail: michal.strzelecki@p.lodz.pl [Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz (Poland); Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur [Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz (Poland)

    2013-02-21

    Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided.

  11. 3D marker-controlled watershed for kidney segmentation in clinical CT exams.

    Science.gov (United States)

    Wieclawek, Wojciech

    2018-02-27

    Image segmentation is an essential and non trivial task in computer vision and medical image analysis. Computed tomography (CT) is one of the most accessible medical examination techniques to visualize the interior of a patient's body. Among different computer-aided diagnostic systems, the applications dedicated to kidney segmentation represent a relatively small group. In addition, literature solutions are verified on relatively small databases. The goal of this research is to develop a novel algorithm for fully automated kidney segmentation. This approach is designed for large database analysis including both physiological and pathological cases. This study presents a 3D marker-controlled watershed transform developed and employed for fully automated CT kidney segmentation. The original and the most complex step in the current proposition is an automatic generation of 3D marker images. The final kidney segmentation step is an analysis of the labelled image obtained from marker-controlled watershed transform. It consists of morphological operations and shape analysis. The implementation is conducted in a MATLAB environment, Version 2017a, using i.a. Image Processing Toolbox. 170 clinical CT abdominal studies have been subjected to the analysis. The dataset includes normal as well as various pathological cases (agenesis, renal cysts, tumors, renal cell carcinoma, kidney cirrhosis, partial or radical nephrectomy, hematoma and nephrolithiasis). Manual and semi-automated delineations have been used as a gold standard. Wieclawek Among 67 delineated medical cases, 62 cases are 'Very good', whereas only 5 are 'Good' according to Cohen's Kappa interpretation. The segmentation results show that mean values of Sensitivity, Specificity, Dice, Jaccard, Cohen's Kappa and Accuracy are 90.29, 99.96, 91.68, 85.04, 91.62 and 99.89% respectively. All 170 medical cases (with and without outlines) have been classified by three independent medical experts as 'Very good' in 143

  12. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.

    Science.gov (United States)

    Fechter, Tobias; Adebahr, Sonja; Baltas, Dimos; Ben Ayed, Ismail; Desrosiers, Christian; Dolz, Jose

    2017-12-01

    Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images. First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation were done on 50 CTs from two different datasets, with clinically used peer-reviewed esophagus contours. Results were assessed regarding spatial overlap and shape similarity. The esophagus contours generated by the proposed algorithm showed a mean Dice coefficient of 0.76 ± 0.11, an average symmetric square distance of 1.36 ± 0.90 mm, and an average Hausdorff distance of 11.68 ± 6.80, compared to the reference contours. These results translate to a very good agreement with reference contours and an increase in accuracy compared to existing methods. Furthermore, when considering the results reported in the literature for the publicly available Synapse dataset, our method outperformed all existing approaches, which suggests that the proposed method represents the current state-of-the-art for automatic esophagus segmentation. We show that a CNN can yield accurate estimations of esophagus location, and that

  13. 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

    Science.gov (United States)

    Dolz, Jose; Desrosiers, Christian; Ben Ayed, Ismail

    2018-04-15

    This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We address the problem via small kernels, allowing deeper architectures. We further model both local and global context by embedding intermediate-layer outputs in the final prediction, which encourages consistency between features extracted at different scales and embeds fine-grained information directly in the segmentation process. Our model is efficiently trained end-to-end on a graphics processing unit (GPU), in a single stage, exploiting the dense inference capabilities of fully CNNs. We performed comprehensive experiments over two publicly available datasets. First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then, we report a large-scale multi-site evaluation over 1112 unregistered subject datasets acquired from 17 different sites (ABIDE dataset), with ages ranging from 7 to 64 years, showing that our method is robust to various acquisition protocols, demographics and clinical factors. Our method yielded segmentations that are highly consistent with a standard atlas-based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps. This makes it convenient for massive multi-site neuroanatomical imaging studies. To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. 3D knee segmentation based on three MRI sequences from different planes.

    Science.gov (United States)

    Zhou, L; Chav, R; Cresson, T; Chartrand, G; de Guise, J

    2016-08-01

    In clinical practice, knee MRI sequences with 3.5~5 mm slice distance in sagittal, coronal, and axial planes are often requested for the knee examination since its acquisition is faster than high-resolution MRI sequence in a single plane, thereby reducing the probability of motion artifact. In order to take advantage of the three sequences from different planes, a 3D segmentation method based on the combination of three knee models obtained from the three sequences is proposed in this paper. In the method, the sub-segmentation is respectively performed with sagittal, coronal, and axial MRI sequence in the image coordinate system. With each sequence, an initial knee model is hierarchically deformed, and then the three deformed models are mapped to reference coordinate system defined by the DICOM standard and combined to obtain a patient-specific model. The experimental results verified that the three sub-segmentation results can complement each other, and their integration can compensate for the insufficiency of boundary information caused by 3.5~5 mm gap between consecutive slices. Therefore, the obtained patient-specific model is substantially more accurate than each sub-segmentation results.

  15. Fast segmentation of stained nuclei in terabyte-scale, time resolved 3D microscopy image stacks.

    Directory of Open Access Journals (Sweden)

    Johannes Stegmaier

    Full Text Available Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu's method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm's superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results.

  16. TU-F-BRF-06: 3D Pancreas MRI Segmentation Using Dictionary Learning and Manifold Clustering

    International Nuclear Information System (INIS)

    Gou, S; Rapacchi, S; Hu, P; Sheng, K

    2014-01-01

    Purpose: The recent advent of MRI guided radiotherapy machines has lent an exciting platform for soft tissue target localization during treatment. However, tools to efficiently utilize MRI images for such purpose have not been developed. Specifically, to efficiently quantify the organ motion, we develop an automated segmentation method using dictionary learning and manifold clustering (DLMC). Methods: Fast 3D HASTE and VIBE MR images of 2 healthy volunteers and 3 patients were acquired. A bounding box was defined to include pancreas and surrounding normal organs including the liver, duodenum and stomach. The first slice of the MRI was used for dictionary learning based on mean-shift clustering and K-SVD sparse representation. Subsequent images were iteratively reconstructed until the error is less than a preset threshold. The preliminarily segmentation was subject to the constraints of manifold clustering. The segmentation results were compared with the mean shift merging (MSM), level set (LS) and manual segmentation methods. Results: DLMC resulted in consistently higher accuracy and robustness than comparing methods. Using manual contours as the ground truth, the mean Dices indices for all subjects are 0.54, 0.56 and 0.67 for MSM, LS and DLMC, respectively based on the HASTE image. The mean Dices indices are 0.70, 0.77 and 0.79 for the three methods based on VIBE images. DLMC is clearly more robust on the patients with the diseased pancreas while LS and MSM tend to over-segment the pancreas. DLMC also achieved higher sensitivity (0.80) and specificity (0.99) combining both imaging techniques. LS achieved equivalent sensitivity on VIBE images but was more computationally inefficient. Conclusion: We showed that pancreas and surrounding normal organs can be reliably segmented based on fast MRI using DLMC. This method will facilitate both planning volume definition and imaging guidance during treatment

  17. TU-F-BRF-06: 3D Pancreas MRI Segmentation Using Dictionary Learning and Manifold Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Gou, S; Rapacchi, S; Hu, P; Sheng, K [UCLA School of Medicine, Los Angeles, CA (United States)

    2014-06-15

    Purpose: The recent advent of MRI guided radiotherapy machines has lent an exciting platform for soft tissue target localization during treatment. However, tools to efficiently utilize MRI images for such purpose have not been developed. Specifically, to efficiently quantify the organ motion, we develop an automated segmentation method using dictionary learning and manifold clustering (DLMC). Methods: Fast 3D HASTE and VIBE MR images of 2 healthy volunteers and 3 patients were acquired. A bounding box was defined to include pancreas and surrounding normal organs including the liver, duodenum and stomach. The first slice of the MRI was used for dictionary learning based on mean-shift clustering and K-SVD sparse representation. Subsequent images were iteratively reconstructed until the error is less than a preset threshold. The preliminarily segmentation was subject to the constraints of manifold clustering. The segmentation results were compared with the mean shift merging (MSM), level set (LS) and manual segmentation methods. Results: DLMC resulted in consistently higher accuracy and robustness than comparing methods. Using manual contours as the ground truth, the mean Dices indices for all subjects are 0.54, 0.56 and 0.67 for MSM, LS and DLMC, respectively based on the HASTE image. The mean Dices indices are 0.70, 0.77 and 0.79 for the three methods based on VIBE images. DLMC is clearly more robust on the patients with the diseased pancreas while LS and MSM tend to over-segment the pancreas. DLMC also achieved higher sensitivity (0.80) and specificity (0.99) combining both imaging techniques. LS achieved equivalent sensitivity on VIBE images but was more computationally inefficient. Conclusion: We showed that pancreas and surrounding normal organs can be reliably segmented based on fast MRI using DLMC. This method will facilitate both planning volume definition and imaging guidance during treatment.

  18. A TN adaptive ray effect mitigation for Styx 3D

    International Nuclear Information System (INIS)

    Aussourd, Ch.

    2003-01-01

    Ray effect (RE) is a non-physical flaw of discrete ordinate methods characterized by spurious rays becoming sharper when small bright neutron sources radiate through low scattering media. While this effect has low impact on integral parameters (α and K eff ), it can ruin simulations of diagnostic problem in terms of neutron dose received by a detector. 3 cumulative factors may affect the RE intensity: the source size, the scattering cross-sections, and the mesh size. We introduce an adaptive Ray Effect Mitigation (REM) aimed at locally improving the angular accuracy of the 3 dimension AMR (adaptive mesh refinement) deterministic neutron transport code Styx. Promising preliminary results have been obtained using a recursively refined octahedron based sphere tessellation in place of the standard S N quadrature. This angular mesh, referred to as T N , has valuable features for local refinement

  19. Segmentation, Reconstruction, and Analysis of Blood Thrombus Formation in 3D 2-Photon Microscopy Images

    Directory of Open Access Journals (Sweden)

    Xu Zhiliang

    2010-01-01

    Full Text Available We study the problem of segmenting, reconstructing, and analyzing the structure growth of thrombi (clots in blood vessels in vivo based on 2-photon microscopic image data. First, we develop an algorithm for segmenting clots in 3D microscopic images based on density-based clustering and methods for dealing with imaging artifacts. Next, we apply the union-of-balls (or alpha-shape algorithm to reconstruct the boundary of clots in 3D. Finally, we perform experimental studies and analysis on the reconstructed clots and obtain quantitative data of thrombus growth and structures. We conduct experiments on laser-induced injuries in vessels of two types of mice (the wild type and the type with low levels of coagulation factor VII and analyze and compare the developing clot structures based on their reconstructed clots from image data. The results we obtain are of biomedical significance. Our quantitative analysis of the clot composition leads to better understanding of the thrombus development, and is valuable to the modeling and verification of computational simulation of thrombogenesis.

  20. Efficient 3D multi-region prostate MRI segmentation using dual optimization.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron

    2013-01-01

    Efficient and accurate extraction of the prostate, in particular its clinically meaningful sub-regions from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, we propose a novel multi-region segmentation approach to simultaneously locating the boundaries of the prostate and its two major sub-regions: the central gland and the peripheral zone. The proposed method utilizes the prior knowledge of the spatial region consistency and employs a customized prostate appearance model to simultaneously segment multiple clinically meaningful regions. We solve the resulted challenging combinatorial optimization problem by means of convex relaxation, for which we introduce a novel spatially continuous flow-maximization model and demonstrate its duality to the investigated convex relaxed optimization problem with the region consistency constraint. Moreover, the proposed continuous max-flow model naturally leads to a new and efficient continuous max-flow based algorithm, which enjoys great advantages in numerics and can be readily implemented on GPUs. Experiments using 15 T2-weighted 3D prostate MR images, by inter- and intra-operator variability, demonstrate the promising performance of the proposed approach.

  1. Facilitating coronary artery evaluation in MDCT using a 3D automatic vessel segmentation tool

    International Nuclear Information System (INIS)

    Fawad Khan, M.; Gurung, Jessen; Maataoui, Adel; Brehmer, Boris; Herzog, Christopher; Vogl, Thomas J.; Wesarg, Stefan; Dogan, Selami; Ackermann, Hanns; Assmus, Birgit

    2006-01-01

    The purpose of this study was to investigate a 3D coronary artery segmentation algorithm using 16-row MDCT data sets. Fifty patients underwent cardiac CT (Sensation 16, Siemens) and coronary angiography. Automatic and manual detection of coronary artery stenosis was performed. A 3D coronary artery segmentation algorithm (Fraunhofer Institute for Computer Graphics, Darmstadt) was used for automatic evaluation. All significant stenoses (>50%) in vessels >1.5 mm in diameter were protocoled. Each detection tool was used by one reader who was blinded to the results of the other detection method and the results of coronary angiography. Sensitivity and specificity were determined for automatic and manual detection as well as was the time for both CT-based evaluation methods. The overall sensitivity and specificity of the automatic and manual approach were 93.1 vs. 95.83% and 86.1 vs. 81.9%. The time required for automatic evaluation was significantly shorter than with the manual approach, i.e., 246.04±43.17 s for the automatic approach and 526.88±45.71 s for the manual approach (P<0.0001). In 94% of the coronary artery branches, automatic detection required less time than the manual approach. Automatic coronary vessel evaluation is feasible. It reduces the time required for cardiac CT evaluation with similar sensitivity and specificity as well as facilitates the evaluation of MDCT coronary angiography in a standardized fashion. (orig.)

  2. Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge.

    Science.gov (United States)

    Zheng, Guoyan; Chu, Chengwen; Belavý, Daniel L; Ibragimov, Bulat; Korez, Robert; Vrtovec, Tomaž; Hutt, Hugo; Everson, Richard; Meakin, Judith; Andrade, Isabel Lŏpez; Glocker, Ben; Chen, Hao; Dou, Qi; Heng, Pheng-Ann; Wang, Chunliang; Forsberg, Daniel; Neubert, Aleš; Fripp, Jurgen; Urschler, Martin; Stern, Darko; Wimmer, Maria; Novikov, Alexey A; Cheng, Hui; Armbrecht, Gabriele; Felsenberg, Dieter; Li, Shuo

    2017-01-01

    The evaluation of changes in Intervertebral Discs (IVDs) with 3D Magnetic Resonance (MR) Imaging (MRI) can be of interest for many clinical applications. This paper presents the evaluation of both IVD localization and IVD segmentation methods submitted to the Automatic 3D MRI IVD Localization and Segmentation challenge, held at the 2015 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2015) with an on-site competition. With the construction of a manually annotated reference data set composed of 25 3D T2-weighted MR images acquired from two different studies and the establishment of a standard validation framework, quantitative evaluation was performed to compare the results of methods submitted to the challenge. Experimental results show that overall the best localization method achieves a mean localization distance of 0.8 mm and the best segmentation method achieves a mean Dice of 91.8%, a mean average absolute distance of 1.1 mm and a mean Hausdorff distance of 4.3 mm, respectively. The strengths and drawbacks of each method are discussed, which provides insights into the performance of different IVD localization and segmentation methods. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. Repeatability of a 3D multi-segment foot model protocol in presence of foot deformities.

    Science.gov (United States)

    Deschamps, Kevin; Staes, Filip; Bruyninckx, Herman; Busschots, Ellen; Matricali, Giovanni A; Spaepen, Pieter; Meyer, Christophe; Desloovere, Kaat

    2012-07-01

    Repeatability studies on 3D multi-segment foot models (3DMFMs) have mainly considered healthy participants which contrasts with the widespread application of these models to evaluate foot pathologies. The current study aimed at establishing the repeatability of the 3DMFM described by Leardini et al. in presence of foot deformities. Foot kinematics of eight adult participants were analyzed using a repeated-measures design including two therapists with different levels of experience. The inter-trial variability was higher compared to the kinematics of healthy subjects. Consideration of relative angles resulted in the lowest inter-session variability. The absolute 3D rotations between the Sha-Cal and Cal-Met seem to have the lowest variability in both therapists. A general trend towards higher σ(sess)/σ(trial) ratios was observed when the midfoot was involved. The current study indicates that not only relative 3D rotations and planar angles can be measured consistently in patients, also a number of absolute parameters can be consistently measured serving as basis for the decision making process. Copyright © 2012 Elsevier B.V. All rights reserved.

  4. CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

    Science.gov (United States)

    2013-01-01

    The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening. PMID:23938087

  5. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.

    Science.gov (United States)

    Valverde, Sergi; Cabezas, Mariano; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Oliver, Arnau; Lladó, Xavier

    2017-07-15

    In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Pancreas segmentation from 3D abdominal CT images using patient-specific weighted subspatial probabilistic atlases

    Science.gov (United States)

    Karasawa, Kenichi; Oda, Masahiro; Hayashi, Yuichiro; Nimura, Yukitaka; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Rueckert, Daniel; Mori, Kensaku

    2015-03-01

    Abdominal organ segmentations from CT volumes are now widely used in the computer-aided diagnosis and surgery assistance systems. Among abdominal organs, the pancreas is especially difficult to segment because of its large individual differences of the shape and position. In this paper, we propose a new pancreas segmentation method from 3D abdominal CT volumes using patient-specific weighted-subspatial probabilistic atlases. First of all, we perform normalization of organ shapes in training volumes and an input volume. We extract the Volume Of Interest (VOI) of the pancreas from the training volumes and an input volume. We divide each training VOI and input VOI into some cubic regions. We use a nonrigid registration method to register these cubic regions of the training VOI to corresponding regions of the input VOI. Based on the registration results, we calculate similarities between each cubic region of the training VOI and corresponding region of the input VOI. We select cubic regions of training volumes having the top N similarities in each cubic region. We subspatially construct probabilistic atlases weighted by the similarities in each cubic region. After integrating these probabilistic atlases in cubic regions into one, we perform a rough-to-precise segmentation of the pancreas using the atlas. The results of the experiments showed that utilization of the training volumes having the top N similarities in each cubic region led good results of the pancreas segmentation. The Jaccard Index and the average surface distance of the result were 58.9% and 2.04mm on average, respectively.

  7. Segmentation process significantly influences the accuracy of 3D surface models derived from cone beam computed tomography

    NARCIS (Netherlands)

    Fourie, Zacharias; Damstra, Janalt; Schepers, Rutger H; Gerrits, Pieter; Ren, Yijin

    AIMS: To assess the accuracy of surface models derived from 3D cone beam computed tomography (CBCT) with two different segmentation protocols. MATERIALS AND METHODS: Seven fresh-frozen cadaver heads were used. There was no conflict of interests in this study. CBCT scans were made of the heads and 3D

  8. Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields.

    Science.gov (United States)

    Robinson, Sean; Guyon, Laurent; Nevalainen, Jaakko; Toriseva, Mervi; Åkerfelt, Malin; Nees, Matthias

    2015-01-01

    Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.

  9. Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields.

    Directory of Open Access Journals (Sweden)

    Sean Robinson

    Full Text Available Organotypic, three dimensional (3D cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs. The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.

  10. Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images

    Science.gov (United States)

    Zhou, Xiangrong; Yamada, Kazuma; Kojima, Takuya; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

    2018-02-01

    The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

  11. 3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilation

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Kishimoto, Jessica; Chen, Yimin; de Ribaupierre, Sandrine; Chiu, Bernard; Fenster, Aaron

    2015-03-01

    Intraventricular hemorrhage (IVH) or bleed within the brain is a common condition among pre-term infants that occurs in very low birth weight preterm neonates. The prognosis is further worsened by the development of progressive ventricular dilatation, i.e., post-hemorrhagic ventricle dilation (PHVD), which occurs in 10-30% of IVH patients. In practice, predicting PHVD accurately and determining if that specific patient with ventricular dilatation requires the ability to measure accurately ventricular volume. While monitoring of PHVD in infants is typically done by repeated US and not MRI, once the patient has been treated, the follow-up over the lifetime of the patient is done by MRI. While manual segmentation is still seen as a gold standard, it is extremely time consuming, and therefore not feasible in a clinical context, and it also has a large inter- and intra-observer variability. This paper proposes a segmentation algorithm to extract the cerebral ventricles from 3D T1- weighted MR images of pre-term infants with PHVD. The proposed segmentation algorithm makes use of the convex optimization technique combined with the learned priors of image intensities and label probabilistic map, which is built from a multi-atlas registration scheme. The leave-one-out cross validation using 7 PHVD patient T1 weighted MR images showed that the proposed method yielded a mean DSC of 89.7% +/- 4.2%, a MAD of 2.6 +/- 1.1 mm, a MAXD of 17.8 +/- 6.2 mm, and a VD of 11.6% +/- 5.9%, suggesting a good agreement with manual segmentations.

  12. 3D automatic anatomy segmentation based on iterative graph-cut-ASM.

    Science.gov (United States)

    Chen, Xinjian; Bagci, Ulas

    2011-08-01

    This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for

  13. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    International Nuclear Information System (INIS)

    Chen, Xinjian; Bagci, Ulas

    2011-01-01

    Purpose: This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. Methods: The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al.[Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. Results: The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 deg. and 0.03, and over all foot bones are about 3.5709 mm, 0.35 deg. and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and

  14. Acquisition and automated 3-D segmentation of respiratory/cardiac-gated PET transmission images

    International Nuclear Information System (INIS)

    Reutter, B.W.; Klein, G.J.; Brennan, K.M.; Huesman, R.H.

    1996-01-01

    To evaluate the impact of respiratory motion on attenuation correction of cardiac PET data, we acquired and automatically segmented gated transmission data for a dog breathing on its own under gas anesthesia. Data were acquired for 20 min on a CTI/Siemens ECAT EXACT HR (47-slice) scanner configured for 12 gates in a static study, Two respiratory gates were obtained using data from a pneumatic bellows placed around the dog's chest, in conjunction with 6 cardiac gates from standard EKG gating. Both signals were directed to a LabVIEW-controlled Macintosh, which translated them into one of 12 gate addresses. The respiratory gating threshold was placed near end-expiration to acquire 6 cardiac-gated datasets at end-expiration and 6 cardiac-gated datasets during breaths. Breaths occurred about once every 10 sec and lasted about 1-1.5 sec. For each respiratory gate, data were summed over cardiac gates and torso and lung surfaces were segmented automatically using a differential 3-D edge detection algorithm. Three-dimensional visualizations showed that lung surfaces adjacent to the heart translated 9 mm inferiorly during breaths. Our results suggest that respiration-compensated attenuation correction is feasible with a modest amount of gated transmission data and is necessary for accurate quantitation of high-resolution gated cardiac PET data

  15. Active surface model improvement by energy function optimization for 3D segmentation.

    Science.gov (United States)

    Azimifar, Zohreh; Mohaddesi, Mahsa

    2015-04-01

    This paper proposes an optimized and efficient active surface model by improving the energy functions, searching method, neighborhood definition and resampling criterion. Extracting an accurate surface of the desired object from a number of 3D images using active surface and deformable models plays an important role in computer vision especially medical image processing. Different powerful segmentation algorithms have been suggested to address the limitations associated with the model initialization, poor convergence to surface concavities and slow convergence rate. This paper proposes a method to improve one of the strongest and recent segmentation algorithms, namely the Decoupled Active Surface (DAS) method. We consider a gradient of wavelet edge extracted image and local phase coherence as external energy to extract more information from images and we use curvature integral as internal energy to focus on high curvature region extraction. Similarly, we use resampling of points and a line search for point selection to improve the accuracy of the algorithm. We further employ an estimation of the desired object as an initialization for the active surface model. A number of tests and experiments have been done and the results show the improvements with regards to the extracted surface accuracy and computational time of the presented algorithm compared with the best and recent active surface models. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2018-04-01

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

  17. Segmentation process significantly influences the accuracy of 3D surface models derived from cone beam computed tomography

    International Nuclear Information System (INIS)

    Fourie, Zacharias; Damstra, Janalt; Schepers, Rutger H.; Gerrits, Peter O.; Ren Yijin

    2012-01-01

    Aims: To assess the accuracy of surface models derived from 3D cone beam computed tomography (CBCT) with two different segmentation protocols. Materials and methods: Seven fresh-frozen cadaver heads were used. There was no conflict of interests in this study. CBCT scans were made of the heads and 3D surface models were created of the mandible using two different segmentation protocols. The one series of 3D models was segmented by a commercial software company, while the other series was done by an experienced 3D clinician. The heads were then macerated following a standard process. A high resolution laser surface scanner was used to make a 3D model of the macerated mandibles, which acted as the reference 3D model or “gold standard”. The 3D models generated from the two rendering protocols were compared with the “gold standard” using a point-based rigid registration algorithm to superimpose the three 3D models. The linear difference at 25 anatomic and cephalometric landmarks between the laser surface scan and the 3D models generate from the two rendering protocols was measured repeatedly in two sessions with one week interval. Results: The agreement between the repeated measurement was excellent (ICC = 0.923–1.000). The mean deviation from the gold standard by the 3D models generated from the CS group was 0.330 mm ± 0.427, while the mean deviation from the Clinician's rendering was 0.763 mm ± 0.392. The surface models segmented by both CS and DS protocols tend to be larger than those of the reference models. In the DS group, the biggest mean differences with the LSS models were found at the points ConLatR (CI: 0.83–1.23), ConMedR (CI: −3.16 to 2.25), CoLatL (CI: −0.68 to 2.23), Spine (CI: 1.19–2.28), ConAntL (CI: 0.84–1.69), ConSupR (CI: −1.12 to 1.47) and RetMolR (CI: 0.84–1.80). Conclusion: The Commercially segmented models resembled the reality more closely than the Doctor's segmented models. If 3D models are needed for surgical drilling

  18. 3-D segmentation of retinal blood vessels in spectral-domain OCT volumes of the optic nerve head

    Science.gov (United States)

    Lee, Kyungmoo; Abràmoff, Michael D.; Niemeijer, Meindert; Garvin, Mona K.; Sonka, Milan

    2010-03-01

    Segmentation of retinal blood vessels can provide important information for detecting and tracking retinal vascular diseases including diabetic retinopathy, arterial hypertension, arteriosclerosis and retinopathy of prematurity (ROP). Many studies on 2-D segmentation of retinal blood vessels from a variety of medical images have been performed. However, 3-D segmentation of retinal blood vessels from spectral-domain optical coherence tomography (OCT) volumes, which is capable of providing geometrically accurate vessel models, to the best of our knowledge, has not been previously studied. The purpose of this study is to develop and evaluate a method that can automatically detect 3-D retinal blood vessels from spectral-domain OCT scans centered on the optic nerve head (ONH). The proposed method utilized a fast multiscale 3-D graph search to segment retinal surfaces as well as a triangular mesh-based 3-D graph search to detect retinal blood vessels. An experiment on 30 ONH-centered OCT scans (15 right eye scans and 15 left eye scans) from 15 subjects was performed, and the mean unsigned error in 3-D of the computer segmentations compared with the independent standard obtained from a retinal specialist was 3.4 +/- 2.5 voxels (0.10 +/- 0.07 mm).

  19. REGISTRATION OF OVERLAPPING 3D POINT CLO UDS USING EXTRACTED LINE SEGMENTS

    Directory of Open Access Journals (Sweden)

    Poręba Martyna

    2014-12-01

    Full Text Available The registration of 3D point clouds collected from different scanner positions is necessary in order to avoid occlusions, ensure a full coverage of areas, and collect useful data for analyzing an d documenting the surrounding environment. This procedure involves three main stages: 1 choosing appropriate features, which can be reliably extracted; 2 matching conjugate primitives; 3 estimating the transformation parameters. Currently, points and spheres are most frequently chosen as the registration features. However, due to limited point cloud resolution, proper identification and precise measurement of a common point within the overlapping laser data is almost impossible. One possible solution to this problem may be a registration process based on the Iterative Closest Point (ICP algorithm or its variation. Alternatively, planar and linear feature - based registration techniques can also be applied. In this paper, we propose the use of line segments obtained from intersecting planes modelled within individual scans. Such primitives can be easily extracted even from low - density point clouds. Working with synthetic data, several existing line - based registration methods are evaluated according to their robustness to noise and the precision of the estimated transformation parameters. For the purpose of quantitative assessment, an accuracy criterion based on a modified Hausdorff distance is defined. Since a n automated matching of segments is a challenging task that influences the correctness of the transformation parameters, a correspondence - finding algorithm is developed. The tests show that our matching algorithm provides a correct pairing with an accuracy of 99 % at least, and about 8% of omitted line pairs.

  20. Efficient Semi-Automatic 3D Segmentation for Neuron Tracing in Electron Microscopy Images

    Science.gov (United States)

    Jones, Cory; Liu, Ting; Cohan, Nathaniel Wood; Ellisman, Mark; Tasdizen, Tolga

    2015-01-01

    0.1. Background In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming. 0.2. New Method We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links. 0.3. Results We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results. 0.4. Comparison with Existing Methods Post-automatic correction methods have also been used in [1] and [2]. These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as [3] and [4] and are inherently different than our method. 0.5. Conclusion Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication. PMID:25769273

  1. Fast CSF MRI for brain segmentation; Cross-validation by comparison with 3D T1-based brain segmentation methods

    DEFF Research Database (Denmark)

    van der Kleij, Lisa A.; de Bresser, Jeroen; Hendrikse, Jeroen

    2018-01-01

    ObjectiveIn previous work we have developed a fast sequence that focusses on cerebrospinal fluid (CSF) based on the long T-2 of CSF. By processing the data obtained with this CSF MRI sequence, brain parenchymal volume (BPV) and intracranial volume (ICV) can be automatically obtained. The aim...... of this study was to assess the precision of the BPV and ICV measurements of the CSF MRI sequence and to validate the CSF MRI sequence by comparison with 3D T-1-based brain segmentation methods.Materials and methodsTen healthy volunteers (2 females; median age 28 years) were scanned (3T MRI) twice......cc) and CSF HR (5 +/- 5/4 +/- 2cc) were comparable to FSL HR (9 +/- 11/19 +/- 23cc), FSL LR (7 +/- 4,6 +/- 5cc),FreeSurfer HR (5 +/- 3/14 +/- 8cc), FreeSurfer LR (9 +/- 8,12 +/- 10cc), and SPM HR (5 +/- 3/4 +/- 7cc), and SPM LR (5 +/- 4,5 +/- 3cc). The correlation between the measured volumes...

  2. Soft computing approach to 3D lung nodule segmentation in CT.

    Science.gov (United States)

    Badura, P; Pietka, E

    2014-10-01

    This paper presents a novel, multilevel approach to the segmentation of various types of pulmonary nodules in computed tomography studies. It is based on two branches of computational intelligence: the fuzzy connectedness (FC) and the evolutionary computation. First, the image and auxiliary data are prepared for the 3D FC analysis during the first stage of an algorithm - the masks generation. Its main goal is to process some specific types of nodules connected to the pleura or vessels. It consists of some basic image processing operations as well as dedicated routines for the specific cases of nodules. The evolutionary computation is performed on the image and seed points in order to shorten the FC analysis and improve its accuracy. After the FC application, the remaining vessels are removed during the postprocessing stage. The method has been validated using the first dataset of studies acquired and described by the Lung Image Database Consortium (LIDC) and by its latest release - the LIDC-IDRI (Image Database Resource Initiative) database. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. A conservative scheme for 2D and 3D adaptive semi-Lagrangian advection

    OpenAIRE

    Behrens, Jörn; Mentrup, Lars

    2005-01-01

    This article describes a 2D and 3D adaptive and mass conservingsemi-Lagrangian advection scheme for atmospheric transport problems. Fromthe integral form of the conservation law we derive a semi-Lagrangian schemebased on conservation of mass along trajectories. The mapping of mass fromthe old (adaptively refined and possibly different) grid to the upstream controlvolume is performed by a mass packet based scheme, essentially consistingof a sub-grid discretization. We validate the new adaptive...

  4. Computer-aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours

    International Nuclear Information System (INIS)

    Way, Ted W.; Hadjiiski, Lubomir M.; Sahiner, Berkman; Chan, H.-P.; Cascade, Philip N.; Kazerooni, Ella A.; Bogot, Naama; Zhou Chuan

    2006-01-01

    We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (A z ) of 0.83±0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC

  5. Complex adaptation-based LDR image rendering for 3D image reconstruction

    Science.gov (United States)

    Lee, Sung-Hak; Kwon, Hyuk-Ju; Sohng, Kyu-Ik

    2014-07-01

    A low-dynamic tone-compression technique is developed for realistic image rendering that can make three-dimensional (3D) images similar to realistic scenes by overcoming brightness dimming in the 3D display mode. The 3D surround provides varying conditions for image quality, illuminant adaptation, contrast, gamma, color, sharpness, and so on. In general, gain/offset adjustment, gamma compensation, and histogram equalization have performed well in contrast compression; however, as a result of signal saturation and clipping effects, image details are removed and information is lost on bright and dark areas. Thus, an enhanced image mapping technique is proposed based on space-varying image compression. The performance of contrast compression is enhanced with complex adaptation in a 3D viewing surround combining global and local adaptation. Evaluating local image rendering in view of tone and color expression, noise reduction, and edge compensation confirms that the proposed 3D image-mapping model can compensate for the loss of image quality in the 3D mode.

  6. Integrated 3D density modelling and segmentation of the Dead Sea Transform

    Science.gov (United States)

    Götze, H.-J.; El-Kelani, R.; Schmidt, S.; Rybakov, M.; Hassouneh, M.; Förster, H.-J.; Ebbing, J.

    2007-04-01

    A 3D interpretation of the newly compiled Bouguer anomaly in the area of the “Dead Sea Rift” is presented. A high-resolution 3D model constrained with the seismic results reveals the crustal thickness and density distribution beneath the Arava/Araba Valley (AV), the region between the Dead Sea and the Gulf of Aqaba/Elat. The Bouguer anomalies along the axial portion of the AV, as deduced from the modelling results, are mainly caused by deep-seated sedimentary basins ( D > 10 km). An inferred zone of intrusion coincides with the maximum gravity anomaly on the eastern flank of the AV. The intrusion is displaced at different sectors along the NNW-SSE direction. The zone of maximum crustal thinning (depth 30 km) is attained in the western sector at the Mediterranean. The southeastern plateau, on the other hand, shows by far the largest crustal thickness of the region (38-42 km). Linked to the left lateral movement of approx. 105 km at the boundary between the African and Arabian plate, and constrained with recent seismic data, a small asymmetric topography of the Moho beneath the Dead Sea Transform (DST) was modelled. The thickness and density of the crust suggest that the AV is underlain by continental crust. The deep basins, the relatively large intrusion and the asymmetric topography of the Moho lead to the conclusion that a small-scale asthenospheric upwelling could be responsible for the thinning of the crust and subsequent creation of the Dead Sea basin during the left lateral movement. A clear segmentation along the strike of the DST was obtained by curvature analysis: the northern part in the neighbourhood of the Dead Sea is characterised by high curvature of the residual gravity field. Flexural rigidity calculations result in very low values of effective elastic lithospheric thickness ( t e < 5 km). This points to decoupling of crust in the Dead Sea area. In the central, AV the curvature is less pronounced and t e increases to approximately 10 km

  7. Rigid 3D-3D registration of TOF MRA integrating vessel segmentation for quantification of recurrence volumes after coiling cerebral aneurysm

    International Nuclear Information System (INIS)

    Saering, Dennis; Forkert, Nils Daniel; Fiehler, Jens; Ries, Thorsten

    2012-01-01

    A fast and reproducible quantification of the recurrence volume of coiled aneurysms is required to enable a more timely evaluation of new coils. This paper presents two registration schemes for the semi-automatic quantification of aneurysm recurrence volumes based on baseline and follow-up 3D MRA TOF datasets. The quantification of shape changes requires a previous definition of corresponding structures in both datasets. For this, two different rigid registration methods have been developed and evaluated. Besides a state-of-the-art rigid registration method, a second approach integrating vessel segmentations is presented. After registration, the aneurysm recurrence volume can be calculated based on the difference image. The computed volumes were compared to manually extracted volumes. An evaluation based on 20 TOF MRA datasets (baseline and follow-up) of ten patients showed that both registration schemes are generally capable of providing sufficient registration results. Regarding the quantification of aneurysm recurrence volumes, the results suggest that the second segmentation-based registration method yields better results, while a reduction of the computation and interaction time is achieved at the same time. The proposed registration scheme incorporating vessel segmentation enables an improved quantification of recurrence volumes of coiled aneurysms with reduced computation and interaction time. (orig.)

  8. 3D-SoftChip: A Novel Architecture for Next-Generation Adaptive Computing Systems

    Directory of Open Access Journals (Sweden)

    Lee Mike Myung-Ok

    2006-01-01

    Full Text Available This paper introduces a novel architecture for next-generation adaptive computing systems, which we term 3D-SoftChip. The 3D-SoftChip is a 3-dimensional (3D vertically integrated adaptive computing system combining state-of-the-art processing and 3D interconnection technology. It comprises the vertical integration of two chips (a configurable array processor and an intelligent configurable switch through an indium bump interconnection array (IBIA. The configurable array processor (CAP is an array of heterogeneous processing elements (PEs, while the intelligent configurable switch (ICS comprises a switch block, 32-bit dedicated RISC processor for control, on-chip program/data memory, data frame buffer, along with a direct memory access (DMA controller. This paper introduces the novel 3D-SoftChip architecture for real-time communication and multimedia signal processing as a next-generation computing system. The paper further describes the advanced HW/SW codesign and verification methodology, including high-level system modeling of the 3D-SoftChip using SystemC, being used to determine the optimum hardware specification in the early design stage.

  9. In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation

    Directory of Open Access Journals (Sweden)

    Chunlei Xia

    2015-08-01

    Full Text Available In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions.

  10. Simulating streamer discharges in 3D with the parallel adaptive Afivo framework

    NARCIS (Netherlands)

    H.J. Teunissen (Jannis); U. M. Ebert (Ute)

    2017-01-01

    htmlabstractWe present an open-source plasma fluid code for 2D, cylindrical and 3D simulations of streamer discharges, based on the Afivo framework that features adaptive mesh refinement, geometric multigrid methods for Poisson's equation, and OpenMP parallelism. We describe the numerical

  11. Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.

    Science.gov (United States)

    Ren, Xuhua; Xiang, Lei; Nie, Dong; Shao, Yeqin; Zhang, Huan; Shen, Dinggang; Wang, Qian

    2018-02-26

    Accurate 3D image segmentation is a crucial step in radiation therapy planning of head and neck tumors. These segmentation results are currently obtained by manual outlining of tissues, which is a tedious and time-consuming procedure. Automatic segmentation provides an alternative solution, which, however, is often difficult for small tissues (i.e., chiasm and optic nerves in head and neck CT images) because of their small volumes and highly diverse appearance/shape information. In this work, we propose to interleave multiple 3D Convolutional Neural Networks (3D-CNNs) to attain automatic segmentation of small tissues in head and neck CT images. A 3D-CNN was designed to segment each structure of interest. To make full use of the image appearance information, multiscale patches are extracted to describe the center voxel under consideration and then input to the CNN architecture. Next, as neighboring tissues are often highly related in the physiological and anatomical perspectives, we interleave the CNNs designated for the individual tissues. In this way, the tentative segmentation result of a specific tissue can contribute to refine the segmentations of other neighboring tissues. Finally, as more CNNs are interleaved and cascaded, a complex network of CNNs can be derived, such that all tissues can be jointly segmented and iteratively refined. Our method was validated on a set of 48 CT images, obtained from the Medical Image Computing and Computer Assisted Intervention (MICCAI) Challenge 2015. The Dice coefficient (DC) and the 95% Hausdorff Distance (95HD) are computed to measure the accuracy of the segmentation results. The proposed method achieves higher segmentation accuracy (with the average DC: 0.58 ± 0.17 for optic chiasm, and 0.71 ± 0.08 for optic nerve; 95HD: 2.81 ± 1.56 mm for optic chiasm, and 2.23 ± 0.90 mm for optic nerve) than the MICCAI challenge winner (with the average DC: 0.38 for optic chiasm, and 0.68 for optic nerve; 95HD: 3.48 for

  12. Automated segmentation of blood-flow regions in large thoracic arteries using 3D-cine PC-MRI measurements.

    Science.gov (United States)

    van Pelt, Roy; Nguyen, Huy; ter Haar Romeny, Bart; Vilanova, Anna

    2012-03-01

    Quantitative analysis of vascular blood flow, acquired by phase-contrast MRI, requires accurate segmentation of the vessel lumen. In clinical practice, 2D-cine velocity-encoded slices are inspected, and the lumen is segmented manually. However, segmentation of time-resolved volumetric blood-flow measurements is a tedious and time-consuming task requiring automation. Automated segmentation of large thoracic arteries, based solely on the 3D-cine phase-contrast MRI (PC-MRI) blood-flow data, was done. An active surface model, which is fast and topologically stable, was used. The active surface model requires an initial surface, approximating the desired segmentation. A method to generate this surface was developed based on a voxel-wise temporal maximum of blood-flow velocities. The active surface model balances forces, based on the surface structure and image features derived from the blood-flow data. The segmentation results were validated using volunteer studies, including time-resolved 3D and 2D blood-flow data. The segmented surface was intersected with a velocity-encoded PC-MRI slice, resulting in a cross-sectional contour of the lumen. These cross-sections were compared to reference contours that were manually delineated on high-resolution 2D-cine slices. The automated approach closely approximates the manual blood-flow segmentations, with error distances on the order of the voxel size. The initial surface provides a close approximation of the desired luminal geometry. This improves the convergence time of the active surface and facilitates parametrization. An active surface approach for vessel lumen segmentation was developed, suitable for quantitative analysis of 3D-cine PC-MRI blood-flow data. As opposed to prior thresholding and level-set approaches, the active surface model is topologically stable. A method to generate an initial approximate surface was developed, and various features that influence the segmentation model were evaluated. The active surface

  13. Conforming to interface structured adaptive mesh refinement: 3D algorithm and implementation

    Science.gov (United States)

    Nagarajan, Anand; Soghrati, Soheil

    2018-03-01

    A new non-iterative mesh generation algorithm named conforming to interface structured adaptive mesh refinement (CISAMR) is introduced for creating 3D finite element models of problems with complex geometries. CISAMR transforms a structured mesh composed of tetrahedral elements into a conforming mesh with low element aspect ratios. The construction of the mesh begins with the structured adaptive mesh refinement of elements in the vicinity of material interfaces. An r-adaptivity algorithm is then employed to relocate selected nodes of nonconforming elements, followed by face-swapping a small fraction of them to eliminate tetrahedrons with high aspect ratios. The final conforming mesh is constructed by sub-tetrahedralizing remaining nonconforming elements, as well as tetrahedrons with hanging nodes. In addition to studying the convergence and analyzing element-wise errors in meshes generated using CISAMR, several example problems are presented to show the ability of this method for modeling 3D problems with intricate morphologies.

  14. Adaptation of Zerotrees Using Signed Binary Digit Representations for 3D Image Coding

    Directory of Open Access Journals (Sweden)

    Mailhes Corinne

    2007-01-01

    Full Text Available Zerotrees of wavelet coefficients have shown a good adaptability for the compression of three-dimensional images. EZW, the original algorithm using zerotree, shows good performance and was successfully adapted to 3D image compression. This paper focuses on the adaptation of EZW for the compression of hyperspectral images. The subordinate pass is suppressed to remove the necessity to keep the significant pixels in memory. To compensate the loss due to this removal, signed binary digit representations are used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one.

  15. Adaptive fringe-pattern projection for image saturation avoidance in 3D surface-shape measurement.

    Science.gov (United States)

    Li, Dong; Kofman, Jonathan

    2014-04-21

    In fringe-projection 3D surface-shape measurement, image saturation results in incorrect intensities in captured images of fringe patterns, leading to phase and measurement errors. An adaptive fringe-pattern projection (AFPP) method was developed to adapt the maximum input gray level in projected fringe patterns to the local reflectivity of an object surface being measured. The AFPP method demonstrated improved 3D measurement accuracy by avoiding image saturation in highly-reflective surface regions while maintaining high intensity modulation across the entire surface. The AFPP method can avoid image saturation and handle varying surface reflectivity, using only two prior rounds of fringe-pattern projection and image capture to generate the adapted fringe patterns.

  16. Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

    Science.gov (United States)

    Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

    2008-03-01

    In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

  17. A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine.

    Science.gov (United States)

    Mastmeyer, André; Engelke, Klaus; Fuchs, Christina; Kalender, Willi A

    2006-08-01

    We have developed a new hierarchical 3D technique to segment the vertebral bodies in order to measure bone mineral density (BMD) with high trueness and precision in volumetric CT datasets. The hierarchical approach starts with a coarse separation of the individual vertebrae, applies a variety of techniques to segment the vertebral bodies with increasing detail and ends with the definition of an anatomic coordinate system for each vertebral body, relative to which up to 41 trabecular and cortical volumes of interest are positioned. In a pre-segmentation step constraints consisting of Boolean combinations of simple geometric shapes are determined that enclose each individual vertebral body. Bound by these constraints viscous deformable models are used to segment the main shape of the vertebral bodies. Volume growing and morphological operations then capture the fine details of the bone-soft tissue interface. In the volumes of interest bone mineral density and content are determined. In addition, in the segmented vertebral bodies geometric parameters such as volume or the length of the main axes of inertia can be measured. Intra- and inter-operator precision errors of the segmentation procedure were analyzed using existing clinical patient datasets. Results for segmented volume, BMD, and coordinate system position were below 2.0%, 0.6%, and 0.7%, respectively. Trueness was analyzed using phantom scans. The bias of the segmented volume was below 4%; for BMD it was below 1.5%. The long-term goal of this work is improved fracture prediction and patient monitoring in the field of osteoporosis. A true 3D segmentation also enables an accurate measurement of geometrical parameters that may augment the clinical value of a pure BMD analysis.

  18. Segmentation of the lumen and media-adventitia boundaries of the common carotid artery from 3D ultrasound images

    Science.gov (United States)

    Ukwatta, E.; Awad, J.; Ward, A. D.; Samarabandu, J.; Krasinski, A.; Parraga, G.; Fenster, A.

    2011-03-01

    Three-dimensional ultrasound (3D US) vessel wall volume (VWV) measurements provide high measurement sensitivity and reproducibility for the monitoring and assessment of carotid atherosclerosis. In this paper, we describe a semiautomated approach based on the level set method to delineate the media-adventitia and lumen boundaries of the common carotid artery from 3D US images to support the computation of VWV. Due to the presence of plaque and US image artifacts, the carotid arteries are challenging to segment using image information alone. Our segmentation framework combines several image cues with domain knowledge and limited user interaction. Our method was evaluated with respect to manually outlined boundaries on 430 2D US images extracted from 3D US images of 30 patients who have carotid stenosis of 60% or more. The VWV given by our method differed from that given by manual segmentation by 6.7% +/- 5.0%. For the media-adventitia and lumen segmentations, respectively, our method yielded Dice coefficients of 95.2% +/- 1.6%, 94.3% +/- 2.6%, mean absolute distances of 0.3 +/- 0.1 mm, 0.2 +/- 0.1 mm, maximum absolute distances of 0.8 +/- 0.4 mm, 0.6 +/- 0.3 mm, and volume differences of 4.2% +/- 3.1%, 3.4% +/- 2.6%. The realization of a semi-automated segmentation method will accelerate the translation of 3D carotid US to clinical care for the rapid, non-invasive, and economical monitoring of atherosclerotic disease progression and regression during therapy.

  19. MRI segmentation by active contours model, 3D reconstruction, and visualization

    Science.gov (United States)

    Lopez-Hernandez, Juan M.; Velasquez-Aguilar, J. Guadalupe

    2005-02-01

    The advances in 3D data modelling methods are becoming increasingly popular in the areas of biology, chemistry and medical applications. The Nuclear Magnetic Resonance Imaging (NMRI) technique has progressed at a spectacular rate over the past few years, its uses have been spread over many applications throughout the body in both anatomical and functional investigations. In this paper we present the application of Zernike polynomials for 3D mesh model of the head using the contour acquired of cross-sectional slices by active contour model extraction and we propose the visualization with OpenGL 3D Graphics of the 2D-3D (slice-surface) information for the diagnostic aid in medical applications.

  20. Automatically tuned adaptive differencing algorithm for 3-D SN implemented in PENTRAN

    International Nuclear Information System (INIS)

    Sjoden, G.; Courau, T.; Manalo, K.; Yi, C.

    2009-01-01

    We present an adaptive algorithm with an automated tuning feature to augment optimum differencing scheme selection for 3-D S N computations in Cartesian geometry. This adaptive differencing scheme has been implemented in the PENTRAN parallel S N code. Individual fixed zeroth spatial transport moment based schemes, including Diamond Zero (DZ), Directional Theta Weighted (DTW), and Exponential Directional Iterative (EDI) 3-D S N methods were evaluated and compared with solutions generated using a code-tuned adaptive algorithm. Model problems considered include a fixed source slab problem (using reflected y- and z-axes) which contained mixed shielding and diffusive regions, and a 17 x 17 PWR assembly eigenvalue test problem; these problems were benchmarked against multigroup MCNP5 Monte Carlo computations. Both problems were effective in highlighting the performance of the adaptive scheme compared to single schemes, and demonstrated that the adaptive tuning handles exceptions to the standard DZ-DTW-EDI adaptive strategy. The tuning feature includes special scheme selection provisions for optically thin cells, and incorporates the ratio of the angular source density relative to the total angular collision density to best select the differencing method. Overall, the adaptive scheme demonstrated the best overall solution accuracy in the test problems. (authors)

  1. 3D Adaptive Virtual Exhibit for the University of Denver Digital Collections

    Directory of Open Access Journals (Sweden)

    Shea-Tinn Yeh

    2015-07-01

    Full Text Available While the gaming industry has taken the world by storm with its three-dimensional (3D user interfaces, current digital collection exhibits presented by museums, historical societies, and libraries are still limited to a two-dimensional (2D interface display. Why can’t digital collections take advantage of this 3D interface advancement? The prototype discussed in this paper presents to the visitor a 3D virtual exhibit containing a set of digital objects from the University of Denver Libraries’ digital image collections, giving visitors an immersive experience when viewing the collections. In particular, the interface is adaptive to the visitor’s browsing behaviors and alters the selection and display of the objects throughout the exhibit to encourage serendipitous discovery. Social media features were also integrated to allow visitors to share items of interest and to create a sense of virtual community.

  2. Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks

    International Nuclear Information System (INIS)

    Sigala, R; Smerieri, A; Camorani, P; Schüz, A; Erokhin, V

    2013-01-01

    Memristors are passive two-terminal circuit elements that combine resistance and memory. Although in theory memristors are a very promising approach to fabricate hardware with adaptive properties, there are only very few implementations able to show their basic properties. We recently developed stochastic polymeric matrices with a functionality that evidences the formation of self-assembled three-dimensional (3D) networks of memristors. We demonstrated that those networks show the typical hysteretic behavior observed in the ‘one input-one output’ memristive configuration. Interestingly, using different protocols to electrically stimulate the networks, we also observed that their adaptive properties are similar to those present in the nervous system. Here, we model and simulate the electrical properties of these self-assembled polymeric networks of memristors, the topology of which is defined stochastically. First, we show that the model recreates the hysteretic behavior observed in the real experiments. Second, we demonstrate that the networks modeled indeed have a 3D instead of a planar functionality. Finally, we show that the adaptive properties of the networks depend on their connectivity pattern. Our model was able to replicate fundamental qualitative behavior of the real organic 3D memristor networks; yet, through the simulations, we also explored other interesting properties, such as the relation between connectivity patterns and adaptive properties. Our model and simulations represent an interesting tool to understand the very complex behavior of self-assembled memristor networks, which can finally help to predict and formulate hypotheses for future experiments. (paper)

  3. Multi-domain, higher order level set scheme for 3D image segmentation on the GPU

    DEFF Research Database (Denmark)

    Sharma, Ojaswa; Zhang, Qin; Anton, François

    2010-01-01

    to evaluate level set surfaces that are $C^2$ continuous, but are slow due to high computational burden. In this paper, we provide a higher order GPU based solver for fast and efficient segmentation of large volumetric images. We also extend the higher order method to multi-domain segmentation. Our streaming...

  4. Improving Segmentation of 3D Retina Layers Based on Graph Theory Approach for Low Quality OCT Images

    Directory of Open Access Journals (Sweden)

    Stankiewicz Agnieszka

    2016-06-01

    Full Text Available This paper presents signal processing aspects for automatic segmentation of retinal layers of the human eye. The paper draws attention to the problems that occur during the computer image processing of images obtained with the use of the Spectral Domain Optical Coherence Tomography (SD OCT. Accuracy of the retinal layer segmentation for a set of typical 3D scans with a rather low quality was shown. Some possible ways to improve quality of the final results are pointed out. The experimental studies were performed using the so-called B-scans obtained with the OCT Copernicus HR device.

  5. Fuzzy segmentation of cerebral tissues in a 3-D MR image: a possibilistic approach versus other methods

    International Nuclear Information System (INIS)

    Barra, V.; Boire, J.Y.

    1999-01-01

    An algorithm for the segmentation of a single sequence of 3-D magnetic resonance images into cerebrospinal Fluid (CSF), Grey (GM) and White Matter (WM) classes is proposed. The method is a possibilistic clustering algorithm on the wavelet coefficients of the voxels. Possibilistic logic allows for modeling the uncertainty and the impreciseness inherent in MR images of the brain, while the wavelet representation allows to take into account both spatial and textural information. The procedure is fast, unsupervised and totally independent of statistical assumptions. In method is validated on a phantom, and then compared with other very used brain tissues segmentation algorithms. (authors)

  6. Image Quality and Radiation Dose of CT Coronary Angiography with Automatic Tube Current Modulation and Strong Adaptive Iterative Dose Reduction Three-Dimensional (AIDR3D.

    Directory of Open Access Journals (Sweden)

    Hesong Shen

    Full Text Available To investigate image quality and radiation dose of CT coronary angiography (CTCA scanned using automatic tube current modulation (ATCM and reconstructed by strong adaptive iterative dose reduction three-dimensional (AIDR3D.Eighty-four consecutive CTCA patients were collected for the study. All patients were scanned using ATCM and reconstructed with strong AIDR3D, standard AIDR3D and filtered back-projection (FBP respectively. Two radiologists who were blinded to the patients' clinical data and reconstruction methods evaluated image quality. Quantitative image quality evaluation included image noise, signal-to-noise ratio (SNR, and contrast-to-noise ratio (CNR. To evaluate image quality qualitatively, coronary artery is classified into 15 segments based on the modified guidelines of the American Heart Association. Qualitative image quality was evaluated using a 4-point scale. Radiation dose was calculated based on dose-length product.Compared with standard AIDR3D, strong AIDR3D had lower image noise, higher SNR and CNR, their differences were all statistically significant (P<0.05; compared with FBP, strong AIDR3D decreased image noise by 46.1%, increased SNR by 84.7%, and improved CNR by 82.2%, their differences were all statistically significant (P<0.05 or 0.001. Segments with diagnostic image quality for strong AIDR3D were 336 (100.0%, 486 (96.4%, and 394 (93.8% in proximal, middle, and distal part respectively; whereas those for standard AIDR3D were 332 (98.8%, 472 (93.7%, 378 (90.0%, respectively; those for FBP were 217 (64.6%, 173 (34.3%, 114 (27.1%, respectively; total segments with diagnostic image quality in strong AIDR3D (1216, 96.5% were higher than those of standard AIDR3D (1182, 93.8% and FBP (504, 40.0%; the differences between strong AIDR3D and standard AIDR3D, strong AIDR3D and FBP were all statistically significant (P<0.05 or 0.001. The mean effective radiation dose was (2.55±1.21 mSv.Compared with standard AIDR3D and FBP, CTCA

  7. A density-based segmentation for 3D images, an application for X-ray micro-tomography

    International Nuclear Information System (INIS)

    Tran, Thanh N.; Nguyen, Thanh T.; Willemsz, Tofan A.; Kessel, Gijs van; Frijlink, Henderik W.; Voort Maarschalk, Kees van der

    2012-01-01

    Highlights: ► We revised the DBSCAN algorithm for segmentation and clustering of large 3D image dataset and classified multivariate image. ► The algorithm takes into account the coordinate system of the image data to improve the computational performance. ► The algorithm solved the instability problem in boundaries detection of the original DBSCAN. ► The segmentation results were successfully validated with synthetic 3D image and 3D XMT image of a pharmaceutical powder. - Abstract: Density-based spatial clustering of applications with noise (DBSCAN) is an unsupervised classification algorithm which has been widely used in many areas with its simplicity and its ability to deal with hidden clusters of different sizes and shapes and with noise. However, the computational issue of the distance table and the non-stability in detecting the boundaries of adjacent clusters limit the application of the original algorithm to large datasets such as images. In this paper, the DBSCAN algorithm was revised and improved for image clustering and segmentation. The proposed clustering algorithm presents two major advantages over the original one. Firstly, the revised DBSCAN algorithm made it applicable for large 3D image dataset (often with millions of pixels) by using the coordinate system of the image data. Secondly, the revised algorithm solved the non-stability issue of boundary detection in the original DBSCAN. For broader applications, the image dataset can be ordinary 3D images or in general, it can also be a classification result of other type of image data e.g. a multivariate image.

  8. Local Stereo Matching Using Adaptive Local Segmentation

    NARCIS (Netherlands)

    Damjanovic, S.; van der Heijden, Ferdinand; Spreeuwers, Lieuwe Jan

    We propose a new dense local stereo matching framework for gray-level images based on an adaptive local segmentation using a dynamic threshold. We define a new validity domain of the fronto-parallel assumption based on the local intensity variations in the 4-neighborhood of the matching pixel. The

  9. Integrated 3D density modelling and segmentation of the Dead Sea

    OpenAIRE

    H.-J. Götze; R. El-Kelani; Sebastian Schmidt; M. Rybakov; M. Hassouneh; Hans-Jürgen Förster; J. Ebbing; DESERT Group;  ;  ;  

    2007-01-01

    A 3D interpretation of the newly compiled Bouguer anomaly in the area of the '‘Dead Sea Rift’’ is presented. A high-resolution 3D model constrained with the seismic results reveals the crustal thickness and density distribution beneath the Arava/Araba Valley (AV), the region between the Dead Sea and the Gulf of Aqaba/Elat. The Bouguer anomalies along the axial portion of the AV, as deduced from the modelling results, are mainly caused by deep-seated sedimentary basins (D > 10 km). An inferred...

  10. An Effective Approach of Teeth Segmentation within the 3D Cone Beam Computed Tomography Image Based on Deformable Surface Model

    Directory of Open Access Journals (Sweden)

    Xutang Zhang

    2016-01-01

    Full Text Available In order to extract the pixels of teeth from 3D Cone Beam Computed Tomography (CBCT image, in this paper, a novel 3D segmentation approach based on deformable surface mode is developed for 3D tooth model reconstruction. Different forces are formulated to handle the segmentation problem by using different strategies. First, the proposed method estimates the deformation force of vertex model by simulating the deformation process of a bubble under the action of internal pressure and external force field. To handle the blurry boundary, a “braking force” is proposed deriving from the 3D gradient information calculated by transforming the Sobel operator into three-dimension representation. In addition, a “border reinforcement” strategy is developed for handling the cases with complicate structures. Moreover, the proposed method combines affine cell image decomposition (ACID grid reparameterization technique to handle the unstable changes of topological structure and deformability during the deformation process. The proposed method was performed on 510 CBCT images. To validate the performance, the results were compared with those of two other well-studied methods. Experimental results show that the proposed approach had a good performance in handling the cases with complicate structures and blurry boundaries well, is effective to converge, and can successfully achieve the reconstruction task of various types of teeth in oral cavity.

  11. Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm.

    Science.gov (United States)

    Conversano, Francesco; Franchini, Roberto; Demitri, Christian; Massoptier, Laurent; Montagna, Francesco; Maffezzoli, Alfonso; Malvasi, Antonio; Casciaro, Sergio

    2011-04-01

    The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery. A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features. The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively. A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections. Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

  12. Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views

    OpenAIRE

    Massa, Francisco; Russell, Bryan; Aubry, Mathieu

    2015-01-01

    This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection. We demonstrate that the ability to adapt the features of natural images to better align with those of CAD rendered views is critical to the success of our technique. We show that the adaptation can be learned by compositing rendered views of textured object models on natural images. Our approach can be naturally incorporated into a CNN detection pipeline and extends the accuracy and speed benefi...

  13. 3D active shape modeling for cardiac MR and CT image segmentation

    NARCIS (Netherlands)

    Assen, Hans Christiaan van

    2006-01-01

    3D Active Shape Modeling is a technique to capture shape information from a training set containing characteristic shapes of, e.g., a heart. The description contains a mean shape, and shape variations (e.g. eigen deformations and eigen values). Many models based on these statistics, and used for

  14. Plane segmentation and decimation of point clouds for 3D environment reconstruction

    NARCIS (Netherlands)

    Ma, L.; Favier, R.J.J.; Do, Q.L.; Bondarev, E.; With, de P.H.N.

    2013-01-01

    Abstract—Three-dimensional (3D) models of environments are a promising technique for serious gaming and professional engineering applications. In this paper, we introduce a fast and memory-efficient system for the reconstruction of large-scale environments based on point clouds. Our main

  15. A Hybrid 3D Colon Segmentation Method Using Modified Geometric Deformable Models

    Directory of Open Access Journals (Sweden)

    S. Falahieh Hamidpour

    2007-06-01

    Full Text Available Introduction: Nowadays virtual colonoscopy has become a reliable and efficient method of detecting primary stages of colon cancer such as polyp detection. One of the most important and crucial stages of virtual colonoscopy is colon segmentation because an incorrect segmentation may lead to a misdiagnosis.  Materials and Methods: In this work, a hybrid method based on Geometric Deformable Models (GDM in combination with an advanced region growing and thresholding methods is proposed. GDM are found to be an attractive tool for structural based image segmentation particularly for extracting the objects with complicated topology. There are two main parameters influencing the overall performance of GDM algorithm; the distance between the initial contour and the actual object’s contours and secondly the stopping term which controls the deformation. To overcome these limitations, a two stage hybrid based segmentation method is suggested to extract the rough but precise initial contours at the first stage of the segmentation. The extracted boundaries are smoothed and improved using a modified GDM algorithm by improving the stopping terms of the algorithm based on the gradient value of image voxels. Results: The proposed algorithm was implemented on forty data sets each containing 400-480 slices. The results show an improvement in the accuracy and smoothness of the extracted boundaries. The improvement obtained for the accuracy of segmentation is about 6% in comparison to the one achieved by the methods based on thresholding and region growing only. Discussion and Conclusion: The extracted contours using modified GDM are smoother and finer. The improvement achieved in this work on the performance of stopping function of GDM model together with applying two stage segmentation of boundaries have resulted in a great improvement on the computational efficiency of GDM algorithm while making smoother and finer colon borders.

  16. Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

    Directory of Open Access Journals (Sweden)

    Laurent Trassoudaine

    2013-03-01

    Full Text Available Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy.

  17. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

    Science.gov (United States)

    Brosch, Tom; Tang, Lisa Y W; Youngjin Yoo; Li, David K B; Traboulsee, Anthony; Tam, Roger

    2016-05-01

    We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

  18. Quality Assurance of Serial 3D Image Registration, Fusion, and Segmentation

    International Nuclear Information System (INIS)

    Sharpe, Michael; Brock, Kristy K.

    2008-01-01

    Radiotherapy relies on images to plan, guide, and assess treatment. Image registration, fusion, and segmentation are integral to these processes; specifically for aiding anatomic delineation, assessing organ motion, and aligning targets with treatment beams in image-guided radiation therapy (IGRT). Future developments in image registration will also improve estimations of the actual dose delivered and quantitative assessment in patient follow-up exams. This article summarizes common and emerging technologies and reviews the role of image registration, fusion, and segmentation in radiotherapy processes. The current quality assurance practices are summarized, and implications for clinical procedures are discussed

  19. A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy

    International Nuclear Information System (INIS)

    Zhou Jinghao; Kim, Sung; Jabbour, Salma; Goyal, Sharad; Haffty, Bruce; Chen, Ting; Levinson, Lydia; Metaxas, Dimitris; Yue, Ning J.

    2010-01-01

    Purpose: In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. Methods: The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CT (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. Results: The ACRASM segmentation algorithm was compared to the original active shape model (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to

  20. High performance 3D adaptive filtering for DSP based portable medical imaging systems

    Science.gov (United States)

    Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark

    2015-03-01

    Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform. In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform. In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.

  1. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

    Energy Technology Data Exchange (ETDEWEB)

    He, Baochun; Huang, Cheng; Zhou, Shoujun; Hu, Qingmao; Jia, Fucang, E-mail: fc.jia@siat.ac.cn [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055 (China); Sharp, Gregory [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 (United States); Fang, Chihua; Fan, Yingfang [Department of Hepatology (I), Zhujiang Hospital, Southern Medical University, Guangzhou 510280 (China)

    2016-05-15

    Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods—3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration—are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach

  2. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.

    Science.gov (United States)

    He, Baochun; Huang, Cheng; Sharp, Gregory; Zhou, Shoujun; Hu, Qingmao; Fang, Chihua; Fan, Yingfang; Jia, Fucang

    2016-05-01

    A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence. The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. The proposed automatic approach achieves robust, accurate, and fast liver

  3. Multi-atlas-based automatic 3D segmentation for prostate brachytherapy in transrectal ultrasound images

    Science.gov (United States)

    Nouranian, Saman; Mahdavi, S. Sara; Spadinger, Ingrid; Morris, William J.; Salcudean, S. E.; Abolmaesumi, P.

    2013-03-01

    One of the commonly used treatment methods for early-stage prostate cancer is brachytherapy. The standard of care for planning this procedure is segmentation of contours from transrectal ultrasound (TRUS) images, which closely follow the prostate boundary. This process is currently performed either manually or using semi-automatic techniques. This paper introduces a fully automatic segmentation algorithm which uses a priori knowledge of contours in a reference data set of TRUS volumes. A non-parametric deformable registration method is employed to transform the atlas prostate contours to a target image coordinates. All atlas images are sorted based on their registration results and the highest ranked registration results are selected for decision fusion. A Simultaneous Truth and Performance Level Estimation algorithm is utilized to fuse labels from registered atlases and produce a segmented target volume. In this experiment, 50 patient TRUS volumes are obtained and a leave-one-out study on TRUS volumes is reported. We also compare our results with a state-of-the-art semi-automatic prostate segmentation method that has been clinically used for planning prostate brachytherapy procedures and we show comparable accuracy and precision within clinically acceptable runtime.

  4. Towards better segmentation of large floating point 3D astronomical data sets : first results

    NARCIS (Netherlands)

    Moschini, Ugo; Teeninga, Paul; Wilkinson, Michael; Giese, Nadine; Punzo, Davide; van der Hulst, Jan M.; Trager, Scott

    2014-01-01

    In any image segmentation task, noise must be separated from the actual information and the relevant pixels grouped into objects of interest, on which measures can later be applied. This should be done efficiently on large astronomical surveys with floating point datasets with resolution of the

  5. Prosthetic component segmentation with blur compensation: a fast method for 3D fluoroscopy.

    Science.gov (United States)

    Tarroni, Giacomo; Tersi, Luca; Corsi, Cristiana; Stagni, Rita

    2012-06-01

    A new method for prosthetic component segmentation from fluoroscopic images is presented. The hybrid approach we propose combines diffusion filtering, region growing and level-set techniques without exploiting any a priori knowledge of the analyzed geometry. The method was evaluated on a synthetic dataset including 270 images of knee and hip prosthesis merged to real fluoroscopic data simulating different conditions of blurring and illumination gradient. The performance of the method was assessed by comparing estimated contours to references using different metrics. Results showed that the segmentation procedure is fast, accurate, independent on the operator as well as on the specific geometrical characteristics of the prosthetic component, and able to compensate for amount of blurring and illumination gradient. Importantly, the method allows a strong reduction of required user interaction time when compared to traditional segmentation techniques. Its effectiveness and robustness in different image conditions, together with simplicity and fast implementation, make this prosthetic component segmentation procedure promising and suitable for multiple clinical applications including assessment of in vivo joint kinematics in a variety of cases.

  6. Proposal of a novel ensemble learning based segmentation with a shape prior and its application to spleen segmentation from a 3D abdominal CT volume

    International Nuclear Information System (INIS)

    Shindo, Kiyo; Shimizu, Akinobu; Kobatake, Hidefumi; Nawano, Shigeru; Shinozaki, Kenji

    2010-01-01

    An organ segmentation learned by a conventional ensemble learning algorithm suffers from unnatural errors because each voxel is classified independently in the segmentation process. This paper proposes a novel ensemble learning algorithm that can take into account global shape and location of organs. It estimates the shape and location of an organ from a given image by combining an intermediate segmentation result with a statistical shape model. Once an ensemble learning algorithm could not improve the segmentation performance in the iterative learning process, it estimates the shape and location by finding an optimal model parameter set with maximum degree of correspondence between a statistical shape model and the intermediate segmentation result. Novel weak classifiers are generated based on a signed distance from a boundary of the estimated shape and a distance from a barycenter of the intermediate segmentation result. Subsequently it continues the learning process with the novel weak classifiers. This paper presents experimental results where the proposed ensemble learning algorithm generates a segmentation process that can extract a spleen from a 3D CT image more precisely than a conventional one. (author)

  7. Development of an automated 3D segmentation program for volume quantification of body fat distribution using CT

    International Nuclear Information System (INIS)

    Ohshima, Shunsuke; Yamamoto, Shuji; Yamaji, Taiki

    2008-01-01

    The objective of this study was to develop a computing tool for full-automatic segmentation of body fat distributions on volumetric CT images. We developed an algorithm to automatically identify the body perimeter and the inner contour that separates visceral fat from subcutaneous fat. Diaphragmatic surfaces can be extracted by model-based segmentation to match the bottom surface of the lung in CT images for determination of the upper limitation of the abdomen. The functions for quantitative evaluation of abdominal obesity or obesity-related metabolic syndrome were implemented with a prototype three-dimensional (3D) image processing workstation. The volumetric ratios of visceral fat to total fat and visceral fat to subcutaneous fat for each subject can be calculated. Additionally, color intensity mapping of subcutaneous areas and the visceral fat layer is quite obvious in understanding the risk of abdominal obesity with the 3D surface display. Preliminary results obtained have been useful in medical checkups and have contributed to improved efficiency in checking obesity throughout the whole range of the abdomen with 3D visualization and analysis. (author)

  8. Understanding Spatially Complex Segmental and Branch Anatomy Using 3D Printing: Liver, Lung, Prostate, Coronary Arteries, and Circle of Willis.

    Science.gov (United States)

    Javan, Ramin; Herrin, Douglas; Tangestanipoor, Ardalan

    2016-09-01

    Three-dimensional (3D) manufacturing is shaping personalized medicine, in which radiologists can play a significant role, be it as consultants to surgeons for surgical planning or by creating powerful visual aids for communicating with patients, physicians, and trainees. This report illustrates the steps in development of custom 3D models that enhance the understanding of complex anatomy. We graphically designed 3D meshes or modified imported data from cross-sectional imaging to develop physical models targeted specifically for teaching complex segmental and branch anatomy. The 3D printing itself is easily accessible through online commercial services, and the models are made of polyamide or gypsum. Anatomic models of the liver, lungs, prostate, coronary arteries, and the Circle of Willis were created. These models have advantages that include customizable detail, relative low cost, full control of design focusing on subsegments, color-coding potential, and the utilization of cross-sectional imaging combined with graphic design. Radiologists have an opportunity to serve as leaders in medical education and clinical care with 3D printed models that provide beneficial interaction with patients, clinicians, and trainees across all specialties by proactively taking on the educator's role. Complex models can be developed to show normal anatomy or common pathology for medical educational purposes. There is a need for randomized trials, which radiologists can design, to demonstrate the utility and effectiveness of 3D printed models for teaching simple and complex anatomy, simulating interventions, measuring patient satisfaction, and improving clinical care. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  9. Development of adaptive bust for female soft body armour using three dimensional (3D) warp interlock fabrics: Three dimensional (3D) design process

    Science.gov (United States)

    Abtew, M. A.; Bruniaux, P.; Boussu, F.

    2017-10-01

    The traditional two dimensional (2D) pattern making method for developing female body armour has a negative effect on the ballistic protective performance as well as the comfort of the wearer. This is due to, unlike the male body armour, the female body armour manufacturing involves different darts to accommodate the natural curvature of the female body, i.e. bust area, which will reveals the weak parts at the seam and stitch area while ballistic impact. Moreover, the proper bra size also plays an important role not only in bra design but also in the design of a women’s ballistic vest. The present research study tried to propose the novel 3D designing approach for developing different volumes of breast using feature points (both bust surface and outline points) in the specific 3D adaptive mannequin. Later the flattened 3D bra patterns of this method has been also compare with the 2D standard pattern making in order to modify and match with 2D traditional method. The result indicated that the proposed method which conceives the 3D patterns on the 3D bust is easier to implement and can generate patterns with satisfactory fit and comfort as compared to 2D patterns.

  10. Automatic training and reliability estimation for 3D ASM applied to cardiac MRI segmentation.

    Science.gov (United States)

    Tobon-Gomez, Catalina; Sukno, Federico M; Butakoff, Constantine; Huguet, Marina; Frangi, Alejandro F

    2012-07-07

    Training active shape models requires collecting manual ground-truth meshes in a large image database. While shape information can be reused across multiple imaging modalities, intensity information needs to be imaging modality and protocol specific. In this context, this study has two main purposes: (1) to test the potential of using intensity models learned from MRI simulated datasets and (2) to test the potential of including a measure of reliability during the matching process to increase robustness. We used a population of 400 virtual subjects (XCAT phantom), and two clinical populations of 40 and 45 subjects. Virtual subjects were used to generate simulated datasets (MRISIM simulator). Intensity models were trained both on simulated and real datasets. The trained models were used to segment the left ventricle (LV) and right ventricle (RV) from real datasets. Segmentations were also obtained with and without reliability information. Performance was evaluated with point-to-surface and volume errors. Simulated intensity models obtained average accuracy comparable to inter-observer variability for LV segmentation. The inclusion of reliability information reduced volume errors in hypertrophic patients (EF errors from 17 ± 57% to 10 ± 18%; LV MASS errors from -27 ± 22 g to -14 ± 25 g), and in heart failure patients (EF errors from -8 ± 42% to -5 ± 14%). The RV model of the simulated images needs further improvement to better resemble image intensities around the myocardial edges. Both for real and simulated models, reliability information increased segmentation robustness without penalizing accuracy.

  11. New adaptive differencing strategy in the PENTRAN 3-d parallel Sn code

    International Nuclear Information System (INIS)

    Sjoden, G.E.; Haghighat, A.

    1996-01-01

    It is known that three-dimensional (3-D) discrete ordinates (S n ) transport problems require an immense amount of storage and computational effort to solve. For this reason, parallel codes that offer a capability to completely decompose the angular, energy, and spatial domains among a distributed network of processors are required. One such code recently developed is PENTRAN, which iteratively solves 3-D multi-group, anisotropic S n problems on distributed-memory platforms, such as the IBM-SP2. Because large problems typically contain several different material zones with various properties, available differencing schemes should automatically adapt to the transport physics in each material zone. To minimize the memory and message-passing overhead required for massively parallel S n applications, available differencing schemes in an adaptive strategy should also offer reasonable accuracy and positivity, yet require only the zeroth spatial moment of the transport equation; differencing schemes based on higher spatial moments, in spite of their greater accuracy, require at least twice the amount of storage and communication cost for implementation in a massively parallel transport code. This paper discusses a new adaptive differencing strategy that uses increasingly accurate schemes with low parallel memory and communication overhead. This strategy, implemented in PENTRAN, includes a new scheme, exponential directional averaged (EDA) differencing

  12. The new Exponential Directional Iterative (EDI) 3-D Sn scheme for parallel adaptive differencing

    International Nuclear Information System (INIS)

    Sjoden, G.E.

    2005-01-01

    The new Exponential Directional Iterative (EDI) discrete ordinates (Sn) scheme for 3-D Cartesian Coordinates is presented. The EDI scheme is a logical extension of the positive, efficient Exponential Directional Weighted (EDW) Sn scheme currently used as the third level of the adaptive spatial differencing algorithm in the PENTRAN parallel discrete ordinates solver. Here, the derivation and advantages of the EDI scheme are presented; EDI uses EDW-rendered exponential coefficients as initial starting values to begin a fixed point iteration of the exponential coefficients. One issue that required evaluation was an iterative cutoff criterion to prevent the application of an unstable fixed point iteration; although this was needed in some cases, it was readily treated with a default to EDW. Iterative refinement of the exponential coefficients in EDI typically converged in fewer than four fixed point iterations. Moreover, EDI yielded more accurate angular fluxes compared to the other schemes tested, particularly in streaming conditions. Overall, it was found that the EDI scheme was up to an order of magnitude more accurate than the EDW scheme on a given mesh interval in streaming cases, and is potentially a good candidate as a fourth-level differencing scheme in the PENTRAN adaptive differencing sequence. The 3-D Cartesian computational cost of EDI was only about 20% more than the EDW scheme, and about 40% more than Diamond Zero (DZ). More evaluation and testing are required to determine suitable upgrade metrics for EDI to be fully integrated into the current adaptive spatial differencing sequence in PENTRAN. (author)

  13. Segmentation and reconstruction of the 3D geometry of the middle and inner ear

    Directory of Open Access Journals (Sweden)

    Lu Yanfei

    2017-01-01

    Full Text Available The anatomical model of the ear is of great importance in the design of ossicular prosthesis, cochlear implant electrodes, as well as for the preoperative planning and navigation of surgery. By means of micro-computed tomography (micro-CT and technology of 3D reconstruction, an anatomical model of the middle and inner ear was built. Region of interest includes the ossicular chain (malleus, incus, and stapes, cochlea (scala vestibule-ST, scala tympani-ST, basilar membrane-BM, spiral ligament-SL and osseous spiral lamina-OSL, tympanic membrane-TM, oval window membrane-OWM, round window membrane-OWM and stapedial annular ligament-SAL. The micro-CT images of a cadaver’s temporal bone were acquired by “SkyScan 1076” (Kontich, Belgium, www.skyscan.be and then reconstructed to cross-section images by SkyScan NRecon™ (v1.6.10.4. The image processing and 3D geometry reconstruction of temporal bone were performed by software Mimics® (v14.0, Materialise NV, Leuven, Belgium. The obtained structures are measured and validated against literature data and the results are in good agreement.

  14. 3D spatially-adaptive canonical correlation analysis: Local and global methods.

    Science.gov (United States)

    Yang, Zhengshi; Zhuang, Xiaowei; Sreenivasan, Karthik; Mishra, Virendra; Curran, Tim; Byrd, Richard; Nandy, Rajesh; Cordes, Dietmar

    2018-04-01

    Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. A novel magnet based 3D printed marker wand as basis for repeated in-shoe multi segment foot analysis: a proof of concept.

    Science.gov (United States)

    Eerdekens, Maarten; Staes, Filip; Pilkington, Thomas; Deschamps, Kevin

    2017-01-01

    Application of in-shoe multi-segment foot kinematic analyses currently faces a number of challenges, including: (i) the difficulty to apply regular markers onto the skin, (ii) the necessity for an adequate shoe which fits various foot morphologies and (iii) the need for adequate repeatability throughout a repeated measure condition. The aim of this study therefore was to design novel magnet based 3D printed markers for repeated in-shoe measurements while using accordingly adapted modified shoes for a specific multi-segment foot model. Multi-segment foot kinematics of ten participants were recorded and kinematics of hindfoot, midfoot and forefoot were calculated. Dynamic trials were conducted to check for intra and inter-session repeatability when combining novel markers and modified shoes in a repeated measures design. Intraclass correlation coefficients were calculated to determine reliability. Both repeatability and reliability were proven to be good to excellent with maximum joint angle deviations of 1.11° for intra-session variability and 1.29° for same-day inter-session variability respectively and ICC values of >0.91. The novel markers can be reliably used in future research settings using in-shoe multi-segment foot kinematic analyses with multiple shod conditions.

  16. 3D thermal modelling within the Lofoten-Vesterålen segment of the Mid-Norwegian continental margin

    Science.gov (United States)

    Maystrenko, Yuriy P.; Gernigon, Laurent; Gradmann, Sofie; Olesen, Odleiv

    2017-04-01

    A lithosphere-scale 3D structural model has been constructed based on the available structural data to reveal a deep structure of the Lofoten-Vesterålen segment and the northern part of the Vøring segment of the Mid-Norwegian continental margin. The constructed model covers the Vestfjorden, Ribban and Røst basins, the northern parts of the Vøring Basin and the Trøndelag Platform. The model also extends from the Fennoscandian Shield to the north-eastern part of the North Atlantic Ocean. The initial 3D structural model has been refined using a 3D gravity modelling over the whole study area. The final gravity-consistent model has been used as a structural base for a further 3D thermal modelling, which has been made by use of commercial software package COMSOL Multiphysics. As an upper thermal boundary condition, time-dependent temperature at the Earth's surface and sea bottom has been set, considering palaeoclimatic changes due to the last two Europe-scale glaciations (the Saalian and Weichselian glacial periods). The lithosphere-asthenosphere boundary has been used as a lower thermal boundary which corresponds to the 1300 °C isotherm. In addition to the above-mentioned paleoclimatic scenario, the effects of late Cenozoic erosion onshore and sedimentation offshore have been taken into account during the 3D thermal modelling. Results of this thermal modelling indicate that the continent is generally colder than the basin areas within the upper part of the 3D model. In particular, considering the transient perturbations in the near-surface thermal regime, as a result of the post-Paleogene erosion and sedimentation, helps us to understand additional details of subsurface temperature distribution within the study area. The thermal effects of the simultaneous erosion over the mainland and deposition within the basin areas indicate that a positive thermal anomaly should exist onshore, whereas the negative one must occur in the offshore part. These two thermal

  17. Segmentation, surface rendering, and surface simplification of 3-D skull images for the repair of a large skull defect

    Science.gov (United States)

    Wan, Weibing; Shi, Pengfei; Li, Shuguang

    2009-10-01

    Given the potential demonstrated by research into bone-tissue engineering, the use of medical image data for the rapid prototyping (RP) of scaffolds is a subject worthy of research. Computer-aided design and manufacture and medical imaging have created new possibilities for RP. Accurate and efficient design and fabrication of anatomic models is critical to these applications. We explore the application of RP computational methods to the repair of a pediatric skull defect. The focus of this study is the segmentation of the defect region seen in computerized tomography (CT) slice images of this patient's skull and the three-dimensional (3-D) surface rendering of the patient's CT-scan data. We see if our segmentation and surface rendering software can improve the generation of an implant model to fill a skull defect.

  18. Automated torso organ segmentation from 3D CT images using structured perceptron and dual decomposition

    Science.gov (United States)

    Nimura, Yukitaka; Hayashi, Yuichiro; Kitasaka, Takayuki; Mori, Kensaku

    2015-03-01

    This paper presents a method for torso organ segmentation from abdominal CT images using structured perceptron and dual decomposition. A lot of methods have been proposed to enable automated extraction of organ regions from volumetric medical images. However, it is necessary to adjust empirical parameters of them to obtain precise organ regions. This paper proposes an organ segmentation method using structured output learning. Our method utilizes a graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weights of the graphical model by structured perceptron and estimate the best organ label for a given image by dynamic programming and dual decomposition. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 4.4%. The DICE coefficients of left lung, right lung, heart, liver, spleen, pancreas, left kidney, right kidney, and gallbladder were 0.91, 0.95, 0.77, 0.81, 0.74, 0.08, 0.83, 0.84, and 0.03, respectively.

  19. The Impact of Different Levels of Adaptive Iterative Dose Reduction 3D on Image Quality of 320-Row Coronary CT Angiography: A Clinical Trial.

    Directory of Open Access Journals (Sweden)

    Sarah Feger

    Full Text Available The aim of this study was the systematic image quality evaluation of coronary CT angiography (CTA, reconstructed with the 3 different levels of adaptive iterative dose reduction (AIDR 3D and compared to filtered back projection (FBP with quantum denoising software (QDS.Standard-dose CTA raw data of 30 patients with mean radiation dose of 3.2 ± 2.6 mSv were reconstructed using AIDR 3D mild, standard, strong and compared to FBP/QDS. Objective image quality comparison (signal, noise, signal-to-noise ratio (SNR, contrast-to-noise ratio (CNR, contour sharpness was performed using 21 measurement points per patient, including measurements in each coronary artery from proximal to distal.Objective image quality parameters improved with increasing levels of AIDR 3D. Noise was lowest in AIDR 3D strong (p ≤ 0.001 at 20/21 measurement points; compared with FBP/QDS. Signal and contour sharpness analysis showed no significant difference between the reconstruction algorithms for most measurement points. Best coronary SNR and CNR were achieved with AIDR 3D strong. No loss of SNR or CNR in distal segments was seen with AIDR 3D as compared to FBP.On standard-dose coronary CTA images, AIDR 3D strong showed higher objective image quality than FBP/QDS without reducing contour sharpness.Clinicaltrials.gov NCT00967876.

  20. Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation

    International Nuclear Information System (INIS)

    Street, Ethan; Hadjiiski, Lubomir; Sahiner, Berkman; Gujar, Sachin; Ibrahim, Mohannad; Mukherji, Suresh K.; Chan, Heang-Ping

    2007-01-01

    The authors have developed a semiautomatic system for segmentation of a diverse set of lesions in head and neck CT scans. The system takes as input an approximate bounding box, and uses a multistage level set to perform the final segmentation. A data set consisting of 69 lesions marked on 33 scans from 23 patients was used to evaluate the performance of the system. The contours from automatic segmentation were compared to both 2D and 3D gold standard contours manually drawn by three experienced radiologists. Three performance metric measures were used for the comparison. In addition, a radiologist provided quality ratings on a 1 to 10 scale for all of the automatic segmentations. For this pilot study, the authors observed that the differences between the automatic and gold standard contours were larger than the interobserver differences. However, the system performed comparably to the radiologists, achieving an average area intersection ratio of 85.4% compared to an average of 91.2% between two radiologists. The average absolute area error was 21.1% compared to 10.8%, and the average 2D distance was 1.38 mm compared to 0.84 mm between the radiologists. In addition, the quality rating data showed that, despite the very lax assumptions made on the lesion characteristics in designing the system, the automatic contours approximated many of the lesions very well

  1. Global left ventricular function in cardiac CT. Evaluation of an automated 3D region-growing segmentation algorithm

    International Nuclear Information System (INIS)

    Muehlenbruch, Georg; Das, Marco; Hohl, Christian; Wildberger, Joachim E.; Guenther, Rolf W.; Mahnken, Andreas H.; Rinck, Daniel; Flohr, Thomas G.; Koos, Ralf; Knackstedt, Christian

    2006-01-01

    The purpose was to evaluate a new semi-automated 3D region-growing segmentation algorithm for functional analysis of the left ventricle in multislice CT (MSCT) of the heart. Twenty patients underwent contrast-enhanced MSCT of the heart (collimation 16 x 0.75 mm; 120 kV; 550 mAseff). Multiphase image reconstructions with 1-mm axial slices and 8-mm short-axis slices were performed. Left ventricular volume measurements (end-diastolic volume, end-systolic volume, ejection fraction and stroke volume) from manually drawn endocardial contours in the short axis slices were compared to semi-automated region-growing segmentation of the left ventricle from the 1-mm axial slices. The post-processing-time for both methods was recorded. Applying the new region-growing algorithm in 13/20 patients (65%), proper segmentation of the left ventricle was feasible. In these patients, the signal-to-noise ratio was higher than in the remaining patients (3.2±1.0 vs. 2.6±0.6). Volume measurements of both segmentation algorithms showed an excellent correlation (all P≤0.0001); the limits of agreement for the ejection fraction were 2.3±8.3 ml. In the patients with proper segmentation the mean post-processing time using the region-growing algorithm was diminished by 44.2%. On the basis of a good contrast-enhanced data set, a left ventricular volume analysis using the new semi-automated region-growing segmentation algorithm is technically feasible, accurate and more time-effective. (orig.)

  2. 3D CSEM inversion based on goal-oriented adaptive finite element method

    Science.gov (United States)

    Zhang, Y.; Key, K.

    2016-12-01

    We present a parallel 3D frequency domain controlled-source electromagnetic inversion code name MARE3DEM. Non-linear inversion of observed data is performed with the Occam variant of regularized Gauss-Newton optimization. The forward operator is based on the goal-oriented finite element method that efficiently calculates the responses and sensitivity kernels in parallel using a data decomposition scheme where independent modeling tasks contain different frequencies and subsets of the transmitters and receivers. To accommodate complex 3D conductivity variation with high flexibility and precision, we adopt the dual-grid approach where the forward mesh conforms to the inversion parameter grid and is adaptively refined until the forward solution converges to the desired accuracy. This dual-grid approach is memory efficient, since the inverse parameter grid remains independent from fine meshing generated around the transmitter and receivers by the adaptive finite element method. Besides, the unstructured inverse mesh efficiently handles multiple scale structures and allows for fine-scale model parameters within the region of interest. Our mesh generation engine keeps track of the refinement hierarchy so that the map of conductivity and sensitivity kernel between the forward and inverse mesh is retained. We employ the adjoint-reciprocity method to calculate the sensitivity kernels which establish a linear relationship between changes in the conductivity model and changes in the modeled responses. Our code uses a direcy solver for the linear systems, so the adjoint problem is efficiently computed by re-using the factorization from the primary problem. Further computational efficiency and scalability is obtained in the regularized Gauss-Newton portion of the inversion using parallel dense matrix-matrix multiplication and matrix factorization routines implemented with the ScaLAPACK library. We show the scalability, reliability and the potential of the algorithm to deal with

  3. Fast Semantic Segmentation of 3d Point Clouds with Strongly Varying Density

    Science.gov (United States)

    Hackel, Timo; Wegner, Jan D.; Schindler, Konrad

    2016-06-01

    We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point's (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

  4. 3D shape extraction segmentation and representation of soil microstructures using generalized cylinders

    Science.gov (United States)

    Ngom, Ndèye Fatou; Monga, Olivier; Ould Mohamed, Mohamed Mahmoud; Garnier, Patricia

    2012-02-01

    This paper focuses on the modeling of soil microstructures using generalized cylinders, with a specific application to pore space. The geometric modeling of these microstructures is a recent area of study, made possible by the improved performance of computed tomography techniques. X-scanners provide very-high-resolution 3D volume images ( 3-5μm) of soil samples in which pore spaces can be extracted by thresholding. However, in most cases, the pore space defines a complex volume shape that cannot be approximated using simple analytical functions. We propose representing this shape using a compact, stable, and robust piecewise approximation by means of generalized cylinders. This intrinsic shape representation conserves its topological and geometric properties. Our algorithm includes three main processing stages. The first stage consists in describing the volume shape using a minimum number of balls included within the shape, such that their union recovers the shape skeleton. The second stage involves the optimum extraction of simply connected chains of balls. The final stage copes with the approximation of each simply optimal chain using generalized cylinders: circular generalized cylinders, tori, cylinders, and truncated cones. This technique was applied to several data sets formed by real volume computed tomography soil samples. It was possible to demonstrate that our geometric representation supplied a good approximation of the pore space. We also stress the compactness and robustness of this method with respect to any changes affecting the initial data, as well as its coherence with the intuitive notion of pores. During future studies, this geometric pore space representation will be used to simulate biological dynamics.

  5. NCC-RANSAC: a fast plane extraction method for 3-D range data segmentation.

    Science.gov (United States)

    Qian, Xiangfei; Ye, Cang

    2014-12-01

    This paper presents a new plane extraction (PE) method based on the random sample consensus (RANSAC) approach. The generic RANSAC-based PE algorithm may over-extract a plane, and it may fail in case of a multistep scene where the RANSAC procedure results in multiple inlier patches that form a slant plane straddling the steps. The CC-RANSAC PE algorithm successfully overcomes the latter limitation if the inlier patches are separate. However, it fails if the inlier patches are connected. A typical scenario is a stairway with a stair wall where the RANSAC plane-fitting procedure results in inliers patches in the tread, riser, and stair wall planes. They connect together and form a plane. The proposed method, called normal-coherence CC-RANSAC (NCC-RANSAC), performs a normal coherence check to all data points of the inlier patches and removes the data points whose normal directions are contradictory to that of the fitted plane. This process results in separate inlier patches, each of which is treated as a candidate plane. A recursive plane clustering process is then executed to grow each of the candidate planes until all planes are extracted in their entireties. The RANSAC plane-fitting and the recursive plane clustering processes are repeated until no more planes are found. A probabilistic model is introduced to predict the success probability of the NCC-RANSAC algorithm and validated with real data of a 3-D time-of-flight camera-SwissRanger SR4000. Experimental results demonstrate that the proposed method extracts more accurate planes with less computational time than the existing RANSAC-based methods.

  6. A new tissue segmentation method to calculate 3D dose in small animal radiation therapy.

    Science.gov (United States)

    Noblet, C; Delpon, G; Supiot, S; Potiron, V; Paris, F; Chiavassa, S

    2018-02-26

    In pre-clinical animal experiments, radiation delivery is usually delivered with kV photon beams, in contrast to the MV beams used in clinical irradiation, because of the small size of the animals. At this medium energy range, however, the contribution of the photoelectric effect to absorbed dose is significant. Accurate dose calculation therefore requires a more detailed tissue definition because both density (ρ) and elemental composition (Z eff ) affect the dose distribution. Moreover, when applied to cone beam CT (CBCT) acquisitions, the stoichiometric calibration of HU becomes inefficient as it is designed for highly collimated fan beam CT acquisitions. In this study, we propose an automatic tissue segmentation method of CBCT imaging that assigns both density (ρ) and elemental composition (Z eff ) in small animal dose calculation. The method is based on the relationship found between CBCT number and ρ*Z eff product computed from known materials. Monte Carlo calculations were performed to evaluate the impact of ρZ eff variation on the absorbed dose in tissues. These results led to the creation of a tissue database composed of artificial tissues interpolated from tissue values published by the ICRU. The ρZ eff method was validated by measuring transmitted doses through tissue substitute cylinders and a mouse with EBT3 film. Measurements were compared to the results of the Monte Carlo calculations. The study of the impact of ρZ eff variation over the range of materials, from ρZ eff  = 2 g.cm - 3 (lung) to 27 g.cm - 3 (cortical bone) led to the creation of 125 artificial tissues. For tissue substitute cylinders, the use of ρZ eff method led to maximal and average relative differences between the Monte Carlo results and the EBT3 measurements of 3.6% and 1.6%. Equivalent comparison for the mouse gave maximal and average relative differences of 4.4% and 1.2%, inside the 80% isodose area. Gamma analysis led to a 94.9% success rate in the 10% isodose

  7. 3D fast adaptive correlation imaging for large-scale gravity data based on GPU computation

    Science.gov (United States)

    Chen, Z.; Meng, X.; Guo, L.; Liu, G.

    2011-12-01

    In recent years, large scale gravity data sets have been collected and employed to enhance gravity problem-solving abilities of tectonics studies in China. Aiming at the large scale data and the requirement of rapid interpretation, previous authors have carried out a lot of work, including the fast gradient module inversion and Euler deconvolution depth inversion ,3-D physical property inversion using stochastic subspaces and equivalent storage, fast inversion using wavelet transforms and a logarithmic barrier method. So it can be say that 3-D gravity inversion has been greatly improved in the last decade. Many authors added many different kinds of priori information and constraints to deal with nonuniqueness using models composed of a large number of contiguous cells of unknown property and obtained good results. However, due to long computation time, instability and other shortcomings, 3-D physical property inversion has not been widely applied to large-scale data yet. In order to achieve 3-D interpretation with high efficiency and precision for geological and ore bodies and obtain their subsurface distribution, there is an urgent need to find a fast and efficient inversion method for large scale gravity data. As an entirely new geophysical inversion method, 3D correlation has a rapid development thanks to the advantage of requiring no a priori information and demanding small amount of computer memory. This method was proposed to image the distribution of equivalent excess masses of anomalous geological bodies with high resolution both longitudinally and transversely. In order to tranform the equivalence excess masses into real density contrasts, we adopt the adaptive correlation imaging for gravity data. After each 3D correlation imaging, we change the equivalence into density contrasts according to the linear relationship, and then carry out forward gravity calculation for each rectangle cells. Next, we compare the forward gravity data with real data, and

  8. Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies.

    Science.gov (United States)

    Sanz-Requena, Roberto; Moratal, David; García-Sánchez, Diego Ramón; Bodí, Vicente; Rieta, José Joaquín; Sanchis, Juan Manuel

    2007-03-01

    Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study. The methodology described in this work has four steps: a pre-processing of IVUS images, a segmentation of media-adventitia contour, a detection of intima and plaque and a 3D reconstruction of the vessel. Preprocessing is intended to remove noise from the images without blurring the edges. Segmentation of media-adventitia contour is achieved using active contours (snakes). In particular, we use the gradient vector flow (GVF) as external force for the snakes. The detection of lumen border is obtained taking into account gray-level information of the inner part of the previously detected contours. A knowledge-based approach is used to determine which level of gray corresponds statistically to the different regions of interest: intima, plaque and lumen. The catheter region is automatically discarded. An estimate of plaque type is also given. Finally, 3D reconstruction of all detected regions is made. The suitability of this methodology has been verified for the analysis and visualization of plaque length, stenosis severity, automatic detection of the most problematic regions, calculus of plaque volumes and a preliminary estimation of plaque type obtaining for automatic measures of lumen and vessel area an average error smaller than 1mm(2) (equivalent aproximately to 10% of the average measure), for calculus of plaque and lumen volume errors smaller than 0.5mm(3) (equivalent approximately to 20% of the average measure) and for plaque type estimates a mismatch of less than 8% in the analysed frames.

  9. 3-D inversion of airborne electromagnetic data parallelized and accelerated by local mesh and adaptive soundings

    Science.gov (United States)

    Yang, Dikun; Oldenburg, Douglas W.; Haber, Eldad

    2014-03-01

    Airborne electromagnetic (AEM) methods are highly efficient tools for assessing the Earth's conductivity structures in a large area at low cost. However, the configuration of AEM measurements, which typically have widely distributed transmitter-receiver pairs, makes the rigorous modelling and interpretation extremely time-consuming in 3-D. Excessive overcomputing can occur when working on a large mesh covering the entire survey area and inverting all soundings in the data set. We propose two improvements. The first is to use a locally optimized mesh for each AEM sounding for the forward modelling and calculation of sensitivity. This dedicated local mesh is small with fine cells near the sounding location and coarse cells far away in accordance with EM diffusion and the geometric decay of the signals. Once the forward problem is solved on the local meshes, the sensitivity for the inversion on the global mesh is available through quick interpolation. Using local meshes for AEM forward modelling avoids unnecessary computing on fine cells on a global mesh that are far away from the sounding location. Since local meshes are highly independent, the forward modelling can be efficiently parallelized over an array of processors. The second improvement is random and dynamic down-sampling of the soundings. Each inversion iteration only uses a random subset of the soundings, and the subset is reselected for every iteration. The number of soundings in the random subset, determined by an adaptive algorithm, is tied to the degree of model regularization. This minimizes the overcomputing caused by working with redundant soundings. Our methods are compared against conventional methods and tested with a synthetic example. We also invert a field data set that was previously considered to be too large to be practically inverted in 3-D. These examples show that our methodology can dramatically reduce the processing time of 3-D inversion to a practical level without losing resolution

  10. CT and MRI assessment and characterization using segmentation and 3D modeling techniques: applications to muscle, bone and brain

    Directory of Open Access Journals (Sweden)

    Paolo Gargiulo

    2014-03-01

    Full Text Available This paper reviews the novel use of CT and MRI data and image processing tools to segment and reconstruct tissue images in 3D to determine characteristics of muscle, bone and brain.This to study and simulate the structural changes occurring in healthy and pathological conditions as well as in response to clinical treatments. Here we report the application of this methodology to evaluate and quantify: 1. progression of atrophy in human muscle subsequent to permanent lower motor neuron (LMN denervation, 2. muscle recovery as induced by functional electrical stimulation (FES, 3. bone quality in patients undergoing total hip replacement and 4. to model the electrical activity of the brain. Study 1: CT data and segmentation techniques were used to quantify changes in muscle density and composition by associating the Hounsfield unit values of muscle, adipose and fibrous connective tissue with different colors. This method was employed to monitor patients who have permanent muscle LMN denervation in the lower extremities under two different conditions: permanent LMN denervated not electrically stimulated and stimulated. Study 2: CT data and segmentation techniques were employed, however, in this work we assessed bone and muscle conditions in the pre-operative CT scans of patients scheduled to undergo total hip replacement. In this work, the overall anatomical structure, the bone mineral density (BMD and compactness of quadriceps muscles and proximal femoral was computed to provide a more complete view for surgeons when deciding which implant technology to use. Further, a Finite element analysis provided a map of the strains around the proximal femur socket when solicited by typical stresses caused by an implant press fitting. Study 3 describes a method to model the electrical behavior of human brain using segmented MR images. The aim of the work is to use these models to predict the electrical activity of the human brain under normal and pathological

  11. Fully anisotropic goal-oriented mesh adaptation for 3D steady Euler equations

    Science.gov (United States)

    Loseille, A.; Dervieux, A.; Alauzet, F.

    2010-04-01

    This paper studies the coupling between anisotropic mesh adaptation and goal-oriented error estimate. The former is very well suited to the control of the interpolation error. It is generally interpreted as a local geometric error estimate. On the contrary, the latter is preferred when studying approximation errors for PDEs. It generally involves non local error contributions. Consequently, a full and strong coupling between both is hard to achieve due to this apparent incompatibility. This paper shows how to achieve this coupling in three steps. First, a new a priori error estimate is proved in a formal framework adapted to goal-oriented mesh adaptation for output functionals. This estimate is based on a careful analysis of the contributions of the implicit error and of the interpolation error. Second, the error estimate is applied to the set of steady compressible Euler equations which are solved by a stabilized Galerkin finite element discretization. A goal-oriented error estimation is derived. It involves the interpolation error of the Euler fluxes weighted by the gradient of the adjoint state associated with the observed functional. Third, rewritten in the continuous mesh framework, the previous estimate is minimized on the set of continuous meshes thanks to a calculus of variations. The optimal continuous mesh is then derived analytically. Thus, it can be used as a metric tensor field to drive the mesh adaptation. From a numerical point of view, this method is completely automatic, intrinsically anisotropic, and does not depend on any a priori choice of variables to perform the adaptation. 3D examples of steady flows around supersonic and transsonic jets are presented to validate the current approach and to demonstrate its efficiency.

  12. Analysis and adaptive control of a novel 3-D conservative no-equilibrium chaotic system

    Directory of Open Access Journals (Sweden)

    Vaidyanathan Sundarapandian

    2015-09-01

    Full Text Available First, this paper announces a seven-term novel 3-D conservative chaotic system with four quadratic nonlinearities. The conservative chaotic systems are characterized by the important property that they are volume conserving. The phase portraits of the novel conservative chaotic system are displayed and the mathematical properties are discussed. An important property of the proposed novel chaotic system is that it has no equilibrium point. Hence, it displays hidden chaotic attractors. The Lyapunov exponents of the novel conservative chaotic system are obtained as L1 = 0.0395,L2 = 0 and L3 = −0.0395. The Kaplan-Yorke dimension of the novel conservative chaotic system is DKY =3. Next, an adaptive controller is designed to globally stabilize the novel conservative chaotic system with unknown parameters. Moreover, an adaptive controller is also designed to achieve global chaos synchronization of the identical conservative chaotic systems with unknown parameters. MATLAB simulations have been depicted to illustrate the phase portraits of the novel conservative chaotic system and also the adaptive control results.

  13. Adaptive Probabilistic Tracking Embedded in Smart Cameras for Distributed Surveillance in a 3D Model

    Directory of Open Access Journals (Sweden)

    Sven Fleck

    2006-12-01

    Full Text Available Tracking applications based on distributed and embedded sensor networks are emerging today, both in the fields of surveillance and industrial vision. Traditional centralized approaches have several drawbacks, due to limited communication bandwidth, computational requirements, and thus limited spatial camera resolution and frame rate. In this article, we present network-enabled smart cameras for probabilistic tracking. They are capable of tracking objects adaptively in real time and offer a very bandwidthconservative approach, as the whole computation is performed embedded in each smart camera and only the tracking results are transmitted, which are on a higher level of abstraction. Based on this, we present a distributed surveillance system. The smart cameras' tracking results are embedded in an integrated 3D environment as live textures and can be viewed from arbitrary perspectives. Also a georeferenced live visualization embedded in Google Earth is presented.

  14. Adaptive Probabilistic Tracking Embedded in Smart Cameras for Distributed Surveillance in a 3D Model

    Directory of Open Access Journals (Sweden)

    Fleck Sven

    2007-01-01

    Full Text Available Tracking applications based on distributed and embedded sensor networks are emerging today, both in the fields of surveillance and industrial vision. Traditional centralized approaches have several drawbacks, due to limited communication bandwidth, computational requirements, and thus limited spatial camera resolution and frame rate. In this article, we present network-enabled smart cameras for probabilistic tracking. They are capable of tracking objects adaptively in real time and offer a very bandwidthconservative approach, as the whole computation is performed embedded in each smart camera and only the tracking results are transmitted, which are on a higher level of abstraction. Based on this, we present a distributed surveillance system. The smart cameras' tracking results are embedded in an integrated 3D environment as live textures and can be viewed from arbitrary perspectives. Also a georeferenced live visualization embedded in Google Earth is presented.

  15. Adaptive unified continuum FEM modeling of a 3D FSI benchmark problem.

    Science.gov (United States)

    Jansson, Johan; Degirmenci, Niyazi Cem; Hoffman, Johan

    2017-09-01

    In this paper, we address a 3D fluid-structure interaction benchmark problem that represents important characteristics of biomedical modeling. We present a goal-oriented adaptive finite element methodology for incompressible fluid-structure interaction based on a streamline diffusion-type stabilization of the balance equations for mass and momentum for the entire continuum in the domain, which is implemented in the Unicorn/FEniCS software framework. A phase marker function and its corresponding transport equation are introduced to select the constitutive law, where the mesh tracks the discontinuous fluid-structure interface. This results in a unified simulation method for fluids and structures. We present detailed results for the benchmark problem compared with experiments, together with a mesh convergence study. Copyright © 2016 John Wiley & Sons, Ltd.

  16. Adaptive compensation of aberrations in ultrafast 3D microscopy using a deformable mirror

    Science.gov (United States)

    Sherman, Leah R.; Albert, O.; Schmidt, Christoph F.; Vdovin, Gleb V.; Mourou, Gerard A.; Norris, Theodore B.

    2000-05-01

    3D imaging using a multiphoton scanning confocal microscope is ultimately limited by aberrations of the system. We describe a system to adaptively compensate the aberrations with a deformable mirror. We have increased the transverse scanning range of the microscope by three with compensation of off-axis aberrations.We have also significantly increased the longitudinal scanning depth with compensation of spherical aberrations from the penetration into the sample. Our correction is based on a genetic algorithm that uses second harmonic or two-photon fluorescence signal excited by femtosecond pulses from the sample as the enhancement parameter. This allows us to globally optimize the wavefront without a wavefront measurement. To improve the speed of the optimization we use Zernike polynomials as the basis for correction. Corrections can be stored in a database for look-up with future samples.

  17. 3D design and electric simulation of a silicon drift detector using a spiral biasing adapter

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yu-yun; Xiong, Bo [School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105 (China); Center for Semiconductor Particle and photon Imaging Detector, Development and Fabrication, Xiangtan University, Xiangtan 411105 (China); Li, Zheng, E-mail: zhengli58@gmail.com [School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105 (China); Center for Semiconductor Particle and photon Imaging Detector, Development and Fabrication, Xiangtan University, Xiangtan 411105 (China)

    2016-09-21

    The detector system of combining a spiral biasing adapter (SBA) with a silicon drift detector (SBA-SDD) is largely different from the traditional silicon drift detector (SDD), including the spiral SDD. It has a spiral biasing adapter of the same design as a traditional spiral SDD and an SDD with concentric rings having the same radius. Compared with the traditional spiral SDD, the SBA-SDD separates the spiral's functions of biasing adapter and the p–n junction definition. In this paper, the SBA-SDD is simulated using a Sentaurus TCAD tool, which is a full 3D device simulation tool. The simulated electric characteristics include electric potential, electric field, electron concentration, and single event effect. Because of the special design of the SBA-SDD, the SBA can generate an optimum drift electric field in the SDD, comparable with the conventional spiral SDD, while the SDD can be designed with concentric rings to reduce surface area. Also the current and heat generated in the SBA are separated from the SDD. To study the single event response, we simulated the induced current caused by incident heavy ions (20 and 50 μm penetration length) with different linear energy transfer (LET). The SBA-SDD can be used just like a conventional SDD, such as X-ray detector for energy spectroscopy and imaging, etc. - Highlights: • The separation of the spiral biasing adapter and SDD is a new concept. • The distribution of the electric potential is symmetrical around the axis through the anode. • The region with higher electron concentrations defines the drift channel.

  18. Automatic segmentation of low-visibility moving objects through energy analyis of the local 3D spectrum

    Science.gov (United States)

    Nestares, Oscar; Miravet, Carlos; Santamaria, Javier; Fonolla Navarro, Rafael

    1999-05-01

    Automatic object segmentation in highly noisy image sequences, composed by a translating object over a background having a different motion, is achieved through joint motion-texture analysis. Local motion and/or texture is characterized by the energy of the local spatio-temporal spectrum, as different textures undergoing different translational motions display distinctive features in their 3D (x,y,t) spectra. Measurements of local spectrum energy are obtained using a bank of directional 3rd order Gaussian derivative filters in a multiresolution pyramid in space- time (10 directions, 3 resolution levels). These 30 energy measurements form a feature vector describing texture-motion for every pixel in the sequence. To improve discrimination capability and reduce computational cost, we automatically select those 4 features (channels) that best discriminate object from background, under the assumptions that the object is smaller than the background and has a different velocity or texture. In this way we reject features irrelevant or dominated by noise, that could yield wrong segmentation results. This method has been successfully applied to sequences with extremely low visibility and for objects that are even invisible for the eye in absence of motion.

  19. Capabilities of wind tunnels with two-adaptive walls to minimize boundary interference in 3-D model testing

    Science.gov (United States)

    Rebstock, Rainer; Lee, Edwin E., Jr.

    1989-01-01

    An initial wind tunnel test was made to validate a new wall adaptation method for 3-D models in test sections with two adaptive walls. First part of the adaptation strategy is an on-line assessment of wall interference at the model position. The wall induced blockage was very small at all test conditions. Lift interference occurred at higher angles of attack with the walls set aerodynamically straight. The adaptation of the top and bottom tunnel walls is aimed at achieving a correctable flow condition. The blockage was virtually zero throughout the wing planform after the wall adjustment. The lift curve measured with the walls adapted agreed very well with interference free data for Mach 0.7, regardless of the vertical position of the wing in the test section. The 2-D wall adaptation can significantly improve the correctability of 3-D model data. Nevertheless, residual spanwise variations of wall interference are inevitable.

  20. Dynamic implicit 3D adaptive mesh refinement for non-equilibrium radiation diffusion

    Science.gov (United States)

    Philip, B.; Wang, Z.; Berrill, M. A.; Birke, M.; Pernice, M.

    2014-04-01

    The time dependent non-equilibrium radiation diffusion equations are important for solving the transport of energy through radiation in optically thick regimes and find applications in several fields including astrophysics and inertial confinement fusion. The associated initial boundary value problems that are encountered often exhibit a wide range of scales in space and time and are extremely challenging to solve. To efficiently and accurately simulate these systems we describe our research on combining techniques that will also find use more broadly for long term time integration of nonlinear multi-physics systems: implicit time integration for efficient long term time integration of stiff multi-physics systems, local control theory based step size control to minimize the required global number of time steps while controlling accuracy, dynamic 3D adaptive mesh refinement (AMR) to minimize memory and computational costs, Jacobian Free Newton-Krylov methods on AMR grids for efficient nonlinear solution, and optimal multilevel preconditioner components that provide level independent solver convergence.

  1. Dynamic implicit 3D adaptive mesh refinement for non-equilibrium radiation diffusion

    International Nuclear Information System (INIS)

    Philip, B.; Wang, Z.; Berrill, M.A.; Birke, M.; Pernice, M.

    2014-01-01

    The time dependent non-equilibrium radiation diffusion equations are important for solving the transport of energy through radiation in optically thick regimes and find applications in several fields including astrophysics and inertial confinement fusion. The associated initial boundary value problems that are encountered often exhibit a wide range of scales in space and time and are extremely challenging to solve. To efficiently and accurately simulate these systems we describe our research on combining techniques that will also find use more broadly for long term time integration of nonlinear multi-physics systems: implicit time integration for efficient long term time integration of stiff multi-physics systems, local control theory based step size control to minimize the required global number of time steps while controlling accuracy, dynamic 3D adaptive mesh refinement (AMR) to minimize memory and computational costs, Jacobian Free Newton–Krylov methods on AMR grids for efficient nonlinear solution, and optimal multilevel preconditioner components that provide level independent solver convergence

  2. Stereoscopic-3D display design: a new paradigm with Intel Adaptive Stable Image Technology [IA-SIT

    Science.gov (United States)

    Jain, Sunil

    2012-03-01

    Stereoscopic-3D (S3D) proliferation on personal computers (PC) is mired by several technical and business challenges: a) viewing discomfort due to cross-talk amongst stereo images; b) high system cost; and c) restricted content availability. Users expect S3D visual quality to be better than, or at least equal to, what they are used to enjoying on 2D in terms of resolution, pixel density, color, and interactivity. Intel Adaptive Stable Image Technology (IA-SIT) is a foundational technology, successfully developed to resolve S3D system design challenges and deliver high quality 3D visualization at PC price points. Optimizations in display driver, panel timing firmware, backlight hardware, eyewear optical stack, and synch mechanism combined can help accomplish this goal. Agnostic to refresh rate, IA-SIT will scale with shrinking of display transistors and improvements in liquid crystal and LED materials. Industry could profusely benefit from the following calls to action:- 1) Adopt 'IA-SIT S3D Mode' in panel specs (via VESA) to help panel makers monetize S3D; 2) Adopt 'IA-SIT Eyewear Universal Optical Stack' and algorithm (via CEA) to help PC peripheral makers develop stylish glasses; 3) Adopt 'IA-SIT Real Time Profile' for sub-100uS latency control (via BT Sig) to extend BT into S3D; and 4) Adopt 'IA-SIT Architecture' for Monitors and TVs to monetize via PC attach.

  3. A systematic review of image segmentation methodology, used in the additive manufacture of patient-specific 3D printed models of the cardiovascular system

    Directory of Open Access Journals (Sweden)

    N Byrne

    2016-04-01

    Full Text Available Background Shortcomings in existing methods of image segmentation preclude the widespread adoption of patient-specific 3D printing as a routine decision-making tool in the care of those with congenital heart disease. We sought to determine the range of cardiovascular segmentation methods and how long each of these methods takes. Methods A systematic review of literature was undertaken. Medical imaging modality, segmentation methods, segmentation time, segmentation descriptive quality (SDQ and segmentation software were recorded. Results Totally 136 studies met the inclusion criteria (1 clinical trial; 80 journal articles; 55 conference, technical and case reports. The most frequently used image segmentation methods were brightness thresholding, region growing and manual editing, as supported by the most popular piece of proprietary software: Mimics (Materialise NV, Leuven, Belgium, 1992–2015. The use of bespoke software developed by individual authors was not uncommon. SDQ indicated that reporting of image segmentation methods was generally poor with only one in three accounts providing sufficient detail for their procedure to be reproduced. Conclusions and implication of key findings Predominantly anecdotal and case reporting precluded rigorous assessment of risk of bias and strength of evidence. This review finds a reliance on manual and semi-automated segmentation methods which demand a high level of expertise and a significant time commitment on the part of the operator. In light of the findings, we have made recommendations regarding reporting of 3D printing studies. We anticipate that these findings will encourage the development of advanced image segmentation methods.

  4. Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network

    Directory of Open Access Journals (Sweden)

    Trong-Ngoc Le

    2016-01-01

    Full Text Available Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN, which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the “ground truth.” Results. The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively.

  5. A semi-automatic 2D-to-3D video conversion with adaptive key-frame selection

    Science.gov (United States)

    Ju, Kuanyu; Xiong, Hongkai

    2014-11-01

    To compensate the deficit of 3D content, 2D to 3D video conversion (2D-to-3D) has recently attracted more attention from both industrial and academic communities. The semi-automatic 2D-to-3D conversion which estimates corresponding depth of non-key-frames through key-frames is more desirable owing to its advantage of balancing labor cost and 3D effects. The location of key-frames plays a role on quality of depth propagation. This paper proposes a semi-automatic 2D-to-3D scheme with adaptive key-frame selection to keep temporal continuity more reliable and reduce the depth propagation errors caused by occlusion. The potential key-frames would be localized in terms of clustered color variation and motion intensity. The distance of key-frame interval is also taken into account to keep the accumulated propagation errors under control and guarantee minimal user interaction. Once their depth maps are aligned with user interaction, the non-key-frames depth maps would be automatically propagated by shifted bilateral filtering. Considering that depth of objects may change due to the objects motion or camera zoom in/out effect, a bi-directional depth propagation scheme is adopted where a non-key frame is interpolated from two adjacent key frames. The experimental results show that the proposed scheme has better performance than existing 2D-to-3D scheme with fixed key-frame interval.

  6. Multi-view 3D human pose estimation combining single-frame recovery, temporal integration and model adaptation

    NARCIS (Netherlands)

    Hofmann, K.M.; Gavrilla, D.M.

    2009-01-01

    We present a system for the estimation of unconstrained 3D human upper body movement from multiple cameras. Its main novelty lies in the integration of three components: single frame pose recovery, temporal integration and model adaptation. Single frame pose recovery consists of a hypothesis

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

    Science.gov (United States)

    Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

    2018-03-01

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

  8. SU-F-J-93: Automated Segmentation of High-Resolution 3D WholeBrain Spectroscopic MRI for Glioblastoma Treatment Planning

    Energy Technology Data Exchange (ETDEWEB)

    Schreibmann, E; Shu, H [Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA (United States); Cordova, J; Gurbani, S; Holder, C; Cooper, L; Shim, H [Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA (United States)

    2016-06-15

    Purpose: We report on an automated segmentation algorithm for defining radiation therapy target volumes using spectroscopic MR images (sMRI) acquired at nominal voxel resolution of 100 microliters. Methods: Wholebrain sMRI combining 3D echo-planar spectroscopic imaging, generalized auto-calibrating partially-parallel acquisitions, and elliptical k-space encoding were conducted on 3T MRI scanner with 32-channel head coil array creating images. Metabolite maps generated include choline (Cho), creatine (Cr), and N-acetylaspartate (NAA), as well as Cho/NAA, Cho/Cr, and NAA/Cr ratio maps. Automated segmentation was achieved by concomitantly considering sMRI metabolite maps with standard contrast enhancing (CE) imaging in a pipeline that first uses the water signal for skull stripping. Subsequently, an initial blob of tumor region is identified by searching for regions of FLAIR abnormalities that also display reduced NAA activity using a mean ratio correlation and morphological filters. These regions are used as starting point for a geodesic level-set refinement that adapts the initial blob to the fine details specific to each metabolite. Results: Accuracy of the segmentation model was tested on a cohort of 12 patients that had sMRI datasets acquired pre, mid and post-treatment, providing a broad range of enhancement patterns. Compared to classical imaging, where heterogeneity in the tumor appearance and shape across posed a greater challenge to the algorithm, sMRI’s regions of abnormal activity were easily detected in the sMRI metabolite maps when combining the detail available in the standard imaging with the local enhancement produced by the metabolites. Results can be imported in the treatment planning, leading in general increase in the target volumes (GTV60) when using sMRI+CE MRI compared to the standard CE MRI alone. Conclusion: Integration of automated segmentation of sMRI metabolite maps into planning is feasible and will likely streamline acceptance of this

  9. The two sides of complement C3d: evolution of electrostatics in a link between innate and adaptive immunity.

    Science.gov (United States)

    Kieslich, Chris A; Morikis, Dimitrios

    2012-01-01

    The interaction between complement fragment C3d and complement receptor 2 (CR2) is a key aspect of complement immune system activation, and is a component in a link between innate and adaptive immunities. The complement immune system is an ancient mechanism for defense, and can be found in species that have been on Earth for the last 600 million years. However, the link between the complement system and adaptive immunity, which is formed through the association of the B-cell co-receptor complex, including the C3d-CR2 interaction, is a much more recent adaptation. Human C3d and CR2 have net charges of -1 and +7 respectively, and are believed to have evolved favoring the role of electrostatics in their functions. To investigate the role of electrostatics in the function and evolution of human C3d and CR2, we have applied electrostatic similarity methods to identify regions of evolutionarily conserved electrostatic potential based on 24 homologues of complement C3d and 4 homologues of CR2. We also examine the effects of structural perturbation, as introduced through molecular dynamics and mutations, on spatial distributions of electrostatic potential to identify perturbation resistant regions, generated by so-called electrostatic "hot-spots". Distributions of electrostatic similarity based on families of perturbed structures illustrate the presence of electrostatic "hot-spots" at the two functional sites of C3d, while the surface of CR2 lacks electrostatic "hot-spots" despite its excessively positive nature. We propose that the electrostatic "hot-spots" of C3d have evolved to optimize its dual-functionality (covalently attaching to pathogen surfaces and interaction with CR2), which are both necessary for the formation B-cell co-receptor complexes. Comparison of the perturbation resistance of the electrostatic character of the homologues of C3d suggests that there was an emergence of a new role of electrostatics, and a transition in the function of C3d, after the

  10. The two sides of complement C3d: evolution of electrostatics in a link between innate and adaptive immunity.

    Directory of Open Access Journals (Sweden)

    Chris A Kieslich

    Full Text Available The interaction between complement fragment C3d and complement receptor 2 (CR2 is a key aspect of complement immune system activation, and is a component in a link between innate and adaptive immunities. The complement immune system is an ancient mechanism for defense, and can be found in species that have been on Earth for the last 600 million years. However, the link between the complement system and adaptive immunity, which is formed through the association of the B-cell co-receptor complex, including the C3d-CR2 interaction, is a much more recent adaptation. Human C3d and CR2 have net charges of -1 and +7 respectively, and are believed to have evolved favoring the role of electrostatics in their functions. To investigate the role of electrostatics in the function and evolution of human C3d and CR2, we have applied electrostatic similarity methods to identify regions of evolutionarily conserved electrostatic potential based on 24 homologues of complement C3d and 4 homologues of CR2. We also examine the effects of structural perturbation, as introduced through molecular dynamics and mutations, on spatial distributions of electrostatic potential to identify perturbation resistant regions, generated by so-called electrostatic "hot-spots". Distributions of electrostatic similarity based on families of perturbed structures illustrate the presence of electrostatic "hot-spots" at the two functional sites of C3d, while the surface of CR2 lacks electrostatic "hot-spots" despite its excessively positive nature. We propose that the electrostatic "hot-spots" of C3d have evolved to optimize its dual-functionality (covalently attaching to pathogen surfaces and interaction with CR2, which are both necessary for the formation B-cell co-receptor complexes. Comparison of the perturbation resistance of the electrostatic character of the homologues of C3d suggests that there was an emergence of a new role of electrostatics, and a transition in the function of C3

  11. ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques.

    Science.gov (United States)

    Ji, Hongwei; He, Jiangping; Yang, Xin; Deklerck, Rudi; Cornelis, Jan

    2013-05-01

    In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

  12. 3D-segmentation of the 18F-choline PET signal for target volume definition in radiation therapy of the prostate.

    Science.gov (United States)

    Ciernik, I Frank; Brown, Derek W; Schmid, Daniel; Hany, Thomas; Egli, Peter; Davis, J Bernard

    2007-02-01

    Volumetric assessment of PET signals becomes increasingly relevant for radiotherapy (RT) planning. Here, we investigate the utility of 18F-choline PET signals to serve as a structure for semi-automatic segmentation for forward treatment planning of prostate cancer. 18F-choline PET and CT scans of ten patients with histologically proven prostate cancer without extracapsular growth were acquired using a combined PET/CT scanner. Target volumes were manually delineated on CT images using standard software. Volumes were also obtained from 18F-choline PET images using an asymmetrical segmentation algorithm. PTVs were derived from CT 18F-choline PET based clinical target volumes (CTVs) by automatic expansion and comparative planning was performed. As a read-out for dose given to non-target structures, dose to the rectal wall was assessed. Planning target volumes (PTVs) derived from CT and 18F-choline PET yielded comparable results. Optimal matching of CT and 18F-choline PET derived volumes in the lateral and cranial-caudal directions was obtained using a background-subtracted signal thresholds of 23.0+/-2.6%. In antero-posterior direction, where adaptation compensating for rectal signal overflow was required, optimal matching was achieved with a threshold of 49.5+/-4.6%. 3D-conformal planning with CT or 18F-choline PET resulted in comparable doses to the rectal wall. Choline PET signals of the prostate provide adequate spatial information amendable to standardized asymmetrical region growing algorithms for PET-based target volume definition for external beam RT.

  13. Comparison of T1-weighted 2D TSE, 3D SPGR, and two-point 3D Dixon MRI for automated segmentation of visceral adipose tissue at 3 Tesla.

    Science.gov (United States)

    Fallah, Faezeh; Machann, Jürgen; Martirosian, Petros; Bamberg, Fabian; Schick, Fritz; Yang, Bin

    2017-04-01

    To evaluate and compare conventional T1-weighted 2D turbo spin echo (TSE), T1-weighted 3D volumetric interpolated breath-hold examination (VIBE), and two-point 3D Dixon-VIBE sequences for automatic segmentation of visceral adipose tissue (VAT) volume at 3 Tesla by measuring and compensating for errors arising from intensity nonuniformity (INU) and partial volume effects (PVE). The body trunks of 28 volunteers with body mass index values ranging from 18 to 41.2 kg/m 2 (30.02 ± 6.63 kg/m 2 ) were scanned at 3 Tesla using three imaging techniques. Automatic methods were applied to reduce INU and PVE and to segment VAT. The automatically segmented VAT volumes obtained from all acquisitions were then statistically and objectively evaluated against the manually segmented (reference) VAT volumes. Comparing the reference volumes with the VAT volumes automatically segmented over the uncorrected images showed that INU led to an average relative volume difference of -59.22 ± 11.59, 2.21 ± 47.04, and -43.05 ± 5.01 % for the TSE, VIBE, and Dixon images, respectively, while PVE led to average differences of -34.85 ± 19.85, -15.13 ± 11.04, and -33.79 ± 20.38 %. After signal correction, differences of -2.72 ± 6.60, 34.02 ± 36.99, and -2.23 ± 7.58 % were obtained between the reference and the automatically segmented volumes. A paired-sample two-tailed t test revealed no significant difference between the reference and automatically segmented VAT volumes of the corrected TSE (p = 0.614) and Dixon (p = 0.969) images, but showed a significant VAT overestimation using the corrected VIBE images. Under similar imaging conditions and spatial resolution, automatically segmented VAT volumes obtained from the corrected TSE and Dixon images agreed with each other and with the reference volumes. These results demonstrate the efficacy of the signal correction methods and the similar accuracy of TSE and Dixon imaging for automatic volumetry of VAT at 3 Tesla.

  14. 3D adaptable building skin : an invention for freedom in shape of façades

    NARCIS (Netherlands)

    Suma, A.B.

    2006-01-01

    This paper sets out to develop a principle on which a façade element can be deformed in shape. By using a flexible structure with cables, small bars and inflatable tubes, which together form a woven pattern, a 3D freedom of deformability results. The shape of the façade element can be manipulated

  15. 3-D discrete analytical ridgelet transform.

    Science.gov (United States)

    Helbert, David; Carré, Philippe; Andres, Eric

    2006-12-01

    In this paper, we propose an implementation of the 3-D Ridgelet transform: the 3-D discrete analytical Ridgelet transform (3-D DART). This transform uses the Fourier strategy for the computation of the associated 3-D discrete Radon transform. The innovative step is the definition of a discrete 3-D transform with the discrete analytical geometry theory by the construction of 3-D discrete analytical lines in the Fourier domain. We propose two types of 3-D discrete lines: 3-D discrete radial lines going through the origin defined from their orthogonal projections and 3-D planes covered with 2-D discrete line segments. These discrete analytical lines have a parameter called arithmetical thickness, allowing us to define a 3-D DART adapted to a specific application. Indeed, the 3-D DART representation is not orthogonal, It is associated with a flexible redundancy factor. The 3-D DART has a very simple forward/inverse algorithm that provides an exact reconstruction without any iterative method. In order to illustrate the potentiality of this new discrete transform, we apply the 3-D DART and its extension to the Local-DART (with smooth windowing) to the denoising of 3-D image and color video. These experimental results show that the simple thresholding of the 3-D DART coefficients is efficient.

  16. Object-oriented philosophy in designing adaptive finite-element package for 3D elliptic deferential equations

    Science.gov (United States)

    Zhengyong, R.; Jingtian, T.; Changsheng, L.; Xiao, X.

    2007-12-01

    Although adaptive finite-element (AFE) analysis is becoming more and more focused in scientific and engineering fields, its efficient implementations are remain to be a discussed problem as its more complex procedures. In this paper, we propose a clear C++ framework implementation to show the powerful properties of Object-oriented philosophy (OOP) in designing such complex adaptive procedure. In terms of the modal functions of OOP language, the whole adaptive system is divided into several separate parts such as the mesh generation or refinement, a-posterior error estimator, adaptive strategy and the final post processing. After proper designs are locally performed on these separate modals, a connected framework of adaptive procedure is formed finally. Based on the general elliptic deferential equation, little efforts should be added in the adaptive framework to do practical simulations. To show the preferable properties of OOP adaptive designing, two numerical examples are tested. The first one is the 3D direct current resistivity problem in which the powerful framework is efficiently shown as only little divisions are added. And then, in the second induced polarization£¨IP£©exploration case, new adaptive procedure is easily added which adequately shows the strong extendibility and re-usage of OOP language. Finally we believe based on the modal framework adaptive implementation by OOP methodology, more advanced adaptive analysis system will be available in future.

  17. Canine and feline fundus photography and videography using a nonpatented 3D printed lens adapter for a smartphone.

    Science.gov (United States)

    Espinheira Gomes, Filipe; Ledbetter, Eric

    2018-05-11

    To describe an indirect funduscopy imaging technique for dogs and cats using low cost and widely available equipment: a smartphone, a three-dimensional (3D) printed indirect lens adapter, and a 40 diopters (D) indirect ophthalmoscopy lens. Fundus videography was performed in dogs and cats using a 40D indirect ophthalmoscopy lens and a smartphone fitted with a 3D printed indirect lens adapter. All animals were pharmacologically dilated with topical tropicamide 1% solution. Eyelid opening and video recording were performed using standard binocular indirect ophthalmoscopy technique. All videos were uploaded to a computer, and still images were selected and acquired for archiving purposes. Fundic images were manipulated to represent the true anatomy of the fundus. It was possible to promptly obtain good quality images from normal and diseased retinas using the nonpatented 3D printed, lens adapter for a smartphone. Fundic imaging using a smartphone can be performed with minimal investment. This simple imaging modality can be used by veterinary ophthalmologists and general practitioners to acquire, archive, and share images of the retina. The quality of images obtained will likely improve with developments in smartphone camera software and hardware. © 2018 American College of Veterinary Ophthalmologists.

  18. PHISICS/RELAP5-3D Adaptive Time-Step Method Demonstrated for the HTTR LOFC#1 Simulation

    Energy Technology Data Exchange (ETDEWEB)

    Baker, Robin Ivey [Idaho National Lab. (INL), Idaho Falls, ID (United States); Balestra, Paolo [Univ. of Rome (Italy); Strydom, Gerhard [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2017-05-01

    A collaborative effort between Japan Atomic Energy Agency (JAEA) and Idaho National Laboratory (INL) as part of the Civil Nuclear Energy Working Group is underway to model the high temperature engineering test reactor (HTTR) loss of forced cooling (LOFC) transient that was performed in December 2010. The coupled version of RELAP5-3D, a thermal fluids code, and PHISICS, a neutronics code, were used to model the transient. The focus of this report is to summarize the changes made to the PHISICS-RELAP5-3D code for implementing an adaptive time step methodology into the code for the first time, and to test it using the full HTTR PHISICS/RELAP5-3D model developed by JAEA and INL and the LOFC simulation. Various adaptive schemes are available based on flux or power convergence criteria that allow significantly larger time steps to be taken by the neutronics module. The report includes a description of the HTTR and the associated PHISICS/RELAP5-3D model test results as well as the University of Rome sub-contractor report documenting the adaptive time step theory and methodology implemented in PHISICS/RELAP5-3D. Two versions of the HTTR model were tested using 8 and 26 energy groups. It was found that most of the new adaptive methods lead to significant improvements in the LOFC simulation time required without significant accuracy penalties in the prediction of the fission power and the fuel temperature. In the best performing 8 group model scenarios, a LOFC simulation of 20 hours could be completed in real-time, or even less than real-time, compared with the previous version of the code that completed the same transient 3-8 times slower than real-time. A few of the user choice combinations between the methodologies available and the tolerance settings did however result in unacceptably high errors or insignificant gains in simulation time. The study is concluded with recommendations on which methods to use for this HTTR model. An important caveat is that these findings

  19. Segmented bimorph mirrors for adaptive optics: morphing strategy.

    Science.gov (United States)

    Bastaits, Renaud; Alaluf, David; Belloni, Edoardo; Rodrigues, Gonçalo; Preumont, André

    2014-08-01

    This paper discusses the concept of a light weight segmented bimorph mirror for adaptive optics. It focuses on the morphing strategy and addresses the ill-conditioning of the Jacobian of the segments, which are partly outside the optical pupil. Two options are discussed, one based on truncating the singular values and one called damped least squares, which minimizes a combined measure of the sensor error and the voltage vector. A comparison of various configurations of segmented mirrors was conducted; it is shown that segmentation sharply increases the natural frequency of the system with limited deterioration of the image quality.

  20. A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin

    Science.gov (United States)

    Ghanta, Sindhu; Jordan, Michael I.; Kose, Kivanc; Brooks, Dana H.; Rajadhyaksha, Milind; Dy, Jennifer G.

    2016-01-01

    Segmenting objects of interest from 3D datasets is a common problem encountered in biological data. Small field of view and intrinsic biological variability combined with optically subtle changes of intensity, resolution and low contrast in images make the task of segmentation difficult, especially for microscopy of unstained living or freshly excised thick tissues. Incorporating shape information in addition to the appearance of the object of interest can often help improve segmentation performance. However, shapes of objects in tissue can be highly variable and design of a flexible shape model that encompasses these variations is challenging. To address such complex segmentation problems, we propose a unified probabilistic framework that can incorporate the uncertainty associated with complex shapes, variable appearance and unknown locations. The driving application which inspired the development of this framework is a biologically important segmentation problem: the task of automatically detecting and segmenting the dermal-epidermal junction (DEJ) in 3D reflectance confocal microscopy (RCM) images of human skin. RCM imaging allows noninvasive observation of cellular, nuclear and morphological detail. The DEJ is an important morphological feature as it is where disorder, disease and cancer usually start. Detecting the DEJ is challenging because it is a 2D surface in a 3D volume which has strong but highly variable number of irregularly spaced and variably shaped “peaks and valleys”. In addition, RCM imaging resolution, contrast and intensity vary with depth. Thus a prior model needs to incorporate the intrinsic structure while allowing variability in essentially all its parameters. We propose a model which can incorporate objects of interest with complex shapes and variable appearance in an unsupervised setting by utilizing domain knowledge to build appropriate priors of the model. Our novel strategy to model this structure combines a spatial Poisson process

  1. Sci-Thur AM: YIS – 03: Combining sagittally-reconstructed 3D and live-2D ultrasound for high-dose-rate prostate brachytherapy needle segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Hrinivich, Thomas; Hoover, Douglas; Surry, Kathleen; Edirisinghe, Chandima; D’Souza, David; Fenster, Aaron; Wong, Eugene [University of Western Ontario, London Regional Cancer Program/LHSC, London Regional Cancer Program/LHSC, Robarts Research Institute, London Regional Cancer Program/LHSC, Robarts Research Institute, University of Western Ontario (Canada)

    2016-08-15

    Ultrasound-guided high-dose-rate prostate brachytherapy (HDR-BT) needle segmentation is performed clinically using live-2D sagittal images. Organ segmentation is then performed using axial images, introducing a source of geometric uncertainty. Sagittally-reconstructed 3D (SR3D) ultrasound enables both needle and organ segmentation, but suffers from shadow artifacts. We present a needle segmentation technique augmenting SR3D with live-2D sagittal images using mechanical probe tracking to mitigate image artifacts and compare it to the clinical standard. Seven prostate cancer patients underwent TRUS-guided HDR-BT during which the clinical and proposed segmentation techniques were completed in parallel using dual ultrasound video outputs. Calibrated needle end-length measurements were used to calculate insertion depth errors (IDEs), and the dosimetric impact of IDEs was evaluated by perturbing clinical treatment plan source positions. The proposed technique provided smaller IDEs than the clinical approach, with mean±SD of −0.3±2.2 mm and −0.5±3.7mm respectively. The proposed and clinical techniques resulted in 84% and 43% of needles with IDEs within ±3mm, and IDE ranges across all needles of [−7.7mm, 5.9mm] and [−9.3mm, 7.7mm] respectively. The proposed and clinical IDEs lead to mean±SD changes in the volume of the prostate receiving the prescription dose of −0.6±0.9% and −2.0±5.3% respectively. The proposed technique provides improved HDR-BT needle segmentation accuracy over the clinical technique leading to decreased dosimetric uncertainty by eliminating the axial-to-sagittal registration, and mitigates the effect of shadow artifacts by incorporating mechanically registered live-2D sagittal images.

  2. A MATLAB Script for Solving 2D/3D Minimum Compliance Problems using Anisotropic Mesh Adaptation

    DEFF Research Database (Denmark)

    Jensen, Kristian Ejlebjerg

    2017-01-01

    We present a pure MATLAB implementation for solving 2D/3D compliance minimization problems using the density method. A filtered design variable with a minimum length is computed using a Helmholtz-type differential equation. The optimality criteria is used as optimizer and to avoid local minima we...... apply continuation of an exponent that controls the stiffness associated with intermediate design variables. We constrain the volume from above and use the implementation to show that optimizations with dynamic meshes can save significant amounts of computational time compared to fixed meshes without...

  3. TH-CD-206-02: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in MR Images Using Patch-Based Anatomical Signature

    International Nuclear Information System (INIS)

    Yang, X; Jani, A; Rossi, P; Mao, H; Curran, W; Liu, T

    2016-01-01

    Purpose: MRI has shown promise in identifying prostate tumors with high sensitivity and specificity for the detection of prostate cancer. Accurate segmentation of the prostate plays a key role various tasks: to accurately localize prostate boundaries for biopsy needle placement and radiotherapy, to initialize multi-modal registration algorithms or to obtain the region of interest for computer-aided detection of prostate cancer. However, manual segmentation during biopsy or radiation therapy can be time consuming and subject to inter- and intra-observer variation. This study’s purpose it to develop an automated method to address this technical challenge. Methods: We present an automated multi-atlas segmentation for MR prostate segmentation using patch-based label fusion. After an initial preprocessing for all images, all the atlases are non-rigidly registered to a target image. And then, the resulting transformation is used to propagate the anatomical structure labels of the atlas into the space of the target image. The top L similar atlases are further chosen by measuring intensity and structure difference in the region of interest around prostate. Finally, using voxel weighting based on patch-based anatomical signature, the label that the majority of all warped labels predict for each voxel is used for the final segmentation of the target image. Results: This segmentation technique was validated with a clinical study of 13 patients. The accuracy of our approach was assessed using the manual segmentation (gold standard). The mean volume Dice Overlap Coefficient was 89.5±2.9% between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D MRI-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning label fusion framework, demonstrated its clinical feasibility, and validated its accuracy. This segmentation technique could be

  4. TH-CD-206-02: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in MR Images Using Patch-Based Anatomical Signature

    Energy Technology Data Exchange (ETDEWEB)

    Yang, X; Jani, A; Rossi, P; Mao, H; Curran, W; Liu, T [Emory University, Atlanta, GA (United States)

    2016-06-15

    Purpose: MRI has shown promise in identifying prostate tumors with high sensitivity and specificity for the detection of prostate cancer. Accurate segmentation of the prostate plays a key role various tasks: to accurately localize prostate boundaries for biopsy needle placement and radiotherapy, to initialize multi-modal registration algorithms or to obtain the region of interest for computer-aided detection of prostate cancer. However, manual segmentation during biopsy or radiation therapy can be time consuming and subject to inter- and intra-observer variation. This study’s purpose it to develop an automated method to address this technical challenge. Methods: We present an automated multi-atlas segmentation for MR prostate segmentation using patch-based label fusion. After an initial preprocessing for all images, all the atlases are non-rigidly registered to a target image. And then, the resulting transformation is used to propagate the anatomical structure labels of the atlas into the space of the target image. The top L similar atlases are further chosen by measuring intensity and structure difference in the region of interest around prostate. Finally, using voxel weighting based on patch-based anatomical signature, the label that the majority of all warped labels predict for each voxel is used for the final segmentation of the target image. Results: This segmentation technique was validated with a clinical study of 13 patients. The accuracy of our approach was assessed using the manual segmentation (gold standard). The mean volume Dice Overlap Coefficient was 89.5±2.9% between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D MRI-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning label fusion framework, demonstrated its clinical feasibility, and validated its accuracy. This segmentation technique could be

  5. Left-ventricle segmentation in real-time 3D echocardiography using a hybrid active shape model and optimal graph search approach

    Science.gov (United States)

    Zhang, Honghai; Abiose, Ademola K.; Campbell, Dwayne N.; Sonka, Milan; Martins, James B.; Wahle, Andreas

    2010-03-01

    Quantitative analysis of the left ventricular shape and motion patterns associated with left ventricular mechanical dyssynchrony (LVMD) is essential for diagnosis and treatment planning in congestive heart failure. Real-time 3D echocardiography (RT3DE) used for LVMD analysis is frequently limited by heavy speckle noise or partially incomplete data, thus a segmentation method utilizing learned global shape knowledge is beneficial. In this study, the endocardial surface of the left ventricle (LV) is segmented using a hybrid approach combining active shape model (ASM) with optimal graph search. The latter is used to achieve landmark refinement in the ASM framework. Optimal graph search translates the 3D segmentation into the detection of a minimum-cost closed set in a graph and can produce a globally optimal result. Various information-gradient, intensity distributions, and regional-property terms-are used to define the costs for the graph search. The developed method was tested on 44 RT3DE datasets acquired from 26 LVMD patients. The segmentation accuracy was assessed by surface positioning error and volume overlap measured for the whole LV as well as 16 standard LV regions. The segmentation produced very good results that were not achievable using ASM or graph search alone.

  6. 3D SEMANTIC LABELING OF ALS DATA BASED ON DOMAIN ADAPTION BY TRANSFERRING AND FUSING RANDOM FOREST MODELS

    Directory of Open Access Journals (Sweden)

    J. Wu

    2018-04-01

    Full Text Available Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain to new data scenes (target domain, which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were different from each other by using two different decision tree construction algorithms: C4.5 with information gain ratio and CART with Gini index. Secondly, four random forest classifiers adapted to the target domain are derived through transferring each tree in the source random forest models with two types of operations: structure expansion and reduction-SER and structure transfer-STRUT. Finally, points in target domain are labelled by fusing the four newly derived random forest classifiers using weights of evidence based fusion model. To validate our method, experimental analysis was conducted using 3 datasets: one is used as the source domain data (Vaihingen data for 3D Semantic Labelling; another two are used as the target domain data from two cities in China (Jinmen city and Dunhuang city. Overall accuracies of 85.5 % and 83.3 % for 3D labelling were achieved for Jinmen city and Dunhuang city data respectively, with only 1/3 newly labelled samples compared to the cases without domain adaption.

  7. 2D/3D video content adaptation decision engine based on content classification and user assessment

    Science.gov (United States)

    Fernandes, Rui; Andrade, M. T.

    2017-07-01

    Multimedia adaptation depends on several factors, such as the content itself, the consumption device and its characteristics, the transport and access networks and the user. An adaptation decision engine, in order to provide the best possible Quality of Experience to a user, needs to have information about all variables that may influence its decision. For the aforementioned factors, we implement content classification, define device classes, consider limited bandwidth scenarios and categorize user preferences based on a subjective quality evaluation test. The results of these actions generate vital information to pass to the adaptation decision engine so that its operation may provide the indication of the most suitable adaptation to perform that delivers the best possible outcome for the user under the existing constraints.

  8. Analysis, Adaptive Control and Adaptive Synchronization of a Nine-Term Novel 3-D Chaotic System with Four Quadratic Nonlinearities and its Circuit Simulation

    Directory of Open Access Journals (Sweden)

    S. Vaidyanathan

    2014-11-01

    Full Text Available This research work describes a nine-term novel 3-D chaotic system with four quadratic nonlinearities and details its qualitative properties. The phase portraits of the 3-D novel chaotic system simulated using MATLAB, depict the strange chaotic attractor of the system. For the parameter values chosen in this work, the Lyapunov exponents of the novel chaotic system are obtained as L1 = 6.8548, L2 = 0 and L3 = −32.8779. Also, the Kaplan-Yorke dimension of the novel chaotic system is obtained as DKY = 2.2085. Next, an adaptive controller is design to achieve global stabilization of the 3-D novel chaotic system with unknown system parameters. Moreover, an adaptive controller is designed to achieve global chaos synchronization of two identical novel chaotic systems with unknown system parameters. Finally, an electronic circuit realization of the novel chaotic system is presented using SPICE to confirm the feasibility of the theoretical model.

  9. Evaluation of right ventricular function by coronary computed tomography angiography using a novel automated 3D right ventricle volume segmentation approach: a validation study.

    Science.gov (United States)

    Burghard, Philipp; Plank, Fabian; Beyer, Christoph; Müller, Silvana; Dörler, Jakob; Zaruba, Marc-Michael; Pölzl, Leo; Pölzl, Gerhard; Klauser, Andrea; Rauch, Stefan; Barbieri, Fabian; Langer, Christian-Ekkehardt; Schgoer, Wilfried; Williamson, Eric E; Feuchtner, Gudrun

    2018-06-04

    To evaluate right ventricle (RV) function by coronary computed tomography angiography (CTA) using a novel automated three-dimensional (3D) RV volume segmentation tool in comparison with clinical reference modalities. Twenty-six patients with severe end-stage heart failure [left ventricle (LV) ejection fraction (EF) right heart invasive catheterisation (IC). Automated 3D RV volume segmentation was successful in 26 (100%) patients. Read-out time was 3 min 33 s (range, 1 min 50s-4 min 33s). RV EF by CTA was stronger correlated with right atrial pressure (RAP) by IC (r = -0.595; p = 0.006) but weaker with TAPSE (r = 0.366, p = 0.94). When comparing TAPSE with RAP by IC (r = -0.317, p = 0.231), a weak-to-moderate non-significant inverse correlation was found. Interobserver correlation was high with r = 0.96 (p right atrium (RA) and right ventricle (RV) was 196.9 ± 75.3 and 217.5 ± 76.1 HU, respectively. Measurement of RV function by CTA using a novel 3D volumetric segmentation tool is fast and reliable by applying a dedicated biphasic injection protocol. The RV EF from CTA is a closer surrogate of RAP than TAPSE by TTE. • Evaluation of RV function by cardiac CTA by using a novel 3D volume segmentation tool is fast and reliable. • A biphasic contrast agent injection protocol ensures homogenous RV contrast attenuation. • Cardiac CT is a valuable alternative modality to CMR for the evaluation of RV function.

  10. The evaluation of single-view and multi-view fusion 3D echocardiography using image-driven segmentation and tracking.

    Science.gov (United States)

    Rajpoot, Kashif; Grau, Vicente; Noble, J Alison; Becher, Harald; Szmigielski, Cezary

    2011-08-01

    Real-time 3D echocardiography (RT3DE) promises a more objective and complete cardiac functional analysis by dynamic 3D image acquisition. Despite several efforts towards automation of left ventricle (LV) segmentation and tracking, these remain challenging research problems due to the poor-quality nature of acquired images usually containing missing anatomical information, speckle noise, and limited field-of-view (FOV). Recently, multi-view fusion 3D echocardiography has been introduced as acquiring multiple conventional single-view RT3DE images with small probe movements and fusing them together after alignment. This concept of multi-view fusion helps to improve image quality and anatomical information and extends the FOV. We now take this work further by comparing single-view and multi-view fused images in a systematic study. In order to better illustrate the differences, this work evaluates image quality and information content of single-view and multi-view fused images using image-driven LV endocardial segmentation and tracking. The image-driven methods were utilized to fully exploit image quality and anatomical information present in the image, thus purposely not including any high-level constraints like prior shape or motion knowledge in the analysis approaches. Experiments show that multi-view fused images are better suited for LV segmentation and tracking, while relatively more failures and errors were observed on single-view images. Copyright © 2011 Elsevier B.V. All rights reserved.

  11. Intracranial aneurysm segmentation in 3D CT angiography: Method and quantitative validation with and without prior noise filtering

    International Nuclear Information System (INIS)

    Firouzian, Azadeh; Manniesing, Rashindra; Flach, Zwenneke H.; Risselada, Roelof; Kooten, Fop van; Sturkenboom, Miriam C.J.M.; Lugt, Aad van der; Niessen, Wiro J.

    2011-01-01

    Intracranial aneurysm volume and shape are important factors for predicting rupture risk, for pre-surgical planning and for follow-up studies. To obtain these parameters, manual segmentation can be employed; however, this is a tedious procedure, which is prone to inter- and intra-observer variability. Therefore there is a need for an automated method, which is accurate, reproducible and reliable. This study aims to develop and validate an automated method for segmenting intracranial aneurysms in Computed Tomography Angiography (CTA) data. Also, it is investigated whether prior smoothing improves segmentation robustness and accuracy. The proposed segmentation method is implemented in the level set framework, more specifically Geodesic Active Surfaces, in which a surface is evolved to capture the aneurysmal wall via an energy minimization approach. The energy term is composed of three different image features, namely; intensity, gradient magnitude and intensity variance. The method requires minimal user interaction, i.e. a single seed point inside the aneurysm needs to be placed, based on which image intensity statistics of the aneurysm are derived and used in defining the energy term. The method has been evaluated on 15 aneurysms in 11 CTA data sets by comparing the results to manual segmentations performed by two expert radiologists. Evaluation measures were Similarity Index, Average Surface Distance and Volume Difference. The results show that the automated aneurysm segmentation method is reproducible, and performs in the range of inter-observer variability in terms of accuracy. Smoothing by nonlinear diffusion with appropriate parameter settings prior to segmentation, slightly improves segmentation accuracy.

  12. 3D analytical field calculation using triangular magnet segments applied to a skewed linear permanent magnet actuator

    NARCIS (Netherlands)

    Janssen, J.L.G.; Paulides, J.J.H.; Lomonova, E.

    2010-01-01

    This paper presents novel analytical expressions which describe the 3D magnetic field of arbitrarily magnetized triangular-shaped charged surfaces. These versatile expressions are suitable to model triangularshaped permanent magnets and can be expanded to any polyhedral shape. Many applications are

  13. 3D Analytical field calculation using triangular magnet segments applied to a skewed linear permanent magnet actuator

    NARCIS (Netherlands)

    Janssen, J.L.G.; Paulides, J.J.H.; Lomonova, E.

    2009-01-01

    This paper presents novel analytical expressions which describe the 3D magnetic field of arbitrarily magnetized triangular-shaped charged surfaces. These versatile expressions are suitable to model triangularshaped permanent magnets and can be expanded to any polyhedral shape. Many applications are

  14. A model for arterial adaptation combining microstructural collagen remodeling and 3D tissue growth

    NARCIS (Netherlands)

    Machyshyn, I.; Bovendeerd, P.H.M.; Ven, van de A.A.F.; Rongen, P.M.J.; Vosse, van de F.N.

    2010-01-01

    Long-term adaptation of soft tissues is realized through growth and remodeling (G&R). Mathematical models are powerful tools in testing hypotheses on G&R and supporting the design and interpretation of experiments. Most theoretical G&R studies concentrate on description of either growth or

  15. Accessible bioprinting: adaptation of a low-cost 3D-printer for precise cell placement and stem cell differentiation.

    Science.gov (United States)

    Reid, John A; Mollica, Peter A; Johnson, Garett D; Ogle, Roy C; Bruno, Robert D; Sachs, Patrick C

    2016-06-07

    The precision and repeatability offered by computer-aided design and computer-numerically controlled techniques in biofabrication processes is quickly becoming an industry standard. However, many hurdles still exist before these techniques can be used in research laboratories for cellular and molecular biology applications. Extrusion-based bioprinting systems have been characterized by high development costs, injector clogging, difficulty achieving small cell number deposits, decreased cell viability, and altered cell function post-printing. To circumvent the high-price barrier to entry of conventional bioprinters, we designed and 3D printed components for the adaptation of an inexpensive 'off-the-shelf' commercially available 3D printer. We also demonstrate via goal based computer simulations that the needle geometries of conventional commercially standardized, 'luer-lock' syringe-needle systems cause many of the issues plaguing conventional bioprinters. To address these performance limitations we optimized flow within several microneedle geometries, which revealed a short tapered injector design with minimal cylindrical needle length was ideal to minimize cell strain and accretion. We then experimentally quantified these geometries using pulled glass microcapillary pipettes and our modified, low-cost 3D printer. This systems performance validated our models exhibiting: reduced clogging, single cell print resolution, and maintenance of cell viability without the use of a sacrificial vehicle. Using this system we show the successful printing of human induced pluripotent stem cells (hiPSCs) into Geltrex and note their retention of a pluripotent state 7 d post printing. We also show embryoid body differentiation of hiPSC by injection into differentiation conducive environments, wherein we observed continuous growth, emergence of various evaginations, and post-printing gene expression indicative of the presence of all three germ layers. These data demonstrate an

  16. WE-EF-210-08: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in Ultrasound Images Using Patch-Based Anatomical Feature

    Energy Technology Data Exchange (ETDEWEB)

    Yang, X; Rossi, P; Jani, A; Ogunleye, T; Curran, W; Liu, T [Emory Univ, Atlanta, GA (United States)

    2015-06-15

    Purpose: Transrectal ultrasound (TRUS) is the standard imaging modality for the image-guided prostate-cancer interventions (e.g., biopsy and brachytherapy) due to its versatility and real-time capability. Accurate segmentation of the prostate plays a key role in biopsy needle placement, treatment planning, and motion monitoring. As ultrasound images have a relatively low signal-to-noise ratio (SNR), automatic segmentation of the prostate is difficult. However, manual segmentation during biopsy or radiation therapy can be time consuming. We are developing an automated method to address this technical challenge. Methods: The proposed segmentation method consists of two major stages: the training stage and the segmentation stage. During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, because these training images have been mapped to the new patient’ images, and the more informative anatomical features are selected to train the kernel support vector machine (KSVM). During the segmentation stage, the selected anatomical features are extracted from newly acquired image as the input of the well-trained KSVM and the output of this trained KSVM is the segmented prostate of this patient. Results: This segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentation. The mean volume Dice Overlap Coefficient was 89.7±2.3%, and the average surface distance was 1.52 ± 0.57 mm between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D ultrasound-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentation (gold standard). This segmentation technique could be a useful

  17. Optic disc boundary segmentation from diffeomorphic demons registration of monocular fundus image sequences versus 3D visualization of stereo fundus image pairs for automated early stage glaucoma assessment

    Science.gov (United States)

    Gatti, Vijay; Hill, Jason; Mitra, Sunanda; Nutter, Brian

    2014-03-01

    Despite the current availability in resource-rich regions of advanced technologies in scanning and 3-D imaging in current ophthalmology practice, world-wide screening tests for early detection and progression of glaucoma still consist of a variety of simple tools, including fundus image-based parameters such as CDR (cup to disc diameter ratio) and CAR (cup to disc area ratio), especially in resource -poor regions. Reliable automated computation of the relevant parameters from fundus image sequences requires robust non-rigid registration and segmentation techniques. Recent research work demonstrated that proper non-rigid registration of multi-view monocular fundus image sequences could result in acceptable segmentation of cup boundaries for automated computation of CAR and CDR. This research work introduces a composite diffeomorphic demons registration algorithm for segmentation of cup boundaries from a sequence of monocular images and compares the resulting CAR and CDR values with those computed manually by experts and from 3-D visualization of stereo pairs. Our preliminary results show that the automated computation of CDR and CAR from composite diffeomorphic segmentation of monocular image sequences yield values comparable with those from the other two techniques and thus may provide global healthcare with a cost-effective yet accurate tool for management of glaucoma in its early stage.

  18. Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences.

    Science.gov (United States)

    Gloger, Oliver; Bülow, Robin; Tönnies, Klaus; Völzke, Henry

    2017-11-24

    We aimed to develop the first fully automated 3D gallbladder segmentation approach to perform volumetric analysis in volume data of magnetic resonance (MR) cholangiopancreatography (MRCP) sequences. Volumetric gallbladder analysis is performed for non-contrast-enhanced and secretin-enhanced MRCP sequences. Native and secretin-enhanced MRCP volume data were produced with a 1.5-T MR system. Images of coronal maximum intensity projections (MIP) are used to automatically compute 2D characteristic shape features of the gallbladder in the MIP images. A gallbladder shape space is generated to derive 3D gallbladder shape features, which are then combined with 2D gallbladder shape features in a support vector machine approach to detect gallbladder regions in MRCP volume data. A region-based level set approach is used for fine segmentation. Volumetric analysis is performed for both sequences to calculate gallbladder volume differences between both sequences. The approach presented achieves segmentation results with mean Dice coefficients of 0.917 in non-contrast-enhanced sequences and 0.904 in secretin-enhanced sequences. This is the first approach developed to detect and segment gallbladders in MR-based volume data automatically in both sequences. It can be used to perform gallbladder volume determination in epidemiological studies and to detect abnormal gallbladder volumes or shapes. The positive volume differences between both sequences may indicate the quantity of the pancreatobiliary reflux.

  19. Inner and outer coronary vessel wall segmentation from CCTA using an active contour model with machine learning-based 3D voxel context-aware image force

    Science.gov (United States)

    Sivalingam, Udhayaraj; Wels, Michael; Rempfler, Markus; Grosskopf, Stefan; Suehling, Michael; Menze, Bjoern H.

    2016-03-01

    In this paper, we present a fully automated approach to coronary vessel segmentation, which involves calcification or soft plaque delineation in addition to accurate lumen delineation, from 3D Cardiac Computed Tomography Angiography data. Adequately virtualizing the coronary lumen plays a crucial role for simulating blood ow by means of fluid dynamics while additionally identifying the outer vessel wall in the case of arteriosclerosis is a prerequisite for further plaque compartment analysis. Our method is a hybrid approach complementing Active Contour Model-based segmentation with an external image force that relies on a Random Forest Regression model generated off-line. The regression model provides a strong estimate of the distance to the true vessel surface for every surface candidate point taking into account 3D wavelet-encoded contextual image features, which are aligned with the current surface hypothesis. The associated external image force is integrated in the objective function of the active contour model, such that the overall segmentation approach benefits from the advantages associated with snakes and from the ones associated with machine learning-based regression alike. This yields an integrated approach achieving competitive results on a publicly available benchmark data collection (Rotterdam segmentation challenge).

  20. The superiority of 3D-CISS sequence in displaying the cisternal segment of facial, vestibulocochlear nerves and their abnormal changes

    Energy Technology Data Exchange (ETDEWEB)

    Liang Changhu, E-mail: tigerlch@163.co [Shandong University, Shandong Medical Imaging Research Institute, CT Room, 324, Jingwu Road, Jinan, Shandong (China); Zhang Bin, E-mail: liangchangbo.student@sina.co [Liao Cheng City People' s Hospital, Dongchang Road, Liaocheng, Shandong (China); Wu Lebin, E-mail: Lebinwu518@163.co [Shandong University, Shandong Medical Imaging Research Institute, CT Room, 324, Jingwu Road, Jinan, Shandong (China); Du Yinglin, E-mail: duyinglinzhuo@sohu.co [Shandong Provincial Center for Disease Control and Prevention, Public Health Institute, 72, Jingshi Road, Jinan, Shandong (China); Wang Ximing, E-mail: wxminmg369@163.co [Shandong University, Shandong Medical Imaging Research Institute, CT Room, 324, Jingwu Road, Jinan, Shandong (China); Liu Cheng, E-mail: cacab2a@126.co [Shandong University, Shandong Medical Imaging Research Institute, CT Room, 324, Jingwu Road, Jinan, Shandong (China); Yu Fuhua, E-mail: changhu1970@163.co [Weifang Medical College, 7166, West Road Baotong Weifang, Shandong (China)

    2010-06-15

    Objective: To select the best imaging method for clinical otologic patients through evaluating 3D constructive interference of steady state (CISS) image quality in visualizing the facial, vestibulocochlear nerves (CN:VII-VIII) and their abnormal changes. Methods: The CN:VII-VIII as well as inner ear structures in 48 volunteers were examined using 3D-CISS and 3D turbo spin echo (TSE) sequences respectively, and displayed to the full at the reformatted and maximum intensity projection (MIP) images. The nerve identification and image quality were graded for the CN:VII-VIII as well as inner ear structures. Statistical analysis was performed using the Wilcoxin test, p < 0.05 was considered significant. In addition, 8 patients with abnormality in facial or vestibulocochlear nerves were also examined using 3D-CISS sequence. Results: The identification rates for the cisternal segment of facial, vestibulocochlear nerves and corresponding membranous labyrinth were 100%. Abnormal changes of the facial or vestibulocochlear nerves were clearly shown in 8 patients, among them 1 was caused by bilateral acoustic neurinoma, 1 by cholesteatoma at cerebellopontine angle, 1 by arachnoid cyst, 1 by neurovascular adhesion, 4 by neurovascular compression. Conclusion: With 3D-CISS sequence the fine structure of the CN:VII-VIII and corresponding membranous labyrinth can be clearly demonstrated; lesions at the site of cerebellopontine angle can also be found easily.

  1. A T{sub N} adaptive ray effect mitigation for Styx 3D

    Energy Technology Data Exchange (ETDEWEB)

    Aussourd, Ch. [CEA Bruyeres-le-Chatel, 91 (France)

    2003-07-01

    Ray effect (RE) is a non-physical flaw of discrete ordinate methods characterized by spurious rays becoming sharper when small bright neutron sources radiate through low scattering media. While this effect has low impact on integral parameters ({alpha} and K{sub eff}), it can ruin simulations of diagnostic problem in terms of neutron dose received by a detector. 3 cumulative factors may affect the RE intensity: the source size, the scattering cross-sections, and the mesh size. We introduce an adaptive Ray Effect Mitigation (REM) aimed at locally improving the angular accuracy of the 3 dimension AMR (adaptive mesh refinement) deterministic neutron transport code Styx. Promising preliminary results have been obtained using a recursively refined octahedron based sphere tessellation in place of the standard S{sub N} quadrature. This angular mesh, referred to as T{sub N}, has valuable features for local refinement.

  2. 3-D direct current resistivity anisotropic modelling by goal-oriented adaptive finite element methods

    Science.gov (United States)

    Ren, Zhengyong; Qiu, Lewen; Tang, Jingtian; Wu, Xiaoping; Xiao, Xiao; Zhou, Zilong

    2018-01-01

    Although accurate numerical solvers for 3-D direct current (DC) isotropic resistivity models are current available even for complicated models with topography, reliable numerical solvers for the anisotropic case are still an open question. This study aims to develop a novel and optimal numerical solver for accurately calculating the DC potentials for complicated models with arbitrary anisotropic conductivity structures in the Earth. First, a secondary potential boundary value problem is derived by considering the topography and the anisotropic conductivity. Then, two a posteriori error estimators with one using the gradient-recovery technique and one measuring the discontinuity of the normal component of current density are developed for the anisotropic cases. Combing the goal-oriented and non-goal-oriented mesh refinements and these two error estimators, four different solving strategies are developed for complicated DC anisotropic forward modelling problems. A synthetic anisotropic two-layer model with analytic solutions verified the accuracy of our algorithms. A half-space model with a buried anisotropic cube and a mountain-valley model are adopted to test the convergence rates of these four solving strategies. We found that the error estimator based on the discontinuity of current density shows better performance than the gradient-recovery based a posteriori error estimator for anisotropic models with conductivity contrasts. Both error estimators working together with goal-oriented concepts can offer optimal mesh density distributions and highly accurate solutions.

  3. Adaptive Sampling based 3D Profile Measuring Method for Free-Form Surface

    Science.gov (United States)

    Duan, Xianyin; Zou, Yu; Gao, Qiang; Peng, Fangyu; Zhou, Min; Jiang, Guozhang

    2018-03-01

    In order to solve the problem of adaptability and scanning efficiency of the current surface profile detection device, a high precision and high efficiency detection approach is proposed for surface contour of free-form surface parts based on self- adaptability. The contact mechanical probe and the non-contact laser probe are synthetically integrated according to the sampling approach of adaptive front-end path detection. First, the front-end path is measured by the non-contact laser probe, and the detection path is planned by the internal algorithm of the measuring instrument. Then a reasonable measurement sampling is completed according to the planned path by the contact mechanical probe. The detection approach can effectively improve the measurement efficiency of the free-form surface contours and can simultaneously detect the surface contours of unknown free-form surfaces with different curvatures and even different rate of curvature. The detection approach proposed in this paper also has important reference value for free-form surface contour detection.

  4. Dose reduction in chest CT: Comparison of the adaptive iterative dose reduction 3D, adaptive iterative dose reduction, and filtered back projection reconstruction techniques

    Energy Technology Data Exchange (ETDEWEB)

    Yamada, Yoshitake, E-mail: yamada@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Jinzaki, Masahiro, E-mail: jinzaki@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Hosokawa, Takahiro, E-mail: hosokawa@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Tanami, Yutaka, E-mail: tanami@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Sugiura, Hiroaki, E-mail: hsugiura@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Abe, Takayuki, E-mail: tabe@z5.keio.jp [Center for Clinical Research, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Kuribayashi, Sachio, E-mail: skuribay@a5.keio.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan)

    2012-12-15

    Objectives: To assess the effectiveness of adaptive iterative dose reduction (AIDR) and AIDR 3D in improving the image quality in low-dose chest CT (LDCT). Materials and methods: Fifty patients underwent standard-dose chest CT (SDCT) and LDCT simultaneously, performed under automatic exposure control with noise index of 19 and 38 (for a 2-mm slice thickness), respectively. The SDCT images were reconstructed with filtered back projection (SDCT-FBP images), and the LDCT images with FBP, AIDR and AIDR 3D (LDCT-FBP, LDCT-AIDR and LDCT-AIDR 3D images, respectively). On all the 200 lung and 200 mediastinal image series, objective image noise and signal-to-noise ratio (SNR) were measured in several regions, and two blinded radiologists independently assessed the subjective image quality. Wilcoxon's signed rank sum test with Bonferroni's correction was used for the statistical analyses. Results: The mean dose reduction in LDCT was 64.2% as compared with the dose in SDCT. LDCT-AIDR 3D images showed significantly reduced objective noise and significantly increased SNR in all regions as compared to the SDCT-FBP, LDCT-FBP and LDCT-AIDR images (all, P ≤ 0.003). In all assessments of the image quality, LDCT-AIDR 3D images were superior to LDCT-AIDR and LDCT-FBP images. The overall diagnostic acceptability of both the lung and mediastinal LDCT-AIDR 3D images was comparable to that of the lung and mediastinal SDCT-FBP images. Conclusions: AIDR 3D is superior to AIDR. Intra-individual comparisons between SDCT and LDCT suggest that AIDR 3D allows a 64.2% reduction of the radiation dose as compared to SDCT, by substantially reducing the objective image noise and increasing the SNR, while maintaining the overall diagnostic acceptability.

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

    Science.gov (United States)

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

    2014-03-01

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

  6. Adaptive multi-resolution 3D Hartree-Fock-Bogoliubov solver for nuclear structure

    Science.gov (United States)

    Pei, J. C.; Fann, G. I.; Harrison, R. J.; Nazarewicz, W.; Shi, Yue; Thornton, S.

    2014-08-01

    Background: Complex many-body systems, such as triaxial and reflection-asymmetric nuclei, weakly bound halo states, cluster configurations, nuclear fragments produced in heavy-ion fusion reactions, cold Fermi gases, and pasta phases in neutron star crust, are all characterized by large sizes and complex topologies in which many geometrical symmetries characteristic of ground-state configurations are broken. A tool of choice to study such complex forms of matter is an adaptive multi-resolution wavelet analysis. This method has generated much excitement since it provides a common framework linking many diversified methodologies across different fields, including signal processing, data compression, harmonic analysis and operator theory, fractals, and quantum field theory. Purpose: To describe complex superfluid many-fermion systems, we introduce an adaptive pseudospectral method for solving self-consistent equations of nuclear density functional theory in three dimensions, without symmetry restrictions. Methods: The numerical method is based on the multi-resolution and computational harmonic analysis techniques with a multi-wavelet basis. The application of state-of-the-art parallel programming techniques include sophisticated object-oriented templates which parse the high-level code into distributed parallel tasks with a multi-thread task queue scheduler for each multi-core node. The internode communications are asynchronous. The algorithm is variational and is capable of solving coupled complex-geometric systems of equations adaptively, with functional and boundary constraints, in a finite spatial domain of very large size, limited by existing parallel computer memory. For smooth functions, user-defined finite precision is guaranteed. Results: The new adaptive multi-resolution Hartree-Fock-Bogoliubov (HFB) solver madness-hfb is benchmarked against a two-dimensional coordinate-space solver hfb-ax that is based on the B-spline technique and a three-dimensional solver

  7. 3D Adaptive Mesh Refinement Simulations of Pellet Injection in Tokamaks

    International Nuclear Information System (INIS)

    Samtaney, S.; Jardin, S.C.; Colella, P.; Martin, D.F.

    2003-01-01

    We present results of Adaptive Mesh Refinement (AMR) simulations of the pellet injection process, a proven method of refueling tokamaks. AMR is a computationally efficient way to provide the resolution required to simulate realistic pellet sizes relative to device dimensions. The mathematical model comprises of single-fluid MHD equations with source terms in the continuity equation along with a pellet ablation rate model. The numerical method developed is an explicit unsplit upwinding treatment of the 8-wave formulation, coupled with a MAC projection method to enforce the solenoidal property of the magnetic field. The Chombo framework is used for AMR. The role of the E x B drift in mass redistribution during inside and outside pellet injections is emphasized

  8. 3-D thermal effect of late Cenozoic erosion and deposition within the Lofoten-Vesterålen segment of the Mid-Norwegian continental margin

    Science.gov (United States)

    Maystrenko, Yuriy Petrovich; Gernigon, Laurent; Olesen, Odleiv; Ottesen, Dag; Rise, Leif

    2018-05-01

    A 3-D subsurface temperature distribution within the Lofoten-Vesterålen segment of the Mid-Norwegian continental margin and adjacent areas has been studied to understand the thermal effect of late Cenozoic erosion of old sedimentary and crystalline rocks and subsequent deposition of glacial sediments during the Pleistocene. A lithosphere-scale 3-D structural model of the Lofoten-Vesterålen area has been used as a realistic approximation of the geometries of the sedimentary infill, underlying crystalline crust and lithospheric mantle during the 3-D thermal modelling. The influence of late Cenozoic erosion and sedimentation has been included during the 3-D thermal calculations. In addition, the 3-D thermal modelling has been carried out by taking also into account the influence of early Cenozoic continental breakup. The results of the 3-D thermal modelling demonstrate that the mainland is generally colder than the basin areas within the upper part of the 3-D model. The thermal influence of the early Cenozoic breakup is still clearly recognizable within the western and deep parts of the Lofoten-Vesterålen margin segment in terms of the increased temperatures. The thermal effects of the erosion and deposition within the study area also indicate that a positive thermal anomaly exists within the specific subareas where sedimentary and crystalline rocks were eroded. A negative thermal effect occurs in the subareas affected by subsidence and sedimentation. The erosion-related positive thermal anomaly reaches its maximum of more than +27 °C at depths of 17-22 km beneath the eastern part of the Vestfjorden Basin. The most pronounced deposition-related negative anomaly shows a minimum of around -70 °C at 17-20 km depth beneath the Lofoten Basin. The second negative anomaly is located within the northeastern part of the Vøring Basin and has minimal values of around -48 °C at 12-14 km depth. These prominent thermal anomalies are associated with the subareas where

  9. Joint segmentation of lumen and outer wall from femoral artery MR images: Towards 3D imaging measurements of peripheral arterial disease.

    Science.gov (United States)

    Ukwatta, Eranga; Yuan, Jing; Qiu, Wu; Rajchl, Martin; Chiu, Bernard; Fenster, Aaron

    2015-12-01

    Three-dimensional (3D) measurements of peripheral arterial disease (PAD) plaque burden extracted from fast black-blood magnetic resonance (MR) images have shown to be more predictive of clinical outcomes than PAD stenosis measurements. To this end, accurate segmentation of the femoral artery lumen and outer wall is required for generating volumetric measurements of PAD plaque burden. Here, we propose a semi-automated algorithm to jointly segment the femoral artery lumen and outer wall surfaces from 3D black-blood MR images, which are reoriented and reconstructed along the medial axis of the femoral artery to obtain improved spatial coherence between slices of the long, thin femoral artery and to reduce computation time. The developed segmentation algorithm enforces two priors in a global optimization manner: the spatial consistency between the adjacent 2D slices and the anatomical region order between the femoral artery lumen and outer wall surfaces. The formulated combinatorial optimization problem for segmentation is solved globally and exactly by means of convex relaxation using a coupled continuous max-flow (CCMF) model, which is a dual formulation to the convex relaxed optimization problem. In addition, the CCMF model directly derives an efficient duality-based algorithm based on the modern multiplier augmented optimization scheme, which has been implemented on a GPU for fast computation. The computed segmentations from the developed algorithm were compared to manual delineations from experts using 20 black-blood MR images. The developed algorithm yielded both high accuracy (Dice similarity coefficients ≥ 87% for both the lumen and outer wall surfaces) and high reproducibility (intra-class correlation coefficient of 0.95 for generating vessel wall area), while outperforming the state-of-the-art method in terms of computational time by a factor of ≈ 20. Copyright © 2015 Elsevier B.V. All rights reserved.

  10. Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study

    International Nuclear Information System (INIS)

    Way, Ted W; Chan, H-P; Goodsitt, Mitchell M; Sahiner, Berkman; Hadjiiski, Lubomir M; Zhou Chuan; Chughtai, Aamer

    2008-01-01

    The purpose of this study is to investigate the effects of CT scanning and reconstruction parameters on automated segmentation and volumetric measurements of nodules in CT images. Phantom nodules of known sizes were used so that segmentation accuracy could be quantified in comparison to ground-truth volumes. Spherical nodules having 4.8, 9.5 and 16 mm diameters and 50 and 100 mg cc -1 calcium contents were embedded in lung-tissue-simulating foam which was inserted in the thoracic cavity of a chest section phantom. CT scans of the phantom were acquired with a 16-slice scanner at various tube currents, pitches, fields-of-view and slice thicknesses. Scans were also taken using identical techniques either within the same day or five months apart for study of reproducibility. The phantom nodules were segmented with a three-dimensional active contour (3DAC) model that we previously developed for use on patient nodules. The percentage volume errors relative to the ground-truth volumes were estimated under the various imaging conditions. There was no statistically significant difference in volume error for repeated CT scans or scans taken with techniques where only pitch, field of view, or tube current (mA) were changed. However, the slice thickness significantly (p < 0.05) affected the volume error. Therefore, to evaluate nodule growth, consistent imaging conditions and high resolution should be used for acquisition of the serial CT scans, especially for smaller nodules. Understanding the effects of scanning and reconstruction parameters on volume measurements by 3DAC allows better interpretation of data and assessment of growth. Tracking nodule growth with computerized segmentation methods would reduce inter- and intraobserver variabilities

  11. Data-adapted moving least squares method for 3-D image interpolation

    International Nuclear Information System (INIS)

    Jang, Sumi; Lee, Yeon Ju; Jeong, Byeongseon; Nam, Haewon; Lee, Rena; Yoon, Jungho

    2013-01-01

    In this paper, we present a nonlinear three-dimensional interpolation scheme for gray-level medical images. The scheme is based on the moving least squares method but introduces a fundamental modification. For a given evaluation point, the proposed method finds the local best approximation by reproducing polynomials of a certain degree. In particular, in order to obtain a better match to the local structures of the given image, we employ locally data-adapted least squares methods that can improve the classical one. Some numerical experiments are presented to demonstrate the performance of the proposed method. Five types of data sets are used: MR brain, MR foot, MR abdomen, CT head, and CT foot. From each of the five types, we choose five volumes. The scheme is compared with some well-known linear methods and other recently developed nonlinear methods. For quantitative comparison, we follow the paradigm proposed by Grevera and Udupa (1998). (Each slice is first assumed to be unknown then interpolated by each method. The performance of each interpolation method is assessed statistically.) The PSNR results for the estimated volumes are also provided. We observe that the new method generates better results in both quantitative and visual quality comparisons. (paper)

  12. Body segment differences in surface area, skin temperature and 3D displacement and the estimation of heat balance during locomotion in hominins.

    Science.gov (United States)

    Cross, Alan; Collard, Mark; Nelson, Andrew

    2008-06-18

    The conventional method of estimating heat balance during locomotion in humans and other hominins treats the body as an undifferentiated mass. This is problematic because the segments of the body differ with respect to several variables that can affect thermoregulation. Here, we report a study that investigated the impact on heat balance during locomotion of inter-segment differences in three of these variables: surface area, skin temperature and rate of movement. The approach adopted in the study was to generate heat balance estimates with the conventional method and then compare them with heat balance estimates generated with a method that takes into account inter-segment differences in surface area, skin temperature and rate of movement. We reasoned that, if the hypothesis that inter-segment differences in surface area, skin temperature and rate of movement affect heat balance during locomotion is correct, the estimates yielded by the two methods should be statistically significantly different. Anthropometric data were collected on seven adult male volunteers. The volunteers then walked on a treadmill at 1.2 m/s while 3D motion capture cameras recorded their movements. Next, the conventional and segmented methods were used to estimate the volunteers' heat balance while walking in four ambient temperatures. Lastly, the estimates produced with the two methods were compared with the paired t-test. The estimates of heat balance during locomotion yielded by the two methods are significantly different. Those yielded by the segmented method are significantly lower than those produced by the conventional method. Accordingly, the study supports the hypothesis that inter-segment differences in surface area, skin temperature and rate of movement impact heat balance during locomotion. This has important implications not only for current understanding of heat balance during locomotion in hominins but also for how future research on this topic should be approached.

  13. Body segment differences in surface area, skin temperature and 3D displacement and the estimation of heat balance during locomotion in hominins.

    Directory of Open Access Journals (Sweden)

    Alan Cross

    Full Text Available The conventional method of estimating heat balance during locomotion in humans and other hominins treats the body as an undifferentiated mass. This is problematic because the segments of the body differ with respect to several variables that can affect thermoregulation. Here, we report a study that investigated the impact on heat balance during locomotion of inter-segment differences in three of these variables: surface area, skin temperature and rate of movement. The approach adopted in the study was to generate heat balance estimates with the conventional method and then compare them with heat balance estimates generated with a method that takes into account inter-segment differences in surface area, skin temperature and rate of movement. We reasoned that, if the hypothesis that inter-segment differences in surface area, skin temperature and rate of movement affect heat balance during locomotion is correct, the estimates yielded by the two methods should be statistically significantly different. Anthropometric data were collected on seven adult male volunteers. The volunteers then walked on a treadmill at 1.2 m/s while 3D motion capture cameras recorded their movements. Next, the conventional and segmented methods were used to estimate the volunteers' heat balance while walking in four ambient temperatures. Lastly, the estimates produced with the two methods were compared with the paired t-test. The estimates of heat balance during locomotion yielded by the two methods are significantly different. Those yielded by the segmented method are significantly lower than those produced by the conventional method. Accordingly, the study supports the hypothesis that inter-segment differences in surface area, skin temperature and rate of movement impact heat balance during locomotion. This has important implications not only for current understanding of heat balance during locomotion in hominins but also for how future research on this topic should be

  14. A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means+.

    Science.gov (United States)

    Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

    2017-04-01

    Accurate classification of different anatomical structures of teeth from medical images provides crucial information for the stress analysis in dentistry. Usually, the anatomical structures of teeth are manually labeled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing 3 dimensional (3D) information, and classify the tooth by employing unsupervised learning i.e., k-means++ method. In order to evaluate the proposed method, the experiments are conducted on the sufficient and extensive datasets of mandibular molars. The experimental results show that our method can achieve higher accuracy and robustness compared to other three clustering methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Evaluation of multimodal segmentation based on 3D T1-, T2- and FLAIR-weighted images - the difficulty of choosing.

    Science.gov (United States)

    Lindig, Tobias; Kotikalapudi, Raviteja; Schweikardt, Daniel; Martin, Pascal; Bender, Friedemann; Klose, Uwe; Ernemann, Ulrike; Focke, Niels K; Bender, Benjamin

    2018-04-15

    Voxel-based morphometry is still mainly based on T1-weighted MRI scans. Misclassification of vessels and dura mater as gray matter has been previously reported. Goal of the present work was to evaluate the effect of multimodal segmentation methods available in SPM12, and their influence on identification of age related atrophy and lesion detection in epilepsy patients. 3D T1-, T2- and FLAIR-images of 77 healthy adults (mean age 35.8 years, 19-66 years, 45 females), 7 patients with malformation of cortical development (MCD) (mean age 28.1 years,19-40 years, 3 females), and 5 patients with left hippocampal sclerosis (LHS) (mean age 49.0 years, 25-67 years, 3 females) from a 3T scanner were evaluated. Segmentation based on T1-only, T1+T2, T1+FLAIR, T2+FLAIR, and T1+T2+FLAIR were compared in the healthy subjects. Clinical VBM results based on the different segmentation approaches for MCD and for LHS were compared. T1-only segmentation overestimated total intracranial volume by about 80ml compared to the other segmentation methods. This was due to misclassification of dura mater and vessels as GM and CSF. Significant differences were found for several anatomical regions: the occipital lobe, the basal ganglia/thalamus, the pre- and postcentral gyrus, the cerebellum, and the brainstem. None of the segmentation methods yielded completely satisfying results for the basal ganglia/thalamus and the brainstem. The best correlation with age could be found for the multimodal T1+T2+FLAIR segmentation. Highest T-scores for identification of LHS were found for T1+T2 segmentation, while highest T-scores for MCD were dependent on lesion and anatomical location. Multimodal segmentation is superior to T1-only segmentation and reduces the misclassification of dura mater and vessels as GM and CSF. Depending on the anatomical region and the pathology of interest (atrophy, lesion detection, etc.), different combinations of T1, T2 and FLAIR yield optimal results. Copyright © 2017 Elsevier

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-07-15

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

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

    Science.gov (United States)

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

    2015-07-15

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

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  19. A strategy for genetic modification of the spike-encoding segment of human reovirus T3D for reovirus targeting.

    Science.gov (United States)

    van den Wollenberg, D J M; van den Hengel, S K; Dautzenberg, I J C; Cramer, S J; Kranenburg, O; Hoeben, R C

    2008-12-01

    Human Orthoreovirus Type 3 Dearing is not pathogenic to humans and has been evaluated clinically as an oncolytic agent. Its transduction efficiency and the tumor cell selectivity may be enhanced by incorporating ligands for alternative receptors. However, the genetic modification of reoviruses has been difficult, and genetic targeting of reoviruses has not been reported so far. Here we describe a technique for generating genetically targeted reoviruses. The propagation of wild-type reoviruses on cells expressing a modified sigma 1-encoding segment embedded in a conventional RNA polymerase II transcript leads to substitution of the wild-type genome segment by the modified version. This technique was used for generating reoviruses that are genetically targeted to an artificial receptor expressed on U118MG cells. These cells lack the junction adhesion molecule-1 and therefore resist infection by wild-type reoviruses. The targeted reoviruses were engineered to carry the ligand for this receptor at the C terminus of the sigma 1 spike protein. This demonstrates that the C terminus of the sigma 1 protein is a suitable locale for the insertion of oligopeptide ligands and that targeting of reoviruses is feasible. The genetically targeted viruses can be propagated using the modified U118MG cells as helper cells. This technique may be applicable for the improvement of human reoviruses as oncolytic agents.

  20. Intrinsic spatial resolution evaluation of the X'tal cube PET detector based on a 3D crystal block segmented by laser processing.

    Science.gov (United States)

    Yoshida, Eiji; Tashima, Hideaki; Inadama, Naoko; Nishikido, Fumihiko; Moriya, Takahiro; Omura, Tomohide; Watanabe, Mitsuo; Murayama, Hideo; Yamaya, Taiga

    2013-01-01

    The X'tal cube is a depth-of-interaction (DOI)-PET detector which is aimed at obtaining isotropic resolution by effective readout of scintillation photons from the six sides of a crystal block. The X'tal cube is composed of the 3D crystal block with isotropic resolution and arrays of multi-pixel photon counters (MPPCs). In this study, to fabricate the 3D crystal block efficiently and precisely, we applied a sub-surface laser engraving (SSLE) technique to a monolithic crystal block instead of gluing segmented small crystals. The SSLE technique provided micro-crack walls which carve a groove into a monolithic scintillator block. Using the fabricated X'tal cube, we evaluated its intrinsic spatial resolution to show a proof of concept of isotropic resolution. The 3D grids of 2 mm pitch were fabricated into an 18 × 18 × 18 mm(3) monolithic lutetium yttrium orthosilicate (LYSO) crystal by the SSLE technique. 4 × 4 MPPCs were optically coupled to each surface of the crystal block. The X'tal cube was uniformly irradiated by (22)Na gamma rays, and all of the 3D grids on the 3D position histogram were separated clearly by an Anger-type calculation from the 96-channel MPPC signals. Response functions of the X'tal cube were measured by scanning with a (22)Na point source. The gamma-ray beam with a 1.0 mm slit was scanned in 0.25 mm steps by positioning of the X'tal cube at vertical and 45° incident angles. The average FWHM resolution at both incident angles was 2.1 mm. Therefore, we confirmed the isotropic spatial resolution performance of the X'tal cube.

  1. Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

    International Nuclear Information System (INIS)

    Ozekes, Serhat; Osman, Onur; Ucan, N.

    2008-01-01

    The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer aided detection of lung nodules

  2. TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions.

    Science.gov (United States)

    Heinrich, Mattias P; Blendowski, Max; Oktay, Ozan

    2018-05-30

    Deep convolutional neural networks (DCNN) are currently ubiquitous in medical imaging. While their versatility and high-quality results for common image analysis tasks including segmentation, localisation and prediction is astonishing, the large representational power comes at the cost of highly demanding computational effort. This limits their practical applications for image-guided interventions and diagnostic (point-of-care) support using mobile devices without graphics processing units (GPU). We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions. Our solution enables the removal of the expensive floating-point matrix multiplications throughout any convolutional neural network and replaces them by energy- and time-preserving binary operators and population counts. We evaluate our approach for the segmentation of the pancreas in CT. Here, our ternary approximation within a fully convolutional network leads to more than 90% memory reductions and high accuracy (without any post-processing) with a Dice overlap of 71.0% that comes close to the one obtained when using networks with high-precision weights and activations. We further provide a concept for sub-second inference without GPUs and demonstrate significant improvements in comparison with binary quantisation and without our proposed ternary hyperbolic tangent continuation. We present a key enabling technique for highly efficient DCNN inference without GPUs that will help to bring the advances of deep learning to practical clinical applications. It has also great promise for improving accuracies in large-scale medical data retrieval.

  3. Content Adaptive Lagrange Multiplier Selection for Rate-Distortion Optimization in 3-D Wavelet-Based Scalable Video Coding

    Directory of Open Access Journals (Sweden)

    Ying Chen

    2018-03-01

    Full Text Available Rate-distortion optimization (RDO plays an essential role in substantially enhancing the coding efficiency. Currently, rate-distortion optimized mode decision is widely used in scalable video coding (SVC. Among all the possible coding modes, it aims to select the one which has the best trade-off between bitrate and compression distortion. Specifically, this tradeoff is tuned through the choice of the Lagrange multiplier. Despite the prevalence of conventional method for Lagrange multiplier selection in hybrid video coding, the underlying formulation is not applicable to 3-D wavelet-based SVC where the explicit values of the quantization step are not available, with on consideration of the content features of input signal. In this paper, an efficient content adaptive Lagrange multiplier selection algorithm is proposed in the context of RDO for 3-D wavelet-based SVC targeting quality scalability. Our contributions are two-fold. First, we introduce a novel weighting method, which takes account of the mutual information, gradient per pixel, and texture homogeneity to measure the temporal subband characteristics after applying the motion-compensated temporal filtering (MCTF technique. Second, based on the proposed subband weighting factor model, we derive the optimal Lagrange multiplier. Experimental results demonstrate that the proposed algorithm enables more satisfactory video quality with negligible additional computational complexity.

  4. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  5. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    International Nuclear Information System (INIS)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-01-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  6. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection.

    Science.gov (United States)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-11-01

    Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency

  7. An Adaptive Motion Segmentation for Automated Video Surveillance

    Directory of Open Access Journals (Sweden)

    Hossain MJulius

    2008-01-01

    Full Text Available This paper presents an adaptive motion segmentation algorithm utilizing spatiotemporal information of three most recent frames. The algorithm initially extracts the moving edges applying a novel flexible edge matching technique which makes use of a combined distance transformation image. Then watershed-based iterative algorithm is employed to segment the moving object region from the extracted moving edges. The challenges of existing three-frame-based methods include slow movement, edge localization error, minor movement of camera, and homogeneity of background and foreground region. The proposed method represents edges as segments and uses a flexible edge matching algorithm to deal with edge localization error and minor movement of camera. The combined distance transformation image works in favor of accumulating gradient information of overlapping region which effectively improves the sensitivity to slow movement. The segmentation algorithm uses watershed, gradient information of difference image, and extracted moving edges. It helps to segment moving object region with more accurate boundary even some part of the moving edges cannot be detected due to region homogeneity or other reasons during the detection step. Experimental results using different types of video sequences are presented to demonstrate the efficiency and accuracy of the proposed method.

  8. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

    Directory of Open Access Journals (Sweden)

    Richard eBeare

    2016-03-01

    Full Text Available Measuring the distribution of brain tissue types (tissue classification in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation, which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF, hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks’ gestation acquired at 30 weeks’ corrected gestational age (n= 5, coronal T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5 and axial T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5. The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR group, consisted of T2-weighted images of preterm infants (born <30 weeks’ gestation acquired shortly after birth (n= 12, preterm infants acquired at term-equivalent age (n= 12, and healthy term-born infants (born ≥38 weeks’ gestation acquired within the first nine days of life (n= 12. For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for

  9. A fully automatic, threshold-based segmentation method for the estimation of the Metabolic Tumor Volume from PET images: validation on 3D printed anthropomorphic oncological lesions

    Science.gov (United States)

    Gallivanone, F.; Interlenghi, M.; Canervari, C.; Castiglioni, I.

    2016-01-01

    18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) is a standard functional diagnostic technique to in vivo image cancer. Different quantitative paramters can be extracted from PET images and used as in vivo cancer biomarkers. Between PET biomarkers Metabolic Tumor Volume (MTV) has gained an important role in particular considering the development of patient-personalized radiotherapy treatment for non-homogeneous dose delivery. Different imaging processing methods have been developed to define MTV. The different proposed PET segmentation strategies were validated in ideal condition (e.g. in spherical objects with uniform radioactivity concentration), while the majority of cancer lesions doesn't fulfill these requirements. In this context, this work has a twofold objective: 1) to implement and optimize a fully automatic, threshold-based segmentation method for the estimation of MTV, feasible in clinical practice 2) to develop a strategy to obtain anthropomorphic phantoms, including non-spherical and non-uniform objects, miming realistic oncological patient conditions. The developed PET segmentation algorithm combines an automatic threshold-based algorithm for the definition of MTV and a k-means clustering algorithm for the estimation of the background. The method is based on parameters always available in clinical studies and was calibrated using NEMA IQ Phantom. Validation of the method was performed both in ideal (e.g. in spherical objects with uniform radioactivity concentration) and non-ideal (e.g. in non-spherical objects with a non-uniform radioactivity concentration) conditions. The strategy to obtain a phantom with synthetic realistic lesions (e.g. with irregular shape and a non-homogeneous uptake) consisted into the combined use of standard anthropomorphic phantoms commercially and irregular molds generated using 3D printer technology and filled with a radioactive chromatic alginate. The proposed segmentation algorithm was feasible in a

  10. Analysis of linear measurements on 3D surface models using CBCT data segmentation obtained by automatic standard pre-set thresholds in two segmentation software programs: an in vitro study.

    Science.gov (United States)

    Poleti, Marcelo Lupion; Fernandes, Thais Maria Freire; Pagin, Otávio; Moretti, Marcela Rodrigues; Rubira-Bullen, Izabel Regina Fischer

    2016-01-01

    The aim of this in vitro study was to evaluate the reliability and accuracy of linear measurements on three-dimensional (3D) surface models obtained by standard pre-set thresholds in two segmentation software programs. Ten mandibles with 17 silica markers were scanned for 0.3-mm voxels in the i-CAT Classic (Imaging Sciences International, Hatfield, PA, USA). Twenty linear measurements were carried out by two observers two times on the 3D surface models: the Dolphin Imaging 11.5 (Dolphin Imaging & Management Solutions, Chatsworth, CA, USA), using two filters(Translucent and Solid-1), and in the InVesalius 3.0.0 (Centre for Information Technology Renato Archer, Campinas, SP, Brazil). The physical measurements were made by another observer two times using a digital caliper on the dry mandibles. Excellent intra- and inter-observer reliability for the markers, physical measurements, and 3D surface models were found (intra-class correlation coefficient (ICC) and Pearson's r ≥ 0.91). The linear measurements on 3D surface models by Dolphin and InVesalius software programs were accurate (Dolphin Solid-1 > InVesalius > Dolphin Translucent). The highest absolute and percentage errors were obtained for the variable R1-R1 (1.37 mm) and MF-AC (2.53 %) in the Dolphin Translucent and InVesalius software, respectively. Linear measurements on 3D surface models obtained by standard pre-set thresholds in the Dolphin and InVesalius software programs are reliable and accurate compared with physical measurements. Studies that evaluate the reliability and accuracy of the 3D models are necessary to ensure error predictability and to establish diagnosis, treatment plan, and prognosis in a more realistic way.

  11. Effects of a 3D segmental prosthetic system for tricuspid valve annulus remodelling on the right coronary artery: a human cadaveric coronary angiography study.

    Science.gov (United States)

    Riki-Marishani, Mohsen; Gholoobi, Arash; Sazegar, Ghasem; Aazami, Mathias H; Hedjazi, Aria; Sajjadian, Maryam; Ebrahimi, Mahmoud; Aghaii-Zade Torabi, Ahmad

    2017-09-01

    A prosthetic system to repair secondary tricuspid valve regurgitation was developed. The conceptual engineering of the current device is based on 3D segmental remodelling of the tricuspid valve annulus in lieu of reductive annuloplasty. This study was designed to investigate the operational safety of the current prosthetic system with regard to the anatomical integrity of the right coronary artery (RCA) in fresh cadaveric human hearts. During the study period, from January to April 2016, the current prosthetic system was implanted on the tricuspid valve annulus in fresh cadaveric human hearts that met the study's inclusion criteria. The prepared specimens were investigated via selective coronary angiography of the RCA in the catheterization laboratory. The RCA angiographic anatomies were categorized as normal, distorted, kinked or occluded. Sixteen specimens underwent implantation of the current prosthetic system. The mean age of the cadaveric human hearts was 43.24 ± 15.79 years, with vehicle accident being the primary cause of death (59%). A dominant RCA was noticed in 62.5% of the specimens. None of the specimens displayed any injury, distortion, kinking or occlusion in the RCA due to the implantation of the prostheses. In light of the results of the present study, undertaken on fresh cadaveric human heart specimens, the current segmental prosthetic system for 3D remodelling of the tricuspid valve annulus seems to be safe vis-à-vis the anatomical integrity of the RCA. Further in vivo studies are needed to investigate the functional features of the current prosthetic system with a view to addressing the complex pathophysiology of secondary tricuspid valve regurgitation. © The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

  12. 3D membrane segmentation and quantification of intact thick cells using cryo soft X-ray transmission microscopy: A pilot study

    Science.gov (United States)

    Klementieva, Oxana; Werner, Stephan; Guttmann, Peter; Pratsch, Christoph; Cladera, Josep

    2017-01-01

    Structural analysis of biological membranes is important for understanding cell and sub-cellular organelle function as well as their interaction with the surrounding environment. Imaging of whole cells in three dimension at high spatial resolution remains a significant challenge, particularly for thick cells. Cryo-transmission soft X-ray microscopy (cryo-TXM) has recently gained popularity to image, in 3D, intact thick cells (∼10μm) with details of sub-cellular architecture and organization in near-native state. This paper reports a new tool to segment and quantify structural changes of biological membranes in 3D from cryo-TXM images by tracking an initial 2D contour along the third axis of the microscope, through a multi-scale ridge detection followed by an active contours-based model, with a subsequent refinement along the other two axes. A quantitative metric that assesses the grayscale profiles perpendicular to the membrane surfaces is introduced and shown to be linearly related to the membrane thickness. Our methodology has been validated on synthetic phantoms using realistic microscope properties and structure dimensions, as well as on real cryo-TXM data. Results demonstrate the validity of our algorithms for cryo-TXM data analysis. PMID:28376110

  13. Comparison of 3D Adaptive Remeshing Strategies for Finite Element Simulations of Electromagnetic Heating of Gold Nanoparticles

    Directory of Open Access Journals (Sweden)

    Fadhil Mezghani

    2015-01-01

    Full Text Available The optical properties of metallic nanoparticles are well known, but the study of their thermal behavior is in its infancy. However the local heating of surrounding medium, induced by illuminated nanostructures, opens the way to new sensors and devices. Consequently the accurate calculation of the electromagnetically induced heating of nanostructures is of interest. The proposed multiphysics problem cannot be directly solved with the classical refinement method of Comsol Multiphysics and a 3D adaptive remeshing process based on an a posteriori error estimator is used. In this paper the efficiency of three remeshing strategies for solving the multiphysics problem is compared. The first strategy uses independent remeshing for each physical quantity to reach a given accuracy. The second strategy only controls the accuracy on temperature. The third strategy uses a linear combination of the two normalized targets (the electric field intensity and the temperature. The analysis of the performance of each strategy is based on the convergence of the remeshing process in terms of number of elements. The efficiency of each strategy is also characterized by the number of computation iterations, the number of elements, the CPU time, and the RAM required to achieve a given target accuracy.

  14. Evaluation of left atrial function by multidetector computed tomography before left atrial radiofrequency-catheter ablation: Comparison of a manual and automated 3D volume segmentation method

    International Nuclear Information System (INIS)

    Wolf, Florian; Ourednicek, Petr; Loewe, Christian; Richter, Bernhard; Goessinger, Heinz David; Gwechenberger, Marianne; Plank, Christina; Schernthaner, Ruediger Egbert; Toepker, Michael; Lammer, Johannes; Feuchtner, Gudrun M.

    2010-01-01

    Introduction: The purpose of this study was to compare a manual and automated 3D volume segmentation tool for evaluation of left atrial (LA) function by 64-slice multidetector-CT (MDCT). Methods and materials: In 33 patients with paroxysmal atrial fibrillation a MDCT scan was performed before radiofrequency-catheter ablation. Atrial function (minimal volume (LAmin), maximal volume (LAmax), stroke volume (SV), ejection fraction (EF)) was evaluated by two readers using a manual and an automatic tool and measurement time was evaluated. Results: Automated LA volume segmentation failed in one patient due to low LA enhancement (103HU). Mean LAmax, LAmin, SV and EF were 127.7 ml, 93 ml, 34.7 ml, 27.1% by the automated, and 122.7 ml, 89.9 ml, 32.8 ml, 26.3% by the manual method with no significant difference (p > 0.05) and high Pearsons correlation coefficients (r = 0.94, r = 0.94, r = 0.82 and r = 0.85, p < 0.0001), respectively. The automated method was significantly faster (p < 0.001). Interobserver variability was low for both methods with Pearson's correlation coefficients between 0.98 and 0.99 (p < 0.0001). Conclusions: Evaluation of LA volume and function with 64-slice MDCT is feasible with a very low interobserver variability. The automatic method is as accurate as the manual method but significantly less time consuming permitting a routine use in clinical practice before RF-catheter ablation.

  15. Pupil-segmentation-based adaptive optics for microscopy

    Science.gov (United States)

    Ji, Na; Milkie, Daniel E.; Betzig, Eric

    2011-03-01

    Inhomogeneous optical properties of biological samples make it difficult to obtain diffraction-limited resolution in depth. Correcting the sample-induced optical aberrations needs adaptive optics (AO). However, the direct wavefront-sensing approach commonly used in astronomy is not suitable for most biological samples due to their strong scattering of light. We developed an image-based AO approach that is insensitive to sample scattering. By comparing images of the sample taken with different segments of the pupil illuminated, local tilt in the wavefront is measured from image shift. The aberrated wavefront is then obtained either by measuring the local phase directly using interference or with phase reconstruction algorithms similar to those used in astronomical AO. We implemented this pupil-segmentation-based approach in a two-photon fluorescence microscope and demonstrated that diffraction-limited resolution can be recovered from nonbiological and biological samples.

  16. Rate Adaptive Selective Segment Assignment for Reliable Wireless Video Transmission

    Directory of Open Access Journals (Sweden)

    Sajid Nazir

    2012-01-01

    Full Text Available A reliable video communication system is proposed based on data partitioning feature of H.264/AVC, used to create a layered stream, and LT codes for erasure protection. The proposed scheme termed rate adaptive selective segment assignment (RASSA is an adaptive low-complexity solution to varying channel conditions. The comparison of the results of the proposed scheme is also provided for slice-partitioned H.264/AVC data. Simulation results show competitiveness of the proposed scheme compared to optimized unequal and equal error protection solutions. The simulation results also demonstrate that a high visual quality video transmission can be maintained despite the adverse effect of varying channel conditions and the number of decoding failures can be reduced.

  17. M-AMST: an automatic 3D neuron tracing method based on mean shift and adapted minimum spanning tree.

    Science.gov (United States)

    Wan, Zhijiang; He, Yishan; Hao, Ming; Yang, Jian; Zhong, Ning

    2017-03-29

    Understanding the working mechanism of the brain is one of the grandest challenges for modern science. Toward this end, the BigNeuron project was launched to gather a worldwide community to establish a big data resource and a set of the state-of-the-art of single neuron reconstruction algorithms. Many groups contributed their own algorithms for the project, including our mean shift and minimum spanning tree (M-MST). Although M-MST is intuitive and easy to implement, the MST just considers spatial information of single neuron and ignores the shape information, which might lead to less precise connections between some neuron segments. In this paper, we propose an improved algorithm, namely M-AMST, in which a rotating sphere model based on coordinate transformation is used to improve the weight calculation method in M-MST. Two experiments are designed to illustrate the effect of adapted minimum spanning tree algorithm and the adoptability of M-AMST in reconstructing variety of neuron image datasets respectively. In the experiment 1, taking the reconstruction of APP2 as reference, we produce the four difference scores (entire structure average (ESA), different structure average (DSA), percentage of different structure (PDS) and max distance of neurons' nodes (MDNN)) by comparing the neuron reconstruction of the APP2 and the other 5 competing algorithm. The result shows that M-AMST gets lower difference scores than M-MST in ESA, PDS and MDNN. Meanwhile, M-AMST is better than N-MST in ESA and MDNN. It indicates that utilizing the adapted minimum spanning tree algorithm which took the shape information of neuron into account can achieve better neuron reconstructions. In the experiment 2, 7 neuron image datasets are reconstructed and the four difference scores are calculated by comparing the gold standard reconstruction and the reconstructions produced by 6 competing algorithms. Comparing the four difference scores of M-AMST and the other 5 algorithm, we can conclude that

  18. Automatic speech signal segmentation based on the innovation adaptive filter

    Directory of Open Access Journals (Sweden)

    Makowski Ryszard

    2014-06-01

    Full Text Available Speech segmentation is an essential stage in designing automatic speech recognition systems and one can find several algorithms proposed in the literature. It is a difficult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006, and it is a modified version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefficients of an innovation adaptive filter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics defined on the mel spectrum determined from the reflection coefficients. The paper presents the structure of the algorithm, defines its properties, lists parameter values, describes detection efficiency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

  19. Which Fault Segments Ruptured in the 2008 Wenchuan Earthquake and Which Did Not? New Evidence from Near‐Fault 3D Surface Displacements Derived from SAR Image Offsets

    KAUST Repository

    Feng, Guangcai; Jonsson, Sigurjon; Klinger, Yann

    2017-01-01

    The 2008 Mw 7.9 Wenchuan earthquake ruptured a complex thrust‐faulting system at the eastern edge of the Tibetan plateau and west of Sichuan basin. Though the earthquake has been extensively studied, several details about the earthquake, such as which fault segments were activated in the earthquake, are still not clear. This is in part due to difficult field access to the fault zone and in part due to limited near‐fault observations in Interferometric Synthetic Aperture Radar (InSAR) observations because of decorrelation. In this study, we address this problem by estimating SAR image offsets that provide near‐fault ground displacement information and exhibit clear displacement discontinuities across activated fault segments. We begin by reanalyzing the coseismic InSAR observations of the earthquake and then mostly eliminate the strong ionospheric signals that were plaguing previous studies by using additional postevent images. We also estimate the SAR image offsets and use their results to retrieve the full 3D coseismic surface displacement field. The coseismic deformation from the InSAR and image‐offset measurements are compared with both Global Positioning System and field observations. The results indicate that our observations provide significantly better information than previous InSAR studies that were affected by ionospheric disturbances. We use the results to present details of the surface‐faulting offsets along the Beichuan fault from the southwest to the northeast and find that there is an obvious right‐lateral strike‐slip component (as well as thrust faulting) along the southern Beichuan fault (in Yingxiu County), which was strongly underestimated in earlier studies. Based on the results, we provide new evidence to show that the Qingchuan fault was not ruptured in the 2008 Wenchuan earthquake, a topic debated in field observation studies, but show instead that surface faulting occurred on a northward extension of the Beichuan fault during

  20. Which Fault Segments Ruptured in the 2008 Wenchuan Earthquake and Which Did Not? New Evidence from Near‐Fault 3D Surface Displacements Derived from SAR Image Offsets

    KAUST Repository

    Feng, Guangcai

    2017-03-15

    The 2008 Mw 7.9 Wenchuan earthquake ruptured a complex thrust‐faulting system at the eastern edge of the Tibetan plateau and west of Sichuan basin. Though the earthquake has been extensively studied, several details about the earthquake, such as which fault segments were activated in the earthquake, are still not clear. This is in part due to difficult field access to the fault zone and in part due to limited near‐fault observations in Interferometric Synthetic Aperture Radar (InSAR) observations because of decorrelation. In this study, we address this problem by estimating SAR image offsets that provide near‐fault ground displacement information and exhibit clear displacement discontinuities across activated fault segments. We begin by reanalyzing the coseismic InSAR observations of the earthquake and then mostly eliminate the strong ionospheric signals that were plaguing previous studies by using additional postevent images. We also estimate the SAR image offsets and use their results to retrieve the full 3D coseismic surface displacement field. The coseismic deformation from the InSAR and image‐offset measurements are compared with both Global Positioning System and field observations. The results indicate that our observations provide significantly better information than previous InSAR studies that were affected by ionospheric disturbances. We use the results to present details of the surface‐faulting offsets along the Beichuan fault from the southwest to the northeast and find that there is an obvious right‐lateral strike‐slip component (as well as thrust faulting) along the southern Beichuan fault (in Yingxiu County), which was strongly underestimated in earlier studies. Based on the results, we provide new evidence to show that the Qingchuan fault was not ruptured in the 2008 Wenchuan earthquake, a topic debated in field observation studies, but show instead that surface faulting occurred on a northward extension of the Beichuan fault during

  1. Evaluation of left atrial function by multidetector computed tomography before left atrial radiofrequency-catheter ablation: Comparison of a manual and automated 3D volume segmentation method

    Energy Technology Data Exchange (ETDEWEB)

    Wolf, Florian, E-mail: florian.wolf@meduniwien.ac.a [Department of Radiology, Medical University of Vienna, Vienna (Austria); Ourednicek, Petr [Philips Medical Systems, Prague (Czech Republic); Loewe, Christian [Department of Radiology, Medical University of Vienna, Vienna (Austria); Richter, Bernhard; Goessinger, Heinz David; Gwechenberger, Marianne [Department of Cardiology, Medical University of Vienna, Vienna (Austria); Plank, Christina; Schernthaner, Ruediger Egbert; Toepker, Michael; Lammer, Johannes [Department of Radiology, Medical University of Vienna, Vienna (Austria); Feuchtner, Gudrun M. [Department of Radiology, Innsbruck Medical University, Innsbruck (Austria); Institute of Diagnostic Radiology, University Hospital Zurich (Switzerland)

    2010-08-15

    Introduction: The purpose of this study was to compare a manual and automated 3D volume segmentation tool for evaluation of left atrial (LA) function by 64-slice multidetector-CT (MDCT). Methods and materials: In 33 patients with paroxysmal atrial fibrillation a MDCT scan was performed before radiofrequency-catheter ablation. Atrial function (minimal volume (LAmin), maximal volume (LAmax), stroke volume (SV), ejection fraction (EF)) was evaluated by two readers using a manual and an automatic tool and measurement time was evaluated. Results: Automated LA volume segmentation failed in one patient due to low LA enhancement (103HU). Mean LAmax, LAmin, SV and EF were 127.7 ml, 93 ml, 34.7 ml, 27.1% by the automated, and 122.7 ml, 89.9 ml, 32.8 ml, 26.3% by the manual method with no significant difference (p > 0.05) and high Pearsons correlation coefficients (r = 0.94, r = 0.94, r = 0.82 and r = 0.85, p < 0.0001), respectively. The automated method was significantly faster (p < 0.001). Interobserver variability was low for both methods with Pearson's correlation coefficients between 0.98 and 0.99 (p < 0.0001). Conclusions: Evaluation of LA volume and function with 64-slice MDCT is feasible with a very low interobserver variability. The automatic method is as accurate as the manual method but significantly less time consuming permitting a routine use in clinical practice before RF-catheter ablation.

  2. Use of Anisotropy, 3D Segmented Atlas, and Computational Analysis to Identify Gray Matter Subcortical Lesions Common to Concussive Injury from Different Sites on the Cortex.

    Directory of Open Access Journals (Sweden)

    Praveen Kulkarni

    Full Text Available Traumatic brain injury (TBI can occur anywhere along the cortical mantel. While the cortical contusions may be random and disparate in their locations, the clinical outcomes are often similar and difficult to explain. Thus a question that arises is, do concussions at different sites on the cortex affect similar subcortical brain regions? To address this question we used a fluid percussion model to concuss the right caudal or rostral cortices in rats. Five days later, diffusion tensor MRI data were acquired for indices of anisotropy (IA for use in a novel method of analysis to detect changes in gray matter microarchitecture. IA values from over 20,000 voxels were registered into a 3D segmented, annotated rat atlas covering 150 brain areas. Comparisons between left and right hemispheres revealed a small population of subcortical sites with altered IA values. Rostral and caudal concussions were of striking similarity in the impacted subcortical locations, particularly the central nucleus of the amygdala, laterodorsal thalamus, and hippocampal complex. Subsequent immunohistochemical analysis of these sites showed significant neuroinflammation. This study presents three significant findings that advance our understanding and evaluation of TBI: 1 the introduction of a new method to identify highly localized disturbances in discrete gray matter, subcortical brain nuclei without postmortem histology, 2 the use of this method to demonstrate that separate injuries to the rostral and caudal cortex produce the same subcortical, disturbances, and 3 the central nucleus of the amygdala, critical in the regulation of emotion, is vulnerable to concussion.

  3. Non-destructive analysis of flake properties in automotive paints with full-field optical coherence tomography and 3D segmentation.

    Science.gov (United States)

    Zhang, Jinke; Williams, Bryan M; Lawman, Samuel; Atkinson, David; Zhang, Zijian; Shen, Yaochun; Zheng, Yalin

    2017-08-07

    Automotive coating systems are designed to protect vehicle bodies from corrosion and enhance their aesthetic value. The number, size and orientation of small metallic flakes in the base coat of the paint has a significant effect on the appearance of automotive bodies. It is important for quality assurance (QA) to be able to measure the properties of these small flakes, which are approximately 10μm in radius, yet current QA techniques are limited to measuring layer thickness. We design and develop a time-domain (TD) full-field (FF) optical coherence tomography (OCT) system to scan automotive panels volumetrically, non-destructively and without contact. We develop and integrate a segmentation method to automatically distinguish flakes and allow measurement of their properties. We test our integrated system on nine sections of five panels and demonstrate that this integrated approach can characterise small flakes in automotive coating systems in 3D, calculating the number, size and orientation accurately and consistently. This has the potential to significantly impact QA testing in the automotive industry.

  4. Real-Time Adaptive Foreground/Background Segmentation

    Directory of Open Access Journals (Sweden)

    Sridha Sridharan

    2005-08-01

    Full Text Available The automatic analysis of digital video scenes often requires the segmentation of moving objects from a static background. Historically, algorithms developed for this purpose have been restricted to small frame sizes, low frame rates, or offline processing. The simplest approach involves subtracting the current frame from the known background. However, as the background is rarely known beforehand, the key is how to learn and model it. This paper proposes a new algorithm that represents each pixel in the frame by a group of clusters. The clusters are sorted in order of the likelihood that they model the background and are adapted to deal with background and lighting variations. Incoming pixels are matched against the corresponding cluster group and are classified according to whether the matching cluster is considered part of the background. The algorithm has been qualitatively and quantitatively evaluated against three other well-known techniques. It demonstrated equal or better segmentation and proved capable of processing 320×240 PAL video at full frame rate using only 35%–40% of a 1.8 GHz Pentium 4 computer.

  5. Adaptation the Abaqus thermomechanics code to simulate 3D multipellet steady and transient WWER fuel rod behavior

    International Nuclear Information System (INIS)

    Kuznetsov, A.V.; Kuznetsov, V.I.; Krupkin, A.V.; Novikov, V.V.

    2015-01-01

    The study of Abaqus technology capabilities for modeling the behavior of the WWER-1000 fuel element for the campaign, taking into account the following features: multi-contact thermomechanical interaction of fuel pellet and fuel can, accounting for creep and swelling of fuel, consideration of creep of the can, setting the mechanisms of thermophysical and mechanical behavior of the fuel - cladding gap. The code was tested on the following developed finite element models: 3D fuel element model with five fuel pellets, 3D fuel element model with one fuel pellet and cleavage in the gap, 3D model of the fuel rod section with one randomly fragmented tablet. The position of the WWER-1000 fuel rod section in the middle of the core and the loads and material properties corresponding to this location were considered. The principal possibility of using Abaqus technology for solving fuel design problems is shown [ru

  6. Adaptation of pharmaceutical excipients to FDM 3D printing for the fabrication of patient-tailored immediate release tablets.

    Science.gov (United States)

    Sadia, Muzna; Sośnicka, Agata; Arafat, Basel; Isreb, Abdullah; Ahmed, Waqar; Kelarakis, Antonios; Alhnan, Mohamed A

    2016-11-20

    This work aims to employ fused deposition modelling 3D printing to fabricate immediate release pharmaceutical tablets with several model drugs. It investigates the addition of non-melting filler to methacrylic matrix to facilitate FDM 3D printing and explore the impact of (i) the nature of filler, (ii) compatibility with the gears of the 3D printer and iii) polymer: filler ratio on the 3D printing process. Amongst the investigated fillers in this work, directly compressible lactose, spray-dried lactose and microcrystalline cellulose showed a level of degradation at 135°C whilst talc and TCP allowed consistent flow of the filament and a successful 3D printing of the tablet. A specially developed universal filament based on pharmaceutically approved methacrylic polymer (Eudragit EPO) and thermally stable filler, TCP (tribasic calcium phosphate) was optimised. Four model drugs with different physicochemical properties were included into ready-to-use mechanically stable tablets with immediate release properties. Following the two thermal processes (hot melt extrusion (HME) and fused deposition modelling (FDM) 3D printing), drug contents were 94.22%, 88.53%, 96.51% and 93.04% for 5-ASA, captopril, theophylline and prednisolone respectively. XRPD indicated that a fraction of 5-ASA, theophylline and prednisolone remained crystalline whilst captopril was in amorphous form. By combining the advantages of thermally stable pharmaceutically approved polymers and fillers, this unique approach provides a low cost production method for on demand manufacturing of individualised dosage forms. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Large 3D resistivity and induced polarization acquisition using the Fullwaver system: towards an adapted processing methodology

    Science.gov (United States)

    Truffert, Catherine; Leite, Orlando; Gance, Julien; Texier, Benoît; Bernard, Jean

    2017-04-01

    Driven by needs in the mineral exploration market for ever faster and ever easier set-up of large 3D resistivity and induced polarization, autonomous and cableless recorded systems come to the forefront. Opposite to the traditional centralized acquisition, this new system permits a complete random distribution of receivers on the survey area allowing to obtain a real 3D imaging. This work presents the results of a 3 km2 large experiment up to 600m of depth performed with a new type of autonomous distributed receivers: the I&V-Fullwaver. With such system, all usual drawbacks induced by long cable set up over large 3D areas - time consuming, lack of accessibility, heavy weight, electromagnetic induction, etc. - disappear. The V-Fullwavers record the entire time series of voltage on two perpendicular axes, for a good determination of the data quality although I-Fullwaver records injected current simultaneously. For this survey, despite good assessment of each individual signal quality, on each channel of the set of Fullwaver systems, a significant number of negative apparent resistivity and chargeability remains present in the dataset (around 15%). These values are commonly not taken into account in the inversion software although they may be due to complex geological structure of interest (e.g. linked to the presence of sulfides in the earth). Taking into account that such distributed recording system aims to restitute the best 3D resistivity and IP tomography, how can 3D inversion be improved? In this work, we present the dataset, the processing chain and quality control of a large 3D survey. We show that the quality of the data selected is good enough to include it into the inversion processing. We propose a second way of processing based on the modulus of the apparent resistivity that stabilizes the inversion. We then discuss the results of both processing. We conclude that an effort could be made on the inclusion of negative apparent resistivity in the inversion

  8. Adaptation of the three-dimensional wisdom scale (3D-WS) for the Korean cultural context.

    Science.gov (United States)

    Kim, Seungyoun; Knight, Bob G

    2014-10-23

    ABSTRACT Background: Previous research on wisdom has suggested that wisdom is comprised of cognitive, reflective, and affective components and has developed and validated wisdom measures based on samples from Western countries. To apply the measurement to Eastern cultures, the present study revised an existing wisdom scale, the three-dimensional wisdom scale (3D-WS, Ardelt, 2003) for the Korean cultural context. Methods: Participants included 189 Korean heritage adults (age range 19-96) living in Los Angeles. We added a culturally specific factor of wisdom to the 3D-WS: Modesty and Unobtrusiveness (Yang, 2001), which captures an Eastern aspect of wisdom. The structure and psychometrics of the scale were tested. By latent cluster analysis, we determined acculturation subgroups and examined group differences in the means of factors in the revised wisdom scale (3D-WS-K). Results: Three factors, Cognitive Flexibility, Viewpoint Relativism, and Empathic Modesty were found using confirmatory factor analysis. Respondents with high biculturalism were higher on Viewpoint Relativism and lower on Empathic Modesty. Conclusion: This study discovered that a revised wisdom scale had a distinct factor structure and item content in a Korean heritage sample. We also found acculturation influences on the meaning of wisdom.

  9. Motion based segmentation for robot vision using adapted EM algorithm

    NARCIS (Netherlands)

    Zhao, Wei; Roos, Nico

    2016-01-01

    Robots operate in a dynamic world in which objects are often moving. The movement of objects may help the robot to segment the objects from the background. The result of the segmentation can subsequently be used to identify the objects. This paper investigates the possibility of segmenting objects

  10. The superiority of 3D-CISS sequence in displaying the cisternal segment of posterior nerves and their pathological changes%3D-CISS MRI序列对脑池段后组脑神经及其病变显示的优势

    Institute of Scientific and Technical Information of China (English)

    梁长虎; 柳澄; 李坤成; 武乐斌; 庞琦; 乌大尉; 王海燕; 于富华

    2009-01-01

    目的 通过脑池段后组脑神经3D-CISS序列与3D-TSE序列成像质量的比较,评估3D-CISS序列对脑池段后组脑神经及其病变显示的作用.方法 对45例正常体检者和12例患有各种后组脑神经异常症状的病人进行3D-CISS序列、3D-TSE序列扫描,对后组腑神经成像进行评分.结果 舌咽、迷走、副神经及舌下神经在3D-CISS、3D-TSE序列的显示率依次为:100%、57.1%;100%、52.3%;100%、41.1%;91.0%、59.3%.应用3D-CISS序列:对8例血管性神经痛病人显示了责任血管压迫点,对3例后组脑神经微小肿瘤进行了显示,对1例蛛网膜囊肿病人显示了压迫点.结论 对于被脑脊液围绕的后组脑神经显示成像,3D-CISS序列是较好的选择.%Objective To evaluate the efficacy of 3D-CISS on image quality of posterior nerves surrounded by CSF when compared with that of 3D-TSE. Method A total of 45 volunteers and 12 patients with abnormality of posterior cranial nerves were examined using 3D-CISS and 3D-TSE sequences respectively. The image quality were graded for glossopharyngeal nerve、vagus nerve、accessory nerve、 hypoglossal nerves (CN Ⅸ、Ⅹ、Ⅺ、Ⅻ) and their related arteries. Results The identification rates for cisternal segment of posterior nerves were as follows: glossopharyngeal nerve (100% in 3D-CISS and 57.1% in 31)-TSE)、vagus nerve(100% in 3D-CISS and 52.3% in 3I)-TSE)、accessory nerve(100% in 3D-CISS and 41.1% in 3D-TSE)、hypoglossal nerves(91.0% in 3D-CISS and 59.3% in 3D-TSE);12 patients with pathological changes in posterior nerves were all displayed well, among them 8 were pressed by artery, 1 by arachnoid cyst,3 caused by tumors. Conclusions 3D-CISS sequence is preferable when imaging posterior cranial nerves surrounded by CSF.

  11. 3D adaptive finite element method for a phase field model for the moving contact line problems

    KAUST Repository

    Shi, Yi; Bao, Kai; Wang, Xiaoping

    2013-01-01

    variables. There- fore we use an adaptive strategy that will take into account of such difference. Numerical experiments show that our algorithm is both efficient and reliable. © 2013 American Institute of Mathematical Sciences.

  12. 3D quantitative interpretation of archaeo-magnetic data: Adaptation of aero-magnetic interpretation techniques for archaeological purposes

    Science.gov (United States)

    Cheyney, S.; Hill, I. A.; Linford, N.; Fishwick, S.; Leech, C.

    2011-12-01

    High-resolution total-field magnetic data can be collected rapidly and relatively cheaply over large archaeological sites due to recent advances in data collection. However, interpretation of these datasets still generally comprises a sequence of data correction and filtering operations prior to a 2D visual interpretation based on pattern recognition. In contrast, current developments in aero-magnetic interpretation have led to several tools for identifying location, shape and depth information of anomalous sources. These methods often fail when directly applied to archaeo-magnetic data, due to the particular noise content typical in very near-surface surveys. Here techniques are explored that allow these aero-magnetic interpretation tools to be applied to archaeological problems, without the need for extensive, often biased user input. It is shown that full 3D quantitative interpretation of the subsurface is possible from just the magnetic data alone. Inversion of magnetic data is increasingly being applied to aero-magnetic surveys to produce 3D models of the subsurface magnetisation. Typically, an objective function is minimised in order to create a smooth distribution of magnetisation away from a reference model (or halfspace if no a-priori information is available). Often, although a good fit to the observed values may be obtained, the final model will be non-unique and biased by the reference model. Testing of synthetic data shows that when archaeo-magnetic datasets are inverted without applying a-priori information, large discrepancies between the true and modelled depths can occur. Where no a-priori information is available, information regarding the horizontal location of sources can be obtained from derivative-based methods such as the absolute horizontal gradient, tilt-angle and theta-map. Using pseudogravity data with these techniques, overcomes the problem of noise amplification that has previously hampered archaeological uses of these techniques. Depth

  13. 3D-printed adaptive acoustic lens as a disruptive technology for transcranial ultrasound therapy using single-element transducers

    Science.gov (United States)

    Maimbourg, Guillaume; Houdouin, Alexandre; Deffieux, Thomas; Tanter, Mickael; Aubry, Jean-François

    2018-01-01

    The development of multi-element arrays for better control of the shape of ultrasonic beams has opened the way for focusing through highly aberrating media, such as the human skull. As a result, the use of brain therapy with transcranial-focused ultrasound has rapidly grown. Although effective, such technology is expensive. We propose a disruptive, low-cost approach that consists of focusing a 1 MHz ultrasound beam through a human skull with a single-element transducer coupled with a tailored silicone acoustic lens cast in a 3D-printed mold and designed using computed tomography-based numerical acoustic simulation. We demonstrate on N  =  3 human skulls that adding lens-based aberration correction to a single-element transducer increases the deposited energy on the target 10 fold.

  14. 3D adaptive finite element method for a phase field model for the moving contact line problems

    KAUST Repository

    Shi, Yi

    2013-08-01

    In this paper, we propose an adaptive finite element method for simulating the moving contact line problems in three dimensions. The model that we used is the coupled Cahn-Hilliard Navier-Stokes equations with the generalized Navier boundary condition(GNBC) proposed in [18]. In our algorithm, to improve the efficiency of the simulation, we use the residual type adaptive finite element algorithm. It is well known that the phase variable decays much faster away from the interface than the velocity variables. There- fore we use an adaptive strategy that will take into account of such difference. Numerical experiments show that our algorithm is both efficient and reliable. © 2013 American Institute of Mathematical Sciences.

  15. Adapting Mask-RCNN for Automatic Nucleus Segmentation

    OpenAIRE

    Johnson, Jeremiah W.

    2018-01-01

    Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for ...

  16. Goal-Oriented Self-Adaptive hp Finite Element Simulation of 3D DC Borehole Resistivity Simulations

    KAUST Repository

    Calo, Victor M.

    2011-05-14

    In this paper we present a goal-oriented self-adaptive hp Finite Element Method (hp-FEM) with shared data structures and a parallel multi-frontal direct solver. The algorithm automatically generates (without any user interaction) a sequence of meshes delivering exponential convergence of a prescribed quantity of interest with respect to the number of degrees of freedom. The sequence of meshes is generated from a given initial mesh, by performing h (breaking elements into smaller elements), p (adjusting polynomial orders of approximation) or hp (both) refinements on the finite elements. The new parallel implementation utilizes a computational mesh shared between multiple processors. All computational algorithms, including automatic hp goal-oriented adaptivity and the solver work fully in parallel. We describe the parallel self-adaptive hp-FEM algorithm with shared computational domain, as well as its efficiency measurements. We apply the methodology described to the three-dimensional simulation of the borehole resistivity measurement of direct current through casing in the presence of invasion.

  17. Cross-axis adaptation improves 3D vestibulo-ocular reflex alignment during chronic stimulation via a head-mounted multichannel vestibular prosthesis

    Science.gov (United States)

    Dai, Chenkai; Fridman, Gene Y.; Chiang, Bryce; Davidovics, Natan; Melvin, Thuy-Anh; Cullen, Kathleen E.; Della Santina, Charles C.

    2012-01-01

    By sensing three-dimensional (3D) head rotation and electrically stimulating the three ampullary branches of a vestibular nerve to encode head angular velocity, a multichannel vestibular prosthesis (MVP) can restore vestibular sensation to individuals disabled by loss of vestibular hair cell function. However, current spread to afferent fibers innervating non-targeted canals and otolith endorgans can distort the vestibular nerve activation pattern, causing misalignment between the perceived and actual axis of head rotation. We hypothesized that over time, central neural mechanisms can adapt to correct this misalignment. To test this, we rendered five chinchillas vestibular-deficient via bilateral gentamicin treatment and unilaterally implanted them with a head mounted MVP. Comparison of 3D angular vestibulo-ocular reflex (aVOR) responses during 2 Hz, 50°/s peak horizontal sinusoidal head rotations in darkness on the first, third and seventh days of continual MVP use revealed that eye responses about the intended axis remained stable (at about 70% of the normal gain) while misalignment improved significantly by the end of one week of prosthetic stimulation. A comparable time course of improvement was also observed for head rotations about the other two semicircular canal axes and at every stimulus frequency examined (0.2–5 Hz). In addition, the extent of disconjugacy between the two eyes progressively improved during the same time window. These results indicate that the central nervous system rapidly adapts to multichannel prosthetic vestibular stimulation to markedly improve 3D aVOR alignment within the first week after activation. Similar adaptive improvements are likely to occur in other species, including humans. PMID:21374081

  18. Comparison of parameter-adapted segmentation methods for fluorescence micrographs.

    Science.gov (United States)

    Held, Christian; Palmisano, Ralf; Häberle, Lothar; Hensel, Michael; Wittenberg, Thomas

    2011-11-01

    Interpreting images from fluorescence microscopy is often a time-consuming task with poor reproducibility. Various image processing routines that can help investigators evaluate the images are therefore useful. The critical aspect for a reliable automatic image analysis system is a robust segmentation algorithm that can perform accurate segmentation for different cell types. In this study, several image segmentation methods were therefore compared and evaluated in order to identify the most appropriate segmentation schemes that are usable with little new parameterization and robustly with different types of fluorescence-stained cells for various biological and biomedical tasks. The study investigated, compared, and enhanced four different methods for segmentation of cultured epithelial cells. The maximum-intensity linking (MIL) method, an improved MIL, a watershed method, and an improved watershed method based on morphological reconstruction were used. Three manually annotated datasets consisting of 261, 817, and 1,333 HeLa or L929 cells were used to compare the different algorithms. The comparisons and evaluations showed that the segmentation performance of methods based on the watershed transform was significantly superior to the performance of the MIL method. The results also indicate that using morphological opening by reconstruction can improve the segmentation of cells stained with a marker that exhibits the dotted surface of cells. Copyright © 2011 International Society for Advancement of Cytometry.

  19. An $h$-Adaptive Operator Splitting Method for Two-Phase Flow in 3D Heterogeneous Porous Media

    KAUST Repository

    Chueh, Chih-Che; Djilali, Ned; Bangerth, Wolfgang

    2013-01-01

    The simulation of multiphase flow in porous media is a ubiquitous problem in a wide variety of fields, such as fuel cell modeling, oil reservoir simulation, magma dynamics, and tumor modeling. However, it is computationally expensive. This paper presents an interconnected set of algorithms which we show can accelerate computations by more than two orders of magnitude compared to traditional techniques, yet retains the high accuracy necessary for practical applications. Specifically, we base our approach on a new adaptive operator splitting technique driven by an a posteriori criterion to separate the flow from the transport equations, adaptive meshing to reduce the size of the discretized problem, efficient block preconditioned solver techniques for fast solution of the discrete equations, and a recently developed artificial diffusion strategy to stabilize the numerical solution of the transport equation. We demonstrate the accuracy and efficiency of our approach using numerical experiments in one, two, and three dimensions using a program that is made available as part of a large open source library. © 2013 Society for Industrial and Applied Mathematics.

  20. Segmentation of 3D microPET images of the rat brain via the hybrid gaussian mixture method with kernel density estimation.

    Science.gov (United States)

    Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing

    2012-01-01

    Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method.

  1. A fully consistent and conservative vertically adaptive coordinate system for SLIM 3D v0.4 with an application to the thermocline oscillations of Lake Tanganyika

    Science.gov (United States)

    Delandmeter, Philippe; Lambrechts, Jonathan; Legat, Vincent; Vallaeys, Valentin; Naithani, Jaya; Thiery, Wim; Remacle, Jean-François; Deleersnijder, Eric

    2018-03-01

    The discontinuous Galerkin (DG) finite element method is well suited for the modelling, with a relatively small number of elements, of three-dimensional flows exhibiting strong velocity or density gradients. Its performance can be highly enhanced by having recourse to r-adaptivity. Here, a vertical adaptive mesh method is developed for DG finite elements. This method, originally designed for finite difference schemes, is based on the vertical diffusion of the mesh nodes, with the diffusivity controlled by the density jumps at the mesh element interfaces. The mesh vertical movement is determined by means of a conservative arbitrary Lagrangian-Eulerian (ALE) formulation. Though conservativity is naturally achieved, tracer consistency is obtained by a suitable construction of the mesh vertical velocity field, which is defined in such a way that it is fully compatible with the tracer and continuity equations at a discrete level. The vertically adaptive mesh approach is implemented in the three-dimensional version of the geophysical and environmental flow Second-generation Louvain-la-Neuve Ice-ocean Model (SLIM 3D; www.climate.be/slim). Idealised benchmarks, aimed at simulating the oscillations of a sharp thermocline, are dealt with. Then, the relevance of the vertical adaptivity technique is assessed by simulating thermocline oscillations of Lake Tanganyika. The results are compared to measured vertical profiles of temperature, showing similar stratification and outcropping events.

  2. Interior insulation—Characterisation of the historic, solid masonrybuilding segment and analysis of the heat saving potential by 1d, 2d,and 3d simulation

    DEFF Research Database (Denmark)

    Odgaard, Tommy Riviere; Bjarløv, Søren Peter; Rode, Carsten

    2018-01-01

    When considering interior insulation of historic, multi-storey buildings with solid masonry walls, it isimportant to focus on two important factors: How big is the building segment to which it can be applied,and what is the significance of how the multi-dimensional geometry of these fac¸ ade walls......-storey apartments in Denmark. It was investigated,which relative reduction of the average thermal transmittance could be obtained by interior insulationwhen simulated in different dimensions, degrees of insulation and thickness. The analysis showed thatpartial insulation of the spandrels below windows on the 2nd/3...

  3. Emphysema quantification on low-dose CT using percentage of low-attenuation volume and size distribution of low-attenuation lung regions: Effects of adaptive iterative dose reduction using 3D processing

    Energy Technology Data Exchange (ETDEWEB)

    Nishio, Mizuho, E-mail: nmizuho@med.kobe-u.ac.jp [Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Matsumoto, Sumiaki, E-mail: sumatsu@med.kobe-u.ac.jp [Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Seki, Shinichiro, E-mail: sshin@med.kobe-u.ac.jp [Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Koyama, Hisanobu, E-mail: hkoyama@med.kobe-u.ac.jp [Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Ohno, Yoshiharu, E-mail: yosirad@kobe-u.ac.jp [Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Fujisawa, Yasuko, E-mail: yasuko1.fujisawa@toshiba.co.jp [Toshiba Medical Systems Corporation, 1385 Shimoishigami, Otawara, Tochigi 324-8550 (Japan); Sugihara, Naoki, E-mail: naoki.sugihara@toshiba.co.jp [Toshiba Medical Systems Corporation, 1385 Shimoishigami, Otawara, Tochigi 324-8550 (Japan); and others

    2014-12-15

    Highlights: • Emphysema quantification (LAV% and D) was affected by image noise on low-dose CT. • For LAV% and D, AIDR 3D improved agreement of quantification on low-dose CT. • AIDR 3D has the potential to quantify emphysema accurately on low-dose CT. - Abstract: Purpose: To evaluate the effects of adaptive iterative dose reduction using 3D processing (AIDR 3D) for quantification of two measures of emphysema: percentage of low-attenuation volume (LAV%) and size distribution of low-attenuation lung regions. Method and materials: : Fifty-two patients who underwent standard-dose (SDCT) and low-dose CT (LDCT) were included. SDCT without AIDR 3D, LDCT without AIDR 3D, and LDCT with AIDR 3D were used for emphysema quantification. First, LAV% was computed at 10 thresholds from −990 to −900 HU. Next, at the same thresholds, linear regression on a log–log plot was used to compute the power law exponent (D) for the cumulative frequency-size distribution of low-attenuation lung regions. Bland–Altman analysis was used to assess whether AIDR 3D improved agreement between LDCT and SDCT for emphysema quantification of LAV% and D. Results: The mean relative differences in LAV% between LDCT without AIDR 3D and SDCT were 3.73%–88.18% and between LDCT with AIDR 3D and SDCT were −6.61% to 0.406%. The mean relative differences in D between LDCT without AIDR 3D and SDCT were 8.22%–19.11% and between LDCT with AIDR 3D and SDCT were 1.82%–4.79%. AIDR 3D improved agreement between LDCT and SDCT at thresholds from −930 to −990 HU for LAV% and at all thresholds for D. Conclusion: AIDR 3D improved the consistency between LDCT and SDCT for emphysema quantification of LAV% and D.

  4. Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

    Directory of Open Access Journals (Sweden)

    Ayman Habib

    2016-01-01

    Full Text Available 3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or active imaging systems, are an important source for 3D modeling. Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process. Segmentation is usually the first step in the data processing chain. This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e., local point density/spacing and noise level of the point cloud in question. The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing. Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features. Starting from optimally-selected seed regions, planar and pole-like features are then segmented. The paper also introduces a list of hypothesized artifacts/problems that might take place during the region-growing process. Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features. Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework.

  5. SU-E-J-123: Assessing Segmentation Accuracy of Internal Volumes and Sub-Volumes in 4D PET/CT of Lung Tumors Using a Novel 3D Printed Phantom

    International Nuclear Information System (INIS)

    Soultan, D; Murphy, J; James, C; Hoh, C; Moiseenko, V; Cervino, L; Gill, B

    2015-01-01

    Purpose: To assess the accuracy of internal target volume (ITV) segmentation of lung tumors for treatment planning of simultaneous integrated boost (SIB) radiotherapy as seen in 4D PET/CT images, using a novel 3D-printed phantom. Methods: The insert mimics high PET tracer uptake in the core and 50% uptake in the periphery, by using a porous design at the periphery. A lung phantom with the insert was placed on a programmable moving platform. Seven breathing waveforms of ideal and patient-specific respiratory motion patterns were fed to the platform, and 4D PET/CT scans were acquired of each of them. CT images were binned into 10 phases, and PET images were binned into 5 phases following the clinical protocol. Two scenarios were investigated for segmentation: a gate 30–70 window, and no gating. The radiation oncologist contoured the outer ITV of the porous insert with on CT images, while the internal void volume with 100% uptake was contoured on PET images for being indistinguishable from the outer volume in CT images. Segmented ITVs were compared to the expected volumes based on known target size and motion. Results: 3 ideal breathing patterns, 2 regular-breathing patient waveforms, and 2 irregular-breathing patient waveforms were used for this study. 18F-FDG was used as the PET tracer. The segmented ITVs from CT closely matched the expected motion for both no gating and gate 30–70 window, with disagreement of contoured ITV with respect to the expected volume not exceeding 13%. PET contours were seen to overestimate volumes in all the cases, up to more than 40%. Conclusion: 4DPET images of a novel 3D printed phantom designed to mimic different uptake values were obtained. 4DPET contours overestimated ITV volumes in all cases, while 4DCT contours matched expected ITV volume values. Investigation of the cause and effects of the discrepancies is undergoing

  6. 3D Surgical Simulation

    Science.gov (United States)

    Cevidanes, Lucia; Tucker, Scott; Styner, Martin; Kim, Hyungmin; Chapuis, Jonas; Reyes, Mauricio; Proffit, William; Turvey, Timothy; Jaskolka, Michael

    2009-01-01

    This paper discusses the development of methods for computer-aided jaw surgery. Computer-aided jaw surgery allows us to incorporate the high level of precision necessary for transferring virtual plans into the operating room. We also present a complete computer-aided surgery (CAS) system developed in close collaboration with surgeons. Surgery planning and simulation include construction of 3D surface models from Cone-beam CT (CBCT), dynamic cephalometry, semi-automatic mirroring, interactive cutting of bone and bony segment repositioning. A virtual setup can be used to manufacture positioning splints for intra-operative guidance. The system provides further intra-operative assistance with the help of a computer display showing jaw positions and 3D positioning guides updated in real-time during the surgical procedure. The CAS system aids in dealing with complex cases with benefits for the patient, with surgical practice, and for orthodontic finishing. Advanced software tools for diagnosis and treatment planning allow preparation of detailed operative plans, osteotomy repositioning, bone reconstructions, surgical resident training and assessing the difficulties of the surgical procedures prior to the surgery. CAS has the potential to make the elaboration of the surgical plan a more flexible process, increase the level of detail and accuracy of the plan, yield higher operative precision and control, and enhance documentation of cases. Supported by NIDCR DE017727, and DE018962 PMID:20816308

  7. A New Multiphase Soft Segmentation with Adaptive Variants

    Directory of Open Access Journals (Sweden)

    Hongyuan Wang

    2013-01-01

    segmentation model for nearly piecewise constant images based on stochastic principle, where pixel intensities are modeled as random variables with mixed Gaussian distribution. The novelty of this paper lies in three aspects. First, unlike some existing models where the mean of each phase is modeled as a constant and the variances for different phases are assumed to be the same, the mean for each phase in the Gaussian distribution in this paper is modeled as a product of a constant and a bias field, and different phases are assumed to have different variances, which makes the model more flexible. Second, we develop a bidirection projected primal dual hybrid gradient (PDHG algorithm for iterations of membership functions. Third, we also develop a novel algorithm for explicitly computing the projection from RK to simplex ΔK-1 for any dimension K using dual theory, which is more efficient in both coding and implementation than existing projection methods.

  8. Adaptive-optics SLO imaging combined with widefield OCT and SLO enables precise 3D localization of fluorescent cells in the mouse retina.

    Science.gov (United States)

    Zawadzki, Robert J; Zhang, Pengfei; Zam, Azhar; Miller, Eric B; Goswami, Mayank; Wang, Xinlei; Jonnal, Ravi S; Lee, Sang-Hyuck; Kim, Dae Yu; Flannery, John G; Werner, John S; Burns, Marie E; Pugh, Edward N

    2015-06-01

    Adaptive optics scanning laser ophthalmoscopy (AO-SLO) has recently been used to achieve exquisite subcellular resolution imaging of the mouse retina. Wavefront sensing-based AO typically restricts the field of view to a few degrees of visual angle. As a consequence the relationship between AO-SLO data and larger scale retinal structures and cellular patterns can be difficult to assess. The retinal vasculature affords a large-scale 3D map on which cells and structures can be located during in vivo imaging. Phase-variance OCT (pv-OCT) can efficiently image the vasculature with near-infrared light in a label-free manner, allowing 3D vascular reconstruction with high precision. We combined widefield pv-OCT and SLO imaging with AO-SLO reflection and fluorescence imaging to localize two types of fluorescent cells within the retinal layers: GFP-expressing microglia, the resident macrophages of the retina, and GFP-expressing cone photoreceptor cells. We describe in detail a reflective afocal AO-SLO retinal imaging system designed for high resolution retinal imaging in mice. The optical performance of this instrument is compared to other state-of-the-art AO-based mouse retinal imaging systems. The spatial and temporal resolution of the new AO instrumentation was characterized with angiography of retinal capillaries, including blood-flow velocity analysis. Depth-resolved AO-SLO fluorescent images of microglia and cone photoreceptors are visualized in parallel with 469 nm and 663 nm reflectance images of the microvasculature and other structures. Additional applications of the new instrumentation are discussed.

  9. Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study

    International Nuclear Information System (INIS)

    Juneja, Prabhjot; Harris, Emma J.; Kirby, Anna M.; Evans, Philip M.

    2012-01-01

    Purpose: To validate and compare the accuracy of breast tissue segmentation methods applied to computed tomography (CT) scans used for radiation therapy planning and to study the effect of tissue distribution on the segmentation accuracy for the purpose of developing models for use in adaptive breast radiation therapy. Methods and Materials: Twenty-four patients receiving postlumpectomy radiation therapy for breast cancer underwent CT imaging in prone and supine positions. The whole-breast clinical target volume was outlined. Clinical target volumes were segmented into fibroglandular and fatty tissue using the following algorithms: physical density thresholding; interactive thresholding; fuzzy c-means with 3 classes (FCM3) and 4 classes (FCM4); and k-means. The segmentation algorithms were evaluated in 2 stages: first, an approach based on the assumption that the breast composition should be the same in both prone and supine position; and second, comparison of segmentation with tissue outlines from 3 experts using the Dice similarity coefficient (DSC). Breast datasets were grouped into nonsparse and sparse fibroglandular tissue distributions according to expert assessment and used to assess the accuracy of the segmentation methods and the agreement between experts. Results: Prone and supine breast composition analysis showed differences between the methods. Validation against expert outlines found significant differences (P<.001) between FCM3 and FCM4. Fuzzy c-means with 3 classes generated segmentation results (mean DSC = 0.70) closest to the experts' outlines. There was good agreement (mean DSC = 0.85) among experts for breast tissue outlining. Segmentation accuracy and expert agreement was significantly higher (P<.005) in the nonsparse group than in the sparse group. Conclusions: The FCM3 gave the most accurate segmentation of breast tissues on CT data and could therefore be used in adaptive radiation therapy-based on tissue modeling. Breast tissue segmentation

  10. Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study

    Energy Technology Data Exchange (ETDEWEB)

    Juneja, Prabhjot, E-mail: Prabhjot.Juneja@icr.ac.uk [Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom); Harris, Emma J. [Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom); Kirby, Anna M. [Department of Academic Radiotherapy, Royal Marsden National Health Service Foundation Trust, Sutton (United Kingdom); Evans, Philip M. [Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom)

    2012-11-01

    Purpose: To validate and compare the accuracy of breast tissue segmentation methods applied to computed tomography (CT) scans used for radiation therapy planning and to study the effect of tissue distribution on the segmentation accuracy for the purpose of developing models for use in adaptive breast radiation therapy. Methods and Materials: Twenty-four patients receiving postlumpectomy radiation therapy for breast cancer underwent CT imaging in prone and supine positions. The whole-breast clinical target volume was outlined. Clinical target volumes were segmented into fibroglandular and fatty tissue using the following algorithms: physical density thresholding; interactive thresholding; fuzzy c-means with 3 classes (FCM3) and 4 classes (FCM4); and k-means. The segmentation algorithms were evaluated in 2 stages: first, an approach based on the assumption that the breast composition should be the same in both prone and supine position; and second, comparison of segmentation with tissue outlines from 3 experts using the Dice similarity coefficient (DSC). Breast datasets were grouped into nonsparse and sparse fibroglandular tissue distributions according to expert assessment and used to assess the accuracy of the segmentation methods and the agreement between experts. Results: Prone and supine breast composition analysis showed differences between the methods. Validation against expert outlines found significant differences (P<.001) between FCM3 and FCM4. Fuzzy c-means with 3 classes generated segmentation results (mean DSC = 0.70) closest to the experts' outlines. There was good agreement (mean DSC = 0.85) among experts for breast tissue outlining. Segmentation accuracy and expert agreement was significantly higher (P<.005) in the nonsparse group than in the sparse group. Conclusions: The FCM3 gave the most accurate segmentation of breast tissues on CT data and could therefore be used in adaptive radiation therapy-based on tissue modeling. Breast tissue

  11. 3D Animation Essentials

    CERN Document Server

    Beane, Andy

    2012-01-01

    The essential fundamentals of 3D animation for aspiring 3D artists 3D is everywhere--video games, movie and television special effects, mobile devices, etc. Many aspiring artists and animators have grown up with 3D and computers, and naturally gravitate to this field as their area of interest. Bringing a blend of studio and classroom experience to offer you thorough coverage of the 3D animation industry, this must-have book shows you what it takes to create compelling and realistic 3D imagery. Serves as the first step to understanding the language of 3D and computer graphics (CG)Covers 3D anim

  12. 3D video

    CERN Document Server

    Lucas, Laurent; Loscos, Céline

    2013-01-01

    While 3D vision has existed for many years, the use of 3D cameras and video-based modeling by the film industry has induced an explosion of interest for 3D acquisition technology, 3D content and 3D displays. As such, 3D video has become one of the new technology trends of this century.The chapters in this book cover a large spectrum of areas connected to 3D video, which are presented both theoretically and technologically, while taking into account both physiological and perceptual aspects. Stepping away from traditional 3D vision, the authors, all currently involved in these areas, provide th

  13. Adaptive local thresholding for robust nucleus segmentation utilizing shape priors

    Science.gov (United States)

    Wang, Xiuzhong; Srinivas, Chukka

    2016-03-01

    This paper describes a novel local thresholding method for foreground detection. First, a Canny edge detection method is used for initial edge detection. Then, tensor voting is applied on the initial edge pixels, using a nonsymmetric tensor field tailored to encode prior information about nucleus size, shape, and intensity spatial distribution. Tensor analysis is then performed to generate the saliency image and, based on that, the refined edge. Next, the image domain is divided into blocks. In each block, at least one foreground and one background pixel are sampled for each refined edge pixel. The saliency weighted foreground histogram and background histogram are then created. These two histograms are used to calculate a threshold by minimizing the background and foreground pixel classification error. The block-wise thresholds are then used to generate the threshold for each pixel via interpolation. Finally, the foreground is obtained by comparing the original image with the threshold image. The effective use of prior information, combined with robust techniques, results in far more reliable foreground detection, which leads to robust nucleus segmentation.

  14. Analysis of 3-D images

    Science.gov (United States)

    Wani, M. Arif; Batchelor, Bruce G.

    1992-03-01

    Deriving generalized representation of 3-D objects for analysis and recognition is a very difficult task. Three types of representations based on type of an object is used in this paper. Objects which have well-defined geometrical shapes are segmented by using a fast edge region based segmentation technique. The segmented image is represented by plan and elevation of each part of the object if the object parts are symmetrical about their central axis. The plan and elevation concept enables representing and analyzing such objects quickly and efficiently. The second type of representation is used for objects having parts which are not symmetrical about their central axis. The segmented surface patches of such objects are represented by the 3-D boundary and the surface features of each segmented surface. Finally, the third type of representation is used for objects which don't have well-defined geometrical shapes (for example a loaf of bread). These objects are represented and analyzed from its features which are derived using a multiscale contour based technique. Anisotropic Gaussian smoothing technique is introduced to segment the contours at various scales of smoothing. A new merging technique is used which enables getting the current best estimate of break points at each scale. This new technique enables elimination of loss of accuracy of localization effects at coarser scales without using scale space tracking approach.

  15. Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

    Directory of Open Access Journals (Sweden)

    Genyun Sun

    2017-08-01

    Full Text Available The region merging algorithm is a widely used segmentation technique for very high resolution (VHR remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods

  16. Adaptation of the Maracas algorithm for carotid artery segmentation and stenosis quantification on CT images

    International Nuclear Information System (INIS)

    Maria A Zuluaga; Maciej Orkisz; Edgar J F Delgado; Vincent Dore; Alfredo Morales Pinzon; Marcela Hernandez Hoyos

    2010-01-01

    This paper describes the adaptations of Maracas algorithm to the segmentation and quantification of vascular structures in CTA images of the carotid artery. The maracas algorithm, which is based on an elastic model and on a multi-scale Eigen-analysis of the inertia matrix, was originally designed to segment a single artery in MRA images. The modifications are primarily aimed at addressing the specificities of CT images and the bifurcations. The algorithms implemented in this new version are classified into two levels. 1. The low-level processing (filtering of noise and directional artifacts, enhancement and pre-segmentation) to improve the quality of the image and to pre-segment it. These techniques are based on a priori information about noise, artifacts and typical gray levels ranges of lumen, background and calcifications. 2. The high-level processing to extract the centerline of the artery, to segment the lumen and to quantify the stenosis. At this level, we apply a priori knowledge of shape and anatomy of vascular structures. The method was evaluated on 31 datasets from the carotid lumen segmentation and stenosis grading grand challenge 2009. The segmentation results obtained an average of 80:4% dice similarity score, compared to reference segmentation, and the mean stenosis quantification error was 14.4%.

  17. 3D printing of intracranial artery stenosis based on the source images of magnetic resonance angiograph.

    Science.gov (United States)

    Xu, Wei-Hai; Liu, Jia; Li, Ming-Li; Sun, Zhao-Yong; Chen, Jie; Wu, Jian-Huang

    2014-08-01

    Three dimensional (3D) printing techniques for brain diseases have not been widely studied. We attempted to 'print' the segments of intracranial arteries based on magnetic resonance imaging. Three dimensional magnetic resonance angiography (MRA) was performed on two patients with middle cerebral artery (MCA) stenosis. Using scale-adaptive vascular modeling, 3D vascular models were constructed from the MRA source images. The magnified (ten times) regions of interest (ROI) of the stenotic segments were selected and fabricated by a 3D printer with a resolution of 30 µm. A survey to 8 clinicians was performed to evaluate the accuracy of 3D printing results as compared with MRA findings (4 grades, grade 1: consistent with MRA and provide additional visual information; grade 2: consistent with MRA; grade 3: not consistent with MRA; grade 4: not consistent with MRA and provide probable misleading information). If a 3D printing vessel segment was ideally matched to the MRA findings (grade 2 or 1), a successful 3D printing was defined. Seven responders marked "grade 1" to 3D printing results, while one marked "grade 4". Therefore, 87.5% of the clinicians considered the 3D printing were successful. Our pilot study confirms the feasibility of using 3D printing technique in the research field of intracranial artery diseases. Further investigations are warranted to optimize this technique and translate it into clinical practice.

  18. The interaction of neutral evolutionary processes with climatically-driven adaptive changes in the 3D shape of the human os coxae.

    Science.gov (United States)

    Betti, Lia; von Cramon-Taubadel, Noreen; Manica, Andrea; Lycett, Stephen J

    2014-08-01

    Differences in the breadth of the pelvis among modern human populations and among extinct hominin species have often been interpreted in the light of thermoregulatory adaptation, whereby a larger pelvic girdle would help preserve body temperature in cold environments while a narrower pelvis would help dissipate heat in tropical climates. There is, however, a theoretical problem in interpreting a pattern of variation as evidence of selection without first accounting for the effects of neutral evolutionary processes (i.e., mutation, genetic drift and migration). Here, we analyse 3D configurations of 27 landmarks on the os coxae of 1494 modern human individuals representing 30 male and 23 female populations from five continents and a range of climatic conditions. We test for the effects of climate on the size and shape of the pelvic bone, while explicitly accounting for population history (i.e., geographically-mediated gene flow and genetic drift). We find that neutral processes account for a substantial proportion of shape variance in the human os coxae in both sexes. Beyond the neutral pattern due to population history, temperature is a significant predictor of shape and size variation in the os coxae, at least in males. The effect of climate on the shape of the pelvic bone, however, is comparatively limited, explaining only a small percentage of shape variation in males and females. In accordance with previous hypotheses, the size of the os coxae tends to increase with decreasing temperature, although the significance of the association is reduced when population history is taken into account. In conclusion, the shape and size of the human os coxae reflect both neutral evolutionary processes and climatically-driven adaptive changes. Neutral processes have a substantial effect on pelvic variation, suggesting such factors will need to be taken into account in future studies of human and fossil hominin coxal variation. Copyright © 2014 Elsevier Ltd. All rights

  19. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification

    Directory of Open Access Journals (Sweden)

    Hui Liu

    2015-01-01

    Full Text Available The key problem of computer-aided diagnosis (CAD of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO pulmonary nodules than other typical algorithms.

  20. Adaptive Binary Arithmetic Coder-Based Image Feature and Segmentation in the Compressed Domain

    Directory of Open Access Journals (Sweden)

    Hsi-Chin Hsin

    2012-01-01

    Full Text Available Image compression is necessary in various applications, especially for efficient transmission over a band-limited channel. It is thus desirable to be able to segment an image in the compressed domain directly such that the burden of decompressing computation can be avoided. Motivated by the adaptive binary arithmetic coder (MQ coder of JPEG2000, we propose an efficient scheme to segment the feature vectors that are extracted from the code stream of an image. We modify the Compression-based Texture Merging (CTM algorithm to alleviate the influence of overmerging problem by making use of the rate distortion information. Experimental results show that the MQ coder-based image segmentation is preferable in terms of the boundary displacement error (BDE measure. It has the advantage of saving computational cost as the segmentation results even at low rates of bits per pixel (bpp are satisfactory.

  1. EUROPEANA AND 3D

    Directory of Open Access Journals (Sweden)

    D. Pletinckx

    2012-09-01

    Full Text Available The current 3D hype creates a lot of interest in 3D. People go to 3D movies, but are we ready to use 3D in our homes, in our offices, in our communication? Are we ready to deliver real 3D to a general public and use interactive 3D in a meaningful way to enjoy, learn, communicate? The CARARE project is realising this for the moment in the domain of monuments and archaeology, so that real 3D of archaeological sites and European monuments will be available to the general public by 2012. There are several aspects to this endeavour. First of all is the technical aspect of flawlessly delivering 3D content over all platforms and operating systems, without installing software. We have currently a working solution in PDF, but HTML5 will probably be the future. Secondly, there is still little knowledge on how to create 3D learning objects, 3D tourist information or 3D scholarly communication. We are still in a prototype phase when it comes to integrate 3D objects in physical or virtual museums. Nevertheless, Europeana has a tremendous potential as a multi-facetted virtual museum. Finally, 3D has a large potential to act as a hub of information, linking to related 2D imagery, texts, video, sound. We describe how to create such rich, explorable 3D objects that can be used intuitively by the generic Europeana user and what metadata is needed to support the semantic linking.

  2. 3D ground‐motion simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone: Variability of long‐period (T≥1  s) ground motions and sensitivity to kinematic rupture parameters

    Science.gov (United States)

    Moschetti, Morgan P.; Hartzell, Stephen; Ramirez-Guzman, Leonardo; Frankel, Arthur; Angster, Stephen J.; Stephenson, William J.

    2017-01-01

    We examine the variability of long‐period (T≥1  s) earthquake ground motions from 3D simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone, Utah, from a set of 96 rupture models with varying slip distributions, rupture speeds, slip velocities, and hypocenter locations. Earthquake ruptures were prescribed on a 3D fault representation that satisfies geologic constraints and maintained distinct strands for the Warm Springs and for the East Bench and Cottonwood faults. Response spectral accelerations (SA; 1.5–10 s; 5% damping) were measured, and average distance scaling was well fit by a simple functional form that depends on the near‐source intensity level SA0(T) and a corner distance Rc:SA(R,T)=SA0(T)(1+(R/Rc))−1. Period‐dependent hanging‐wall effects manifested and increased the ground motions by factors of about 2–3, though the effects appeared partially attributable to differences in shallow site response for sites on the hanging wall and footwall of the fault. Comparisons with modern ground‐motion prediction equations (GMPEs) found that the simulated ground motions were generally consistent, except within deep sedimentary basins, where simulated ground motions were greatly underpredicted. Ground‐motion variability exhibited strong lateral variations and, at some sites, exceeded the ground‐motion variability indicated by GMPEs. The effects on the ground motions of changing the values of the five kinematic rupture parameters can largely be explained by three predominant factors: distance to high‐slip subevents, dynamic stress drop, and changes in the contributions from directivity. These results emphasize the need for further characterization of the underlying distributions and covariances of the kinematic rupture parameters used in 3D ground‐motion simulations employed in probabilistic seismic‐hazard analyses.

  3. 3D imaging, 3D printing and 3D virtual planning in endodontics.

    Science.gov (United States)

    Shah, Pratik; Chong, B S

    2018-03-01

    The adoption and adaptation of recent advances in digital technology, such as three-dimensional (3D) printed objects and haptic simulators, in dentistry have influenced teaching and/or management of cases involving implant, craniofacial, maxillofacial, orthognathic and periodontal treatments. 3D printed models and guides may help operators plan and tackle complicated non-surgical and surgical endodontic treatment and may aid skill acquisition. Haptic simulators may assist in the development of competency in endodontic procedures through the acquisition of psycho-motor skills. This review explores and discusses the potential applications of 3D printed models and guides, and haptic simulators in the teaching and management of endodontic procedures. An understanding of the pertinent technology related to the production of 3D printed objects and the operation of haptic simulators are also presented.

  4. Utility of real-time prospective motion correction (PROMO) for segmentation of cerebral cortex on 3D T1-weighted imaging: Voxel-based morphometry analysis for uncooperative patients

    International Nuclear Information System (INIS)

    Igata, Natsuki; Kakeda, Shingo; Watanabe, Keita; Narimatsu, Hidekuni; Ide, Satoru; Korogi, Yukunori; Nozaki, Atsushi; Rettmann, Dan; Abe, Osamu

    2017-01-01

    To assess the utility of the motion correction method with prospective motion correction (PROMO) in a voxel-based morphometry (VBM) analysis for 'uncooperative' patient populations. High-resolution 3D T1-weighted imaging both with and without PROMO were performed in 33 uncooperative patients with Parkinson's disease (n = 11) or dementia (n = 22). We compared the grey matter (GM) volumes and cortical thickness between the scans with and without PROMO. For the mean total GM volume with the VBM analysis, the scan without PROMO showed a significantly smaller volume than that with PROMO (p < 0.05), which was caused by segmentation problems due to motion during acquisition. The whole-brain VBM analysis showed significant GM volume reductions in some regions in the scans without PROMO (familywise error corrected p < 0.05). In the cortical thickness analysis, the scans without PROMO also showed decreased cortical thickness compared to the scan with PROMO (p < 0.05). Our results with the uncooperative patients indicate that the use of PROMO can reduce misclassification during segmentation of the VBM analyses, although it may not prevent GM volume reduction. (orig.)

  5. Utility of real-time prospective motion correction (PROMO) for segmentation of cerebral cortex on 3D T1-weighted imaging: Voxel-based morphometry analysis for uncooperative patients

    Energy Technology Data Exchange (ETDEWEB)

    Igata, Natsuki; Kakeda, Shingo; Watanabe, Keita; Narimatsu, Hidekuni; Ide, Satoru; Korogi, Yukunori [University of Occupational and Environmental Health School of Medicine, Department of Radiology, Kitakyushu (Japan); Nozaki, Atsushi [MR Applications and Workflow Asia Pacific GE Healthcare Japan, Tokyo (Japan); Rettmann, Dan [MR Applications and Workflow GE Healthcare, Rochester, MN (United States); Abe, Osamu [University of Tokyo, Department of Radiology, Graduate School of Medicine, Tokyo (Japan)

    2017-08-15

    To assess the utility of the motion correction method with prospective motion correction (PROMO) in a voxel-based morphometry (VBM) analysis for 'uncooperative' patient populations. High-resolution 3D T1-weighted imaging both with and without PROMO were performed in 33 uncooperative patients with Parkinson's disease (n = 11) or dementia (n = 22). We compared the grey matter (GM) volumes and cortical thickness between the scans with and without PROMO. For the mean total GM volume with the VBM analysis, the scan without PROMO showed a significantly smaller volume than that with PROMO (p < 0.05), which was caused by segmentation problems due to motion during acquisition. The whole-brain VBM analysis showed significant GM volume reductions in some regions in the scans without PROMO (familywise error corrected p < 0.05). In the cortical thickness analysis, the scans without PROMO also showed decreased cortical thickness compared to the scan with PROMO (p < 0.05). Our results with the uncooperative patients indicate that the use of PROMO can reduce misclassification during segmentation of the VBM analyses, although it may not prevent GM volume reduction. (orig.)

  6. Open 3D Projects

    Directory of Open Access Journals (Sweden)

    Felician ALECU

    2010-01-01

    Full Text Available Many professionals and 3D artists consider Blender as being the best open source solution for 3D computer graphics. The main features are related to modeling, rendering, shading, imaging, compositing, animation, physics and particles and realtime 3D/game creation.

  7. Dosimetric Evaluation of Automatic Segmentation for Adaptive IMRT for Head-and-Neck Cancer

    International Nuclear Information System (INIS)

    Tsuji, Stuart Y.; Hwang, Andrew; Weinberg, Vivian; Yom, Sue S.; Quivey, Jeanne M.; Xia Ping

    2010-01-01

    Purpose: Adaptive planning to accommodate anatomic changes during treatment requires repeat segmentation. This study uses dosimetric endpoints to assess automatically deformed contours. Methods and Materials: Sixteen patients with head-and-neck cancer had adaptive plans because of anatomic change during radiotherapy. Contours from the initial planning computed tomography (CT) were deformed to the mid-treatment CT using an intensity-based free-form registration algorithm then compared with the manually drawn contours for the same CT using the Dice similarity coefficient and an overlap index. The automatic contours were used to create new adaptive plans. The original and automatic adaptive plans were compared based on dosimetric outcomes of the manual contours and on plan conformality. Results: Volumes from the manual and automatic segmentation were similar; only the gross tumor volume (GTV) was significantly different. Automatic plans achieved lower mean coverage for the GTV: V95: 98.6 ± 1.9% vs. 89.9 ± 10.1% (p = 0.004) and clinical target volume: V95: 98.4 ± 0.8% vs. 89.8 ± 6.2% (p 3 of the spinal cord 39.9 ± 3.7 Gy vs. 42.8 ± 5.4 Gy (p = 0.034), but no difference for the remaining structures. Conclusions: Automatic segmentation is not robust enough to substitute for physician-drawn volumes, particularly for the GTV. However, it generates normal structure contours of sufficient accuracy when assessed by dosimetric end points.

  8. 3D Integration for Wireless Multimedia

    Science.gov (United States)

    Kimmich, Georg

    The convergence of mobile phone, internet, mapping, gaming and office automation tools with high quality video and still imaging capture capability is becoming a strong market trend for portable devices. High-density video encode and decode, 3D graphics for gaming, increased application-software complexity and ultra-high-bandwidth 4G modem technologies are driving the CPU performance and memory bandwidth requirements close to the PC segment. These portable multimedia devices are battery operated, which requires the deployment of new low-power-optimized silicon process technologies and ultra-low-power design techniques at system, architecture and device level. Mobile devices also need to comply with stringent silicon-area and package-volume constraints. As for all consumer devices, low production cost and fast time-to-volume production is key for success. This chapter shows how 3D architectures can bring a possible breakthrough to meet the conflicting power, performance and area constraints. Multiple 3D die-stacking partitioning strategies are described and analyzed on their potential to improve the overall system power, performance and cost for specific application scenarios. Requirements and maturity of the basic process-technology bricks including through-silicon via (TSV) and die-to-die attachment techniques are reviewed. Finally, we highlight new challenges which will arise with 3D stacking and an outlook on how they may be addressed: Higher power density will require thermal design considerations, new EDA tools will need to be developed to cope with the integration of heterogeneous technologies and to guarantee signal and power integrity across the die stack. The silicon/wafer test strategies have to be adapted to handle high-density IO arrays, ultra-thin wafers and provide built-in self-test of attached memories. New standards and business models have to be developed to allow cost-efficient assembly and testing of devices from different silicon and technology

  9. Using the Technology Acceptance Model to explore community dwelling older adults' perceptions of a 3D interior design application to facilitate pre-discharge home adaptations.

    Science.gov (United States)

    Money, Arthur G; Atwal, Anita; Young, Katherine L; Day, Yasmin; Wilson, Lesley; Money, Kevin G

    2015-08-26

    In the UK occupational therapy pre-discharge home visits are routinely carried out as a means of facilitating safe transfer from the hospital to home. Whilst they are an integral part of practice, there is little evidence to demonstrate they have a positive outcome on the discharge process. Current issues for patients are around the speed of home visits and the lack of shared decision making in the process, resulting in less than 50 % of the specialist equipment installed actually being used by patients on follow-up. To improve practice there is an urgent need to examine other ways of conducting home visits to facilitate safe discharge. We believe that Computerised 3D Interior Design Applications (CIDAs) could be a means to support more efficient, effective and collaborative practice. A previous study explored practitioners perceptions of using CIDAs; however it is important to ascertain older adult's views about the usability of technology and to compare findings. This study explores the perceptions of community dwelling older adults with regards to adopting and using CIDAs as an assistive tool for the home adaptations process. Ten community dwelling older adults participated in individual interactive task-focused usability sessions with a customised CIDA, utilising the think-aloud protocol and individual semi-structured interviews. Template analysis was used to carry out both deductive and inductive analysis of the think-aloud and interview data. Initially, a deductive stance was adopted, using the three pre-determined high-level themes of the technology acceptance model (TAM): Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Actual Use (AU). Inductive template analysis was then carried out on the data within these themes, from which a number of sub-thmes emerged. Regarding PU, participants believed CIDAs served as a useful visual tool and saw clear potential to facilitate shared understanding and partnership in care delivery. For PEOU, participants were

  10. Sealing Clay Text Segmentation Based on Radon-Like Features and Adaptive Enhancement Filters

    Directory of Open Access Journals (Sweden)

    Xia Zheng

    2015-01-01

    Full Text Available Text extraction is a key issue in sealing clay research. The traditional method based on rubbings increases the risk of sealing clay damage and is unfavorable to sealing clay protection. Therefore, using digital image of sealing clay, a new method for text segmentation based on Radon-like features and adaptive enhancement filters is proposed in this paper. First, adaptive enhancement LM filter bank is used to get the maximum energy image; second, the edge image of the maximum energy image is calculated; finally, Radon-like feature images are generated by combining maximum energy image and its edge image. The average image of Radon-like feature images is segmented by the image thresholding method. Compared with 2D Otsu, GA, and FastFCM, the experiment result shows that this method can perform better in terms of accuracy and completeness of the text.

  11. YouDash3D: exploring stereoscopic 3D gaming for 3D movie theaters

    Science.gov (United States)

    Schild, Jonas; Seele, Sven; Masuch, Maic

    2012-03-01

    Along with the success of the digitally revived stereoscopic cinema, events beyond 3D movies become attractive for movie theater operators, i.e. interactive 3D games. In this paper, we present a case that explores possible challenges and solutions for interactive 3D games to be played by a movie theater audience. We analyze the setting and showcase current issues related to lighting and interaction. Our second focus is to provide gameplay mechanics that make special use of stereoscopy, especially depth-based game design. Based on these results, we present YouDash3D, a game prototype that explores public stereoscopic gameplay in a reduced kiosk setup. It features live 3D HD video stream of a professional stereo camera rig rendered in a real-time game scene. We use the effect to place the stereoscopic effigies of players into the digital game. The game showcases how stereoscopic vision can provide for a novel depth-based game mechanic. Projected trigger zones and distributed clusters of the audience video allow for easy adaptation to larger audiences and 3D movie theater gaming.

  12. Accurate and reproducible reconstruction of coronary arteries and endothelial shear stress calculation using 3D OCT: comparative study to 3D IVUS and 3D QCA.

    Science.gov (United States)

    Toutouzas, Konstantinos; Chatzizisis, Yiannis S; Riga, Maria; Giannopoulos, Andreas; Antoniadis, Antonios P; Tu, Shengxian; Fujino, Yusuke; Mitsouras, Dimitrios; Doulaverakis, Charalampos; Tsampoulatidis, Ioannis; Koutkias, Vassilis G; Bouki, Konstantina; Li, Yingguang; Chouvarda, Ioanna; Cheimariotis, Grigorios; Maglaveras, Nicos; Kompatsiaris, Ioannis; Nakamura, Sunao; Reiber, Johan H C; Rybicki, Frank; Karvounis, Haralambos; Stefanadis, Christodoulos; Tousoulis, Dimitris; Giannoglou, George D

    2015-06-01

    Geometrically-correct 3D OCT is a new imaging modality with the potential to investigate the association of local hemodynamic microenvironment with OCT-derived high-risk features. We aimed to describe the methodology of 3D OCT and investigate the accuracy, inter- and intra-observer agreement of 3D OCT in reconstructing coronary arteries and calculating ESS, using 3D IVUS and 3D QCA as references. 35 coronary artery segments derived from 30 patients were reconstructed in 3D space using 3D OCT. 3D OCT was validated against 3D IVUS and 3D QCA. The agreement in artery reconstruction among 3D OCT, 3D IVUS and 3D QCA was assessed in 3-mm-long subsegments using lumen morphometry and ESS parameters. The inter- and intra-observer agreement of 3D OCT, 3D IVUS and 3D QCA were assessed in a representative sample of 61 subsegments (n = 5 arteries). The data processing times for each reconstruction methodology were also calculated. There was a very high agreement between 3D OCT vs. 3D IVUS and 3D OCT vs. 3D QCA in terms of total reconstructed artery length and volume, as well as in terms of segmental morphometric and ESS metrics with mean differences close to zero and narrow limits of agreement (Bland-Altman analysis). 3D OCT exhibited excellent inter- and intra-observer agreement. The analysis time with 3D OCT was significantly lower compared to 3D IVUS. Geometrically-correct 3D OCT is a feasible, accurate and reproducible 3D reconstruction technique that can perform reliable ESS calculations in coronary arteries. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  13. Refined 3d-3d correspondence

    Energy Technology Data Exchange (ETDEWEB)

    Alday, Luis F.; Genolini, Pietro Benetti; Bullimore, Mathew; Loon, Mark van [Mathematical Institute, University of Oxford, Andrew Wiles Building,Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG (United Kingdom)

    2017-04-28

    We explore aspects of the correspondence between Seifert 3-manifolds and 3d N=2 supersymmetric theories with a distinguished abelian flavour symmetry. We give a prescription for computing the squashed three-sphere partition functions of such 3d N=2 theories constructed from boundary conditions and interfaces in a 4d N=2{sup ∗} theory, mirroring the construction of Seifert manifold invariants via Dehn surgery. This is extended to include links in the Seifert manifold by the insertion of supersymmetric Wilson-’t Hooft loops in the 4d N=2{sup ∗} theory. In the presence of a mass parameter for the distinguished flavour symmetry, we recover aspects of refined Chern-Simons theory with complex gauge group, and in particular construct an analytic continuation of the S-matrix of refined Chern-Simons theory.

  14. A 3d-3d appetizer

    Energy Technology Data Exchange (ETDEWEB)

    Pei, Du; Ye, Ke [Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, 91125 (United States)

    2016-11-02

    We test the 3d-3d correspondence for theories that are labeled by Lens spaces. We find a full agreement between the index of the 3d N=2 “Lens space theory” T[L(p,1)] and the partition function of complex Chern-Simons theory on L(p,1). In particular, for p=1, we show how the familiar S{sup 3} partition function of Chern-Simons theory arises from the index of a free theory. For large p, we find that the index of T[L(p,1)] becomes a constant independent of p. In addition, we study T[L(p,1)] on the squashed three-sphere S{sub b}{sup 3}. This enables us to see clearly, at the level of partition function, to what extent G{sub ℂ} complex Chern-Simons theory can be thought of as two copies of Chern-Simons theory with compact gauge group G.

  15. An adaptive segment method for smoothing lidar signal based on noise estimation

    Science.gov (United States)

    Wang, Yuzhao; Luo, Pingping

    2014-10-01

    An adaptive segmentation smoothing method (ASSM) is introduced in the paper to smooth the signal and suppress the noise. In the ASSM, the noise is defined as the 3σ of the background signal. An integer number N is defined for finding the changing positions in the signal curve. If the difference of adjacent two points is greater than 3Nσ, the position is recorded as an end point of the smoothing segment. All the end points detected as above are recorded and the curves between them will be smoothed separately. In the traditional method, the end points of the smoothing windows in the signals are fixed. The ASSM creates changing end points in different signals and the smoothing windows could be set adaptively. The windows are always set as the half of the segmentations and then the average smoothing method will be applied in the segmentations. The Iterative process is required for reducing the end-point aberration effect in the average smoothing method and two or three times are enough. In ASSM, the signals are smoothed in the spacial area nor frequent area, that means the frequent disturbance will be avoided. A lidar echo was simulated in the experimental work. The echo was supposed to be created by a space-born lidar (e.g. CALIOP). And white Gaussian noise was added to the echo to act as the random noise resulted from environment and the detector. The novel method, ASSM, was applied to the noisy echo to filter the noise. In the test, N was set to 3 and the Iteration time is two. The results show that, the signal could be smoothed adaptively by the ASSM, but the N and the Iteration time might be optimized when the ASSM is applied in a different lidar.

  16. 3D future internet media

    CERN Document Server

    Dagiuklas, Tasos

    2014-01-01

    This book describes recent innovations in 3D media and technologies, with coverage of 3D media capturing, processing, encoding, and adaptation, networking aspects for 3D Media, and quality of user experience (QoE). The main contributions are based on the results of the FP7 European Projects ROMEO, which focus on new methods for the compression and delivery of 3D multi-view video and spatial audio, as well as the optimization of networking and compression jointly across the Future Internet (www.ict-romeo.eu). The delivery of 3D media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics and user terminal requirements, as well as the user’s context such as their preferences and location. As the number of visual views increases, current systems will struggle to meet the demanding requirements in terms of delivery of constant video quality to both fixed and mobile users. ROMEO will design and develop hybrid-networking solutions that co...

  17. Novel 3D media technologies

    CERN Document Server

    Dagiuklas, Tasos

    2015-01-01

    This book describes recent innovations in 3D media and technologies, with coverage of 3D media capturing, processing, encoding, and adaptation, networking aspects for 3D Media, and quality of user experience (QoE). The contributions are based on the results of the FP7 European Project ROMEO, which focuses on new methods for the compression and delivery of 3D multi-view video and spatial audio, as well as the optimization of networking and compression jointly across the future Internet. The delivery of 3D media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics and user terminal requirements, as well as the user’s context such as their preferences and location. As the number of visual views increases, current systems will struggle to meet the demanding requirements in terms of delivery of consistent video quality to fixed and mobile users. ROMEO will present hybrid networking solutions that combine the DVB-T2 and DVB-NGH broadcas...

  18. Dimensional accuracy of 3D printed vertebra

    Science.gov (United States)

    Ogden, Kent; Ordway, Nathaniel; Diallo, Dalanda; Tillapaugh-Fay, Gwen; Aslan, Can

    2014-03-01

    3D printer applications in the biomedical sciences and medical imaging are expanding and will have an increasing impact on the practice of medicine. Orthopedic and reconstructive surgery has been an obvious area for development of 3D printer applications as the segmentation of bony anatomy to generate printable models is relatively straightforward. There are important issues that should be addressed when using 3D printed models for applications that may affect patient care; in particular the dimensional accuracy of the printed parts needs to be high to avoid poor decisions being made prior to surgery or therapeutic procedures. In this work, the dimensional accuracy of 3D printed vertebral bodies derived from CT data for a cadaver spine is compared with direct measurements on the ex-vivo vertebra and with measurements made on the 3D rendered vertebra using commercial 3D image processing software. The vertebra was printed on a consumer grade 3D printer using an additive print process using PLA (polylactic acid) filament. Measurements were made for 15 different anatomic features of the vertebral body, including vertebral body height, endplate width and depth, pedicle height and width, and spinal canal width and depth, among others. It is shown that for the segmentation and printing process used, the results of measurements made on the 3D printed vertebral body are substantially the same as those produced by direct measurement on the vertebra and measurements made on the 3D rendered vertebra.

  19. Adaptive iterative dose reduction (AIDR) 3D in low dose CT abdomen-pelvis: Effects on image quality and radiation exposure

    International Nuclear Information System (INIS)

    Ang, W C; Hashim, S; Karim, M K A; Bahruddin, N A; Salehhon, N; Musa, Y

    2017-01-01

    The widespread use of computed tomography (CT) has increased the medical radiation exposure and cancer risk. We aimed to evaluate the impact of AIDR 3D in CT abdomen-pelvic examinations based on image quality and radiation dose in low dose (LD) setting compared to standard dose (STD) with filtered back projection (FBP) reconstruction. We retrospectively reviewed the images of 40 patients who underwent CT abdomen-pelvic using a 80 slice CT scanner. Group 1 patients ( n =20, mean age 41 ± 17 years) were performed at LD with AIDR 3D reconstruction and Group 2 patients ( n =20, mean age 52 ± 21 years) were scanned with STD using FBP reconstruction. Objective image noise was assessed by region of interest (ROI) measurements in the liver and aorta as standard deviation (SD) of the attenuation value (Hounsfield Unit, HU) while subjective image quality was evaluated by two radiologists. Statistical analysis was used to compare the scan length, CT dose index volume (CTDI vol ) and image quality of both patient groups. Although both groups have similar mean scan length, the CTDI vol significantly decreased by 38% in LD CT compared to STD CT ( p <0.05). Objective and subjective image quality were statistically improved with AIDR 3D ( p <0.05). In conclusion, AIDR 3D enables significant dose reduction of 38% with superior image quality in LD CT abdomen-pelvis. (paper)

  20. 3D virtuel udstilling

    DEFF Research Database (Denmark)

    Tournay, Bruno; Rüdiger, Bjarne

    2006-01-01

    3d digital model af Arkitektskolens gård med virtuel udstilling af afgangsprojekter fra afgangen sommer 2006. 10 s.......3d digital model af Arkitektskolens gård med virtuel udstilling af afgangsprojekter fra afgangen sommer 2006. 10 s....

  1. Underwater 3D filming

    Directory of Open Access Journals (Sweden)

    Roberto Rinaldi

    2014-12-01

    Full Text Available After an experimental phase of many years, 3D filming is now effective and successful. Improvements are still possible, but the film industry achieved memorable success on 3D movie’s box offices due to the overall quality of its products. Special environments such as space (“Gravity” and the underwater realm look perfect to be reproduced in 3D. “Filming in space” was possible in “Gravity” using special effects and computer graphic. The underwater realm is still difficult to be handled. Underwater filming in 3D was not that easy and effective as filming in 2D, since not long ago. After almost 3 years of research, a French, Austrian and Italian team realized a perfect tool to film underwater, in 3D, without any constrains. This allows filmmakers to bring the audience deep inside an environment where they most probably will never have the chance to be.

  2. Reactive power and voltage control strategy based on dynamic and adaptive segment for DG inverter

    Science.gov (United States)

    Zhai, Jianwei; Lin, Xiaoming; Zhang, Yongjun

    2018-03-01

    The inverter of distributed generation (DG) can support reactive power to help solve the problem of out-of-limit voltage in active distribution network (ADN). Therefore, a reactive voltage control strategy based on dynamic and adaptive segment for DG inverter is put forward to actively control voltage in this paper. The proposed strategy adjusts the segmented voltage threshold of Q(U) droop curve dynamically and adaptively according to the voltage of grid-connected point and the power direction of adjacent downstream line. And then the reactive power reference of DG inverter can be got through modified Q(U) control strategy. The reactive power of inverter is controlled to trace the reference value. The proposed control strategy can not only control the local voltage of grid-connected point but also help to maintain voltage within qualified range considering the terminal voltage of distribution feeder and the reactive support for adjacent downstream DG. The scheme using the proposed strategy is compared with the scheme without the reactive support of DG inverter and the scheme using the Q(U) control strategy with constant segmented voltage threshold. The simulation results suggest that the proposed method has a significant improvement on solving the problem of out-of-limit voltage, restraining voltage variation and improving voltage quality.

  3. Magnet system optimization for segmented adaptive-gap in-vacuum undulator

    Energy Technology Data Exchange (ETDEWEB)

    Kitegi, C., E-mail: ckitegi@bnl.gov; Chubar, O.; Eng, C. [Energy Sciences Directorates, Brookhaven National Laboratory, Upton NY1 1973 (United States)

    2016-07-27

    Segmented Adaptive Gap in-vacuum Undulator (SAGU), in which different segments have different gaps and periods, promises a considerable spectral performance gain over a conventional undulator with uniform gap and period. According to calculations, this gain can be comparable to the gain achievable with a superior undulator technology (e.g. a room-temperature in-vacuum hybrid SAGU would perform as a cryo-cooled hybrid in-vacuum undulator with uniform gap and period). However, for reaching the high spectral performance, SAGU magnetic design has to include compensation of kicks experienced by the electron beam at segment junctions because of different deflection parameter values in the segments. We show that such compensation to large extent can be accomplished by using a passive correction, however, simple correction coils are nevertheless required as well to reach perfect compensation over a whole SAGU tuning range. Magnetic optimizations performed with Radia code, and the resulting undulator radiation spectra calculated using SRW code, demonstrating a possibility of nearly perfect correction, are presented.

  4. Underwater 3D filming

    OpenAIRE

    Rinaldi, Roberto

    2014-01-01

    After an experimental phase of many years, 3D filming is now effective and successful. Improvements are still possible, but the film industry achieved memorable success on 3D movie’s box offices due to the overall quality of its products. Special environments such as space (“Gravity” ) and the underwater realm look perfect to be reproduced in 3D. “Filming in space” was possible in “Gravity” using special effects and computer graphic. The underwater realm is still difficult to be handled. Unde...

  5. Blender 3D cookbook

    CERN Document Server

    Valenza, Enrico

    2015-01-01

    This book is aimed at the professionals that already have good 3D CGI experience with commercial packages and have now decided to try the open source Blender and want to experiment with something more complex than the average tutorials on the web. However, it's also aimed at the intermediate Blender users who simply want to go some steps further.It's taken for granted that you already know how to move inside the Blender interface, that you already have 3D modeling knowledge, and also that of basic 3D modeling and rendering concepts, for example, edge-loops, n-gons, or samples. In any case, it'

  6. In situ repair of bone and cartilage defects using 3D scanning and 3D printing

    OpenAIRE

    Li, Lan; Yu, Fei; Shi, Jianping; Shen, Sheng; Teng, Huajian; Yang, Jiquan; Wang, Xingsong; Jiang, Qing

    2017-01-01

    Three-dimensional (3D) printing is a rapidly emerging technology that promises to transform tissue engineering into a commercially successful biomedical industry. However, the use of robotic bioprinters alone is not sufficient for disease treatment. This study aimed to report the combined application of 3D scanning and 3D printing for treating bone and cartilage defects. Three different kinds of defect models were created to mimic three orthopedic diseases: large segmental defects of long bon...

  7. DELTA 3D PRINTER

    Directory of Open Access Journals (Sweden)

    ȘOVĂILĂ Florin

    2016-07-01

    Full Text Available 3D printing is a very used process in industry, the generic name being “rapid prototyping”. The essential advantage of a 3D printer is that it allows the designers to produce a prototype in a very short time, which is tested and quickly remodeled, considerably reducing the required time to get from the prototype phase to the final product. At the same time, through this technique we can achieve components with very precise forms, complex pieces that, through classical methods, could have been accomplished only in a large amount of time. In this paper, there are presented the stages of a 3D model execution, also the physical achievement after of a Delta 3D printer after the model.

  8. Professional Papervision3D

    CERN Document Server

    Lively, Michael

    2010-01-01

    Professional Papervision3D describes how Papervision3D works and how real world applications are built, with a clear look at essential topics such as building websites and games, creating virtual tours, and Adobe's Flash 10. Readers learn important techniques through hands-on applications, and build on those skills as the book progresses. The companion website contains all code examples, video step-by-step explanations, and a collada repository.

  9. Seismic processing using Parallel 3D FMM

    OpenAIRE

    Borlaug, Idar

    2007-01-01

    This thesis develops and tests 3D Fast Marching Method (FMM) algorithm and apply these to seismic simulations. The FMM is a general method for monotonically advancing fronts, originally developed by Sethian. It calculates the first arrival time for an advancing front or wave. FMM methods are used for a variety of applications including, fatigue cracks in materials, lymph node segmentation in CT images, computing skeletons and centerlines in 3D objects and for finding salt formations in seismi...

  10. 3D Reasoning from Blocks to Stability.

    Science.gov (United States)

    Zhaoyin Jia; Gallagher, Andrew C; Saxena, Ashutosh; Chen, Tsuhan

    2015-05-01

    Objects occupy physical space and obey physical laws. To truly understand a scene, we must reason about the space that objects in it occupy, and how each objects is supported stably by each other. In other words, we seek to understand which objects would, if moved, cause other objects to fall. This 3D volumetric reasoning is important for many scene understanding tasks, ranging from segmentation of objects to perception of a rich 3D, physically well-founded, interpretations of the scene. In this paper, we propose a new algorithm to parse a single RGB-D image with 3D block units while jointly reasoning about the segments, volumes, supporting relationships, and object stability. Our algorithm is based on the intuition that a good 3D representation of the scene is one that fits the depth data well, and is a stable, self-supporting arrangement of objects (i.e., one that does not topple). We design an energy function for representing the quality of the block representation based on these properties. Our algorithm fits 3D blocks to the depth values corresponding to image segments, and iteratively optimizes the energy function. Our proposed algorithm is the first to consider stability of objects in complex arrangements for reasoning about the underlying structure of the scene. Experimental results show that our stability-reasoning framework improves RGB-D segmentation and scene volumetric representation.

  11. Adaptation Measurement of CAD/CAM Dental Crowns with X-Ray Micro-CT: Metrological Chain Standardization and 3D Gap Size Distribution

    Directory of Open Access Journals (Sweden)

    L. Tapie

    2016-01-01

    Full Text Available Computer-Aided Design and Manufacturing systems are increasingly used to produce dental prostheses, but the parts produced suffer from a lack of evaluation, especially concerning the internal gap of the final assembly, that is, the space between the prepared tooth and the prosthesis. X-ray micro-Computed Tomography (micro-CT is a noninvasive imaging technique enabling the internal inspection of the assembly. It has proved to be an efficient tool for measuring the gap. In this study, a critical review of the protocols using micro-CT to quantify the gap is proposed as an introduction to a new protocol aimed at minimizing errors and enabling comparison between CAD/CAM systems. To compare different systems, a standardized protocol is proposed including two reference geometries. Micro-CT is used to acquire the reference geometries. A new 3D method is then proposed and a new indicator is defined (Gap Size Distribution (GSD. In addition, the usual 2D measurements are described and discussed. The 3D gap measurement method proposed can be used in clinical case geometries and has the considerable advantage of minimizing the data processing steps before performing the measurements.

  12. Segmented socioeconomic adaptation of New Eastern European professionals in the United States.

    Science.gov (United States)

    Michalikova, Nina

    2018-01-01

    This study examines the socioeconomic adaptation of post-1991 Eastern European professionals in the United States. The data were obtained from the pooled 2006-2010 American Community Surveys. The analysis includes recent immigrants between ages of 25-65 who have at least an associate's degree. Skilled immigrants in professional or managerial occupations are compared with non-professionals or managers to examine and compare socioeconomic outcomes. The findings presented in this study support the segmented assimilation theory and reveal cross-group and cross-country disparities in socioeconomic adaptation. Despite the high amount of human capital, Eastern European skilled immigrants tend to have a lower share of professionals and managers than other groups. Their average income is lower than the income of some other groups in the analysis, especially immigrants from Northern and Western Europe, suggesting these immigrants experience difficulties in transferring human capital. Among the three largest Eastern European groups - Russia, Ukraine, and Poland - there is a clear hierarchy in socioeconomic status with Russian professionals having the highest educational attainment and income, followed by immigrants from Ukraine and Poland. Results also revealed gender differences in socioeconomic adaptation. Women from Eastern Europe are highly professional, but they tend to be concentrated in different occupations than men, leading to a significant gender-wage gap. The effect of selected individual and country-level characteristics on skilled immigrants' socioeconomic adaptation is discussed.

  13. Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

    International Nuclear Information System (INIS)

    Nanayakkara, Nuwan D; Samarabandu, Jagath; Fenster, Aaron

    2006-01-01

    Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 ± 0.51 pixels (0.54 ± 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts

  14. Wearable 3D measurement

    Science.gov (United States)

    Manabe, Yoshitsugu; Imura, Masataka; Tsuchiya, Masanobu; Yasumuro, Yoshihiro; Chihara, Kunihiro

    2003-01-01

    Wearable 3D measurement realizes to acquire 3D information of an objects or an environment using a wearable computer. Recently, we can send voice and sound as well as pictures by mobile phone in Japan. Moreover it will become easy to capture and send data of short movie by it. On the other hand, the computers become compact and high performance. And it can easy connect to Internet by wireless LAN. Near future, we can use the wearable computer always and everywhere. So we will be able to send the three-dimensional data that is measured by wearable computer as a next new data. This paper proposes the measurement method and system of three-dimensional data of an object with the using of wearable computer. This method uses slit light projection for 3D measurement and user"s motion instead of scanning system.

  15. 3D Digital Modelling

    DEFF Research Database (Denmark)

    Hundebøl, Jesper

    wave of new building information modelling tools demands further investigation, not least because of industry representatives' somewhat coarse parlance: Now the word is spreading -3D digital modelling is nothing less than a revolution, a shift of paradigm, a new alphabet... Research qeustions. Based...... on empirical probes (interviews, observations, written inscriptions) within the Danish construction industry this paper explores the organizational and managerial dynamics of 3D Digital Modelling. The paper intends to - Illustrate how the network of (non-)human actors engaged in the promotion (and arrest) of 3...... important to appreciate the analysis. Before turning to the presentation of preliminary findings and a discussion of 3D digital modelling, it begins, however, with an outline of industry specific ICT strategic issues. Paper type. Multi-site field study...

  16. 3D ARCHITECTURAL VIDEOMAPPING

    Directory of Open Access Journals (Sweden)

    R. Catanese

    2013-07-01

    Full Text Available 3D architectural mapping is a video projection technique that can be done with a survey of a chosen building in order to realize a perfect correspondence between its shapes and the images in projection. As a performative kind of audiovisual artifact, the real event of the 3D mapping is a combination of a registered video animation file with a real architecture. This new kind of visual art is becoming very popular and its big audience success testifies new expressive chances in the field of urban design. My case study has been experienced in Pisa for the Luminara feast in 2012.

  17. Interaktiv 3D design

    DEFF Research Database (Denmark)

    Villaume, René Domine; Ørstrup, Finn Rude

    2002-01-01

    Projektet undersøger potentialet for interaktiv 3D design via Internettet. Arkitekt Jørn Utzons projekt til Espansiva blev udviklet som et byggesystem med det mål, at kunne skabe mangfoldige planmuligheder og mangfoldige facade- og rumudformninger. Systemets bygningskomponenter er digitaliseret som...... 3D elementer og gjort tilgængelige. Via Internettet er det nu muligt at sammenstille og afprøve en uendelig  række bygningstyper som  systemet blev tænkt og udviklet til....

  18. 3D Projection Installations

    DEFF Research Database (Denmark)

    Halskov, Kim; Johansen, Stine Liv; Bach Mikkelsen, Michelle

    2014-01-01

    Three-dimensional projection installations are particular kinds of augmented spaces in which a digital 3-D model is projected onto a physical three-dimensional object, thereby fusing the digital content and the physical object. Based on interaction design research and media studies, this article ...... Fingerplan to Loop City, is a 3-D projection installation presenting the history and future of city planning for the Copenhagen area in Denmark. The installation was presented as part of the 12th Architecture Biennale in Venice in 2010....

  19. Herramientas SIG 3D

    Directory of Open Access Journals (Sweden)

    Francisco R. Feito Higueruela

    2010-04-01

    Full Text Available Applications of Geographical Information Systems on several Archeology fields have been increasing during the last years. Recent avances in these technologies make possible to work with more realistic 3D models. In this paper we introduce a new paradigm for this system, the GIS Thetrahedron, in which we define the fundamental elements of GIS, in order to provide a better understanding of their capabilities. At the same time the basic 3D characteristics of some comercial and open source software are described, as well as the application to some samples on archeological researchs

  20. Bootstrapping 3D fermions

    Energy Technology Data Exchange (ETDEWEB)

    Iliesiu, Luca [Joseph Henry Laboratories, Princeton University, Princeton, NJ 08544 (United States); Kos, Filip; Poland, David [Department of Physics, Yale University, New Haven, CT 06520 (United States); Pufu, Silviu S. [Joseph Henry Laboratories, Princeton University, Princeton, NJ 08544 (United States); Simmons-Duffin, David [School of Natural Sciences, Institute for Advanced Study, Princeton, NJ 08540 (United States); Yacoby, Ran [Joseph Henry Laboratories, Princeton University, Princeton, NJ 08544 (United States)

    2016-03-17

    We study the conformal bootstrap for a 4-point function of fermions 〈ψψψψ〉 in 3D. We first introduce an embedding formalism for 3D spinors and compute the conformal blocks appearing in fermion 4-point functions. Using these results, we find general bounds on the dimensions of operators appearing in the ψ×ψ OPE, and also on the central charge C{sub T}. We observe features in our bounds that coincide with scaling dimensions in the Gross-Neveu models at large N. We also speculate that other features could coincide with a fermionic CFT containing no relevant scalar operators.

  1. Development of a tomographic system adapted to 3D measurement of contaminated wounds based on the Cacao concept (Computer aided collimation Gamma Camera)

    International Nuclear Information System (INIS)

    Douiri, A.

    2002-03-01

    The computer aided collimation gamma camera (CACAO in French) is a gamma camera using a collimator with large holes, a supplementary linear scanning motion during the acquisition and a dedicated reconstruction program taking full account of the source depth. The CACAO system was introduced to improve both the sensitivity and the resolution in nuclear medicine. This thesis focuses on the design of a fast and robust reconstruction algorithm in the CACAO project. We start by an overview of tomographic imaging techniques in nuclear medicine. After modelling the physical CACAO system, we present the complete reconstruction program which involves three steps: 1) shift and sum 2) deconvolution and filtering 3) rotation and sum. The deconvolution is the critical step that decreases the signal to noise ratio of the reconstructed images. We propose a regularized multi-channel algorithm to solve the deconvolution problem. We also present a fast algorithm based on Splines functions and preserving the high quality of the reconstructed images for the shift and the rotation steps. Comparisons of simulated reconstructed images in 2D and 3D for the conventional system (CPHC) and CACAO demonstrate the ability of CACAO system to increase the quality of the SPECT images. Finally, this study concludes with an experimental approach with a pixellated detector conceived for a 3D measurement of contaminated wounds. This experimentation proves the possible advantages of coupling the CACAO project with pixellated detectors. Moreover, a variety of applications could fully benefit from the CACAO system, such as low activity imaging, the use of high-energy gamma isotopes and the visualization of deep organs. Moreover the combination of the CACAO system with a pixels detector may open up further possibilities for the future of nuclear medicine. (author)

  2. Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

    Directory of Open Access Journals (Sweden)

    Luis Daniel Lledó

    2015-03-01

    Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.

  3. Shaping 3-D boxes

    DEFF Research Database (Denmark)

    Stenholt, Rasmus; Madsen, Claus B.

    2011-01-01

    Enabling users to shape 3-D boxes in immersive virtual environments is a non-trivial problem. In this paper, a new family of techniques for creating rectangular boxes of arbitrary position, orientation, and size is presented and evaluated. These new techniques are based solely on position data...

  4. 3D Wire 2015

    DEFF Research Database (Denmark)

    Jordi, Moréton; F, Escribano; J. L., Farias

    This document is a general report on the implementation of gamification in 3D Wire 2015 event. As the second gamification experience in this event, we have delved deeply in the previous objectives (attracting public areas less frequented exhibition in previous years and enhance networking) and have...

  5. 3D Harmonic Echocardiography:

    NARCIS (Netherlands)

    M.M. Voormolen (Marco)

    2007-01-01

    textabstractThree dimensional (3D) echocardiography has recently developed from an experimental technique in the ’90 towards an imaging modality for the daily clinical practice. This dissertation describes the considerations, implementation, validation and clinical application of a unique

  6. 2D to 3D conversion implemented in different hardware

    Science.gov (United States)

    Ramos-Diaz, Eduardo; Gonzalez-Huitron, Victor; Ponomaryov, Volodymyr I.; Hernandez-Fragoso, Araceli

    2015-02-01

    Conversion of available 2D data for release in 3D content is a hot topic for providers and for success of the 3D applications, in general. It naturally completely relies on virtual view synthesis of a second view given by original 2D video. Disparity map (DM) estimation is a central task in 3D generation but still follows a very difficult problem for rendering novel images precisely. There exist different approaches in DM reconstruction, among them manually and semiautomatic methods that can produce high quality DMs but they demonstrate hard time consuming and are computationally expensive. In this paper, several hardware implementations of designed frameworks for an automatic 3D color video generation based on 2D real video sequence are proposed. The novel framework includes simultaneous processing of stereo pairs using the following blocks: CIE L*a*b* color space conversions, stereo matching via pyramidal scheme, color segmentation by k-means on an a*b* color plane, and adaptive post-filtering, DM estimation using stereo matching between left and right images (or neighboring frames in a video), adaptive post-filtering, and finally, the anaglyph 3D scene generation. Novel technique has been implemented on DSP TMS320DM648, Matlab's Simulink module over a PC with Windows 7, and using graphic card (NVIDIA Quadro K2000) demonstrating that the proposed approach can be applied in real-time processing mode. The time values needed, mean Similarity Structural Index Measure (SSIM) and Bad Matching Pixels (B) values for different hardware implementations (GPU, Single CPU, and DSP) are exposed in this paper.

  7. 3D Surgical Simulation

    OpenAIRE

    Cevidanes, Lucia; Tucker, Scott; Styner, Martin; Kim, Hyungmin; Chapuis, Jonas; Reyes, Mauricio; Proffit, William; Turvey, Timothy; Jaskolka, Michael

    2010-01-01

    This paper discusses the development of methods for computer-aided jaw surgery. Computer-aided jaw surgery allows us to incorporate the high level of precision necessary for transferring virtual plans into the operating room. We also present a complete computer-aided surgery (CAS) system developed in close collaboration with surgeons. Surgery planning and simulation include construction of 3D surface models from Cone-beam CT (CBCT), dynamic cephalometry, semi-automatic mirroring, interactive ...

  8. Uncovering the true nature of deformation microstructures using 3D analysis methods

    Science.gov (United States)

    Ferry, M.; Quadir, M. Z.; Afrin, N.; Xu, W.; Loeb, A.; Soe, B.; McMahon, C.; George, C.; Bassman, L.

    2015-08-01

    Three-dimensional electron backscatter diffraction (3D EBSD) has emerged as a powerful technique for generating 3D crystallographic information in reasonably large volumes of a microstructure. The technique uses a focused ion beam (FIB) as a high precision serial sectioning device for generating consecutive ion milled surfaces of a material, with each milled surface subsequently mapped by EBSD. The successive EBSD maps are combined using a suitable post-processing method to generate a crystallographic volume of the microstructure. The first part of this paper shows the usefulness of 3D EBSD for understanding the origin of various structural features associated with the plastic deformation of metals. The second part describes a new method for automatically identifying the various types of low and high angle boundaries found in deformed and annealed metals, particularly those associated with grains exhibiting subtle and gradual variations in orientation. We have adapted a 2D image segmentation technique, fast multiscale clustering, to 3D EBSD data using a novel variance function to accommodate quaternion data. This adaptation is capable of segmenting based on subtle and gradual variation as well as on sharp boundaries within the data. We demonstrate the excellent capabilities of this technique with application to 3D EBSD data sets generated from a range of cold rolled and annealed metals described in the paper.

  9. Adaptive output-based command shaping for sway control of a 3D overhead crane with payload hoisting and wind disturbance

    Science.gov (United States)

    Abdullahi, Auwalu M.; Mohamed, Z.; Selamat, H.; Pota, Hemanshu R.; Zainal Abidin, M. S.; Ismail, F. S.; Haruna, A.

    2018-01-01

    Payload hoisting and wind disturbance during crane operations are among the challenging factors that affect a payload sway and thus, affect the crane's performance. This paper proposes a new online adaptive output-based command shaping (AOCS) technique for an effective payload sway reduction of an overhead crane under the influence of those effects. This technique enhances the previously developed output-based command shaping (OCS) which was effective only for a fixed system and without external disturbances. Unlike the conventional input shaping design technique which requires the system's natural frequency and damping ratio, the proposed technique is designed by using the output signal and thus, an online adaptive algorithm can be formulated. To test the effectiveness of the AOCS, experiments are carried out using a laboratory overhead crane with a payload hoisting in the presence of wind, and with different payloads. The superiority of the method is confirmed by 82% and 29% reductions in the overall sway and the maximum transient sway respectively, when compared to the OCS, and two robust input shapers namely Zero Vibration Derivative-Derivative and Extra-Insensitive shapers. Furthermore, the method demonstrates a uniform crane's performance under all conditions. It is envisaged that the proposed method can be very useful in designing an effective controller for a crane system with an unknown payload and under the influence of external disturbances.

  10. EVALUATION OF METHODS FOR COREGISTRATION AND FUSION OF RPAS-BASED 3D POINT CLOUDS AND THERMAL INFRARED IMAGES

    Directory of Open Access Journals (Sweden)

    L. Hoegner

    2016-06-01

    Full Text Available This paper discusses the automatic coregistration and fusion of 3d point clouds generated from aerial image sequences and corresponding thermal infrared (TIR images. Both RGB and TIR images have been taken from a RPAS platform with a predefined flight path where every RGB image has a corresponding TIR image taken from the same position and with the same orientation with respect to the accuracy of the RPAS system and the inertial measurement unit. To remove remaining differences in the exterior orientation, different strategies for coregistering RGB and TIR images are discussed: (i coregistration based on 2D line segments for every single TIR image and the corresponding RGB image. This method implies a mainly planar scene to avoid mismatches; (ii coregistration of both the dense 3D point clouds from RGB images and from TIR images by coregistering 2D image projections of both point clouds; (iii coregistration based on 2D line segments in every single TIR image and 3D line segments extracted from intersections of planes fitted in the segmented dense 3D point cloud; (iv coregistration of both the dense 3D point clouds from RGB images and from TIR images using both ICP and an adapted version based on corresponding segmented planes; (v coregistration of both image sets based on point features. The quality is measured by comparing the differences of the back projection of homologous points in both corrected RGB and TIR images.

  11. In situ repair of bone and cartilage defects using 3D scanning and 3D printing.

    Science.gov (United States)

    Li, Lan; Yu, Fei; Shi, Jianping; Shen, Sheng; Teng, Huajian; Yang, Jiquan; Wang, Xingsong; Jiang, Qing

    2017-08-25

    Three-dimensional (3D) printing is a rapidly emerging technology that promises to transform tissue engineering into a commercially successful biomedical industry. However, the use of robotic bioprinters alone is not sufficient for disease treatment. This study aimed to report the combined application of 3D scanning and 3D printing for treating bone and cartilage defects. Three different kinds of defect models were created to mimic three orthopedic diseases: large segmental defects of long bones, free-form fracture of femoral condyle, and International Cartilage Repair Society grade IV chondral lesion. Feasibility of in situ 3D bioprinting for these diseases was explored. The 3D digital models of samples with defects and corresponding healthy parts were obtained using high-resolution 3D scanning. The Boolean operation was used to achieve the shape of the defects, and then the target geometries were imported in a 3D bioprinter. Two kinds of photopolymerized hydrogels were synthesized as bioinks. Finally, the defects of bone and cartilage were restored perfectly in situ using 3D bioprinting. The results of this study suggested that 3D scanning and 3D bioprinting could provide another strategy for tissue engineering and regenerative medicine.

  12. Tangible 3D Modelling

    DEFF Research Database (Denmark)

    Hejlesen, Aske K.; Ovesen, Nis

    2012-01-01

    This paper presents an experimental approach to teaching 3D modelling techniques in an Industrial Design programme. The approach includes the use of tangible free form models as tools for improving the overall learning. The paper is based on lecturer and student experiences obtained through...... facilitated discussions during the course as well as through a survey distributed to the participating students. The analysis of the experiences shows a mixed picture consisting of both benefits and limits to the experimental technique. A discussion about the applicability of the technique and about...

  13. FUN3D Manual: 13.3

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2018-01-01

    This manual describes the installation and execution of FUN3D version 13.3, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  14. FUN3D Manual: 12.8

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2015-01-01

    This manual describes the installation and execution of FUN3D version 12.8, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  15. FUN3D Manual: 13.1

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2017-01-01

    This manual describes the installation and execution of FUN3D version 13.1, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  16. FUN3D Manual: 13.2

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, William L.; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2017-01-01

    This manual describes the installation and execution of FUN3D version 13.2, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  17. FUN3D Manual: 12.9

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2016-01-01

    This manual describes the installation and execution of FUN3D version 12.9, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  18. FUN3D Manual: 13.0

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bill; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2016-01-01

    This manual describes the installation and execution of FUN3D version 13.0, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  19. FUN3D Manual: 12.7

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2015-01-01

    This manual describes the installation and execution of FUN3D version 12.7, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  20. BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

    Science.gov (United States)

    Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana

    2006-01-01

    Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

  1. 3.0T MR3D-COSMIC序列结合多平面重建对12对脑神经脑池段的成像价值%Imaging the cisternal segment of cranial nerves with 3.0T MR 3D-COSMIC and multiplanar reconstruction

    Institute of Scientific and Technical Information of China (English)

    郑红伟; 马秀华; 薛鹏; 漆剑频; 胡道予; 吕富荣

    2012-01-01

    Objective To investigate the applying value of 3D-COSMIC MRI sequences for cranial nerves I-M.Methods 3D-COSMIC were used in 20 volunteers and 10 patients with complaint of cranial nerves.480 nerves were evaluated.Two radiologists independently divided the imaging findings into 3 grades: 1 (completely visualized) ,2 (partially visualized) ,and 3 (not visualized).The first two grades were defined as visualization and the last grade was no visualization,the relationship between nerves and surrounding vessels were divided into grades;1 (notcontact) ,2(contact) ,3 (compression).Results The rate of visualization of these cranial nerves with 3D-COSMIC sequences were as follows respectively: nerve I (86%);nerve Ⅱ (100%);nerve Ⅲ (97% );nerve Ⅳ(23%);nerve Ⅴ(100%) ;nerve Ⅵ (90%) ;nerve Ⅶ(100%);nerve 1(100%);nerve IX-XI complex(63% ,100% and 53%);nerve Ⅻ(40%)3 of 10 patients with complaint of cranial nerves displayed nerve had vascular compression or contact.Conclusion 3D-COSMIC sequence can show detailed anatomy of the cranial nerves and relationship between nerves and surrounding vessels ,especially Ⅱ,Ⅲ,Ⅳ,Ⅴ,Ⅵ,Ⅶ,Ⅷ,Ⅹ cranial nerves?to help clinical treatment.%目的 探讨3.0T MR 3D-COSMIC成像序列对Ⅰ~Ⅻ对脑神经脑池段显示的临床价值.方法 采用3.0T MR扫描仪对20例健康志愿者和10例疑因血管等原因压迫相应颅脑神经产生临床症状的患者进行MR检查,扫描序列包括3D-COSMIC及3D-TOF-SPGR.并由2名神经放射学医师根据20例健康志愿者480支颅神经显示的清晰程度分为清晰、较清晰、不清晰3个等级,清晰和较清晰定义为显示,不清晰定义为未显示,而神经与血管的关系分为无接触、接触、压迫.结果 脑神经在3D-COSMIC序列的显示率分别为:嗅神经(86%),视神经(100%),动眼神经(97%),滑车神经(23%),三叉神经(100%),外展神经(90%),面神经(100%),前庭蜗神经(100%),后组颅神经复合体[舌咽神经(63%)

  2. Adaptive Stress Response in Segmental Progeria Resembles Long-Lived Dwarfism and Calorie Restriction in Mice

    Science.gov (United States)

    Holcomb, Valerie B; von Lindern, Marieke; Jong, Willeke M. C; Zeeuw, Chris I. De; Suh, Yousin; Hasty, Paul; Hoeijmakers, Jan H. J; van der Horst, Gijsbertus T. J; Mitchell, James R

    2006-01-01

    How congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular senescence is a likely contributing mechanism. Contrary to expectations, neither accelerated senescence nor acute oxidative stress hypersensitivity was detected in primary fibroblast or erythroblast cultures from multiple progeroid mouse models for defects in the nucleotide excision DNA repair pathway, which share premature aging features including postnatal growth retardation, cerebellar ataxia, and death before weaning. Instead, we report a prominent phenotypic overlap with long-lived dwarfism and calorie restriction during postnatal development (2 wk of age), including reduced size, reduced body temperature, hypoglycemia, and perturbation of the growth hormone/insulin-like growth factor 1 neuroendocrine axis. These symptoms were also present at 2 wk of age in a novel progeroid nucleotide excision repair-deficient mouse model (XPDG602D/R722W/XPA−/−) that survived weaning with high penetrance. However, despite persistent cachectic dwarfism, blood glucose and serum insulin-like growth factor 1 levels returned to normal by 10 wk, with hypoglycemia reappearing near premature death at 5 mo of age. These data strongly suggest changes in energy metabolism as part of an adaptive response during the stressful period of postnatal growth. Interestingly, a similar perturbation of the postnatal growth axis was not detected in another progeroid mouse model, the double-strand DNA break repair deficient Ku80 −/− mouse. Specific (but not all) types of genome instability may thus engage a conserved response to stress that evolved to cope with environmental pressures such as food shortage. PMID:17173483

  3. Adaptive stress response in segmental progeria resembles long-lived dwarfism and calorie restriction in mice.

    Science.gov (United States)

    van de Ven, Marieke; Andressoo, Jaan-Olle; Holcomb, Valerie B; von Lindern, Marieke; Jong, Willeke M C; De Zeeuw, Chris I; Suh, Yousin; Hasty, Paul; Hoeijmakers, Jan H J; van der Horst, Gijsbertus T J; Mitchell, James R

    2006-12-15

    How congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular senescence is a likely contributing mechanism. Contrary to expectations, neither accelerated senescence nor acute oxidative stress hypersensitivity was detected in primary fibroblast or erythroblast cultures from multiple progeroid mouse models for defects in the nucleotide excision DNA repair pathway, which share premature aging features including postnatal growth retardation, cerebellar ataxia, and death before weaning. Instead, we report a prominent phenotypic overlap with long-lived dwarfism and calorie restriction during postnatal development (2 wk of age), including reduced size, reduced body temperature, hypoglycemia, and perturbation of the growth hormone/insulin-like growth factor 1 neuroendocrine axis. These symptoms were also present at 2 wk of age in a novel progeroid nucleotide excision repair-deficient mouse model (XPD(G602D/R722W)/XPA(-/-)) that survived weaning with high penetrance. However, despite persistent cachectic dwarfism, blood glucose and serum insulin-like growth factor 1 levels returned to normal by 10 wk, with hypoglycemia reappearing near premature death at 5 mo of age. These data strongly suggest changes in energy metabolism as part of an adaptive response during the stressful period of postnatal growth. Interestingly, a similar perturbation of the postnatal growth axis was not detected in another progeroid mouse model, the double-strand DNA break repair deficient Ku80(-/-) mouse. Specific (but not all) types of genome instability may thus engage a conserved response to stress that evolved to cope with environmental pressures such as food shortage.

  4. Adaptive stress response in segmental progeria resembles long-lived dwarfism and calorie restriction in mice.

    Directory of Open Access Journals (Sweden)

    Marieke van de Ven

    2006-12-01

    Full Text Available How congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular senescence is a likely contributing mechanism. Contrary to expectations, neither accelerated senescence nor acute oxidative stress hypersensitivity was detected in primary fibroblast or erythroblast cultures from multiple progeroid mouse models for defects in the nucleotide excision DNA repair pathway, which share premature aging features including postnatal growth retardation, cerebellar ataxia, and death before weaning. Instead, we report a prominent phenotypic overlap with long-lived dwarfism and calorie restriction during postnatal development (2 wk of age, including reduced size, reduced body temperature, hypoglycemia, and perturbation of the growth hormone/insulin-like growth factor 1 neuroendocrine axis. These symptoms were also present at 2 wk of age in a novel progeroid nucleotide excision repair-deficient mouse model (XPD(G602D/R722W/XPA(-/- that survived weaning with high penetrance. However, despite persistent cachectic dwarfism, blood glucose and serum insulin-like growth factor 1 levels returned to normal by 10 wk, with hypoglycemia reappearing near premature death at 5 mo of age. These data strongly suggest changes in energy metabolism as part of an adaptive response during the stressful period of postnatal growth. Interestingly, a similar perturbation of the postnatal growth axis was not detected in another progeroid mouse model, the double-strand DNA break repair deficient Ku80(-/- mouse. Specific (but not all types of genome instability may thus engage a conserved response to stress that evolved to cope with environmental pressures such as food shortage.

  5. Adaptive stress response in segmental progeria resembles long-lived dwarfism and calorie restriction in mice

    NARCIS (Netherlands)

    van de Ven, Marieke; Andressoo, Jaan-Olle; Holcomb, Valerie B.; von Lindern, Marieke; Jong, Willeke M. C.; de Zeeuw, Chris I.; Suh, Yousin; Hasty, Paul; Hoeijmakers, Jan H. J.; van der Horst, Gijsbertus T. J.; Mitchell, James R.

    2006-01-01

    How congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular senescence is a

  6. Adaptive stress response in segmental progeria resembles long-lived dwarfism and calorie restriction in mice

    NARCIS (Netherlands)

    H.W.M. van de Ven (Marieke); J.-O. Andressoo (Jaan-Olle); V.B. Holcomb (Valerie); M.M. von Lindern (Marieke); W.M.C. Jong (Willeke); C.I. de Zeeuw (Chris); Y. Suh (Yousin); P. Hasty (Paul); J.H.J. Hoeijmakers (Jan); G.T.J. van der Horst (Gijsbertus); J.R. Mitchell (James)

    2006-01-01

    textabstractHow congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular

  7. 3.0T MR3D FIESTA+C成像序列结合多平面重建对Ⅰ~Ⅻ对脑神经脑池段的成像研究%Study on the cisternal segment of cranial nerves with 3.0T MR 3D- FIESTA+C and multiplanar reconstruction

    Institute of Scientific and Technical Information of China (English)

    郑红伟; 祁佩红; 薛鹏; 陈勇; 张道培; 漆剑频; 夏黎明; 胡道予

    2012-01-01

    目的 探讨3.0T MR 3D FIESTA+C成像序列对Ⅰ~Ⅻ对脑神经脑池段显示的临床价值.方法 对20例健康志愿者和20例相应颅脑神经症状患者进行3.0T MR检查,扫描序列为3D FIESTA+C及3D FIESTA.根据20例志愿者480支颅神经显示的清晰程度分为3个等级:清晰、较清晰、不清晰,前两者定义为显示,后者定义为未显示,而神经与血管的关系分为无接触、接触、压迫.结果 3D FIESTA+C及3D FIESTA 2种序列脑神经的显示率分别为:嗅神经(36/40、29/40 ),视神经(40/40、40/40),动眼神经(39/40、37/40),滑车神经(7/40、7/40),三叉神经(40/40、40/40),外展神经(38/40、37/40 ),面神经(40/40、40/40),前庭蜗神经(40/40、40/40),后组脑神经复合体(舌咽神经、迷走神经、副神经) (36/40、30/40;40/40、40/40;32/40、25/40),舌下神经(23/40、21/40).20例神经症状患者中4例显示血管与神经接触或压迫,3例显示神经本身肿瘤或肿瘤压迫神经,其中5例为手术或病理证实.结论 3D-FIESTA+C成像序列较为清晰显示除滑车神经之外其余11对脑神经脑池段形态及其与血管的关系,尤其对I、Ⅱ、Ⅲ、Ⅴ、Ⅵ、Ⅶ、Ⅷ、Ⅹ8对脑神经脑池段的显示效果较好.%Objective To investigate the applied value of MR 3D-FIESTA + C sequences for showing cranial nerves Ⅰ -Ⅶ. Methods 20 volunteers and 20 patients with complaint of cranial nerves underwent MR imaging at 3. 0T MR scanner using 3D-FIESTA + C and 3D)-FIESTA sequences. According to showing condition of the cranial nerves by MR imaging in 20 volunteers, which were divided into three grades: 1 ( completely visualized) , 2 (partially visualized) , and 3 ( not visualized). The first two grades were defined as visualization and the last grade was no visualization. The relationships between nerves and surrounding vessels were divided into three grades: 1 (not contact) ,2(contact) and 3( compression). Results The rates of visualization of these cranial

  8. Imaging the Cisternal Segment of Cranial Nerves with 3.0TMR 3D-COSMIC and Multiplanar Reconstruction%3.0TMR3D-COSMIC成像序列结合MRP对Ⅰ-Ⅻ对脑神经脑池段的成像研究

    Institute of Scientific and Technical Information of China (English)

    李家才; 郑红伟; 漆剑频; 夏黎明; 胡道予

    2012-01-01

    Objective To investigate the applying value of To compare 3D-COSMIC MRI sequences for cranial nerves I ~XII. Methods 3D-C0SMIC were used in 20 volunteers and 10 patients with complaint of cranial nerves. 480 nerves were evaluated. Two radiologists independently divided the imaging findings into 3 grades: 1 (completely visualized), 2 (partially visualized), and 3 (not visualized). The first two grades were defined as visualization and the last grade was no visualization, the relationship between nerves and surrounding vessels were divided into grades: 1 (notcontact),2(contact),3(compression). Results The rate of visualization of these cranial nerves with 3D-COSMIC sequenceswere as follows respectively: nerve I (86%); nerve 11(100%); nerve 111(97%); nerve IV(23%); nerve V (100%); nerve VT(90%); nerve W( 100%); nerve Wl( 100%); nerve K-XI complex (63%, 100% and53%); nerve XE(40%). 3 of 10 patients with complaint of cranial nerves displayed nerve had vascular compression or contact. Conclusion 3D-COSM1C sequence can show detailed anatomy of the cranial nerves and relationship between nerves and surrounding vessels, especially II -> UL V-, VI-. VII-. V1IK X cranial nerves, to help clinical treatment.%目的 探讨3.0T MR 3D-COSMIC 成像序列对Ⅰ-Ⅻ对脑神经脑池段显示的临床价值.方法 采用3.0TMR扫描仪对20例健康志愿者和10例疑因血管等原因压迫相应颅脑神经产生临床症状的患者进行MR检查.并由两名神经放射学医师根据20例志愿者480支颅神经显示的清晰程度分为以下3个等级:清晰、较清晰、不清晰,前两者定义为显示,后者定义为未显示,而神经与血管的关系分为无接触、接触、压迫.结果 脑神经在3D-COSMIC序列的显示率分别为:嗅神经(86%),视神经(100%),动眼神经(97%),滑车神经(23%),三叉神经(100%),外展神经(90%),面神经(100%),前庭蜗神经 (100%),后组颅神经复合体(舌咽神经、迷走神经、副神经)(63%、100%、53%),

  9. Mobile 3D tomograph

    International Nuclear Information System (INIS)

    Illerhaus, Bernhard; Goebbels, Juergen; Onel, Yener; Sauerwein, Christoph

    2008-01-01

    Mobile tomographs often have the problem that high spatial resolution is impossible owing to the position or setup of the tomograph. While the tree tomograph developed by Messrs. Isotopenforschung Dr. Sauerwein GmbH worked well in practice, it is no longer used as the spatial resolution and measuring time are insufficient for many modern applications. The paper shows that the mechanical base of the method is sufficient for 3D CT measurements with modern detectors and X-ray tubes. CT measurements with very good statistics take less than 10 min. This means that mobile systems can be used, e.g. in examinations of non-transportable cultural objects or monuments. Enhancement of the spatial resolution of mobile tomographs capable of measuring in any position is made difficult by the fact that the tomograph has moving parts and will therefore have weight shifts. With the aid of tomographies whose spatial resolution is far higher than the mechanical accuracy, a correction method is presented for direct integration of the Feldkamp algorithm [de

  10. 3D Printing and 3D Bioprinting in Pediatrics.

    Science.gov (United States)

    Vijayavenkataraman, Sanjairaj; Fuh, Jerry Y H; Lu, Wen Feng

    2017-07-13

    Additive manufacturing, commonly referred to as 3D printing, is a technology that builds three-dimensional structures and components layer by layer. Bioprinting is the use of 3D printing technology to fabricate tissue constructs for regenerative medicine from cell-laden bio-inks. 3D printing and bioprinting have huge potential in revolutionizing the field of tissue engineering and regenerative medicine. This paper reviews the application of 3D printing and bioprinting in the field of pediatrics.

  11. 3D Printing and 3D Bioprinting in Pediatrics

    OpenAIRE

    Vijayavenkataraman, Sanjairaj; Fuh, Jerry Y H; Lu, Wen Feng

    2017-01-01

    Additive manufacturing, commonly referred to as 3D printing, is a technology that builds three-dimensional structures and components layer by layer. Bioprinting is the use of 3D printing technology to fabricate tissue constructs for regenerative medicine from cell-laden bio-inks. 3D printing and bioprinting have huge potential in revolutionizing the field of tissue engineering and regenerative medicine. This paper reviews the application of 3D printing and bioprinting in the field of pediatrics.

  12. 3D Maps Representation Using GNG

    Directory of Open Access Journals (Sweden)

    Vicente Morell

    2014-01-01

    Full Text Available Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly become unmanageable. In this work, we focus on 3D maps representation and propose the use of the Growing Neural Gas (GNG network as a model to represent 3D input data. GNG method is able to represent the input data with a desired amount of neurons or resolution while preserving the topology of the input space. Experiments show how GNG method yields a better input space adaptation than other state-of-the-art 3D map representation methods.

  13. 3D printing for dummies

    CERN Document Server

    Hausman, Kalani Kirk

    2014-01-01

    Get started printing out 3D objects quickly and inexpensively! 3D printing is no longer just a figment of your imagination. This remarkable technology is coming to the masses with the growing availability of 3D printers. 3D printers create 3-dimensional layered models and they allow users to create prototypes that use multiple materials and colors.  This friendly-but-straightforward guide examines each type of 3D printing technology available today and gives artists, entrepreneurs, engineers, and hobbyists insight into the amazing things 3D printing has to offer. You'll discover methods for

  14. Ultralow-dose CT of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction and model-based iterative reconstruction: 2D and 3D image quality.

    Science.gov (United States)

    Widmann, Gerlig; Schullian, Peter; Gassner, Eva-Maria; Hoermann, Romed; Bale, Reto; Puelacher, Wolfgang

    2015-03-01

    OBJECTIVE. The purpose of this article is to evaluate 2D and 3D image quality of high-resolution ultralow-dose CT images of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) in comparison with standard filtered backprojection (FBP). MATERIALS AND METHODS. A formalin-fixed human cadaver head was scanned using a clinical reference protocol at a CT dose index volume of 30.48 mGy and a series of five ultralow-dose protocols at 3.48, 2.19, 0.82, 0.44, and 0.22 mGy using FBP and ASIR at 50% (ASIR-50), ASIR at 100% (ASIR-100), and MBIR. Blinded 2D axial and 3D volume-rendered images were compared with each other by three readers using top-down scoring. Scores were analyzed per protocol or dose and reconstruction. All images were compared with the FBP reference at 30.48 mGy. A nonparametric Mann-Whitney U test was used. Statistical significance was set at p ASIR-100 at 3.48 mGy, ASIR-100 at 2.19 mGy, and MBIR at 0.82 mGy. MBIR at 2.19 and 3.48 mGy scored statistically significantly better than the FBP reference (p = 0.032 and 0.001, respectively). For 3D images, the FBP reference at 30.48 mGy did not statistically significantly differ from all reconstructions at 3.48 mGy; FBP and ASIR-100 at 2.19 mGy; FBP, ASIR-100, and MBIR at 0.82 mGy; MBIR at 0.44 mGy; and MBIR at 0.22 mGy. CONCLUSION. MBIR (2D and 3D) and ASIR-100 (2D) may significantly improve subjective image quality of ultralow-dose images and may allow more than 90% dose reductions.

  15. 3D Volume Rendering and 3D Printing (Additive Manufacturing).

    Science.gov (United States)

    Katkar, Rujuta A; Taft, Robert M; Grant, Gerald T

    2018-07-01

    Three-dimensional (3D) volume-rendered images allow 3D insight into the anatomy, facilitating surgical treatment planning and teaching. 3D printing, additive manufacturing, and rapid prototyping techniques are being used with satisfactory accuracy, mostly for diagnosis and surgical planning, followed by direct manufacture of implantable devices. The major limitation is the time and money spent generating 3D objects. Printer type, material, and build thickness are known to influence the accuracy of printed models. In implant dentistry, the use of 3D-printed surgical guides is strongly recommended to facilitate planning and reduce risk of operative complications. Copyright © 2018 Elsevier Inc. All rights reserved.

  16. 3D game environments create professional 3D game worlds

    CERN Document Server

    Ahearn, Luke

    2008-01-01

    The ultimate resource to help you create triple-A quality art for a variety of game worlds; 3D Game Environments offers detailed tutorials on creating 3D models, applying 2D art to 3D models, and clear concise advice on issues of efficiency and optimization for a 3D game engine. Using Photoshop and 3ds Max as his primary tools, Luke Ahearn explains how to create realistic textures from photo source and uses a variety of techniques to portray dynamic and believable game worlds.From a modern city to a steamy jungle, learn about the planning and technological considerations for 3D modelin

  17. Development of a tomographic system adapted to 3D measurement of contaminated wounds based on the Cacao concept (Computer aided collimation Gamma Camera); Developpement a partir du concept CACAO (Camera A Collimation Assistee par Ordinateur) d'un systeme tomographique adapte a la mesure 3D de plaies contaminees

    Energy Technology Data Exchange (ETDEWEB)

    Douiri, A

    2002-03-01

    The computer aided collimation gamma camera (CACAO in French) is a gamma camera using a collimator with large holes, a supplementary linear scanning motion during the acquisition and a dedicated reconstruction program taking full account of the source depth. The CACAO system was introduced to improve both the sensitivity and the resolution in nuclear medicine. This thesis focuses on the design of a fast and robust reconstruction algorithm in the CACAO project. We start by an overview of tomographic imaging techniques in nuclear medicine. After modelling the physical CACAO system, we present the complete reconstruction program which involves three steps: 1) shift and sum 2) deconvolution and filtering 3) rotation and sum. The deconvolution is the critical step that decreases the signal to noise ratio of the reconstructed images. We propose a regularized multi-channel algorithm to solve the deconvolution problem. We also present a fast algorithm based on Splines functions and preserving the high quality of the reconstructed images for the shift and the rotation steps. Comparisons of simulated reconstructed images in 2D and 3D for the conventional system (CPHC) and CACAO demonstrate the ability of CACAO system to increase the quality of the SPECT images. Finally, this study concludes with an experimental approach with a pixellated detector conceived for a 3D measurement of contaminated wounds. This experimentation proves the possible advantages of coupling the CACAO project with pixellated detectors. Moreover, a variety of applications could fully benefit from the CACAO system, such as low activity imaging, the use of high-energy gamma isotopes and the visualization of deep organs. Moreover the combination of the CACAO system with a pixels detector may open up further possibilities for the future of nuclear medicine. (author)

  18. Automatic Segmentation and Online virtualCT in Head-and-Neck Adaptive Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Peroni, Marta, E-mail: marta.peroni@mail.polimi.it [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Ciardo, Delia [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Spadea, Maria Francesca [Department of Experimental and Clinical Medicine, Universita degli Studi Magna Graecia, Catanzaro (Italy); Riboldi, Marco [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy); Comi, Stefania; Alterio, Daniela [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Baroni, Guido [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy); Orecchia, Roberto [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Universita degli Studi di Milano, Milano (Italy); Medical Department, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy)

    2012-11-01

    Purpose: The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. Method: We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. Results: Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. Conclusion: The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.

  19. Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy.

    Science.gov (United States)

    Peroni, Marta; Ciardo, Delia; Spadea, Maria Francesca; Riboldi, Marco; Comi, Stefania; Alterio, Daniela; Baroni, Guido; Orecchia, Roberto

    2012-11-01

    The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization. Copyright © 2012 Elsevier Inc. All rights reserved.

  20. The Future Is 3D

    Science.gov (United States)

    Carter, Luke

    2015-01-01

    3D printers are a way of producing a 3D model of an item from a digital file. The model builds up in successive layers of material placed by the printer controlled by the information in the computer file. In this article the author argues that 3D printers are one of the greatest technological advances of recent times. He discusses practical uses…

  1. The 3D additivist cookbook

    NARCIS (Netherlands)

    Allahyari, Morehshin; Rourke, Daniel; Rasch, Miriam

    The 3D Additivist Cookbook, devised and edited by Morehshin Allahyari & Daniel Rourke, is a free compendium of imaginative, provocative works from over 100 world-leading artists, activists and theorists. The 3D Additivist Cookbook contains .obj and .stl files for the 3D printer, as well as critical

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

    Directory of Open Access Journals (Sweden)

    Ahmed Elazab

    2015-01-01

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

  3. Adaptive stress response in segmental progeria resembles long-lived dwarfism and calorie restriction in mice

    OpenAIRE

    Ven, Marieke; Andressoo, Jaan-Olle; Holcomb, Valerie; Lindern, Marieke; Jong, Willeke; Zeeuw, Chris; Suh, Yousin; Hasty, Paul; Hoeijmakers, Jan; Horst, Gijsbertus; Mitchell, James

    2006-01-01

    textabstractHow congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular senescence is a likely contributing mechanism. Contrary to expectations, neither accelerated senescence nor acute oxidative stress hypersensitivity was detected in primary fibroblast or erythroblast cul...

  4. Adaptive segmentation of nuclei in H&S stained tendon microscopy

    Science.gov (United States)

    Chuang, Bo-I.; Wu, Po-Ting; Hsu, Jian-Han; Jou, I.-Ming; Su, Fong-Chin; Sun, Yung-Nien

    2015-12-01

    Tendiopathy is a popular clinical issue in recent years. In most cases like trigger finger or tennis elbow, the pathology change can be observed under H and E stained tendon microscopy. However, the qualitative analysis is too subjective and thus the results heavily depend on the observers. We develop an automatic segmentation procedure which segments and counts the nuclei in H and E stained tendon microscopy fast and precisely. This procedure first determines the complexity of images and then segments the nuclei from the image. For the complex images, the proposed method adopts sampling-based thresholding to segment the nuclei. While for the simple images, the Laplacian-based thresholding is employed to re-segment the nuclei more accurately. In the experiments, the proposed method is compared with the experts outlined results. The nuclei number of proposed method is closed to the experts counted, and the processing time of proposed method is much faster than the experts'.

  5. Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models

    DEFF Research Database (Denmark)

    Puonti, Oula; Van Leemput, Koen

    2016-01-01

    In this paper we propose a new generative model for simultaneous brain parcellation and white matter lesion segmentation from multi-contrast magnetic resonance images. The method combines an existing whole-brain segmentation technique with a novel spatial lesion model based on a convolutional...... restricted Boltzmann machine. Unlike current state-of-the-art lesion detection techniques based on discriminative modeling, the proposed method is not tuned to one specific scanner or imaging protocol, and simultaneously segments dozens of neuroanatomical structures. Experiments on a public benchmark dataset...... in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations....

  6. Proposal of custom made wrist orthoses based on 3D modelling and 3D printing.

    Science.gov (United States)

    Abreu de Souza, Mauren; Schmitz, Cristiane; Marega Pinhel, Marcelo; Palma Setti, Joao A; Nohama, Percy

    2017-07-01

    Accessibility to three-dimensional (3D) technologies, such as 3D scanning systems and additive manufacturing (like 3D printers), allows a variety of 3D applications. For medical applications in particular, these modalities are gaining a lot of attention enabling several opportunities for healthcare applications. The literature brings several cases applying both technologies, but none of them focus on the spreading of how this technology could benefit the health segment. This paper proposes a new methodology, which employs both 3D modelling and 3D printing for building orthoses, which could better fit the demands of different patients. Additionally, there is an opportunity for sharing expertise, as it represents a trendy in terms of the maker-movement. Therefore, as a result of the proposed approach, we present a case study based on a volunteer who needs an immobilization orthosis, which was built for exemplification of the whole process. This proposal also employs freely available 3D models and software, having a strong social impact. As a result, it enables the implementation and effective usability for a variety of built to fit solutions, hitching useful and smarter technologies for the healthcare sector.

  7. ROOFN3D: DEEP LEARNING TRAINING DATA FOR 3D BUILDING RECONSTRUCTION

    Directory of Open Access Journals (Sweden)

    A. Wichmann

    2018-05-01

    Full Text Available Machine learning methods have gained in importance through the latest development of artificial intelligence and computer hardware. Particularly approaches based on deep learning have shown that they are able to provide state-of-the-art results for various tasks. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a new 3D point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. It can be used, among others, to train semantic segmentation networks or to learn the structure of buildings and the geometric model construction. Further details about RoofN3D and the developed data preparation framework, which enables the automatic derivation of training data, are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to unstructured and not gridded 3D point cloud data are presented.

  8. 3D Structure of Tillage Soils

    Science.gov (United States)

    González-Torre, Iván; Losada, Juan Carlos; Falconer, Ruth; Hapca, Simona; Tarquis, Ana M.

    2015-04-01

    application of multifractal analysis methods in images for the study of soil structure. Master thesis, UPM, 2014. Houston, A.N.; S. Schmidt, A.M. Tarquis, W. Otten, P.C. Baveye, S.M. Hapca. Effect of scanning and image reconstruction settings in X-ray computed tomography on soil image quality and segmentation performance. Geoderma, 207-208, 154-165, 2013a. Houston, A, Otten, W., Baveye, Ph., Hapca, S. Adaptive-Window Indicator Kriging: A Thresholding Method for Computed Tomography, Computers & Geosciences, 54, 239-248, 2013b. Tarquis, A.M., R.J. Heck, D. Andina, A. Alvarez and J.M. Antón. Multifractal analysis and thresholding of 3D soil images. Ecological Complexity, 6, 230-239, 2009. Tarquis, A.M.; D. Giménez, A. Saa, M.C. Díaz. and J.M. Gascó. Scaling and Multiscaling of Soil Pore Systems Determined by Image Analysis. Scaling Methods in Soil Systems. Pachepsky, Radcliffe and Selim Eds., 19-33, 2003. CRC Press, Boca Ratón, Florida. Acknowledgements First author acknowledges the financial support obtained from Soil Imaging Laboratory (University of Gueplh, Canada) in 2014.

  9. Fast and Sequence-Adaptive Whole-Brain Segmentation Using Parametric Bayesian Modeling

    DEFF Research Database (Denmark)

    Puonti, Oula; Iglesias, Juan Eugenio; Van Leemput, Koen

    2016-01-01

    the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable...

  10. Spatially adapted augmentation of age-specific atlas-based segmentation using patch-based priors

    Science.gov (United States)

    Liu, Mengyuan; Seshamani, Sharmishtaa; Harrylock, Lisa; Kitsch, Averi; Miller, Steven; Chau, Van; Poskitt, Kenneth; Rousseau, Francois; Studholme, Colin

    2014-03-01

    One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.

  11. Assessing hippocampal development and language in early childhood: Evidence from a new application of the Automatic Segmentation Adapter Tool.

    Science.gov (United States)

    Lee, Joshua K; Nordahl, Christine W; Amaral, David G; Lee, Aaron; Solomon, Marjorie; Ghetti, Simona

    2015-11-01

    Volumetric assessments of the hippocampus and other brain structures during childhood provide useful indices of brain development and correlates of cognitive functioning in typically and atypically developing children. Automated methods such as FreeSurfer promise efficient and replicable segmentation, but may include errors which are avoided by trained manual tracers. A recently devised automated correction tool that uses a machine learning algorithm to remove systematic errors, the Automatic Segmentation Adapter Tool (ASAT), was capable of substantially improving the accuracy of FreeSurfer segmentations in an adult sample [Wang et al., 2011], but the utility of ASAT has not been examined in pediatric samples. In Study 1, the validity of FreeSurfer and ASAT corrected hippocampal segmentations were examined in 20 typically developing children and 20 children with autism spectrum disorder aged 2 and 3 years. We showed that while neither FreeSurfer nor ASAT accuracy differed by disorder or age, the accuracy of ASAT corrected segmentations were substantially better than FreeSurfer segmentations in every case, using as few as 10 training examples. In Study 2, we applied ASAT to 89 typically developing children aged 2 to 4 years to examine relations between hippocampal volume, age, sex, and expressive language. Girls had smaller hippocampi overall, and in left hippocampus this difference was larger in older than younger girls. Expressive language ability was greater in older children, and this difference was larger in those with larger hippocampi, bilaterally. Overall, this research shows that ASAT is highly reliable and useful to examinations relating behavior to hippocampal structure. © 2015 Wiley Periodicals, Inc.

  12. 3D Spectroscopy in Astronomy

    Science.gov (United States)

    Mediavilla, Evencio; Arribas, Santiago; Roth, Martin; Cepa-Nogué, Jordi; Sánchez, Francisco

    2011-09-01

    Preface; Acknowledgements; 1. Introductory review and technical approaches Martin M. Roth; 2. Observational procedures and data reduction James E. H. Turner; 3. 3D Spectroscopy instrumentation M. A. Bershady; 4. Analysis of 3D data Pierre Ferruit; 5. Science motivation for IFS and galactic studies F. Eisenhauer; 6. Extragalactic studies and future IFS science Luis Colina; 7. Tutorials: how to handle 3D spectroscopy data Sebastian F. Sánchez, Begona García-Lorenzo and Arlette Pécontal-Rousset.

  13. 3D Elevation Program—Virtual USA in 3D

    Science.gov (United States)

    Lukas, Vicki; Stoker, J.M.

    2016-04-14

    The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) uses a laser system called ‘lidar’ (light detection and ranging) to create a virtual reality map of the Nation that is very accurate. 3D maps have many uses with new uses being discovered all the time.  

  14. 3D IBFV : Hardware-Accelerated 3D Flow Visualization

    NARCIS (Netherlands)

    Telea, Alexandru; Wijk, Jarke J. van

    2003-01-01

    We present a hardware-accelerated method for visualizing 3D flow fields. The method is based on insertion, advection, and decay of dye. To this aim, we extend the texture-based IBFV technique for 2D flow visualization in two main directions. First, we decompose the 3D flow visualization problem in a

  15. 3D IBFV : hardware-accelerated 3D flow visualization

    NARCIS (Netherlands)

    Telea, A.C.; Wijk, van J.J.

    2003-01-01

    We present a hardware-accelerated method for visualizing 3D flow fields. The method is based on insertion, advection, and decay of dye. To this aim, we extend the texture-based IBFV technique presented by van Wijk (2001) for 2D flow visualization in two main directions. First, we decompose the 3D

  16. 3D for Graphic Designers

    CERN Document Server

    Connell, Ellery

    2011-01-01

    Helping graphic designers expand their 2D skills into the 3D space The trend in graphic design is towards 3D, with the demand for motion graphics, animation, photorealism, and interactivity rapidly increasing. And with the meteoric rise of iPads, smartphones, and other interactive devices, the design landscape is changing faster than ever.2D digital artists who need a quick and efficient way to join this brave new world will want 3D for Graphic Designers. Readers get hands-on basic training in working in the 3D space, including product design, industrial design and visualization, modeling, ani

  17. Using 3D in Visualization

    DEFF Research Database (Denmark)

    Wood, Jo; Kirschenbauer, Sabine; Döllner, Jürgen

    2005-01-01

    to display 3D imagery. The extra cartographic degree of freedom offered by using 3D is explored and offered as a motivation for employing 3D in visualization. The use of VR and the construction of virtual environments exploit navigational and behavioral realism, but become most usefil when combined...... with abstracted representations embedded in a 3D space. The interactions between development of geovisualization, the technology used to implement it and the theory surrounding cartographic representation are explored. The dominance of computing technologies, driven particularly by the gaming industry...

  18. Qademah Fault 3D Survey

    KAUST Repository

    Hanafy, Sherif M.

    2014-01-01

    Objective: Collect 3D seismic data at Qademah Fault location to 1. 3D traveltime tomography 2. 3D surface wave migration 3. 3D phase velocity 4. Possible reflection processing Acquisition Date: 26 – 28 September 2014 Acquisition Team: Sherif, Kai, Mrinal, Bowen, Ahmed Acquisition Layout: We used 288 receiver arranged in 12 parallel lines, each line has 24 receiver. Inline offset is 5 m and crossline offset is 10 m. One shot is fired at each receiver location. We use the 40 kgm weight drop as seismic source, with 8 to 15 stacks at each shot location.