WorldWideScience

Sample records for adaptive 3-d segmentation

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

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    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. An efficient topology adaptation system for parametric active contour segmentation of 3D images

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    Abhau, Jochen; Scherzer, Otmar

    2008-03-01

    Active contour models have already been used succesfully for segmentation of organs from medical images in 3D. In implicit models, the contour is given as the isosurface of a scalar function, and therefore topology adaptations are handled naturally during a contour evolution. Nevertheless, explicit or parametric models are often preferred since user interaction and special geometric constraints are usually easier to incorporate. Although many researchers have studied topology adaptation algorithms in explicit mesh evolutions, no stable algorithm is known for interactive applications. In this paper, we present a topology adaptation system, which consists of two novel ingredients: A spatial hashing technique is used to detect self-colliding triangles of the mesh whose expected running time is linear with respect to the number of mesh vertices. For the topology change procedure, we have developed formulas by homology theory. During a contour evolution, we just have to choose between a few possible mesh retriangulations by local triangle-triangle intersection tests. Our algorithm has several advantages compared to existing ones: Since the new algorithm does not require any global mesh reparametrizations, it is very efficient. Since the topology adaptation system does not require constant sampling density of the mesh vertices nor especially smooth meshes, mesh evolution steps can be performed in a stable way with a rather coarse mesh. We apply our algorithm to 3D ultrasonic data, showing that accurate segmentation is obtained in some seconds.

  3. 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.

  4. Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm.

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    Onoma, D P; Ruan, S; Thureau, S; Nkhali, L; Modzelewski, R; Monnehan, G A; Vera, P; Gardin, I

    2014-12-01

    A segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake.

  5. A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography.

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    Boegel, Marco; Hoelter, Philip; Redel, Thomas; Maier, Andreas; Hornegger, Joachim; Doerfler, Arnd

    2015-01-01

    Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.

  6. Heat Equation to 3D Image Segmentation

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    Nikolay Sirakov

    2006-04-01

    Full Text Available This paper presents a new approach, capable of 3D image segmentation and objects' surface reconstruction. The main advantages of the method are: large capture range; quick segmentation of a 3D scene/image to regions; multiple 3D objects reconstruction. The method uses centripetal force and penalty function to segment the entire 3D scene/image to regions containing a single 3D object. Each region is inscribed in a convex, smooth closed surface, which defines a centripetal force. Then the surface is evolved by the geometric heat differential equation toward the force's direction. The penalty function is defined to stop evolvement of those surface patches, whose normal vectors encountered object's surface. On the base of the theoretical model Forward Difference Algorithm was developed and coded by Mathematica. Stability convergence condition, truncation error and calculation complexity of the algorithm are determined. The obtained results, advantages and disadvantages of the method are discussed at the end of this paper.

  7. Freehand 3D ultrasound breast tumor segmentation

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    Liu, Qi; Ge, Yinan; Ou, Yue; Cao, Biao

    2007-12-01

    It is very important for physicians to accurately determine breast tumor location, size and shape in ultrasound image. The precision of breast tumor volume quantification relies on the accurate segmentation of the images. Given the known location and orientation of the ultrasound probe, We propose using freehand three dimensional (3D) ultrasound to acquire original images of the breast tumor and the surrounding tissues in real-time, after preprocessing with anisotropic diffusion filtering, the segmentation operation is performed slice by slice based on the level set method in the image stack. For the segmentation on each slice, the user can adjust the parameters to fit the requirement in the specified image in order to get the satisfied result. By the quantification procedure, the user can know the tumor size varying in different images in the stack. Surface rendering and interpolation are used to reconstruct the 3D breast tumor image. And the breast volume is constructed by the segmented contours in the stack of images. After the segmentation, the volume of the breast tumor in the 3D image data can be obtained.

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

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    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.

  9. Adaptive 3D image segmentation based on optimized PCNN%优化的PCNN自适应三维图像分割算法

    Institute of Scientific and Technical Information of China (English)

    唐宁; 江贵平; 吕庆文

    2012-01-01

    The segmentation algorithm with PCNN model has many natural advantages. But the typical PCNN model has too many parameters difficult to determinate and consumes too much time. This paper proposed an effective three dimension image segmentation model that integrated multiple PCNN models and the statistical model. It was used to segment brain MRI image into gray matter( GM) , white matter( WM) and cerebrospinal fluid( CSF). And the segmentation results were compared with those of standard PCNN, traditional Otsu threshold, SPM8 toolbox and expert segmentation. It demonstrates this adaptive method is fairly accurate and effective.%脉冲耦合神经网络(pulse coupled neural network,PCNN)对图像分割具有天然的优势,但是传统的PCNN模型参数难以确定,且算法耗时多.对多种PCNN模型进行研究改进,并利用统计学知识提出了一种精简高效的自适应三维分割算法.将其用于脑部磁共振成像(magnetic resonance imaging,MRI)图像的分割,把脑组织分成白质、灰质和脑脊液.与标准PCNN、传统的Otsu阈值方法、SPM8工具箱及专家手动分割结果的对比实验表明,该自适应算法具有精确性、高效性.

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

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    Spiegel, M; Hornegger, J [Pattern Recognition Lab, University Erlangen-Nuremberg, Erlangen (Germany); Redel, T [Siemens AG Healthcare Sector, Forchheim (Germany); Struffert, T; Doerfler, A, E-mail: martin.spiegel@informatik.uni-erlangen.de [Department of Neuroradiology, University Erlangen-Nuremberg, Erlangen (Germany)

    2011-10-07

    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.

  11. 3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches

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    Shadi AlZu'bi

    2010-01-01

    that 3D methodologies can accurately detect the Region Of Interest (ROI. Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations.

  12. Chest Wall Segmentation in Automated 3D Breast Ultrasound Scans

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    Tan, T.; Platel, B.; Mann, R.M.; Huisman, H.; Karssemeijer, N.

    2013-01-01

    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 be

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

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

    2013-04-01

    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. 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. 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. The proposed needle segmentation algorithm is accurate, robust, and

  14. Adaptive interrogation for 3D-PIV

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    Novara, Matteo; Ianiro, Andrea; Scarano, Fulvio

    2013-02-01

    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

  15. Medical image segmentation using 3D MRI data

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    Voronin, V.; Marchuk, V.; Semenishchev, E.; Cen, Yigang; Agaian, S.

    2017-05-01

    Precise segmentation of three-dimensional (3D) magnetic resonance imaging (MRI) image can be a very useful computer aided diagnosis (CAD) tool in clinical routines. Accurate automatic extraction a 3D component from images obtained by magnetic resonance imaging (MRI) is a challenging segmentation problem due to the small size objects of interest (e.g., blood vessels, bones) in each 2D MRA slice and complex surrounding anatomical structures. Our objective is to develop a specific segmentation scheme for accurately extracting parts of bones from MRI images. In this paper, we use a segmentation algorithm to extract the parts of bones from Magnetic Resonance Imaging (MRI) data sets based on modified active contour method. As a result, the proposed method demonstrates good accuracy in a comparison between the existing segmentation approaches on real MRI data.

  16. 3D Medical Image Segmentation Based on Rough Set Theory

    Institute of Scientific and Technical Information of China (English)

    CHEN Shi-hao; TIAN Yun; WANG Yi; HAO Chong-yang

    2007-01-01

    This paper presents a method which uses multiple types of expert knowledge together in 3D medical image segmentation based on rough set theory. The focus of this paper is how to approximate a ROI (region of interest) when there are multiple types of expert knowledge. Based on rough set theory, the image can be split into three regions:positive regions; negative regions; boundary regions. With multiple knowledge we refine ROI as an intersection of all of the expected shapes with single knowledge. At last we show the results of implementing a rough 3D image segmentation and visualization system.

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

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    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

  18. Unsupervised fuzzy segmentation of 3D magnetic resonance brain images

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    Velthuizen, Robert P.; Hall, Lawrence O.; Clarke, Laurence P.; Bensaid, Amine M.; Arrington, J. A.; Silbiger, Martin L.

    1993-07-01

    Unsupervised fuzzy methods are proposed for segmentation of 3D Magnetic Resonance images of the brain. Fuzzy c-means (FCM) has shown promising results for segmentation of single slices. FCM has been investigated for volume segmentations, both by combining results of single slices and by segmenting the full volume. Different strategies and initializations have been tried. In particular, two approaches have been used: (1) a method by which, iteratively, the furthest sample is split off to form a new cluster center, and (2) the traditional FCM in which the membership grade matrix is initialized in some way. Results have been compared with volume segmentations by k-means and with two supervised methods, k-nearest neighbors and region growing. Results of individual segmentations are presented as well as comparisons on the application of the different methods to a number of tumor patient data sets.

  19. Calculation of prestressed anchor segment by 3D infiniteelement

    Institute of Scientific and Technical Information of China (English)

    Yanfen WANG; Hongyang XIE; Yuanhan WANG

    2009-01-01

    Based on 1D infinite element theory, the coordinate transformation and shape function of 3D point-radiation 4-node infinite elements were derived.They were coupled with 8-node finite elements to compute the compressive deformation of the prestressed anchor segment. The results indicate that when the prestressed force acts on the anchor segment, the stresses and displacements in the rock around the anchor segment are concentrated in the zone center with the anchor axis and are subjected to exponential decay. Therefore, the stresses and the displacement spindles are formed. The calculation results of the infinite element are close to the theoretical results.

  20. Midbrain segmentation in transcranial 3D ultrasound for Parkinson diagnosis.

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    Ahmadi, Seyed-Ahmad; Baust, Maximilian; Karamalis, Athanasios; Plate, Annika; Boetzel, Kai; Klein, Tassilo; Navab, Nassir

    2011-01-01

    Ultrasound examination of the human brain through the temporal bone window, also called transcranial ultrasound (TC-US), is a completely non-invasive and cost-efficient technique, which has established itself for differential diagnosis of Parkinson's Disease (PD) in the past decade. The method requires spatial analysis of ultrasound hyperechogenicities produced by pathological changes within the Substantia Nigra (SN), which belongs to the basal ganglia within the midbrain. Related work on computer aided PD diagnosis shows the urgent need for an accurate and robust segmentation of the midbrain from 3D TC-US, which is an extremely difficult task due to poor image quality of TC-US. In contrast to 2D segmentations within earlier approaches, we develop the first method for semi-automatic midbrain segmentation from 3D TC-US and demonstrate its potential benefit on a database of 11 diagnosed Parkinson patients and 11 healthy controls.

  1. Quality assessment of adaptive 3D video streaming

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    Tavakoli, Samira; Gutiérrez, Jesús; García, Narciso

    2013-03-01

    The streaming of 3D video contents is currently a reality to expand the user experience. However, because of the variable bandwidth of the networks used to deliver multimedia content, a smooth and high-quality playback experience could not always be guaranteed. Using segments in multiple video qualities, HTTP adaptive streaming (HAS) of video content is a relevant advancement with respect to classic progressive download streaming. Mainly, it allows resolving these issues by offering significant advantages in terms of both user-perceived Quality of Experience (QoE) and resource utilization for content and network service providers. In this paper we discuss the impact of possible HAS client's behavior while adapting to the network capacity on enduser. This has been done through an experiment of testing the end-user response to the quality variation during the adaptation procedure. The evaluation has been carried out through a subjective test of the end-user response to various possible clients' behaviors for increasing, decreasing, and oscillation of quality in 3D video. In addition, some of the HAS typical impairments during the adaptation has been simulated and their effects on the end-user perception are assessed. The experimental conclusions have made good insight into the user's response to different adaptation scenarios and visual impairments causing the visual discomfort that can be used to develop the adaptive streaming algorithm to improve the end-user experience.

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

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    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.

  3. [An integrated segmentation method for 3D ultrasound carotid artery].

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    Yang, Xin; Wu, Huihui; Liu, Yang; Xu, Hongwei; Liang, Huageng; Cai, Wenjuan; Fang, Mengjie; Wang, Yujie

    2013-07-01

    An integrated segmentation method for 3D ultrasound carotid artery was proposed. 3D ultrasound image was sliced into transverse, coronal and sagittal 2D images on the carotid bifurcation point. Then, the three images were processed respectively, and the carotid artery contours and thickness were obtained finally. This paper tries to overcome the disadvantages of current computer aided diagnosis method, such as high computational complexity, easily introduced subjective errors et al. The proposed method could get the carotid artery overall information rapidly, accurately and completely. It could be transplanted into clinical usage for atherosclerosis diagnosis and prevention.

  4. NIHmagic: 3D visualization, registration, and segmentation tool

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    Freidlin, Raisa Z.; Ohazama, Chikai J.; Arai, Andrew E.; McGarry, Delia P.; Panza, Julio A.; Trus, Benes L.

    2000-05-01

    Interactive visualization of multi-dimensional biological images has revolutionized diagnostic and therapy planning. Extracting complementary anatomical and functional information from different imaging modalities provides a synergistic analysis capability for quantitative and qualitative evaluation of the objects under examination. We have been developing NIHmagic, a visualization tool for research and clinical use, on the SGI OnyxII Infinite Reality platform. Images are reconstructed into a 3D volume by volume rendering, a display technique that employs 3D texture mapping to provide a translucent appearance to the object. A stack of slices is rendered into a volume by an opacity mapping function, where the opacity is determined by the intensity of the voxel and its distance from the viewer. NIHmagic incorporates 3D visualization of time-sequenced images, manual registration of 2D slices, segmentation of anatomical structures, and color-coded re-mapping of intensities. Visualization of MIR, PET, CT, Ultrasound, and 3D reconstructed electron microscopy images has been accomplished using NIHmagic.

  5. Automated 3D renal segmentation based on image partitioning

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    Yeghiazaryan, Varduhi; Voiculescu, Irina D.

    2016-03-01

    Despite several decades of research into segmentation techniques, automated medical image segmentation is barely usable in a clinical context, and still at vast user time expense. This paper illustrates unsupervised organ segmentation through the use of a novel automated labelling approximation algorithm followed by a hypersurface front propagation method. The approximation stage relies on a pre-computed image partition forest obtained directly from CT scan data. We have implemented all procedures to operate directly on 3D volumes, rather than slice-by-slice, because our algorithms are dimensionality-independent. The results picture segmentations which identify kidneys, but can easily be extrapolated to other body parts. Quantitative analysis of our automated segmentation compared against hand-segmented gold standards indicates an average Dice similarity coefficient of 90%. Results were obtained over volumes of CT data with 9 kidneys, computing both volume-based similarity measures (such as the Dice and Jaccard coefficients, true positive volume fraction) and size-based measures (such as the relative volume difference). The analysis considered both healthy and diseased kidneys, although extreme pathological cases were excluded from the overall count. Such cases are difficult to segment both manually and automatically due to the large amplitude of Hounsfield unit distribution in the scan, and the wide spread of the tumorous tissue inside the abdomen. In the case of kidneys that have maintained their shape, the similarity range lies around the values obtained for inter-operator variability. Whilst the procedure is fully automated, our tools also provide a light level of manual editing.

  6. Automatic airline baggage counting using 3D image segmentation

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    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.

  7. 3D GEOMARKETING SEGMENTATION: A HIGHER SPATIAL DIMENSION PLANNING PERSPECTIVE

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    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

  8. Comparison of thyroid segmentation techniques for 3D ultrasound

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    Wunderling, T.; Golla, B.; Poudel, P.; Arens, C.; Friebe, M.; Hansen, C.

    2017-02-01

    The segmentation of the thyroid in ultrasound images is a field of active research. The thyroid is a gland of the endocrine system and regulates several body functions. Measuring the volume of the thyroid is regular practice of diagnosing pathological changes. In this work, we compare three approaches for semi-automatic thyroid segmentation in freehand-tracked three-dimensional ultrasound images. The approaches are based on level set, graph cut and feature classification. For validation, sixteen 3D ultrasound records were created with ground truth segmentations, which we make publicly available. The properties analyzed are the Dice coefficient when compared against the ground truth reference and the effort of required interaction. Our results show that in terms of Dice coefficient, all algorithms perform similarly. For interaction, however, each algorithm has advantages over the other. The graph cut-based approach gives the practitioner direct influence on the final segmentation. Level set and feature classifier require less interaction, but offer less control over the result. All three compared methods show promising results for future work and provide several possible extensions.

  9. Efficient segmentation of 3D fluoroscopic datasets from mobile C-arm

    Science.gov (United States)

    Styner, Martin A.; Talib, Haydar; Singh, Digvijay; Nolte, Lutz-Peter

    2004-05-01

    The emerging mobile fluoroscopic 3D technology linked with a navigation system combines the advantages of CT-based and C-arm-based navigation. The intra-operative, automatic segmentation of 3D fluoroscopy datasets enables the combined visualization of surgical instruments and anatomical structures for enhanced planning, surgical eye-navigation and landmark digitization. We performed a thorough evaluation of several segmentation algorithms using a large set of data from different anatomical regions and man-made phantom objects. The analyzed segmentation methods include automatic thresholding, morphological operations, an adapted region growing method and an implicit 3D geodesic snake method. In regard to computational efficiency, all methods performed within acceptable limits on a standard Desktop PC (30sec-5min). In general, the best results were obtained with datasets from long bones, followed by extremities. The segmentations of spine, pelvis and shoulder datasets were generally of poorer quality. As expected, the threshold-based methods produced the worst results. The combined thresholding and morphological operations methods were considered appropriate for a smaller set of clean images. The region growing method performed generally much better in regard to computational efficiency and segmentation correctness, especially for datasets of joints, and lumbar and cervical spine regions. The less efficient implicit snake method was able to additionally remove wrongly segmented skin tissue regions. This study presents a step towards efficient intra-operative segmentation of 3D fluoroscopy datasets, but there is room for improvement. Next, we plan to study model-based approaches for datasets from the knee and hip joint region, which would be thenceforth applied to all anatomical regions in our continuing development of an ideal segmentation procedure for 3D fluoroscopic images.

  10. Methods for 2-D and 3-D Endobronchial Ultrasound Image Segmentation.

    Science.gov (United States)

    Zang, Xiaonan; Bascom, Rebecca; Gilbert, Christopher; Toth, Jennifer; Higgins, William

    2016-07-01

    Endobronchial ultrasound (EBUS) is now commonly used for cancer-staging bronchoscopy. Unfortunately, EBUS is challenging to use and interpreting EBUS video sequences is difficult. Other ultrasound imaging domains, hampered by related difficulties, have benefited from computer-based image-segmentation methods. Yet, so far, no such methods have been proposed for EBUS. We propose image-segmentation methods for 2-D EBUS frames and 3-D EBUS sequences. Our 2-D method adapts the fast-marching level-set process, anisotropic diffusion, and region growing to the problem of segmenting 2-D EBUS frames. Our 3-D method builds upon the 2-D method while also incorporating the geodesic level-set process for segmenting EBUS sequences. Tests with lung-cancer patient data showed that the methods ran fully automatically for nearly 80% of test cases. For the remaining cases, the only user-interaction required was the selection of a seed point. When compared to ground-truth segmentations, the 2-D method achieved an overall Dice index = 90.0% ±4.9%, while the 3-D method achieved an overall Dice index = 83.9 ± 6.0%. In addition, the computation time (2-D, 0.070 s/frame; 3-D, 0.088 s/frame) was two orders of magnitude faster than interactive contour definition. Finally, we demonstrate the potential of the methods for EBUS localization in a multimodal image-guided bronchoscopy system.

  11. 3D Brain Tumors and Internal Brain Structures Segmentation in MR Images

    Directory of Open Access Journals (Sweden)

    P.NARENDRAN

    2012-02-01

    Full Text Available The main topic of this paper is to segment brain tumors, their components (edema and necrosis and internal structures of the brain in 3D MR images. For tumor segmentation we propose a framework that is a combination of region-based and boundary-based paradigms. In this framework, segment the brain using a method adapted for pathological cases and extract some global information on the tumor by symmetry based histogram analysis. We propose a new and original method that combines region and boundary information in two phases: initialization and refinement. The method relies on symmetry-based histogram analysis. The initial segmentation of the tumor is refined relying on boundary information of the image. We use a deformable model which is again constrained by the fused spatial relations of the structure. The method was also evaluated on 10 contrast enhanced T1-weighted images to segment the ventricles, caudate nucleus and thalamus.

  12. 3D geometric split-merge segmentation of brain MRI datasets.

    Science.gov (United States)

    Marras, Ioannis; Nikolaidis, Nikolaos; Pitas, Ioannis

    2014-05-01

    In this paper, a novel method for MRI volume segmentation based on region adaptive splitting and merging is proposed. The method, called Adaptive Geometric Split Merge (AGSM) segmentation, aims at finding complex geometrical shapes that consist of homogeneous geometrical 3D regions. In each volume splitting step, several splitting strategies are examined and the most appropriate is activated. A way to find the maximal homogeneity axis of the volume is also introduced. Along this axis, the volume splitting technique divides the entire volume in a number of large homogeneous 3D regions, while at the same time, it defines more clearly small homogeneous regions within the volume in such a way that they have greater probabilities of survival at the subsequent merging step. Region merging criteria are proposed to this end. The presented segmentation method has been applied to brain MRI medical datasets to provide segmentation results when each voxel is composed of one tissue type (hard segmentation). The volume splitting procedure does not require training data, while it demonstrates improved segmentation performance in noisy brain MRI datasets, when compared to the state of the art methods.

  13. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution

    Science.gov (United States)

    Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing

    2016-12-01

    The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of 80.3+/- 4.5 , yielding a mean Dice similarity coefficient of 97.25+/- 0.65 % , and an average symmetric surface distance of 0.84+/- 0.25 mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

  14. 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).

  15. 3D Printing device adaptable to Computer Numerical Control (CNC)

    OpenAIRE

    Gardan, Julien; DANESI, Frédéric; 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...

  16. Medical anatomy segmentation kit: combining 2D and 3D segmentation methods to enhance functionality

    Science.gov (United States)

    Tracton, Gregg S.; Chaney, Edward L.; Rosenman, Julian G.; Pizer, Stephen M.

    1994-07-01

    Image segmentation, in particular, defining normal anatomic structures and diseased or malformed tissue from tomographic images, is common in medical applications. Defining tumors or arterio-venous malformation from computed tomography or magnetic resonance images are typical examples. This paper describes a program, Medical Anatomy Segmentation Kit (MASK), whose design acknowledges that no single segmentation technique has proven to be successful or optimal for all object definition tasks associated with medical images. A practical solution is offered through a suite of complementary user-guided segmentation techniques and extensive manual editing functions to reach the final object definition goal. Manual editing can also be used to define objects which are abstract or otherwise not well represented in the image data and so require direct human definition - e.g., a radiotherapy target volume which requires human knowledge and judgement regarding image interpretation and tumor spread characteristics. Results are either in the form of 2D boundaries or regions of labeled pixels or voxels. MASK currently uses thresholding and edge detection to form contours, and 2D or 3D scale-sensitive fill and region algebra to form regions. In addition to these proven techniques, MASK's architecture anticipates clinically practical automatic 2D and 3D segmentation methods of the future.

  17. 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

    2017-10-02

    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, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

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

    Directory of Open Access Journals (Sweden)

    Z.F. Shi

    2012-04-01

    Full Text Available 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 integrated into the segmentation framework. Experimental results show that the noise and over-segmentation are avoided. It also demonstrates that the proposed scheme has the capability of combining the geometric and topology information of the 3D mesh.

  19. 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models

    Science.gov (United States)

    Khalifa, Fahmi; Soliman, Ahmed; Gimel'farb, Georgy

    2017-01-01

    Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images' inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels' appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.

  20. Parameterization adaption for 3D shape optimization in aerodynamics

    Directory of Open Access Journals (Sweden)

    Badr Abou El Majd

    2013-10-01

    Full Text Available When solving a PDE problem numerically, a certain mesh-refinement process is always implicit, and very classically, mesh adaptivity is a very effective means to accelerate grid convergence. Similarly, when optimizing a shape by means of an explicit geometrical representation, it is natural to seek for an analogous concept of parameterization adaptivity. We propose here an adaptive parameterization for three-dimensional optimum design in aerodynamics by using the so-called “Free-Form Deformation” approach based on 3D tensorial Bézier parameterization. The proposed procedure leads to efficient numerical simulations with highly reduced computational costs.[How to cite this article:  Majd, B.A.. 2014. Parameterization adaption for 3D shape optimization in aerodynamics. International Journal of Science and Engineering, 6(1:61-69. Doi: 10.12777/ijse.6.1.61-69

  1. A vectorizable adaptive grid solver for PDEs in 3D

    NARCIS (Netherlands)

    Blom, J.G.; Verwer, J.G.

    1993-01-01

    This paper describes the application of an adaptive-grid finite-difference solver to some time-dependent three-dimensional systems of partial differential equations. The code is a 3D extension of the 2D code VLUGR2[3].

  2. 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.

  3. 3D plant phenotyping in sunflower using architecture-based organ segmentation from 3D point clouds

    OpenAIRE

    Gélard, William; Burger, Philippe; Casadebaig, Pierre; Langlade, Nicolas; Debaeke, Philippe; Devy, Michel; Herbulot, Ariane

    2016-01-01

    International audience; This paper presents a 3D phenotyping method applied to sunflower, allowing to compute the leaf area of an isolated plant. This is a preliminary step towards the automated monitoring of leaf area and plant growth through the plant life cycle. First, a model-based segmentation method is applied to 3D data derived from RGB images acquired on sunflower plants grown in pots. The RGB image acquisitions are made all around the isolated plant with a single hand-held standard c...

  4. Blood Pool Segmentation Results in Superior Virtual Cardiac Models than Myocardial Segmentation for 3D Printing.

    Science.gov (United States)

    Farooqi, Kanwal M; Lengua, Carlos Gonzalez; Weinberg, Alan D; Nielsen, James C; Sanz, Javier

    2016-08-01

    The method of cardiac magnetic resonance (CMR) three-dimensional (3D) image acquisition and post-processing which should be used to create optimal virtual models for 3D printing has not been studied systematically. Patients (n = 19) who had undergone CMR including both 3D balanced steady-state free precession (bSSFP) imaging and contrast-enhanced magnetic resonance angiography (MRA) were retrospectively identified. Post-processing for the creation of virtual 3D models involved using both myocardial (MS) and blood pool (BP) segmentation, resulting in four groups: Group 1-bSSFP/MS, Group 2-bSSFP/BP, Group 3-MRA/MS and Group 4-MRA/BP. The models created were assessed by two raters for overall quality (1-poor; 2-good; 3-excellent) and ability to identify predefined vessels (1-5: superior vena cava, inferior vena cava, main pulmonary artery, ascending aorta and at least one pulmonary vein). A total of 76 virtual models were created from 19 patient CMR datasets. The mean overall quality scores for Raters 1/2 were 1.63 ± 0.50/1.26 ± 0.45 for Group 1, 2.12 ± 0.50/2.26 ± 0.73 for Group 2, 1.74 ± 0.56/1.53 ± 0.61 for Group 3 and 2.26 ± 0.65/2.68 ± 0.48 for Group 4. The numbers of identified vessels for Raters 1/2 were 4.11 ± 1.32/4.05 ± 1.31 for Group 1, 4.90 ± 0.46/4.95 ± 0.23 for Group 2, 4.32 ± 1.00/4.47 ± 0.84 for Group 3 and 4.74 ± 0.56/4.63 ± 0.49 for Group 4. Models created using BP segmentation (Groups 2 and 4) received significantly higher ratings than those created using MS for both overall quality and number of vessels visualized (p printed on desktop 3D printers with good quality and accurate representation of the virtual 3D models. We recommend using BP segmentation with either MRA or bSSFP source datasets to create virtual 3D models for 3D printing. Desktop 3D printers can offer good quality printed models with accurate representation of anatomic detail.

  5. 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.

  6. Hybrid Method for 3D Segmentation of Magnetic Resonance Images

    Institute of Scientific and Technical Information of China (English)

    ZHANGXiang; ZHANGDazhi; TIANJinwen; LIUJian

    2003-01-01

    Segmentation of some complex images, especially in magnetic resonance brain images, is often difficult to perform satisfactory results using only single approach of image segmentation. An approach towards the integration of several techniques seems to be the best solution. In this paper a new hybrid method for 3-dimension segmentation of the whole brain is introduced, based on fuzzy region growing, edge detection and mathematical morphology, The gray-level threshold, controlling the process of region growing, is determined by fuzzy technique. The image gradient feature is obtained by the 3-dimension sobel operator considering a 3×3×3 data block with the voxel to be evaluated at the center, while the gradient magnitude threshold is defined by the gradient magnitude histogram of brain magnetic resonance volume. By the combined methods of edge detection and region growing, the white matter volume of human brain is segmented perfectly. By the post-processing using mathematical morphological techniques, the whole brain region is obtained. In order to investigate the validity of the hybrid method, two comparative experiments, the region growing method using only gray-level feature and the thresholding method by combining gray-level and gradient features, are carried out. Experimental results indicate that the proposed method provides much better results than the traditional method using a single technique in the 3-dimension segmentation of human brain magnetic resonance data sets.

  7. Correlation based 3-D segmentation of the left ventricle in pediatric echocardiographic images using radio-frequency data.

    Science.gov (United States)

    Nillesen, Maartje M; Lopata, Richard G P; Huisman, H J; Thijssen, Johan M; Kapusta, Livia; de Korte, Chris L

    2011-09-01

    Clinical diagnosis of heart disease might be substantially supported by automated segmentation of the endocardial surface in three-dimensional (3-D) echographic images. Because of the poor echogenicity contrast between blood and myocardial tissue in some regions and the inherent speckle noise, automated analysis of these images is challenging. A priori knowledge on the shape of the heart cannot always be relied on, e.g., in children with congenital heart disease, segmentation should be based on the echo features solely. The objective of this study was to investigate the merit of using temporal cross-correlation of radio-frequency (RF) data for automated segmentation of 3-D echocardiographic images. Maximum temporal cross-correlation (MCC) values were determined locally from the RF-data using an iterative 3-D technique. MCC values as well as a combination of MCC values and adaptive filtered, demodulated RF-data were used as an additional, external force in a deformable model approach to segment the endocardial surface and were tested against manually segmented surfaces. Results on 3-D full volume images (Philips, iE33) of 10 healthy children demonstrate that MCC values derived from the RF signal yield a useful parameter to distinguish between blood and myocardium in regions with low echogenicity contrast and incorporation of MCC improves the segmentation results significantly. Further investigation of the MCC over the whole cardiac cycle is required to exploit the full benefit of it for automated segmentation.

  8. 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......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...

  9. 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....

  10. Segmented Domain Decomposition Multigrid For 3-D Turbomachinery Flows

    Science.gov (United States)

    Celestina, M. L.; Adamczyk, J. J.; Rubin, S. G.

    2001-01-01

    A Segmented Domain Decomposition Multigrid (SDDMG) procedure was developed for three-dimensional viscous flow problems as they apply to turbomachinery flows. The procedure divides the computational domain into a coarse mesh comprised of uniformly spaced cells. To resolve smaller length scales such as the viscous layer near a surface, segments of the coarse mesh are subdivided into a finer mesh. This is repeated until adequate resolution of the smallest relevant length scale is obtained. Multigrid is used to communicate information between the different grid levels. To test the procedure, simulation results will be presented for a compressor and turbine cascade. These simulations are intended to show the ability of the present method to generate grid independent solutions. Comparisons with data will also be presented. These comparisons will further demonstrate the usefulness of the present work for they allow an estimate of the accuracy of the flow modeling equations independent of error attributed to numerical discretization.

  11. Esophagus Segmentation from 3D CT Data Using Skeleton Prior-Based Graph Cut

    Directory of Open Access Journals (Sweden)

    Damien Grosgeorge

    2013-01-01

    Full Text Available The segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper, we focus on esophagus segmentation, a challenging application since the wall of the esophagus, made of muscle tissue, has very low contrast in CT images. We propose in this paper an original method to segment in thoracic CT scans the 3D esophagus using a skeleton-shape model to guide the segmentation. Our method is composed of two steps: a 3D segmentation by graph cut with skeleton prior, followed by a 2D propagation. Our method yields encouraging results over 6 patients.

  12. Segmentation of the ovine lung in 3D CT Images

    Science.gov (United States)

    Shi, Lijun; Hoffman, Eric A.; Reinhardt, Joseph M.

    2004-04-01

    Pulmonary CT images can provide detailed information about the regional structure and function of the respiratory system. Prior to any of these analyses, however, the lungs must be identified in the CT data sets. A popular animal model for understanding lung physiology and pathophysiology is the sheep. In this paper we describe a lung segmentation algorithm for CT images of sheep. The algorithm has two main steps. The first step is lung extraction, which identifies the lung region using a technique based on optimal thresholding and connected components analysis. The second step is lung separation, which separates the left lung from the right lung by identifying the central fissure using an anatomy-based method incorporating dynamic programming and a line filter algorithm. The lung segmentation algorithm has been validated by comparing our automatic method to manual analysis for five pulmonary CT datasets. The RMS error between the computer-defined and manually-traced boundary is 0.96 mm. The segmentation requires approximately 10 minutes for a 512x512x400 dataset on a PC workstation (2.40 GHZ CPU, 2.0 GB RAM), while it takes human observer approximately two hours to accomplish the same task.

  13. A Hybrid 3D Learning-and-Interaction-based Segmentation Approach Applied on CT Liver Volumes

    Directory of Open Access Journals (Sweden)

    M. Danciu

    2013-04-01

    Full Text Available Medical volume segmentation in various imaging modalities using real 3D approaches (in contrast to slice-by-slice segmentation represents an actual trend. The increase in the acquisition resolution leads to large amount of data, requiring solutions to reduce the dimensionality of the segmentation problem. In this context, the real-time interaction with the large medical data volume represents another milestone. This paper addresses the twofold problem of the 3D segmentation applied to large data sets and also describes an intuitive neuro-fuzzy trained interaction method. We present a new hybrid semi-supervised 3D segmentation, for liver volumes obtained from computer tomography scans. This is a challenging medical volume segmentation task, due to the acquisition and inter-patient variability of the liver parenchyma. The proposed solution combines a learning-based segmentation stage (employing 3D discrete cosine transform and a probabilistic support vector machine classifier with a post-processing stage (automatic and manual segmentation refinement. Optionally, an optimization of the segmentation can be achieved by level sets, using as initialization the segmentation provided by the learning-based solution. The supervised segmentation is applied on elementary cubes in which the CT volume is decomposed by tilling, thus ensuring a significant reduction of the data to be classified by the support vector machine into liver/not liver. On real volumes, the proposed approach provides good segmentation accuracy, with a significant reduction in the computational complexity.

  14. Method, Software and Aparatus for Segmenting a Series of 2D or 3D Images

    NARCIS (Netherlands)

    Noble, Nicholas M.I.; Spreeuwers, Lieuwe Jan; Breeuwer, Marcel

    2005-01-01

    he invention relates to an apparatus having means for segmenting a series of 2D or 3D images obtained by monitoring a patient's organ or other body part, wherein a first segmentation is carried out on a first image of the series of images and wherein the first segmentation is used for the subsequent

  15. Method, Software and Aparatus for Segmenting a Series of 2D or 3D Images

    NARCIS (Netherlands)

    Noble, Nicholas Michael Ian; Spreeuwers, Lieuwe Jan; Breeuwer, Marcel

    2010-01-01

    he invention relates to an apparatus having means for segmenting a series of 2D or 3D images obtained by monitoring a patient's organ or other body part, wherein a first segmentation is carried out on a first image of the series of images and wherein the first segmentation is used for the subsequent

  16. Rotationally resliced 3D prostate TRUS segmentation using convex optimization with shape priors.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Fenster, Aaron

    2015-02-01

    Efficient and accurate segmentations of 3D end-firing transrectal ultrasound (TRUS) images play an important role in planning of 3D TRUS guided prostate biopsy. However, poor image quality of the input 3D TRUS images, such as strong imaging artifacts and speckles, often makes it a challenging task to extract the prostate boundaries accurately and efficiently. In this paper, the authors propose a novel convex optimization-based approach to delineate the prostate surface from a given 3D TRUS image, which reduces the original 3D segmentation problem to a sequence of simple 2D segmentation subproblems over the rotational reslices of the 3D TRUS volume. Essentially, the authors introduce a novel convex relaxation-based contour evolution approach to each 2D slicewise image segmentation with the joint optimization of shape information, where the learned 2D nonlinear statistical shape prior is incorporated to segment the initial slice, its result is propagated as a shape constraint to the segmentation of the following slices. In practice, the proposed segmentation algorithm is implemented on a GPU to achieve the high computational performance. Experimental results using 30 patient 3D TRUS images show that the proposed method can achieve a mean Dice similarity coefficient of 93.4% ± 2.2% in 20 s for one 3D image, outperforming the existing local-optimization-based methods, e.g., level-set and active-contour, in terms of accuracy and efficiency. In addition, inter- and intraobserver variability experiments show its good reproducibility. A semiautomatic segmentation approach is proposed and evaluated to extract the prostate boundary from 3D TRUS images acquired by a 3D end-firing TRUS guided prostate biopsy system. Experimental results suggest that it may be suitable for the clinical use involving the image guided prostate biopsy procedures.

  17. 3DSEM++: Adaptive and intelligent 3D SEM surface reconstruction.

    Science.gov (United States)

    Tafti, Ahmad P; Holz, Jessica D; Baghaie, Ahmadreza; Owen, Heather A; He, Max M; Yu, Zeyun

    2016-08-01

    Structural analysis of microscopic objects is a longstanding topic in several scientific disciplines, such as biological, mechanical, and materials sciences. The scanning electron microscope (SEM), as a promising imaging equipment has been around for decades to determine the surface properties (e.g., compositions or geometries) of specimens by achieving increased magnification, contrast, and resolution greater than one nanometer. Whereas SEM micrographs still remain two-dimensional (2D), many research and educational questions truly require knowledge and facts about their three-dimensional (3D) structures. 3D surface reconstruction from SEM images leads to remarkable understanding of microscopic surfaces, allowing informative and qualitative visualization of the samples being investigated. In this contribution, we integrate several computational technologies including machine learning, contrario methodology, and epipolar geometry to design and develop a novel and efficient method called 3DSEM++ for multi-view 3D SEM surface reconstruction in an adaptive and intelligent fashion. The experiments which have been performed on real and synthetic data assert the approach is able to reach a significant precision to both SEM extrinsic calibration and its 3D surface modeling.

  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...

  19. 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.

  20. 3D Game Content Distributed Adaptation in Heterogeneous Environments

    Science.gov (United States)

    Morán, Francisco; Preda, Marius; Lafruit, Gauthier; Villegas, Paulo; Berretty, Robert-Paul

    2007-12-01

    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.

  1. 3D conformal planning using low segment multi-criteria IMRT optimization

    CERN Document Server

    Khan, Fazal

    2014-01-01

    Purpose: To evaluate automated multicriteria optimization (MCO)-- designed for intensity modulated radiation therapy (IMRT), but invoked with limited segmentation -- to efficiently produce high quality 3D conformal treatment (3D-CRT) plans. Methods: Ten patients previously planned with 3D-CRT were replanned with a low-segment inverse multicriteria optimized technique. The MCO-3D plans used the same number of beams, beam geometry and machine parameters of the corresponding 3D plans, but were limited to an energy of 6 MV. The MCO-3D plans were optimized using a fluence-based MCO IMRT algorithm and then, after MCO navigation, segmented with a low number of segments. The 3D and MCO-3D plans were compared by evaluating mean doses to individual organs at risk (OARs), mean doses to combined OARs, homogeneity indexes (HI), monitor units (MUs), physician preference, and qualitative assessments of planning time and plan customizability. Results: The MCO-3D plans significantly reduced the OAR mean doses and monitor unit...

  2. An improved Marching Cube algorithm for 3D data segmentation

    Science.gov (United States)

    Masala, G. L.; Golosio, B.; Oliva, P.

    2013-03-01

    The marching cube algorithm is one of the most popular algorithms for isosurface triangulation. It is based on a division of the data volume into elementary cubes, followed by a standard triangulation inside each cube. In the original formulation, the marching cube algorithm is based on 15 basic triangulations and a total of 256 elementary triangulations are obtained from the basic ones by rotation, reflection, conjugation, and combinations of these operations. The original formulation of the algorithm suffers from well-known problems of connectivity among triangles of adjacent cubes, which has been solved in various ways. We developed a variant of the marching cube algorithm that makes use of 21 basic triangulations. Triangles of adjacent cubes are always well connected in this approach. The output of the code is a triangulated model of the isosurface in raw format or in VRML (Virtual Reality Modelling Language) format. Catalogue identifier: AENS_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENS_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 147558 No. of bytes in distributed program, including test data, etc.: 26084066 Distribution format: tar.gz Programming language: C. Computer: Pentium 4, CPU 3.2 GHz and 3.24 GB of RAM (2.77 GHz). Operating system: Tested on several Linux distribution, but generally works in all Linux-like platforms. RAM: Approximately 2 MB Classification: 6.5. Nature of problem: Given a scalar field μ(x,y,z) sampled on a 3D regular grid, build a discrete model of the isosurface associated to the isovalue μIso, which is defined as the set of points that satisfy the equation μ(x,y,z)=μIso. Solution method: The proposed solution is an improvement of the Marching Cube algorithm, which approximates the isosurface using a set of

  3. 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

  4. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours.

    Science.gov (United States)

    Gao, Yi; Kikinis, Ron; Bouix, Sylvain; Shenton, Martha; Tannenbaum, Allen

    2012-08-01

    Extracting anatomical and functional significant structures renders one of the important tasks for both the theoretical study of the medical image analysis, and the clinical and practical community. In the past, much work has been dedicated only to the algorithmic development. Nevertheless, for clinical end users, a well designed algorithm with an interactive software is necessary for an algorithm to be utilized in their daily work. Furthermore, the software would better be open sourced in order to be used and validated by not only the authors but also the entire community. Therefore, the contribution of the present work is twofolds: first, we propose a new robust statistics based conformal metric and the conformal area driven multiple active contour framework, to simultaneously extract multiple targets from MR and CT medical imagery in 3D. Second, an open source graphically interactive 3D segmentation tool based on the aforementioned contour evolution is implemented and is publicly available for end users on multiple platforms. In using this software for the segmentation task, the process is initiated by the user drawn strokes (seeds) in the target region in the image. Then, the local robust statistics are used to describe the object features, and such features are learned adaptively from the seeds under a non-parametric estimation scheme. Subsequently, several active contours evolve simultaneously with their interactions being motivated by the principles of action and reaction-this not only guarantees mutual exclusiveness among the contours, but also no longer relies upon the assumption that the multiple objects fill the entire image domain, which was tacitly or explicitly assumed in many previous works. In doing so, the contours interact and converge to equilibrium at the desired positions of the desired multiple objects. Furthermore, with the aim of not only validating the algorithm and the software, but also demonstrating how the tool is to be used, we provide

  5. 3D semi-automatic segmentation of the cochlea and inner ear.

    Science.gov (United States)

    Xianfen, Diao; Siping, Chen; Changhong, Liang; Yuanmei, Wang

    2005-01-01

    Though interactive direct volume rendering produces meaningful images with high quality, it cannot display separate inner ear labyrinth or cochlea only by adjusting imaging parameters to suppress the surrounding structures. Novel semi-automatic segmentation methods were presented to extract the cochlea and inner ear from spiral CT images. The cochlea was separated from the medical image volume by applying the 3D narrow band level set segmentation algorithm with user interaction introduced to locate the initial contour and adjust the parameters. The inner ear was extracted with a similar semi-automatic segmentation method: manual segmentation was first applied to remove several closely interconnected regions in boundary by viewing image volume slice by slice, then the 3D narrow band level set segmentation algorithm was used to complete fine segmentation on image volume. Generating 3D models of cochlea and inner ear structures with such methods not only takes advantage of the combination of 2D images with 3D volume but also saves much time of post-processing. The segmented results were rendered with the Marching Cubes surface rendering method. The correlation of the point on the resultant surface to the three orthogonal sections that intersect at that point on the surface was built to evaluate the segmented object and display the spatial relations of the anatomical structures. The performance of the presented semi-automatic segmentation methods is tested using spiral CT images of the temporal bone.

  6. A method for the evaluation of thousands of automated 3D stem cell segmentations.

    Science.gov (United States)

    Bajcsy, P; Simon, M; Florczyk, S J; Simon, C G; Juba, D; Brady, M C

    2015-12-01

    There is no segmentation method that performs perfectly with any dataset in comparison to human segmentation. Evaluation procedures for segmentation algorithms become critical for their selection. The problems associated with segmentation performance evaluations and visual verification of segmentation results are exaggerated when dealing with thousands of three-dimensional (3D) image volumes because of the amount of computation and manual inputs needed. We address the problem of evaluating 3D segmentation performance when segmentation is applied to thousands of confocal microscopy images (z-stacks). Our approach is to incorporate experimental imaging and geometrical criteria, and map them into computationally efficient segmentation algorithms that can be applied to a very large number of z-stacks. This is an alternative approach to considering existing segmentation methods and evaluating most state-of-the-art algorithms. We designed a methodology for 3D segmentation performance characterization that consists of design, evaluation and verification steps. The characterization integrates manual inputs from projected surrogate 'ground truth' of statistically representative samples and from visual inspection into the evaluation. The novelty of the methodology lies in (1) designing candidate segmentation algorithms by mapping imaging and geometrical criteria into algorithmic steps, and constructing plausible segmentation algorithms with respect to the order of algorithmic steps and their parameters, (2) evaluating segmentation accuracy using samples drawn from probability distribution estimates of candidate segmentations and (3) minimizing human labour needed to create surrogate 'truth' by approximating z-stack segmentations with 2D contours from three orthogonal z-stack projections and by developing visual verification tools. We demonstrate the methodology by applying it to a dataset of 1253 mesenchymal stem cells. The cells reside on 10 different types of biomaterial

  7. Adaptive Segmentation for Scientific Databases

    NARCIS (Netherlands)

    Ivanova, M.G.; Kersten, M.L.; Nes, N.J.

    2008-01-01

    In this paper we explore database segmentation in the context of a column-store DBMS targeted at a scientific database. We present a novel hardware- and scheme-oblivious segmentation algorithm, which learns and adapts to the workload immediately. The approach taken is to capitalize on (intermediate)

  8. Adaptive segmentation for scientific databases

    NARCIS (Netherlands)

    Ivanova, M.; Kersten, M.L.; Nes, N.

    2008-01-01

    In this paper we explore database segmentation in the context of a column-store DBMS targeted at a scientific database. We present a novel hardware- and scheme-oblivious segmentation algorithm, which learns and adapts to the workload immediately. The approach taken is to capitalize on (intermediate)

  9. Adaptive segmentation for scientific databases

    NARCIS (Netherlands)

    Ivanova, M.; Kersten, M.L.; Nes, N.

    2008-01-01

    In this paper we explore database segmentation in the context of a column-store DBMS targeted at a scientific database. We present a novel hardware- and scheme-oblivious segmentation algorithm, which learns and adapts to the workload immediately. The approach taken is to capitalize on (intermediate)

  10. Model-based segmentation and quantification of subcellular structures in 2D and 3D fluorescent microscopy images

    Science.gov (United States)

    Wörz, Stefan; Heinzer, Stephan; Weiss, Matthias; Rohr, Karl

    2008-03-01

    We introduce a model-based approach for segmenting and quantifying GFP-tagged subcellular structures of the Golgi apparatus in 2D and 3D microscopy images. The approach is based on 2D and 3D intensity models, which are directly fitted to an image within 2D circular or 3D spherical regions-of-interest (ROIs). We also propose automatic approaches for the detection of candidates, for the initialization of the model parameters, and for adapting the size of the ROI used for model fitting. Based on the fitting results, we determine statistical information about the spatial distribution and the total amount of intensity (fluorescence) of the subcellular structures. We demonstrate the applicability of our new approach based on 2D and 3D microscopy images.

  11. 3D modeling of geological anomalies based on segmentation of multiattribute fusion

    Science.gov (United States)

    Liu, Zhi-Ning; Song, Cheng-Yun; Li, Zhi-Yong; Cai, Han-Peng; Yao, Xing-Miao; Hu, Guang-Min

    2016-09-01

    3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset-based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly. Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.

  12. 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...

  13. Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method

    Institute of Scientific and Technical Information of China (English)

    Shi-yan WANG; Hui-min YU

    2012-01-01

    A method based on 3D videos is proposed for multi-target segmentation and tracking with a moving viewing system.A spatiotemporal energy functional is built up to perform motion segmentation and estimation simultaneously.To overcome the limitation of the local minimum problem with the level set method,a convex relaxation method is applied to the 3D spatiotemporal segmentation model.The relaxed convex model is independent of the initial condition.A primal-dual algorithm is used to improve computational efficiency.Several indoor experiments show the validity of the proposed method.

  14. Active contours extension and similarity indicators for improved 3D segmentation of thyroid ultrasound images

    Science.gov (United States)

    Poudel, P.; Illanes, A.; Arens, C.; Hansen, C.; Friebe, M.

    2017-03-01

    Thyroid segmentation in tracked 2D ultrasound (US) using active contours has a low segmentation accuracy mainly due to the fact that smaller structures cannot be efficiently recognized and segmented. To address this issue, we propose a new similarity indicator with the main objective to provide information to the active contour algorithm concerning the regions that the active contour should continue to expand or should stop. First, a preprocessing step is carried out in order to attenuate the noise present in the US image and to increase its contrast, using histogram equalization and a median filter. In the second step, active contours are used to segment the thyroid in each 2D image of the dataset. After performing a first segmentation, two similarity indicators (ratio of mean square error, MSE and correlation between histograms) are computed at each contour point of the initial segmented thyroid between rectangles located inside and outside the obtained contour. A threshold is used on a final indicator computed from the other two indicators to find the probable regions for further segmentation using active contours. This process is repeated until no new segmentation region is identified. Finally, all the segmented thyroid images passed through a 3D reconstruction algorithm to obtain a 3D volume segmented thyroid. The results showed that including similarity indicators based on histogram equalization and MSE between inside and outside regions of the contour can help to segment difficult areas that active contours have problem to segment.

  15. Iterative Mesh Transformation for 3D Segmentation of Livers with Cancers in CT Images

    OpenAIRE

    Lu, Difei; Wu, Yin; Harris, Gordon; Cai, Wenli

    2015-01-01

    Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh ...

  16. Effects of CT image segmentation methods on the accuracy of long bone 3D reconstructions.

    Science.gov (United States)

    Rathnayaka, Kanchana; Sahama, Tony; Schuetz, Michael A; Schmutz, Beat

    2011-03-01

    An accurate and accessible image segmentation method is in high demand for generating 3D bone models from CT scan data, as such models are required in many areas of medical research. Even though numerous sophisticated segmentation methods have been published over the years, most of them are not readily available to the general research community. Therefore, this study aimed to quantify the accuracy of three popular image segmentation methods, two implementations of intensity thresholding and Canny edge detection, for generating 3D models of long bones. In order to reduce user dependent errors associated with visually selecting a threshold value, we present a new approach of selecting an appropriate threshold value based on the Canny filter. A mechanical contact scanner in conjunction with a microCT scanner was utilised to generate the reference models for validating the 3D bone models generated from CT data of five intact ovine hind limbs. When the overall accuracy of the bone model is considered, the three investigated segmentation methods generated comparable results with mean errors in the range of 0.18-0.24 mm. However, for the bone diaphysis, Canny edge detection and Canny filter based thresholding generated 3D models with a significantly higher accuracy compared to those generated through visually selected thresholds. This study demonstrates that 3D models with sub-voxel accuracy can be generated utilising relatively simple segmentation methods that are available to the general research community.

  17. Automated 3D ultrasound image segmentation for assistant diagnosis of breast cancer

    Science.gov (United States)

    Wang, Yuxin; Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Du, Sidan; Yuan, Jie; Wang, Xueding; Carson, Paul L.

    2016-04-01

    Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

  18. Automated 3D ultrasound image segmentation to aid breast cancer image interpretation.

    Science.gov (United States)

    Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A; Yuan, Jie; Wang, Xueding; Carson, Paul L

    2016-02-01

    Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

  19. 3D TEM reconstruction and segmentation process of laminar bio-nanocomposites

    Energy Technology Data Exchange (ETDEWEB)

    Iturrondobeitia, M., E-mail: maider.iturrondobeitia@ehu.es; Okariz, A.; Fernandez-Martinez, R.; Jimbert, P.; Guraya, T.; Ibarretxe, J. [eMERG, University of the Basque Country, Rafael Moreno Pitxitxi street 2 and 3, 48013, Bilbao (Spain)

    2015-03-30

    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{sub clay} (%) and the actual one. The method is first validated using a fictitious set of objects, and then applied on a nanocomposite.

  20. 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

    that provide a better resolution and therefore a more accurate segmentation. The imaging modalities used for comparison are scanning electron microscopy, optical microscopy and synchrotron CT. The validation methods are applied to the asses the segmentation of individual fibres from X-ray microtomograms.......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...

  1. Minimum Error Thresholding Segmentation Algorithm Based on 3D Grayscale Histogram

    Directory of Open Access Journals (Sweden)

    Jin Liu

    2014-01-01

    Full Text Available Threshold segmentation is a very important technique. The existing threshold algorithms do not work efficiently for noisy grayscale images. This paper proposes a novel algorithm called three-dimensional minimum error thresholding (3D-MET, which is used to solve the problem. The proposed approach is implemented by an optimal threshold discriminant based on the relative entropy theory and the 3D histogram. The histogram is comprised of gray distribution information of pixels and relevant information of neighboring pixels in an image. Moreover, a fast recursive method is proposed to reduce the time complexity of 3D-MET from O(L6 to O(L3, where L stands for gray levels. Experimental results demonstrate that the proposed approach can provide superior segmentation performance compared to other methods for gray image segmentation.

  2. 3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images.

    Science.gov (United States)

    Pei, Yuru; Ai, Xingsheng; Zha, Hongbin; Xu, Tianmin; Ma, Gengyu

    2016-09-01

    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. 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. The proposed method was applied for tooth segmentation of twenty clinically captured CBCT images. Three metrics

  3. 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.

  4. 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.

  5. Multi-Camera Sensor System for 3D Segmentation and Localization of Multiple Mobile Robots

    Directory of Open Access Journals (Sweden)

    Cristina Losada

    2010-04-01

    Full Text Available This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space using a multi-camera sensor system. The set of calibrated and synchronized cameras are placed in fixed positions within the environment (intelligent space. The proposed algorithm for motion segmentation and 3D localization is based on the minimization of an objective function. This function includes information from all the cameras, and it does not rely on previous knowledge or invasive landmarks on board the robots. The proposed objective function depends on three groups of variables: the segmentation boundaries, the motion parameters and the depth. For the objective function minimization, we use a greedy iterative algorithm with three steps that, after initialization of segmentation boundaries and depth, are repeated until convergence.

  6. Multi-camera sensor system for 3D segmentation and localization of multiple mobile robots.

    Science.gov (United States)

    Losada, Cristina; Mazo, Manuel; Palazuelos, Sira; Pizarro, Daniel; Marrón, Marta

    2010-01-01

    This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space using a multi-camera sensor system. The set of calibrated and synchronized cameras are placed in fixed positions within the environment (intelligent space). The proposed algorithm for motion segmentation and 3D localization is based on the minimization of an objective function. This function includes information from all the cameras, and it does not rely on previous knowledge or invasive landmarks on board the robots. The proposed objective function depends on three groups of variables: the segmentation boundaries, the motion parameters and the depth. For the objective function minimization, we use a greedy iterative algorithm with three steps that, after initialization of segmentation boundaries and depth, are repeated until convergence.

  7. Identifying fault segments from 3D fault drag analysis (Vienna Basin, Austria)

    Science.gov (United States)

    Spahić, Darko; Grasemann, Bernhard; Exner, Ulrike

    2013-10-01

    The segmented growth of the Markgrafneusiedl normal fault in the late Miocene clastic sediments of the central Vienna Basin (Austria) was investigated by construction of a detailed three-dimensional (3D) structural model. Using high resolution 3D seismic data, the fault surface and marker horizons in the hanging wall and the footwall of the Markgrafneusiedl Fault were mapped and orientation, displacement and morphology of the fault surface were quantified. Individual, fault segments were identified by direct mapping of the deflection of the marker horizons close to the fault surface. Correlating the size of the identified segments with the magnitude of fault drag and displacement distribution showed that fault evolution progressed in several stages. The proposed method allows the detection of segments that are not recorded by the magnitude of displacement or fault morphology. Most importantly, detailed mapping of marker deflections in the hanging wall could help to constrain equivalent structures in the footwall, which may represent potential hydrocarbon traps.

  8. Adaptive Kalman snake for semi-autonomous 3D vessel tracking.

    Science.gov (United States)

    Lee, Sang-Hoon; Lee, Sanghoon

    2015-10-01

    In this paper, we propose a robust semi-autonomous algorithm for 3D vessel segmentation and tracking based on an active contour model and a Kalman filter. For each computed tomography angiography (CTA) slice, we use the active contour model to segment the vessel boundary and the Kalman filter to track position and shape variations of the vessel boundary between slices. For successful segmentation via active contour, we select an adequate number of initial points from the contour of the first slice. The points are set manually by user input for the first slice. For the remaining slices, the initial contour position is estimated autonomously based on segmentation results of the previous slice. To obtain refined segmentation results, an adaptive control spacing algorithm is introduced into the active contour model. Moreover, a block search-based initial contour estimation procedure is proposed to ensure that the initial contour of each slice can be near the vessel boundary. Experiments were performed on synthetic and real chest CTA images. Compared with the well-known Chan-Vese (CV) model, the proposed algorithm exhibited better performance in segmentation and tracking. In particular, receiver operating characteristic analysis on the synthetic and real CTA images demonstrated the time efficiency and tracking robustness of the proposed model. In terms of computational time redundancy, processing time can be effectively reduced by approximately 20%.

  9. 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.

  10. 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.

  11. Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization.

    Science.gov (United States)

    Rathke, Fabian; Schmidt, Stefan; Schnörr, Christoph

    2014-07-01

    With the introduction of spectral-domain optical coherence tomography (OCT), resulting in a significant increase in acquisition speed, the fast and accurate segmentation of 3-D OCT scans has become evermore important. This paper presents a novel probabilistic approach, that models the appearance of retinal layers as well as the global shape variations of layer boundaries. Given an OCT scan, the full posterior distribution over segmentations is approximately inferred using a variational method enabling efficient probabilistic inference in terms of computationally tractable model components: Segmenting a full 3-D volume takes around a minute. Accurate segmentations demonstrate the benefit of using global shape regularization: We segmented 35 fovea-centered 3-D volumes with an average unsigned error of 2.46 ± 0.22 μm as well as 80 normal and 66 glaucomatous 2-D circular scans with errors of 2.92 ± 0.5 μm and 4.09 ± 0.98 μm respectively. Furthermore, we utilized the inferred posterior distribution to rate the quality of the segmentation, point out potentially erroneous regions and discriminate normal from pathological scans. No pre- or postprocessing was required and we used the same set of parameters for all data sets, underlining the robustness and out-of-the-box nature of our approach.

  12. Automatic 3D Segmentation of Ultrasound Images Using Atlas Registration and Statistical Texture Prior

    Science.gov (United States)

    Yang, Xiaofeng; Schuster, David; Master, Viraj; Nieh, Peter; Fenster, Aaron; Fei, Baowei

    2012-01-01

    We are developing a molecular image-directed, 3D ultrasound-guided, targeted biopsy system for improved detection of prostate cancer. In this paper, we propose an automatic 3D segmentation method for transrectal ultrasound (TRUS) images, which is based on multi-atlas registration and statistical texture prior. The atlas database includes registered TRUS images from previous patients and their segmented prostate surfaces. Three orthogonal Gabor filter banks are used to extract texture features from each image in the database. Patient-specific Gabor features from the atlas database are used to train kernel support vector machines (KSVMs) and then to segment the prostate image from a new patient. The segmentation method was tested in TRUS data from 5 patients. The average surface distance between our method and manual segmentation is 1.61 ± 0.35 mm, indicating that the atlas-based automatic segmentation method works well and could be used for 3D ultrasound-guided prostate biopsy. PMID:22708024

  13. Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI.

    Science.gov (United States)

    Lee, Junghoon; Woo, Jonghye; Xing, Fangxu; Murano, Emi Z; Stone, Maureen; Prince, Jerry L

    2014-12-01

    Dynamic MRI has been widely used to track the motion of the tongue and measure its internal deformation during speech and swallowing. Accurate segmentation of the tongue is a prerequisite step to define the target boundary and constrain the tracking to tissue points within the tongue. Segmentation of 2D slices or 3D volumes is challenging because of the large number of slices and time frames involved in the segmentation, as well as the incorporation of numerous local deformations that occur throughout the tongue during motion. In this paper, we propose a semi-automatic approach to segment 3D dynamic MRI of the tongue. The algorithm steps include seeding a few slices at one time frame, propagating seeds to the same slices at different time frames using deformable registration, and random walker segmentation based on these seed positions. This method was validated on the tongue of five normal subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semi-automatic segmentations of a total of 130 volumes showed an average dice similarity coefficient (DSC) score of 0.92 with less segmented volume variability between time frames than in manual segmentations.

  14. Graph-based segmentation for RGB-D data using 3-D geometry enhanced superpixels.

    Science.gov (United States)

    Yang, Jingyu; Gan, Ziqiao; Li, Kun; Hou, Chunping

    2015-05-01

    With the advances of depth sensing technologies, color image plus depth information (referred to as RGB-D data hereafter) is more and more popular for comprehensive description of 3-D scenes. This paper proposes a two-stage segmentation method for RGB-D data: 1) oversegmentation by 3-D geometry enhanced superpixels and 2) graph-based merging with label cost from superpixels. In the oversegmentation stage, 3-D geometrical information is reconstructed from the depth map. Then, a K-means-like clustering method is applied to the RGB-D data for oversegmentation using an 8-D distance metric constructed from both color and 3-D geometrical information. In the merging stage, treating each superpixel as a node, a graph-based model is set up to relabel the superpixels into semantically-coherent segments. In the graph-based model, RGB-D proximity, texture similarity, and boundary continuity are incorporated into the smoothness term to exploit the correlations of neighboring superpixels. To obtain a compact labeling, the label term is designed to penalize labels linking to similar superpixels that likely belong to the same object. Both the proposed 3-D geometry enhanced superpixel clustering method and the graph-based merging method from superpixels are evaluated by qualitative and quantitative results. By the fusion of color and depth information, the proposed method achieves superior segmentation performance over several state-of-the-art algorithms.

  15. A 3D image filter for parameter-free segmentation of macromolecular structures from electron tomograms.

    Directory of Open Access Journals (Sweden)

    Rubbiya A Ali

    Full Text Available 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.

  16. 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.

  17. 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.

  18. Segmentation of the central-chest lymph nodes in 3D MDCT images.

    Science.gov (United States)

    Lu, Kongkuo; Higgins, William E

    2011-09-01

    Central-chest lymph nodes play a vital role in lung-cancer staging. The definition of lymph nodes from three-dimensional (3D) multidetector computed-tomography (MDCT) images, however, remains an open problem. We propose two methods for computer-based segmentation of the central-chest lymph nodes from a 3D MDCT scan: the single-section live wire and the single-click live wire. For the single-section live wire, the user first applies the standard live wire to a single two-dimensional (2D) section after which automated analysis completes the segmentation process. The single-click live wire is similar but is almost completely automatic. Ground-truth studies involving human 3D MDCT scans demonstrate the robustness, efficiency, and intra-observer and inter-observer reproducibility of the methods.

  19. MRI Slice Segmentation and 3D Modelling of Temporomandibular Joint Measured by Microscopic Coil

    Science.gov (United States)

    Smirg, O.; Liberda, O.; Smekal, Z.; Sprlakova-Pukova, A.

    2012-01-01

    The paper focuses on the segmentation of magnetic resonance imaging (MRI) slices and 3D modelling of the temporomandibular joint disc in order to help physicians diagnose patients with dysfunction of the temporomandibular joint (TMJ). The TMJ is one of the most complex joints in the human body. The most common joint dysfunction is due to the disc. The disc is a soft tissue, which in principle cannot be diagnosed by the CT method. Therefore, a 3D model is made from the MRI slices, which can image soft tissues. For the segmentation of the disc in individual slices a new method is developed based on spatial distribution and anatomical TMJ structure with automatic thresholding. The thresholding is controlled by a genetic algorithm. The 3D model is realized using the marching cube method.

  20. Improved 3D Superresolution Localization Microscopy Using Adaptive Optics

    CERN Document Server

    Piro, Nicolas; Olivier, Nicolas; Manley, Suliana

    2014-01-01

    We demonstrate a new versatile method for 3D super-resolution microscopy by using a deformable mirror to shape the point spread function of our microscope in a continuous and controllable way. We apply this for 3D STORM imaging of microtubules.

  1. 3D Materials image segmentation by 2D propagation: a graph-cut approach considering homomorphism.

    Science.gov (United States)

    Waggoner, Jarrell; Zhou, Youjie; Simmons, Jeff; De Graef, Marc; Wang, Song

    2013-12-01

    Segmentation propagation, similar to tracking, is the problem of transferring a segmentation of an image to a neighboring image in a sequence. This problem is of particular importance to materials science, where the accurate segmentation of a series of 2D serial-sectioned images of multiple, contiguous 3D structures has important applications. Such structures may have distinct shape, appearance, and topology, which can be considered to improve segmentation accuracy. For example, some materials images may have structures with a specific shape or appearance in each serial section slice, which only changes minimally from slice to slice, and some materials may exhibit specific inter-structure topology that constrains their neighboring relations. Some of these properties have been individually incorporated to segment specific materials images in prior work. In this paper, we develop a propagation framework for materials image segmentation where each propagation is formulated as an optimal labeling problem that can be efficiently solved using the graph-cut algorithm. Our framework makes three key contributions: 1) a homomorphic propagation approach, which considers the consistency of region adjacency in the propagation; 2) incorporation of shape and appearance consistency in the propagation; and 3) a local non-homomorphism strategy to handle newly appearing and disappearing substructures during this propagation. To show the effectiveness of our framework, we conduct experiments on various 3D materials images, and compare the performance against several existing image segmentation methods.

  2. - and Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

    Science.gov (United States)

    Xu, Y.; Hoegner, L.; Tuttas, S.; Stilla, U.

    2017-05-01

    Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

  3. 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.

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

    Science.gov (United States)

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

    2011-12-01

    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 T2-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.

  5. How to Extract the Geometry and Topology from Very Large 3D Segmentations

    CERN Document Server

    Andres, Bjoern; Kroeger, Thorben; Hamprecht, Fred A

    2010-01-01

    Segmentation is often an essential intermediate step in image analysis. A volume segmentation characterizes the underlying volume image in terms of geometric information--segments, faces between segments, curves in which several faces meet--as well as a topology on these objects. Existing algorithms encode this information in designated data structures, but require that these data structures fit entirely in Random Access Memory (RAM). Today, 3D images with several billion voxels are acquired, e.g. in structural neurobiology. Since these large volumes can no longer be processed with existing methods, we present a new algorithm which performs geometry and topology extraction with a runtime linear in the number of voxels and log-linear in the number of faces and curves. The parallelizable algorithm proceeds in a block-wise fashion and constructs a consistent representation of the entire volume image on the hard drive, making the structure of very large volume segmentations accessible to image analysis. The paral...

  6. 3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework

    Science.gov (United States)

    Yang, Xiaofeng; Rossi, Peter J.; Jani, Ashesh B.; Mao, Hui; Curran, Walter J.; Liu, Tian

    2016-03-01

    We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework. Patient-specific anatomical features are extracted from aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified by the feature selection process to train the kernel support vector machine (KSVM). The well-trained SVM was used to localize the prostate of the new patient. Our segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentations (gold standard). The mean volume Dice overlap coefficient was 89.7%. In this study, 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 segmentations.

  7. Iterative mesh transformation for 3D segmentation of livers with cancers in CT images.

    Science.gov (United States)

    Lu, Difei; Wu, Yin; Harris, Gordon; Cai, Wenli

    2015-07-01

    Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semi-automated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases.

  8. 3D video bit rate adaptation decision taking using ambient illumination context

    Directory of Open Access Journals (Sweden)

    G. Nur Yilmaz

    2014-09-01

    Full Text Available 3-Dimensional (3D video adaptation decision taking is an open field in which not many researchers have carried out investigations yet compared to 3D video display, coding, etc. Moreover, utilizing ambient illumination as an environmental context for 3D video adaptation decision taking has particularly not been studied in literature to date. In this paper, a user perception model, which is based on determining perception characteristics of a user for a 3D video content viewed under a particular ambient illumination condition, is proposed. Using the proposed model, a 3D video bit rate adaptation decision taking technique is developed to determine the adapted bit rate for the 3D video content to maintain 3D video quality perception by considering the ambient illumination condition changes. Experimental results demonstrate that the proposed technique is capable of exploiting the changes in ambient illumination level to use network resources more efficiently without sacrificing the 3D video quality perception.

  9. 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.

  10. A segmentation method for 3D visualization of neurons imaged with a confocal laser scanning microscope

    Science.gov (United States)

    Anderson, Jeffrey R.; Barrett, Steven F.; Wilcox, Michael J.

    2005-04-01

    Our understanding of the world around us is based primarily on three-dimensional information because of the environment in which we live and interact. Medical or biological image information is often collected in the form of two-dimensional, serial section images. As such, it is difficult for the observer to mentally reconstruct the three dimensional features of each object. Although many image rendering software packages allow for 3D views of the serial sections, they lack the ability to segment, or isolate different objects in the data set. Segmentation is the key to creating 3D renderings of distinct objects from serial slice images, like separate pieces to a puzzle. This paper describes a segmentation method for objects recorded with serial section images. The user defines threshold levels and object labels on a single image of the data set that are subsequently used to automatically segment each object in the remaining images of the same data set, while maintaining boundaries between contacting objects. The performance of the algorithm is verified using mathematically defined shapes. It is then applied to the visual neurons of the housefly, Musca domestica. Knowledge of the fly"s visual system may lead to improved machine visions systems. This effort has provided the impetus to develop this segmentation algorithm. The described segmentation method can be applied to any high contrast serial slice data set that is well aligned and registered. The medical field alone has many applications for rapid generation of 3D segmented models from MRI and other medical imaging modalities.

  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

    2017-07-21

    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. Intervertebral disc segmentation in MR images with 3D convolutional networks

    Science.gov (United States)

    Korez, Robert; Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2017-02-01

    The vertebral column is a complex anatomical construct, composed of vertebrae and intervertebral discs (IVDs) supported by ligaments and muscles. During life, all components undergo degenerative changes, which may in some cases cause severe, chronic and debilitating low back pain. The main diagnostic challenge is to locate the pain generator, and degenerated IVDs have been identified to act as such. Accurate and robust segmentation of IVDs is therefore a prerequisite for computer-aided diagnosis and quantification of IVD degeneration, and can be also used for computer-assisted planning and simulation in spinal surgery. In this paper, we present a novel fully automated framework for supervised segmentation of IVDs from three-dimensional (3D) magnetic resonance (MR) spine images. By considering global intensity appearance and local shape information, a landmark-based approach is first used for the detection of IVDs in the observed image, which then initializes the segmentation of IVDs by coupling deformable models with convolutional networks (ConvNets). For this purpose, a 3D ConvNet architecture was designed that learns rich high-level appearance representations from a training repository of IVDs, and then generates spatial IVD probability maps that guide deformable models towards IVD boundaries. By applying the proposed framework to 15 3D MR spine images containing 105 IVDs, quantitative comparison of the obtained against reference IVD segmentations yielded an overall mean Dice coefficient of 92.8%, mean symmetric surface distance of 0.4 mm and Hausdorff surface distance of 3.7 mm.

  13. 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.

  14. 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.

  15. Can segmented 3D images be used for stenosis evaluation in coronary CT angiography?

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Chunliang [Linkoeping Univ., Center for Medical Image Science and Visualization, Linkoeping (Sweden); Linkoeping Univ., Div. of Radiological Sciences, Linkoeping (Sweden)], e-mail: chunliang.wang@liu.se; Persson, Anders; De Geer, Jakob; Smedby, Oerjan [Linkoeping Univ., Center for Medical Image Science and Visualization, Linkoeping (Sweden); Linkoeping Univ., Div. of Radiological Sciences, Linkoeping (Sweden); Linkoeping Univ. Hospital, Dept. of Radiology, Linkoeping (Sweden); Engvall, Jan [Linkoeping Univ., Center for Medical Image Science and Visualization, Linkoeping (Sweden); Linkoeping Univ. Hospital, Dept. of Clinical Physiology, Linkoeping (Sweden); Czekierda, Waldemar; Bjoerkholm, Anders [Linkoeping Univ. Hospital, Dept. of Radiology, Linkoeping (Sweden); Fransson, Sven-Goeran [Linkoeping Univ., Div. of Radiological Sciences, Linkoeping (Sweden); Linkoeping Univ. Hospital, Dept. of Radiology, Linkoeping (Sweden)

    2012-10-15

    Background Thanks to the development of computed tomography (CT) scanners and computer software, accurate coronary artery segmentation can be achieved with minimum user interaction. However, the question remains whether we can use these segmented images for reliable diagnosis. Purpose To retrospectively evaluate the diagnostic accuracy of coronary CT angiography (CCTA) using segmented 3D data for the detection of significant stenosis. Material and Methods CCTA data-sets from 30 patients were acquired with a 64-slice CT scanner and segmented using the region growing (RG) method and the 'virtual contrast injection' (VC) method. Three types of images of each patient were reviewed by different reviewers for the presence of stenosis with diameter reduction of 50% or more. The evaluation was performed on four main arteries of each patient (120 arteries in total). For the original series, the reviewer was allowed to use all the 2D and 3D visualization tools available (conventional method). For the segmented results from RG and VC, only maximum intensity projection was used. Evaluation results were compared with catheter angiography (CA) for each artery in a blinded fashion. Results Overall, 34 arteries with significant stenosis were identified by CA. The percentage of evaluable arteries, accuracy and negative predictive value for detecting stenosis were, respectively, 86%, 74%, and 93% for the conventional method, 83%, 71%, and 92% for VC, and 64%, 56%, and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (P < 0.01), whereas there was no significant difference in accuracy between the VC method and the conventional method (P = 0.22). Conclusion The diagnostic accuracy for the RG-segmented 3D data is lower than those with access to 2D images, whereas the VC method shows diagnostic accuracy similar to the conventional method.

  16. 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...

  17. 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.

  18. Free-hand 3D reconstruction and tumor segmentation of Laparoscopic Ultrasounds for pancreatic MIS interventions

    OpenAIRE

    Fernández Pena, A.; Viana Matesanz, M.; Rodríguez Vila, Borja; Oropesa García, Ignacio; Sánchez González, Patricia; Sánchez Margallo, Juan Antonio; Moyano García-Cuevas, J.L.; Sánchez Margallo, Francisco Miguel; Gómez Aguilera, Enrique J.

    2015-01-01

    Pancreatic cancer's treatment dilemma comes while trying to determine the precise nature of the lesion. The best approach is defined by diagnose of the tumor cells' staging. This paper presents a fast approach towards acquiring an estimation of the tumor positioning and size through laparoscopic ultrasound (LUS) images. The method segments 2D images of pancreas and lesions before reconstructing the extracted tumors into a full 3D volume. The whole method is integrated into a visualization and...

  19. Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

    OpenAIRE

    Cuingnet, Rémi; Prevost, Raphaël; Lesage, David; Cohen, Laurent D.; Mory, Benoît; Ardon, Roberto

    2012-01-01

    International audience; Kidney segmentation in 3D CT images allows extracting useful information for nephrologists. For practical use in clinical routine, such an algorithm should be fast, automatic and robust to contrast-agent enhancement and elds of view. By combining and re ning state-of-the-art techniques (random forests and template deformation), we demonstrate the possibility of building an algorithm that meets these requirements. Kidneys are localized with random forests following a co...

  20. 3D Models of Pelvic Floor Muscles Developed by Manual Segmentation to FEM

    OpenAIRE

    Cristina S Saleme; Renato N. Jorge; Marcos P Barbosa; Marco Parente; Agnaldo L S Filho; Thuane Roza; João Manuel RS Tavares; Teresa Mascarenhas

    2009-01-01

    The female pelvic floor is an understudied region of the body from the biomechanical perspective. MRI has been used in the diagnostic evaluation of the pelvic floor dysfunctions. Static images show their morphology while dynamic images show the functional changes that occur on straining and contraction of the pelvic floor. In the present work, MR images contribute to generate 3D solids of pelvic floor muscles through manual segmentation. To study the biomechanical behavior of pelvic floor mus...

  1. 3D Curvelet-Based Segmentation and Quantification of Drusen in Optical Coherence Tomography Images

    Directory of Open Access Journals (Sweden)

    M. Esmaeili

    2017-01-01

    Full Text Available Spectral-Domain Optical Coherence Tomography (SD-OCT is a widely used interferometric diagnostic technique in ophthalmology that provides novel in vivo information of depth-resolved inner and outer retinal structures. This imaging modality can assist clinicians in monitoring the progression of Age-related Macular Degeneration (AMD by providing high-resolution visualization of drusen. Quantitative tools for assessing drusen volume that are indicative of AMD progression may lead to appropriate metrics for selecting treatment protocols. To address this need, a fully automated algorithm was developed to segment drusen area and volume from SD-OCT images. The proposed algorithm consists of three parts: (1 preprocessing, which includes creating binary mask and removing possible highly reflective posterior hyaloid that is used in accurate detection of inner segment/outer segment (IS/OS junction layer and Bruch’s membrane (BM retinal layers; (2 coarse segmentation, in which 3D curvelet transform and graph theory are employed to get the possible candidate drusenoid regions; (3 fine segmentation, in which morphological operators are used to remove falsely extracted elongated structures and get the refined segmentation results. The proposed method was evaluated in 20 publically available volumetric scans acquired by using Bioptigen spectral-domain ophthalmic imaging system. The average true positive and false positive volume fractions (TPVF and FPVF for the segmentation of drusenoid regions were found to be 89.15% ± 3.76 and 0.17% ± .18%, respectively.

  2. Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

    Directory of Open Access Journals (Sweden)

    Sungdae Sim

    2012-12-01

    Full Text Available Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

  3. Intuitive terrain reconstruction using height observation-based ground segmentation and 3D object boundary estimation.

    Science.gov (United States)

    Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

    2012-12-12

    Mobile robot operators must make rapid decisions based on information about the robot's surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot's array of sensors, but some upper parts of objects are beyond the sensors' measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

  4. A customized model for 3D human segmental kinematic coupling analysis by optoelectronic stereophotogrammetry

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The study of three-dimensional human kinematics has significant impacts on medical and healthcare technology innovations. As a non-invasive technology, optoelectronic stereophotogrammetry is widely used for in-vivo locomotor evaluations. However, relatively high testing difficulties, poor testing accuracies, and high analysis complexities prohibit its further employment. The objective of this study is to explore an improved modeling technique for quantitative measurement and analysis of human locomotion. Firstly, a 3D whole body model of 17 rigid segments was developed to describe human locomotion. Subsequently, a novel infrared reflective marker cluster for 17 body segments was constructed to calibrate and record the 3D segmental position and orientation of each functional body region simultaneously with high spatial accuracy. In addition, the novel calibration procedure and the conception of kinematic coupling of human locomotion were proposed to investigate the segmental functional characteristics of human motion. Eight healthy male subjects were evaluated with walking and running experiments using the Qualisys motion capture system. The experimental results demonstrated the followings: (i) The kinematic coupling of the upper limbs and the lower limbs both showed the significant characteristics of joint motion, while the torso motion of human possessed remarkable features of segmental motion; (ii) flexion/extension was the main motion feature in sagittal plane, while the lateral bending in coronal plane and the axial rotation in transverse plane were subsidiary motions during an entire walking cycle regarding to all the segments of the human body; (iii) compared with conventional methods, the improved techniques have a competitive advantage in the convenient measurement and accurate analysis of the segmental dynamic functional characteristics during human locomotion. The modeling technique proposed in this paper has great potentials in rehabilitation engineering

  5. 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.

  6. 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.

  7. Automated Segmentation of the Right Ventricle in 3D Echocardiography: A Kalman Filter State Estimation Approach.

    Science.gov (United States)

    Bersvendsen, Jorn; Orderud, Fredrik; Massey, Richard John; Fosså, Kristian; Gerard, Olivier; Urheim, Stig; Samset, Eigil

    2016-01-01

    As the right ventricle's (RV) role in cardiovascular diseases is being more widely recognized, interest in RV imaging, function and quantification is growing. However, there are currently few RV quantification methods for 3D echocardiography presented in the literature or commercially available. In this paper we propose an automated RV segmentation method for 3D echocardiographic images. We represent the RV geometry by a Doo-Sabin subdivision surface with deformation modes derived from a training set of manual segmentations. The segmentation is then represented as a state estimation problem and solved with an extended Kalman filter by combining the RV geometry with a motion model and edge detection. Validation was performed by comparing surface-surface distances, volumes and ejection fractions in 17 patients with aortic insufficiency between the proposed method, magnetic resonance imaging (MRI), and a manual echocardiographic reference. The algorithm was efficient with a mean computation time of 2.0 s. The mean absolute distances between the proposed and manual segmentations were 3.6 ± 0.7 mm. Good agreements of end diastolic volume, end systolic volume and ejection fraction with respect to MRI ( -26±24 mL , -16±26 mL and 0 ± 10%, respectively) and a manual echocardiographic reference (7 ± 30 mL, 13 ± 17 mL and -5±7% , respectively) were observed.

  8. Robust 3-D airway tree segmentation for image-guided peripheral bronchoscopy.

    Science.gov (United States)

    Graham, Michael W; Gibbs, Jason D; Cornish, Duane C; Higgins, William E

    2010-04-01

    A vital task in the planning of peripheral bronchoscopy is the segmentation of the airway tree from a 3-D multidetector computed tomography chest scan. Unfortunately, existing methods typically do not sufficiently extract the necessary peripheral airways needed to plan a procedure. We present a robust method that draws upon both local and global information. The method begins with a conservative segmentation of the major airways. Follow-on stages then exhaustively search for additional candidate airway locations. Finally, a graph-based optimization method counterbalances both the benefit and cost of retaining candidate airway locations for the final segmentation. Results demonstrate that the proposed method typically extracts 2-3 more generations of airways than several other methods, and that the extracted airway trees enable image-guided bronchoscopy deeper into the human lung periphery than past studies.

  9. 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.

  10. Weakly Supervised Segmentation-Aided Classification of Urban Scenes from 3d LIDAR Point Clouds

    Science.gov (United States)

    Guinard, S.; Landrieu, L.

    2017-05-01

    We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.

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

    Energy Technology Data Exchange (ETDEWEB)

    Narayanan, R; Suri, J S [Eigen Inc, Grass Valley, CA (United States); Werahera, P N; Barqawi, A; Crawford, E D [University of Colorado, Denver, CO (United States); Shinohara, K [University of California, San Francisco, CA (United States); Simoneau, A R [University of California, Irvine, CA (United States)], E-mail: jas.suri@eigen.com

    2008-10-21

    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

  12. 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.

  13. Semi-automatic 3D segmentation of costal cartilage in CT data from Pectus Excavatum patients

    Science.gov (United States)

    Barbosa, Daniel; Queirós, Sandro; Rodrigues, Nuno; Correia-Pinto, Jorge; Vilaça, J.

    2015-03-01

    One of the current frontiers in the clinical management of Pectus Excavatum (PE) patients is the prediction of the surgical outcome prior to the intervention. This can be done through computerized simulation of the Nuss procedure, which requires an anatomically correct representation of the costal cartilage. To this end, we take advantage of the costal cartilage tubular structure to detect it through multi-scale vesselness filtering. This information is then used in an interactive 2D initialization procedure which uses anatomical maximum intensity projections of 3D vesselness feature images to efficiently initialize the 3D segmentation process. We identify the cartilage tissue centerlines in these projected 2D images using a livewire approach. We finally refine the 3D cartilage surface through region-based sparse field level-sets. We have tested the proposed algorithm in 6 noncontrast CT datasets from PE patients. A good segmentation performance was found against reference manual contouring, with an average Dice coefficient of 0.75±0.04 and an average mean surface distance of 1.69+/-0.30mm. The proposed method requires roughly 1 minute for the interactive initialization step, which can positively contribute to an extended use of this tool in clinical practice, since current manual delineation of the costal cartilage can take up to an hour.

  14. Focused shape models for hip joint segmentation in 3D magnetic resonance images.

    Science.gov (United States)

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

    2014-04-01

    Deformable models incorporating shape priors have proved to be a successful approach in segmenting anatomical regions and specific structures in medical images. This paper introduces weighted shape priors for deformable models in the context of 3D magnetic resonance (MR) image segmentation of the bony elements of the human hip joint. The fully automated approach allows the focusing of the shape model energy to a priori selected anatomical structures or regions of clinical interest by preferentially ordering the shape representation (or eigen-modes) within this type of model to the highly weighted areas. This focused shape model improves accuracy of the shape constraints in those regions compared to standard approaches. The proposed method achieved femoral head and acetabular bone segmentation mean absolute surface distance errors of 0.55±0.18mm and 0.75±0.20mm respectively in 35 3D unilateral MR datasets from 25 subjects acquired at 3T with different limited field of views for individual bony components of the hip joint.

  15. Lateral ventricle segmentation of 3D pre-term neonates US using convex optimization.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Kishimoto, Jessica; Ukwatta, Eranga; Fenster, Aaron

    2013-01-01

    Intraventricular hemorrhage (IVH) is a common disease among preterm infants with an occurrence of 12-20% in those born at less than 35 weeks gestational age. Neonates at risk of IVH are monitored by conventional 2D ultrasound (US) for hemorrhage and potential ventricular dilation. Compared to 2D US relying on linear measurements from a single slice and visually estimates to determine ventricular dilation, 3D US can provide volumetric ventricle measurements, more sensitive to longitudinal changes in ventricular volume. In this work, we propose a global optimization-based surface evolution approach to the segmentation of the lateral ventricles in preterm neonates with IVH. The proposed segmentation approach makes use of convex optimization technique in combination with a subject-specific shape model. We show that the introduced challenging combinatorial optimization problem can be solved globally by means of convex relaxation. In this regard, we propose a coupled continuous max-flow model, which derives a new and efficient dual based algorithm, that can be implemented on GPUs to achieve a high-performance in numerics. Experiments demonstrate the advantages of our approach in both accuracy and efficiency. To the best of our knowledge, this paper reports the first study on semi-automatic segmentation of lateral ventricles in neonates with IVH from 3D US images.

  16. Segmentation of vascular structures and hematopoietic cells in 3D microscopy images and quantitative analysis

    Science.gov (United States)

    Mu, Jian; Yang, Lin; Kamocka, Malgorzata M.; Zollman, Amy L.; Carlesso, Nadia; Chen, Danny Z.

    2015-03-01

    In this paper, we present image processing methods for quantitative study of how the bone marrow microenvironment changes (characterized by altered vascular structure and hematopoietic cell distribution) caused by diseases or various factors. We develop algorithms that automatically segment vascular structures and hematopoietic cells in 3-D microscopy images, perform quantitative analysis of the properties of the segmented vascular structures and cells, and examine how such properties change. In processing images, we apply local thresholding to segment vessels, and add post-processing steps to deal with imaging artifacts. We propose an improved watershed algorithm that relies on both intensity and shape information and can separate multiple overlapping cells better than common watershed methods. We then quantitatively compute various features of the vascular structures and hematopoietic cells, such as the branches and sizes of vessels and the distribution of cells. In analyzing vascular properties, we provide algorithms for pruning fake vessel segments and branches based on vessel skeletons. Our algorithms can segment vascular structures and hematopoietic cells with good quality. We use our methods to quantitatively examine the changes in the bone marrow microenvironment caused by the deletion of Notch pathway. Our quantitative analysis reveals property changes in samples with deleted Notch pathway. Our tool is useful for biologists to quantitatively measure changes in the bone marrow microenvironment, for developing possible therapeutic strategies to help the bone marrow microenvironment recovery.

  17. 3D Finite Element Analysis of TBM Water Diversion Tunnel Segment Coupled with Seepage Field

    Institute of Scientific and Technical Information of China (English)

    钟登华; 胡能明; 程正飞; 吕鹏; 佟大威

    2016-01-01

    In most studies of tunnel boring machine(TBM)tunnelling, the groundwater pressure was not consid-ered, or was simplified and exerted on the boundary of lining structure. Meanwhile, the leakage, which mainly oc-curs in the segment joints, was often ignored in the relevant studies of TBM tunnelling. Additionally, the geological models in these studies were simplified to different extents, and mostly were simplified as homogenous bodies. Considering the deficiencies above, a 3D refined model of the surrounding rock of a tunnel is firstly established using NURBS-TIN-BReP hybrid data structure in this paper. Then the seepage field of the surrounding rock con-sidering the leakage in the segment joints is simulated. Finally, the stability of TBM water diversion tunnel is stud-ied coupled with the seepage simulation, to analyze the stress-strain conditions, the axial force and the bending moment of tunnel segment considering the leakage in the segment joints. The results illustrate that the maximum radial displacement, the minimum principal stress, the maximum principal stress and the axial force of segment lining considering the seepage effect are all larger than those disregarding the seepage effect.

  18. Semantic segmentation of 3D textured meshes for urban scene analysis

    Science.gov (United States)

    Rouhani, Mohammad; Lafarge, Florent; Alliez, Pierre

    2017-01-01

    Classifying 3D measurement data has become a core problem in photogrammetry and 3D computer vision, since the rise of modern multiview geometry techniques, combined with affordable range sensors. We introduce a Markov Random Field-based approach for segmenting textured meshes generated via multi-view stereo into urban classes of interest. The input mesh is first partitioned into small clusters, referred to as superfacets, from which geometric and photometric features are computed. A random forest is then trained to predict the class of each superfacet as well as its similarity with the neighboring superfacets. Similarity is used to assign the weights of the Markov Random Field pairwise-potential and to account for contextual information between the classes. The experimental results illustrate the efficacy and accuracy of the proposed framework.

  19. 3-D segmentation and quantitative analysis of inner and outer walls of thrombotic abdominal aortic aneurysms

    Science.gov (United States)

    Lee, Kyungmoo; Yin, Yin; Wahle, Andreas; Olszewski, Mark E.; Sonka, Milan

    2008-03-01

    An abdominal aortic aneurysm (AAA) is an area of a localized widening of the abdominal aorta, with a frequent presence of thrombus. A ruptured aneurysm can cause death due to severe internal bleeding. AAA thrombus segmentation and quantitative analysis are of paramount importance for diagnosis, risk assessment, and determination of treatment options. Until now, only a small number of methods for thrombus segmentation and analysis have been presented in the literature, either requiring substantial user interaction or exhibiting insufficient performance. We report a novel method offering minimal user interaction and high accuracy. Our thrombus segmentation method is composed of an initial automated luminal surface segmentation, followed by a cost function-based optimal segmentation of the inner and outer surfaces of the aortic wall. The approach utilizes the power and flexibility of the optimal triangle mesh-based 3-D graph search method, in which cost functions for thrombus inner and outer surfaces are based on gradient magnitudes. Sometimes local failures caused by image ambiguity occur, in which case several control points are used to guide the computer segmentation without the need to trace borders manually. Our method was tested in 9 MDCT image datasets (951 image slices). With the exception of a case in which the thrombus was highly eccentric, visually acceptable aortic lumen and thrombus segmentation results were achieved. No user interaction was used in 3 out of 8 datasets, and 7.80 +/- 2.71 mouse clicks per case / 0.083 +/- 0.035 mouse clicks per image slice were required in the remaining 5 datasets.

  20. OCT Segmentation Survey and Summary Reviews and a Novel 3D Segmentation Algorithm and a Proof of Concept Implementation

    CERN Document Server

    Mokhov, Serguei A

    2012-01-01

    We overview the existing OCT work, especially the practical aspects of it. We create a novel algorithm for 3D OCT segmentation with the goals of speed and/or accuracy while remaining flexible in the design and implementation for future extensions and improvements. The document at this point is a running draft being iteratively "developed" as a progress report as the work and survey advance. It contains the review and summarization of select OCT works, the design and implementation of the OCTMARF experimentation application and some results.

  1. 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

  2. Real-time 3D medical structure segmentation using fast evolving active contours

    Science.gov (United States)

    Wang, Xiaotao; Wang, Qiang; Hao, Zhihui; Xu, Kuanhong; Guo, Ping; Ren, Haibing; Jang, Wooyoung; Kim, Jung-bae

    2014-03-01

    Segmentation of 3D medical structures in real-time is an important as well as intractable problem for clinical applications due to the high computation and memory cost. We propose a novel fast evolving active contour model in this paper to reduce the requirements of computation and memory. The basic idea is to evolve the brief represented dynamic contour interface as far as possible per iteration. Our method encodes zero level set via a single unordered list, and evolves the list recursively by adding activated adjacent neighbors to its end, resulting in active parts of the zero level set moves far enough per iteration along with list scanning. To guarantee the robustness of this process, a new approximation of curvature for integer valued level set is proposed as the internal force to penalize the list smoothness and restrain the list continual growth. Besides, list scanning times are also used as an upper hard constraint to control the list growing. Together with the internal force, efficient regional and constrained external forces, whose computations are only performed along the unordered list, are also provided to attract the list toward object boundaries. Specially, our model calculates regional force only in a narrowband outside the zero level set and can efficiently segment multiple regions simultaneously as well as handle the background with multiple components. Compared with state-of-the-art algorithms, our algorithm is one-order of magnitude faster with similar segmentation accuracy and can achieve real-time performance for the segmentation of 3D medical structures on a standard PC.

  3. Visualising, segmenting and analysing heterogenous glacigenic sediments using 3D x-ray CT.

    Science.gov (United States)

    Carr, Simon; Diggens, Lucy; Groves, John; O'Sullivan, Catherine; Marsland, Rhona

    2015-04-01

    , especially with regard to using such data to improve understanding of mechanisms of particle motion and fabric development during subglacial strain. In this study, we present detailed investigation of subglacial tills from the UK, Iceland and Poland, to explore the challenges in segmenting these highly variable sediment bodies for 3D microfabric analysis. A calibration study is reported to compare various approaches to CT data segmentation to manually segmented datasets, from which an optimal workflow is developed, using a combination of the WEKA Trainable Segmentation tool within ImageJ to segment the data, followed by object-based analysis using Blob3D. We then demonstrate the value of this analysis through the analysis of true 3D microfabric data from a Last Glacial Maximum till deposit located at Morston, North Norfolk. Seven undisturbed sediment samples were scanned and analysed using high-resolution 3D X-ray computed tomography. Large (~5,000 to ~16,000) populations of individual particles are objectively and systematically segmented and identified. These large datasets are then subject to detailed interrogation using bespoke code for analysing particle fabric within Matlab, including the application of fabric-tensor analysis, by which fabrics can be weighted and scaled by key variables such as size and shape. We will present initial findings from these datasets, focusing particularly on overcoming the methodological challenges of obtaining robust datasets of sediments with highly complex, mixed compositional sediments.

  4. 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.

  5. Shape representation for efficient landmark-based segmentation in 3-d.

    Science.gov (United States)

    Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2014-04-01

    In this paper, we propose a novel approach to landmark-based shape representation that is based on transportation theory, where landmarks are considered as sources and destinations, all possible landmark connections as roads, and established landmark connections as goods transported via these roads. Landmark connections, which are selectively established, are identified through their statistical properties describing the shape of the object of interest, and indicate the least costly roads for transporting goods from sources to destinations. From such a perspective, we introduce three novel shape representations that are combined with an existing landmark detection algorithm based on game theory. To reduce computational complexity, which results from the extension from 2-D to 3-D segmentation, landmark detection is augmented by a concept known in game theory as strategy dominance. The novel shape representations, game-theoretic landmark detection and strategy dominance are combined into a segmentation framework that was evaluated on 3-D computed tomography images of lumbar vertebrae and femoral heads. The best shape representation yielded symmetric surface distance of 0.75 mm and 1.11 mm, and Dice coefficient of 93.6% and 96.2% for lumbar vertebrae and femoral heads, respectively. By applying strategy dominance, the computational costs were further reduced for up to three times.

  6. Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis elegans

    Science.gov (United States)

    Rodrigues, Pedro L.; Moreira, António H. J.; Teixeira-Castro, Andreia; Oliveira, João; Dias, Nuno; Rodrigues, Nuno F.; Vilaça, João L.

    2012-03-01

    In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals' transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey's biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention.

  7. 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.

  8. 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.

  9. 3-D carotid multi-region MRI segmentation by globally optimal evolution of coupled surfaces.

    Science.gov (United States)

    Ukwatta, Eranga; Yuan, Jing; Rajchl, Martin; Qiu, Wu; Tessier, David; Fenster, Aaron

    2013-04-01

    In this paper, we propose a novel global optimization based 3-D multi-region segmentation algorithm for T1-weighted black-blood carotid magnetic resonance (MR) images. The proposed algorithm partitions a 3-D carotid MR image into three regions: wall, lumen, and background. The algorithm performs such partitioning by simultaneously evolving two coupled 3-D surfaces of carotid artery adventitia boundary (AB) and lumen-intima boundary (LIB) while preserving their anatomical inter-surface consistency such that the LIB is always located within the AB. In particular, we show that the proposed algorithm results in a fully time implicit scheme that propagates the two linearly ordered surfaces of the AB and LIB to their globally optimal positions during each discrete time frame by convex relaxation. In this regard, we introduce the continuous max-flow model and prove its duality/equivalence to the convex relaxed optimization problem with respect to each evolution step. We then propose a fully parallelized continuous max-flow-based algorithm, which can be readily implemented on a GPU to achieve high computational efficiency. Extensive experiments, with four users using 12 3T MR and 26 1.5T MR images, demonstrate that the proposed algorithm yields high accuracy and low operator variability in computing vessel wall volume. In addition, we show the algorithm outperforms previous methods in terms of high computational efficiency and robustness with fewer user interactions.

  10. The effect of pose variability and repeated reliability of segmental centres of mass acquisition when using 3D photonic scanning.

    Science.gov (United States)

    Chiu, Chuang-Yuan; Pease, David L; Sanders, Ross H

    2016-12-01

    Three-dimensional (3D) photonic scanning is an emerging technique to acquire accurate body segment parameter data. This study established the repeated reliability of segmental centres of mass when using 3D photonic scanning (3DPS). Seventeen male participants were scanned twice by a 3D whole-body laser scanner. The same operators conducted the reconstruction and segmentation processes to obtain segmental meshes for calculating the segmental centres of mass. The segmental centres of mass obtained from repeated 3DPS were compared by relative technical error of measurement (TEM). Hypothesis tests were conducted to determine the size of change required for each segment to be determined a true variation. The relative TEMs for all segments were less than 5%. The relative changes in centres of mass at ±1.5% for most segments can be detected (p 3D photonic scanning and emphasised that the error for arm segments need to be considered while using this technique to acquire centres of mass.

  11. Automated segmentation of acetabulum and femoral head from 3-D CT images.

    Science.gov (United States)

    Zoroofi, Reza A; Sato, Yoshinobu; Sasama, Toshihiko; Nishii, Takashi; Sugano, Nobuhiko; Yonenobu, Kazuo; Yoshikawa, Hideki; Ochi, Takahiro; Tamura, Shinichi

    2003-12-01

    This paper describes several new methods and software for automatic segmentation of the pelvis and the femur, based on clinically obtained multislice computed tomography (CT) data. The hip joint is composed of the acetabulum, cavity of the pelvic bone, and the femoral head. In vivo CT data sets of 60 actual patients were used in the study. The 120 (60 x 2) hip joints in the data sets were divided into four groups according to several key features for segmentation. Conventional techniques for classification of bony tissues were first employed to distinguish the pelvis and the femur from other CT tissue images in the hip joint. Automatic techniques were developed to extract the boundary between the acetabulum and the femoral head. An automatic method was built up to manage the segmentation task according to image intensity of bone tissues, size, center, shape of the femoral heads, and other characters. The processing scheme consisted of the following five steps: 1) preprocessing, including resampling 3-D CT data by a modified Sinc interpolation to create isotropic volume and to avoid Gibbs ringing, and smoothing the resulting images by a 3-D Gaussian filter; 2) detecting bone tissues from CT images by conventional techniques including histogram-based thresholding and binary morphological operations; 3) estimating initial boundary of the femoral head and the joint space between the acetabulum and the femoral head by a new approach utilizing the constraints of the greater trochanter and the shapes of the femoral head; 4) enhancing the joint space by a Hessian filter; and 5) refining the rough boundary obtained in step 3) by a moving disk technique and the filtered images obtained in step 4). The above method was implemented in a Microsoft Windows software package and the resulting software is freely available on the Internet. The feasibility of this method was tested on the data sets of 60 clinical cases (5000 CT images).

  12. 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.

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

    Science.gov (United States)

    Hodneland, Erlend; Kögel, Tanja; Frei, Dominik Michael; Gerdes, Hans-Hermann; Lundervold, Arvid

    2013-08-09

    : 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.

  14. Chest-wall segmentation in automated 3D breast ultrasound images using thoracic volume classification

    Science.gov (United States)

    Tan, Tao; van Zelst, Jan; Zhang, Wei; Mann, Ritse M.; Platel, Bram; Karssemeijer, Nico

    2014-03-01

    Computer-aided detection (CAD) systems are expected to improve effectiveness and efficiency of radiologists in reading automated 3D breast ultrasound (ABUS) images. One challenging task on developing CAD is to reduce a large number of false positives. A large amount of false positives originate from acoustic shadowing caused by ribs. Therefore determining the location of the chestwall in ABUS is necessary in CAD systems to remove these false positives. Additionally it can be used as an anatomical landmark for inter- and intra-modal image registration. In this work, we extended our previous developed chestwall segmentation method that fits a cylinder to automated detected rib-surface points and we fit the cylinder model by minimizing a cost function which adopted a term of region cost computed from a thoracic volume classifier to improve segmentation accuracy. We examined the performance on a dataset of 52 images where our previous developed method fails. Using region-based cost, the average mean distance of the annotated points to the segmented chest wall decreased from 7.57±2.76 mm to 6.22±2.86 mm.art.

  15. Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

    Science.gov (United States)

    Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

    2017-02-01

    Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

  16. 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

    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

  17. The influence of the segmentation process on 3D measurements from cone beam computed tomography-derived surface models

    NARCIS (Netherlands)

    Engelbrecht, Willem P.; Fourie, Zacharias; Damstra, Janalt; Gerrits, Peter O.; Ren, Yijin

    2013-01-01

    To compare the accuracy of linear and angular measurements between cephalometric and anatomic landmarks on surface models derived from 3D cone beam computed tomography (CBCT) with two different segmentation protocols was the aim of this study. CBCT scans were made of cadaver heads and 3D surface mod

  18. An adaptive 3-D discrete cosine transform coder for medical image compression.

    Science.gov (United States)

    Tai, S C; Wu, Y G; Lin, C W

    2000-09-01

    In this communication, a new three-dimensional (3-D) discrete cosine transform (DCT) coder for medical images is presented. In the proposed method, a segmentation technique based on the local energy magnitude is used to segment subblocks of the image into different energy levels. Then, those subblocks with the same energy level are gathered to form a 3-D cuboid. Finally, 3-D DCT is employed to compress the 3-D cuboid individually. Simulation results show that the reconstructed images achieve a bit rate lower than 0.25 bit per pixel even when the compression ratios are higher than 35. As compared with the results by JPEG and other strategies, it is found that the proposed method achieves better qualities of decoded images than by JPEG and the other strategies.

  19. 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

    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. PMID:26630674

  20. 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.

  1. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Xinjian; Bagci, Ulas [Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10 Room 1C515, Bethesda, Maryland 20892-1182 and Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi' an 710071 (China); Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10 Room 1C515, Bethesda, Maryland 20892-1182 (United States)

    2011-08-15

    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

  2. Segmentation and Classification of Human Actions and Actor Characteristics with 3d Motion Data

    Directory of Open Access Journals (Sweden)

    S. Ali Etemad

    2012-08-01

    Full Text Available In this paper we have used 3D motion capture data with the aim of detecting and classifying specifichuman actions. In addition to recognition of basic action classes, actor styles and characteristics such asgender, age, and energy level have also been subject to classification. We have applied and compared threemain methods: nearest neighbour search, hidden Markov models, and artificial neural networks. Usingthese techniques, we have proposed exhaustive algorithms for detection of actions in a motion piece andsubsequently classifying the segmented actions and respective characteristics of the actors. We have testedthe methods for various sequences and compared the results for a comprehensive evaluation of each of theproposed techniques. Our findings can be largely used for general classification of human motion data formultimedia applications as well as sorting and classifying data sets of human motion data especially thoseacquired using visual marker-based motion capture systems such as the one employed in this research.

  3. Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images.

    Science.gov (United States)

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

    2014-04-01

    We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a "global" 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2%±2.0% for 3-D TRUS images and 88.5%±3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.

  4. 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.

  5. Electro-bending characterization of adaptive 3D fiber reinforced plastics based on shape memory alloys

    Science.gov (United States)

    Ashir, Moniruddoza; Hahn, Lars; Kluge, Axel; Nocke, Andreas; Cherif, Chokri

    2016-03-01

    The industrial importance of fiber reinforced plastics (FRPs) is growing steadily in recent years, which are mostly used in different niche products, has been growing steadily in recent years. The integration of sensors and actuators in FRP is potentially valuable for creating innovative applications and therefore the market acceptance of adaptive FRP is increasing. In particular, in the field of highly stressed FRP, structural integrated systems for continuous component parts monitoring play an important role. This presented work focuses on the electro-mechanical characterization of adaptive three-dimensional (3D)FRP with integrated textile-based actuators. Here, the friction spun hybrid yarn, consisting of shape memory alloy (SMA) in wire form as core, serves as an actuator. Because of the shape memory effect, the SMA-hybrid yarn returns to its original shape upon heating that also causes the deformation of adaptive 3D FRP. In order to investigate the influences of the deformation behavior of the adaptive 3D FRP, investigations in this research are varied according to the structural parameters such as radius of curvature of the adaptive 3D FRP, fabric types and number of layers of the fabric in the composite. Results show that reproducible deformations can be realized with adaptive 3D FRP and that structural parameters have a significant impact on the deformation capability.

  6. Segmentation of Textures Defined on Flat vs. Layered Surfaces using Neural Networks: Comparison of 2D vs. 3D Representations.

    Science.gov (United States)

    Oh, Sejong; Choe, Yoonsuck

    2007-08-01

    Texture boundary detection (or segmentation) is an important capability in human vision. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct (i.e., occluded) surfaces. Hence, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this paper, we conducted computational experiments with artificial neural networks to investigate the relative difficulty of learning to segment textures defined on flat 2D surfaces vs. those in 3D configurations where the boundaries are defined by occluding surfaces and their change over time due to the observer's motion. It turns out that learning is faster and more accurate in 3D, very much in line with our expectation. Furthermore, our results showed that the neural network's learned ability to segment texture in 3D transfers well into 2D texture segmentation, bolstering our initial hypothesis, and providing insights on the possible developmental origin of 2D texture segmentation function in human vision.

  7. 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).

  8. Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models

    Science.gov (United States)

    Neubert, A.; Fripp, J.; Engstrom, C.; Schwarz, R.; Lauer, L.; Salvado, O.; Crozier, S.

    2012-12-01

    Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

  9. Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map.

    Science.gov (United States)

    Kafieh, Raheleh; Rabbani, Hossein; Abramoff, Michael D; Sonka, Milan

    2013-12-01

    Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information in localizing most of boundaries and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping applied to 2D and 3D OCT datasets is composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers. In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node. The weights of the graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity. The first diffusion map clusters the data into three parts, the second of which is the area of interest. The other two sections are eliminated from the remaining calculations. In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities. The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals). The mean unsigned border positioning errors (mean ± SD) was 8.52 ± 3.13 and 7.56 ± 2.95 μm for the 2D and 3D methods, respectively.

  10. 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.

  11. Joint segmentation of 3D femoral lumen and outer wall surfaces from MR images.

    Science.gov (United States)

    Ukwatta, Eranga; Yuan, Jing; Qiu, Wu; Rajchl, Martin; Chiu, Bernard; Shavakh, Shadi; Xu, Jianrong; Fenster, Aaron

    2013-01-01

    We propose a novel algorithm to jointly delineate the femoral artery lumen and outer wall surfaces from 3D black-blood MR images, while enforcing the spatial consistency of the reoriented MR slices along the medial axis of the femoral artery. We demonstrate that the resulting optimization problem of the proposed segmentation can be solved globally and exactly by means of convex relaxation, for which we introduce a novel coupled continuous max-flow (CCOMF) model based on an Ishikawa-type flow configuration and show its duality to the studied convex relaxed optimization problem. Using the proposed CCMF model, the exactness and globalness of its dual convex relaxation problem is proven. Experiment results demonstrate that the proposed method yielded high accuracy (i.e. Dice similarity coefficient > 85%) for both the lumen and outer wall and high reproducibility (intra-class correlation coefficient of 0.95) for generating vessel wall area. The proposed method outperformed the previous method, in terms of computation time, by a factor of pproximately 20.

  12. Segmentation and tracking of adherens junctions in 3D for the analysis of epithelial tissue morphogenesis.

    Science.gov (United States)

    Cilla, Rodrigo; Mechery, Vinodh; Hernandez de Madrid, Beatriz; Del Signore, Steven; Dotu, Ivan; Hatini, Victor

    2015-04-01

    Epithelial morphogenesis generates the shape of tissues, organs and embryos and is fundamental for their proper function. It is a dynamic process that occurs at multiple spatial scales from macromolecular dynamics, to cell deformations, mitosis and apoptosis, to coordinated cell rearrangements that lead to global changes of tissue shape. Using time lapse imaging, it is possible to observe these events at a system level. However, to investigate morphogenetic events it is necessary to develop computational tools to extract quantitative information from the time lapse data. Toward this goal, we developed an image-based computational pipeline to preprocess, segment and track epithelial cells in 4D confocal microscopy data. The computational pipeline we developed, for the first time, detects the adherens junctions of epithelial cells in 3D, without the need to first detect cell nuclei. We accentuate and detect cell outlines in a series of steps, symbolically describe the cells and their connectivity, and employ this information to track the cells. We validated the performance of the pipeline for its ability to detect vertices and cell-cell contacts, track cells, and identify mitosis and apoptosis in surface epithelia of Drosophila imaginal discs. We demonstrate the utility of the pipeline to extract key quantitative features of cell behavior with which to elucidate the dynamics and biomechanical control of epithelial tissue morphogenesis. We have made our methods and data available as an open-source multiplatform software tool called TTT (http://github.com/morganrcu/TTT).

  13. 3D Segmentation with an application of level set-method using MRI volumes for image guided surgery.

    Science.gov (United States)

    Bosnjak, A; Montilla, G; Villegas, R; Jara, I

    2007-01-01

    This paper proposes an innovation in the application for image guided surgery using a comparative study of three different method of segmentation. This segmentation method is faster than the manual segmentation of images, with the advantage that it allows to use the same patient as anatomical reference, which has more precision than a generic atlas. This new methodology for 3D information extraction is based on a processing chain structured of the following modules: 1) 3D Filtering: the purpose is to preserve the contours of the structures and to smooth the homogeneous areas; several filters were tested and finally an anisotropic diffusion filter was used. 2) 3D Segmentation. This module compares three different methods: Region growing Algorithm, Cubic spline hand assisted, and Level Set Method. It then proposes a Level Set-based on the front propagation method that allows the making of the reconstruction of the internal walls of the anatomical structures of the brain. 3) 3D visualization. The new contribution of this work consists on the visualization of the segmented model and its use in the pre-surgery planning.

  14. METHOD FOR ADAPTIVE MESH GENERATION BASED ON GEOMETRICAL FEATURES OF 3D SOLID

    Institute of Scientific and Technical Information of China (English)

    HUANG Xiaodong; DU Qungui; YE Bangyan

    2006-01-01

    In order to provide a guidance to specify the element size dynamically during adaptive finite element mesh generation, adaptive criteria are firstly defined according to the relationships between the geometrical features and the elements of 3D solid. Various modes based on different datum geometrical elements, such as vertex, curve, surface, and so on, are then designed for generating local refmed mesh. With the guidance of the defined criteria, different modes are automatically selected to apply on the appropriate datum objects to program the element size in the local special areas. As a result, the control information of element size is successfully programmed coveting the entire domain based on the geometrical features of 3D solid. A new algorithm based on Delaunay triangulation is then developed for generating 3D adaptive fmite element mesh, in which the element size is dynamically specified to catch the geometrical features and suitable tetrahedron facets are selected to locate interior nodes continuously. As a result, adaptive mesh with good-quality elements is generated. Examples show that the proposed method can be successfully applied to adaptive finite element mesh automatic generation based on the geometrical features of 3D solid.

  15. Efficient global optimization based 3D carotid AB-LIB MRI segmentation by simultaneously evolving coupled surfaces.

    Science.gov (United States)

    Ukwatta, Eranga; Yuan, Jing; Rajchl, Martin; Fenster, Aaron

    2012-01-01

    Magnetic resonance (MR) imaging of carotid atherosclerosis biomarkers are increasingly being investigated for the risk assessment of vulnerable plaques. A fast and robust 3D segmentation of the carotid adventitia (AB) and lumen-intima (LIB) boundaries can greatly alleviate the measurement burden of generating quantitative imaging biomarkers in clinical research. In this paper, we propose a novel global optimization-based approach to segment the carotid AB and LIB from 3D T1-weighted black blood MR images, by simultaneously evolving two coupled surfaces with enforcement of anatomical consistency of the AB and LIB. We show that the evolution of two surfaces at each discrete time-frame can be optimized exactly and globally by means of convex relaxation. Our continuous max-flow based algorithm is implemented in GPUs to achieve high computational performance. The experiment results from 16 carotid MR images show that the algorithm obtained high agreement with manual segmentations and achieved high repeatability in segmentation.

  16. 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.

  17. Left-Atrial Segmentation From 3-D Ultrasound Using B-Spline Explicit Active Surfaces With Scale Uncoupling.

    Science.gov (United States)

    Almeida, Nuno; Friboulet, Denis; Sarvari, Sebastian Imre; Bernard, Olivier; Barbosa, Daniel; Samset, Eigil; Dhooge, Jan

    2016-02-01

    Segmentation of the left atrium (LA) of the heart allows quantification of LA volume dynamics which can give insight into cardiac function. However, very little attention has been given to LA segmentation from three-dimensional (3-D) ultrasound (US), most efforts being focused on the segmentation of the left ventricle (LV). The B-spline explicit active surfaces (BEAS) framework has been shown to be a very robust and efficient methodology to perform LV segmentation. In this study, we propose an extension of the BEAS framework, introducing B-splines with uncoupled scaling. This formulation improves the shape support for less regular and more variable structures, by giving independent control over smoothness and number of control points. Semiautomatic segmentation of the LA endocardium using this framework was tested in a setup requiring little user input, on 20 volumetric sequences of echocardiographic data from healthy subjects. The segmentation results were evaluated against manual reference delineations of the LA. Relevant LA morphological and functional parameters were derived from the segmented surfaces, in order to assess the performance of the proposed method on its clinical usage. The results showed that the modified BEAS framework is capable of accurate semiautomatic LA segmentation in 3-D transthoracic US, providing reliable quantification of the LA morphology and function.

  18. Quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images

    Directory of Open Access Journals (Sweden)

    Claudia Chevrefils

    2012-08-01

    Full Text Available Abstract Background For some scoliotic patients the spinal instrumentation is inevitable. Among these patients, those with stiff curvature will need thoracoscopic disk resection. The removal of the intervertebral disk with only thoracoscopic images is a tedious and challenging task for the surgeon. With computer aided surgery and 3D visualisation of the interverterbral disk during surgery, surgeons will have access to additional information such as the remaining disk tissue or the distance of surgical tools from critical anatomical structures like the aorta or spinal canal. We hypothesized that automatically extracting 3D information of the intervertebral disk from MR images would aid the surgeons to evaluate the remaining disk and would add a security factor to the patient during thoracoscopic disk resection. Methods This paper presents a quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images. The automatic segmentation method is based on the watershed technique and morphological operators. The 3D Dice Similarity Coefficient (DSC is the main statistical metric used to validate the automatically detected preoperative disk volumes. The automatic detections of intervertebral disks of real clinical MR images are compared to manual segmentation done by clinicians. Results Results show that depending on the type of MR acquisition sequence, the 3D DSC can be as high as 0.79 (±0.04. These 3D results are also supported by a 2D quantitative evaluation as well as by robustness and variability evaluations. The mean discrepancy (in 2D between the manual and automatic segmentations for regions around the spinal canal is of 1.8 (±0.8 mm. The robustness study shows that among the five factors evaluated, only the type of MRI acquisition sequence can affect the segmentation results. Finally, the variability of the automatic segmentation method is lower than the variability associated

  19. Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT.

    Science.gov (United States)

    Korfiatis, P; Skiadopoulos, S; Sakellaropoulos, P; Kalogeropoulou, C; Costaridou, L

    2007-12-01

    The first step in lung analysis by CT is the identification of the lung border. To deal with the increased number of sections per scan in thin-slice multidetector CT, it has been crucial to develop accurate and automated lung segmentation algorithms. In this study, an automated method for lung segmentation of thin-slice CT data is presented. The method exploits the advantages of a two-dimensional wavelet edge-highlighting step in lung border delineation. Lung volume segmentation is achieved with three-dimensional (3D) grey level thresholding, using a minimum error technique. 3D thresholding, combined with the wavelet pre-processing step, successfully deals with lung border segmentation challenges, such as anterior or posterior junction lines and juxtapleural nodules. Finally, to deal with mediastinum border under-segmentation, 3D morphological closing with a spherical structural element is applied. The performance of the proposed method is quantitatively assessed on a dataset originating from the Lung Imaging Database Consortium (LIDC) by comparing automatically derived borders with the manually traced ones. Segmentation performance, averaged over left and right lung volumes, for lung volume overlap is 0.983+/-0.008, whereas for shape differentiation in terms of mean distance it is 0.770+/-0.251 mm (root mean square distance is 0.520+/-0.008 mm; maximum distance is 3.327+/-1.637 mm). The effect of the wavelet pre-processing step was assessed by comparing the proposed method with the 3D thresholding technique (applied on original volume data). This yielded statistically significant differences for all segmentation metrics (p<0.01). Results demonstrate an accurate method that could be used as a first step in computer lung analysis by CT.

  20. Segmentation of Façades from Urban 3D Point Clouds Using Geometrical and Morphological Attribute-Based Operators

    Directory of Open Access Journals (Sweden)

    Andrés Serna

    2016-01-01

    Full Text Available 3D building segmentation is an important research issue in the remote sensing community with relevant applications to urban modeling, cloud-to-cloud and cloud-to-model registration, 3D cartography, virtual reality, cultural heritage documentation, among others. In this paper, we propose automatic, parametric and robust approaches to segment façades from 3D point clouds. Processing is carried out using elevation images and 3D decomposition, and the final result can be reprojected onto the 3D point cloud for visualization or evaluation purposes. Our methods are based on geometrical and geodesic constraints. Parameters are related to urban and architectural constraints. Thus, they can be set up to manage façades of any height, length and elongation. We propose two methods based on façade marker extraction and a third method without markers based on the maximal elongation image. This work is developed in the framework of TerraMobilita project. The performance of our methods is proved in our experiments on TerraMobilita databases using 2D and 3D ground truth annotations.

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

    Science.gov (United States)

    Xia, Chunlei; Wang, Longtan; Chung, Bu-Keun; Lee, Jang-Myung

    2015-01-01

    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. PMID:26295395

  2. 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.

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

    Science.gov (United States)

    Xia, Chunlei; Wang, Longtan; Chung, Bu-Keun; Lee, Jang-Myung

    2015-08-19

    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.

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

    Directory of Open Access Journals (Sweden)

    Emmanuel Christophe

    2007-02-01

    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.

  5. 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.

  6. Grid-Adapted FUN3D Computations for the Second High Lift Prediction Workshop

    Science.gov (United States)

    Lee-Rausch, E. M.; Rumsey, C. L.; Park, M. A.

    2014-01-01

    Contributions of the unstructured Reynolds-averaged Navier-Stokes code FUN3D to the 2nd AIAA CFD High Lift Prediction Workshop are described, and detailed comparisons are made with experimental data. Using workshop-supplied grids, results for the clean wing configuration are compared with results from the structured code CFL3D Using the same turbulence model, both codes compare reasonably well in terms of total forces and moments, and the maximum lift is similarly over-predicted for both codes compared to experiment. By including more representative geometry features such as slat and flap brackets and slat pressure tube bundles, FUN3D captures the general effects of the Reynolds number variation, but under-predicts maximum lift on workshop-supplied grids in comparison with the experimental data, due to excessive separation. However, when output-based, off-body grid adaptation in FUN3D is employed, results improve considerably. In particular, when the geometry includes both brackets and the pressure tube bundles, grid adaptation results in a more accurate prediction of lift near stall in comparison with the wind-tunnel data. Furthermore, a rotation-corrected turbulence model shows improved pressure predictions on the outboard span when using adapted grids.

  7. Segment adaptive gradient angle interpolation.

    Science.gov (United States)

    Zwart, Christine M; Frakes, David H

    2013-08-01

    We introduce a new edge-directed interpolator based on locally defined, straight line approximations of image isophotes. Spatial derivatives of image intensity are used to describe the principal behavior of pixel-intersecting isophotes in terms of their slopes. The slopes are determined by inverting a tridiagonal matrix and are forced to vary linearly from pixel-to-pixel within segments. Image resizing is performed by interpolating along the approximated isophotes. The proposed method can accommodate arbitrary scaling factors, provides state-of-the-art results in terms of PSNR as well as other quantitative visual quality metrics, and has the advantage of reduced computational complexity that is directly proportional to the number of pixels.

  8. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

    OpenAIRE

    Taha, Abdel Aziz; Hanbury, Allan

    2015-01-01

    Background Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by ...

  9. User-guided segmentation of preterm neonate ventricular system from 3-D ultrasound images using convex optimization.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Kishimoto, Jessica; McLeod, Jonathan; Chen, Yimin; de Ribaupierre, Sandrine; Fenster, Aaron

    2015-02-01

    A three-dimensional (3-D) ultrasound (US) system has been developed to monitor the intracranial ventricular system of preterm neonates with intraventricular hemorrhage (IVH) and the resultant dilation of the ventricles (ventriculomegaly). To measure ventricular volume from 3-D US images, a semi-automatic convex optimization-based approach is proposed for segmentation of the cerebral ventricular system in preterm neonates with IVH from 3-D US images. The proposed semi-automatic segmentation method makes use of the convex optimization technique supervised by user-initialized information. Experiments using 58 patient 3-D US images reveal that our proposed approach yielded a mean Dice similarity coefficient of 78.2% compared with the surfaces that were manually contoured, suggesting good agreement between these two segmentations. Additional metrics, the mean absolute distance of 0.65 mm and the maximum absolute distance of 3.2 mm, indicated small distance errors for a voxel spacing of 0.22 × 0.22 × 0.22 mm(3). The Pearson correlation coefficient (r = 0.97, p < 0.001) indicated a significant correlation of algorithm-generated ventricular system volume (VSV) with the manually generated VSV. The calculated minimal detectable difference in ventricular volume change indicated that the proposed segmentation approach with 3-D US images is capable of detecting a VSV difference of 6.5 cm(3) with 95% confidence, suggesting that this approach might be used for monitoring IVH patients' ventricular changes using 3-D US imaging. The mean segmentation times of the graphics processing unit (GPU)- and central processing unit-implemented algorithms were 50 ± 2 and 205 ± 5 s for one 3-D US image, respectively, in addition to 120 ± 10 s for initialization, less than the approximately 35 min required by manual segmentation. In addition, repeatability experiments indicated that the intra-observer variability ranges from 6.5% to 7.5%, and the inter-observer variability is 8.5% in terms

  10. Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT

    Directory of Open Access Journals (Sweden)

    Daniel Markel

    2013-01-01

    Full Text Available Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET. First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT with K-nearest neighbours (KNN classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians.

  11. Sample based 3D face reconstruction from a single frontal image by adaptive locally linear embedding

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jian; ZHUANG Yue-ting

    2007-01-01

    In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Beforehand, an active appearance model (AAM) is trained for automatic feature extraction and adaptive locally linear embedding (ALLE) algorithm is utilized to reduce the dimensionality of the 3D database. Then, given an input frontal face image, the corresponding weights between 3D samples and the image are synthesized adaptively according to the AAM selected facial features. Finally, geometry reconstruction is achieved by linear weighted combination of adaptively selected samples. Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry. The texture of invisible regions between the face and the ears is interpolated by sampling from the frontal image. This approach has several advantages: (1) Only a single frontal face image is needed for highly automatic face reconstruction; (2) Compared with former works, our reconstruction approach provides higher accuracy; (3) Constraint based RBF texture mapping provides natural appearance for reconstructed face.

  12. 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.

  13. 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.

  14. 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

  15. Web-based Visualization and Query of semantically segmented multiresolution 3D Models in the Field of Cultural Heritage

    Science.gov (United States)

    Auer, M.; Agugiaro, G.; Billen, N.; Loos, L.; Zipf, A.

    2014-05-01

    Many important Cultural Heritage sites have been studied over long periods of time by different means of technical equipment, methods and intentions by different researchers. This has led to huge amounts of heterogeneous "traditional" datasets and formats. The rising popularity of 3D models in the field of Cultural Heritage in recent years has brought additional data formats and makes it even more necessary to find solutions to manage, publish and study these data in an integrated way. The MayaArch3D project aims to realize such an integrative approach by establishing a web-based research platform bringing spatial and non-spatial databases together and providing visualization and analysis tools. Especially the 3D components of the platform use hierarchical segmentation concepts to structure the data and to perform queries on semantic entities. This paper presents a database schema to organize not only segmented models but also different Levels-of-Details and other representations of the same entity. It is further implemented in a spatial database which allows the storing of georeferenced 3D data. This enables organization and queries by semantic, geometric and spatial properties. As service for the delivery of the segmented models a standardization candidate of the OpenGeospatialConsortium (OGC), the Web3DService (W3DS) has been extended to cope with the new database schema and deliver a web friendly format for WebGL rendering. Finally a generic user interface is presented which uses the segments as navigation metaphor to browse and query the semantic segmentation levels and retrieve information from an external database of the German Archaeological Institute (DAI).

  16. Parallel Adaptive Computation of Blood Flow in a 3D ``Whole'' Body Model

    Science.gov (United States)

    Zhou, M.; Figueroa, C. A.; Taylor, C. A.; Sahni, O.; Jansen, K. E.

    2008-11-01

    Accurate numerical simulations of vascular trauma require the consideration of a larger portion of the vasculature than previously considered, due to the systemic nature of the human body's response. A patient-specific 3D model composed of 78 connected arterial branches extending from the neck to the lower legs is constructed to effectively represent the entire body. Recently developed outflow boundary conditions that appropriately represent the downstream vasculature bed which is not included in the 3D computational domain are applied at 78 outlets. In this work, the pulsatile blood flow simulations are started on a fairly uniform, unstructured mesh that is subsequently adapted using a solution-based approach to efficiently resolve the flow features. The adapted mesh contains non-uniform, anisotropic elements resulting in resolution that conforms with the physical length scales present in the problem. The effects of the mesh resolution on the flow field are studied, specifically on relevant quantities of pressure, velocity and wall shear stress.

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

    Science.gov (United States)

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

    2016-03-01

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

  18. Estimation of 3-D pore network coordination number of rocks from watershed segmentation of a single 2-D image

    Science.gov (United States)

    Rabbani, Arash; Ayatollahi, Shahab; Kharrat, Riyaz; Dashti, Nader

    2016-08-01

    In this study, we have utilized 3-D micro-tomography images of real and synthetic rocks to introduce two mathematical correlations which estimate the distribution parameters of 3-D coordination number using a single 2-D cross-sectional image. By applying a watershed segmentation algorithm, it is found that the distribution of 3-D coordination number is acceptably predictable by statistical analysis of the network extracted from 2-D images. In this study, we have utilized 25 volumetric images of rocks in order to propose two mathematical formulas. These formulas aim to approximate the average and standard deviation of coordination number in 3-D pore networks. Then, the formulas are applied for five independent test samples to evaluate the reliability. Finally, pore network flow modeling is used to find the error of absolute permeability prediction using estimated and measured coordination numbers. Results show that the 2-D images are considerably informative about the 3-D network of the rocks and can be utilized to approximate the 3-D connectivity of the porous spaces with determination coefficient of about 0.85 that seems to be acceptable considering the variety of the studied samples.

  19. CONVERGENCE OF ADAPTIVE EDGE ELEMENT METHODS FOR THE 3D EDDY CURRENTS EQUATIONS

    Institute of Scientific and Technical Information of China (English)

    R.H.W. Hoppe; J. Sch(o)berl

    2009-01-01

    We consider an Adaptive Edge Finite Element Method (AEFEM) for the 3D eddy cur-rents equations with variable coefficients using a residual-type a posteriori error estimator. Both the components of the estimator and certain oscillation terms, due to the occurrence of the variable coefficients, have to be controlled properly within the adaptive loop which is taken care of by appropriate bulk criteria. Convergence of the AEFEM in terms of reductions of the energy norm of the discretization error and of the oscillations is shown. Numerical results are given to illustrate the performance of the AEFEM.

  20. Estimation of regeneration coverage in a temperate forest by 3D segmentation using airborne laser scanning data

    Science.gov (United States)

    Amiri, Nina; Yao, Wei; Heurich, Marco; Krzystek, Peter; Skidmore, Andrew K.

    2016-10-01

    Forest understory and regeneration are important factors in sustainable forest management. However, understanding their spatial distribution in multilayered forests requires accurate and continuously updated field data, which are difficult and time-consuming to obtain. Therefore, cost-efficient inventory methods are required, and airborne laser scanning (ALS) is a promising tool for obtaining such information. In this study, we examine a clustering-based 3D segmentation in combination with ALS data for regeneration coverage estimation in a multilayered temperate forest. The core of our method is a two-tiered segmentation of the 3D point clouds into segments associated with regeneration trees. First, small parts of trees (super-voxels) are constructed through mean shift clustering, a nonparametric procedure for finding the local maxima of a density function. In the second step, we form a graph based on the mean shift clusters and merge them into larger segments using the normalized cut algorithm. These segments are used to obtain regeneration coverage of the target plot. Results show that, based on validation data from field inventory and terrestrial laser scanning (TLS), our approach correctly estimates up to 70% of regeneration coverage across the plots with different properties, such as tree height and tree species. The proposed method is negatively impacted by the density of the overstory because of decreasing ground point density. In addition, the estimated coverage has a strong relationship with the overstory tree species composition.

  1. 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.

  2. Manifold learning for shape guided segmentation of cardiac boundaries: application to 3D+t cardiac MRI.

    Science.gov (United States)

    Eslami, Abouzar; Yigitsoy, Mehmet; Navab, Nassir

    2011-01-01

    In this paper we propose a new method for shape guided segmentation of cardiac boundaries based on manifold learning of the shapes represented by the phase field approximation of the Mumford-Shah functional. A novel distance is defined to measure the similarity of shapes without requiring deformable registration. Cardiac motion is compensated and phases are mapped into one reference phase, that is the end of diastole, to avoid time warping and synchronization at all cardiac phases. Non-linear embedding of these 3D shapes extracts the manifold of the inter-subject variation of the heart shape to be used for guiding the segmentation for a new subject. For validation the method is applied to a comprehensive dataset of 3D+t cardiac Cine MRI from normal subjects and patients.

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

    Energy Technology Data Exchange (ETDEWEB)

    Tran, Thanh N., E-mail: thanh.tran@merck.com [Center for Mathematical Sciences Merck, MSD Molenstraat 110, 5342 CC Oss, PO Box 20, 5340 BH Oss (Netherlands); Nguyen, Thanh T.; Willemsz, Tofan A. [Department of Pharmaceutical Technology and Biopharmacy, University of Groningen, Groningen (Netherlands); Pharmaceutical Sciences and Clinical Supplies, Merck MSD, PO Box 20, 5340 BH Oss (Netherlands); Kessel, Gijs van [Center for Mathematical Sciences Merck, MSD Molenstraat 110, 5342 CC Oss, PO Box 20, 5340 BH Oss (Netherlands); Frijlink, Henderik W. [Department of Pharmaceutical Technology and Biopharmacy, University of Groningen, Groningen (Netherlands); Voort Maarschalk, Kees van der [Department of Pharmaceutical Technology and Biopharmacy, University of Groningen, Groningen (Netherlands); Competence Center Process Technology, Purac Biochem, Gorinchem (Netherlands)

    2012-05-06

    Highlights: Black-Right-Pointing-Pointer We revised the DBSCAN algorithm for segmentation and clustering of large 3D image dataset and classified multivariate image. Black-Right-Pointing-Pointer The algorithm takes into account the coordinate system of the image data to improve the computational performance. Black-Right-Pointing-Pointer The algorithm solved the instability problem in boundaries detection of the original DBSCAN. Black-Right-Pointing-Pointer 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.

  4. Adaptive clutter rejection for 3D color Doppler imaging: preliminary clinical study.

    Science.gov (United States)

    Yoo, Yang Mo; Sikdar, Siddhartha; Karadayi, Kerem; Kolokythas, Orpheus; Kim, Yongmin

    2008-08-01

    In three-dimensional (3D) ultrasound color Doppler imaging (CDI), effective rejection of flash artifacts caused by tissue motion (clutter) is important for improving sensitivity in visualizing blood flow in vessels. Since clutter characteristics can vary significantly during volume acquisition, a clutter rejection technique that can adapt to the underlying clutter conditions is desirable for 3D CDI. We have previously developed an adaptive clutter rejection (ACR) method, in which an optimum filter is dynamically selected from a set of predesigned clutter filters based on the measured clutter characteristics. In this article, we evaluated the ACR method with 3D in vivo data acquired from 37 kidney transplant patients clinically indicated for a duplex ultrasound examination. We compared ACR against a conventional clutter rejection method, down-mixing (DM), using a commonly-used flow signal-to-clutter ratio (SCR) and a new metric called fractional residual clutter area (FRCA). The ACR method was more effective in removing the flash artifacts while providing higher sensitivity in detecting blood flow in the arcuate arteries and veins in the parenchyma of transplanted kidneys. ACR provided 3.4 dB improvement in SCR over the DM method (11.4 +/- 1.6 dB versus 8.0 +/- 2.0 dB, p < 0.001) and had lower average FRCA values compared with the DM method (0.006 +/- 0.003 versus 0.036 +/- 0.022, p < 0.001) for all study subjects. These results indicate that the new ACR method is useful for removing nonstationary tissue motion while improving the image quality for visualizing 3D vascular structure in 3D CDI.

  5. From voxels to knowledge: a practical guide to the segmentation of complex electron microscopy 3D-data.

    Science.gov (United States)

    Tsai, Wen-Ting; Hassan, Ahmed; Sarkar, Purbasha; Correa, Joaquin; Metlagel, Zoltan; Jorgens, Danielle M; Auer, Manfred

    2014-08-13

    Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation. The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data

  6. A Simple Fault-Tolerant Adaptive and Minimal Routing Approach in 3-D Meshes

    Institute of Scientific and Technical Information of China (English)

    WU Jie(吴杰)

    2003-01-01

    In this paper we propose a sufficient condition for minimal routing in 3-dimensional (3-D) meshes with faulty nodes. It is based on an early work of the author on minimal routing in 2-dimensional (2-D) meshes. Unlike many traditional models that assume all the nodes know global fault distribution or just adjacent fault information, our approach is based on the concept of limited global fault information. First, we propose a fault model called faulty cube in which all faulty nodes in the system are contained in a set of faulty cubes. Fault information is then distributed to limited number of nodes while it is still sufficient to support minimal routing. The limited fault information collected at each node is represented by a vector called extended safety level. The extended safety level associated with a node can be used to determine the existence of a minimal path from this node to a given destination. Specifically, we study the existence of minimal paths at a given source node, limited distribution of fault information, minimal routing, and deadlock-free and livelock-free routing. Our results show that any minimal routing that is partially adaptive can be applied in our model as long as the destination node meets a certain condition. We also propose a dynamic planar-adaptive routing scheme that offers better fault tolerance and adaptivity than the planar-adaptive routing scheme in 3-D meshes. Our approach is the first attempt to address adaptive and minimal routing in 3-D meshes with faulty nodes using limited fault information.

  7. Combining population and patient-specific characteristics for prostate segmentation on 3D CT images

    Science.gov (United States)

    Ma, Ling; Guo, Rongrong; Tian, Zhiqiang; Venkataraman, Rajesh; Sarkar, Saradwata; Liu, Xiabi; Tade, Funmilayo; Schuster, David M.; Fei, Baowei

    2016-03-01

    Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy.

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

    Science.gov (United States)

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

    2008-10-01

    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

  9. A 3-D Novel Conservative Chaotic System and its Generalized Projective Synchronization via Adaptive Control

    Directory of Open Access Journals (Sweden)

    S. Vaidyanathan

    2014-11-01

    Full Text Available This research work proposes a five-term 3-D novel conservative chaotic system with a quadratic nonlinearity and a quartic nonlinearity. The conservative chaotic systems have the important property that they are volume conserving. The Lyapunov exponents of the 3-D novel chaotic system are obtained as �! = 0.0836, �! = 0 and �! = −0.0836. Since the sum of the Lyapunov exponents is zero, the 3-D novel chaotic system is conservative. Thus, the Kaplan-Yorke dimension of the 3-D novel chaotic system is easily seen as 3.0000. The phase portraits of the novel chaotic system simulated using MATLAB depict the chaotic attractor of the novel system. This research work also discusses other qualitative properties of the system. Next, an adaptive controller is designed to achieve Generalized Projective Synchronization (GPS of two identical novel chaotic systems with unknown system parameters. MATLAB simulations are shown to validate and demonstrate the GPS results derived in this work.

  10. 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 med

  11. 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.

  12. 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.

  13. 3D-FIESTA Magnetic Resonance Angiography Fusion Imaging of Distal Segment of Occluded Middle Cerebral Artery.

    Science.gov (United States)

    Kuribara, Tomoyoshi; Haraguchi, Koichi; Ogane, Kazumi; Matsuura, Nobuki; Ito, Takeo

    2015-01-01

    Middle cerebral artery (MCA) occlusion was examined with basi-parallel anatomical scanning (BPAS) using three-dimensional fast imaging employing steady-state acquisition (3D-FIESTA), and 3D-FIESTA and magnetic resonance angiography (MRA) fusion images were created. We expected that an incidence of hemorrhagic complications due to vessel perforations would be decreased by obtaining vascular information beyond the occlusion and thus acute endovascular revascularization could be performed using such techniques. We performed revascularization for acute MCA occlusion for five patients who were admitted in our hospital from October 2012 to October 2014. Patients consisted of 1 man and 4 women with a mean age of 76.2 years (range: 59-86 years). Fusion images were created from three-dimensional time of flight (3D-TOF) MRA and 3D-FIESTA with phase cycling (3D-FIESTA-C). Then thrombectomy was performed in all the 5 patients. Merci retriever to 1 patient, Penumbra system to 1, urokinase infusion to 2, and Solitaire to 1 using such techniques. In all cases, a 3D-FIESTA-MRA fusion imaging could depict approximately clear vascular information to at least the M3 segment beyond the occlusion. And each acute revascularization was able to perform smoothly using these imaging techniques. In all cases, there was no symptomatic hemorrhagic complication. It showed that 3D-FIESTA MRA fusion imaging technique could obtain vascular information beyond the MCA occlusion. In this study, no symptomatic hemorrhagic complications were detected. It could imply that such techniques were useful not only to improve treatment efficiency but also to reduce the risk of development of hemorrhagic complications caused by vessel perforations in acute revascularization.

  14. Metastatic liver tumour segmentation with a neural network-guided 3D deformable model.

    Science.gov (United States)

    Vorontsov, Eugene; Tang, An; Roy, David; Pal, Christopher J; Kadoury, Samuel

    2017-01-01

    The segmentation of liver tumours in CT images is useful for the diagnosis and treatment of liver cancer. Furthermore, an accurate assessment of tumour volume aids in the diagnosis and evaluation of treatment response. Currently, segmentation is performed manually by an expert, and because of the time required, a rough estimate of tumour volume is often done instead. We propose a semi-automatic segmentation method that makes use of machine learning within a deformable surface model. Specifically, we propose a deformable model that uses a voxel classifier based on a multilayer perceptron (MLP) to interpret the CT image. The new deformable model considers vertex displacement towards apparent tumour boundaries and regularization that promotes surface smoothness. During operation, a user identifies the target tumour and the mesh then automatically delineates the tumour from the MLP processed image. The method was tested on a dataset of 40 abdominal CT scans with a total of 95 colorectal metastases collected from a variety of scanners with variable spatial resolution. The segmentation results are encouraging with a Dice similarity metric of [Formula: see text] and demonstrates that the proposed method can deal with highly variable data. This work motivates further research into tumour segmentation using machine learning with more data and deeper neural networks.

  15. 3D Modeling of Murine Abdominal Aortic Aneurysms: Quantification of Segmentation and Volumetric Reconstruction

    OpenAIRE

    Sarmiento, Paula A; Adelsperger, Amelia R; Goergen, Craig J.

    2016-01-01

    Abdominal Aortic Aneurysms (AAA) cause 5,900 deaths in the United States each year. Surgical intervention is clinically studied by non-invasive techniques such as computed tomography and magnetic resonance imaging. However, three-dimensional (3D) ultrasound imaging has become an inexpensive alternative and useful tool to characterize aneurysms, allowing for reconstruction of the vessel, quantification of hemodynamics through computational fluid dynamics (CFD) simulation, and possible predicti...

  16. SATZ An Adaptive Sentence Segmentation System

    CERN Document Server

    Palmer, D D

    1995-01-01

    This paper provides a detailed description of the sentence segmentation system first introduced in cmp-lg/9411022. It provides results of systematic experiments involving sentence boundary determination, including context size, lexicon size, and single-case texts. Also included are the results of successfully adapting the system to German and French. The source code for the system is available as a compressed tar file at ftp://cs-tr.CS.Berkeley.EDU/pub/cstr/satz.tar.Z .

  17. 3D cerebral MR image segmentation using multiple-classifier system.

    Science.gov (United States)

    Amiri, Saba; Movahedi, Mohammad Mehdi; Kazemi, Kamran; Parsaei, Hossein

    2017-03-01

    The three soft brain tissues white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) identified in a magnetic resonance (MR) image via image segmentation techniques can aid in structural and functional brain analysis, brain's anatomical structures measurement and visualization, neurodegenerative disorders diagnosis, and surgical planning and image-guided interventions, but only if obtained segmentation results are correct. This paper presents a multiple-classifier-based system for automatic brain tissue segmentation from cerebral MR images. The developed system categorizes each voxel of a given MR image as GM, WM, and CSF. The algorithm consists of preprocessing, feature extraction, and supervised classification steps. In the first step, intensity non-uniformity in a given MR image is corrected and then non-brain tissues such as skull, eyeballs, and skin are removed from the image. For each voxel, statistical features and non-statistical features were computed and used a feature vector representing the voxel. Three multilayer perceptron (MLP) neural networks trained using three different datasets were used as the base classifiers of the multiple-classifier system. The output of the base classifiers was fused using majority voting scheme. Evaluation of the proposed system was performed using Brainweb simulated MR images with different noise and intensity non-uniformity and internet brain segmentation repository (IBSR) real MR images. The quantitative assessment of the proposed method using Dice, Jaccard, and conformity coefficient metrics demonstrates improvement (around 5 % for CSF) in terms of accuracy as compared to single MLP classifier and the existing methods and tools such FSL-FAST and SPM. As accurately segmenting a MR image is of paramount importance for successfully promoting the clinical application of MR image segmentation techniques, the improvement obtained by using multiple-classifier-based system is encouraging.

  18. Fast algorithm for optimal graph-Laplacian based 3D image segmentation

    Science.gov (United States)

    Harizanov, S.; Georgiev, I.

    2016-10-01

    In this paper we propose an iterative steepest-descent-type algorithm that is observed to converge towards the exact solution of the ℓ0 discrete optimization problem, related to graph-Laplacian based image segmentation. Such an algorithm allows for significant additional improvements on the segmentation quality once the minimizer of the associated relaxed ℓ1 continuous optimization problem is computed, unlike the standard strategy of simply hard-thresholding the latter. Convergence analysis of the algorithm is not a subject of this work. Instead, various numerical experiments, confirming the practical value of the algorithm, are documented.

  19. 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

    Level set method based segmentation provides an efficient tool for topological and geometrical shape handling. Conventional level set surfaces are only $C^0$ continuous since the level set evolution involves linear interpolation to compute derivatives. Bajaj et al. present a higher order method t...

  20. 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 orde

  1. Graph-cut Based Interactive Segmentation of 3D Materials-Science Images

    Science.gov (United States)

    2014-04-26

    problems in computational vision. J. Am. Stat. Assoc. 76–89 (1987) 31. Martin , D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural...Medical Image Computing and Computer-Assisted Intervention vol. 6893, pp. 603–610 (2011) 54. Unger, M., Pock, T., Trobin, W., Cremers , D., Bischof, H

  2. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images

    Science.gov (United States)

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

    2014-12-01

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

  3. 3D segmentation of abdominal aorta from CT-scan and MR images.

    Science.gov (United States)

    Duquette, Anthony Adam; Jodoin, Pierre-Marc; Bouchot, Olivier; Lalande, Alain

    2012-06-01

    We designed a generic method for segmenting the aneurismal sac of an abdominal aortic aneurysm (AAA) both from multi-slice MR and CT-scan examinations. It is a semi-automatic method requiring little human intervention and based on graph cut theory to segment the lumen interface and the aortic wall of AAAs. Our segmentation method works independently on MRI and CT-scan volumes and has been tested on a 44 patient dataset and 10 synthetic images. Segmentation and maximum diameter estimation were compared to manual tracing from 4 experts. An inter-observer study was performed in order to measure the variability range of a human observer. Based on three metrics (the maximum aortic diameter, the volume overlap and the Hausdorff distance) the variability of the results obtained by our method is shown to be similar to that of a human operator, both for the lumen interface and the aortic wall. As will be shown, the average distance obtained with our method is less than one standard deviation away from each expert, both for healthy subjects and for patients with AAA. Our semi-automatic method provides reliable contours of the abdominal aorta from CT-scan or MRI, allowing rapid and reproducible evaluations of AAA.

  4. Model-based 3D segmentation of the bones of joints in medical images

    Science.gov (United States)

    Liu, Jiamin; Udupa, Jayaram K.; Saha, Punam K.; Odhner, Dewey; Hirsch, Bruce E.; Siegler, Sorin; Simon, Scott; Winkelstein, Beth A.

    2005-04-01

    There are several medical application areas that require the segmentation and separation of the component bones of joints in a sequence of acquired images of the joint under various loading conditions, our own target area being joint motion analysis. This is a challenging problem due to the proximity of bones at the joint, partial volume effects, and other imaging modality-specific factors that confound boundary contrast. A model-based strategy is proposed in this paper wherein a rigid model of the bone is generated from a segmentation of the bone in the image corresponding to one position of the joint by using the live wire method. In other images of the joint, this model is used to search for the same bone by minimizing an energy functional that utilizes both boundary- and region-based information. An evaluation of the method by utilizing a total of 60 data sets on MR and CT images of the ankle complex and cervical spine indicates that the segmentations agree very closely with the live wire segmentations yielding true positive and false positive volume fractions in the range 89-97% and 0.2-0.7%. The method requires 1-2 minutes of operator time and 6-7 minutes of computer time, which makes it significantly more efficient than live wire - the only method currently available for the task.

  5. Shape-adaptive DCT for denoising of 3D scalar and tensor valued images.

    Science.gov (United States)

    Bergmann, Ørjan; Christiansen, Oddvar; Lie, Johan; Lundervold, Arvid

    2009-06-01

    During the last ten years or so, diffusion tensor imaging has been used in both research and clinical medical applications. To construct the diffusion tensor images, a large set of direction sensitive magnetic resonance image (MRI) acquisitions are required. These acquisitions in general have a lower signal-to-noise ratio than conventional MRI acquisitions. In this paper, we discuss computationally effective algorithms for noise removal for diffusion tensor magnetic resonance imaging (DTI) using the framework of 3-dimensional shape-adaptive discrete cosine transform. We use local polynomial approximations for the selection of homogeneous regions in the DTI data. These regions are transformed to the frequency domain by a modified discrete cosine transform. In the frequency domain, the noise is removed by thresholding. We perform numerical experiments on 3D synthetical MRI and DTI data and real 3D DTI brain data from a healthy volunteer. The experiments indicate good performance compared to current state-of-the-art methods. The proposed method is well suited for parallelization and could thus dramatically improve the computation speed of denoising schemes for large scale 3D MRI and DTI.

  6. Anterior segment optical coherence tomography for the diagnosis of corneal dystrophies according to the IC3D classification.

    Science.gov (United States)

    Siebelmann, Sebastian; Scholz, Paula; Sonnenschein, Simon; Bachmann, Björn; Matthaei, Mario; Cursiefen, Claus; Heindl, Ludwig M

    2017-08-09

    Corneal dystrophies are categorized according to the International Committee for Classification of Corneal Dystrophies (IC3D) classification, and their treatment depends on the affected structures and layer of the cornea. Therefore, estimating the depth and extent of the morphological changes due to the specific dystrophy is crucial when deciding between different treatment options. Besides superficial laser treatments and penetrating keratoplasty, minimal invasive lamellar keratoplasties such as Descemet membrane endothelial keratoplasty, deep anterior lamellar keratoplasty, or Descemet stripping automated keratoplasty have become increasingly popular to exchange the specific opaque layers in dystrophic eyes. To determine the morphological changes of the cornea in the different dystrophies, in addition to slit-lamp examination, anterior segment optical coherence tomography has become an important tool with nearly histological resolution. Nonetheless, only a few case series describe the characteristics of changes seen on anterior segment optical coherence tomography. Therefore, we summarize anterior segment optical coherence tomography signs and correlate with slit-lamp examination, as well as the histopathological findings, of corneal dystrophies according to the IC3D classification. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. 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.

  8. Intracranial aneurysm segmentation in 3D CT angiography: method and quantitative validation

    Science.gov (United States)

    Firouzian, Azadeh; Manniesing, R.; Flach, Z. H.; Risselada, R.; van Kooten, F.; Sturkenboom, M. C. J. M.; van der Lugt, A.; Niessen, W. J.

    2010-03-01

    Accurately quantifying aneurysm shape parameters is of clinical importance, as it is an important factor in choosing the right treatment modality (i.e. coiling or clipping), in predicting rupture risk and operative risk and for pre-surgical planning. The first step in aneurysm quantification is to segment it from other structures that are present in the image. As manual segmentation is a tedious procedure and prone to inter- and intra-observer variability, there is a need for an automated method which is accurate and reproducible. In this paper a novel semi-automated method for segmenting aneurysms in Computed Tomography Angiography (CTA) data based on Geodesic Active Contours is presented and quantitatively evaluated. Three different image features are used to steer the level set to the boundary of the aneurysm, namely intensity, gradient magnitude and variance in intensity. The method requires minimum user interaction, i.e. clicking a single seed point inside the aneurysm which is used to estimate the vessel intensity distribution and to initialize the level set. The results show that the developed method is reproducible, and performs in the range of interobserver variability in terms of accuracy.

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

    Science.gov (United States)

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

    1994-05-01

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

  10. Computer-aided segmentation and 3D analysis of in vivo MRI examinations of the human vocal tract during phonation

    Science.gov (United States)

    Wismüller, Axel; Behrends, Johannes; Hoole, Phil; Leinsinger, Gerda L.; Meyer-Baese, Anke; Reiser, Maximilian F.

    2008-03-01

    We developed, tested, and evaluated a 3D segmentation and analysis system for in vivo MRI examinations of the human vocal tract during phonation. For this purpose, six professionally trained speakers, age 22-34y, were examined using a standardized MRI protocol (1.5 T, T1w FLASH, ST 4mm, 23 slices, acq. time 21s). The volunteers performed a prolonged (>=21s) emission of sounds of the German phonemic inventory. Simultaneous audio tape recording was obtained to control correct utterance. Scans were made in axial, coronal, and sagittal planes each. Computer-aided quantitative 3D evaluation included (i) automated registration of the phoneme-specific data acquired in different slice orientations, (ii) semi-automated segmentation of oropharyngeal structures, (iii) computation of a curvilinear vocal tract midline in 3D by nonlinear PCA, (iv) computation of cross-sectional areas of the vocal tract perpendicular to this midline. For the vowels /a/,/e/,/i/,/o/,/ø/,/u/,/y/, the extracted area functions were used to synthesize phoneme sounds based on an articulatory-acoustic model. For quantitative analysis, recorded and synthesized phonemes were compared, where area functions extracted from 2D midsagittal slices were used as a reference. All vowels could be identified correctly based on the synthesized phoneme sounds. The comparison between synthesized and recorded vowel phonemes revealed that the quality of phoneme sound synthesis was improved for phonemes /a/ and /y/, if 3D instead of 2D data were used, as measured by the average relative frequency shift between recorded and synthesized vowel formants (pproduction.

  11. Object-adaptive depth compensated inter prediction for depth video coding in 3D video system

    Science.gov (United States)

    Kang, Min-Koo; Lee, Jaejoon; Lim, Ilsoon; Ho, Yo-Sung

    2011-01-01

    Nowadays, the 3D video system using the MVD (multi-view video plus depth) data format is being actively studied. The system has many advantages with respect to virtual view synthesis such as an auto-stereoscopic functionality, but compression of huge input data remains a problem. Therefore, efficient 3D data compression is extremely important in the system, and problems of low temporal consistency and viewpoint correlation should be resolved for efficient depth video coding. In this paper, we propose an object-adaptive depth compensated inter prediction method to resolve the problems where object-adaptive mean-depth difference between a current block, to be coded, and a reference block are compensated during inter prediction. In addition, unique properties of depth video are exploited to reduce side information required for signaling decoder to conduct the same process. To evaluate the coding performance, we have implemented the proposed method into MVC (multiview video coding) reference software, JMVC 8.2. Experimental results have demonstrated that our proposed method is especially efficient for depth videos estimated by DERS (depth estimation reference software) discussed in the MPEG 3DV coding group. The coding gain was up to 11.69% bit-saving, and it was even increased when we evaluated it on synthesized views of virtual viewpoints.

  12. Scalable and Adaptive Streaming of 3D Mesh to Heterogeneous Devices

    Science.gov (United States)

    Abderrahim, Zeineb; Bouhlel, Mohamed Salim

    2016-12-01

    This article comprises a presentation of a web platform for the diffusion and visualization of 3D compressed data on the web. Indeed, the major goal of this work resides in the proposal of the transfer adaptation of the three-dimensional data to resources (network bandwidth, the type of visualization terminals, display resolution, user's preferences...). Also, it is an attempt to provide an effective consultation adapted to the user's request (preferences, levels of the requested detail, etc.). Such a platform can adapt the levels of detail to the change in the bandwidth and the rendering time when loading the mesh at the client level. In addition, the levels of detail are adapted to the distance between the object and the camera. These features are able to minimize the latency time and to make the real time interaction possible. The experiences as well as the comparison with the existing solutions show auspicious results in terms of latency, scalability and the quality of the experience offered to the users.

  13. 3D Simulation of Flow with Free Surface Based on Adaptive Octree Mesh System

    Institute of Scientific and Technical Information of China (English)

    Li Shaowu; Zhuang Qian; Huang Xiaoyun; Wang Dong

    2015-01-01

    The technique of adaptive tree mesh is an effective way to reduce computational cost through automatic adjustment of cell size according to necessity. In the present study, the 2D numerical N-S solver based on the adaptive quadtree mesh system was extended to a 3D one, in which a spatially adaptive octree mesh system and multiple parti-cle level set method were adopted for the convenience to deal with the air-water-structure multiple-medium coexisting domain. The stretching process of a dumbbell was simulated and the results indicate that the meshes are well adaptable to the free surface. The collapsing process of water column impinging a circle cylinder was simulated and from the results, it can be seen that the processes of fluid splitting and merging are properly simulated. The interaction of sec-ond-order Stokes waves with a square cylinder was simulated and the obtained drag force is consistent with the result by the Morison’s wave force formula with the coefficient values of the stable drag component and the inertial force component being set as 2.54.

  14. Thrust fault segmentation and downward fault propagation in accretionary wedges: New Insights from 3D seismic reflection data

    Science.gov (United States)

    Orme, Haydn; Bell, Rebecca; Jackson, Christopher

    2016-04-01

    The shallow parts of subduction megathrust faults are typically thought to be aseismic and incapable of propagating seismic rupture. The 2011 Tohoku-Oki earthquake, however, ruptured all the way to the trench, proving that in some locations rupture can propagate through the accretionary wedge. An improved understanding of the structural character and physical properties of accretionary wedges is therefore crucial to begin to assess why such anomalously shallow seismic rupture occurs. Despite its importance, we know surprisingly little regarding the 3D geometry and kinematics of thrust network development in accretionary prisms, largely due to a lack of 3D seismic reflection data providing high-resolution, 3D images of entire networks. Thus our current understanding is largely underpinned by observations from analogue and numerical modelling, with limited observational data from natural examples. In this contribution we use PSDM, 3D seismic reflection data from the Nankai margin (3D Muroto dataset, available from the UTIG Academic Seismic Portal, Marine Geoscience Data System) to examine how imbricate thrust fault networks evolve during accretionary wedge growth. We unravel the evolution of faults within the protothrust and imbricate thrust zones by interpreting multiple horizons across faults and measuring fault displacement and fold amplitude along-strike; by doing this, we are able to investigate the three dimensional accrual of strain. We document a number of local displacement minima along-strike of faults, suggesting that, the protothrust and imbricate thrusts developed from the linkage of smaller, previously isolated fault segments. Although we often assume imbricate faults are likely to have propagated upwards from the décollement we show strong evidence for fault nucleation at shallow depths and downward propagation to intersect the décollement. The complex fault interactions documented here have implications for hydraulic compartmentalisation and pore

  15. Adaptive associative memories capable of pattern segmentation.

    Science.gov (United States)

    Ma, Q

    1996-01-01

    This paper presents an adaptive type of associative memory (AAM) that can separate patterns from composite inputs which might be degraded by deficiency or noise and that can recover incomplete or noisy single patterns. The behavior of AAM is analyzed in terms of stability, giving the stable solutions (results of recall), and the recall of spurious memories (the undesired solutions) is shown to be greatly reduced compared with earlier types of associative memory that can perform pattern segmentation. Two conditions that guarantee the nonexistence of undesired solutions are also given. Results of computer experiments show that the performance of AAM is much better than that of the earlier types of associative memory in terms of pattern segmentation and pattern recovery.

  16. 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

  17. Adaptive multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model

    Science.gov (United States)

    Navarro, Cristóbal A.; Huang, Wei; Deng, Youjin

    2016-08-01

    This work presents an adaptive multi-GPU Exchange Monte Carlo approach for the simulation of the 3D Random Field Ising Model (RFIM). The design is based on a two-level parallelization. The first level, spin-level parallelism, maps the parallel computation as optimal 3D thread-blocks that simulate blocks of spins in shared memory with minimal halo surface, assuming a constant block volume. The second level, replica-level parallelism, uses multi-GPU computation to handle the simulation of an ensemble of replicas. CUDA's concurrent kernel execution feature is used in order to fill the occupancy of each GPU with many replicas, providing a performance boost that is more notorious at the smallest values of L. In addition to the two-level parallel design, the work proposes an adaptive multi-GPU approach that dynamically builds a proper temperature set free of exchange bottlenecks. The strategy is based on mid-point insertions at the temperature gaps where the exchange rate is most compromised. The extra work generated by the insertions is balanced across the GPUs independently of where the mid-point insertions were performed. Performance results show that spin-level performance is approximately two orders of magnitude faster than a single-core CPU version and one order of magnitude faster than a parallel multi-core CPU version running on 16-cores. Multi-GPU performance is highly convenient under a weak scaling setting, reaching up to 99 % efficiency as long as the number of GPUs and L increase together. The combination of the adaptive approach with the parallel multi-GPU design has extended our possibilities of simulation to sizes of L = 32 , 64 for a workstation with two GPUs. Sizes beyond L = 64 can eventually be studied using larger multi-GPU systems.

  18. Adaptive image segmentation applied to plant reproduction by tissue culture

    Science.gov (United States)

    Vazquez Rueda, Martin G.; Hahn, Federico; Zapata, Jose L.

    1997-04-01

    This paper presents that experimental results obtained on indoor tissue culture using the adaptive image segmentation system. The performance of the adaptive technique is contrasted with different non-adaptive techniques commonly used in the computer vision field to demonstrate the improvement provided by the adaptive image segmentation system.

  19. 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.

  20. Automatic Detection, Segmentation and Classification of Retinal Horizontal Neurons in Large-scale 3D Confocal Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Karakaya, Mahmut [ORNL; Kerekes, Ryan A [ORNL; Gleason, Shaun Scott [ORNL; Martins, Rodrigo [St. Jude Children' s Research Hospital; Dyer, Michael [St. Jude Children' s Research Hospital

    2011-01-01

    Automatic analysis of neuronal structure from wide-field-of-view 3D image stacks of retinal neurons is essential for statistically characterizing neuronal abnormalities that may be causally related to neural malfunctions or may be early indicators for a variety of neuropathies. In this paper, we study classification of neuron fields in large-scale 3D confocal image stacks, a challenging neurobiological problem because of the low spatial resolution imagery and presence of intertwined dendrites from different neurons. We present a fully automated, four-step processing approach for neuron classification with respect to the morphological structure of their dendrites. In our approach, we first localize each individual soma in the image by using morphological operators and active contours. By using each soma position as a seed point, we automatically determine an appropriate threshold to segment dendrites of each neuron. We then use skeletonization and network analysis to generate the morphological structures of segmented dendrites, and shape-based features are extracted from network representations of each neuron to characterize the neuron. Based on qualitative results and quantitative comparisons, we show that we are able to automatically compute relevant features that clearly distinguish between normal and abnormal cases for postnatal day 6 (P6) horizontal neurons.

  1. Automatic detection, segmentation and characterization of retinal horizontal neurons in large-scale 3D confocal imagery

    Science.gov (United States)

    Karakaya, Mahmut; Kerekes, Ryan A.; Gleason, Shaun S.; Martins, Rodrigo A. P.; Dyer, Michael A.

    2011-03-01

    Automatic analysis of neuronal structure from wide-field-of-view 3D image stacks of retinal neurons is essential for statistically characterizing neuronal abnormalities that may be causally related to neural malfunctions or may be early indicators for a variety of neuropathies. In this paper, we study classification of neuron fields in large-scale 3D confocal image stacks, a challenging neurobiological problem because of the low spatial resolution imagery and presence of intertwined dendrites from different neurons. We present a fully automated, four-step processing approach for neuron classification with respect to the morphological structure of their dendrites. In our approach, we first localize each individual soma in the image by using morphological operators and active contours. By using each soma position as a seed point, we automatically determine an appropriate threshold to segment dendrites of each neuron. We then use skeletonization and network analysis to generate the morphological structures of segmented dendrites, and shape-based features are extracted from network representations of each neuron to characterize the neuron. Based on qualitative results and quantitative comparisons, we show that we are able to automatically compute relevant features that clearly distinguish between normal and abnormal cases for postnatal day 6 (P6) horizontal neurons.

  2. Ellipsoid Segmentation Model for Analyzing Light-Attenuated 3D Confocal Image Stacks of Fluorescent Multi-Cellular Spheroids.

    Directory of Open Access Journals (Sweden)

    Michaël Barbier

    Full Text Available In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation.

  3. 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.

  4. A novel 3-D jerk chaotic system with three quadratic nonlinearities and its adaptive control

    Directory of Open Access Journals (Sweden)

    Vaidyanathan Sundarapandian

    2016-03-01

    Full Text Available This paper announces an eight-term novel 3-D jerk chaotic system with three quadratic nonlinearities. The phase portraits of the novel jerk chaotic system are displayed and the qualitative properties of the jerk system are described. The novel jerk chaotic system has two equilibrium points, which are saddle-foci and unstable. The Lyapunov exponents of the novel jerk chaotic system are obtained as L1 = 0.20572,L2 = 0 and L3 = −1.20824. Since the sum of the Lyapunov exponents of the jerk chaotic system is negative, we conclude that the chaotic system is dissipative. The Kaplan-Yorke dimension of the novel jerk chaotic system is derived as DKY = 2.17026. Next, an adaptive controller is designed via backstepping control method to globally stabilize the novel jerk chaotic system with unknown parameters. Moreover, an adaptive controller is also designed via backstepping control method to achieve global chaos synchronization of the identical jerk chaotic systems with unknown parameters. The backstepping control method is a recursive procedure that links the choice of a Lyapunov function with the design of a controller and guarantees global asymptotic stability of strict feedback systems. MATLAB simulations have been depicted to illustrate the phase portraits of the novel jerk chaotic system and also the adaptive backstepping control results.

  5. 3D design and electric simulation of a silicon drift detector using a spiral biasing adapter

    Science.gov (United States)

    Li, Yu-yun; Xiong, Bo; Li, Zheng

    2016-09-01

    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.

  6. 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.

  7. Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform.

    Science.gov (United States)

    De Queiroz, Ricardo; Chou, Philip A

    2016-06-01

    In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time and with the recent possibility of real time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably to the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this work represents the state-of-the-art in intra-frame compression of point clouds for real-time 3D video.

  8. 3D Continuum Radiative Transfer. An adaptive grid construction algorithm based on the Monte Carlo method

    Science.gov (United States)

    Niccolini, G.; Alcolea, J.

    Solving the radiative transfer problem is a common problematic to may fields in astrophysics. With the increasing angular resolution of spatial or ground-based telescopes (VLTI, HST) but also with the next decade instruments (NGST, ALMA, ...), astrophysical objects reveal and will certainly reveal complex spatial structures. Consequently, it is necessary to develop numerical tools being able to solve the radiative transfer equation in three dimensions in order to model and interpret these observations. I present a 3D radiative transfer program, using a new method for the construction of an adaptive spatial grid, based on the Monte Claro method. With the help of this tools, one can solve the continuum radiative transfer problem (e.g. a dusty medium), computes the temperature structure of the considered medium and obtain the flux of the object (SED and images).

  9. 3D level set methods for evolving fronts on tetrahedral meshes with adaptive mesh refinement

    Science.gov (United States)

    Morgan, Nathaniel R.; Waltz, Jacob I.

    2017-05-01

    The level set method is commonly used to model dynamically evolving fronts and interfaces. In this work, we present new methods for evolving fronts with a specified velocity field or in the surface normal direction on 3D unstructured tetrahedral meshes with adaptive mesh refinement (AMR). The level set field is located at the nodes of the tetrahedral cells and is evolved using new upwind discretizations of Hamilton-Jacobi equations combined with a Runge-Kutta method for temporal integration. The level set field is periodically reinitialized to a signed distance function using an iterative approach with a new upwind gradient. The details of these level set and reinitialization methods are discussed. Results from a range of numerical test problems are presented.

  10. 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.

  11. An adaptive learning approach for 3-D surface reconstruction from point clouds.

    Science.gov (United States)

    Junior, Agostinho de Medeiros Brito; Neto, Adrião Duarte Dória; de Melo, Jorge Dantas; Goncalves, Luiz Marcos Garcia

    2008-06-01

    In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.

  12. The Singularity Threshold of the Nonlinear Sigma Model Using 3D Adaptive Mesh Refinement

    CERN Document Server

    Liebling, S L

    2002-01-01

    Numerical solutions to the nonlinear sigma model (NLSM), a wave map from 3+1 Minkowski space to S^3, are computed in three spatial dimensions (3D) using adaptive mesh refinement (AMR). For initial data with compact support the model is known to have two regimes, one in which regular initial data forms a singularity and another in which the energy is dispersed to infinity. The transition between these regimes has been shown in spherical symmetry to demonstrate threshold behavior similar to that between black hole formation and dispersal in gravitating theories. Here, I generalize the result by removing the assumption of spherical symmetry. The evolutions suggest that the spherically symmetric critical solution remains an intermediate attractor separating the two end states.

  13. Geometric and topological feature extraction of linear segments from 2D cross-section data of 3D point clouds

    Science.gov (United States)

    Ramamurthy, Rajesh; Harding, Kevin; Du, Xiaoming; Lucas, Vincent; Liao, Yi; Paul, Ratnadeep; Jia, Tao

    2015-05-01

    Optical measurement techniques are often employed to digitally capture three dimensional shapes of components. The digital data density output from these probes range from a few discrete points to exceeding millions of points in the point cloud. The point cloud taken as a whole represents a discretized measurement of the actual 3D shape of the surface of the component inspected to the measurement resolution of the sensor. Embedded within the measurement are the various features of the part that make up its overall shape. Part designers are often interested in the feature information since those relate directly to part function and to the analytical models used to develop the part design. Furthermore, tolerances are added to these dimensional features, making their extraction a requirement for the manufacturing quality plan of the product. The task of "extracting" these design features from the point cloud is a post processing task. Due to measurement repeatability and cycle time requirements often automated feature extraction from measurement data is required. The presence of non-ideal features such as high frequency optical noise and surface roughness can significantly complicate this feature extraction process. This research describes a robust process for extracting linear and arc segments from general 2D point clouds, to a prescribed tolerance. The feature extraction process generates the topology, specifically the number of linear and arc segments, and the geometry equations of the linear and arc segments automatically from the input 2D point clouds. This general feature extraction methodology has been employed as an integral part of the automated post processing algorithms of 3D data of fine features.

  14. Analysis and adaptive synchronization of eight-term 3-D polynomial chaotic systems with three quadratic nonlinearities

    Science.gov (United States)

    Vaidyanathan, S.

    2014-06-01

    This paper proposes a eight-term 3-D polynomial chaotic system with three quadratic nonlinearities and describes its properties. The maximal Lyapunov exponent (MLE) of the proposed 3-D chaotic system is obtained as L 1 = 6.5294. Next, new results are derived for the global chaos synchronization of the identical eight-term 3-D chaotic systems with unknown system parameters using adaptive control. Lyapunov stability theory has been applied for establishing the adaptive synchronization results. Numerical simulations are shown using MATLAB to describe the main results derived in this paper.

  15. Compressible magma/mantle dynamics: 3-D, adaptive simulations in ASPECT

    Science.gov (United States)

    Dannberg, Juliane; Heister, Timo

    2016-12-01

    Melt generation and migration are an important link between surface processes and the thermal and chemical evolution of the Earth's interior. However, their vastly different timescales make it difficult to study mantle convection and melt migration in a unified framework, especially for 3-D global models. And although experiments suggest an increase in melt volume of up to 20 per cent from the depth of melt generation to the surface, previous computations have neglected the individual compressibilities of the solid and the fluid phase. Here, we describe our extension of the finite element mantle convection code ASPECT that adds melt generation and migration. We use the original compressible formulation of the McKenzie equations, augmented by an equation for the conservation of energy. Applying adaptive mesh refinement to this type of problems is particularly advantageous, as the resolution can be increased in areas where melt is present and viscosity gradients are high, whereas a lower resolution is sufficient in regions without melt. Together with a high-performance, massively parallel implementation, this allows for high-resolution, 3-D, compressible, global mantle convection simulations coupled with melt migration. We evaluate the functionality and potential of this method using a series of benchmarks and model setups, compare results of the compressible and incompressible formulation, and show the effectiveness of adaptive mesh refinement when applied to melt migration. Our model of magma dynamics provides a framework for modelling processes on different scales and investigating links between processes occurring in the deep mantle and melt generation and migration. This approach could prove particularly useful applied to modelling the generation of komatiites or other melts originating in greater depths. The implementation is available in the Open Source ASPECT repository.

  16. 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

  17. CT and MRI Assessment and Characterization Using Segmentation and 3D Modeling Techniques: Applications to Muscle, Bone and Brain

    Science.gov (United States)

    Helgason, Thordur; Ramon, Ceon; jr, Halldór Jónsson; Carraro, Ugo

    2014-01-01

    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 conditions by

  18. Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network

    Directory of Open Access Journals (Sweden)

    K. Gayathri Devi

    2015-01-01

    Full Text Available Purpose. Colon segmentation is an essential step in the development of computer-aided diagnosis systems based on computed tomography (CT images. The requirement for the detection of the polyps which lie on the walls of the colon is much needed in the field of medical imaging for diagnosis of colorectal cancer. Methods. The proposed work is focused on designing an efficient automatic colon segmentation algorithm from abdominal slices consisting of colons, partial volume effect, bowels, and lungs. The challenge lies in determining the exact colon enhanced with partial volume effect of the slice. In this work, adaptive thresholding technique is proposed for the segmentation of air packets, machine learning based cascade feed forward neural network enhanced with boundary detection algorithms are used which differentiate the segments of the lung and the fluids which are sediment at the side wall of colon and by rejecting bowels based on the slice difference removal method. The proposed neural network method is trained with Bayesian regulation algorithm to determine the partial volume effect. Results. Experiment was conducted on CT database images which results in 98% accuracy and minimal error rate. Conclusions. The main contribution of this work is the exploitation of neural network algorithm for removal of opacified fluid to attain desired colon segmentation result.

  19. Rotational-slice-Based prostate segmentation using level set with shape constraint for 3D end-firing TRUS guided biopsy.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Tessier, David; Fenster, Aaron

    2012-01-01

    Prostate segmentation in 3D ultrasound images is an important step in the planning and treatment of 3D end-firing transrectal ultrasound (TRUS) guided prostate biopsy. A semi-automatic prostate segmentation method is presented in this paper, which integrates a modified distance regularization level set formulation with shape constraint to a rotational-slice-based 3D prostate segmentation method. Its performance, using different metrics, has been evaluated on a set of twenty 3D patient prostate images by comparison with expert delineations. The volume overlap ratio of 93.39 +/- 1.26% and the mean absolute surface distance of 1.16 +/- 0.34 mm were found in the quantitative validation result.

  20. Adaptive Multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model

    CERN Document Server

    Navarro, C A; Deng, Youjin

    2015-01-01

    The study of disordered spin systems through Monte Carlo simulations has proven to be a hard task due to the adverse energy landscape present at the low temperature regime, making it difficult for the simulation to escape from a local minimum. Replica based algorithms such as the Exchange Monte Carlo (also known as parallel tempering) are effective at overcoming this problem, reaching equilibrium on disordered spin systems such as the Spin Glass or Random Field models, by exchanging information between replicas of neighbor temperatures. In this work we present a multi-GPU Exchange Monte Carlo method designed for the simulation of the 3D Random Field Model. The implementation is based on a two-level parallelization scheme that allows the method to scale its performance in the presence of faster and GPUs as well as multiple GPUs. In addition, we modified the original algorithm by adapting the set of temperatures according to the exchange rate observed from short trial runs, leading to an increased exchange rate...

  1. Three-dimensional prostate segmentation using level set with shape constraint based on rotational slices for 3D end-firing TRUS guided biopsy.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Tessier, David; Fenster, Aaron

    2013-07-01

    Prostate segmentation is an important step in the planning and treatment of 3D end-firing transrectal ultrasound (TRUS) guided prostate biopsy. In order to improve the accuracy and efficiency of prostate segmentation in 3D TRUS images, an improved level set method is incorporated into a rotational-slice-based 3D prostate segmentation to decrease the accumulated segmentation errors produced by the slice-by-slice segmentation method. A 3D image is first resliced into 2D slices in a rotational manner in both the clockwise and counterclockwise directions. All slices intersect approximately along the rotational scanning axis and have an equal angular spacing. Six to eight boundary points are selected to initialize a level set function to extract the prostate contour within the first slice. The segmented contour is then propagated to the adjacent slice and is used as the initial contour for segmentation. This process is repeated until all slices are segmented. A modified distance regularization level set method is used to segment the prostate in all resliced 2D slices. In addition, shape-constraint and local-region-based energies are imposed to discourage the evolved level set function to leak in regions with weak edges or without edges. An anchor point based energy is used to promote the level set function to pass through the initial selected boundary points. The algorithm's performance was evaluated using distance- and volume-based metrics (sensitivity (Se), Dice similarity coefficient (DSC), mean absolute surface distance (MAD), maximum absolute surface distance (MAXD), and volume difference) by comparison with expert delineations. The validation results using thirty 3D patient images showed that the authors' method can obtain a DSC of 93.1% ± 1.6%, a sensitivity of 93.0% ± 2.0%, a MAD of 1.18 ± 0.36 mm, a MAXD of 3.44 ± 0.8 mm, and a volume difference of 2.6 ± 1.9 cm(3) for the entire prostate. A reproducibility experiment demonstrated that the proposed method

  2. Adaptive Iterative Dose Reduction Using Three Dimensional Processing (AIDR3D improves chest CT image quality and reduces radiation exposure.

    Directory of Open Access Journals (Sweden)

    Tsuneo Yamashiro

    Full Text Available To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D for image quality improvement and dose reduction for chest computed tomography (CT.Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D. Using a 5-point scale from 1 (non-diagnostic to 5 (excellent, three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease, and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts. Differences in these scores were assessed by Scheffe's test.At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p<0.0001 and all mediastinal measurements (p<0.01. For lung diseases, significant improvements with AIDR3D were frequently observed at 120 and 60 mA. Scans with AIDR3D at 120 mA had significantly higher scores than those without AIDR3D at 240 mA for lung zones and mediastinal streak artifacts (p<0.0001, and slightly higher or equal scores for all other measurements. Scans with AIDR3D at 60 mA were also judged superior or equivalent to those without AIDR3D at 120 mA.For chest CT, AIDR3D provides better image quality and can reduce radiation exposure by 50%.

  3. Cell type-specific adaptation of cellular and nuclear volume in micro-engineered 3D environments.

    Science.gov (United States)

    Greiner, Alexandra M; Klein, Franziska; Gudzenko, Tetyana; Richter, Benjamin; Striebel, Thomas; Wundari, Bayu G; Autenrieth, Tatjana J; Wegener, Martin; Franz, Clemens M; Bastmeyer, Martin

    2015-11-01

    Bio-functionalized three-dimensional (3D) structures fabricated by direct laser writing (DLW) are structurally and mechanically well-defined and ideal for systematically investigating the influence of three-dimensionality and substrate stiffness on cell behavior. Here, we show that different fibroblast-like and epithelial cell lines maintain normal proliferation rates and form functional cell-matrix contacts in DLW-fabricated 3D scaffolds of different mechanics and geometry. Furthermore, the molecular composition of cell-matrix contacts forming in these 3D micro-environments and under conventional 2D culture conditions is identical, based on the analysis of several marker proteins (paxillin, phospho-paxillin, phospho-focal adhesion kinase, vinculin, β1-integrin). However, fibroblast-like and epithelial cells differ markedly in the way they adapt their total cell and nuclear volumes in 3D environments. While fibroblast-like cell lines display significantly increased cell and nuclear volumes in 3D substrates compared to 2D substrates, epithelial cells retain similar cell and nuclear volumes in 2D and 3D environments. Despite differential cell volume regulation between fibroblasts and epithelial cells in 3D environments, the nucleus-to-cell (N/C) volume ratios remain constant for all cell types and culture conditions. Thus, changes in cell and nuclear volume during the transition from 2D to 3D environments are strongly cell type-dependent, but independent of scaffold stiffness, while cells maintain the N/C ratio regardless of culture conditions.

  4. A 3-D Novel Conservative Chaotic System and its Generalized Projective Synchronization via Adaptive Control

    OpenAIRE

    Vaidyanathan, S.; S. Pakiriswamy

    2014-01-01

    This research work proposes a five-term 3-D novel conservative chaotic system with a quadratic nonlinearity and a quartic nonlinearity. The conservative chaotic systems have the important property that they are volume conserving. The Lyapunov exponents of the 3-D novel chaotic system are obtained as �! = 0.0836, �! = 0 and �! = −0.0836. Since the sum of the Lyapunov exponents is zero, the 3-D novel chaotic system is conservative. Thus, the Kaplan-Yorke dimension of the 3-D novel c...

  5. A Rapid and Efficient 2D/3D Nuclear Segmentation Method for Analysis of Early Mouse Embryo and Stem Cell Image Data

    Directory of Open Access Journals (Sweden)

    Xinghua Lou

    2014-03-01

    Full Text Available Segmentation is a fundamental problem that dominates the success of microscopic image analysis. In almost 25 years of cell detection software development, there is still no single piece of commercial software that works well in practice when applied to early mouse embryo or stem cell image data. To address this need, we developed MINS (modular interactive nuclear segmentation as a MATLAB/C++-based segmentation tool tailored for counting cells and fluorescent intensity measurements of 2D and 3D image data. Our aim was to develop a tool that is accurate and efficient yet straightforward and user friendly. The MINS pipeline comprises three major cascaded modules: detection, segmentation, and cell position classification. An extensive evaluation of MINS on both 2D and 3D images, and comparison to related tools, reveals improvements in segmentation accuracy and usability. Thus, its accuracy and ease of use will allow MINS to be implemented for routine single-cell-level image analyses.

  6. The PCNN adaptive segmentation algorithm based on visual perception

    Science.gov (United States)

    Zhao, Yanming

    To solve network adaptive parameter determination problem of the pulse coupled neural network (PCNN), and improve the image segmentation results in image segmentation. The PCNN adaptive segmentation algorithm based on visual perception of information is proposed. Based on the image information of visual perception and Gabor mathematical model of Optic nerve cells receptive field, the algorithm determines adaptively the receptive field of each pixel of the image. And determines adaptively the network parameters W, M, and β of PCNN by the Gabor mathematical model, which can overcome the problem of traditional PCNN parameter determination in the field of image segmentation. Experimental results show that the proposed algorithm can improve the region connectivity and edge regularity of segmentation image. And also show the PCNN of visual perception information for segmentation image of advantage.

  7. Comparative evaluation of a novel 3D segmentation algorithm on in-treatment radiotherapy cone beam CT images

    Science.gov (United States)

    Price, Gareth; Moore, Chris

    2007-03-01

    Image segmentation and delineation is at the heart of modern radiotherapy, where the aim is to deliver as high a radiation dose as possible to a cancerous target whilst sparing the surrounding healthy tissues. This, of course, requires that a radiation oncologist dictates both where the tumour and any nearby critical organs are located. As well as in treatment planning, delineation is of vital importance in image guided radiotherapy (IGRT): organ motion studies demand that features across image databases are accurately segmented, whilst if on-line adaptive IGRT is to become a reality, speedy and correct target identification is a necessity. Recently, much work has been put into the development of automatic and semi-automatic segmentation tools, often using prior knowledge to constrain some grey level, or derivative thereof, interrogation algorithm. It is hoped that such techniques can be applied to organ at risk and tumour segmentation in radiotherapy. In this work, however, we make the assumption that grey levels do not necessarily determine a tumour's extent, especially in CT where the attenuation coefficient can often vary little between cancerous and normal tissue. In this context we present an algorithm that generates a discontinuity free delineation surface driven by user placed, evidence based support points. In regions of sparse user supplied information, prior knowledge, in the form of a statistical shape model, provides guidance. A small case study is used to illustrate the method. Multiple observers (between 3 and 7) used both the presented tool and a commercial manual contouring package to delineate the bladder on a serially imaged (10 cone beam CT volumes ) prostate patient. A previously presented shape analysis technique is used to quantitatively compare the observer variability.

  8. 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.

  9. 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.

  10. Controllable liquid crystal gratings for an adaptive 2D/3D auto-stereoscopic display

    Science.gov (United States)

    Zhang, Y. A.; Jin, T.; He, L. C.; Chu, Z. H.; Guo, T. L.; Zhou, X. T.; Lin, Z. X.

    2017-02-01

    2D/3D switchable, viewpoint controllable and 2D/3D localizable auto-stereoscopic displays based on controllable liquid crystal gratings are proposed in this work. Using the dual-layer staggered structure on the top substrate and bottom substrate as driven electrodes within a liquid crystal cell, the ratio between transmitting region and shielding region can be selectively controlled by the corresponding driving circuit, which indicates that 2D/3D switch and 3D video sources with different disparity images can reveal in the same auto-stereoscopic display system. Furthermore, the controlled region in the liquid crystal gratings presents 3D model while other regions maintain 2D model in the same auto-stereoscopic display by the corresponding driving circuit. This work demonstrates that the controllable liquid crystal gratings have potential applications in the field of auto-stereoscopic display.

  11. CT image artifacts from brachytherapy seed implants: a postprocessing 3D adaptive median filter.

    Science.gov (United States)

    Basran, Parminder S; Robertson, Andrew; Wells, Derek

    2011-02-01

    To design a postprocessing 3D adaptive median filter that minimizes streak artifacts and improves soft-tissue contrast in postoperative CT images of brachytherapy seed implantations. The filter works by identifying voxels that are likely streaks and estimating more reflective voxel intensity by using voxel intensities in adjacent CT slices and applying a median filter over voxels not identified as seeds. Median values are computed over a 5 x 5 x 5 mm region of interest (ROI) within the CT volume. An acrylic phantom simulating a clinical seed implant arrangement and containing nonradioactive seeds was created. Low contrast subvolumes of tissuelike material were also embedded in the phantom. Pre- and postprocessed image quality metrics were compared using the standard deviation of ROIs between the seeds, the CT numbers of low contrast ROIs embedded within the phantom, the signal to noise ratio (SNR), and the contrast to noise ratio (CNR) of the low contrast ROIs. The method was demonstrated with a clinical postimplant CT dataset. After the filter was applied, the standard deviation of CT values in streak artifact regions was significantly reduced from 76.5 to 7.2 HU. Within the observable low contrast plugs, the mean of all ROI standard deviations was significantly reduced from 60.5 to 3.9 HU, SNR significantly increased from 2.3 to 22.4, and CNR significantly increased from 0.2 to 4.1 (all P mean CT in the low contrast plugs remained within 5 HU of the original values. An efficient postprocessing filter that does not require access to projection data, which can be applied irrespective of CT scan parameters has been developed, provided the slice thickness and spacing is 3 mm or less.

  12. CT image artifacts from brachytherapy seed implants: A postprocessing 3D adaptive median filter

    Energy Technology Data Exchange (ETDEWEB)

    Basran, Parminder S.; Robertson, Andrew; Wells, Derek [Department of Medical Physics, Vancouver Island Cancer Centre, 2410 Lee Avenue, Victoria, British Columbia V8R 6V5 (Canada) and Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia V8W 3P6 (Canada); Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia V8W 3P6 (Canada); Department of Medical Physics, Vancouver Island Cancer Centre, 2410 Lee Avenue, Victoria, British Columbia V8R 6V5 (Canada) and Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia V8W 3P6 (Canada)

    2011-02-15

    Purpose: To design a postprocessing 3D adaptive median filter that minimizes streak artifacts and improves soft-tissue contrast in postoperative CT images of brachytherapy seed implantations. Methods: The filter works by identifying voxels that are likely streaks and estimating more reflective voxel intensity by using voxel intensities in adjacent CT slices and applying a median filter over voxels not identified as seeds. Median values are computed over a 5x5x5 mm region of interest (ROI) within the CT volume. An acrylic phantom simulating a clinical seed implant arrangement and containing nonradioactive seeds was created. Low contrast subvolumes of tissuelike material were also embedded in the phantom. Pre- and postprocessed image quality metrics were compared using the standard deviation of ROIs between the seeds, the CT numbers of low contrast ROIs embedded within the phantom, the signal to noise ratio (SNR), and the contrast to noise ratio (CNR) of the low contrast ROIs. The method was demonstrated with a clinical postimplant CT dataset. Results: After the filter was applied, the standard deviation of CT values in streak artifact regions was significantly reduced from 76.5 to 7.2 HU. Within the observable low contrast plugs, the mean of all ROI standard deviations was significantly reduced from 60.5 to 3.9 HU, SNR significantly increased from 2.3 to 22.4, and CNR significantly increased from 0.2 to 4.1 (all P<0.01). The mean CT in the low contrast plugs remained within 5 HU of the original values. Conclusion: An efficient postprocessing filter that does not require access to projection data, which can be applied irrespective of CT scan parameters has been developed, provided the slice thickness and spacing is 3 mm or less.

  13. 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.

  14. Efficient global wave propagation adapted to 3-D structural complexity: a pseudospectral/spectral-element approach

    Science.gov (United States)

    Leng, Kuangdai; Nissen-Meyer, Tarje; van Driel, Martin

    2016-12-01

    We present a new, computationally efficient numerical method to simulate global seismic wave propagation in realistic 3-D Earth models. We characterize the azimuthal dependence of 3-D wavefields in terms of Fourier series, such that the 3-D equations of motion reduce to an algebraic system of coupled 2-D meridian equations, which is then solved by a 2-D spectral element method (SEM). Computational efficiency of such a hybrid method stems from lateral smoothness of 3-D Earth models and axial singularity of seismic point sources, which jointly confine the Fourier modes of wavefields to a few lower orders. We show novel benchmarks for global wave solutions in 3-D structures between our method and an independent, fully discretized 3-D SEM with remarkable agreement. Performance comparisons are carried out on three state-of-the-art tomography models, with seismic period ranging from 34 s down to 11 s. It turns out that our method has run up to two orders of magnitude faster than the 3-D SEM, featured by a computational advantage expanding with seismic frequency.

  15. 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.

  16. Gebiss: an ImageJ plugin for the specification of ground truth and the performance evaluation of 3d segmentation algorithms

    Directory of Open Access Journals (Sweden)

    Yee Kwo

    2011-06-01

    Full Text Available Abstract Background Image segmentation is a crucial step in quantitative microscopy that helps to define regions of tissues, cells or subcellular compartments. Depending on the degree of user interactions, segmentation methods can be divided into manual, automated or semi-automated approaches. 3D image stacks usually require automated methods due to their large number of optical sections. However, certain applications benefit from manual or semi-automated approaches. Scenarios include the quantification of 3D images with poor signal-to-noise ratios or the generation of so-called ground truth segmentations that are used to evaluate the accuracy of automated segmentation methods. Results We have developed Gebiss; an ImageJ plugin for the interactive segmentation, visualisation and quantification of 3D microscopic image stacks. We integrated a variety of existing plugins for threshold-based segmentation and volume visualisation. Conclusions We demonstrate the application of Gebiss to the segmentation of nuclei in live Drosophila embryos and the quantification of neurodegeneration in Drosophila larval brains. Gebiss was developed as a cross-platform ImageJ plugin and is freely available on the web at http://imaging.bii.a-star.edu.sg/projects/gebiss/.

  17. 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 generat

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

    NARCIS (Netherlands)

    Hofmann, K.M.; Gavrila, 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 generat

  19. Computer-aided classification of liver tumors in 3D ultrasound images with combined deformable model segmentation and support vector machine

    Science.gov (United States)

    Lee, Myungeun; Kim, Jong Hyo; Park, Moon Ho; Kim, Ye-Hoon; Seong, Yeong Kyeong; Cho, Baek Hwan; Woo, Kyoung-Gu

    2014-03-01

    In this study, we propose a computer-aided classification scheme of liver tumor in 3D ultrasound by using a combination of deformable model segmentation and support vector machine. For segmentation of tumors in 3D ultrasound images, a novel segmentation model was used which combined edge, region, and contour smoothness energies. Then four features were extracted from the segmented tumor including tumor edge, roundness, contrast, and internal texture. We used a support vector machine for the classification of features. The performance of the developed method was evaluated with a dataset of 79 cases including 20 cysts, 20 hemangiomas, and 39 hepatocellular carcinomas, as determined by the radiologist's visual scoring. Evaluation of the results showed that our proposed method produced tumor boundaries that were equal to or better than acceptable in 89.8% of cases, and achieved 93.7% accuracy in classification of cyst and hemangioma.

  20. Adaptive textural segmentation of medical images

    Science.gov (United States)

    Kuklinski, Walter S.; Frost, Gordon S.; MacLaughlin, Thomas

    1992-06-01

    A number of important problems in medical imaging can be described as segmentation problems. Previous fractal-based image segmentation algorithms have used either the local fractal dimension alone or the local fractal dimension and the corresponding image intensity as features for subsequent pattern recognition algorithms. An image segmentation algorithm that utilized the local fractal dimension, image intensity, and the correlation coefficient of the local fractal dimension regression analysis computation, to produce a three-dimension feature space that was partitioned to identify specific pixels of dental radiographs as being either bone, teeth, or a boundary between bone and teeth also has been reported. In this work we formulated the segmentation process as a configurational optimization problem and discuss the application of simulated annealing optimization methods to the solution of this specific optimization problem. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.

  1. Application of adaptive non-linear 2D and 3D postprocessing filters for reduced dose abdominal CT.

    Science.gov (United States)

    Borgen, Lars; Kalra, Mannudeep K; Laerum, Frode; Hachette, Isabelle W; Fredriksson, Carina H; Sandborg, Michael; Smedby, Orjan

    2012-04-01

    Abdominal computed tomography (CT) is a frequently performed imaging procedure, resulting in considerable radiation doses to the patient population. Postprocessing filters are one of several dose reduction measures that might help to reduce radiation doses without loss of image quality. To assess and compare the effect of two- and three-dimensional (2D, 3D) non-linear adaptive filters on reduced dose abdominal CT images. Two baseline abdominal CT image series with a volume computer tomography dose index (CTDI (vol)) of 12 mGy and 6 mGy were acquired for 12 patients. Reduced dose images were postprocessed with 2D and 3D filters. Six radiologists performed blinded randomized, side-by-side image quality assessments. Objective noise was measured. Data were analyzed using visual grading regression and mixed linear models. All image quality criteria were rated as superior for 3D filtered images compared to reduced dose baseline and 2D filtered images (P 0.05). There were no significant variations of objective noise between standard dose and 2D or 3D filtered images. The quality of 3D filtered reduced dose abdominal CT images is superior compared to reduced dose unfiltered and 2D filtered images. For patients with BMI < 30 kg/m(2), 3D filtered images are comparable to standard dose images.

  2. Adaptive geometric tessellation for 3D reconstruction of anisotropically developing cells in multilayer tissues from sparse volumetric microscopy images.

    Directory of Open Access Journals (Sweden)

    Anirban Chakraborty

    Full Text Available The need for quantification of cell growth patterns in a multilayer, multi-cellular tissue necessitates the development of a 3D reconstruction technique that can estimate 3D shapes and sizes of individual cells from Confocal Microscopy (CLSM image slices. However, the current methods of 3D reconstruction using CLSM imaging require large number of image slices per cell. But, in case of Live Cell Imaging of an actively developing tissue, large depth resolution is not feasible in order to avoid damage to cells from prolonged exposure to laser radiation. In the present work, we have proposed an anisotropic Voronoi tessellation based 3D reconstruction framework for a tightly packed multilayer tissue with extreme z-sparsity (2-4 slices/cell and wide range of cell shapes and sizes. The proposed method, named as the 'Adaptive Quadratic Voronoi Tessellation' (AQVT, is capable of handling both the sparsity problem and the non-uniformity in cell shapes by estimating the tessellation parameters for each cell from the sparse data-points on its boundaries. We have tested the proposed 3D reconstruction method on time-lapse CLSM image stacks of the Arabidopsis Shoot Apical Meristem (SAM and have shown that the AQVT based reconstruction method can correctly estimate the 3D shapes of a large number of SAM cells.

  3. 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

  4. 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

  5. Application of adaptive non-linear 2D and 3D postprocessing filters for reduced dose abdominal CT

    Energy Technology Data Exchange (ETDEWEB)

    Borgen, Lars (Dept. of Radiology, Drammen Hospital, Drammen and Buskerud Univ. College, Drammen (Norway)), Email: lars.borgen@vestreviken.no; Kalra, Mannudeep K. (Massachusetts General Hospital Imaging, Harvard Medical School, Massachusetts General Hospital, Boston (United States)); Laerum, Frode (Dept. of Radiology, Akershus Univ. Hospital, Loerenskog (Norway)); Hachette, Isabelle W.; Fredriksson, Carina H. (ContextVision AB, Linkoeping (Sweden)); Sandborg, Michael (Dept. of Medical Physics, IMH, Faculty of Health Sciences, Linkoeping Univ., County Council of Oestergoetland, Linkoeping (Sweden); Center for Medical Image Science and Visualization, Linkoeping (Sweden)); Smedby, Oerjan (Center for Medical Image Science and Visualization, Linkoeping (Sweden); Dept. of Radiology, Linkoeping Univ., Linkoeping (Sweden))

    2012-04-15

    Background: Abdominal computed tomography (CT) is a frequently performed imaging procedure, resulting in considerable radiation doses to the patient population. Postprocessing filters are one of several dose reduction measures that might help to reduce radiation doses without loss of image quality. Purpose: To assess and compare the effect of two- and three-dimensional (2D, 3D) non-linear adaptive filters on reduced dose abdominal CT images. Material and Methods: Two baseline abdominal CT image series with a volume computer tomography dose index (CTDI{sub vol}) of 12 mGy and 6 mGy were acquired for 12 patients. Reduced dose images were postprocessed with 2D and 3D filters. Six radiologists performed blinded randomized, side-by-side image quality assessments. Objective noise was measured. Data were analyzed using visual grading regression and mixed linear models. Results: All image quality criteria were rated as superior for 3D filtered images compared to reduced dose baseline and 2D filtered images (P < 0.01). Standard dose images had better image quality than reduced dose 3D filtered images (P < 0.01), but similar image noise. For patients with body mass index (BMI) < 30 kg/m2 however, 3D filtered images were rated significantly better than normal dose images for two image criteria (P < 0.05), while no significant difference was found for the remaining three image criteria (P > 0.05). There were no significant variations of objective noise between standard dose and 2D or 3D filtered images. Conclusion: The quality of 3D filtered reduced dose abdominal CT images is superior compared to reduced dose unfiltered and 2D filtered images. For patients with BMI < 30 kg/m2, 3D filtered images are comparable to standard dose images

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

    Science.gov (United States)

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

    2009-02-01

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

  7. A mesh adaptivity scheme on the Landau-de Gennes functional minimization case in 3D, and its driving efficiency

    Science.gov (United States)

    Bajc, Iztok; Hecht, Frédéric; Žumer, Slobodan

    2016-09-01

    This paper presents a 3D mesh adaptivity strategy on unstructured tetrahedral meshes by a posteriori error estimates based on metrics derived from the Hessian of a solution. The study is made on the case of a nonlinear finite element minimization scheme for the Landau-de Gennes free energy functional of nematic liquid crystals. Newton's iteration for tensor fields is employed with steepest descent method possibly stepping in. Aspects relating the driving of mesh adaptivity within the nonlinear scheme are considered. The algorithmic performance is found to depend on at least two factors: when to trigger each single mesh adaptation, and the precision of the correlated remeshing. Each factor is represented by a parameter, with its values possibly varying for every new mesh adaptation. We empirically show that the time of the overall algorithm convergence can vary considerably when different sequences of parameters are used, thus posing a question about optimality. The extensive testings and debugging done within this work on the simulation of systems of nematic colloids substantially contributed to the upgrade of an open source finite element-oriented programming language to its 3D meshing possibilities, as also to an outer 3D remeshing module.

  8. Adaptive segmentation of digital mammograms through reinforcement learning

    Institute of Scientific and Technical Information of China (English)

    LIU Xin-yue; FANG Xiao-xuan; HUANG Lian-qing

    2005-01-01

    An approach based on reinfocement learning for the automated segmentation is presented. The approach consists of two modules:segmentation module and learning module. The segmentation module uses the region-growing algorithm combined with the smooth filtering and the morphological filtering to segment mammograms. The learning module uses the segmentation output as the feedback to learn to select the optimal parameter settings of the segmentation algorithm according to the image properties using reinforcement learning techniques. The approach can adapt itself to various kinds of mammograms through training and therefore obviates the tedious and error-prone tuning of parameter settings manually. Quantitative test results show that the approach is accurate for several kinds of mammograms. Compared to previously proposed approaches,the approach is more adaptable to different mammograms.

  9. 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.

  10. A stabilized adaptive appearance changes model for 3D head tracking

    NARCIS (Netherlands)

    Zivkovic, Zoran; Heijden, van der Ferdinand; Williams, A.Denise

    2001-01-01

    A simple method is presented for 3D head pose estimation and tracking in monocular image sequences. A generic geometric model is used. The initialization consists of aligning the perspective projection of the geometric model with the subjects head in the initial image. After the initialization, the

  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 dynamic rupture with anelastic wave propagation using an hp-adaptive Discontinuous Galerkin method

    Science.gov (United States)

    Tago, J.; Cruz-Atienza, V. M.; Etienne, V.; Virieux, J.; Benjemaa, M.; Sanchez-Sesma, F. J.

    2010-12-01

    Simulating any realistic seismic scenario requires incorporating physical basis into the model. Considering both the dynamics of the rupture process and the anelastic attenuation of seismic waves is essential to this purpose and, therefore, we choose to extend the hp-adaptive Discontinuous Galerkin finite-element method to integrate these physical aspects. The 3D elastodynamic equations in an unstructured tetrahedral mesh are solved with a second-order time marching approach in a high-performance computing environment. The first extension incorporates the viscoelastic rheology so that the intrinsic attenuation of the medium is considered in terms of frequency dependent quality factors (Q). On the other hand, the extension related to dynamic rupture is integrated through explicit boundary conditions over the crack surface. For this visco-elastodynamic formulation, we introduce an original discrete scheme that preserves the optimal code performance of the elastodynamic equations. A set of relaxation mechanisms describes the behavior of a generalized Maxwell body. We approximate almost constant Q in a wide frequency range by selecting both suitable relaxation frequencies and anelastic coefficients characterizing these mechanisms. In order to do so, we solve an optimization problem which is critical to minimize the amount of relaxation mechanisms. Two strategies are explored: 1) a least squares method and 2) a genetic algorithm (GA). We found that the improvement provided by the heuristic GA method is negligible. Both optimization strategies yield Q values within the 5% of the target constant Q mechanism. Anelastic functions (i.e. memory variables) are introduced to efficiently evaluate the time convolution terms involved in the constitutive equations and thus to minimize the computational cost. The incorporation of anelastic functions implies new terms with ordinary differential equations in the mathematical formulation. We solve these equations using the same order

  13. 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.

  14. µCT-3D visualization analysis of resin composite polymerization and dye penetration test of composite adaptation.

    Science.gov (United States)

    Yoshikawa, Takako; Sadr, Alireza; Tagami, Junji

    2017-08-25

    This study evaluated the effects of the light curing methods and resin composite composition on composite polymerization contraction behavior and resin composite adaptation to the cavity wall using μCT-3D visualization analysis and dye penetration test. Cylindrical cavities were restored using Clearfil tri-S Bond ND Quick adhesive and filled with Clearfil AP-X or Clearfil Photo Bright composite. The composites were cured using the conventional or the slow-start curing method. The light-cured resin composite, which had increased contrast ratio during polymerization, improved adaptation to the cavity wall using the slow-start curing method. In the μCT-3D visualization method, the slow-start curing method reduced polymerization shrinkage volume of resin composite restoration to half of that produced by the conventional curing method in the cavity with adhesive for both composites. Moreover, μCT-3D visualization method can be used to detect and analyze resin composite polymerization contraction behavior and shrinkage volume as 3D image in the cavity.

  15. Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach

    Science.gov (United States)

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

    2017-02-01

    We have proposed an end-to-end learning approach that trained a deep convolutional neural network (CNN) for automatic CT image segmentation, which accomplished a voxel-wised multiple classification to directly map each voxel on 3D CT images to an anatomical label automatically. The novelties of our proposed method were (1) transforming the anatomical structures segmentation on 3D CT images into a majority voting of the results of 2D semantic image segmentation on a number of 2D-slices from different image orientations, and (2) using "convolution" and "deconvolution" networks to achieve the conventional "coarse recognition" and "fine extraction" functions which were integrated into a compact all-in-one deep CNN for CT image segmentation. The advantage comparing to previous works was its capability to accomplish real-time image segmentations on 2D slices of arbitrary CT-scan-range (e.g. body, chest, abdomen) and produced correspondingly-sized output. In this paper, we propose an improvement of our proposed approach by adding an organ localization module to limit CT image range for training and testing deep CNNs. A database consisting of 240 3D CT scans and a human annotated ground truth was used for training (228 cases) and testing (the remaining 12 cases). We applied the improved method to segment pancreas and left kidney regions, respectively. The preliminary results showed that the accuracies of the segmentation results were improved significantly (pancreas was 34% and kidney was 8% increased in Jaccard index from our previous results). The effectiveness and usefulness of proposed improvement for CT image segmentations were confirmed.

  16. 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.

  17. Metallic Material Image Segmentation by using 3D Grain Structure Consistency and Intra/Inter-Grain Model Information

    Science.gov (United States)

    2015-01-05

    steps: 1) A geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks; 2) building a crack probability map to enhance...appearance (top row) and disappearance (bottom row). (a,d) Segmentation on U . (b,e) Segmentation with global labeling. Green dots and yellow dashed lines ...segment topology by fixing the label of pixels along the ring boundary and in the center segment (dashed lines ). Red numbers indicate the numbers of

  18. 3-D grid refinement using the University of Michigan adaptive mesh library for a pure advective test

    Science.gov (United States)

    Oehmke, R.; Vandenberg, D.; Andronova, N.; Penner, J.; Stout, Q.; Zubov, V.; Jablonowski, C.

    2008-05-01

    The numerical representation of the partial differential equations (PDE) for high resolution atmospheric dynamical and physical features requires division of the atmospheric volume into a set of 3D grids, each of which has a not quite rectangular form. Each location on the grid contains multiple data that together represent the state of Earth's atmosphere. For successful numerical integration of the PDEs the size of each grid box is used to define the Courant-Friedrichs-Levi criterion in setting the time step. 3D adaptive representations of a sphere are needed to represent the evolution of clouds. In this paper we present the University of Michigan adaptive mesh library - a library that supports the production of parallel codes with use of adaptation on a sphere. The library manages the block-structured data layout, handles ghost cell updates among neighboring blocks and splits blocks as refinements occur. The library has several modules that provide a layer of abstraction for adaptive refinement: blocks, which contain individual cells of user data; shells — the global geometry for the problem, including a sphere, reduced sphere, and now a 3D sphere; a load balancer for placement of blocks onto processors; and a communication support layer which encapsulates all data movement. Users provide data manipulation functions for performing interpolation of user data when refining blocks. We rigorously test the library using refinement of the modeled vertical transport of a tracer with prescribed atmospheric sources and sinks. It is both a 2 and a 3D test, and bridges the performance of the model's dynamics and physics needed for inclusion of cloud formation.

  19. Adaptive optofluidic lens(es) for switchable 2D and 3D imaging

    Science.gov (United States)

    Huang, Hanyang; Wei, Kang; Zhao, Yi

    2016-03-01

    The stereoscopic image is often captured using dual cameras arranged side-by-side and optical path switching systems such as two separate solid lenses or biprism/mirrors. The miniaturization of the overall size of current stereoscopic devices down to several millimeters is at a sacrifice of further device size shrinkage. The limited light entry worsens the final image resolution and brightness. It is known that optofluidics offer good re-configurability for imaging systems. Leveraging this technique, we report a reconfigurable optofluidic system whose optical layout can be swapped between a singlet lens with 10 mm in diameter and a pair of binocular lenses with each lens of 3 mm in diameter for switchable two-dimensional (2D) and three-dimensional (3D) imaging. The singlet and the binoculars share the same optical path and the same imaging sensor. The singlet acquires a 3D image with better resolution and brightness, while the binoculars capture stereoscopic image pairs for 3D vision and depth perception. The focusing power tuning capability of the singlet and the binoculars enable image acquisition at varied object planes by adjusting the hydrostatic pressure across the lens membrane. The vari-focal singlet and binoculars thus work interchangeably and complementarily. The device is thus expected to have applications in robotic vision, stereoscopy, laparoendoscopy and miniaturized zoom lens system.

  20. 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

  1. Computer-aided diagnosis: a 3D segmentation method for lung nodules in CT images by use of a spiral-scanning technique

    Science.gov (United States)

    Wang, Jiahui; Engelmann, Roger; Li, Qiang

    2008-03-01

    Lung nodule segmentation in computed tomography (CT) plays an important role in computer-aided detection, diagnosis, and quantification systems for lung cancer. In this study, we developed a simple but accurate nodule segmentation method in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. We then transformed the VOI into a two-dimensional (2D) image by use of a "spiral-scanning" technique, in which a radial line originating from the center of the VOI spirally scanned the VOI. The voxels scanned by the radial line were arranged sequentially to form a transformed 2D image. Because the surface of a nodule in 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified our segmentation method and enabled us to obtain accurate segmentation results. We employed a dynamic programming technique to delineate the "optimal" outline of a nodule in the 2D image, which was transformed back into the 3D image space to provide the interior of the nodule. The proposed segmentation method was trained on the first and was tested on the second Lung Image Database Consortium (LIDC) datasets. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric. The experimental results on the LIDC database demonstrated that our segmentation method provided relatively robust and accurate segmentation results with mean overlap values of 66% and 64% for the nodules in the first and second LIDC datasets, respectively, and would be useful for the quantification, detection, and diagnosis of lung cancer.

  2. MRI Brain Tumor Segmentation and Necrosis Detection Using Adaptive Sobolev Snakes

    Science.gov (United States)

    Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

    2014-01-01

    Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D diffusion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation. PMID:25302005

  3. 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.

  4. The image variations in mastoid segment of facial nerve and sinus tympani in congenital aural atresia by HRCT and 3D VR CT.

    Science.gov (United States)

    Wang, Zhen; Hou, Qian; Wang, Pu; Sun, Zhaoyong; Fan, Yue; Wang, Yun; Xue, Huadan; Jin, Zhengyu; Chen, Xiaowei

    2015-09-01

    To find the variations of middle ear structures including the spatial pattern of mastoid segment of facial nerve and the shapes of the sinus tympani in patients with congenital aural atresia (CAA) by using the high-resolution (HR) CT and 3D volume rendered (VR) CT images. HRCT was performed in 25 patients with congenital aural atresia including six bilateral atresia patients (n=25, 21 males, 4 females, mean age 13.8 years, range 6-19). Along the long axis of the posterior semicircular canal ampulla, the oblique axial multiplanar reconstruction (MPR) was set to view the depiction of the round window and the mastoid segment of facial nerve. Volumetric rending technique was used to demonstrate the morphologic features. HRCT and 3D VR findings in atresia ears were compared with those in 19 normal ears of the unilateral ears of atresia patients. On the basic plane, the horizontal line distances between the mastoid segment of the facial nerve and the round window (h-RF) in atresia ears significantly decreased compared to the control ears (PVR CT images. HRCT and 3D VR CT could help a better understanding of different kinds of variations in mastoid segment of facial nerve and sinus tympani in CAA ears. And it may further help surgeons to make the correct decision for hearing rehabilitation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  5. 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.

  6. Semi-automated 3D segmentation of major tracts in the rat brain: comparing DTI with standard histological methods.

    Science.gov (United States)

    Gyengesi, Erika; Calabrese, Evan; Sherrier, Matthew C; Johnson, G Allan; Paxinos, George; Watson, Charles

    2014-03-01

    Researchers working with rodent models of neurological disease often require an accurate map of the anatomical organization of the white matter of the rodent brain. With the increasing popularity of small animal MRI techniques, including diffusion tensor imaging (DTI), there is considerable interest in rapid segmentation methods of neurological structures for quantitative comparisons. DTI-derived tractography allows simple and rapid segmentation of major white matter tracts, but the anatomic accuracy of these computer-generated fibers is open to question and has not been rigorously evaluated in the rat brain. In this study, we examine the anatomic accuracy of tractography-based segmentation in the adult rat brain. We analysed 12 major white matter pathways using semi-automated tractography-based segmentation alongside manual segmentation of Gallyas silver-stained histology sections. We applied four fiber-tracking algorithms to the DTI data-two integration methods and two deflection methods. In many cases, tractography-based segmentation closely matched histology-based segmentation; however different tractography algorithms produced dramatically different results. Results suggest that certain white matter pathways are more amenable to tractography-based segmentation than others. We believe that these data will help researchers decide whether it is appropriate to use tractography-based segmentation of white matter structures for quantitative DTI-based analysis of neurologic disease models.

  7. 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

    2017-01-01

    Segmenting objects of interest from 3D data sets 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, the 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 that 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

  8. A comparison study of atlas-based 3D cardiac MRI segmentation: global versus global and local transformations

    Science.gov (United States)

    Daryanani, Aditya; Dangi, Shusil; Ben-Zikri, Yehuda Kfir; Linte, Cristian A.

    2016-03-01

    Magnetic Resonance Imaging (MRI) is a standard-of-care imaging modality for cardiac function assessment and guidance of cardiac interventions thanks to its high image quality and lack of exposure to ionizing radiation. Cardiac health parameters such as left ventricular volume, ejection fraction, myocardial mass, thickness, and strain can be assessed by segmenting the heart from cardiac MRI images. Furthermore, the segmented pre-operative anatomical heart models can be used to precisely identify regions of interest to be treated during minimally invasive therapy. Hence, the use of accurate and computationally efficient segmentation techniques is critical, especially for intra-procedural guidance applications that rely on the peri-operative segmentation of subject-specific datasets without delaying the procedure workflow. Atlas-based segmentation incorporates prior knowledge of the anatomy of interest from expertly annotated image datasets. Typically, the ground truth atlas label is propagated to a test image using a combination of global and local registration. The high computational cost of non-rigid registration motivated us to obtain an initial segmentation using global transformations based on an atlas of the left ventricle from a population of patient MRI images and refine it using well developed technique based on graph cuts. Here we quantitatively compare the segmentations obtained from the global and global plus local atlases and refined using graph cut-based techniques with the expert segmentations according to several similarity metrics, including Dice correlation coefficient, Jaccard coefficient, Hausdorff distance, and Mean absolute distance error.

  9. Adaptive Multi-resolution 3D Hartree-Fock-Bogoliubov Solver for Nuclear Structure

    CERN Document Server

    Pei, Junchen; Harrison, Robert; Nazarewicz, Witold; Shi, Yue; Thornton, Scott

    2014-01-01

    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, they 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. To describe complex superfluid many-fermion systems, we introduce an adaptive pseudo-spectral method for solving self-consistent equations of nuclear density functional theory in three dimensions, without symmetry restrictions. The new adaptive mult...

  10. Structured light 3D depth map enhancement and gesture recognition using image content adaptive filtering

    Science.gov (United States)

    Ramachandra, Vikas; Nash, James; Atanassov, Kalin; Goma, Sergio

    2013-03-01

    A structured-light system for depth estimation is a type of 3D active sensor that consists of a structured-light projector that projects an illumination pattern on the scene (e.g. mask with vertical stripes) and a camera which captures the illuminated scene. Based on the received patterns, depths of different regions in the scene can be inferred. In this paper, we use side information in the form of image structure to enhance the depth map. This side information is obtained from the received light pattern image reflected by the scene itself. The processing steps run real time. This post-processing stage in the form of depth map enhancement can be used for better hand gesture recognition, as is illustrated in this paper.

  11. Sodium MRI using a density-adapted 3D radial acquisition technique.

    Science.gov (United States)

    Nagel, Armin M; Laun, Frederik B; Weber, Marc-André; Matthies, Christian; Semmler, Wolfhard; Schad, Lothar R

    2009-12-01

    A density-adapted three-dimensional radial projection reconstruction pulse sequence is presented which provides a more efficient k-space sampling than conventional three-dimensional projection reconstruction sequences. The gradients of the density-adapted three-dimensional radial projection reconstruction pulse sequence are designed such that the averaged sampling density in each spherical shell of k-space is constant. Due to hardware restrictions, an inner sphere of k-space is sampled without density adaption. This approach benefits from both the straightforward handling of conventional three-dimensional projection reconstruction sequence trajectories and an enhanced signal-to-noise ratio (SNR) efficiency akin to the commonly used three-dimensional twisted projection imaging trajectories. Benefits for low SNR applications, when compared to conventional three-dimensional projection reconstruction sequences, are demonstrated with the example of sodium imaging. In simulations of the point-spread function, the SNR of small objects is increased by a factor 1.66 for the density-adapted three-dimensional radial projection reconstruction pulse sequence sequence. Using analytical and experimental phantoms, it is shown that the density-adapted three-dimensional radial projection reconstruction pulse sequence allows higher resolutions and is more robust in the presence of field inhomogeneities. High-quality in vivo images of the healthy human leg muscle and the healthy human brain are acquired. For equivalent scan times, the SNR is up to a factor of 1.8 higher and anatomic details are better resolved using density-adapted three-dimensional radial projection reconstruction pulse sequence. (c) 2009 Wiley-Liss, Inc.

  12. 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

  13. A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images.

    Science.gov (United States)

    Lin, Gang; Chawla, Monica K; Olson, Kathy; Barnes, Carol A; Guzowski, John F; Bjornsson, Christopher; Shain, William; Roysam, Badrinath

    2007-09-01

    Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model-based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user-provided training examples. Starting with an initial over-segmentation produced by a gradient-weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi-model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations.

  14. Multi-surface and multi-field co-segmentation of 3-D retinal optical coherence tomography.

    Science.gov (United States)

    Bogunovic, Hrvoje; Sonka, Milan; Kwon, Young H; Kemp, Pavlina; Abramoff, Michael D; Wu, Xiaodong

    2014-12-01

    When segmenting intraretinal layers from multiple optical coherence tomography (OCT) images forming a mosaic or a set of repeated scans, it is attractive to exploit the additional information from the overlapping areas rather than discarding it as redundant, especially in low contrast and noisy images. However, it is currently not clear how to effectively combine the multiple information sources available in the areas of overlap. In this paper, we propose a novel graph-theoretic method for multi-surface multi-field co-segmentation of intraretinal layers, assuring consistent segmentation of the fields across the overlapped areas. After 2-D en-face alignment, all the fields are segmented simultaneously, imposing a priori soft interfield-intrasurface constraints for each pair of overlapping fields. The constraints penalize deviations from the expected surface height differences, taken to be the depth-axis shifts that produce the maximum cross-correlation of pairwise-overlapped areas. The method's accuracy and reproducibility are evaluated qualitatively and quantitatively on 212 OCT images (20 nine-field, 32 single-field acquisitions) from 26 patients with glaucoma. Qualitatively, the obtained thickness maps show no stitching artifacts, compared to pronounced stitches when the fields are segmented independently. Quantitatively, two ophthalmologists manually traced four intraretinal layers on 10 patients, and the average error ( 4.58 ±1.46 μm) was comparable to the average difference between the observers ( 5.86±1.72 μm). Furthermore, we show the benefit of the proposed approach in co-segmenting longitudinal scans. As opposed to segmenting layers in each of the fields independently, the proposed co-segmentation method obtains consistent segmentations across the overlapped areas, producing accurate, reproducible, and artifact-free results.

  15. An efficient content-adaptive motion-compensated 3-D DWT with enhanced spatial and temporal scalability.

    Science.gov (United States)

    Mehrseresht, Nagita; Taubman, David

    2006-06-01

    We propose a novel, content adaptive method for motion-compensated three-dimensional wavelet transformation (MC 3-D DWT) of video. The proposed method overcomes problems of ghosting and nonaligned aliasing artifacts which can arise in regions of motion model failure, when the video is reconstructed at reduced temporal or spatial resolutions. Previous MC 3-D DWT structures either take the form of MC temporal DWT followed by a spatial transform ("t+2D"), or perform the spatial transform first ("2D + t"), limiting the spatial frequencies which can be jointly compensated in the temporal transform, and hence limiting the compression efficiency. When the motion model fails, the "t + 2D" structure causes nonaligned aliasing artifacts in reduced spatial resolution sequences. Essentially, the proposed transform continuously adapts itself between the "t + 2D" and "2D + t" structures, based on information available within the compressed bit stream. Ghosting artifacts may also appear in reduced frame-rate sequences due to temporal low-pass filtering along invalid motion trajectories. To avoid the ghosting artifacts, we continuously select between different low-pass temporal filters, based on the estimated accuracy of the motion model. Experimental results indicate that the proposed adaptive transform preserves high compression efficiency while substantially improving the quality of reduced spatial and temporal resolution sequences.

  16. Three-dimensional whole breast segmentation in sagittal MR images with dense depth field modeling and localized self-adaptation

    Science.gov (United States)

    Wei, Dong; Weinstein, Susan; Hsieh, Meng-Kang; Pantalone, Lauren; Schnall, Mitchell; Kontos, Despina

    2017-02-01

    Whole breast segmentation is the first step in quantitative analysis of breast MR images. This task is challenging due mainly to the chest-wall line's (CWL) spatially varying appearance and nearby distracting structures, both being complex. In this paper, we propose an automatic three-dimensional (3-D) segmentation method of whole breast in sagittal MR images. This method distinguishes itself from others in two main aspects. First, it reformulates the challenging problem of CWL localization into an equivalence that searches for an optimal smooth depth field and so fully utilizes the 3-D continuity of the CWLs. Second, it employs a localized self- adapting algorithm to adjust to the CWL's spatial variation. Experimental results on real patient data with expert-outlined ground truth show that the proposed method can segment breasts accurately and reliably, and that its segmentation is superior to that of previously established methods.

  17. 基于特征点集搜索的三维序列livewire分割方法%3D livewire segmentation based on feature point set searching

    Institute of Scientific and Technical Information of China (English)

    金勇; 蒋建国; 郝世杰; 鲁清凯; 李鸿; 杨青青

    2011-01-01

    On account of the large amount of the three-dimensional(3D) medical image data sets such as computed tomography images(CT) and magnetic resonance image(MRI), the manual image segmentation is time consuming and operator-dependent. Considering the similarity of shape and texture of the segmentation targets between adjacent slices, a 3D livewire segmentation method based on feature point set searching is proposed in this paper. With minimal human interaction, the effective segmentation of objectives in 3D medical image data is achieved. The experiments on the lung CT and cancer MRI show that the temporal cost of the segmentation dramatically falls while its accuracy is close to the manual one.%三维计算机断层图像(CT)或核磁共振图像(MRI)数据量较大,仅仅依靠人工分割整个数据集相当耗时,且分割结果因操作者不同而带有主观性.三维序列图像数据相邻切面间的分割目标形状和纹理通常具有一定的相关性,文章充分利用了这样的先验知识,提出了基于特征点集搜索的三维序列Live Wire 分割方法,旨在尽可能少的人工交互下,完成整个三维医学图像数据中目标的有效分割.实验中,对肺部CT图像和肿瘤MRI图像进行了三维分割,在分割精度与人工分割相当的前提下,分割速度大大提高.

  18. Use of 3D adaptive raw-data filter in CT of the lung: effect on radiation dose reduction.

    Science.gov (United States)

    Kubo, Takeshi; Ohno, Yoshiharu; Gautam, Shiva; Lin, Pei-Jan P; Kauczor, Hans-Ulrich; Hatabu, Hiroto

    2008-10-01

    The purpose of this study was to determine the effectiveness of a 3D adaptive raw-data filter in improving image quality and the role of the filter in radiation dose reduction in lung CT. Fifty-eight chest CT examinations were performed with a 16-MDCT scanner. Two acquisitions were performed with different tube current-exposure time settings (50 and 150 mAs, 120 kVp). Four series of lung images were prepared from two sets of raw data with and without application of a 3D adaptive filter (50 mAs, 50 mAs with filter, 150 mAs, 150 mAs with filter). Three blinded readers using a 5-point scale from 1 (nondiagnostic) to 5 (excellent) independently evaluated image quality in five lobes and the lingula. A set of images was considered acceptable when scores in all six regions were 3 (acceptable) or higher. The SD of attenuation was calculated in 24 regions of interest. The overall mean image quality scores were 3.09, 3.53, 4.02, and 4.38 for the 50 mAs, 50 mAs with filter, 150 mAs, and 150 mAs with filter sets, respectively. Scores were significantly better with filter application (p filter application (p images, 18, 52, 50, and 58 sets were judged acceptable with no significant difference in acceptability between images obtained at 50 mAs with a filter and at 150 mAs (p = 0.72). With filter application, the acceptability of 50-mAs images became comparable with that of 150-mAs images, making dose reduction to 50 mAs practical. Use of a 3D adaptive raw-data filter improved the quality of lung images, making dose reduction to 50 mAs attainable with use of the filter.

  19. Adaptive Optics Assisted 3D spectroscopy observations for black hole mass measurements

    OpenAIRE

    Pastorini, Guia

    2006-01-01

    The very high spatial resolution provided by Adaptive Optics assisted spectroscopic observations at 8m-class telescopes (e.g. with SINFONI at the VLT) will allow to greatly increase the number of direct black hole (BH) mass measurements which is currently very small. This is a fundamental step to investigate the tight link between galaxy evolution and BH growth, revealed by the existing scaling relations between $M_{BH}$ and galaxy structural parameters. I present preliminary results from SIN...

  20. Adaptive Filters for 2-D and 3-D Digital Images Processing

    OpenAIRE

    Martišek, Karel

    2012-01-01

    Práce se zabývá adaptivními filtry pro vizualizaci obrazů s vysokým rozlišením. V teoretické části je popsán princip činnosti konfokálního mikroskopu a matematicky korektně zaveden pojem digitální obraz. Pro zpracování obrazů je volen jak frekvenční přístup (s využitím 2-D a 3-D diskrétní Fourierovy transformace a frekvenčních filtrů), tak přístup pomocí digitální geometrie (s využitím adaptivní ekvalizace histogramu s adaptivním okolím). Dále jsou popsány potřebné úpravy pro práci s neideáln...

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

    Energy Technology Data Exchange (ETDEWEB)

    Firouzian, Azadeh, E-mail: a.firouzian@erasmusmc.nl [Department of Medical Informatics, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Department of Radiology, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Manniesing, Rashindra, E-mail: r.manniesing@erasmusmc.nl [Department of Medical Informatics, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Department of Radiology, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Flach, Zwenneke H., E-mail: zwenneke.flach@gmail.com [Department of Radiology, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Risselada, Roelof, E-mail: r.risselada@erasmusmc.nl [Department of Medical Informatics, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Kooten, Fop van, E-mail: f.vankooten@erasmusmc.nl [Department of Neurology, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Sturkenboom, Miriam C.J.M., E-mail: m.sturkenboom@erasmusmc.nl [Department of Medical Informatics, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Department of Epidemiology, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Lugt, Aad van der, E-mail: a.vanderlugt@erasmusmc.nl [Department of Radiology, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Niessen, Wiro J., E-mail: w.niessen@erasmusmc.nl [Department of Medical Informatics, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Department of Radiology, Erasmus MC, University Medical Centre Rotterdam (Netherlands); Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology (Netherlands)

    2011-08-15

    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.

  2. 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.

  3. Image segmentation on adaptive edge-preserving smoothing

    Science.gov (United States)

    He, Kun; Wang, Dan; Zheng, Xiuqing

    2016-09-01

    Nowadays, typical active contour models are widely applied in image segmentation. However, they perform badly on real images with inhomogeneous subregions. In order to overcome the drawback, this paper proposes an edge-preserving smoothing image segmentation algorithm. At first, this paper analyzes the edge-preserving smoothing conditions for image segmentation and constructs an edge-preserving smoothing model inspired by total variation. The proposed model has the ability to smooth inhomogeneous subregions and preserve edges. Then, a kind of clustering algorithm, which reasonably trades off edge-preserving and subregion-smoothing according to the local information, is employed to learn the edge-preserving parameter adaptively. At last, according to the confidence level of segmentation subregions, this paper constructs a smoothing convergence condition to avoid oversmoothing. Experiments indicate that the proposed algorithm has superior performance in precision, recall, and F-measure compared with other segmentation algorithms, and it is insensitive to noise and inhomogeneous-regions.

  4. 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).

  5. 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.

  6. Repercussion of geometric and dynamic constraints on the 3D rendering quality in structurally adaptive multi-view shooting systems

    Science.gov (United States)

    Ali-Bey, Mohamed; Moughamir, Saïd; Manamanni, Noureddine

    2011-12-01

    in this paper a simulator of a multi-view shooting system with parallel optical axes and structurally variable configuration is proposed. The considered system is dedicated to the production of 3D contents for auto-stereoscopic visualization. The global shooting/viewing geometrical process, which is the kernel of this shooting system, is detailed and the different viewing, transformation and capture parameters are then defined. An appropriate perspective projection model is afterward derived to work out a simulator. At first, this latter is used to validate the global geometrical process in the case of a static configuration. Next, the simulator is used to show the limitations of a static configuration of this shooting system type by considering the case of dynamic scenes and then a dynamic scheme is achieved to allow a correct capture of this kind of scenes. After that, the effect of the different geometrical capture parameters on the 3D rendering quality and the necessity or not of their adaptation is studied. Finally, some dynamic effects and their repercussions on the 3D rendering quality of dynamic scenes are analyzed using error images and some image quantization tools. Simulation and experimental results are presented throughout this paper to illustrate the different studied points. Some conclusions and perspectives end the paper. [Figure not available: see fulltext.

  7. 3D positional control of magnetic levitation system using adaptive control: improvement of positioning control in horizontal plane

    Science.gov (United States)

    Nishino, Toshimasa; Fujitani, Yasuhiro; Kato, Norihiko; Tsuda, Naoaki; Nomura, Yoshihiko; Matsui, Hirokazu

    2012-01-01

    The objective of this paper is to establish a technique that levitates and conveys a hand, a kind of micro-robot, by applying magnetic forces: the hand is assumed to have a function of holding and detaching the objects. The equipment to be used in our experiments consists of four pole-pieces of electromagnets, and is expected to work as a 4DOF drive unit within some restricted range of 3D space: the three DOF are corresponding to 3D positional control and the remaining one DOF, rotational oscillation damping control. Having used the same equipment, Khamesee et al. had manipulated the impressed voltages on the four electric magnetics by a PID controller by the use of the feedback signal of the hand's 3D position, the controlled variable. However, in this system, there were some problems remaining: in the horizontal direction, when translating the hand out of restricted region, positional control performance was suddenly degraded. The authors propose a method to apply an adaptive control to the horizontal directional control. It is expected that the technique to be presented in this paper contributes not only to the improvement of the response characteristic but also to widening the applicable range in the horizontal directional control.

  8. 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

  9. A novel 3D graph cut based co-segmentation of lung tumor on PET-CT images with Gaussian mixture models

    Science.gov (United States)

    Yu, Kai; Chen, Xinjian; Shi, Fei; Zhu, Weifang; Zhang, Bin; Xiang, Dehui

    2016-03-01

    Positron Emission Tomography (PET) and Computed Tomography (CT) have been widely used in clinical practice for radiation therapy. Most existing methods only used one image modality, either PET or CT, which suffers from the low spatial resolution in PET or low contrast in CT. In this paper, a novel 3D graph cut method is proposed, which integrated Gaussian Mixture Models (GMMs) into the graph cut method. We also employed the random walk method as an initialization step to provide object seeds for the improvement of the graph cut based segmentation on PET and CT images. The constructed graph consists of two sub-graphs and a special link between the sub-graphs which penalize the difference segmentation between the two modalities. Finally, the segmentation problem is solved by the max-flow/min-cut method. The proposed method was tested on 20 patients' PET-CT images, and the experimental results demonstrated the accuracy and efficiency of the proposed algorithm.

  10. Adaptive backstepping control, synchronization and circuit simulation of a 3-D novel jerk chaotic system with two hyperbolic sinusoidal nonlinearities

    Directory of Open Access Journals (Sweden)

    Vaidyanathan Sundarapandian

    2014-09-01

    Full Text Available In this research work, a six-term 3-D novel jerk chaotic system with two hyperbolic sinusoidal nonlinearities has been proposed, and its qualitative properties have been detailed. The Lyapunov exponents of the novel jerk system are obtained as L1 = 0.07765,L2 = 0, and L3 = −0.87912. The Kaplan-Yorke dimension of the novel jerk system is obtained as DKY = 2.08833. Next, an adaptive backstepping controller is designed to stabilize the novel jerk chaotic system with two unknown parameters. Moreover, an adaptive backstepping controller is designed to achieve complete chaos synchronization of the identical novel jerk chaotic systems with two unknown parameters. Finally, an electronic circuit realization of the novel jerk chaotic system using Spice is presented in detail to confirm the feasibility of the theoretical model

  11. Vertical Scan (V-SCAN) for 3-D Grid Adaptive Mesh Refinement for an atmospheric Model Dynamical Core

    Science.gov (United States)

    Andronova, N. G.; Vandenberg, D.; Oehmke, R.; Stout, Q. F.; Penner, J. E.

    2009-12-01

    One of the major building blocks of a rigorous representation of cloud evolution in global atmospheric models is a parallel adaptive grid MPI-based communication library (an Adaptive Blocks for Locally Cartesian Topologies library -- ABLCarT), which manages the block-structured data layout, handles ghost cell updates among neighboring blocks and splits a block as refinements occur. The library has several modules that provide a layer of abstraction for adaptive refinement: blocks, which contain individual cells of user data; shells - the global geometry for the problem, including a sphere, reduced sphere, and now a 3D sphere; a load balancer for placement of blocks onto processors; and a communication support layer which encapsulates all data movement. A major performance concern with adaptive mesh refinement is how to represent calculations that have need to be sequenced in a particular order in a direction, such as calculating integrals along a specific path (e.g. atmospheric pressure or geopotential in the vertical dimension). This concern is compounded if the blocks have varying levels of refinement, or are scattered across different processors, as can be the case in parallel computing. In this paper we describe an implementation in ABLCarT of a vertical scan operation, which allows computing along vertical paths in the correct order across blocks transparent to their resolution and processor location. We test this functionality on a 2D and a 3D advection problem, which tests the performance of the model’s dynamics (transport) and physics (sources and sinks) for different model resolutions needed for inclusion of cloud formation.

  12. Semi-automatic segmentation and quantification of the internal carotid artery from 3D contrast-enhanced MR angiograms

    Science.gov (United States)

    van Bemmel, Cornelis M.; Niessen, Wiro J.

    2004-05-01

    A technique is presented for segmentation and quantification of stenosed internal carotid arteries in three-dimensional contrast-enhanced magnetic resonance angiography. Segmentation with sub-voxel accuracy of the internal carotid arteries (ICAs) has been achieved via level-set techniques in which the central axis serves as initialization. The central axis is determined with minmal user-interaction, viz. two user-defined points. Quantification is performed by measuring the cross-sectional area in the stenosis and at a reference segment in planes perpendicular to the central axis. The technique was applied to 52 ICAs. It is demonstrated that the method's reproducibility is better than the intra-observer agreement. Furthermore, the agreement between the presented method and the observers is better than the inter-observer agreement.

  13. Quantitative Evaluation of Tissue Surface Adaption of CAD-Designed and 3D Printed Wax Pattern of Maxillary Complete Denture

    Directory of Open Access Journals (Sweden)

    Hu Chen

    2015-01-01

    Full Text Available Objective. To quantitatively evaluate the tissue surface adaption of a maxillary complete denture wax pattern produced by CAD and 3DP. Methods. A standard edentulous maxilla plaster cast model was used, for which a wax pattern of complete denture was designed using CAD software developed in our previous study and printed using a 3D wax printer, while another wax pattern was manufactured by the traditional manual method. The cast model and the two wax patterns were scanned in the 3D scanner as “DataModel,” “DataWaxRP,” and “DataWaxManual.” After setting each wax pattern on the plaster cast, the whole model was scanned for registration. After registration, the deviations of tissue surface between “DataModel” and “DataWaxRP” and between “DataModel” and “DataWaxManual” were measured. The data was analyzed by paired t-test. Results. For both wax patterns produced by the CAD&RP method and the manual method, scanning data of tissue surface and cast surface showed a good fit in the majority. No statistically significant (P>0.05 difference was observed between the CAD&RP method and the manual method. Conclusions. Wax pattern of maxillary complete denture produced by the CAD&3DP method is comparable with traditional manual method in the adaption to the edentulous cast model.

  14. Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information.

    Science.gov (United States)

    Klein, Stefan; van der Heide, Uulke A; Lips, Irene M; van Vulpen, Marco; Staring, Marius; Pluim, Josien P W

    2008-04-01

    An automatic method for delineating the prostate (including the seminal vesicles) in three-dimensional magnetic resonance scans is presented. The method is based on nonrigid registration of a set of prelabeled atlas images. Each atlas image is nonrigidly registered with the target patient image. Subsequently, the deformed atlas label images are fused to yield a single segmentation of the patient image. The proposed method is evaluated on 50 clinical scans, which were manually segmented by three experts. The Dice similarity coefficient (DSC) is used to quantify the overlap between the automatic and manual segmentations. We investigate the impact of several factors on the performance of the segmentation method. For the registration, two similarity measures are compared: Mutual information and a localized version of mutual information. The latter turns out to be superior (median DeltaDSC approximately equal 0.02, p 0.05). To assess the influence of the atlas composition, two atlas sets are compared. The first set consists of 38 scans of healthy volunteers. The second set is constructed by a leave-one-out approach using the 50 clinical scans that are used for evaluation. The second atlas set gives substantially better performance (DeltaDSC=0.04, p definition. With the best settings, a median DSC of around 0.85 is achieved, which is close to the median interobserver DSC of 0.87. The segmentation quality is especially good at the prostate-rectum interface, where the segmentation error remains below 1 mm in 50% of the cases and below 1.5 mm in 75% of the cases.

  15. 3D multi-object segmentation of cardiac MSCT imaging by using a multi-agent approach.

    Science.gov (United States)

    Fleureau, Julien; Garreau, Mireille; Boulmier, Dominique; Hernández, Alfredo

    2007-01-01

    We propose a new technique for general purpose, semi-interactive and multi-object segmentation in N-dimensional images, applied to the extraction of cardiac structures in MultiSlice Computed Tomography (MSCT) imaging. The proposed approach makes use of a multi-agent scheme combined with a supervised classification methodology allowing the introduction of a priori information and presenting fast computing times. The multi-agent system is organised around a communicating agent which manages a population of situated agents which segment the image through cooperative and competitive interactions. The proposed technique has been tested on several patient data sets. Some typical results are finally presented and discussed.

  16. Graph-based Active Learning of Agglomeration (GALA: a Python library to segment 2D and 3D neuroimages

    Directory of Open Access Journals (Sweden)

    Juan eNunez-Iglesias

    2014-04-01

    Full Text Available The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM. Thus, a common approach is to perform automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration, improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others. We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the limitations of the gala library and how we intend to address them.

  17. Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages.

    Science.gov (United States)

    Nunez-Iglesias, Juan; Kennedy, Ryan; Plaza, Stephen M; Chakraborty, Anirban; Katz, William T

    2014-01-01

    The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

  18. Adaptive Optics Assisted 3D spectroscopy observations for black hole mass measurements

    CERN Document Server

    Pastorini, G

    2006-01-01

    The very high spatial resolution provided by Adaptive Optics assisted spectroscopic observations at 8m-class telescopes (e.g. with SINFONI at the VLT) will allow to greatly increase the number of direct black hole (BH) mass measurements which is currently very small. This is a fundamental step to investigate the tight link between galaxy evolution and BH growth, revealed by the existing scaling relations between $M_{BH}$ and galaxy structural parameters. I present preliminary results from SINFONI K-band spectroscopic observations of a sample of 5 objects with $M_{BH}$ measurements obtained with the Reverberation Mapping (RM) technique. This technique is the starting point to derive the so-called virial $M_{BH}$ estimates, currently the only way to measure $M_{BH}$ at high redshift. Our goal is to assess the reliability of RM by measuring $M_{BH}$ with both gas and stellar kinematical methods and to investigate whether active galaxies follow the same $M_{BH}$-galaxy correlations as normal ones.

  19. ELTs Adaptive Optics for Multi-Objects 3D Spectroscopy Key Parameters and Design Rules

    CERN Document Server

    Neichel, B; Fusco, T; Gendron, E; Puech, M; Rousset, G; Hammer, F

    2006-01-01

    In the last few years, new Adaptive Optics [AO] techniques have emerged to answer new astronomical challenges: Ground-Layer AO [GLAO] and Multi-Conjugate AO [MCAO] to access a wider Field of View [FoV], Multi-Object AO [MOAO] for the simultaneous observation of several faint galaxies, eXtreme AO [XAO] for the detection of faint companions. In this paper, we focus our study to one of these applications : high red-shift galaxy observations using MOAO techniques in the framework of Extremely Large Telescopes [ELTs]. We present the high-level specifications of a dedicated instrument. We choose to describe the scientific requirements with the following criteria : 40% of Ensquared Energy [EE] in H band (1.65um) and in an aperture size from 25 to 150 mas. Considering these specifications we investigate different AO solutions thanks to Fourier based simulations. Sky Coverage [SC] is computed for Natural and Laser Guide Stars [NGS, LGS] systems. We show that specifications are met for NGS-based systems at the cost of ...

  20. Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis.

    Science.gov (United States)

    Liu, Jiamin; Udupa, Jayaram K; Saha, Punam K; Odhner, Dewey; Hirsch, Bruce E; Siegler, Sorin; Simon, Scott; Winkelstein, Beth A

    2008-08-01

    There are several medical application areas that require the segmentation and separation of the component bones of joints in a sequence of images of the joint acquired under various loading conditions, our own target area being joint motion analysis. This is a challenging problem due to the proximity of bones at the joint, partial volume effects, and other imaging modality-specific factors that confound boundary contrast. In this article, a two-step model-based segmentation strategy is proposed that utilizes the unique context of the current application wherein the shape of each individual bone is preserved in all scans of a particular joint while the spatial arrangement of the bones alters significantly among bones and scans. In the first step, a rigid deterministic model of the bone is generated from a segmentation of the bone in the image corresponding to one position of the joint by using the live wire method. Subsequently, in other images of the same joint, this model is used to search for the same bone by minimizing an energy function that utilizes both boundary- and region-based information. An evaluation of the method by utilizing a total of 60 data sets on MR and CT images of the ankle complex and cervical spine indicates that the segmentations agree very closely with the live wire segmentations, yielding true positive and false positive volume fractions in the range 89%-97% and 0.2%-0.7%. The method requires 1-2 minutes of operator time and 6-7 min of computer time per data set, which makes it significantly more efficient than live wire-the method currently available for the task that can be used routinely.

  1. An adaptive multi-feature segmentation model for infrared image

    Science.gov (United States)

    Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

    2016-04-01

    Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

  2. Investigation of Adaptive Responses in Bystander Cells in 3D Cultures Containing Tritium-Labeled and Unlabeled Normal Human Fibroblasts

    Science.gov (United States)

    Pinto, Massimo; Azzam, Edouard I.; Howell, Roger W.

    2010-01-01

    The study of radiation-induced bystander effects in normal human cells maintained in three-dimensional (3D) architecture provides more in vivo-like conditions and is relevant to human risk assessment. Linear energy transfer, dose and dose rate have been considered as critical factors in propagating radiation-induced effects. This investigation uses an in vitro 3D tissue culture model in which normal AG1522 human fibroblasts are grown in a carbon scaffold to investigate induction of a G1 arrest in bystander cells that neighbor radiolabeled cells. Cell cultures were co-pulse-labeled with [3H]deoxycytidine (3HdC) to selectively irradiate a minor fraction of cells with 1–5 keV/μm β particles and bromodeoxyuridine (BrdU) to identify the radiolabeled cells using immunofluorescence. The induction of a G1 arrest was measured specifically in unlabeled cells (i.e. bystander cells) using a flow cytometry-based version of the cumulative labeling index assay. To investigate the relationship between bystander effects and adaptive responses, cells were challenged with an acute 4 Gy γ-radiation dose after they had been kept under the bystander conditions described above for several hours, and the regulation of the radiation-induced G1 arrest was measured selectively in bystander cells. When the average dose rate in 3HdC-labeled cells (bystander effects or adaptive bystander effects were observed as measured by magnitude of the G1 arrest, micronucleus formation, or changes in mitochondrial membrane potential. Higher dose rates and/or higher LET may be required to observe stressful bystander effects in this experimental system, whereas lower dose rates and challenge doses may be required to detect adaptive bystander responses. PMID:20681788

  3. HIFI-C: a robust and fast method for determining NMR couplings from adaptive 3D to 2D projections.

    Science.gov (United States)

    Cornilescu, Gabriel; Bahrami, Arash; Tonelli, Marco; Markley, John L; Eghbalnia, Hamid R

    2007-08-01

    We describe a novel method for the robust, rapid, and reliable determination of J couplings in multi-dimensional NMR coupling data, including small couplings from larger proteins. The method, "High-resolution Iterative Frequency Identification of Couplings" (HIFI-C) is an extension of the adaptive and intelligent data collection approach introduced earlier in HIFI-NMR. HIFI-C collects one or more optimally tilted two-dimensional (2D) planes of a 3D experiment, identifies peaks, and determines couplings with high resolution and precision. The HIFI-C approach, demonstrated here for the 3D quantitative J method, offers vital features that advance the goal of rapid and robust collection of NMR coupling data. (1) Tilted plane residual dipolar couplings (RDC) data are collected adaptively in order to offer an intelligent trade off between data collection time and accuracy. (2) Data from independent planes can provide a statistical measure of reliability for each measured coupling. (3) Fast data collection enables measurements in cases where sample stability is a limiting factor (for example in the presence of an orienting medium required for residual dipolar coupling measurements). (4) For samples that are stable, or in experiments involving relatively stronger couplings, robust data collection enables more reliable determinations of couplings in shorter time, particularly for larger biomolecules. As a proof of principle, we have applied the HIFI-C approach to the 3D quantitative J experiment to determine N-C' RDC values for three proteins ranging from 56 to 159 residues (including a homodimer with 111 residues in each subunit). A number of factors influence the robustness and speed of data collection. These factors include the size of the protein, the experimental set up, and the coupling being measured, among others. To exhibit a lower bound on robustness and the potential for time saving, the measurement of dipolar couplings for the N-C' vector represents a realistic

  4. Improved Gaussian Mixture Models for Adaptive Foreground Segmentation

    DEFF Research Database (Denmark)

    Katsarakis, Nikolaos; Pnevmatikakis, Aristodemos; Tan, Zheng-Hua

    2016-01-01

    Adaptive foreground segmentation is traditionally performed using Stauffer & Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic...... elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated...

  5. Individual fibre segmentation from 3D X-ray computed tomography for characterising the fibre orientation in unidirectional composite materials

    DEFF Research Database (Denmark)

    Emerson, Monica Jane; Jespersen, Kristine Munk; Dahl, Anders Bjorholm

    2017-01-01

    The aim of this paper is to characterise the fibre orientation in unidirectional fibre reinforced polymers, namely glass and carbon fibre composites. The compression strength of the composite is related to the orientation of the fibres. Thus the orientation is essential when designing materials...... for wind turbine blades. The calculation of the fibre orientation distribution is based on segmenting the individual fibres from volumes that have been acquired through X-ray tomography. The segmentation method presented in this study can accurately extract individual fibres from low contrast X-ray scans...... of composites with high fibre volume fraction. From the individual fibre orientations, it is possible to obtain results which are independent of the scanning quality. The compression strength for both composites is estimated from the average fibre orientations and is found to be of the same order of magnitude...

  6. 3D segmentation and quantification of magnetic resonance data: application to the osteonecrosis of the femoral head

    Science.gov (United States)

    Klifa, Catherine S.; Lynch, John A.; Zaim, Souhil; Genant, Harry K.

    1999-05-01

    The general objective of our study is the development of a clinically robust three-dimensional segmentation and quantification technique of Magnetic Resonance (MR) data, for the objective and quantitative evaluation of the osteonecrosis (ON) of the femoral head. This method will help evaluate the effects of joint preserving treatments for femoral head osteonecrosis from MR data. The disease is characterized by tissue changes (death of bone and marrow cells) within the weight-bearing portion of the femoral head. Due to the fuzzy appearance of lesion tissues and their different intensity patterns in various MR sequences, we proposed a semi-automatic multispectral segmentation of MR data introducing data constraints (anatomical and geometrical) and using a classical K-means unsupervised clustering algorithm. The method was applied on ON patient data. Results of volumetric measurements and configuration of various tissues obtained with the semi- automatic method were compared with quantitative results delineated by a trained radiologist.

  7. Chest wall segmentation in automated 3D breast ultrasound using rib shadow enhancement and multi-plane cumulative probability enhanced map

    Science.gov (United States)

    Kim, Hyeonjin; Kim, Hannah; Hong, Helen

    2015-03-01

    We propose an automatic segmentation method of chest wall in 3D ABUS images using rib shadow enhancement and multi-planar cumulative probability enhanced map. For the identification of individual dark rib shadows, each rib shadow is enhanced using intensity transfer function and 3D sheet-like enhancement filtering. Then, wrongly enhanced intercostal regions and small fatty tissues are removed using coronal and sagittal cumulative probability enhanced maps. The large fatty tissues with globular and sheet-like shapes at the top of rib shadow are removed using shape and orientation analysis based on moment matrix. Detected chest walls are connected with cubic B-spline interpolation. Experimental results show that the Dice similarity coefficient of proposed method as comparison with two manually outlining results provides over 90% in average.

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

    OpenAIRE

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

    2008-01-01

    Objective 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. Materials and Methods 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 lu...

  9. Parametric 3D Atmospheric Reconstruction in Highly Variable Terrain with Recycled Monte Carlo Paths and an Adapted Bayesian Inference Engine

    Science.gov (United States)

    Langmore, Ian; Davis, Anthony B.; Bal, Guillaume; Marzouk, Youssef M.

    2012-01-01

    We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.

  10. Fully-3D PET image reconstruction using scanner-independent, adaptive projection data and highly rotation-symmetric voxel assemblies.

    Science.gov (United States)

    Scheins, J J; Herzog, H; Shah, N J

    2011-03-01

    For iterative, fully 3D positron emission tomography (PET) image reconstruction intrinsic symmetries can be used to significantly reduce the size of the system matrix. The precalculation and beneficial memory-resident storage of all nonzero system matrix elements is possible where sufficient compression exists. Thus, reconstruction times can be minimized independently of the used projector and more elaborate weighting schemes, e.g., volume-of-intersection (VOI), are applicable. A novel organization of scanner-independent, adaptive 3D projection data is presented which can be advantageously combined with highly rotation-symmetric voxel assemblies. In this way, significant system matrix compression is achieved. Applications taking into account all physical lines-of-response (LORs) with individual VOI projectors are presented for the Siemens ECAT HR+ whole-body scanner and the Siemens BrainPET, the PET component of a novel hybrid-MR/PET imaging system. Measured and simulated data were reconstructed using the new method with ordered-subset-expectation-maximization (OSEM). Results are compared to those obtained by the sinogram-based OSEM reconstruction provided by the manufacturer. The higher computational effort due to the more accurate image space sampling provides significantly improved images in terms of resolution and noise.

  11. Fast 3-D large-scale gravity and magnetic modeling using unstructured grids and an adaptive multilevel fast multipole method

    Science.gov (United States)

    Ren, Zhengyong; Tang, Jingtian; Kalscheuer, Thomas; Maurer, Hansruedi

    2017-01-01

    A novel fast and accurate algorithm is developed for large-scale 3-D gravity and magnetic modeling problems. An unstructured grid discretization is used to approximate sources with arbitrary mass and magnetization distributions. A novel adaptive multilevel fast multipole (AMFM) method is developed to reduce the modeling time. An observation octree is constructed on a set of arbitrarily distributed observation sites, while a source octree is constructed on a source tetrahedral grid. A novel characteristic is the independence between the observation octree and the source octree, which simplifies the implementation of different survey configurations such as airborne and ground surveys. Two synthetic models, a cubic model and a half-space model with mountain-valley topography, are tested. As compared to analytical solutions of gravity and magnetic signals, excellent agreements of the solutions verify the accuracy of our AMFM algorithm. Finally, our AMFM method is used to calculate the terrain effect on an airborne gravity data set for a realistic topography model represented by a triangular surface retrieved from a digital elevation model. Using 16 threads, more than 5800 billion interactions between 1,002,001 observation points and 5,839,830 tetrahedral elements are computed in 453.6 s. A traditional first-order Gaussian quadrature approach requires 3.77 days. Hence, our new AMFM algorithm not only can quickly compute the gravity and magnetic signals for complicated problems but also can substantially accelerate the solution of 3-D inversion problems.

  12. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ozekes, Serhat; Osman, Onur; Ucan, N. [Istanbul Commerce University, Ragip Gumuspala Cad. No: 84 34378 Eminonu, Istanbul (Turkmenistan)

    2008-02-15

    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.

  14. A novel method of target recognition based on 3D-color-space locally adaptive regression kernels model

    Science.gov (United States)

    Liu, Jiaqi; Han, Jing; Zhang, Yi; Bai, Lianfa

    2015-10-01

    Locally adaptive regression kernels model can describe the edge shape of images accurately and graphic trend of images integrally, but it did not consider images' color information while the color is an important element of an image. Therefore, we present a novel method of target recognition based on 3-D-color-space locally adaptive regression kernels model. Different from the general additional color information, this method directly calculate the local similarity features of 3-D data from the color image. The proposed method uses a few examples of an object as a query to detect generic objects with incompact, complex and changeable shapes. Our method involves three phases: First, calculating the novel color-space descriptors from the RGB color space of query image which measure the likeness of a voxel to its surroundings. Salient features which include spatial- dimensional and color -dimensional information are extracted from said descriptors, and simplifying them to construct a non-similar local structure feature set of the object class by principal components analysis (PCA). Second, we compare the salient features with analogous features from the target image. This comparison is done using a matrix generalization of the cosine similarity measure. Then the similar structures in the target image are obtained using local similarity structure statistical matching. Finally, we use the method of non-maxima suppression in the similarity image to extract the object position and mark the object in the test image. Experimental results demonstrate that our approach is effective and accurate in improving the ability to identify targets.

  15. Combining 3D tracking and surgical instrumentation to determine the stiffness of spinal motion segments: a validation study.

    Science.gov (United States)

    Reutlinger, C; Gédet, P; Büchler, P; Kowal, J; Rudolph, T; Burger, J; Scheffler, K; Hasler, C

    2011-04-01

    The spine is a complex structure that provides motion in three directions: flexion and extension, lateral bending and axial rotation. So far, the investigation of the mechanical and kinematic behavior of the basic unit of the spine, a motion segment, is predominantly a domain of in vitro experiments on spinal loading simulators. Most existing approaches to measure spinal stiffness intraoperatively in an in vivo environment use a distractor. However, these concepts usually assume a planar loading and motion. The objective of our study was to develop and validate an apparatus, that allows to perform intraoperative in vivo measurements to determine both the applied force and the resulting motion in three dimensional space. The proposed setup combines force measurement with an instrumented distractor and motion tracking with an optoelectronic system. As the orientation of the applied force and the three dimensional motion is known, not only force-displacement, but also moment-angle relations could be determined. The validation was performed using three cadaveric lumbar ovine spines. The lateral bending stiffness of two motion segments per specimen was determined with the proposed concept and compared with the stiffness acquired on a spinal loading simulator which was considered to be gold standard. The mean values of the stiffness computed with the proposed concept were within a range of ±15% compared to data obtained with the spinal loading simulator under applied loads of less than 5 Nm.

  16. 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.

  17. 手腕骨三维图像分割方法%A 3D Segmentation Technique for Carpal Bone Images

    Institute of Scientific and Technical Information of China (English)

    李晶; 赵海燕

    2013-01-01

    Targeted at kinematic analysis of the carpal bones and design of fracture fixation for corresponding surgeries, this paper proposes a 3D (Three-Dimensional) technique for segmentation of the carpal bone images on the basis of the spatial position, which contributes to independent investigation of each segmented carpal bone on kinematic features and load-carrying capabilities under different circumstances. As a new segmentation technique in diagnosis and treatment of carpal bone diseases, it can divide the carpal bone images into eight segments that are available separately for review, conifguration, analysis and measurement.%为了对手腕骨进行运动学分析并对骨折手术固定辅助设计,本文提出一种对手腕骨的三维分割方法,即采用基于空间位置的方法将手腕骨独立分开,以便独立研究各部分在不同情况下的运动与受力。该方法将手腕8块腕骨分割开来,并能独立显示控制测量。为手腕骨疾病的诊断治疗提供,新的技术方法。

  18. Registration of overlapping 3D point clouds using extracted line segments. (Polish Title: Rejestracja chmur punktów 3D w oparciu o wyodrębnione krawędzie)

    Science.gov (United States)

    Poręba, M.; Goulette, F.

    2014-12-01

    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 and 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 an 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 p airing with an accuracy of 99 % at least, and about 8% of omitted line pairs.

  19. Automating measurement of subtle changes in articular cartilage from MRI of the knee by combining 3D image registration and segmentation

    Science.gov (United States)

    Lynch, John A.; Zaim, Souhil; Zhao, Jenny; Peterfy, Charles G.; Genant, Harry K.

    2001-07-01

    In osteoarthritis, articular cartilage loses integrity and becomes thinned. This usually occurs at sites which bear weight during normal use. Measurement of such loss from MRI scans, requires precise and reproducible techniques, which can overcome the difficulties of patient repositioning within the scanner. In this study, we combine a previously described technique for segmentation of cartilage from MRI of the knee, with a technique for 3D image registration that matches localized regions of interest at followup and baseline. Two patients, who had recently undergone meniscal surgery, and developed lesions during the 12 month followup period were examined. Image registration matched regions of interest (ROI) between baseline and followup, and changes within the cartilage lesions were estimate to be about a 16% reduction in cartilage volume within each ROI. This was more than 5 times the reproducibility of the measurement, but only represented a change of between 1 and 2% in total femoral cartilage volume. Changes in total cartilage volume may be insensitive for quantifying changes in cartilage morphology. A combined used of automated image segmentation, with 3D image registration could be a useful tool for the precise and sensitive measurement of localized changes in cartilage from MRI of the knee.

  20. Assessment of a Microsoft Kinect-based 3D scanning system for taking body segment girth measurements: a comparison to ISAK and ISO standards.

    Science.gov (United States)

    Clarkson, Sean; Wheat, Jon; Heller, Ben; Choppin, Simon

    2016-01-01

    Use of anthropometric data to infer sporting performance is increasing in popularity, particularly within elite sport programmes. Measurement typically follows standards set by the International Society for the Advancement of Kinanthropometry (ISAK). However, such techniques are time consuming, which reduces their practicality. Schranz et al. recently suggested 3D body scanners could replace current measurement techniques; however, current systems are costly. Recent interest in natural user interaction has led to a range of low-cost depth cameras capable of producing 3D body scans, from which anthropometrics can be calculated. A scanning system comprising 4 depth cameras was used to scan 4 cylinders, representative of the body segments. Girth measurements were calculated from the 3D scans and compared to gold standard measurements. Requirements of a Level 1 ISAK practitioner were met in all 4 cylinders, and ISO standards for scan-derived girth measurements were met in the 2 larger cylinders only. A fixed measurement bias was identified that could be corrected with a simple offset factor. Further work is required to determine comparable performance across a wider range of measurements performed upon living participants. Nevertheless, findings of the study suggest such a system offers many advantages over current techniques, having a range of potential applications.

  1. Adaptive distance metric learning for diffusion tensor image segmentation.

    Science.gov (United States)

    Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

    2014-01-01

    High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

  2. Adaptive distance metric learning for diffusion tensor image segmentation.

    Directory of Open Access Journals (Sweden)

    Youyong Kong

    Full Text Available High quality segmentation of diffusion tensor images (DTI is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

  3. 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

  4. A 3-D Novel Highly Chaotic System with Four Quadratic Nonlinearities, its Adaptive Control and Anti-Synchronization with Unknown Parameters

    Directory of Open Access Journals (Sweden)

    S. Vaidyanathan

    2014-11-01

    Full Text Available This research work proposes a seven-term 3-D novel dissipative chaotic system with four quadratic nonlinearities. The Lyapunov exponents of the 3-D novel chaotic system are obtained as L1 = 11.36204, L2 = 0 and L3 = –47.80208. Since the sum of the Lyapunov exponents is negative, the 3-D novel chaotic system is dissipative. Also, the Kaplan-Yorke dimension of the 3-D novel chaotic system is obtained as DKY = 2.23769. The maximal Lyapunov exponent (MLE of the novel chaotic system is L1 = 11.36204, which is a large value for a polynomial chaotic system. Thus, the proposed 3-D novel chaotic system is highly chaotic. The phase portraits of the novel chaotic system simulated using MATLAB depict the highly chaotic attractor of the novel system. This research work also discusses other qualitative properties of the system. Next, an adaptive controller is designed to stabilize the 3-D novel chaotic system with unknown parameters. Also, an adaptive synchronizer is designed to achieve anti-synchronization of the identical 3-D novel chaotic systems with unknown parameters. The adaptive results derived in this work are established using Lyapunov stability theory. MATLAB simulations have been shown to illustrate and validate all the main results derived in this work.

  5. 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.

  6. 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

  7. Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores.

    Science.gov (United States)

    Zhang, Jiong; Dashtbozorg, Behdad; Bekkers, Erik; Pluim, Josien P W; Duits, Remco; Ter Haar Romeny, Bart M

    2016-12-01

    This paper presents a robust and fully automatic filter-based approach for retinal vessel segmentation. We propose new filters based on 3D rotating frames in so-called orientation scores, which are functions on the Lie-group domain of positions and orientations [Formula: see text]. By means of a wavelet-type transform, a 2D image is lifted to a 3D orientation score, where elongated structures are disentangled into their corresponding orientation planes. In the lifted domain [Formula: see text], vessels are enhanced by means of multi-scale second-order Gaussian derivatives perpendicular to the line structures. More precisely, we use a left-invariant rotating derivative (LID) frame, and a locally adaptive derivative (LAD) frame. The LAD is adaptive to the local line structures and is found by eigensystem analysis of the left-invariant Hessian matrix (computed with the LID). After multi-scale filtering via the LID or LAD in the orientation score domain, the results are projected back to the 2D image plane giving us the enhanced vessels. Then a binary segmentation is obtained through thresholding. The proposed methods are validated on six retinal image datasets with different image types, on which competitive segmentation performances are achieved. In particular, the proposed algorithm of applying the LAD filter on orientation scores (LAD-OS) outperforms most of the state-of-the-art methods. The LAD-OS is capable of dealing with typically difficult cases like crossings, central arterial reflex, closely parallel and tiny vessels. The high computational speed of the proposed methods allows processing of large datasets in a screening setting.

  8. 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

  9. 3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets.

    Science.gov (United States)

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

    2015-09-01

    Intraventricular hemorrhage (IVH) or bleed within the cerebral ventricles is a common condition among very low birth weight pre-term neonates. The prognosis for these patients is worsened should they develop progressive ventricular dilatation, i.e., post-hemorrhagic ventricle dilatation (PHVD), which occurs in 10-30% of IVH patients. Accurate measurement of ventricular volume would be valuable information and could be used to predict PHVD and determine whether that specific patient with ventricular dilatation requires treatment. While the monitoring of PHVD in infants is typically done by repeated transfontanell 2D ultrasound (US) and not MRI, once the patient's fontanels have closed around 12-18months of life, the follow-up patient scans are done by MRI. Manual segmentation of ventricles from MR images is still seen as a gold standard. However, it is extremely time- and labor-consuming, and it also has observer variability. This paper proposes an accurate multiphase geodesic level-set segmentation algorithm for the extraction of the cerebral ventricle system of pre-term PHVD neonates from 3D T1 weighted MR images. The proposed segmentation algorithm makes use of multi-region segmentation technique associated with spatial priors built from a multi-atlas registration scheme. The leave-one-out cross validation with 19 patients with mild enlargement of ventricles and 7 hydrocephalus patients shows that the proposed method is accurate, suggesting that the proposed approach could be potentially used for volumetric and morphological analysis of the ventricle system of IVH neonatal brains in clinical practice. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. 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.

  11. Disk-Halo-Disk Circulation and the Evolution of the ISM - 3D HD and MHD Adaptive Mesh Refinement Simulations

    CERN Document Server

    D'Avillez, M A; Breitschwerdt, Dieter

    2005-01-01

    State of the art models of the ISM use adaptive mesh refinement to capture small scale structures, by refining on the fly those regions of the grid where density and pressure gradients occur, keeping at the same time the existing resolution in the other regions. With this technique it became possible to study the ISM in star-forming galaxies in a global way by following matter circulation between stars and the interstellar gas, and, in particular the energy input by random and clustered supernova explosions, which determine the dynamical and chemical evolution of the ISM, and hence of the galaxy as a whole. In this paper we review the conditions for a self-consistent modelling of the ISM and present the results from the latest developments in the 3D HD/MHD global models of the ISM. Special emphasis is put on the effects of the magnetic field with respect to the volume and mass fractions of the different ISM ``phases'', the relative importance of ram, thermal and magnetic pressures, and whether the field can p...

  12. 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.

  13. Generalization of Hindi OCR Using Adaptive Segmentation and Font Files

    Science.gov (United States)

    Agrawal, Mudit; Ma, Huanfeng; Doermann, David

    In this chapter, we describe an adaptive Indic OCR system implemented as part of a rapidly retargetable language tool effort and extend work found in [20, 2]. The system includes script identification, character segmentation, training sample creation, and character recognition. For script identification, Hindi words are identified in bilingual or multilingual document images using features of the Devanagari script and support vector machine (SVM). Identified words are then segmented into individual characters, using a font-model-based intelligent character segmentation and recognition system. Using characteristics of structurally similar TrueType fonts, our system automatically builds a model to be used for the segmentation and recognition of the new script, independent of glyph composition. The key is a reliance on known font attributes. In our recognition system three feature extraction methods are used to demonstrate the importance of appropriate features for classification. The methods are tested on both Latin and non-Latin scripts. Results show that the character-level recognition accuracy exceeds 92% for non-Latin and 96% for Latin text on degraded documents. This work is a step toward the recognition of scripts of low-density languages which typically do not warrant the development of commercial OCR, yet often have complete TrueType font descriptions.

  14. Integration of 3D scale-based pseudo-enhancement correction and partial volume image segmentation for improving electronic colon cleansing in CT colonograpy.

    Science.gov (United States)

    Zhang, Hao; Li, Lihong; Zhu, Hongbin; Han, Hao; Song, Bowen; Liang, Zhengrong

    2014-01-01

    Orally administered tagging agents are usually used in CT colonography (CTC) to differentiate residual bowel content from native colonic structures. However, the high-density contrast agents tend to introduce pseudo-enhancement (PE) effect on neighboring soft tissues and elevate their observed CT attenuation value toward that of the tagged materials (TMs), which may result in an excessive electronic colon cleansing (ECC) since the pseudo-enhanced soft tissues are incorrectly identified as TMs. To address this issue, we integrated a 3D scale-based PE correction into our previous ECC pipeline based on the maximum a posteriori expectation-maximization partial volume (PV) segmentation. The newly proposed ECC scheme takes into account both the PE and PV effects that commonly appear in CTC images. We evaluated the new scheme on 40 patient CTC scans, both qualitatively through display of segmentation results, and quantitatively through radiologists' blind scoring (human observer) and computer-aided detection (CAD) of colon polyps (computer observer). Performance of the presented algorithm has shown consistent improvements over our previous ECC pipeline, especially for the detection of small polyps submerged in the contrast agents. The CAD results of polyp detection showed that 4 more submerged polyps were detected for our new ECC scheme over the previous one.

  15. 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.

  16. 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

  17. Atlas Based Automatic Liver 3D CT Image Segmentation%基于图谱的肝脏CT三维自动分割研究

    Institute of Scientific and Technical Information of China (English)

    刘伟; 贾富仓; 胡庆茂; 王俊

    2011-01-01

    目的 在肝脏外科手术或肝脏病理研究中,计算肝脏体积是重要步骤.由于肝脏外形复杂、临近组织灰度值与之接近等特点,肝脏的自动医学图像分割仍是医学图像处理中的难点之一.方法 本文采用图谱结合3D非刚性配准的方法,同时加入肝脏区域搜索算法,实现了鲁棒性较高的肝脏自动分割程序.首先,利用20套训练图像创建图谱,然后程序自动搜索肝脏区域,最后将图谱与待分割CT图像依次进行仿射配准和B样条配准.配准以后的图谱肝脏轮廓即可表示为目标肝脏分割轮廓,进而计算出肝脏体积.结果 评估结果显示,上述方法在肝脏体积误差方面表现出色,达到77分,但在局部(主要在肝脏尖端)出现较大的误差.结论 该方法分割临床肝脏CT图像具有可行性.%Objective Liver segmentation is an important step for the planning and navigation in liver surgery. Accurate, fast and robust automatic segmentation methods for clinical routine data are urgently needed. Because of the liver- s characteristics, such as the complexity of the external form, the similarity between the intensities of the liver and the tissues around it, automatic segmentation of the liver is one of the difficulties in medical image processing. Methods In this paper, 3D non-rigid registration from a refined atlas to liver CT images is used for segmentation. Firstly, twenty sets of training images are utilized to create an atlas. Then the liver initial region is searched and located automatically. After that threshold filtering is used to enhance the robustness of segmentation. Finally, this atlas is non-rigidly registered to the liver in CT images with affine and B-spline in succession. The registered segmentation of liver- s atlas represented the segmentation of the target liver, and then the liver volume was calculated. Results The evaluation show that the proposed method works well in liver volume error, with the 77 score

  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. Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging.

    Science.gov (United States)

    Fu, J C; Chen, C C; Chai, J W; Wong, S T C; Li, I C

    2010-06-01

    We propose an automatic hybrid image segmentation model that integrates the statistical expectation maximization (EM) model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation. In addition, an adaptive mechanism is developed to fine tune the PCNN parameters. The EM model serves two functions: evaluation of the PCNN image segmentation and adaptive adjustment of the PCNN parameters for optimal segmentation. To evaluate the performance of the adaptive EM-PCNN, we use it to segment MR brain image into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The performance of the adaptive EM-PCNN is compared with that of the non-adaptive EM-PCNN, EM, and Bias Corrected Fuzzy C-Means (BCFCM) algorithms. The result is four sets of boundaries for the GM and the brain parenchyma (GM+WM), the two regions of most interest in medical research and clinical applications. Each set of boundaries is compared with the golden standard to evaluate the segmentation performance. The adaptive EM-PCNN significantly outperforms the non-adaptive EM-PCNN, EM, and BCFCM algorithms in gray mater segmentation. In brain parenchyma segmentation, the adaptive EM-PCNN significantly outperforms the BCFCM only. However, the adaptive EM-PCNN is better than the non-adaptive EM-PCNN and EM on average. We conclude that of the three approaches, the adaptive EM-PCNN yields the best results for gray matter and brain parenchyma segmentation.

  20. 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.

  1. 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

  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. 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

  4. 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.

  5. Energy-consistent small-core pseudopotentials for 3d-transition metals adapted to quantum Monte Carlo calculations

    NARCIS (Netherlands)

    Burkatzki, M.; Filippi, Claudia; Dolg, M.

    2008-01-01

    We extend our recently published set of energy-consistent scalar-relativistic Hartree–Fock pseudopotentials by the 3d-transition metal elements, scandium through zinc. The pseudopotentials do not exhibit a singularity at the nucleus and are therefore suitable for quantum Monte Carlo (QMC)

  6. Optimized adaptation algorithm for HEVC/H.265 dynamic adaptive streaming over HTTP using variable segment duration

    Science.gov (United States)

    Irondi, Iheanyi; Wang, Qi; Grecos, Christos

    2016-04-01

    Adaptive video streaming using HTTP has become popular in recent years for commercial video delivery. The recent MPEG-DASH standard allows interoperability and adaptability between servers and clients from different vendors. The delivery of the MPD (Media Presentation Description) files in DASH and the DASH client behaviours are beyond the scope of the DASH standard. However, the different adaptation algorithms employed by the clients do affect the overall performance of the system and users' QoE (Quality of Experience), hence the need for research in this field. Moreover, standard DASH delivery is based on fixed segments of the video. However, there is no standard segment duration for DASH where various fixed segment durations have been employed by different commercial solutions and researchers with their own individual merits. Most recently, the use of variable segment duration in DASH has emerged but only a few preliminary studies without practical implementation exist. In addition, such a technique requires a DASH client to be aware of segment duration variations, and this requirement and the corresponding implications on the DASH system design have not been investigated. This paper proposes a segment-duration-aware bandwidth estimation and next-segment selection adaptation strategy for DASH. Firstly, an MPD file extension scheme to support variable segment duration is proposed and implemented in a realistic hardware testbed. The scheme is tested on a DASH client, and the tests and analysis have led to an insight on the time to download next segment and the buffer behaviour when fetching and switching between segments of different playback durations. Issues like sustained buffering when switching between segments of different durations and slow response to changing network conditions are highlighted and investigated. An enhanced adaptation algorithm is then proposed to accurately estimate the bandwidth and precisely determine the time to download the next

  7. Analogue modeling of 3-D structural segmentation in fold-and-thrust belts: interactions between frictional and viscous provinces in foreland basins

    Science.gov (United States)

    Borderie, Sandra; Graveleau, Fabien; Witt, César; Vendeville, Bruno C.

    2016-04-01

    Accretionary wedges are generally segmented both across and along strike because of diverse factors including tectonic and stratigraphic inheritance. In fold-and-thrust belts, along-strike stratigraphic changes in the foreland sequence are classically observed and cause a curvature of the deformation front. Although the parameters controlling this curvature are well documented, the structural interactions and mutual influences between adjacent provinces are much less analyzed. To investigate this question, we deformed analogue models in a compressional box equipped with digital cameras and a topographic measurement apparatus. Models where shortened above a basal frictional detachment (glass microbeads) and segmentation was tested by having a region in which we added an interbedded viscous level (silicone polymer) within the sedimentary cover (dry sand). By changing the number (2 or 3) and the relative width of the purely frictional and viscous provinces, our goal was to characterize geometrically and kinematically the interactions between the viscous and the purely frictional provinces. We used a commercial geomodeller to generate 3-D geometrical models. The results indicate that regardless of the relative width of the purely frictional vs. viscous provinces, the deformation style in the frictional province is not influenced by the presence of the adjacent viscous province. On the contrary, the structural style and the deformation kinematics in the viscous province is significantly impacted by the presence or absence of an adjacent purely frictional province. At first order, the deformation style in the viscous province depends on its width, and three structural styles can be defined along strike. Far from the frictional area, structures are primarily of salt-massif type, and they do not seem to be influenced by the frictional wedge province. Towards the frictional province, deformation changes gradually to a zone of purely forethrusts (foreland verging), and

  8. Adaptive Image Restoration and Segmentation Method Using Different Neighborhood Sizes

    Directory of Open Access Journals (Sweden)

    Chengcheng Li

    2003-04-01

    Full Text Available The image restoration methods based on the Bayesian's framework and Markov random fields (MRF have been widely used in the image-processing field. The basic idea of all these methods is to use calculus of variation and mathematical statistics to average or estimate a pixel value by the values of its neighbors. After applying this averaging process to the whole image a number of times, the noisy pixels, which are abnormal values, are filtered out. Based on the Tea-trade model, which states that the closer the neighbor, more contribution it makes, almost all of these methods use only the nearest four neighbors for calculation. In our previous research [1, 2], we extended the research on CLRS (image restoration and segmentation by using competitive learning algorithm to enlarge the neighborhood size. The results showed that the longer neighborhood range could improve or worsen the restoration results. We also found that the autocorrelation coefficient was an important factor to determine the proper neighborhood size. We then further realized that the computational complexity increased dramatically along with the enlargement of the neighborhood size. This paper is to further the previous research and to discuss the tradeoff between the computational complexity and the restoration improvement by using longer neighborhood range. We used a couple of methods to construct the synthetic images with the exact correlation coefficients we want and to determine the corresponding neighborhood size. We constructed an image with a range of correlation coefficients by blending some synthetic images. Then an adaptive method to find the correlation coefficients of this image was constructed. We restored the image by applying different neighborhood CLRS algorithm to different parts of the image according to its correlation coefficient. Finally, we applied this adaptive method to some real-world images to get improved restoration results than by using single

  9. Adaptive Hydraulics/Hydrology (AdH) Pilot Point Specification: Guidelines for Solving 3D Groundwater Problems Utilizing Pilot Points

    Science.gov (United States)

    2013-11-01

    Shepard’s method inverse-distance weighted ( IDW )  ordinary kriging and two aforementioned dimensionalities:  3D  2D horizontal for a...Modified Shepard’s method IDW : ( ) ( ) ( )ˆ n loi i loi i i v x w x v x = =å 1 (3) ( ) ( ) ( ) i loi i loi n j loij m x w x m x = = å 1 (4...loi k k loi loi k R d x x m x R d x x æ ö- ÷ç ÷ç= ÷ç ÷÷çè ø 2 (5) where: = IDW estimator = location of interest = IDW

  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 Image Modelling and Specific Treatments in Orthodontics Domain

    Directory of Open Access Journals (Sweden)

    Dionysis Goularas

    2007-01-01

    Full Text Available In this article, we present a 3D specific dental plaster treatment system for orthodontics. From computer tomography scanner images, we propose first a 3D image modelling and reconstruction method of the Mandible and Maxillary based on an adaptive triangulation allowing management of contours meant for the complex topologies. Secondly, we present two specific treatment methods directly achieved on obtained 3D model allowing the automatic correction for the setting in occlusion of the Mandible and the Maxillary, and the teeth segmentation allowing more specific dental examinations. Finally, these specific treatments are presented via a client/server application with the aim of allowing a telediagnosis and treatment.

  12. Pattern matching and adaptive image segmentation applied to plant reproduction by tissue culture

    Science.gov (United States)

    Vazquez Rueda, Martin G.; Hahn, Federico

    1999-03-01

    This paper shows the results obtained in a system vision applied to plant reproduction by tissue culture using adaptive image segmentation and pattern matching algorithms, this analysis improves the number of tissue obtained and minimize errors, the image features of tissue are considered join to statistical analysis to determine the best match and results. Tests make on potato plants are used to present comparative results with original images processed with adaptive segmentation algorithm and non adaptive algorithms and pattern matching.

  13. Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Jing; TANG Jilong; LIU Jibin; REN Chunying; LIU Xiangnan; FENG Jiang

    2009-01-01

    Remote sensing image segmentation is the basis of image understanding and analysis. However, the precision and the speed of segmentation can not meet the need of image analysis, due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm (AGA) and Alternative Fuzzy C-Means (AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased, and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means (FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.

  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. Energy-consistent small-core pseudopotentials for 3d-transition metals adapted to quantum Monte Carlo calculations.

    Science.gov (United States)

    Burkatzki, M; Filippi, Claudia; Dolg, M

    2008-10-28

    We extend our recently published set of energy-consistent scalar-relativistic Hartree-Fock pseudopotentials by the 3d-transition metal elements, scandium through zinc. The pseudopotentials do not exhibit a singularity at the nucleus and are therefore suitable for quantum Monte Carlo (QMC) calculations. The pseudopotentials and the accompanying basis sets (VnZ with n=T,Q) are given in standard Gaussian representation and their parameter sets are presented. Coupled cluster, configuration interaction, and QMC studies are carried out for the scandium and titanium atoms and their oxides, demonstrating the good performance of the pseudopotentials. Even though the choice of pseudopotential form is motivated by QMC, these pseudopotentials can also be employed in other quantum chemical approaches.

  16. 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 S; Melvin, Thuy-Anh; Cullen, Kathleen E; Della Santina, Charles C

    2011-05-01

    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 end organs 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 1 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.

  17. Adaptive RT in rectal cancer: superior to 3D-CRT? A simple question, a complex answer

    Energy Technology Data Exchange (ETDEWEB)

    Haustermans, K.; Roels, S.; Verstraete, J.; Depuydt, T. [Leuvens Kanker Inst., Dept. of Radiotherapy, Univ. Hospital Gasthuisberg, Leuven (Belgium); Slagmolen, P. [ESAT/Radiology, Medical Image Computing, Univ. Hospital Gasthuisberg, ESAT, Leuven (Belgium)

    2007-12-15

    Although the introduction of modern treatment techniques such as Total Mesorectal Excision (TME) and combined preoperative chemoradiation has strongly reduced local recurrence rates, there is still room for improvement in treatment in high-risk patients (T3-T4, lymph node positive). In these selected patients, progress may be achieved with higher preoperative doses and by integrating novel chemotherapeutic and molecular targeted agents. Better treatment techniques such as adaptive radiotherapy may ultimately lead to less local recurrences and more sphincter and even organ preservation. But before adaptive radiation can be introduced into clinical routine several steps have to be taken going from target definition over target localisation and positioning to re-planning and cumulative dosimetry. (orig.)

  18. 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.

  19. The human dorsal stream adapts to real actions and 3D shape processing: a functional magnetic resonance imaging study.

    Science.gov (United States)

    Króliczak, G; McAdam, T D; Quinlan, D J; Culham, J C

    2008-11-01

    We tested whether the control of real actions in an ever-changing environment would show any dependence on prior actions elicited by instructional cues a few seconds before. To this end, adaptation of the functional magnetic resonance imaging signal was measured while human participants sequentially grasped three-dimensional objects in an event-related design, using grasps oriented along the same or a different axis of either the same or a different object shape. We found that the bilateral anterior intraparietal sulcus, an area previously linked to the control of visually guided grasping, along with other areas of the intraparietal sulcus, the left supramarginal gyrus, and the right mid superior parietal lobe showed clear adaptation following both repeated grasps and repeated objects. In contrast, the left ventral premotor cortex and the bilateral dorsal premotor cortex, the two premotor areas often linked to response selection, action planning, and execution, showed only grasp-selective adaptation. These results suggest that, even in real action guidance, parietofrontal areas demonstrate differential involvement in visuomotor processing dependent on whether the action or the object has been previously experienced.

  20. Effective Immune Genetic Algorithm for Segmentation of 3D Brain Images%基于免疫遗传算法的三维大脑图像分割

    Institute of Scientific and Technical Information of China (English)

    王毅; 樊养余; 牛奕龙; Monika Lehmpfuhl; 齐敏; 郝重阳

    2008-01-01

    To solve large time-consumption of the complete search (CS), and the instability and inaccurateness of the simple genetic algorithm (SGA), an effective 3D brain images segmentation procedure, utilizing optimal entropy multi-thresholding method, was proposed. Global maximum entropy for the segmentation was yielded fast by the combination of the immune genetic algorithm (IGA) and simulated annealing (SA). Compared to the SGA, the IGA constructs a better selection scheme and ensures various individuals to be selected for preserving the diversity of the population. Meanwhile, the optimal entropy function of 3D medical images is stretched by the SA to construct the new fitness function, and the general expressing form of the selection probability for IGA is also given. Furthermore, to enhance the convergence of our algorithm, the proposed method includes the elitist strategy and the adaptive crossover and mutation mechanism. Results of 100 simulations demonstrate that the 3D brain volume can be successfully classified into three parts: the white matter, the gray matter and the cerebrospinal fluid on the IDL platform. The stability and accuracy of the algorithm, compared with the SGA and IGA, are all improved according to their performance contrasts.%利用最大熵多阈值方法对三维大脑数据进行分割时,穷尽搜索法耗时长,而简单遗传算法的搜索结果又不够稳定和精确.针对该问题,提出了一种免疫遗传和模拟退火相结合的新算法来快速求解全局最大熵.与简单遗传算法相比,免疫遗传算法采用了更佳的选择操作,以确保更多不同个体被选择来保存种群的多样性,而模拟退火机制用于拉伸免疫遗传算法的适应度函数.算法给出了选择概率的一般表达式,并采用精英策略和自适应的交叉、变异机制以改善算法的收敛性.基于IDL平台的100次仿真结果表明,三维大脑数据被成功地分为:脑白质、脑灰质和脑脊液三部

  1. Simultaneous 3D segmentation of three bone compartments on high resolution knee MR images from osteoarthritis initiative (OAI) using graph cuts

    Science.gov (United States)

    Shim, Hackjoon; Kwoh, C. Kent; Yun, Il Dong; Lee, Sang Uk; Bae, Kyongtae

    2009-02-01

    Osteoarthritis (OA) is associated with degradation of cartilage and related changes in the underlying bone. Quantitative measurement of those changes from MR images is an important biomarker to study the progression of OA and it requires a reliable segmentation of knee bone and cartilage. As the most popular method, manual segmentation of knee joint structures by boundary delineation is highly laborious and subject to user-variation. To overcome these difficulties, we have developed a semi-automated method for segmentation of knee bones, which consisted of two steps: placement of seeds and computation of segmentation. In the first step, seeds were placed by the user on a number of slices and then were propagated automatically to neighboring images. The seed placement could be performed on any of sagittal, coronal, and axial planes. The second step, computation of segmentation, was based on a graph-cuts algorithm where the optimal segmentation is the one that minimizes a cost function, which integrated the seeds specified by the user and both the regional and boundary properties of the regions to be segmented. The algorithm also allows simultaneous segmentation of three compartments of the knee bone (femur, tibia, patella). Our method was tested on the knee MR images of six subjects from the osteoarthritis initiative (OAI). The segmentation processing time (mean+/-SD) was (22+/-4)min, which is much shorter than that by the manual boundary delineation method (typically several hours). With this improved efficiency, our segmentation method will facilitate the quantitative morphologic analysis of changes in knee bones associated with osteoarthritis.

  2. A Feature-adaptive Subdivision Method for Real-time 3D Reconstruction of Repeated Topology Surfaces

    Science.gov (United States)

    Lin, Jinhua; Wang, Yanjie; Sun, Honghai

    2017-03-01

    It's well known that rendering for a large number of triangles with GPU hardware tessellation has made great progress. However, due to the fixed nature of GPU pipeline, many off-line methods that perform well can not meet the on-line requirements. In this paper, an optimized Feature-adaptive subdivision method is proposed, which is more suitable for reconstructing surfaces with repeated cusps or creases. An Octree primitive is established in irregular regions where there are the same sharp vertices or creases, this method can find the neighbor geometry information quickly. Because of having the same topology structure between Octree primitive and feature region, the Octree feature points can match the arbitrary vertices in feature region more precisely. In the meanwhile, the patches is re-encoded in the Octree primitive by using the breadth-first strategy, resulting in a meta-table which allows for real-time reconstruction by GPU hardware tessellation unit. There is only one feature region needed to be calculated under Octree primitive, other regions with the same repeated feature generate their own meta-table directly, the reconstruction time is saved greatly for this step. With regard to the meshes having a large number of repeated topology feature, our algorithm improves the subdivision time by 17.575% and increases the average frame drawing time by 0.2373 ms compared to the traditional FAS (Feature-adaptive Subdivision), at the same time the model can be reconstructed in a watertight manner.

  3. An $h$-Adaptive Operator Splitting Method for Two-Phase Flow in 3D Heterogeneous Porous Media

    KAUST Repository

    Chueh, Chih-Che

    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.

  4. Online Event Segmentation in Active Perception using Adaptive Strong Anticipation

    CERN Document Server

    Nery, Bruno

    2010-01-01

    Most cognitive architectures rely on discrete representation, both in space (e.g., objects) and in time (e.g., events). However, a robot interaction with the world is inherently continuous, both in space and in time. The segmentation of the stream of perceptual inputs a robot receives into discrete and meaningful events poses as a challenge in bridging the gap between internal cognitive representations, and the external world. Event Segmentation Theory, recently proposed in the context of cognitive systems research, sustains that humans segment time into events based on matching perceptual input with predictions. In this work we propose a framework for online event segmentation, targeting robots endowed with active perception. Moreover, sensory processing systems have an intrinsic latency, resulting from many factors such as sampling rate, and computational processing, and which is seldom accounted for. This framework is founded on the theory of dynamical systems synchronization, where the system considered i...

  5. Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information

    OpenAIRE

    Temitope Mapayi; Serestina Viriri; Jules-Raymond Tapamo

    2015-01-01

    Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal image demo...

  6. 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.

  7. Development 3D model of adaptation of the Azerbaijan coastal zone at the various levels of Caspian Sea

    Science.gov (United States)

    Mammadov, Ramiz

    2013-04-01

    coastal areas at hydraulic engineering projects the sea level should be considered as multistage process, what we have considered by development of adaptation of a coastal zone The exact three-dimensional map of a coastal zone has been created. For different scenario sea levels, or example, -30.0; -29.0; -28.0; -27.0; -26.0; -25.0 and -24.0 exact coastal lines have been certain. Further maps of a vegetative cover, ground, social and economic and ecological conditions have been developed for different level and respective alterations are certain. More vulnerable coastal zone, flooded area and socio-economic damage were estimated.

  8. An approach to offline handwritten Chinese character recognition based on segment evaluation of adaptive duration

    Institute of Scientific and Technical Information of China (English)

    李国宏; 施鹏飞

    2004-01-01

    This paper presents a methodology for off-line handwritten Chinese character recognition based on mergence of consecutive segments of adaptive duration. The handwritten Chinese character string is partitioned into a sequence of consecutive segments, which are combined to implement dissimilarity evaluation within a sliding window whose durations are determined adaptively by the integration of shapes and context of evaluations. The average stroke width is estimated for the handwritten Chinese character string, and a set of candidate character segmentation boundaries is found by using the integration of pixel and stroke features. The final decisions on segmentation and recognition are made under minimal arithmetical mean dissimilarities. Experiments proved that the proposed approach of adaptive duration outperforms the method of fixed duration, and is very effective for the recognition of overlapped, broken, touched, loosely configured Chinese characters.

  9. An approach to offline handwritten Chinese character recognition based on segment evaluation of adaptive duration

    Institute of Scientific and Technical Information of China (English)

    李国宏; 施鹏飞

    2004-01-01

    This paper presents a methodology for off-line handwritten Chinese character recognition based on mergence of consecutive segments of adaptive duration. The handwritten Chinese character string is partitioned into a sequence of consecutive segments,which are combined to implement dissimilarity evaluation within a sliding window whose durations are determined adaptively by the integration of shapes and context of evaluations. The average stroke width is estimated for the handwritten Chinese character string,and a set of candidate character segmentation boundaries is found by using the integration of pixel and stroke features. The final decisions on segmentation and recognition are made under minimal arithmetical mean dissimilarities. Experiments proved that the proposed approach of adaptive duration outperforms the method of fixed duration,and is very effective for the recognition of overlapped,broken,touched,loosely configured Chinese characters.

  10. A Bintree Energy Approach for Colour Image Segmentation Using Adaptive Channel Selection

    Institute of Scientific and Technical Information of China (English)

    TU Sheng-xian; ZHANG Su; CHEN Ya-zhu; XIAO Chang-yan; ZHANG Lei

    2008-01-01

    A new hierarchical approach called bintree energy segmentation was presented for color image seg-mentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images,from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the "best" chan-nel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.

  11. 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.

  12. Adaptive Cell Segmentation and Tracking for Volumetric Confocal Microscopy Images of a Developing Plant Meristem

    Institute of Scientific and Technical Information of China (English)

    Min Liu; Anirban Chakraborty; Damanpreet Singh; Ram Kishor Yadav; Gopi Meenakshisundaram; G. Venugopala Reddy; Amit Roy-Chowdhury

    2011-01-01

    Automated segmentation and tracking of cells in actively developing tissues can provide high-throughput and quantitative spatiotemporal measurements of a range of cell behaviors; cell expansion and cell-division kinetics leading to a better understanding of the underlying dynamics of morphogenesis.Here,we have studied the problem of constructing cell lineages in time-lapse volumetric image stacks obtained using Confocal Laser Scanning Microscopy (CLSM).The novel contribution of the work lies in its ability to segment and track cells in densely packed tissue,the shoot apical meristem (SAM),through the use of a close-loop,adaptive segmentation,and tracking approach.The tracking output acts as an indicator of the quality of segmentation and,in turn,the segmentation can be improved to obtain better tracking results.We construct an optimization function that minimizes the segmentation error,which is,in turn,estimated from the tracking results.This adaptive approach significantly improves both tracking and segmentation when compared to an open loop framework in which segmentation and tracking modules operate separately.

  13. Speckle imaging through turbulent atmosphere based on adaptable pupil segmentation.

    Science.gov (United States)

    Loktev, Mikhail; Soloviev, Oleg; Savenko, Svyatoslav; Vdovin, Gleb

    2011-07-15

    We report on the first results to our knowledge obtained with adaptable multiaperture imaging through turbulence on a horizontal atmospheric path. We show that the resolution can be improved by adaptively matching the size of the subaperture to the characteristic size of the turbulence. Further improvement is achieved by the deconvolution of a number of subimages registered simultaneously through multiple subapertures. Different implementations of multiaperture geometry, including pupil multiplication, pupil image sampling, and a plenoptic telescope, are considered. Resolution improvement has been demonstrated on a ∼550 m horizontal turbulent path, using a combination of aperture sampling, speckle image processing, and, optionally, frame selection. © 2011 Optical Society of America

  14. 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.

  15. Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information.

    Science.gov (United States)

    Mapayi, Temitope; Viriri, Serestina; Tapamo, Jules-Raymond

    2015-01-01

    Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal image demonstrates the high performance of the proposed local adaptive thresholding technique. The maximum average accuracy rates of 0.9511 and 0.9510 with maximum average sensitivity rates of 0.7650 and 0.7641 were achieved on DRIVE and STARE databases, respectively. When compared to the widely previously used techniques on the databases, the proposed adaptive thresholding technique is time efficient with a higher average sensitivity and average accuracy rates in the same range of very good specificity.

  16. Determining adaptive thresholds for image segmentation for a license plate recognition system

    Directory of Open Access Journals (Sweden)

    Siti Norul Huda Sheikh Abdullah

    2016-06-01

    Full Text Available A vehicle license plate recognition (LPR system is useful to many applications, such as entrance admission, security, parking control, airport and cargo, traffic and speed control. This paper describe an adaptive threshold for image segmentation applied to a system for Malaysian intelligent license plate recognition (MyiLPR. Due to the different types of license plates used, the requirements of an automatic LPR system are rather different for each country. Upon receiving the input car image, this system (MyiLPR detects and segments the license plate based on proposed adaptive threshold via image and blob histogram, and blob agglomeration, and finally, it extracts geometric character features and classifies them using neural network. The use of the proposed adaptive threshold increased the detection, segmentation and recognition rate to 99%, 94.98% and 90% correspondingly, from 95%, 78.27% and 71.08% obtained with the fixed threshold used in the originally proposed system.

  17. 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

    Energy Technology Data Exchange (ETDEWEB)

    Soultan, D [University of California-San Diego, San Diego State University, La Jolla, CA (United States); Murphy, J; James, C; Hoh, C; Moiseenko, V; Cervino, L [University of California, San Diego, La Jolla, CA (United States); Gill, B [British Columbia Cancer Agency, Windsor, ON (Canada)

    2015-06-15

    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.

  18. 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

  19. 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.

  20. 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.

  1. An improved 3-D constrained stochastic gravity inversion method, adapted to the crustal-scale study of offshore rifted continental margins

    Science.gov (United States)

    Geng, Meixia; Welford, J. Kim; Farquharson, Colin

    2017-04-01

    While seismic methods provide the best geophysical methods for characterizing crustal structure, regional potential field studies and, specifically, constrained 3-D potential field inversion studies, provide an efficient means of bridging between seismic lines and obtaining regional views of deep structure. Most existing potential field inversion codes have been developed for the mining industry with the goal of delineating dense bodies within less dense half-spaces. While these codes can be successfully applied to crustal-scale targets, they are not designed to generate models with the kind of depth-dependent layering expected within the crust and upper mantle and consequently, the results must be interpreted with such limitations in mind. The development of improved inversion codes that will produce results that better conform to known density distributions within the crust and uppermost mantle will revolutionize the application of potential field methods for the study of rifted continental margins where only limited seismic constraints are available. Through insights gained from using existing inversion codes, we have developed a 3D inversion algorithm based on the constrained stochastic method and adapted it for use in regional crustal-scale studies. The new method honours existing sparse seismic constraints and generates models that can reproduce sharp boundaries at the base of the crust as well as more gradational density variations with depth for the crust to upper mantle transition. The improved regional crustal models provide crustal thickness estimates and crustal stretching factors that agree with the sparsely available seismic constraints, while also generating more realistic Earth models. Both synthetic and real examples from offshore eastern Canada, will be used to demonstrate the power of the new method.

  2. Motion-capture-based walking simulation of digital human adapted to laser-scanned 3D as-is environments for accessibility evaluation

    Directory of Open Access Journals (Sweden)

    Tsubasa Maruyama

    2016-07-01

    Full Text Available Owing to our rapidly aging society, accessibility evaluation to enhance the ease and safety of access to indoor and outdoor environments for the elderly and disabled is increasing in importance. Accessibility must be assessed not only from the general standard aspect but also in terms of physical and cognitive friendliness for users of different ages, genders, and abilities. Meanwhile, human behavior simulation has been progressing in the areas of crowd behavior analysis and emergency evacuation planning. However, in human behavior simulation, environment models represent only “as-planned” situations. In addition, a pedestrian model cannot generate the detailed articulated movements of various people of different ages and genders in the simulation. Therefore, the final goal of this research was to develop a virtual accessibility evaluation by combining realistic human behavior simulation using a digital human model (DHM with “as-is” environment models. To achieve this goal, we developed an algorithm for generating human-like DHM walking motions, adapting its strides, turning angles, and footprints to laser-scanned 3D as-is environments including slopes and stairs. The DHM motion was generated based only on a motion-capture (MoCap data for flat walking. Our implementation constructed as-is 3D environment models from laser-scanned point clouds of real environments and enabled a DHM to walk autonomously in various environment models. The difference in joint angles between the DHM and MoCap data was evaluated. Demonstrations of our environment modeling and walking simulation in indoor and outdoor environments including corridors, slopes, and stairs are illustrated in this study.

  3. Automatic Spatially-Adaptive Balancing of Energy Terms for Image Segmentation

    CERN Document Server

    Rao, Josna; Abugharbieh, Rafeef

    2009-01-01

    Image segmentation techniques are predominately based on parameter-laden optimization. The objective function typically involves weights for balancing competing image fidelity and segmentation regularization cost terms. Setting these weights suitably has been a painstaking, empirical process. Even if such ideal weights are found for a novel image, most current approaches fix the weight across the whole image domain, ignoring the spatially-varying properties of object shape and image appearance. We propose a novel technique that autonomously balances these terms in a spatially-adaptive manner through the incorporation of image reliability in a graph-based segmentation framework. We validate on synthetic data achieving a reduction in mean error of 47% (p-value << 0.05) when compared to the best fixed parameter segmentation. We also present results on medical images (including segmentations of the corpus callosum and brain tissue in MRI data) and on natural images.

  4. Research on adaptive segmentation and activity classification method of filamentous fungi image in microbe fermentation

    Science.gov (United States)

    Cai, Xiaochun; Hu, Yihua; Wang, Peng; Sun, Dujuan; Hu, Guilan

    2009-10-01

    The paper presents an adaptive segmentation and activity classification method for filamentous fungi image. Firstly, an adaptive structuring element (SE) construction algorithm is proposed for image background suppression. Based on watershed transform method, the color labeled segmentation of fungi image is taken. Secondly, the fungi elements feature space is described and the feature set for fungi hyphae activity classification is extracted. The growth rate evaluation of fungi hyphae is achieved by using SVM classifier. Some experimental results demonstrate that the proposed method is effective for filamentous fungi image processing.

  5. MDCT and 3D evaluation of type 2 hypoplastic pulmonary artery sling associated with right lung agenesis, hypoplastic aortic arch, and long segment tracheal stenosis.

    Science.gov (United States)

    Lee, Edward Y

    2007-11-01

    The early diagnosis and complete anatomic evaluation of pulmonary artery sling, a congenital vascular anomaly in which left pulmonary artery arises from the right pulmonary artery, is paramount for proper patient management, because patients with this disorder frequently have other congenital anomalies resulting in high morbidity and mortality. Until recently, pulmonary artery sling in the neonate has been established with standard radiologic imaging studies such as plain radiographs, barium swallow studies, fluoroscopy-guided airway studies, and echocardiograms. However, with the development and widespread availability of multidetector computed tomography, pulmonary artery sling is increasingly evaluated with this newer technology. This case report presents a rare incidence of type 2 hypoplastic pulmonary artery sling in a neonate associated with right lung agenesis, hypoplastic aortic arch, and long segment tracheal stenosis. Multidetector computed tomography combined with 3-dimensional evaluation was particularly helpful in making a correct diagnosis of the complicated anatomic anomalies found in this case.

  6. 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

  7. 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

  8. Adaptive-weighted cubic B-spline using lookup tables for fast and efficient axial resampling of 3D confocal microscopy images.

    Science.gov (United States)

    Indhumathi, C; Cai, Y Y; Guan, Y Q; Opas, M; Zheng, J

    2012-01-01

    Confocal laser scanning microscopy has become a most powerful tool to visualize and analyze the dynamic behavior of cellular molecules. Photobleaching of fluorochromes is a major problem with confocal image acquisition that will lead to intensity attenuation. Photobleaching effect can be reduced by optimizing the collection efficiency of the confocal image by fast z-scanning. However, such images suffer from distortions, particularly in the z dimension, which causes disparities in the x, y, and z directions of the voxels with the original image stacks. As a result, reliable segmentation and feature extraction of these images may be difficult or even impossible. Image interpolation is especially needed for the correction of undersampling artifact in the axial plane of three-dimensional images generated by a confocal microscope to obtain cubic voxels. In this work, we present an adaptive cubic B-spline-based interpolation with the aid of lookup tables by deriving adaptive weights based on local gradients for the sampling nodes in the interpolation formulae. Thus, the proposed method enhances the axial resolution of confocal images by improving the accuracy of the interpolated value simultaneously with great reduction in computational cost. Numerical experimental results confirm the effectiveness of the proposed interpolation approach and demonstrate its superiority both in terms of accuracy and speed compared to other interpolation algorithms.

  9. 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.

  10. Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

    Science.gov (United States)

    Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

    2015-03-01

    Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.

  11. Structure Segmentation and Transfer Faults in the Marcellus Shale, Clearfield County, Pennsylvania: Implications for Gas Recovery Efficiency and Risk Assessment Using 3D Seismic Attribute Analysis

    Science.gov (United States)

    Roberts, Emily D.

    The Marcellus Shale has become an important unconventional gas reservoir in the oil and gas industry. Fractures within this organic-rich black shale serve as an important component of porosity and permeability useful in enhancing production. Horizontal drilling is the primary approach for extracting hydrocarbons in the Marcellus Shale. Typically, wells are drilled perpendicular to natural fractures in an attempt to intersect fractures for effective hydraulic stimulation. If the fractures are contained within the shale, then hydraulic fracturing can enhance permeability by further breaking the already weakened rock. However, natural fractures can affect hydraulic stimulations by absorbing and/or redirecting the energy away from the wellbore, causing a decreased efficiency in gas recovery, as has been the case for the Clearfield County, Pennsylvania study area. Estimating appropriate distances away from faults and fractures, which may limit hydrocarbon recovery, is essential to reducing the risk of injection fluid migration along these faults. In an attempt to mitigate the negative influences of natural fractures on hydrocarbon extraction within the Marcellus Shale, fractures were analyzed through the aid of both traditional and advanced seismic attributes including variance, curvature, ant tracking, and waveform model regression. Through the integration of well log interpretations and seismic data, a detailed assessment of structural discontinuities that may decrease the recovery efficiency of hydrocarbons was conducted. High-quality 3D seismic data in Central Pennsylvania show regional folds and thrusts above the major detachment interval of the Salina Salt. In addition to the regional detachment folds and thrusts, cross-regional, northwest-trending lineaments were mapped. These lineaments may pose a threat to hydrocarbon productivity and recovery efficiency due to faults and fractures acting as paths of least resistance for induced hydraulic stimulation fluids

  12. Infants adapt to speaking rate differences in word segmentation.

    Science.gov (United States)

    Wang, Yuanyuan; Llanos, Fernando; Seidl, Amanda

    2017-04-01

    Throughout their development, infants are exposed to varying speaking rates. Thus, it is important to determine whether they are able to adapt to speech at varying rates and recognize target words from continuous speech despite speaking rate differences. To address this question, a series of four experiments were conducted to test whether infants can recognize words in continuous speech when rate is variable. In addition, the underlying mechanisms that infants may use to cope with variations induced by different speaking rates were also examined. Specifically, using the Headturn Preference procedure [Jusczyk and Aslin (1995). Cognitive Psychol. 29, 1-23], infants were familiarized with normal-rate passages containing two trisyllabic target words (e.g., elephants and dinosaurs), and tested with familiar (elephants and dinosaurs) and unfamiliar (crocodiles and platypus) words embedded in normal-rate (experiment 1), fast-rate (experiments 2 and 3), or slow-rate passages (experiment 4). The results indicate that 14-month-olds, but not 11-month-olds, recognized target words in passages with a fast speaking rate. In addition, findings suggest that infants used context to normalize speech across different speaking rates.

  13. An Adaptive Frame Skipping and VOP Interpolation Algorithm for Video Object Segmentation

    Institute of Scientific and Technical Information of China (English)

    YANGGaobo; ZHANGZhaoyang

    2004-01-01

    Video object segmentation is a key step for the successful use of MPEG-4. However, most of the current available segmentation algorithms are still far away from real-time performance. In order to improve the processing speed, an adaptive frame skipping and VOP interpolation algorithm is proposed in this paper. It adaptively determines the number of skipped frames based on the rigidity and motion complexity of video object. To interpolate the VOPs for skipped frames, a hi-directional projection scheme is adopted. Its principle is to perform a classification of those regions obtained by spatial segmentation for every frame in the sequence. It is valid for both rigid object and non-rigid object and can get good localization of object boundaries. Experimental results show that the proposed approach can improve the processing speed greatly while maintaining visually pleasant results.

  14. 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.

  15. Adaptively Active Contours Based on Variable Exponent Lp(|∇I| Norm for Image Segmentation

    Directory of Open Access Journals (Sweden)

    Wenying Wen

    2012-01-01

    Full Text Available We propose an Lp(|∇I|-based adaptively active contours model for image segmentation which is derived from the well-known Chan-Vese (C-V model. Unlike the C-V model, the proposed model uses the Lp(|∇I| (p(|∇I|>2 norm instead of the L2 norm to define the external energy and incorporates an extra internal energy into the overall energy. Due to the variable exponent p(|∇I|  which could fit the image gradient information adaptively, the proposed Lp(|∇I|-based model has the hope of segmenting those images with low contrast and blurred boundaries. Experimental results show that the proposed model with p(|∇I|>2 really can effectively and quickly segment those images with low contrast and blurred boundaries.

  16. Electric field theory based approach to search-direction line definition in image segmentation: application to optimal femur-tibia cartilage segmentation in knee-joint 3-D MR

    Science.gov (United States)

    Yin, Y.; Sonka, M.

    2010-03-01

    A novel method is presented for definition of search lines in a variety of surface segmentation approaches. The method is inspired by properties of electric field direction lines and is applicable to general-purpose n-D shapebased image segmentation tasks. Its utility is demonstrated in graph construction and optimal segmentation of multiple mutually interacting objects. The properties of the electric field-based graph construction guarantee that inter-object graph connecting lines are non-intersecting and inherently covering the entire object-interaction space. When applied to inter-object cross-surface mapping, our approach generates one-to-one and all-to-all vertex correspondent pairs between the regions of mutual interaction. We demonstrate the benefits of the electric field approach in several examples ranging from relatively simple single-surface segmentation to complex multiobject multi-surface segmentation of femur-tibia cartilage. The performance of our approach is demonstrated in 60 MR images from the Osteoarthritis Initiative (OAI), in which our approach achieved a very good performance as judged by surface positioning errors (average of 0.29 and 0.59 mm for signed and unsigned cartilage positioning errors, respectively).

  17. A class-adaptive spatially variant mixture model for image segmentation.

    Science.gov (United States)

    Nikou, Christophoros; Galatsanos, Nikolaos P; Likas, Aristidis C

    2007-04-01

    We propose a new approach for image segmentation based on a hierarchical and spatially variant mixture model. According to this model, the pixel labels are random variables and a smoothness prior is imposed on them. The main novelty of this work is a new family of smoothness priors for the label probabilities in spatially variant mixture models. These Gauss-Markov random field-based priors allow all their parameters to be estimated in closed form via the maximum a posteriori (MAP) estimation using the expectation-maximization methodology. Thus, it is possible to introduce priors with multiple parameters that adapt to different aspects of the data. Numerical experiments are presented where the proposed MAP algorithms were tested in various image segmentation scenarios. These experiments demonstrate that the proposed segmentation scheme compares favorably to both standard and previous spatially constrained mixture model-based segmentation.

  18. 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.

  19. A 3-D Novel Highly Chaotic System with Four Quadratic Nonlinearities, its Adaptive Control and Anti-Synchronization with Unknown Parameters

    OpenAIRE

    Vaidyanathan, S.

    2014-01-01

    This research work proposes a seven-term 3-D novel dissipative chaotic system with four quadratic nonlinearities. The Lyapunov exponents of the 3-D novel chaotic system are obtained as L1 = 11.36204, L2 = 0 and L3 = –47.80208. Since the sum of the Lyapunov exponents is negative, the 3-D novel chaotic system is dissipative. Also, the Kaplan-Yorke dimension of the 3-D novel chaotic system is obtained as DKY = 2.23769. The maximal Lyapunov exponent (MLE) of the novel chaotic system i...

  20. Beowulf 3D: a case study

    Science.gov (United States)

    Engle, Rob

    2008-02-01

    This paper discusses the creative and technical challenges encountered during the production of "Beowulf 3D," director Robert Zemeckis' adaptation of the Old English epic poem and the first film to be simultaneously released in IMAX 3D and digital 3D formats.

  1. An adaptive spatial clustering method for automatic brain MR image segmentation

    Institute of Scientific and Technical Information of China (English)

    Jingdan Zhang; Daoqing Dai

    2009-01-01

    In this paper, an adaptive spatial clustering method is presented for automatic brain MR image segmentation, which is based on a competitive learning algorithm-self-organizing map (SOM). We use a pattern recognition approach in terms of feature generation and classifier design. Firstly, a multi-dimensional feature vector is constructed using local spatial information. Then, an adaptive spatial growing hierarchical SOM (ASGHSOM) is proposed as the classifier, which is an extension of SOM, fusing multi-scale segmentation with the competitive learning clustering algorithm to overcome the problem of overlapping grey-scale intensities on boundary regions. Furthermore, an adaptive spatial distance is integrated with ASGHSOM, in which local spatial information is considered in the cluster-ing process to reduce the noise effect and the classification ambiguity. Our proposed method is validated by extensive experiments using both simulated and real MR data with varying noise level, and is compared with the state-of-the-art algorithms.

  2. 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.

  3. Co-adaptability solution to conflict events in construction projects by segmented hierarchical algorithm

    Institute of Scientific and Technical Information of China (English)

    HOU XueLiang; LU Mei

    2008-01-01

    In order to seek the co-adaptability solution to conflict events in construction en-gineering projects,a new method referred to as segmented hierarchical algorithm is proposed in this paper by means of comparing co-adaptability evolution process of conflict events to the stackelberg model.By this new algorithm,local solutions to the first-order transformation of co-adaptability for conflict events can be ob-tained,based upon which,a global solution to the second-order transformation of co-adaptability for conflict events can also be decided by judging satisfaction de-gree of local solutions.The research results show that this algorithm can be used not only for obtaining co-adaptability solution to conflict events efficiently,but also for other general decision-making problems with multi-layers and multi-subsidi-aries in project management field.

  4. Co-adaptability solution to conflict events in construction projects by segmented hierarchical algorithm

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In order to seek the co-adaptability solution to conflict events in construction engineering projects, a new method referred to as segmented hierarchical algorithm is proposed in this paper by means of comparing co-adaptability evolution process of conflict events to the stackelberg model. By this new algorithm, local solutions to the first-order transformation of co-adaptability for conflict events can be obtained, based upon which, a global solution to the second-order transformation of co-adaptability for conflict events can also be decided by judging satisfaction degree of local solutions. The research results show that this algorithm can be used not only for obtaining co-adaptability solution to conflict events efficiently, but also for other general decision-making problems with multi-layers and multi-subsidi-aries in project management field.

  5. Impossible expectations: fMRI adaptation in the lateral occipital complex (LOC) is modulated by the statistical regularities of 3D structural information.

    Science.gov (United States)

    Freud, Erez; Ganel, Tzvi; Avidan, Galia

    2015-11-15

    fMRI adaptation (fMRIa), the attenuation of fMRI signal which follows repeated presentation of a stimulus, is a well-documented phenomenon. Yet, the underlying neural mechanisms supporting this effect are not fully understood. Recently, short-term perceptual expectations, induced by specific experimental settings, were shown to play an important modulating role in fMRIa. Here we examined the role of long-term expectations, based on 3D structural statistical regularities, in the modulation of fMRIa. To this end, human participants underwent fMRI scanning while performing a same-different task on pairs of possible (regular, expected) objects and spatially impossible (irregular, unexpected) objects. We hypothesized that given the spatial irregularity of impossible objects in relation to real-world visual experience, the visual system would always generate a prediction which is biased to the possible version of the objects. Consistently, fMRIa effects in the lateral occipital cortex (LOC) were found for possible, but not for impossible objects. Additionally, in alternating trials the order of stimulus presentation modulated LOC activity. That is, reduced activation was observed in trials in which the impossible version of the object served as the prime object (i.e. first object) and was followed by the possible version compared to the reverse order. These results were also supported by the behavioral advantage observed for trials that were primed by possible objects. Together, these findings strongly emphasize the importance of perceptual expectations in object representation and provide novel evidence for the role of real-world statistical regularities in eliciting fMRIa.

  6. SU-C-9A-01: Parameter Optimization in Adaptive Region-Growing for Tumor Segmentation in PET

    Energy Technology Data Exchange (ETDEWEB)

    Tan, S [University of Maryland School of Medicine, Baltimore, MD (United States); Huazhong University of Science and Technology, Wuhan, Hubei (China); Xue, M; Chen, W; D' Souza, W; Lu, W [University of Maryland School of Medicine, Baltimore, MD (United States); Li, H [Washington University School of Medicine, Saint Louis, MO. (United States)

    2014-06-01

    Purpose: To design a reliable method to determine the optimal parameter in the adaptive region-growing (ARG) algorithm for tumor segmentation in PET. Methods: The ARG uses an adaptive similarity criterion m - fσ ≤ I-PET ≤ m + fσ, so that a neighboring voxel is appended to the region based on its similarity to the current region. When increasing the relaxing factor f (f ≥ 0), the resulting volumes monotonically increased with a sharp increase when the region just grew into the background. The optimal f that separates the tumor from the background is defined as the first point with the local maximum curvature on an Error function fitted to the f-volume curve. The ARG was tested on a tumor segmentation Benchmark that includes ten lung cancer patients with 3D pathologic tumor volume as ground truth. For comparison, the widely used 42% and 50% SUVmax thresholding, Otsu optimal thresholding, Active Contours (AC), Geodesic Active Contours (GAC), and Graph Cuts (GC) methods were tested. The dice similarity index (DSI), volume error (VE), and maximum axis length error (MALE) were calculated to evaluate the segmentation accuracy. Results: The ARG provided the highest accuracy among all tested methods. Specifically, the ARG has an average DSI, VE, and MALE of 0.71, 0.29, and 0.16, respectively, better than the absolute 42% thresholding (DSI=0.67, VE= 0.57, and MALE=0.23), the relative 42% thresholding (DSI=0.62, VE= 0.41, and MALE=0.23), the absolute 50% thresholding (DSI=0.62, VE=0.48, and MALE=0.21), the relative 50% thresholding (DSI=0.48, VE=0.54, and MALE=0.26), OTSU (DSI=0.44, VE=0.63, and MALE=0.30), AC (DSI=0.46, VE= 0.85, and MALE=0.47), GAC (DSI=0.40, VE= 0.85, and MALE=0.46) and GC (DSI=0.66, VE= 0.54, and MALE=0.21) methods. Conclusions: The results suggest that the proposed method reliably identified the optimal relaxing factor in ARG for tumor segmentation in PET. This work was supported in part by National Cancer Institute Grant R01 CA172638; The

  7. Performance of adaptive iterative dose reduction 3D integrated with automatic tube current modulation in radiation dose and image noise reduction compared with filtered-back projection for 80-kVp abdominal CT: Anthropomorphic phantom and patient study.

    Science.gov (United States)

    Chen, Chien-Ming; Lin, Yang-Yu; Hsu, Ming-Yi; Hung, Chien-Fu; Liao, Ying-Lan; Tsai, Hui-Yu

    2016-09-01

    Evaluate the performance of Adaptive Iterative Dose Reduction 3D (AIDR 3D) and compare with filtered-back projection (FBP) regarding radiation dosage and image quality for an 80-kVp abdominal CT. An abdominal phantom underwent four CT acquisitions and reconstruction algorithms (FBP; AIDR 3D mild, standard and strong). Sixty-three patients underwent unenhanced liver CT with FBP and standard level AIDR 3D. Further post-acquisition reconstruction with strong level AIDR 3D was made. Patients were divided into two groups (radiation dose by 72% in the phantom and 47.1% in the patient study compared with FBP. There was no difference in mean attenuations. Image noise was the lowest and signal-to-noise ratio the highest using strong level AIDR 3D in both patient groups. For Deffradiation dose and maintenance of image quality compared with FBP. Using AIDR 3D reconstruction, patients with larger abdomen circumference could be imaged at 80kVp. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  8. SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering

    Science.gov (United States)

    Zhang, Xiangrong; Yang, Jie; Hou, Biao; Jiao, Licheng

    2010-10-01

    Spectral clustering has become one of the most popular modern clustering algorithms in recent years. In this paper, a new algorithm named entropy ranking based adaptive semi-supervised spectral clustering for SAR image segme