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

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    Hai-Yun Wang

    2004-06-01

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

  3. Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

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    Yu, Kai; Shi, Fei; Gao, Enting; Zhu, Weifang; Chen, Haoyu; Chen, Xinjian

    2018-01-01

    Optic nerve head (ONH) is a crucial region for glaucoma detection and tracking based on spectral domain optical coherence tomography (SD-OCT) images. In this region, the existence of a “hole” structure makes retinal layer segmentation and analysis very challenging. To improve retinal layer segmentation, we propose a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints. With the proposed method, both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD-OCT images. An overall mean unsigned border positioning error of 7.27 ± 5.40 µm was achieved for layer segmentation, and a mean Dice coefficient of 0.925 ± 0.03 was achieved for optic disc region detection. PMID:29541497

  4. Authoring Adaptive 3D Virtual Learning Environments

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    Ewais, Ahmed; De Troyer, Olga

    2014-01-01

    The use of 3D and Virtual Reality is gaining interest in the context of academic discussions on E-learning technologies. However, the use of 3D for learning environments also has drawbacks. One way to overcome these drawbacks is by having an adaptive learning environment, i.e., an environment that dynamically adapts to the learner and the…

  5. 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.

  6. 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.

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

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

    2011-01-01

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

  8. Hybrid segmentation framework for 3D medical image analysis

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    Chen, Ting; Metaxas, Dimitri N.

    2003-05-01

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

  9. 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

    2013-01-01

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

  10. Prostate MR image segmentation using 3D active appearance models

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    Maan, Bianca; van der Heijden, Ferdinand

    2012-01-01

    This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain

  11. Adaptive interrogation for 3D-PIV

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    Novara, Matteo; Scarano, Fulvio; Ianiro, Andrea

    2013-01-01

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

  12. Automatic 3D lesion segmentation on breast ultrasound images

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    Kuo, Hsien-Chi; Giger, Maryellen L.; Reiser, Ingrid; Drukker, Karen; Edwards, Alexandra; Sennett, Charlene A.

    2013-02-01

    Automatically acquired and reconstructed 3D breast ultrasound images allow radiologists to detect and evaluate breast lesions in 3D. However, assessing potential cancers in 3D ultrasound can be difficult and time consuming. In this study, we evaluate a 3D lesion segmentation method, which we had previously developed for breast CT, and investigate its robustness on lesions on 3D breast ultrasound images. Our dataset includes 98 3D breast ultrasound images obtained on an ABUS system from 55 patients containing 64 cancers. Cancers depicted on 54 US images had been clinically interpreted as negative on screening mammography and 44 had been clinically visible on mammography. All were from women with breast density BI-RADS 3 or 4. Tumor centers and margins were indicated and outlined by radiologists. Initial RGI-eroded contours were automatically calculated and served as input to the active contour segmentation algorithm yielding the final lesion contour. Tumor segmentation was evaluated by determining the overlap ratio (OR) between computer-determined and manually-drawn outlines. Resulting average overlap ratios on coronal, transverse, and sagittal views were 0.60 +/- 0.17, 0.57 +/- 0.18, and 0.58 +/- 0.17, respectively. All OR values were significantly higher the 0.4, which is deemed "acceptable". Within the groups of mammogram-negative and mammogram-positive cancers, the overlap ratios were 0.63 +/- 0.17 and 0.56 +/- 0.16, respectively, on the coronal views; with similar results on the other views. The segmentation performance was not found to be correlated to tumor size. Results indicate robustness of the 3D lesion segmentation technique in multi-modality 3D breast imaging.

  13. 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

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

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    M. Arfan Jaffar

    2012-06-01

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

  15. 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. Copyright © 2012 Elsevier B.V. All rights reserved.

  16. Automatic segmentation of the puborectalis muscle in 3D transperineal ultrasound.

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    van den Noort, Frieda; Grob, Anique T M; Slump, Cornelis H; van der Vaart, Carl H; van Stralen, Marijn

    2017-10-11

    The introduction of 3D analysis of the puborectalis muscle, for diagnostic purposes, into daily practice is hindered by the need for appropriate training of the observers. Automatic 3D segmentation of the puborectalis muscle in 3D transperineal ultrasound may aid to its adaption in clinical practice. A manual 3D segmentation protocol was developed to segment the puborectalis muscle. The data of 20 women, in their first trimester of pregnancy, was used to validate the reproducibility of this protocol. For automatic segmentation, active appearance models of the puborectalis muscle were developed. Those models were trained using manual segmentation data of 50 women. The performance of both manual and automatic segmentation was analyzed by measuring the overlap and distance between the segmentations. Also, the interclass correlation coefficients and their 95% confidence intervals were determined for mean echogenicity and volume of the puborectalis muscle. The ICC values of mean echogenicity (0.968-0.991) and volume (0.626-0.910) are good to very good for both automatic and manual segmentation. The results of overlap and distance for manual segmentation are as expected, showing only few pixels (2-3) mismatch on average and a reasonable overlap. Based on overlap and distance 5 mismatches in automatic segmentation were detected, resulting in an automatic segmentation a success rate of 90%. In conclusion, this study presents a reliable manual and automatic 3D segmentation of the puborectalis muscle. This will facilitate future investigation of the puborectalis muscle. It also allows for reliable measurements of clinically potentially valuable parameters like mean echogenicity. This article is protected by copyright. All rights reserved.

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

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

    2013-01-01

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

  18. 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.

  19. 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

  20. Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis.

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    Agarwal, Richa; Diaz, Oliver; Lladó, Xavier; Gubern-Mérida, Albert; Vilanova, Joan C; Martí, Robert

    2018-03-01

    Mammography is the gold standard screening technique in breast cancer, but it has some limitations for women with dense breasts. In such cases, sonography is usually recommended as an additional imaging technique. A traditional sonogram produces a two-dimensional (2D) visualization of the breast and is highly operator dependent. Automated breast ultrasound (ABUS) has also been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency, facilitating double reading and comparison with past exams. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the three-dimensional (3D) nature of the images makes the analysis difficult and tedious for radiologists. The goal of this work is to develop a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. The effect of different de-noising methods on segmentation is studied showing a significant impact ([Formula: see text]) on the performance using a dataset of 28 temporal pairs resulting in a total of 56 ABUS volumes. The volumetric analysis is also used to evaluate the performance of the developed framework. A mean Dice Similarity Coefficient of [Formula: see text] with a mean False Positive ratio [Formula: see text] has been obtained. The Pearson correlation coefficient between the segmented volumes and the corresponding ground truth volumes is [Formula: see text] ([Formula: see text]). Similar analysis, performed on 28 temporal (prior and current) pairs, resulted in a good correlation coefficient [Formula: see text] ([Formula: see text]) for prior and [Formula: see text] ([Formula: see text]) for current cases. The developed framework showed prospects to help radiologists to perform an assessment of ABUS lesion volumes, as well as to quantify volumetric changes during lesions diagnosis and follow-up.

  1. 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.

  2. Object Segmentation and Ground Truth in 3D Embryonic Imaging.

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    Bhavna Rajasekaran

    Full Text Available Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.

  3. Adaptive fuzzy system for 3-D vision

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    Mitra, Sunanda

    1993-01-01

    An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

  4. 3D geometric split-merge segmentation of brain MRI datasets.

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    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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. A combined learning algorithm for prostate segmentation on 3D CT images.

    Science.gov (United States)

    Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

    2017-11-01

    Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation. By combining the population learning and patient-specific learning methods, the proposed method is

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

    Science.gov (United States)

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

    2016-08-01

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

  7. 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.

  8. 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

    2016-12-21

    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 [Formula: see text], yielding a mean Dice similarity coefficient of [Formula: see text], and an average symmetric surface distance of [Formula: see text] mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

  9. 3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

    Science.gov (United States)

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

    2015-03-01

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

  10. 3D mesh segmentation of historic buildings for architectural surveys

    Directory of Open Access Journals (Sweden)

    Borja Javier Herráez

    2018-01-01

    Full Text Available Advances in three-dimensional (3D acquisition systems have introduced this technology to more fields of study, such as archaeology or architecture. In the architectural field, scanning a building is one of the first possible steps from which a 3D model can be obtained and can be later used for visualisation and/or feature analysis, thanks to computer-based pattern recognition tools. The automation of these tools allows for temporal savings and has become a strong aid for professionals, so that more and more methods are developed with this objective. In this article, a method for 3D mesh segmentation focused  on  the representation  of  historic  buildings  is  proposed.  This  type  of  buildings is characterised  by  having singularities  and features in  façades, such  as  doors  or  windows. The  main  objective  is  to  recognise  these  features, understanding them as those parts of the model that differ from the main structure of the building. The idea is to use a recognition algorithm for planar faces that allows users to create a graph showing the connectivity between them, therefore allowing the reflection of the shape of the 3Dmodel. At a later step, this graph is matched against some pre-defined graphs that  represent  the  patterns  to  look  for. Each  coincidence  between  both  graphs  indicate  the  position  of  one  of  the characteristics sought. The developed method has proved to be effective for feature detection and suitable for inclusion in architectural surveying applications.

  11. A random walk-based segmentation framework for 3D ultrasound images of the prostate.

    Science.gov (United States)

    Ma, Ling; Guo, Rongrong; Tian, Zhiqiang; Fei, Baowei

    2017-10-01

    Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images. The proposed segmentation method uses a context-classification-based random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision-tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance. We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist. The random walk-based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures. © 2017

  12. 3D Object Segmentation of Point Clouds using Profiling Techniques ...

    African Journals Online (AJOL)

    In the automatic processing of point clouds, higher level information in the form of point segments is required for classification and object detection purposes. Segmentation allows for the definition of these segments. Because of the increasing size of point clouds faster and more reliable segmentation methods are being ...

  13. 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).

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

    OpenAIRE

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

    2012-01-01

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

  15. Adaptive optics enables 3D STED microscopy in aberrating specimens.

    Science.gov (United States)

    Gould, Travis J; Burke, Daniel; Bewersdorf, Joerg; Booth, Martin J

    2012-09-10

    Stimulated emission depletion (STED) microscopy allows fluorescence far-field imaging with diffraction-unlimited resolution. Unfortunately, extending this technique to three-dimensional (3D) imaging of thick specimens has been inhibited by sample-induced aberrations. Here we present the first implementation of adaptive optics in STED microscopy to allow 3D super-resolution imaging in strongly aberrated imaging conditions, such as those introduced by thick biological tissue.

  16. Adaptive optics enables 3D STED microscopy in aberrating specimens

    Science.gov (United States)

    Gould, Travis J.; Burke, Daniel; Bewersdorf, Joerg; Booth, Martin J.

    2012-01-01

    Stimulated emission depletion (STED) microscopy allows fluorescence far-field imaging with diffraction-unlimited resolution. Unfortunately, extending this technique to three-dimensional (3D) imaging of thick specimens has been inhibited by sample-induced aberrations. Here we present the first implementation of adaptive optics in STED microscopy to allow 3D super-resolution imaging in strongly aberrated imaging conditions, such as those introduced by thick biological tissue. PMID:23037223

  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

    2018-05-01

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

  18. 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...

  19. 3D pulmonary nodules detection using fast marching segmentation ...

    African Journals Online (AJOL)

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

  20. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.

    Science.gov (United States)

    Kamnitsas, Konstantinos; Ledig, Christian; Newcombe, Virginia F J; Simpson, Joanna P; Kane, Andrew D; Menon, David K; Rueckert, Daniel; Glocker, Ben

    2017-02-01

    We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  1. 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

  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. 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 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.

  4. Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes.

    Science.gov (United States)

    Eapen, Maya; Korah, Reeba; Geetha, G

    2015-01-01

    The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method.

  5. Virtual bacterium colony in 3D image segmentation.

    Science.gov (United States)

    Badura, Pawel

    2018-04-01

    Several heuristic, biologically inspired strategies have been discovered in recent decades, including swarm intelligence algorithms. So far, their application to volumetric imaging data mining is, however, limited. This paper presents a new flexible swarm intelligence optimization technique for segmentation of various structures in three- or two-dimensional images. The agents of a self-organizing colony explore their host, use stigmergy to communicate themselves, and mark regions of interest leading to the object extraction. Detailed specification of the bacterium colony segmentation (BCS) technique in terms of both individual and social behaviour is described in this paper. The method is illustrated and evaluated using several experiments involving synthetic data, computed tomography studies, and ultrasonography images. The obtained results and observations are discussed in terms of parameter settings and potential application of the method in various segmentation tasks. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. 3D Building Models Segmentation Based on K-Means++ Cluster Analysis

    Science.gov (United States)

    Zhang, C.; Mao, B.

    2016-10-01

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

  7. 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.

  8. Left Ventricular Myocardial Segmentation in 3D Ultrasound Recordings: Effect of Different Endoand Epicardial Coupling Strategies.

    Science.gov (United States)

    Pedrosa, Joao; Barbosa, Daniel; Heyde, Brecht; Schnell, Frederic; Rosner, Assami; Claus, Piet; D Hooge, Jan

    2016-12-12

    Cardiac volume/function assessment remains a critical step in daily cardiology and 3D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this study, different ways of coupling the endo- and epicardial segmentation are contrasted and compared to uncoupled segmentation. For this purpose, the B-spline Explicit Active Surfaces framework was used. Twenty-seven 3D echocardiographic images were used to validate the different coupling strategies which were compared to manual contouring of the endo- and epicardial borders performed by an expert. It is shown that an independent segmentation of the endocardium followed by an epicardial segmentation coupled to the endocardium is the most advantageous. In this way, a framework for fully automatic 3D myocardial segmentation is proposed using a novel coupling strategy.

  9. 3d object segmentation of point clouds using profiling techniques

    African Journals Online (AJOL)

    Administrator

    combination of data structuring, point reduction, segmentation and memory optimization strategies. With data structuring the point cloud is arranged in a fashion that makes ... curvature and facet normals, while radiometric properties include colour and shading. The success of region growing depends on the selection of the ...

  10. Viewpoint-independent 3D object segmentation for randomly stacked objects using optical object detection

    International Nuclear Information System (INIS)

    Chen, Liang-Chia; Nguyen, Thanh-Hung; Lin, Shyh-Tsong

    2015-01-01

    This work proposes a novel approach to segmenting randomly stacked objects in unstructured 3D point clouds, which are acquired by a random-speckle 3D imaging system for the purpose of automated object detection and reconstruction. An innovative algorithm is proposed; it is based on a novel concept of 3D watershed segmentation and the strategies for resolving over-segmentation and under-segmentation problems. Acquired 3D point clouds are first transformed into a corresponding orthogonally projected depth map along the optical imaging axis of the 3D sensor. A 3D watershed algorithm based on the process of distance transformation is then performed to detect the boundary, called the edge dam, between stacked objects and thereby to segment point clouds individually belonging to two stacked objects. Most importantly, an object-matching algorithm is developed to solve the over- and under-segmentation problems that may arise during the watershed segmentation. The feasibility and effectiveness of the method are confirmed experimentally. The results reveal that the proposed method is a fast and effective scheme for the detection and reconstruction of a 3D object in a random stack of such objects. In the experiments, the precision of the segmentation exceeds 95% and the recall exceeds 80%. (paper)

  11. 3D adaptive optics in a light sheet microscope.

    Science.gov (United States)

    Bourgenot, Cyril; Saunter, Christopher D; Taylor, Jonathan M; Girkin, John M; Love, Gordon D

    2012-06-04

    We report on a single plane illumination microscope (SPIM) incorporating adaptive optics in the imaging arm. We show how aberrations can occur from the sample mounting tube and quantify the aberrations both experimentally and computationally. A wavefront sensorless approach was taken to imaging a green fluorescent protein (GFP) labelled transgenic zebrafish. We show improvements in image quality whilst recording a 3D "z-stack" and show how the aberrations come from varying depths in the fish.

  12. 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....

  13. 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.

  14. 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.

  15. 3D deeply supervised network for automated segmentation of volumetric medical images.

    Science.gov (United States)

    Dou, Qi; Yu, Lequan; Chen, Hao; Jin, Yueming; Yang, Xin; Qin, Jing; Heng, Pheng-Ann

    2017-10-01

    While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our proposed 3D DSN. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Diffusive smoothing of 3D segmented medical data

    Directory of Open Access Journals (Sweden)

    Giuseppe Patané

    2015-05-01

    Full Text Available This paper proposes an accurate, computationally efficient, and spectrum-free formulation of the heat diffusion smoothing on 3D shapes, represented as triangle meshes. The idea behind our approach is to apply a (r,r-degree Padé–Chebyshev rational approximation to the solution of the heat diffusion equation. The proposed formulation is equivalent to solve r sparse, symmetric linear systems, is free of user-defined parameters, and is robust to surface discretization. We also discuss a simple criterion to select the time parameter that provides the best compromise between approximation accuracy and smoothness of the solution. Finally, our experiments on anatomical data show that the spectrum-free approach greatly reduces the computational cost and guarantees a higher approximation accuracy than previous work.

  17. 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

  18. 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

  19. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. 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.

  1. 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

  2. Segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image registration.

    Science.gov (United States)

    Biesdorf, Andreas; Rohr, Karl; Feng, Duan; von Tengg-Kobligk, Hendrik; Rengier, Fabian; Böckler, Dittmar; Kauczor, Hans-Ulrich; Wörz, Stefan

    2012-08-01

    Accurate quantification of the morphology of vessels is important for diagnosis and treatment of cardiovascular diseases. We introduce a new joint segmentation and registration approach for the quantification of the aortic arch morphology that combines 3D model-based segmentation with elastic image registration. With this combination, the approach benefits from the robustness of model-based segmentation and the accuracy of elastic registration. The approach can cope with a large spectrum of vessel shapes and particularly with pathological shapes that deviate significantly from the underlying model used for segmentation. The performance of the approach has been evaluated on the basis of 3D synthetic images, 3D phantom data, and clinical 3D CTA images including pathologies. We also performed a quantitative comparison with previous approaches. Copyright © 2012 Elsevier B.V. All rights reserved.

  3. SEGMENTATION OF UAV-BASED IMAGES INCORPORATING 3D POINT CLOUD INFORMATION

    Directory of Open Access Journals (Sweden)

    A. Vetrivel

    2015-03-01

    Full Text Available Numerous applications related to urban scene analysis demand automatic recognition of buildings and distinct sub-elements. For example, if LiDAR data is available, only 3D information could be leveraged for the segmentation. However, this poses several risks, for instance, the in-plane objects cannot be distinguished from their surroundings. On the other hand, if only image based segmentation is performed, the geometric features (e.g., normal orientation, planarity are not readily available. This renders the task of detecting the distinct sub-elements of the building with similar radiometric characteristic infeasible. In this paper the individual sub-elements of buildings are recognized through sub-segmentation of the building using geometric and radiometric characteristics jointly. 3D points generated from Unmanned Aerial Vehicle (UAV images are used for inferring the geometric characteristics of roofs and facades of the building. However, the image-based 3D points are noisy, error prone and often contain gaps. Hence the segmentation in 3D space is not appropriate. Therefore, we propose to perform segmentation in image space using geometric features from the 3D point cloud along with the radiometric features. The initial detection of buildings in 3D point cloud is followed by the segmentation in image space using the region growing approach by utilizing various radiometric and 3D point cloud features. The developed method was tested using two data sets obtained with UAV images with a ground resolution of around 1-2 cm. The developed method accurately segmented most of the building elements when compared to the plane-based segmentation using 3D point cloud alone.

  4. 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

  5. Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

    Science.gov (United States)

    Liu, Cheng-Yi; Iglesias, Juan Eugenio; Tu, Zhuowen

    2013-10-01

    Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

  6. Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images.

    Science.gov (United States)

    Ma, Ling; Guo, Rongrong; Tian, Zhiqiang; Venkataraman, Rajesh; Sarkar, Saradwata; Liu, Xiabi; Nieh, Peter T; Master, Viraj V; Schuster, David M; Fei, Baowei

    2016-02-27

    This paper proposes a new semi-automatic segmentation method for the prostate on 3D transrectal ultrasound images (TRUS) by combining the region and classification information. We use a random walk algorithm to express the region information efficiently and flexibly because it can avoid segmentation leakage and shrinking bias. We further use the decision tree as the classifier to distinguish the prostate from the non-prostate tissue because of its fast speed and superior performance, especially for a binary classification problem. Our segmentation algorithm is initialized with the user roughly marking the prostate and non-prostate points on the mid-gland slice which are fitted into an ellipse for obtaining more points. Based on these fitted seed points, we run the random walk algorithm to segment the prostate on the mid-gland slice. The segmented contour and the information from the decision tree classification are combined to determine the initial seed points for the other slices. The random walk algorithm is then used to segment the prostate on the adjacent slice. We propagate the process until all slices are segmented. The segmentation method was tested in 32 3D transrectal ultrasound images. Manual segmentation by a radiologist serves as the gold standard for the validation. The experimental results show that the proposed method achieved a Dice similarity coefficient of 91.37±0.05%. The segmentation method can be applied to 3D ultrasound-guided prostate biopsy and other applications.

  7. 3D Multi-segment foot kinematics in children: A developmental study in typically developing boys.

    Science.gov (United States)

    Deschamps, Kevin; Staes, Filip; Peerlinck, Kathelijne; Van Geet, Christel; Hermans, Cedric; Matricali, Giovanni Arnoldo; Lobet, Sebastien

    2017-02-01

    The relationship between age and 3D rotations objectivized with multisegment foot models has not been quantified until now. The purpose of this study was therefore to investigate the relationship between age and multi-segment foot kinematics in a cross-sectional database. Barefoot multi-segment foot kinematics of thirty two typically developing boys, aged 6-20 years, were captured with the Rizzoli Multi-segment Foot Model. One-dimensional statistical parametric mapping linear regression was used to examine the relationship between age and 3D inter-segment rotations of the dominant leg during the full gait cycle. Age was significantly correlated with sagittal plane kinematics of the midfoot and the calcaneus-metatarsus inter-segment angle (pplane kinematics of the calcaneus-metatarsus angle (pfoot models, however, the current study highlights that this is of particular relevance for foot models which incorporate a separate midfoot segment. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. 3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

    Science.gov (United States)

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

    2017-10-01

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

  9. 3D segmentation of scintigraphic images with validation on realistic GATE simulations

    International Nuclear Information System (INIS)

    Burg, Samuel

    2011-01-01

    The objective of this thesis was to propose a new 3D segmentation method for scintigraphic imaging. The first part of the work was to simulate 3D volumes with known ground truth in order to validate a segmentation method over other. Monte-Carlo simulations were performed using the GATE software (Geant4 Application for Emission Tomography). For this, we characterized and modeled the gamma camera 'γ Imager' Biospace TM by comparing each measurement from a simulated acquisition to his real equivalent. The 'low level' segmentation tool that we have developed is based on a modeling of the levels of the image by probabilistic mixtures. Parameters estimation is done by an SEM algorithm (Stochastic Expectation Maximization). The 3D volume segmentation is achieved by an ICM algorithm (Iterative Conditional Mode). We compared the segmentation based on Gaussian and Poisson mixtures to segmentation by thresholding on the simulated volumes. This showed the relevance of the segmentations obtained using probabilistic mixtures, especially those obtained with Poisson mixtures. Those one has been used to segment real 18 FDG PET images of the brain and to compute descriptive statistics of the different tissues. In order to obtain a 'high level' segmentation method and find anatomical structures (necrotic part or active part of a tumor, for example), we proposed a process based on the point processes formalism. A feasibility study has yielded very encouraging results. (author) [fr

  10. Segmentation and quantification of human vessels using a 3-D cylindrical intensity model.

    Science.gov (United States)

    Wörz, Stefan; Rohr, Karl

    2007-08-01

    We introduce a new approach for 3-D segmentation and quantification of vessels. The approach is based on a 3-D cylindrical parametric intensity model, which is directly fitted to the image intensities through an incremental process based on a Kalman filter. Segmentation results are the vessel centerline and shape, i.e., we estimate the local vessel radius, the 3-D position and 3-D orientation, the contrast, as well as the fitting error. We carried out an extensive validation using 3-D synthetic images and also compared the new approach with an approach based on a Gaussian model. In addition, the new model has been successfully applied to segment vessels from 3-D MRA and computed tomography angiography image data. In particular, we compared our approach with an approach based on the randomized Hough transform. Moreover, a validation of the segmentation results based on ground truth provided by a radiologist confirms the accuracy of the new approach. Our experiments show that the new model yields superior results in estimating the vessel radius compared to previous approaches based on a Gaussian model as well as the Hough transform.

  11. 3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images.

    Science.gov (United States)

    Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

    2016-08-01

    Automatic 3D liver segmentation is a fundamental step in the liver disease diagnosis and surgery planning. This paper presents a novel fully automatic algorithm for 3D liver segmentation in clinical 3D computed tomography (CT) images. Based on image features, we propose a new Mahalanobis distance cost function using an active shape model (ASM). We call our method MD-ASM. Unlike the standard active shape model (ST-ASM), the proposed method introduces a new feature-constrained Mahalanobis distance cost function to measure the distance between the generated shape during the iterative step and the mean shape model. The proposed Mahalanobis distance function is learned from a public database of liver segmentation challenge (MICCAI-SLiver07). As a refinement step, we propose the use of a 3D graph-cut segmentation. Foreground and background labels are automatically selected using texture features of the learned Mahalanobis distance. Quantitatively, the proposed method is evaluated using two clinical 3D CT scan databases (MICCAI-SLiver07 and MIDAS). The evaluation of the MICCAI-SLiver07 database is obtained by the challenge organizers using five different metric scores. The experimental results demonstrate the availability of the proposed method by achieving an accurate liver segmentation compared to the state-of-the-art methods.

  12. 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. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2014-01-01

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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-07-01

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

  16. 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.

  17. 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

  18. 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.

  19. 2D segmented large inkjet printhead for high speed 3D printers

    International Nuclear Information System (INIS)

    Einat, Moshe; Bar-Levav, Elkana

    2015-01-01

    Three-dimensional (3D) printing is a fast-developing technology these days. However, 3D printing of a model takes many hours. Therefore, the enlargement of the printhead and the increase of the printing speed are important to this technology. In order to enable the enlargement of the printhead a different approach and design are suggested and tested experimentally. The printhead is divided into small segments; each one is autonomous, and not fluid-connected to the neighboring segment. Each segment contains a micro reservoir and few nozzles. The segments are manufactured together in close proximity to each other on the same substrate enabling area coverage. A segmented printhead based on this approach was built and tested. The micro reservoir ink-filling method and operation of the segments were experimentally proven. Ink drops were obtained and the lifetime of the resistors was measured. Electrical characteristics of power and energy for proper operation were obtained. A 3D model printed according to the suggested approach can be completed in less than a minute. (paper)

  20. 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.

    2014-01-01

    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. PMID:25155697

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

    International Nuclear Information System (INIS)

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

    1991-01-01

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

  2. 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.

  3. 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...

  4. 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.

  5. Denoising imaging polarimetry by adapted BM3D method.

    Science.gov (United States)

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

    2018-04-01

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

  6. VOXEL- AND GRAPH-BASED POINT CLOUD SEGMENTATION OF 3D SCENES USING PERCEPTUAL GROUPING LAWS

    Directory of Open Access Journals (Sweden)

    Y. Xu

    2017-05-01

    Full Text Available 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.

  7. - 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.

  8. 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.

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

    Science.gov (United States)

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

    2018-03-01

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

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

    Science.gov (United States)

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

    2011-12-07

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

  11. 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

    Science.gov (United States)

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

    2012-07-01

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

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

    Science.gov (United States)

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

    2012-01-01

    Purpose: Liver segmentation is an important prerequisite for the assessment of liver cancer treatment options like tumor resection, image-guided radiation therapy (IGRT), radiofrequency ablation, etc. The purpose of this work was to evaluate a new approach for liver segmentation. Methods: A graph cuts segmentation method was combined with a three-dimensional virtual reality based segmentation refinement approach. The developed interactive segmentation system allowed the user to manipulate volume chunks and/or surfaces instead of 2D contours in cross-sectional images (i.e, slice-by-slice). The method was evaluated on twenty routinely acquired portal-phase contrast enhanced multislice computed tomography (CT) data sets. An independent reference was generated by utilizing a currently clinically utilized slice-by-slice segmentation method. After 1 h of introduction to the developed segmentation system, three experts were asked to segment all twenty data sets with the proposed method. Results: Compared to the independent standard, the relative volumetric segmentation overlap error averaged over all three experts and all twenty data sets was 3.74%. Liver segmentation required on average 16 min of user interaction per case. The calculated relative volumetric overlap errors were not found to be significantly different [analysis of variance (ANOVA) test,p = 0.82] between experts who utilized the proposed 3D system. In contrast, the time required by each expert for segmentation was found to be significantly different (ANOVA test, p = 0.0009). Major differences between generated segmentations and independent references were observed in areas were vessels enter or leave the liver and no accepted criteria for defining liver boundaries exist. In comparison, slice-by-slice based generation of the independent standard utilizing a live wire tool took 70.1 min on average. A standard 2D segmentation refinement approach applied to all twenty data sets required on average 38

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    Science.gov (United States)

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

    2012-03-01

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

  15. 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.

  16. Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction.

    Science.gov (United States)

    Li, Rongjian; Zeng, Tao; Peng, Hanchuan; Ji, Shuiwang

    2017-07-01

    Digital reconstruction, or tracing, of 3-D neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anatomy of a brain. Despite a number of prior attempts, this task remains very challenging, especially when images are contaminated by noises or have discontinued segments of neurite patterns. An approach for addressing such problems is to identify the locations of neuronal voxels using image segmentation methods, prior to applying tracing or reconstruction techniques. This preprocessing step is expected to remove noises in the data, thereby leading to improved reconstruction results. In this paper, we proposed to use 3-D convolutional neural networks (CNNs) for segmenting the neuronal microscopy images. Specifically, we designed a novel CNN architecture, that takes volumetric images as the inputs and their voxel-wise segmentation maps as the outputs. The developed architecture allows us to train and predict using large microscopy images in an end-to-end manner. We evaluated the performance of our model on a variety of challenging 3-D microscopy images from different organisms. Results showed that the proposed methods improved the tracing performance significantly when combined with different reconstruction algorithms.

  17. Automatic renal segmentation for MR urography using 3D-GrabCut and random forests.

    Science.gov (United States)

    Yoruk, Umit; Hargreaves, Brian A; Vasanawala, Shreyas S

    2018-03-01

    To introduce and evaluate a fully automated renal segmentation technique for glomerular filtration rate (GFR) assessment in children. An image segmentation method based on iterative graph cuts (GrabCut) was modified to work on time-resolved 3D dynamic contrast-enhanced MRI data sets. A random forest classifier was trained to further segment the renal tissue into cortex, medulla, and the collecting system. The algorithm was tested on 26 subjects and the segmentation results were compared to the manually drawn segmentation maps using the F1-score metric. A two-compartment model was used to estimate the GFR of each subject using both automatically and manually generated segmentation maps. Segmentation maps generated automatically showed high similarity to the manually drawn maps for the whole-kidney (F1 = 0.93) and renal cortex (F1 = 0.86). GFR estimations using whole-kidney segmentation maps from the automatic method were highly correlated (Spearman's ρ = 0.99) to the GFR values obtained from manual maps. The mean GFR estimation error of the automatic method was 2.98 ± 0.66% with an average segmentation time of 45 s per patient. The automatic segmentation method performs as well as the manual segmentation for GFR estimation and reduces the segmentation time from several hours to 45 s. Magn Reson Med 79:1696-1707, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  18. 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.

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

    Science.gov (United States)

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

    2017-06-01

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

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

    Science.gov (United States)

    Taha, Abdel Aziz; Hanbury, Allan

    2015-08-12

    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 existing metrics. First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.

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

    Science.gov (United States)

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

    2017-10-01

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

  2. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

    Science.gov (United States)

    Chen, Hao; Dou, Qi; Yu, Lequan; Qin, Jing; Heng, Pheng-Ann

    2018-04-15

    Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet. Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain

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

    Science.gov (United States)

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

    2017-09-01

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

  4. 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...

  5. 3D segmentation of coronary arteries based on advanced mathematical morphology techniques.

    Science.gov (United States)

    Bouraoui, B; Ronse, C; Baruthio, J; Passat, N; Germain, P

    2010-07-01

    In this article, we propose an automatic algorithm for coronary artery segmentation from 3D X-ray data sequences of a cardiac cycle (3D-CT scan, 64 detectors, 10 phases). This method is based on recent mathematical morphology techniques (some of them being extended in this article). It is also guided by anatomical knowledge, using discrete geometric tools to fit on the artery shape independently from any perturbation of the data. The application of the method on a validation dataset (60 images: 20 patients in 3 phases) led to 90% correct (and automatically obtained) segmentations, the 10% remaining cases corresponding to images where the SNR was very low. 2010 Elsevier Ltd. All rights reserved.

  6. Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.

    Science.gov (United States)

    Wahabzada, Mirwaes; Paulus, Stefan; Kersting, Kristian; Mahlein, Anne-Katrin

    2015-08-08

    Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation. The automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture. An automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated - even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant

  7. Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters.

    Science.gov (United States)

    Schneider, Matthias; Hirsch, Sven; Weber, Bruno; Székely, Gábor; Menze, Bjoern H

    2015-01-01

    We propose a novel framework for joint 3-D vessel segmentation and centerline extraction. The approach is based on multivariate Hough voting and oblique random forests (RFs) that we learn from noisy annotations. It relies on steerable filters for the efficient computation of local image features at different scales and orientations. We validate both the segmentation performance and the centerline accuracy of our approach both on synthetic vascular data and four 3-D imaging datasets of the rat visual cortex at 700 nm resolution. First, we evaluate the most important structural components of our approach: (1) Orthogonal subspace filtering in comparison to steerable filters that show, qualitatively, similarities to the eigenspace filters learned from local image patches. (2) Standard RF against oblique RF. Second, we compare the overall approach to different state-of-the-art methods for (1) vessel segmentation based on optimally oriented flux (OOF) and the eigenstructure of the Hessian, and (2) centerline extraction based on homotopic skeletonization and geodesic path tracing. Our experiments reveal the benefit of steerable over eigenspace filters as well as the advantage of oblique split directions over univariate orthogonal splits. We further show that the learning-based approach outperforms different state-of-the-art methods and proves highly accurate and robust with regard to both vessel segmentation and centerline extraction in spite of the high level of label noise in the training data. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network

    Science.gov (United States)

    Patel, Ajay; van de Leemput, Sil C.; Prokop, Mathias; van Ginneken, Bram; Manniesing, Rashindra

    2017-03-01

    Segmentation of anatomical structures is fundamental in the development of computer aided diagnosis systems for cerebral pathologies. Manual annotations are laborious, time consuming and subject to human error and observer variability. Accurate quantification of cerebrospinal fluid (CSF) can be employed as a morphometric measure for diagnosis and patient outcome prediction. However, segmenting CSF in non-contrast CT images is complicated by low soft tissue contrast and image noise. In this paper we propose a state-of-the-art method using a multi-scale three-dimensional (3D) fully convolutional neural network (CNN) to automatically segment all CSF within the cranial cavity. The method is trained on a small dataset comprised of four manually annotated cerebral CT images. Quantitative evaluation of a separate test dataset of four images shows a mean Dice similarity coefficient of 0.87 +/- 0.01 and mean absolute volume difference of 4.77 +/- 2.70 %. The average prediction time was 68 seconds. Our method allows for fast and fully automated 3D segmentation of cerebral CSF in non-contrast CT, and shows promising results despite a limited amount of training data.

  9. Segmentation of 3D images of plant tissues at multiple scales using the level set method.

    Science.gov (United States)

    Kiss, Annamária; Moreau, Typhaine; Mirabet, Vincent; Calugaru, Cerasela Iliana; Boudaoud, Arezki; Das, Pradeep

    2017-01-01

    Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue. We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue. The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations.

  10. 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.

  11. Left Ventricular Myocardial Segmentation in 3-D Ultrasound Recordings: Effect of Different Endocardial and Epicardial Coupling Strategies.

    Science.gov (United States)

    Pedrosa, Joao; Barbosa, Daniel; Heyde, Brecht; Schnell, Frederic; Rosner, Assami; Claus, Piet; D'hooge, Jan

    2017-03-01

    Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this paper, different ways of coupling the endocardial and epicardial segmentations are contrasted and compared with uncoupled segmentation. For this purpose, the B-spline explicit active surfaces framework was used; 27 3-D echocardiographic images were used to validate the different coupling strategies, which were compared with manual contouring of the endocardial and epicardial borders performed by an expert. It is shown that an independent segmentation of the endocardium followed by an epicardial segmentation coupled to the endocardium is the most advantageous. In this way, a framework for fully automatic 3-D myocardial segmentation is proposed using a novel coupling strategy.

  12. Fully automatic segmentation of left ventricular anatomy in 3-D LGE-MRI.

    Science.gov (United States)

    Kurzendorfer, Tanja; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Maier, Andreas; Brost, Alexander

    2017-07-01

    The current challenge for electrophysiology procedures, targeting the left ventricle, is the localization and qualification of myocardial scar. Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the current gold standard to visualize regions of myocardial infarction. Commonly, a stack of 2-D images is acquired of the left ventricle in short-axis orientation. Recently, 3-D LGE-MRI methods were proposed that continuously cover the whole heart with a high resolution within a single acquisition. The acquisition promises an accurate quantification of the myocardium to the extent of myocardial scarring. The major challenge arises in the analysis of the resulting images, as the accurate segmentation of the myocardium is a requirement for a precise scar tissue quantification. In this work, we propose a novel approach for fully automatic left ventricle segmentation in 3-D whole-heart LGE-MRI, to address this limitation. First, a two-step registration is performed to initialize the left ventricle. In the next step, the principal components are computed and a pseudo short axis view of the left ventricle is estimated. The refinement of the endocardium and epicardium is performed in polar space. Prior knowledge for shape and inter-slice smoothness is used during segmentation. The proposed method was evaluated on 30 clinical 3-D LGE-MRI datasets from individual patients obtained at two different clinical sites and were compared to gold standard segmentations of two clinical experts. This comparison resulted in a Dice coefficient of 0.83 for the endocardium and 0.80 for the epicardium. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. 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.

  14. A Doubly Adaptive Algorithm for Edge Detection in 3D Images

    Directory of Open Access Journals (Sweden)

    Sagrario Lantarón

    2016-01-01

    Full Text Available This paper proposes a new algorithm (DA3DED for edge detection in 3D images. DA3DED is doubly adaptive because it is based on the adaptive algorithm EDAS-1 for detecting edges in functions of one variable and a second adaptive procedure based on the concept of projective complexity of a 3D image. DA3DED has been tested on 3D images that modelize real problems (composites and fractures. It has been much faster than the 1D edge detection algorithm for 3D images derived from EDAS-1.

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  16. Development of an Amendment to X3D to Create a Standard Specification of Medical Image Volume Rendering, Segmentation, and Registration

    National Research Council Canada - National Science Library

    Ressler, Sandy; Aratow, Mike

    2006-01-01

    .... The Web3D Consortium, which administers X3D, has developed a draft extension to X3D for a volume rendering, registration and segmentation component to define a file format and display of 3D medical imaging data...

  17. A modular segmented-flow platform for 3D cell cultivation.

    Science.gov (United States)

    Lemke, Karen; Förster, Tobias; Römer, Robert; Quade, Mandy; Wiedemeier, Stefan; Grodrian, Andreas; Gastrock, Gunter

    2015-07-10

    In vitro 3D cell cultivation is promised to equate tissue in vivo more realistically than 2D cell cultivation corresponding to cell-cell and cell-matrix interactions. Therefore, a scalable 3D cultivation platform was developed. This platform, called pipe-based bioreactors (pbb), is based on the segmented-flow technology: aqueous droplets are embedded in a water-immiscible carrier fluid. The droplet volumes range from 60 nL to 20 μL and are used as bioreactors lined up in a tubing like pearls on a string. The modular automated platform basically consists of several modules like a fluid management for a high throughput droplet generation for self-assembly or scaffold-based 3D cell cultivation, a storage module for incubation and storage, and an analysis module for monitoring cell aggregation and proliferation basing on microscopy or photometry. In this report, the self-assembly of murine embryonic stem cells (mESCs) to uniformly sized embryoid bodies (EBs), the cell proliferation, the cell viability as well as the influence on the cell differentiation to cardiomyocytes are described. The integration of a dosage module for medium exchange or agent addition will enable pbb as long-term 3D cell cultivation system for studying stem cell differentiation, e.g. cardiac myogenesis or for diagnostic and therapeutic testing in personalized medicine. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    An, Lu; Guo, Baolong

    2018-03-01

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

  19. 3D segmentation of liver, kidneys and spleen from CT images

    International Nuclear Information System (INIS)

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

    2007-01-01

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

  20. 3D medical image segmentation based on a continuous modelling of the volume

    International Nuclear Information System (INIS)

    Marque, I.

    1990-12-01

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

  1. 3D marker-controlled watershed for kidney segmentation in clinical CT exams.

    Science.gov (United States)

    Wieclawek, Wojciech

    2018-02-27

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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Johannes Stegmaier

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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  5. 3D robust Chan-Vese model for industrial computed tomography volume data segmentation

    Science.gov (United States)

    Liu, Linghui; Zeng, Li; Luan, Xiao

    2013-11-01

    Industrial computed tomography (CT) has been widely applied in many areas of non-destructive testing (NDT) and non-destructive evaluation (NDE). In practice, CT volume data to be dealt with may be corrupted by noise. This paper addresses the segmentation of noisy industrial CT volume data. Motivated by the research on the Chan-Vese (CV) model, we present a region-based active contour model that draws upon intensity information in local regions with a controllable scale. In the presence of noise, a local energy is firstly defined according to the intensity difference within a local neighborhood. Then a global energy is defined to integrate local energy with respect to all image points. In a level set formulation, this energy is represented by a variational level set function, where a surface evolution equation is derived for energy minimization. Comparative analysis with the CV model indicates the comparable performance of the 3D robust Chan-Vese (RCV) model. The quantitative evaluation also shows the segmentation accuracy of 3D RCV. In addition, the efficiency of our approach is validated under several types of noise, such as Poisson noise, Gaussian noise, salt-and-pepper noise and speckle noise.

  6. 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.

  7. Left Ventricular Myocardial Segmentation in 3-D Ultrasound Recordings: Effect of Different Endocardial and Epicardial Coupling Strategies

    OpenAIRE

    Pedrosa , Joao; Barbosa , Daniel; Heyde , Brecht; Schnell , Frédéric; Rosner , Assami; Claus , Piet; D'Hooge , Jan

    2017-01-01

    International audience; Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this paper, different ways of coupling the endocardial and epicardial segmentations are contrasted and compared with uncoupl...

  8. Automatic 3D liver location and segmentation via convolutional neural network and graph cut.

    Science.gov (United States)

    Lu, Fang; Wu, Fa; Hu, Peijun; Peng, Zhiyi; Kong, Dexing

    2017-02-01

    Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.

  9. 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.

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

    Science.gov (United States)

    2013-01-01

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

  11. 3D Segmentation of Rodent Brain Structures Using Hierarchical Shape Priors and Deformable Models

    Science.gov (United States)

    Zhang, Shaoting; Huang, Junzhou; Uzunbas, Mustafa; Shen, Tian; Delis, Foteini; Huang, Xiaolei; Volkow, Nora; Thanos, Panayotis; Metaxas, Dimitris N.

    2016-01-01

    In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple structures at both global and local levels. At the global level, the statistics of relative positions among different structures are modeled. At the local level, the shape statistics within each structure is learned from training samples. Our segmentation method adaptively employs both priors to constrain the intermediate deformation result. This prior constraint improves the robustness of the model and benefits the segmentation accuracy. Another merit of our prior module is that the size of the training data can be small, because the shape prior module models each structure individually and combines them using global statistics. This scheme can preserve shape details better than directly applying PCA on all structures. We use this method to segment rodent brain structures, such as the cerebellum, the left and right striatum, and the left and right hippocampus. The experiments show that our method works effectively and this hierarchical prior improves the segmentation performance. PMID:22003750

  12. 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.

  13. 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

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

    NARCIS (Netherlands)

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

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

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

    Science.gov (United States)

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

    2015-01-01

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

  16. 3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilation

    Science.gov (United States)

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

    2015-03-01

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

  17. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    International Nuclear Information System (INIS)

    Chen, Xinjian; Bagci, Ulas

    2011-01-01

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

  18. 3D automatic anatomy segmentation based on iterative graph-cut-ASM.

    Science.gov (United States)

    Chen, Xinjian; Bagci, Ulas

    2011-08-01

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

  19. Progressive Minimal Path Method for Segmentation of 2D and 3D Line Structures.

    Science.gov (United States)

    Liao, Wei; Worz, Stefan; Kang, Chang-Ki; Cho, Zang-Hee; Rohr, Karl

    2018-03-01

    We propose a novel minimal path method for the segmentation of 2D and 3D line structures. Minimal path methods perform propagation of a wavefront emanating from a start point at a speed derived from image features, followed by path extraction using backtracing. Usually, the computation of the speed and the propagation of the wave are two separate steps, and point features are used to compute a static speed. We introduce a new continuous minimal path method which steers the wave propagation progressively using dynamic speed based on path features. We present three instances of our method, using an appearance feature of the path, a geometric feature based on the curvature of the path, and a joint appearance and geometric feature based on the tangent of the wavefront. These features have not been used in previous continuous minimal path methods. We compute the features dynamically during the wave propagation, and also efficiently using a fast numerical scheme and a low-dimensional parameter space. Our method does not suffer from discretization or metrication errors. We performed qualitative and quantitative evaluations using 2D and 3D images from different application areas.

  20. 3-D design method for welding groove and seal weld of reactor CRDM adapter

    International Nuclear Information System (INIS)

    Ma Baiyong; Wang Xiaobin; Zhu Xiaoyong

    2008-01-01

    Based on the analysis of 2 D and 3 D shapes of welding groove and seal weld of reactor CRDM adapter, four intersecting curves are defined, and a method and gist to 3 D design of adapter welding groove and seal weld is proposed. Parameterized design of adapter welding groove and seal weld has been realized using UG software, and the main factors which affect the welding section areas have been analyzed. Compared with the measurement, the error of weld section area of each adapter created by spline fitting method is less than 0.8%. (authors)

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

    International Nuclear Information System (INIS)

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

    1996-01-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

  3. 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.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Poręba Martyna

    2014-12-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  7. Dental measurements and Bolton index reliability and accuracy obtained from 2D digital, 3D segmented CBCT, and 3d intraoral laser scanner

    Science.gov (United States)

    San José, Verónica; Bellot-Arcís, Carlos; Tarazona, Beatriz; Zamora, Natalia; O Lagravère, Manuel

    2017-01-01

    Background To compare the reliability and accuracy of direct and indirect dental measurements derived from two types of 3D virtual models: generated by intraoral laser scanning (ILS) and segmented cone beam computed tomography (CBCT), comparing these with a 2D digital model. Material and Methods One hundred patients were selected. All patients’ records included initial plaster models, an intraoral scan and a CBCT. Patients´ dental arches were scanned with the iTero® intraoral scanner while the CBCTs were segmented to create three-dimensional models. To obtain 2D digital models, plaster models were scanned using a conventional 2D scanner. When digital models had been obtained using these three methods, direct dental measurements were measured and indirect measurements were calculated. Differences between methods were assessed by means of paired t-tests and regression models. Intra and inter-observer error were analyzed using Dahlberg´s d and coefficients of variation. Results Intraobserver and interobserver error for the ILS model was less than 0.44 mm while for segmented CBCT models, the error was less than 0.97 mm. ILS models provided statistically and clinically acceptable accuracy for all dental measurements, while CBCT models showed a tendency to underestimate measurements in the lower arch, although within the limits of clinical acceptability. Conclusions ILS and CBCT segmented models are both reliable and accurate for dental measurements. Integration of ILS with CBCT scans would get dental and skeletal information altogether. Key words:CBCT, intraoral laser scanner, 2D digital models, 3D models, dental measurements, reliability. PMID:29410764

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

    Science.gov (United States)

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

    2014-07-01

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

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

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  10. Solving 2D/3D Heat Conduction Problems by Combining Topology Optimization and Anisotropic Mesh Adaptation

    DEFF Research Database (Denmark)

    Jensen, Kristian

    2018-01-01

    Topology optimization was recently combined with anisotropic mesh adaptation to solve 3D minimum compliance problems in a fast and robust way. This paper demonstrates that the methodology is also applicable to 2D/3D heat conduction problems. Nodal design variables are used and the objective...... function is chosen such that the problem is self-adjoint. There is no way around the book keeping associated with mesh adaptation, so the whole 5527 line MATLAB code is published (https://github.com/kristianE86/trullekrul). The design variables as well as the sensitivities have to be interpolated between...

  11. 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.

  12. 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts.

    Science.gov (United States)

    Wu, Weiwei; Wu, Shuicai; Zhou, Zhuhuang; Zhang, Rui; Zhang, Yanhua

    2017-01-01

    Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C -means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.

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

    NARCIS (Netherlands)

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

    2017-01-01

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

  14. 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.

  15. 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.

  16. Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-Learning Based Cascade Framework

    Science.gov (United States)

    Nie, Dong; Wang, Li; Trullo, Roger; Li, Jianfu; Yuan, Peng; Xia, James

    2017-01-01

    Computed tomography (CT) is commonly used as a diagnostic and treatment planning imaging modality in craniomaxillofacial (CMF) surgery to correct patient’s bony defects. A major disadvantage of CT is that it emits harmful ionizing radiation to patients during the exam. Magnetic resonance imaging (MRI) is considered to be much safer and noninvasive, and often used to study CMF soft tissues (e.g., temporomandibular joint and brain). However, it is extremely difficult to accurately segment CMF bony structures from MRI since both bone and air appear to be black in MRI, along with low signal-to-noise ratio and partial volume effect. To this end, we proposed a 3D deep-learning based cascade framework to solve these issues. Specifically, a 3D fully convolutional network (FCN) architecture is first adopted to coarsely segment the bony structures. As the coarsely segmented bony structures by FCN tend to be thicker, convolutional neural network (CNN) is further utilized for fine-grained segmentation. To enhance the discriminative ability of the CNN, we particularly concatenate the predicted probability maps from FCN and the original MRI, and feed them together into the CNN to provide more context information for segmentation. Experimental results demonstrate a good performance and also the clinical feasibility of our proposed 3D deep-learning based cascade framework. PMID:29417097

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

    Science.gov (United States)

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

    2018-02-26

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

  18. Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN.

    Science.gov (United States)

    Xu, Xuanang; Zhou, Fugen; Liu, Bo

    2018-03-19

    Automatic approach for bladder segmentation from computed tomography (CT) images is highly desirable in clinical practice. It is a challenging task since the bladder usually suffers large variations of appearance and low soft-tissue contrast in CT images. In this study, we present a deep learning-based approach which involves a convolutional neural network (CNN) and a 3D fully connected conditional random fields recurrent neural network (CRF-RNN) to perform accurate bladder segmentation. We also propose a novel preprocessing method, called dual-channel preprocessing, to further advance the segmentation performance of our approach. The presented approach works as following: first, we apply our proposed preprocessing method on the input CT image and obtain a dual-channel image which consists of the CT image and an enhanced bladder density map. Second, we exploit a CNN to predict a coarse voxel-wise bladder score map on this dual-channel image. Finally, a 3D fully connected CRF-RNN refines the coarse bladder score map and produce final fine-localized segmentation result. We compare our approach to the state-of-the-art V-net on a clinical dataset. Results show that our approach achieves superior segmentation accuracy, outperforming the V-net by a significant margin. The Dice Similarity Coefficient of our approach (92.24%) is 8.12% higher than that of the V-net. Moreover, the bladder probability maps performed by our approach present sharper boundaries and more accurate localizations compared with that of the V-net. Our approach achieves higher segmentation accuracy than the state-of-the-art method on clinical data. Both the dual-channel processing and the 3D fully connected CRF-RNN contribute to this improvement. The united deep network composed of the CNN and 3D CRF-RNN also outperforms a system where the CRF model acts as a post-processing method disconnected from the CNN.

  19. 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.

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

    Science.gov (United States)

    Li, Dong; Kofman, Jonathan

    2014-04-21

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

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

    Science.gov (United States)

    Nagarajan, Anand; Soghrati, Soheil

    2018-03-01

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

  2. Automatic 3D Segmentation and Quantification of Lenticulostriate Arteries from High-Resolution 7 Tesla MRA Images.

    Science.gov (United States)

    Wei Liao; Rohr, Karl; Chang-Ki Kang; Zang-Hee Cho; Worz, Stefan

    2016-01-01

    We propose a novel hybrid approach for automatic 3D segmentation and quantification of high-resolution 7 Tesla magnetic resonance angiography (MRA) images of the human cerebral vasculature. Our approach consists of two main steps. First, a 3D model-based approach is used to segment and quantify thick vessels and most parts of thin vessels. Second, remaining vessel gaps of the first step in low-contrast and noisy regions are completed using a 3D minimal path approach, which exploits directional information. We present two novel minimal path approaches. The first is an explicit approach based on energy minimization using probabilistic sampling, and the second is an implicit approach based on fast marching with anisotropic directional prior. We conducted an extensive evaluation with over 2300 3D synthetic images and 40 real 3D 7 Tesla MRA images. Quantitative and qualitative evaluation shows that our approach achieves superior results compared with a previous minimal path approach. Furthermore, our approach was successfully used in two clinical studies on stroke and vascular dementia.

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

    Science.gov (United States)

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

    2011-03-01

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

  4. An adaptive and fully automatic method for estimating the 3D position of bendable instruments using endoscopic images.

    Science.gov (United States)

    Cabras, Paolo; Nageotte, Florent; Zanne, Philippe; Doignon, Christophe

    2017-12-01

    Flexible bendable instruments are key tools for performing surgical endoscopy. Being able to measure the 3D position of such instruments can be useful for various tasks, such as controlling automatically robotized instruments and analyzing motions. An automatic method is proposed to infer the 3D pose of a single bending section instrument, using only the images provided by a monocular camera embedded at the tip of the endoscope. The proposed method relies on colored markers attached onto the bending section. The image of the instrument is segmented using a graph-based method and the corners of the markers are extracted by detecting the color transitions along Bézier curves fitted on edge points. These features are accurately located and then used to estimate the 3D pose of the instrument using an adaptive model that takes into account the mechanical play between the instrument and its housing channel. The feature extraction method provides good localization of marker corners with images of the in vivo environment despite sensor saturation due to strong lighting. The RMS error on estimation of the tip position of the instrument for laboratory experiments was 2.1, 1.96, and 3.18 mm in the x, y and z directions, respectively. Qualitative analysis in the case of in vivo images shows the ability to correctly estimate the 3D position of the instrument tip during real motions. The proposed method provides an automatic and accurate estimation of the 3D position of the tip of a bendable instrument in realistic conditions, where standard approaches fail. Copyright © 2017 John Wiley & Sons, Ltd.

  5. Unsupervised 3D Prostate Segmentation Based on Diffusion-Weighted Imaging MRI Using Active Contour Models with a Shape Prior

    Directory of Open Access Journals (Sweden)

    Xin Liu

    2011-01-01

    plays a crucial role in various clinical applications. Recently, magnetic resonance imaging (MRI is proposed as a promising modality to detect and monitor prostate-related diseases. In this paper, we propose an unsupervised algorithm to segment prostate with 3D apparent diffusion coefficient (ADC images derived from diffusion-weighted imaging (DWI MRI without the need of a training dataset, whereas previous methods for this purpose require training datasets. We first apply a coarse segmentation to extract the shape information. Then, the shape prior is incorporated into the active contour model. Finally, morphological operations are applied to refine the segmentation results. We apply our method to an MR dataset obtained from three patients and provide segmentation results obtained by our method and an expert. Our experimental results show that the performance of the proposed method is quite successful.

  6. 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....

  7. 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.

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

    International Nuclear Information System (INIS)

    Barra, V.; Boire, J.Y.

    1999-01-01

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

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  10. Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm.

    Science.gov (United States)

    Maglietta, Rosalia; Amoroso, Nicola; Boccardi, Marina; Bruno, Stefania; Chincarini, Andrea; Frisoni, Giovanni B; Inglese, Paolo; Redolfi, Alberto; Tangaro, Sabina; Tateo, Andrea; Bellotti, Roberto

    The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice's index of [Formula: see text] ([Formula: see text]) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi.

  11. 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.

  12. Improvements to the ShipIR/NTCS adaptive track gate algorithm and 3D flare particle model

    Science.gov (United States)

    Ramaswamy, Srinivasan; Vaitekunas, David A.; Gunter, Willem H.; February, Faith J.

    2017-05-01

    A key component in any image-based tracking system is the adaptive tracking algorithm used to segment the image into potential targets, rank-and-select the best candidate target, and gate the selected target to further improve tracker performance. Similarly, a key component in any soft-kill response to an incoming guided missile is the flare/chaff decoy used to distract or seduce the seeker homing system away from the naval platform. This paper describes the recent improvements to the naval threat countermeasure simulator (NTCS) of the NATO-standard ship signature model (ShipIR). Efforts to analyse and match the 3D flare particle model against actual IR measurements of the Chemring TALOS IR round resulted in further refinement of the 3D flare particle distribution. The changes in the flare model characteristics were significant enough to require an overhaul to the adaptive track gate (ATG) algorithm in the way it detects the presence of flare decoys and reacquires the target after flare separation. A series of test scenarios are used to demonstrate the impact of the new flare and ATG on IR tactics simulation.

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

    Science.gov (United States)

    2014-04-26

    segmentation from the automatic propa- gation approach ( Si ) saved for retrieval. A cache allows multiple inter- actions that modify the segmentation Si to be...can be min- imized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004) 28. Kuang , Z., Schnieders, D., Zhou, H., Wong, K.Y...2004) 40. Rowenhorst, D., Lewis, A., Spanos, G.: Three-dimensional analy- sis of grain topology and interface curvature in a β-titanium alloy. Acta

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

    Science.gov (United States)

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

    2013-03-01

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

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

    Science.gov (United States)

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

    2011-04-01

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

  16. 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

  17. Local Stereo Matching Using Adaptive Local Segmentation

    NARCIS (Netherlands)

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

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

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

    Directory of Open Access Journals (Sweden)

    S. Falahieh Hamidpour

    2007-06-01

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

  19. 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.

  20. 3D morphometry using automated aortic segmentation in native MR angiography: an alternative to contrast enhanced MRA?

    Science.gov (United States)

    Müller-Eschner, Matthias; Müller, Tobias; Biesdorf, Andreas; Wörz, Stefan; Rengier, Fabian; Böckler, Dittmar; Kauczor, Hans-Ulrich; Rohr, Karl; von Tengg-Kobligk, Hendrik

    2014-04-01

    Native-MR angiography (N-MRA) is considered an imaging alternative to contrast enhanced MR angiography (CE-MRA) for patients with renal insufficiency. Lower intraluminal contrast in N-MRA often leads to failure of the segmentation process in commercial algorithms. This study introduces an in-house 3D model-based segmentation approach used to compare both sequences by automatic 3D lumen segmentation, allowing for evaluation of differences of aortic lumen diameters as well as differences in length comparing both acquisition techniques at every possible location. Sixteen healthy volunteers underwent 1.5-T-MR Angiography (MRA). For each volunteer, two different MR sequences were performed, CE-MRA: gradient echo Turbo FLASH sequence and N-MRA: respiratory-and-cardiac-gated, T2-weighted 3D SSFP. Datasets were segmented using a 3D model-based ellipse-fitting approach with a single seed point placed manually above the celiac trunk. The segmented volumes were manually cropped from left subclavian artery to celiac trunk to avoid error due to side branches. Diameters, volumes and centerline length were computed for intraindividual comparison. For statistical analysis the Wilcoxon-Signed-Ranked-Test was used. Average centerline length obtained based on N-MRA was 239.0±23.4 mm compared to 238.6±23.5 mm for CE-MRA without significant difference (P=0.877). Average maximum diameter obtained based on N-MRA was 25.7±3.3 mm compared to 24.1±3.2 mm for CE-MRA (P<0.001). In agreement with the difference in diameters, volumes obtained based on N-MRA (100.1±35.4 cm(3)) were consistently and significantly larger compared to CE-MRA (89.2±30.0 cm(3)) (P<0.001). 3D morphometry shows highly similar centerline lengths for N-MRA and CE-MRA, but systematically higher diameters and volumes for N-MRA.

  1. Automatic 3D segmentation of the kidney in MR images using wavelet feature extraction and probability shape model

    Science.gov (United States)

    Akbari, Hamed; Fei, Baowei

    2012-02-01

    Numerical estimation of the size of the kidney is useful in evaluating conditions of the kidney, especially, when serial MR imaging is performed to evaluate the kidney function. This paper presents a new method for automatic segmentation of the kidney in three-dimensional (3D) MR images, by extracting texture features and statistical matching of geometrical shape of the kidney. A set of Wavelet-based support vector machines (W-SVMs) is trained on the MR images. The W-SVMs capture texture priors of MRI for classification of the kidney and non-kidney tissues in different zones around the kidney boundary. In the segmentation procedure, these W-SVMs are trained to tentatively label each voxel around the kidney model as a kidney or non-kidney voxel by texture matching. A probability kidney model is created using 10 segmented MRI data. The model is initially localized based on the intensity profiles in three directions. The weight functions are defined for each labeled voxel for each Wavelet-based, intensity-based, and model-based label. Consequently, each voxel has three labels and three weights for the Wavelet feature, intensity, and probability model. Using a 3D edge detection method, the model is re-localized and the segmented kidney is modified based on a region growing method in the model region. The probability model is re-localized based on the results and this loop continues until the segmentation converges. Experimental results with mouse MRI data show the good performance of the proposed method in segmenting the kidney in MR images.

  2. 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.

  3. Solving Elliptical Equations in 3D by Means of an Adaptive Refinement in Generalized Finite Differences

    Directory of Open Access Journals (Sweden)

    Luis Gavete

    2018-01-01

    Full Text Available We apply a 3D adaptive refinement procedure using meshless generalized finite difference method for solving elliptic partial differential equations. This adaptive refinement, based on an octree structure, allows adding nodes in a regular way in order to obtain smooth transitions with different nodal densities in the model. For this purpose, we define an error indicator as stop condition of the refinement, a criterion for choosing nodes with the highest errors, and a limit for the number of nodes to be added in each adaptive stage. This kind of equations often appears in engineering problems such as simulation of heat conduction, electrical potential, seepage through porous media, or irrotational flow of fluids. The numerical results show the high accuracy obtained.

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

    Science.gov (United States)

    Teunissen, Jannis; Ebert, Ute

    2017-11-01

    We present an open-source plasma fluid code for 2D, cylindrical and 3D simulations of streamer discharges. The code is based on the Afivo framework, which features adaptive mesh refinement on quadtree/octree grids, geometric multigrid methods for Poisson’s equation, and OpenMP parallelism. We describe the numerical implementation of a fluid model of the drift-diffusion-reaction type, combined with the local field approximation. Then we demonstrate its functionality with 3D simulations of long positive streamers in nitrogen in undervolted gaps. Three examples are presented. The first one shows how a stochastic background density affects streamer propagation and branching. The second one focuses on the interaction of a streamer with preionized regions, and the third one investigates the interaction between two streamers. The simulations use up to 108 grid cells and run in less than a day; without mesh refinement they would require more than 1012 grid cells.

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

    International Nuclear Information System (INIS)

    Sharpe, Michael; Brock, Kristy K.

    2008-01-01

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

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  7. 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...... solver is efficient in memory usage....

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

    Science.gov (United States)

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

    2013-03-01

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

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

    Science.gov (United States)

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

    2017-10-01

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

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

    International Nuclear Information System (INIS)

    Sjoden, G.E.; Haghighat, A.

    1996-01-01

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

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

    International Nuclear Information System (INIS)

    Ohshima, Shunsuke; Yamamoto, Shuji; Yamaji, Taiki

    2008-01-01

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

  12. 3D spatially-adaptive canonical correlation analysis: Local and global methods.

    Science.gov (United States)

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

    2018-04-01

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

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

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

    Science.gov (United States)

    Javan, Ramin; Herrin, Douglas; Tangestanipoor, Ardalan

    2016-09-01

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

  15. Parallel goal-oriented adaptive finite element modeling for 3D electromagnetic exploration

    Science.gov (United States)

    Zhang, Y.; Key, K.; Ovall, J.; Holst, M.

    2014-12-01

    We present a parallel goal-oriented adaptive finite element method for accurate and efficient electromagnetic (EM) modeling of complex 3D structures. An unstructured tetrahedral mesh allows this approach to accommodate arbitrarily complex 3D conductivity variations and a priori known boundaries. The total electric field is approximated by the lowest order linear curl-conforming shape functions and the discretized finite element equations are solved by a sparse LU factorization. Accuracy of the finite element solution is achieved through adaptive mesh refinement that is performed iteratively until the solution converges to the desired accuracy tolerance. Refinement is guided by a goal-oriented error estimator that uses a dual-weighted residual method to optimize the mesh for accurate EM responses at the locations of the EM receivers. As a result, the mesh refinement is highly efficient since it only targets the elements where the inaccuracy of the solution corrupts the response at the possibly distant locations of the EM receivers. We compare the accuracy and efficiency of two approaches for estimating the primary residual error required at the core of this method: one uses local element and inter-element residuals and the other relies on solving a global residual system using a hierarchical basis. For computational efficiency our method follows the Bank-Holst algorithm for parallelization, where solutions are computed in subdomains of the original model. To resolve the load-balancing problem, this approach applies a spectral bisection method to divide the entire model into subdomains that have approximately equal error and the same number of receivers. The finite element solutions are then computed in parallel with each subdomain carrying out goal-oriented adaptive mesh refinement independently. We validate the newly developed algorithm by comparison with controlled-source EM solutions for 1D layered models and with 2D results from our earlier 2D goal oriented

  16. Soft tissue coverage on the segmentation accuracy of the 3D surface-rendered model from cone-beam CT.

    Science.gov (United States)

    Dusseldorp, J K; Stamatakis, H C; Ren, Y

    2017-04-01

    The aim of this study is to investigate the effect of soft tissue presence on the segmentation accuracy of the 3D hard tissue models from cone-beam computed tomography (CBCT). Seven pairs of CBCT Digital Imaging and Communication in Medicine (DICOM) datasets, containing data of human cadaver heads and their respective dry skulls, were used. The effect of the soft tissue presence on the accuracy of the segmented models was evaluated by performing linear and angular measurements and by superimposition and color mapping of the surface discrepancies after splitting the mandible and maxillo-facial complex in the midsagittal plane. The linear and angular measurements showed significant differences for the more posterior transversal measurements on the mandible (p  0.05). The RMSE value for the mandible, however, significantly decreased from 1.240 to 0.981 mm after splitting (p cone-beam CT, below a generally accepted level of clinical significance of 1 mm. However, this level of accuracy may not meet the requirement for applications where high precision is paramount. Accuracy of CBCT-based 3D surface-rendered models, especially of the hard tissues, are crucial in several dental and medical applications, such as implant planning and virtual surgical planning on patients undergoing orthognathic and navigational surgeries. When used in applications where high precision is paramount, the effect of soft tissue presence should be taken into consideration during the segmentation process.

  17. Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.

    Science.gov (United States)

    Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

    2016-12-01

    This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  18. 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.

  19. Capturing Multiscale Phenomena via Adaptive Mesh Refinement (AMR) in 2D and 3D Atmospheric Flows

    Science.gov (United States)

    Ferguson, J. O.; Jablonowski, C.; Johansen, H.; McCorquodale, P.; Ullrich, P. A.; Langhans, W.; Collins, W. D.

    2017-12-01

    Extreme atmospheric events such as tropical cyclones are inherently complex multiscale phenomena. Such phenomena are a challenge to simulate in conventional atmosphere models, which typically use rather coarse uniform-grid resolutions. To enable study of these systems, Adaptive Mesh Refinement (AMR) can provide sufficient local resolution by dynamically placing high-resolution grid patches selectively over user-defined features of interest, such as a developing cyclone, while limiting the total computational burden of requiring such high-resolution globally. This work explores the use of AMR with a high-order, non-hydrostatic, finite-volume dynamical core, which uses the Chombo AMR library to implement refinement in both space and time on a cubed-sphere grid. The characteristics of the AMR approach are demonstrated via a series of idealized 2D and 3D test cases designed to mimic atmospheric dynamics and multiscale flows. In particular, new shallow-water test cases with forcing mechanisms are introduced to mimic the strengthening of tropical cyclone-like vortices and to include simplified moisture and convection processes. The forced shallow-water experiments quantify the improvements gained from AMR grids, assess how well transient features are preserved across grid boundaries, and determine effective refinement criteria. In addition, results from idealized 3D test cases are shown to characterize the accuracy and stability of the non-hydrostatic 3D AMR dynamical core.

  20. 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.

  1. Adaptive robotic end-effector with embedded 3D-printed sensing Circuits

    Directory of Open Access Journals (Sweden)

    Zapciu Aurelian

    2017-01-01

    Full Text Available The paper presents the development and testing of an adaptive robotic end-effector used for manipulation of sensitive objects such as fruits and vegetables. The end-effector uses Fin-Ray-structured 3D-printed fingers with embedded conductive 3D-printed sensing circuits, which give the end-effector capacitive touch sensing and bend sensing capabilities. The conductive 3D-printed circuit is connected to a control circuit consisting of a low-current DC power source and a microcontroller. As the end-effector finger is subjected to various forces and other external stimuli, changes in the electric signals that run through the conductive circuit of the end-effector finger are detected by the microcontroller. The electric signal is processed in order to provide real-time information about contact detection, finger position or gripping force. This information was used for process monitoring purposes and as feedback for the end-effector actuator.

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

    Directory of Open Access Journals (Sweden)

    Sarah Feger

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

  3. SEGMENTATION OF 2D AND 3D TEXTURES FROM ESTIMATES OF THE LOCAL ORIENTATION

    Directory of Open Access Journals (Sweden)

    Dominique Jeulin

    2011-05-01

    Full Text Available We use a method to estimate local orientations in the n-dimensional space from the covariance matrix of the gradient, which can be implemented either in the image space or in the Fourier space. In a second step, two methods allow us to detect sudden changes of orientation in images. The first one uses an index of confidence of the estimated orientation, and the second one the detection of minima of scalar products in a neighbourhood. This is illustrated on 2D Transmission Electrons Microscope images of cellulose cryofracture (to display the organisation of cellulose whiskers and the points of germination, and to 3D images of a TA6V alloy (lamellar microstructure obtained by microtomography.

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

    OpenAIRE

    Le, Trong-Ngoc; Bao, Pham The; Huynh, Hieu Trung

    2016-01-01

    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 co...

  5. 3D CSEM inversion based on goal-oriented adaptive finite element method

    Science.gov (United States)

    Zhang, Y.; Key, K.

    2016-12-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  7. 3D thermal modelling within the Lofoten-Vesterålen segment of the Mid-Norwegian continental margin

    Science.gov (United States)

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

    2017-04-01

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

  8. Multiscale Hessian fracture filtering for the enhancement and segmentation of narrow fractures in 3D image data

    Science.gov (United States)

    Voorn, Maarten; Exner, Ulrike; Rath, Alexander

    2013-08-01

    Narrow fractures—or more generally narrow planar features—can be difficult to extract from 3D image datasets, and available methods are often unsuitable or inapplicable. A proper extraction is however in many cases required for visualisation or future processing steps. We use the example of 3D X-ray micro-Computed Tomography (µCT) data of narrow fractures through core samples from a dolomitic hydrocarbon reservoir (Hauptdolomit below the Vienna Basin, Austria). The extraction and eventual binary segmentation of the fractures in these datasets is required for porosity determination and permeability modelling. In this paper, we present the multiscale Hessian fracture filtering technique for extracting narrow fractures from a 3D image dataset. The second-order information in the Hessian matrix is used to distinguish planar features from the dataset. Different results are obtained for different scales of analysis in the calculation of the Hessian matrix. By combining these various scales of analysis, the final output is multiscale; i.e. narrow fractures of different apertures are detected. The presented technique is implemented and made available as macro code for the multiplatform public domain image processing software ImageJ. Serial processing of blocks of data ensures that full 3D processing of relatively large datasets (example dataset: 1670×1670×1546 voxels) is possible on a desktop computer. Here, several hours of processing time are required, but interaction is only required in the beginning. Various post-processing steps (calibration, connectivity filtering, and binarisation) can be applied, depending on the goals of research. The multiscale Hessian fracture filtering technique provides very good results for extracting the narrow fractures in our example dataset, despite several drawbacks inherent to the use of the Hessian matrix. Although we apply the technique on a specific example, the general implementation makes the filter suitable for different

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

    International Nuclear Information System (INIS)

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

    2007-01-01

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

  10. Segmentation of the Aortic Valve Apparatus in 3D Echocardiographic Images: Deformable Modeling of a Branching Medial Structure.

    Science.gov (United States)

    Pouch, Alison M; Tian, Sijie; Takabe, Manabu; Wang, Hongzhi; Yuan, Jiefu; Cheung, Albert T; Jackson, Benjamin M; Gorman, Joseph H; Gorman, Robert C; Yushkevich, Paul A

    2015-01-01

    3D echocardiographic (3DE) imaging is a useful tool for assessing the complex geometry of the aortic valve apparatus. Segmentation of this structure in 3DE images is a challenging task that benefits from shape-guided deformable modeling methods, which enable inter-subject statistical shape comparison. Prior work demonstrates the efficacy of using continuous medial representation (cm-rep) as a shape descriptor for valve leaflets. However, its application to the entire aortic valve apparatus is limited since the structure has a branching medial geometry that cannot be explicitly parameterized in the original cm-rep framework. In this work, we show that the aortic valve apparatus can be accurately segmented using a new branching medial modeling paradigm. The segmentation method achieves a mean boundary displacement of 0.6 ± 0.1 mm (approximately one voxel) relative to manual segmentation on 11 3DE images of normal open aortic valves. This study demonstrates a promising approach for quantitative 3DE analysis of aortic valve morphology.

  11. Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement

    Science.gov (United States)

    Yin, Yin; Fotin, Sergei V.; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, François; Turkbey, Baris; Choyke, Peter

    2012-02-01

    Manual delineation of the prostate is a challenging task for a clinician due to its complex and irregular shape. Furthermore, the need for precisely targeting the prostate boundary continues to grow. Planning for radiation therapy, MR-ultrasound fusion for image-guided biopsy, multi-parametric MRI tissue characterization, and context-based organ retrieval are examples where accurate prostate delineation can play a critical role in a successful patient outcome. Therefore, a robust automated full prostate segmentation system is desired. In this paper, we present an automated prostate segmentation system for 3D MR images. In this system, the prostate is segmented in two steps: the prostate displacement and size are first detected, and then the boundary is refined by a shape model. The detection approach is based on normalized gradient fields cross-correlation. This approach is fast, robust to intensity variation and provides good accuracy to initialize a prostate mean shape model. The refinement model is based on a graph-search based framework, which contains both shape and topology information during deformation. We generated the graph cost using trained classifiers and used coarse-to-fine search and region-specific classifier training. The proposed algorithm was developed using 261 training images and tested on another 290 cases. The segmentation performance using mean DSC ranging from 0.89 to 0.91 depending on the evaluation subset demonstrates state of the art performance. Running time for the system is about 20 to 40 seconds depending on image size and resolution.

  12. 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

  13. 3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images.

    Science.gov (United States)

    Li, Xiaomeng; Dou, Qi; Chen, Hao; Fu, Chi-Wing; Qi, Xiaojuan; Belavý, Daniel L; Armbrecht, Gabriele; Felsenberg, Dieter; Zheng, Guoyan; Heng, Pheng-Ann

    2018-04-01

    Intervertebral discs (IVDs) are small joints that lie between adjacent vertebrae. The localization and segmentation of IVDs are important for spine disease diagnosis and measurement quantification. However, manual annotation is time-consuming and error-prone with limited reproducibility, particularly for volumetric data. In this work, our goal is to develop an automatic and accurate method based on fully convolutional networks (FCN) for the localization and segmentation of IVDs from multi-modality 3D MR data. Compared with single modality data, multi-modality MR images provide complementary contextual information, which contributes to better recognition performance. However, how to effectively integrate such multi-modality information to generate accurate segmentation results remains to be further explored. In this paper, we present a novel multi-scale and modality dropout learning framework to locate and segment IVDs from four-modality MR images. First, we design a 3D multi-scale context fully convolutional network, which processes the input data in multiple scales of context and then merges the high-level features to enhance the representation capability of the network for handling the scale variation of anatomical structures. Second, to harness the complementary information from different modalities, we present a random modality voxel dropout strategy which alleviates the co-adaption issue and increases the discriminative capability of the network. Our method achieved the 1st place in the MICCAI challenge on automatic localization and segmentation of IVDs from multi-modality MR images, with a mean segmentation Dice coefficient of 91.2% and a mean localization error of 0.62 mm. We further conduct extensive experiments on the extended dataset to validate our method. We demonstrate that the proposed modality dropout strategy with multi-modality images as contextual information improved the segmentation accuracy significantly. Furthermore, experiments conducted on

  14. 3D shape extraction segmentation and representation of soil microstructures using generalized cylinders

    Science.gov (United States)

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

    2012-02-01

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

  15. An optimisation design of adaptive illumination for a multi-reflective 3D scene

    Science.gov (United States)

    Lyu, Chengang; Gao, Shuang; Yang, Jiachen

    2017-06-01

    An illumination optimisation technique applied to multi-reflective 3-D machine vision based on a projector-camera system is introduced, in which the projector plays a key role to compensate for surface reflectance at each pixel to be inversely proportional to the brightness of the pixel under ambient light. The adaptive illumination technology was achieved by iterations emphasising different illumination intensities according to different surface orientations and requiring an accurate correspondence between the projector pixels and the camera pixels. In order to establish the most effective correspondence to prepare for subsequent adaptive illumination, 4 kinds of grating patterns, including sinusoidal, rectangular, triangular, and dual-frequency sinusoidal grating patterns, were projected and compared. The iterations were halted when an optimally lit scene was obtained; the further experiments under weak and strong light searched for the best method of illumination optimisation and confirmed the reliability of the adaptive illumination. The proposed optimisation design could run in real time and became a viable solution for industry.

  16. 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.

  17. 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.

  18. 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.

  19. User Modeling Approaches towards Adaptation of Users’ Roles to Improve Group Interaction in Collaborative 3D Games

    OpenAIRE

    OCTAVIA, Johanna; BEZNOSYK, Anastasiia; CONINX, Karin; QUAX, Peter; LUYTEN, Kris

    2011-01-01

    This paper focuses on how adaptation of users' roles based on a collaborative user model can improve group interaction in collaborative 3D games. We aim to provide adaptation for users based on their individual performance and preferences while collaborating in a 3D puzzle game. Four different user modeling approaches are considered to build collaborative user models. Through an experiment, we present the validation of these approaches for two different cases: co-located collaboration and rem...

  20. 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.

  1. Model-based adaptive 3D sonar reconstruction in reverberating environments.

    Science.gov (United States)

    Saucan, Augustin-Alexandru; Sintes, Christophe; Chonavel, Thierry; Caillec, Jean-Marc Le

    2015-10-01

    In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.

  2. 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

  3. A new 3-D jerk chaotic system with two cubic nonlinearities and its adaptive backstepping control

    Directory of Open Access Journals (Sweden)

    Vaidyanathan Sundarapandian

    2017-09-01

    Full Text Available This paper presents a new seven-term 3-D jerk chaotic system with two cubic 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 a unique equilibrium at the origin, which is a saddle-focus and unstable. The Lyapunov exponents of the novel jerk chaotic system are obtained as L1 = 0:2974, L2 = 0 and L3 = −3:8974. 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 new jerk chaotic system is found as DKY = 2:0763. Next, an adaptive backstepping controller is designed to globally stabilize the new jerk chaotic system with unknown parameters. Moreover, an adaptive backstepping controller is also designed 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 are shown to illustrate all the main results derived in this work.

  4. Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling.

    Science.gov (United States)

    Pouch, A M; Wang, H; Takabe, M; Jackson, B M; Gorman, J H; Gorman, R C; Yushkevich, P A; Sehgal, C M

    2014-01-01

    Comprehensive visual and quantitative analysis of in vivo human mitral valve morphology is central to the diagnosis and surgical treatment of mitral valve disease. Real-time 3D transesophageal echocardiography (3D TEE) is a practical, highly informative imaging modality for examining the mitral valve in a clinical setting. To facilitate visual and quantitative 3D TEE image analysis, we describe a fully automated method for segmenting the mitral leaflets in 3D TEE image data. The algorithm integrates complementary probabilistic segmentation and shape modeling techniques (multi-atlas joint label fusion and deformable modeling with continuous medial representation) to automatically generate 3D geometric models of the mitral leaflets from 3D TEE image data. These models are unique in that they establish a shape-based coordinate system on the valves of different subjects and represent the leaflets volumetrically, as structures with locally varying thickness. In this work, expert image analysis is the gold standard for evaluating automatic segmentation. Without any user interaction, we demonstrate that the automatic segmentation method accurately captures patient-specific leaflet geometry at both systole and diastole in 3D TEE data acquired from a mixed population of subjects with normal valve morphology and mitral valve disease. Copyright © 2013 Elsevier B.V. All rights reserved.

  5. Automatic left-atrial segmentation from cardiac 3D ultrasound: a dual-chamber model-based approach

    Science.gov (United States)

    Almeida, Nuno; Sarvari, Sebastian I.; Orderud, Fredrik; Gérard, Olivier; D'hooge, Jan; Samset, Eigil

    2016-04-01

    In this paper, we present an automatic solution for segmentation and quantification of the left atrium (LA) from 3D cardiac ultrasound. A model-based framework is applied, making use of (deformable) active surfaces to model the endocardial surfaces of cardiac chambers, allowing incorporation of a priori anatomical information in a simple fashion. A dual-chamber model (LA and left ventricle) is used to detect and track the atrio-ventricular (AV) plane, without any user input. Both chambers are represented by parametric surfaces and a Kalman filter is used to fit the model to the position of the endocardial walls detected in the image, providing accurate detection and tracking during the whole cardiac cycle. This framework was tested in 20 transthoracic cardiac ultrasound volumetric recordings of healthy volunteers, and evaluated using manual traces of a clinical expert as a reference. The 3D meshes obtained with the automatic method were close to the reference contours at all cardiac phases (mean distance of 0.03+/-0.6 mm). The AV plane was detected with an accuracy of -0.6+/-1.0 mm. The LA volumes assessed automatically were also in agreement with the reference (mean +/-1.96 SD): 0.4+/-5.3 ml, 2.1+/-12.6 ml, and 1.5+/-7.8 ml at end-diastolic, end-systolic and pre-atrial-contraction frames, respectively. This study shows that the proposed method can be used for automatic volumetric assessment of the LA, considerably reducing the analysis time and effort when compared to manual analysis.

  6. 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.

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

    Science.gov (United States)

    Byrne, N; Velasco Forte, M; Tandon, A; Valverde, I; Hussain, T

    2016-01-01

    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. A systematic review of literature was undertaken. Medical imaging modality, segmentation methods, segmentation time, segmentation descriptive quality (SDQ) and segmentation software were recorded. 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. 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.

  8. 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.

  9. 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/.

  10. 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

  11. 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

  12. 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

  13. 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

  14. Image-guided depth propagation for 2-D-to-3-D video conversion using superpixel matching and adaptive autoregressive model

    Science.gov (United States)

    Cai, Jiji; Jung, Cheolkon

    2017-09-01

    We propose image-guided depth propagation for two-dimensional (2-D)-to-three-dimensional (3-D) video conversion using superpixel matching and the adaptive autoregressive (AR) model. We adopt key frame-based depth propagation that propagates the depth map in the key frame to nonkey frames. Moreover, we use the adaptive AR model for depth refinement to penalize depth-color inconsistency. First, we perform superpixel matching to estimate motion vectors at the superpixel level instead of block matching based on the fixed block size. Then, we conduct depth compensation based on motion vectors to generate the depth map in the nonkey frame. However, the size of two superpixels is not exactly the same due to the segment-based matching, which causes matching errors in the compensated depth map. Thus, we introduce an adaptive image-guided AR model to minimize matching errors and produce the final depth map by minimizing AR prediction errors. Finally, we employ depth-image-based rendering to generate stereoscopic views from 2-D videos and their depth maps. Experimental results demonstrate that the proposed method successfully performs depth propagation and produces high-quality depth maps for 2-D-to-3-D video conversion.

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

    Science.gov (United States)

    Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

    2018-03-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-06-15

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

  17. Adaptive 3D Virtual Learning Environments--A Review of the Literature

    Science.gov (United States)

    Scott, Ezequiel; Soria, Alvaro; Campo, Marcelo

    2017-01-01

    New ways of learning have emerged in the last years by using computers in education. For instance, many Virtual Learning Environments have been widely adopted by educators, obtaining promising outcomes. Recently, these environments have evolved into more advanced ones using 3D technologies and taking into account the individual learner needs and…

  18. 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.

  19. 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.

  20. 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.

  1. Active shape models exploiting slice-to-slice correlation in segmentation of 3D CTA AAA images

    NARCIS (Netherlands)

    Bruijne, M. de; Ginneken, B. van; Niessen, W.J.; Maintz, J.B.A.; Viergever, M.A.

    2001-01-01

    An automated method for the segmentation of thrombus in abdominal aortic aneurysms (AAA) from CTA data is presented. Three segmentation schemes, inspired by Active Shape Model (ASM) segmentation, were investigated. (1) The original ASM scheme as proposed by Cootes and Taylor [1], applied to

  2. 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

    2018-01-30

    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. 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.

  3. 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.

  4. Gd-EOB enhanced MRI T1-weighted 3D-GRE with and without elevated flip angle modulation for threshold-based liver volume segmentation.

    Science.gov (United States)

    Grieser, Christian; Denecke, Timm; Rothe, Jan-Holger; Geisel, Dominik; Stelter, Lars; Cannon Walter, Thula; Seehofer, Daniel; Steffen, Ingo G

    2015-12-01

    Despite novel software solutions, liver volume segmentation is still a time-consuming procedure and often requires further manual optimization. With the high signal intensity of the liver parenchyma in Gd-EOB enhanced magnetic resonance imaging (MRI), liver volume segmentation may be improved. To evaluate the practicability of threshold-based segmentation of the liver volume using Gd-EOB-enhanced MRI including a customized three-dimensional (3D) sequence. A total of 20 patients examined with Gd-EOB MRI (hepatobiliary phase T1-weighted (T1W) 3D sequence [VIBE]; flip angle [FA], 10° and 30°) were enrolled in this retrospective study. The datasets were independently processed by two blinded observers (O1 and O2) in two ways: manual (man) and threshold-based (thresh; study method) segmentation of the liver each followed by an optimization step (man+opt and thresh+opt; man+opt [FA10°] served as reference method). Resulting liver volumes and segmentation times were compared. A liver conversion factor was calculated in percent, describing the non-hepatocellular fraction of the total liver volume, i.e. bile ducts and vessels. Thresh+opt (FA10°) was significantly faster compared to the reference method leading to a median volume overestimation of 4%/8% (P segmentation was even faster (P  0.2). The liver conversion factor was found to be 10%. Threshold-based liver segmentation employing Gd-EOB-enhanced hepatobiliary phase standard T1W 3D sequence is accurate and time-saving. The performance of this approach can be further improved by increasing the FA. © The Foundation Acta Radiologica 2014.

  5. 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

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

    DEFF Research Database (Denmark)

    Jensen, Kristian Ejlebjerg

    2017-01-01

    We present a pure MATLAB implementation for solving 2D/3D compliance minimization problems using the density method. A filtered design variable with a minimum length is computed using a Helmholtz-type differential equation. The optimality criteria is used as optimizer and to avoid local minima we...... the implementation totals some 5,000 lines of MAT-LAB code, but the functions associated with the forward analysis, geometry/mesh setup and optimization are concise and well documented, so the implementation can be used as a starting point for research on related topics....

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

    Science.gov (United States)

    Fernandes, Rui; Andrade, M. T.

    2017-07-01

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

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

    Directory of Open Access Journals (Sweden)

    S. Vaidyanathan

    2014-11-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  10. 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.

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-06-15

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

  13. 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.

  14. 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 (Patresia group is larger (P<0.05). The shapes of the sinus tympani were classified into three categories: the cup-shaped, the pear-shaped and the boot-shaped. Area measurement indicated that the boot-shaped sinus tympani was a special variation with a large area, which only appears in CAA group. There were a significant difference between the area of the boot-shaped group and the other two groups (P<0.05). The morphologic differences of ST and other middle ear structures can also be observed visually in 3D VR 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.

  15. Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D)

    OpenAIRE

    Yu, Shaode; Wu, Shibin; Zhuang, Ling; Wei, Xinhua; Sak, Mark; Neb, Duric; Hu, Jiani; Xie, Yaoqin

    2017-01-01

    As an emerging modality for whole breast imaging, ultrasound tomography (UST), has been adopted for diagnostic purposes. Efficient segmentation of an entire breast in UST images plays an important role in quantitative tissue analysis and cancer diagnosis, while major existing methods suffer from considerable time consumption and intensive user interaction. This paper explores three-dimensional GrabCut (GC3D) for breast isolation in thirty reflection (B-mode) UST volumetric images. The algorit...

  16. 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

  17. 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...

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

  20. IFCPT S-Duct Grid-Adapted FUN3D Computations for the Third Propulsion Aerodynamics Works

    Science.gov (United States)

    Davis, Zach S.; Park, M. A.

    2017-01-01

    Contributions of the unstructured Reynolds-averaged Navier-Stokes code, FUN3D, to the 3rd AIAA Propulsion Aerodynamics Workshop are described for the diffusing IFCPT S-Duct. Using workshop-supplied grids, results for the baseline S-Duct, baseline S-Duct with Aerodynamic Interface Plane (AIP) rake hardware, and baseline S-Duct with flow control devices are compared with experimental data and results computed with output-based, off-body grid adaptation in FUN3D. Due to the absence of influential geometry components, total pressure recovery is overpredicted on the baseline S-Duct and S-Duct with flow control vanes when compared to experimental values. An estimate for the exact value of total pressure recovery is derived for these cases given an infinitely refined mesh. When results from output-based mesh adaptation are compared with those computed on workshop-supplied grids, a considerable improvement in predicting total pressure recovery is observed. By including more representative geometry, output-based mesh adaptation compares very favorably with experimental data in terms of predicting the total pressure recovery cost-function; whereas, results computed using the workshop-supplied grids are underpredicted.

  1. 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.

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

    Science.gov (United States)

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

    2017-11-24

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

  3. Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method.

    Directory of Open Access Journals (Sweden)

    Chengwen Chu

    Full Text Available In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1 3D T2-weighted spine MR images from 23 patients and 2 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5, we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.

  4. Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method.

    Science.gov (United States)

    Chu, Chengwen; Belavý, Daniel L; Armbrecht, Gabriele; Bansmann, Martin; Felsenberg, Dieter; Zheng, Guoyan

    2015-01-01

    In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.

  5. 3D Adaptive Mesh Refinement Simulations of Pellet Injection in Tokamaks

    International Nuclear Information System (INIS)

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

    2003-01-01

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

  6. 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

  7. [Tumor segmentation of brain MRI with adaptive bandwidth mean shift].

    Science.gov (United States)

    Hou, Xiaowen; Liu, Qi

    2014-10-01

    In order to get the adaptive bandwidth of mean shift to make the tumor segmentation of brain magnetic resonance imaging (MRI) to be more accurate, we in this paper present an advanced mean shift method. Firstly, we made use of the space characteristics of brain image to eliminate the impact on segmentation of skull; and then, based on the characteristics of spatial agglomeration of different tissues of brain (includes tumor), we applied edge points to get the optimal initial mean value and the respectively adaptive bandwidth, in order to improve the accuracy of tumor segmentation. The results of experiment showed that, contrast to the fixed bandwidth mean shift method, the method in this paper could segment the tumor more accurately.

  8. 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.

  9. 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.

  10. A self-tuning adaptive controller for 3-D image-guided ultrasound cancer therapy.

    Science.gov (United States)

    Goharrizi, Amin Yazdanpanah; Kwong, Raymond H; Chopra, Rajiv

    2014-03-01

    One of the challenges in MRI-controlled hyperthermia cancer treatment for localized tumor is that the tissue properties are dynamic and difficult to measure. Therefore, tuning the optimal gains for a constant gain controller can be challenging. In this paper, a new multi-input single-output adaptive controller strategy is proposed to address these problems. The inputs to the controller block are the frequency, rotation rate, and applied power level of an interstitial applicator, and the output is the boundary temperature during treatment. The time-varying gains of the new controller are updated over time using Lyapunov-based stability analysis. The robustness of the new controller to changes in the parameters of the tissue is investigated and compared to a constant gain controller through simulation studies. Simulations take into account changes in tissue properties and other conditions that may be encountered in a practical clinical situation. Finally, the effectiveness of the proposed controller is validated through an experimental study.

  11. A convolution-adapted ratio-TAR algorithm for 3D photon beam treatment planning.

    Science.gov (United States)

    Zhu, X R; Low, D A; Harms, W B; Purdy, J A

    1995-08-01

    A convolution-adapted ratio of tissue-air ratios (CARTAR) method of dose calculation has been developed at the Mallinckrodt Institute of Radiology. This photon pencil-beam algorithm has been developed and implemented specifically for three-dimensional treatment planning. In a standard ratio of tissue-air ratios (RTAR) algorithm, doses to points in irregular field geometries are not adequately modeled. This is inconsistent with the advent of conformal therapy, the goal of which is to conform the dose distribution to the target volume while sparing neighboring sensitive normal critical structures. This motivated us to develop an algorithm that can model the beam penumbra near irregular field edges, while retaining much of the speed for the original RTAR algorithm. The dose calculation algorithm uses two-dimensional (2D) convolutions, computed by 2D fast Fourier transform, of pencil-beam kernels with a beam transmission array to calculate 2D off-axis profiles at a series of depths. These profiles are used to replace the product of the transmission function and measured square-field boundary factors used in the standard RTAR calculation. The 2D pencil-beam kernels were derived from measured data for each modality using commonly available dosimetry equipment. The CARTAR algorithm is capable of modeling the penumbra near block edges as well as the loss of primary and scattered beam in partially blocked regions. This paper describes the dose calculation algorithm, implementation, and verification.

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

    Science.gov (United States)

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

    2014-03-01

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

  13. 3D thermal effect of late Cenozoic erosion and deposition within the Lofoten-Vesterålen segment of the Mid-Norwegian continental margin

    Science.gov (United States)

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

    2018-01-01

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

  14. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features.

    Science.gov (United States)

    Zheng, Yefeng; Barbu, Adrian; Georgescu, Bogdan; Scheuering, Michael; Comaniciu, Dorin

    2008-11-01

    We propose an automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics are discussed: heart modeling and automatic model fitting to an unseen volume. Heart modeling is a nontrivial task since the heart is a complex nonrigid organ. The model must be anatomically accurate, allow manual editing, and provide sufficient information to guide automatic detection and segmentation. Unlike previous work, we explicitly represent important landmarks (such as the valves and the ventricular septum cusps) among the control points of the model. The control points can be detected reliably to guide the automatic model fitting process. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3-D CT volumes. We formulate the segmentation as a two-step learning problem: anatomical structure localization and boundary delineation. In both steps, we exploit the recent advances in learning discriminative models. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-D similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3-D shape through learning-based boundary delineation. The proposed method has been extensively tested on the largest dataset (with 323 volumes from 137 patients) ever reported in the literature. To the best of our knowledge, our system is the fastest with a speed of 4.0 s per volume (on a dual-core 3.2-GHz processor) for the automatic segmentation of all four chambers.

  15. 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...

  16. 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.

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

    Science.gov (United States)

    2015-01-05

    fully-automatic method to detect cracks from pavement images, that can be used for pavement road maintenance. The developed method consists of three...steps: 1) A geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks ; 2) building a crack probability map to enhance... cracks . Cracktree was evaluated on real pavement images and it achieves better performance than existing methods. 1 Multi-label Segmentation Propagation

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  19. 3D multi-slab diffusion-weighted readout-segmented EPI with real-time cardiac-reordered K-space acquisition.

    Science.gov (United States)

    Frost, Robert; Miller, Karla L; Tijssen, Rob H N; Porter, David A; Jezzard, Peter

    2014-12-01

    The aim of this study was to develop, implement, and demonstrate a three-dimensional (3D) extension of the readout-segmented echo-planar imaging (rs-EPI) sequence for diffusion imaging. Potential k-space acquisition schemes were assessed by simulating their associated spatial point spread functions. Motion-induced phase artifacts were also simulated to test navigator corrections and a real-time reordering of the k-space acquisition relative to the cardiac cycle. The cardiac reordering strategy preferentially chooses readout segments closer to the center of 3D k-space during diastole. Motion-induced phase artifacts were quantified by calculating the voxel-wise temporal variation in a set of repeated diffusion-weighted acquisitions. Based on the results of these simulations, a 2D navigated multi-slab rs-EPI sequence with real-time cardiac reordering was implemented. The multi-slab implementation enables signal-to-noise ratio-optimal repetition times of 1-2 s. Cardiac reordering was validated in simulations and in vivo using the multi-slab rs-EPI sequence. In comparisons with standard k-space acquisitions, cardiac reordering was shown to reduce the variability due to motion-induced phase artifacts by 30-50%. High-resolution diffusion tensor imaging data acquired with the cardiac-reordered multi-slab rs-EPI sequence are presented. A 3D multi-slab rs-EPI sequence with cardiac reordering has been demonstrated in vivo and is shown to provide high-quality 3D diffusion-weighted data sets. © 2013 Wiley Periodicals, Inc.

  20. Classification and segmentation of orbital space based objects against terrestrial distractors for the purpose of finding holes in shape from motion 3D reconstruction

    Science.gov (United States)

    Mundhenk, T. Nathan; Flores, Arturo; Hoffman, Heiko

    2013-12-01

    3D reconstruction of objects via Shape from Motion (SFM) has made great strides recently. Utilizing images from a variety of poses, objects can be reconstructed in 3D without knowing a priori the camera pose. These feature points can then be bundled together to create large scale scene reconstructions automatically. A shortcoming of current methods of SFM reconstruction is in dealing with specular or flat low feature surfaces. The inability of SFM to handle these places creates holes in a 3D reconstruction. This can cause problems when the 3D reconstruction is used for proximity detection and collision avoidance by a space vehicle working around another space vehicle. As such, we would like the automatic ability to recognize when a hole in a 3D reconstruction is in fact not a hole, but is a place where reconstruction has failed. Once we know about such a location, methods can be used to try to either more vigorously fill in that region or to instruct a space vehicle to proceed with more caution around that area. Detecting such areas in earth orbiting objects is non-trivial since we need to parse out complex vehicle features from complex earth features, particularly when the observing vehicle is overhead the target vehicle. To do this, we have created a Space Object Classifier and Segmenter (SOCS) hole finder. The general principle we use is to classify image features into three categories (earth, man-made, space). Classified regions are then clustered into probabilistic regions which can then be segmented out. Our categorization method uses an augmentation of a state of the art bag of visual words method for object categorization. This method works by first extracting PHOW (dense SIFT like) features which are computed over an image and then quantized via KD Tree. The quantization results are then binned into histograms and results classified by the PEGASOS support vector machine solver. This gives a probability that a patch in the image corresponds to one of three

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

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

    2018-04-15

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

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

    Science.gov (United States)

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

    2015-07-15

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

  4. 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.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Ying Chen

    2018-03-01

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

  7. 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.

  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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  9. A minimally interactive method to segment enlarged lymph nodes in 3D thoracic CT images using a rotatable spiral-scanning technique

    Science.gov (United States)

    Wang, Lei; Moltz, Jan H.; Bornemann, Lars; Hahn, Horst K.

    2012-03-01

    Precise size measurement of enlarged lymph nodes is a significant indicator for diagnosing malignancy, follow-up and therapy monitoring of cancer diseases. The presence of diverse sizes and shapes, inhomogeneous enhancement and the adjacency to neighboring structures with similar intensities, make the segmentation task challenging. We present a semi-automatic approach requiring minimal user interactions to fast and robustly segment the enlarged lymph nodes. First, a stroke approximating the largest diameter of a specific lymph node is drawn manually from which a volume of interest (VOI) is determined. Second, Based on the statistical analysis of the intensities on the dilated stroke area, a region growing procedure is utilized within the VOI to create an initial segmentation of the target lymph node. Third, a rotatable spiral-scanning technique is proposed to resample the 3D boundary surface of the lymph node to a 2D boundary contour in a transformed polar image. The boundary contour is found by seeking the optimal path in 2D polar image with dynamic programming algorithm and eventually transformed back to 3D. Ultimately, the boundary surface of the lymph node is determined using an interpolation scheme followed by post-processing steps. To test the robustness and efficiency of our method, a quantitative evaluation was conducted with a dataset of 315 lymph nodes acquired from 79 patients with lymphoma and melanoma. Compared to the reference segmentations, an average Dice coefficient of 0.88 with a standard deviation of 0.08, and an average absolute surface distance of 0.54mm with a standard deviation of 0.48mm, were achieved.

  10. 3D MULTI-MODAL SPATIAL FUZZY SEGMENTATION OF INTRA-MUSCULAR CONNECTIVE AND ADIPOSE TISSUE FROM ULTRA-SHORT TE MR IMAGES OF CALF MUSCLE

    Science.gov (United States)

    Ugarte, Vincent; Sinha, Usha; Malis, Vadim; Csapo, Robert; Sinha, Shantanu

    2016-01-01

    Purpose To develop and evaluate an automated algorithm to segment intramuscular adipose (IMAT) and connective (IMCT) tissue from musculoskeletal MRI images acquired with a dual echo Ultrashort TE (UTE) sequence. Theory and Methods The dual echo images and calculated structure tensor images are the inputs to the multichannel fuzzy cluster mean (MCFCM) algorithm. Modifications to the basic MCFCM include an adaptive spatial term and bias shading correction. The algorithm was tested on digital phantoms simulating IMAT/IMCT tissue under varying conditions of image noise and bias and on ten subjects with varying amounts of IMAT/IMCT. Results The MCFCM including the adaptive spatial term and bias shading correction performed better than the original MCFCM and adaptive spatial MCFCM algorithms. IMAT/IMCT was segmented from the unsmoothed simulated phantom data with a mean Dice coefficient of 0.933 ± 0.001 when contrast-to-noise (CNR) was 140 and bias was varied between 30% and 65%. The algorithm yielded accurate in-vivo segmentations of IMAT/IMCT with a mean Dice coefficient of 0.977 ± 0.066. Conclusion The proposed algorithm is completely automated and yielded accurate segmentation of intramuscular adipose and connective tissue in the digital phantom and in human calf data. PMID:26892499

  11. 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.

  12. Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN

    Science.gov (United States)

    Zhou, Xiangrong; Kano, Takuya; Koyasu, Hiromi; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Matsuo, Masayuki; Fujita, Hiroshi

    2017-03-01

    This paper describes a novel approach for the automatic assessment of breast density in non-contrast three-dimensional computed tomography (3D CT) images. The proposed approach trains and uses a deep convolutional neural network (CNN) from scratch to classify breast tissue density directly from CT images without segmenting the anatomical structures, which creates a bottleneck in conventional approaches. Our scheme determines breast density in a 3D breast region by decomposing the 3D region into several radial 2D-sections from the nipple, and measuring the distribution of breast tissue densities on each 2D section from different orientations. The whole scheme is designed as a compact network without the need for post-processing and provides high robustness and computational efficiency in clinical settings. We applied this scheme to a dataset of 463 non-contrast CT scans obtained from 30- to 45-year-old-women in Japan. The density of breast tissue in each CT scan was assigned to one of four categories (glandular tissue within the breast 75%) by a radiologist as ground truth. We used 405 CT scans for training a deep CNN and the remaining 58 CT scans for testing the performance. The experimental results demonstrated that the findings of the proposed approach and those of the radiologist were the same in 72% of the CT scans among the training samples and 76% among the testing samples. These results demonstrate the potential use of deep CNN for assessing breast tissue density in non-contrast 3D CT images.

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

    OpenAIRE

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

    2016-01-01

    This work aims to employ fused deposition modelling 3D printing to fabricate immediate release pharmaceutical tablets with various 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, and iii) polymer: filler ratio on the 3D printing process. A specially developed filament based on pharmaceutically approved methacrylic poly...

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

    Science.gov (United States)

    Liu, Guoying; Zhang, Yun; Wang, Aimin

    2015-11-01

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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-11-01

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

  17. 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

  18. 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

    International Nuclear Information System (INIS)

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

    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

  19. 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.

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

    Science.gov (United States)

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

    2017-09-01

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

  1. 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

  2. Development of an Amendment to X3D to Create a Standard Specification of Medical Image Volume Rendering, Segmentation, and Registration

    National Research Council Canada - National Science Library

    Ressler, Sandy; Aratow, Mike

    2006-01-01

    ...) medical imaging data. Extensible 3D (X3D) is an International Standards Organization (ISO) ratified, freely available standard that defines a runtime system and delivery mechanism for 3D graphics on the World Wide Web...

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

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

    Science.gov (United States)

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

    2017-08-07

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

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  11. 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

  12. 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.

  13. 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

  14. 3D-printed adaptive acoustic lens as a disruptive technology for transcranial ultrasound therapy using single-element transducers

    Science.gov (United States)

    Maimbourg, Guillaume; Houdouin, Alexandre; Deffieux, Thomas; Tanter, Mickael; Aubry, Jean-François

    2018-01-01

    The development of multi-element arrays for better control of the shape of ultrasonic beams has opened the way for focusing through highly aberrating media, such as the human skull. As a result, the use of brain therapy with transcranial-focused ultrasound has rapidly grown. Although effective, such technology is expensive. We propose a disruptive, low-cost approach that consists of focusing a 1 MHz ultrasound beam through a human skull with a single-element transducer coupled with a tailored silicone acoustic lens cast in a 3D-printed mold and designed using computed tomography-based numerical acoustic simulation. We demonstrate on N  =  3 human skulls that adding lens-based aberration correction to a single-element transducer increases the deposited energy on the target 10 fold.

  15. 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.

  16. 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.

  17. An improved adaptive genetic algorithm for image segmentation and vision alignment used in microelectronic bonding

    OpenAIRE

    Wang, Fujun; Li, Junlan; Liu, Shiwei; Zhao, Xingyu; Zhang, Dawei; Tian, Yanling

    2014-01-01

    In order to improve the precision and efficiency of microelectronic bonding, this paper presents an improved adaptive genetic algorithm (IAGA) for the image segmentation and vision alignment of the solder joints in the microelectronic chips. The maximum between-cluster variance (OTSU) threshold segmentation method was adopted for the image segmentation of microchips, and the IAGA was introduced to the threshold segmentation considering the features of the images. The performance of the image ...

  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. 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.

  20. 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.

  1. Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

    Science.gov (United States)

    Yan, Liang; Zhang, Lu; Zhu, Bo; Zhang, Jingying; Jiao, Zongxia

    2017-10-01

    Permanent magnet spherical actuator (PMSA) is a multi-variable featured and inter-axis coupled nonlinear system, which unavoidably compromises its motion control implementation. Uncertainties such as external load and friction torque of ball bearing and manufacturing errors also influence motion performance significantly. Therefore, the objective of this paper is to propose a controller based on a single neural adaptive (SNA) algorithm and a neural network (NN) identifier optimized with a particle swarm optimization (PSO) algorithm to improve the motion stability of PMSA with three-dimensional magnet arrays. The dynamic model and computed torque model are formulated for the spherical actuator, and a dynamic decoupling control algorithm is developed. By utilizing the global-optimization property of the PSO algorithm, the NN identifier is trained to avoid locally optimal solution and achieve high-precision compensations to uncertainties. The employment of the SNA controller helps to reduce the effect of compensation errors and convert the system to a stable one, even if there is difference between the compensations and uncertainties due to external disturbances. A simulation model is established, and experiments are conducted on the research prototype to validate the proposed control algorithm. The amplitude of the parameter perturbation is set to 5%, 10%, and 15%, respectively. The strong robustness of the proposed hybrid algorithm is validated by the abundant simulation data. It shows that the proposed algorithm can effectively compensate the influence of uncertainties and eliminate the effect of inter-axis couplings of the spherical actuator.

  2. EEG alpha map series: brain micro-states by space-oriented adaptive segmentation.

    Science.gov (United States)

    Lehmann, D; Ozaki, H; Pal, I

    1987-09-01

    The spontaneous EEG, viewed as a series of momentary scalp field maps, shows stable map configurations (of periodically reversed polarity) for varying durations, and discontinuous changes of the configurations. For adaptive segmentation of map series into spatially stationary epochs, the maps at the times of maximal map relief are selected and spatially described by the two locations of maximal and minimal (extreme) potentials; a segment ends if over time an extreme leaves its pre-set spatial window. Over 6 subjects, the resting alpha EEG showed 210 msec mean segment duration; segments longer than 323 msec covered 50% of the total time; the most prominent segment class (1.5% of all classes) covered 20% of total time (prominence varied strongly over classes; not all possible classes occurred). Spectral power and phase of averages of adaptive and pre-determined segments demonstrated the adequacy of the strategy, and the homogeneity of adaptive segment classes by their reduced within-class variance. It is suggested that different segment classes manifest different brain functional states exerting different effects on information processing. The spatially stationary segments might be basic building blocks of brain information processing, possibly operationalizing consciousness time and offering a common phenomenology for spontaneous activity and event-related potentials. The functional significance of segments might be modes or steps of information processing or performance, tested, e.g., as reaction time.

  3. A fully consistent and conservative vertically adaptive coordinate system for SLIM 3D v0.4 with an application to the thermocline oscillations of Lake Tanganyika

    Science.gov (United States)

    Delandmeter, Philippe; Lambrechts, Jonathan; Legat, Vincent; Vallaeys, Valentin; Naithani, Jaya; Thiery, Wim; Remacle, Jean-François; Deleersnijder, Eric

    2018-03-01

    The discontinuous Galerkin (DG) finite element method is well suited for the modelling, with a relatively small number of elements, of three-dimensional flows exhibiting strong velocity or density gradients. Its performance can be highly enhanced by having recourse to r-adaptivity. Here, a vertical adaptive mesh method is developed for DG finite elements. This method, originally designed for finite difference schemes, is based on the vertical diffusion of the mesh nodes, with the diffusivity controlled by the density jumps at the mesh element interfaces. The mesh vertical movement is determined by means of a conservative arbitrary Lagrangian-Eulerian (ALE) formulation. Though conservativity is naturally achieved, tracer consistency is obtained by a suitable construction of the mesh vertical velocity field, which is defined in such a way that it is fully compatible with the tracer and continuity equations at a discrete level. The vertically adaptive mesh approach is implemented in the three-dimensional version of the geophysical and environmental flow Second-generation Louvain-la-Neuve Ice-ocean Model (SLIM 3D; www.climate.be/slim). Idealised benchmarks, aimed at simulating the oscillations of a sharp thermocline, are dealt with. Then, the relevance of the vertical adaptivity technique is assessed by simulating thermocline oscillations of Lake Tanganyika. The results are compared to measured vertical profiles of temperature, showing similar stratification and outcropping events.

  4. A 3D interactive method for estimating body segmental parameters in animals: application to the turning and running performance of Tyrannosaurus rex.

    Science.gov (United States)

    Hutchinson, John R; Ng-Thow-Hing, Victor; Anderson, Frank C

    2007-06-21

    We developed a method based on interactive B-spline solids for estimating and visualizing biomechanically important parameters for animal body segments. Although the method is most useful for assessing the importance of unknowns in extinct animals, such as body contours, muscle bulk, or inertial parameters, it is also useful for non-invasive measurement of segmental dimensions in extant animals. Points measured directly from bodies or skeletons are digitized and visualized on a computer, and then a B-spline solid is fitted to enclose these points, allowing quantification of segment dimensions. The method is computationally fast enough so that software implementations can interactively deform the shape of body segments (by warping the solid) or adjust the shape quantitatively (e.g., expanding the solid boundary by some percentage or a specific distance beyond measured skeletal coordinates). As the shape changes, the resulting changes in segment mass, center of mass (CM), and moments of inertia can be recomputed immediately. Volumes of reduced or increased density can be embedded to represent lungs, bones, or other structures within the body. The method was validated by reconstructing an ostrich body from a fleshed and defleshed carcass and comparing the estimated dimensions to empirically measured values from the original carcass. We then used the method to calculate the segmental masses, centers of mass, and moments of inertia for an adult Tyrannosaurus rex, with measurements taken directly from a complete skeleton. We compare these results to other estimates, using the model to compute the sensitivities of unknown parameter values based upon 30 different combinations of trunk, lung and air sac, and hindlimb dimensions. The conclusion that T. rex was not an exceptionally fast runner remains strongly supported by our models-the main area of ambiguity for estimating running ability seems to be estimating fascicle lengths, not body dimensions. Additionally, the

  5. 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.

  6. 3D Surgical Simulation

    Science.gov (United States)

    Cevidanes, Lucia; Tucker, Scott; Styner, Martin; Kim, Hyungmin; Chapuis, Jonas; Reyes, Mauricio; Proffit, William; Turvey, Timothy; Jaskolka, Michael

    2009-01-01

    This paper discusses the development of methods for computer-aided jaw surgery. Computer-aided jaw surgery allows us to incorporate the high level of precision necessary for transferring virtual plans into the operating room. We also present a complete computer-aided surgery (CAS) system developed in close collaboration with surgeons. Surgery planning and simulation include construction of 3D surface models from Cone-beam CT (CBCT), dynamic cephalometry, semi-automatic mirroring, interactive cutting of bone and bony segment repositioning. A virtual setup can be used to manufacture positioning splints for intra-operative guidance. The system provides further intra-operative assistance with the help of a computer display showing jaw positions and 3D positioning guides updated in real-time during the surgical procedure. The CAS system aids in dealing with complex cases with benefits for the patient, with surgical practice, and for orthodontic finishing. Advanced software tools for diagnosis and treatment planning allow preparation of detailed operative plans, osteotomy repositioning, bone reconstructions, surgical resident training and assessing the difficulties of the surgical procedures prior to the surgery. CAS has the potential to make the elaboration of the surgical plan a more flexible process, increase the level of detail and accuracy of the plan, yield higher operative precision and control, and enhance documentation of cases. Supported by NIDCR DE017727, and DE018962 PMID:20816308

  7. Interior insulation—Characterisation of the historic, solid masonrybuilding segment and analysis of the heat saving potential by 1d, 2d,and 3d simulation

    DEFF Research Database (Denmark)

    Odgaard, Tommy Riviere; Bjarløv, Søren Peter; Rode, Carsten

    2018-01-01

    When considering interior insulation of historic, multi-storey buildings with solid masonry walls, it isimportant to focus on two important factors: How big is the building segment to which it can be applied,and what is the significance of how the multi-dimensional geometry of these fac¸ ade walls......-storey apartments in Denmark. It was investigated,which relative reduction of the average thermal transmittance could be obtained by interior insulationwhen simulated in different dimensions, degrees of insulation and thickness. The analysis showed thatpartial insulation of the spandrels below windows on the 2nd/3...

  8. Hierarchical image segmentation via recursive superpixel with adaptive regularity

    Science.gov (United States)

    Nakamura, Kensuke; Hong, Byung-Woo

    2017-11-01

    A fast and accurate segmentation algorithm in a hierarchical way based on a recursive superpixel technique is presented. We propose a superpixel energy formulation in which the trade-off between data fidelity and regularization is dynamically determined based on the local residual in the energy optimization procedure. We also present an energy optimization algorithm that allows a pixel to be shared by multiple regions to improve the accuracy and appropriate the number of segments. The qualitative and quantitative evaluations demonstrate that our algorithm, combining the proposed energy and optimization, outperforms the conventional k-means algorithm by up to 29.10% in F-measure. We also perform comparative analysis with state-of-the-art algorithms in the hierarchical segmentation. Our algorithm yields smooth regions throughout the hierarchy as opposed to the others that include insignificant details. Our algorithm overtakes the other algorithms in terms of balance between accuracy and computational time. Specifically, our method runs 36.48% faster than the region-merging approach, which is the fastest of the comparing algorithms, while achieving a comparable accuracy.

  9. SU-E-J-123: Assessing Segmentation Accuracy of Internal Volumes and Sub-Volumes in 4D PET/CT of Lung Tumors Using a Novel 3D Printed Phantom

    International Nuclear Information System (INIS)

    Soultan, D; Murphy, J; James, C; Hoh, C; Moiseenko, V; Cervino, L; Gill, B

    2015-01-01

    Purpose: To assess the accuracy of internal target volume (ITV) segmentation of lung tumors for treatment planning of simultaneous integrated boost (SIB) radiotherapy as seen in 4D PET/CT images, using a novel 3D-printed phantom. Methods: The insert mimics high PET tracer uptake in the core and 50% uptake in the periphery, by using a porous design at the periphery. A lung phantom with the insert was placed on a programmable moving platform. Seven breathing waveforms of ideal and patient-specific respiratory motion patterns were fed to the platform, and 4D PET/CT scans were acquired of each of them. CT images were binned into 10 phases, and PET images were binned into 5 phases following the clinical protocol. Two scenarios were investigated for segmentation: a gate 30–70 window, and no gating. The radiation oncologist contoured the outer ITV of the porous insert with on CT images, while the internal void volume with 100% uptake was contoured on PET images for being indistinguishable from the outer volume in CT images. Segmented ITVs were compared to the expected volumes based on known target size and motion. Results: 3 ideal breathing patterns, 2 regular-breathing patient waveforms, and 2 irregular-breathing patient waveforms were used for this study. 18F-FDG was used as the PET tracer. The segmented ITVs from CT closely matched the expected motion for both no gating and gate 30–70 window, with disagreement of contoured ITV with respect to the expected volume not exceeding 13%. PET contours were seen to overestimate volumes in all the cases, up to more than 40%. Conclusion: 4DPET images of a novel 3D printed phantom designed to mimic different uptake values were obtained. 4DPET contours overestimated ITV volumes in all cases, while 4DCT contours matched expected ITV volume values. Investigation of the cause and effects of the discrepancies is undergoing

  10. An adaptive quantum-behaved particle swarm optimization approach for license plate segmentation

    Science.gov (United States)

    Zhu, Rong; Zhuo, Junfei

    2009-10-01

    Automatic license plate segmentation plays an essential role in intelligent transportation systems. But it can be a challenging task when segmenting the vehicle images with poor quality in real-world applications. For segmenting license plates out of the vehicle images efficiently, a novel two-stage segmentation strategy that contains a rough localization stage and a fine segmentation stage is proposed in this paper. Firstly, during the rough localization stage, the texture characteristic of Chinese license plates is utilized to get candidate rectangle regions. The license plate region is then identified and extracted from these regions based on projection property and geometric information. In the fine segmentation stage, two enhancement algorithms are applied to improve image quality and reduce segmentation error. And then an adaptive threshold-based segmentation approach based on quantum-behaved particle swarm optimization is presented to deal with the threshold selection of distinguishing the constitution codes from the background in the obtained license plate region. The experiments of segmenting the vehicle images are illustrated to show that the proposed method can achieve an ideal segmentation result with less computational cost.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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).

  20. Extraction of fluorescent cell puncta by adaptive fuzzy segmentation.

    Science.gov (United States)

    Pham, Tuan D; Crane, Denis I; Tran, Tuan H; Nguyen, Tam H

    2004-09-22

    The discrimination and measurement of fluorescent-labeled vesicles using microscopic analysis of fixed cells presents a challenge for biologists interested in quantifying the abundance, size and distribution of such vesicles in normal and abnormal cellular situations. In the specific application reported here, we were interested in quantifying changes to the population of a major organelle, the peroxisome, in cells from normal control patients and from patients with a defect in peroxisome biogenesis. In the latter, peroxisomes are present as larger vesicular structures with a more restricted cytoplasmic distribution. Existing image processing methods for extracting fluorescent cell puncta do not provide useful results and therefore, there is a need to develop some new approaches for dealing with such a task effectively. We present an effective implementation of the fuzzy c-means algorithm for extracting puncta (spots), representing fluorescent-labeled peroxisomes, which are subject to low contrast. We make use of the quadtree partition to enhance the fuzzy c-means based segmentation and to disregard regions which contain no target objects (peroxisomes) in order to minimize considerable time taken by the iterative process of the fuzzy c-means algorithm. We finally isolate touching peroxisomes by an aspect-ratio criterion. The proposed approach has been applied to extract peroxisomes contained in several sets of color images and the results are superior to those obtained from a number of standard techniques for spot extraction. Image data and computer codes written in Matlab are available upon request from the first author.

  1. Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

    Directory of Open Access Journals (Sweden)

    Genyun Sun

    2017-08-01

    Full Text Available The region merging algorithm is a widely used segmentation technique for very high resolution (VHR remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods

  2. Adaptation of the Maracas algorithm for carotid artery segmentation and stenosis quantification on CT images

    International Nuclear Information System (INIS)

    Maria A Zuluaga; Maciej Orkisz; Edgar J F Delgado; Vincent Dore; Alfredo Morales Pinzon; Marcela Hernandez Hoyos

    2010-01-01

    This paper describes the adaptations of Maracas algorithm to the segmentation and quantification of vascular structures in CTA images of the carotid artery. The maracas algorithm, which is based on an elastic model and on a multi-scale Eigen-analysis of the inertia matrix, was originally designed to segment a single artery in MRA images. The modifications are primarily aimed at addressing the specificities of CT images and the bifurcations. The algorithms implemented in this new version are classified into two levels. 1. The low-level processing (filtering of noise and directional artifacts, enhancement and pre-segmentation) to improve the quality of the image and to pre-segment it. These techniques are based on a priori information about noise, artifacts and typical gray levels ranges of lumen, background and calcifications. 2. The high-level processing to extract the centerline of the artery, to segment the lumen and to quantify the stenosis. At this level, we apply a priori knowledge of shape and anatomy of vascular structures. The method was evaluated on 31 datasets from the carotid lumen segmentation and stenosis grading grand challenge 2009. The segmentation results obtained an average of 80:4% dice similarity score, compared to reference segmentation, and the mean stenosis quantification error was 14.4%.

  3. Europeana and 3D

    Science.gov (United States)

    Pletinckx, D.

    2011-09-01

    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.

  4. 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.

  5. 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.

  6. Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation.

    Science.gov (United States)

    Wenzel, Fabian; Meyer, Carsten; Stehle, Thomas; Peters, Jochen; Siemonsen, Susanne; Thaler, Christian; Zagorchev, Lyubomir

    2018-03-09

    This work presents a novel approach for the rapid segmentation of clinically relevant subcortical brain structures in T1-weighted MRI by utilizing a shape-constrained deformable surface model. In contrast to other approaches for segmenting brain structures, its design allows for parallel segmentation of individual brain structures within a flexible and robust hierarchical framework such that accurate adaptation and volume computation can be achieved within a minute of processing time. Furthermore, adaptation is driven by local and not global contrast, potentially relaxing requirements with respect to preprocessing steps such as bias-field correction. Detailed evaluation experiments on more than 1000 subjects, including comparisons to FSL FIRST and FreeSurfer as well as a clinical assessment, demonstrate high accuracy and test-retest consistency of the presented segmentation approach, leading, for example, to an average segmentation error of less than 0.5 mm. The presented approach might be useful in both, research as well as clinical routine, for automated segmentation and volume quantification of subcortical brain structures in order to increase confidence in the diagnosis of neuro-degenerative disorders, such as Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, or clinical applications for other neurologic and psychiatric diseases. Copyright © 2018 Elsevier B.V. All rights reserved.

  7. 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.

  8. 3D Geometric Analysis of the Pediatric Aorta in 3D MRA Follow-Up Images with Application to Aortic Coarctation.

    Science.gov (United States)

    Wörz, Stefan; Schenk, Jens-Peter; Alrajab, Abdulsattar; von Tengg-Kobligk, Hendrik; Rohr, Karl; Arnold, Raoul

    2016-10-17

    Coarctation of the aorta is one of the most common congenital heart diseases. Despite different treatment opportunities, long-term outcome after surgical or interventional therapy is diverse. Serial morphologic follow-up of vessel growth is necessary, because vessel growth cannot be predicted by primer morphology or a therapeutic option. For the analysis of the long-term outcome after therapy of congenital diseases such as aortic coarctation, accurate 3D geometric analysis of the aorta from follow-up 3D medical image data such as magnetic resonance angiography (MRA) is important. However, for an objective, fast, and accurate 3D geometric analysis, an automatic approach for 3D segmentation and quantification of the aorta from pediatric images is required. We introduce a new model-based approach for the segmentation of the thoracic aorta and its main branches from follow-up pediatric 3D MRA image data. For robust segmentation of vessels even in difficult cases (e.g., neighboring structures), we propose a new extended parametric cylinder model that requires only relatively few model parameters. Moreover, we include a novel adaptive background-masking scheme used for least-squares model fitting, we use a spatial normalization scheme to align the segmentation results from follow-up examinations, and we determine relevant 3D geometric parameters of the aortic arch. We have evaluated our proposed approach using different 3D synthetic images. Moreover, we have successfully applied the approach to follow-up pediatric 3D MRA image data, we have normalized the 3D segmentation results of follow-up images of individual patients, and we have combined the results of all patients. We also present a quantitative evaluation of our approach for four follow-up 3D MRA images of a patient, which confirms that our approach yields accurate 3D segmentation results. An experimental comparison with two previous approaches demonstrates that our approach yields superior results. From the

  9. Open 3D Projects

    Directory of Open Access Journals (Sweden)

    Felician ALECU

    2010-01-01

    Full Text Available Many professionals and 3D artists consider Blender as being the best open source solution for 3D computer graphics. The main features are related to modeling, rendering, shading, imaging, compositing, animation, physics and particles and realtime 3D/game creation.

  10. Open 3D Projects

    OpenAIRE

    Felician ALECU

    2010-01-01

    Many professionals and 3D artists consider Blender as being the best open source solution for 3D computer graphics. The main features are related to modeling, rendering, shading, imaging, compositing, animation, physics and particles and realtime 3D/game creation.

  11. 3D imaging, 3D printing and 3D virtual planning in endodontics.

    Science.gov (United States)

    Shah, Pratik; Chong, B S

    2018-03-01

    The adoption and adaptation of recent advances in digital technology, such as three-dimensional (3D) printed objects and haptic simulators, in dentistry have influenced teaching and/or management of cases involving implant, craniofacial, maxillofacial, orthognathic and periodontal treatments. 3D printed models and guides may help operators plan and tackle complicated non-surgical and surgical endodontic treatment and may aid skill acquisition. Haptic simulators may assist in the development of competency in endodontic procedures through the acquisition of psycho-motor skills. This review explores and discusses the potential applications of 3D printed models and guides, and haptic simulators in the teaching and management of endodontic procedures. An understanding of the pertinent technology related to the production of 3D printed objects and the operation of haptic simulators are also presented.

  12. An automatic segmentation method of a parameter-adaptive PCNN for medical images.

    Science.gov (United States)

    Lian, Jing; Shi, Bin; Li, Mingcong; Nan, Ziwei; Ma, Yide

    2017-09-01

    Since pre-processing and initial segmentation steps in medical images directly affect the final segmentation results of the regions of interesting, an automatic segmentation method of a parameter-adaptive pulse-coupled neural network is proposed to integrate the above-mentioned two segmentation steps into one. This method has a low computational complexity for different kinds of medical images and has a high segmentation precision. The method comprises four steps. Firstly, an optimal histogram threshold is used to determine the parameter [Formula: see text] for different kinds of images. Secondly, we acquire the parameter [Formula: see text] according to a simplified pulse-coupled neural network (SPCNN). Thirdly, we redefine the parameter V of the SPCNN model by sub-intensity distribution range of firing pixels. Fourthly, we add an offset [Formula: see text] to improve initial segmentation precision. Compared with the state-of-the-art algorithms, the new method achieves a comparable performance by the experimental results from ultrasound images of the gallbladder and gallstones, magnetic resonance images of the left ventricle, and mammogram images of the left and the right breast, presenting the overall metric UM of 0.9845, CM of 0.8142, TM of 0.0726. The algorithm has a great potential to achieve the pre-processing and initial segmentation steps in various medical images. This is a premise for assisting physicians to detect and diagnose clinical cases.

  13. 3D ground‐motion simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone: Variability of long‐period (T≥1  s) ground motions and sensitivity to kinematic rupture parameters

    Science.gov (United States)

    Moschetti, Morgan P.; Hartzell, Stephen; Ramirez-Guzman, Leonardo; Frankel, Arthur; Angster, Stephen J.; Stephenson, William J.

    2017-01-01

    We examine the variability of long‐period (T≥1  s) earthquake ground motions from 3D simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone, Utah, from a set of 96 rupture models with varying slip distributions, rupture speeds, slip velocities, and hypocenter locations. Earthquake ruptures were prescribed on a 3D fault representation that satisfies geologic constraints and maintained distinct strands for the Warm Springs and for the East Bench and Cottonwood faults. Response spectral accelerations (SA; 1.5–10 s; 5% damping) were measured, and average distance scaling was well fit by a simple functional form that depends on the near‐source intensity level SA0(T) and a corner distance Rc:SA(R,T)=SA0(T)(1+(R/Rc))−1. Period‐dependent hanging‐wall effects manifested and increased the ground motions by factors of about 2–3, though the effects appeared partially attributable to differences in shallow site response for sites on the hanging wall and footwall of the fault. Comparisons with modern ground‐motion prediction equations (GMPEs) found that the simulated ground motions were generally consistent, except within deep sedimentary basins, where simulated ground motions were greatly underpredicted. Ground‐motion variability exhibited strong lateral variations and, at some sites, exceeded the ground‐motion variability indicated by GMPEs. The effects on the ground motions of changing the values of the five kinematic rupture parameters can largely be explained by three predominant factors: distance to high‐slip subevents, dynamic stress drop, and changes in the contributions from directivity. These results emphasize the need for further characterization of the underlying distributions and covariances of the kinematic rupture parameters used in 3D ground‐motion simulations employed in probabilistic seismic‐hazard analyses.

  14. 3D Integration for Wireless Multimedia

    Science.gov (United States)

    Kimmich, Georg

    The convergence of mobile phone, internet, mapping, gaming and office automation tools with high quality video and still imaging capture capability is becoming a strong market trend for portable devices. High-density video encode and decode, 3D graphics for gaming, increased application-software complexity and ultra-high-bandwidth 4G modem technologies are driving the CPU performance and memory bandwidth requirements close to the PC segment. These portable multimedia devices are battery operated, which requires the deployment of new low-power-optimized silicon process technologies and ultra-low-power design techniques at system, architecture and device level. Mobile devices also need to comply with stringent silicon-area and package-volume constraints. As for all consumer devices, low production cost and fast time-to-volume production is key for success. This chapter shows how 3D architectures can bring a possible breakthrough to meet the conflicting power, performance and area constraints. Multiple 3D die-stacking partitioning strategies are described and analyzed on their potential to improve the overall system power, performance and cost for specific application scenarios. Requirements and maturity of the basic process-technology bricks including through-silicon via (TSV) and die-to-die attachment techniques are reviewed. Finally, we highlight new challenges which will arise with 3D stacking and an outlook on how they may be addressed: Higher power density will require thermal design considerations, new EDA tools will need to be developed to cope with the integration of heterogeneous technologies and to guarantee signal and power integrity across the die stack. The silicon/wafer test strategies have to be adapted to handle high-density IO arrays, ultra-thin wafers and provide built-in self-test of attached memories. New standards and business models have to be developed to allow cost-efficient assembly and testing of devices from different silicon and technology

  15. IZDELAVA TISKALNIKA 3D

    OpenAIRE

    Brdnik, Lovro

    2015-01-01

    Diplomsko delo analizira trenutno stanje 3D tiskalnikov na trgu. Prikazan je razvoj in principi delovanja 3D tiskalnikov. Predstavljeni so tipi 3D tiskalnikov, njihove prednosti in slabosti. Podrobneje je predstavljena zgradba in delovanje koračnih motorjev. Opravljene so meritve koračnih motorjev. Opisana je programska oprema za rokovanje s 3D tiskalniki in komponente, ki jih potrebujemo za izdelavo. Diploma se oklepa vprašanja, ali je izdelava 3D tiskalnika bolj ekonomična kot pa naložba v ...

  16. Utility of real-time prospective motion correction (PROMO) for segmentation of cerebral cortex on 3D T1-weighted imaging: Voxel-based morphometry analysis for uncooperative patients

    International Nuclear Information System (INIS)

    Igata, Natsuki; Kakeda, Shingo; Watanabe, Keita; Narimatsu, Hidekuni; Ide, Satoru; Korogi, Yukunori; Nozaki, Atsushi; Rettmann, Dan; Abe, Osamu

    2017-01-01

    To assess the utility of the motion correction method with prospective motion correction (PROMO) in a voxel-based morphometry (VBM) analysis for 'uncooperative' patient populations. High-resolution 3D T1-weighted imaging both with and without PROMO were performed in 33 uncooperative patients with Parkinson's disease (n = 11) or dementia (n = 22). We compared the grey matter (GM) volumes and cortical thickness between the scans with and without PROMO. For the mean total GM volume with the VBM analysis, the scan without PROMO showed a significantly smaller volume than that with PROMO (p < 0.05), which was caused by segmentation problems due to motion during acquisition. The whole-brain VBM analysis showed significant GM volume reductions in some regions in the scans without PROMO (familywise error corrected p < 0.05). In the cortical thickness analysis, the scans without PROMO also showed decreased cortical thickness compared to the scan with PROMO (p < 0.05). Our results with the uncooperative patients indicate that the use of PROMO can reduce misclassification during segmentation of the VBM analyses, although it may not prevent GM volume reduction. (orig.)

  17. Utility of real-time prospective motion correction (PROMO) for segmentation of cerebral cortex on 3D T1-weighted imaging: Voxel-based morphometry analysis for uncooperative patients

    Energy Technology Data Exchange (ETDEWEB)

    Igata, Natsuki; Kakeda, Shingo; Watanabe, Keita; Narimatsu, Hidekuni; Ide, Satoru; Korogi, Yukunori [University of Occupational and Environmental Health School of Medicine, Department of Radiology, Kitakyushu (Japan); Nozaki, Atsushi [MR Applications and Workflow Asia Pacific GE Healthcare Japan, Tokyo (Japan); Rettmann, Dan [MR Applications and Workflow GE Healthcare, Rochester, MN (United States); Abe, Osamu [University of Tokyo, Department of Radiology, Graduate School of Medicine, Tokyo (Japan)

    2017-08-15

    To assess the utility of the motion correction method with prospective motion correction (PROMO) in a voxel-based morphometry (VBM) analysis for 'uncooperative' patient populations. High-resolution 3D T1-weighted imaging both with and without PROMO were performed in 33 uncooperative patients with Parkinson's disease (n = 11) or dementia (n = 22). We compared the grey matter (GM) volumes and cortical thickness between the scans with and without PROMO. For the mean total GM volume with the VBM analysis, the scan without PROMO showed a significantly smaller volume than that with PROMO (p < 0.05), which was caused by segmentation problems due to motion during acquisition. The whole-brain VBM analysis showed significant GM volume reductions in some regions in the scans without PROMO (familywise error corrected p < 0.05). In the cortical thickness analysis, the scans without PROMO also showed decreased cortical thickness compared to the scan with PROMO (p < 0.05). Our results with the uncooperative patients indicate that the use of PROMO can reduce misclassification during segmentation of the VBM analyses, although it may not prevent GM volume reduction. (orig.)

  18. Using the Technology Acceptance Model to explore community dwelling older adults' perceptions of a 3D interior design application to facilitate pre-discharge home adaptations.

    Science.gov (United States)

    Money, Arthur G; Atwal, Anita; Young, Katherine L; Day, Yasmin; Wilson, Lesley; Money, Kevin G

    2015-08-26

    In the UK occupational therapy pre-discharge home visits are routinely carried out as a means of facilitating safe transfer from the hospital to home. Whilst they are an integral part of practice, there is little evidence to demonstrate they have a positive outcome on the discharge process. Current issues for patients are around the speed of home visits and the lack of shared decision making in the process, resulting in less than 50 % of the specialist equipment installed actually being used by patients on follow-up. To improve practice there is an urgent need to examine other ways of conducting home visits to facilitate safe discharge. We believe that Computerised 3D Interior Design Applications (CIDAs) could be a means to support more efficient, effective and collaborative practice. A previous study explored practitioners perceptions of using CIDAs; however it is important to ascertain older adult's views about the usability of technology and to compare findings. This study explores the perceptions of community dwelling older adults with regards to adopting and using CIDAs as an assistive tool for the home adaptations process. Ten community dwelling older adults participated in individual interactive task-focused usability sessions with a customised CIDA, utilising the think-aloud protocol and individual semi-structured interviews. Template analysis was used to carry out both deductive and inductive analysis of the think-aloud and interview data. Initially, a deductive stance was adopted, using the three pre-determined high-level themes of the technology acceptance model (TAM): Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Actual Use (AU). Inductive template analysis was then carried out on the data within these themes, from which a number of sub-thmes emerged. Regarding PU, participants believed CIDAs served as a useful visual tool and saw clear potential to facilitate shared understanding and partnership in care delivery. For PEOU, participants were

  19. A novel white blood cells segmentation algorithm based on adaptive neutrosophic similarity score.

    Science.gov (United States)

    Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

    2018-12-01

    White blood cells (WBCs) play a crucial role in the diagnosis of many diseases according to their numbers or morphology. The recent digital pathology equipments investigate and analyze the blood smear images automatically. The previous automated segmentation algorithms worked on healthy and non-healthy WBCs separately. Also, such algorithms had employed certain color components which leak adaptively with different datasets. In this paper, a novel segmentation algorithm for WBCs in the blood smear images is proposed using multi-scale similarity measure based on the neutrosophic domain. We employ neutrosophic similarity score to measure the similarity between different color components of the blood smear image. Since we utilize different color components from different color spaces, we modify the neutrosphic score algorithm to be adaptive. Two different segmentation frameworks are proposed: one for the segmentation of nucleus, and the other for the cytoplasm of WBCs. Moreover, our proposed algorithm is applied to both healthy and non-healthy WBCs. in some cases, the single blood smear image gather between healthy and non-healthy WBCs which is considered in our proposed algorithm. Also, our segmentation algorithm is performed without any external morphological binary enhancement methods which may effect on the original shape of the WBC. Different public datasets with different resolutions were used in our experiments. We evaluate the system performance based on both qualitative and quantitative measurements. The quantitative results indicates high precision rates of the segmentation performance measurement A1 = 96.5% and A2 = 97.2% of the proposed method. The average segmentation performance results for different WBCs types reach to 97.6%. In this paper, a method based on adaptive neutrosphic sets similarity score is proposed in order to detect WBCs from a blood smear microscopic image and segment its components (nucleus and the cytoplasm). The proposed

  20. Refined 3d-3d correspondence

    Science.gov (United States)

    Alday, Luis F.; Genolini, Pietro Benetti; Bullimore, Mathew; van Loon, Mark

    2017-04-01

    We explore aspects of the correspondence between Seifert 3-manifolds and 3d N = 2 supersymmetric theories with a distinguished abelian flavour symmetry. We give a prescription for computing the squashed three-sphere partition functions of such 3d N = 2 theories constructed from boundary conditions and interfaces in a 4d N = 2∗ theory, mirroring the construction of Seifert manifold invariants via Dehn surgery. This is extended to include links in the Seifert manifold by the insertion of supersymmetric Wilson-'t Hooft loops in the 4d N = 2∗ theory. In the presence of a mass parameter cfor the distinguished flavour symmetry, we recover aspects of refined Chern-Simons theory with complex gauge group, and in particular construct an analytic continuation of the S-matrix of refined Chern-Simons theory.

  1. Dosimetric Evaluation of Automatic Segmentation for Adaptive IMRT for Head-and-Neck Cancer

    International Nuclear Information System (INIS)

    Tsuji, Stuart Y.; Hwang, Andrew; Weinberg, Vivian; Yom, Sue S.; Quivey, Jeanne M.; Xia Ping

    2010-01-01

    Purpose: Adaptive planning to accommodate anatomic changes during treatment requires repeat segmentation. This study uses dosimetric endpoints to assess automatically deformed contours. Methods and Materials: Sixteen patients with head-and-neck cancer had adaptive plans because of anatomic change during radiotherapy. Contours from the initial planning computed tomography (CT) were deformed to the mid-treatment CT using an intensity-based free-form registration algorithm then compared with the manually drawn contours for the same CT using the Dice similarity coefficient and an overlap index. The automatic contours were used to create new adaptive plans. The original and automatic adaptive plans were compared based on dosimetric outcomes of the manual contours and on plan conformality. Results: Volumes from the manual and automatic segmentation were similar; only the gross tumor volume (GTV) was significantly different. Automatic plans achieved lower mean coverage for the GTV: V95: 98.6 ± 1.9% vs. 89.9 ± 10.1% (p = 0.004) and clinical target volume: V95: 98.4 ± 0.8% vs. 89.8 ± 6.2% (p 3 of the spinal cord 39.9 ± 3.7 Gy vs. 42.8 ± 5.4 Gy (p = 0.034), but no difference for the remaining structures. Conclusions: Automatic segmentation is not robust enough to substitute for physician-drawn volumes, particularly for the GTV. However, it generates normal structure contours of sufficient accuracy when assessed by dosimetric end points.

  2. YouDash3D: exploring stereoscopic 3D gaming for 3D movie theaters

    Science.gov (United States)

    Schild, Jonas; Seele, Sven; Masuch, Maic

    2012-03-01

    Along with the success of the digitally revived stereoscopic cinema, events beyond 3D movies become attractive for movie theater operators, i.e. interactive 3D games. In this paper, we present a case that explores possible challenges and solutions for interactive 3D games to be played by a movie theater audience. We analyze the setting and showcase current issues related to lighting and interaction. Our second focus is to provide gameplay mechanics that make special use of stereoscopy, especially depth-based game design. Based on these results, we present YouDash3D, a game prototype that explores public stereoscopic gameplay in a reduced kiosk setup. It features live 3D HD video stream of a professional stereo camera rig rendered in a real-time game scene. We use the effect to place the stereoscopic effigies of players into the digital game. The game showcases how stereoscopic vision can provide for a novel depth-based game mechanic. Projected trigger zones and distributed clusters of the audience video allow for easy adaptation to larger audiences and 3D movie theater gaming.

  3. A 3d-3d appetizer

    Energy Technology Data Exchange (ETDEWEB)

    Pei, Du; Ye, Ke [Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, 91125 (United States)

    2016-11-02

    We test the 3d-3d correspondence for theories that are labeled by Lens spaces. We find a full agreement between the index of the 3d N=2 “Lens space theory” T[L(p,1)] and the partition function of complex Chern-Simons theory on L(p,1). In particular, for p=1, we show how the familiar S{sup 3} partition function of Chern-Simons theory arises from the index of a free theory. For large p, we find that the index of T[L(p,1)] becomes a constant independent of p. In addition, we study T[L(p,1)] on the squashed three-sphere S{sub b}{sup 3}. This enables us to see clearly, at the level of partition function, to what extent G{sub ℂ} complex Chern-Simons theory can be thought of as two copies of Chern-Simons theory with compact gauge group G.

  4. Accurate and reproducible reconstruction of coronary arteries and endothelial shear stress calculation using 3D OCT: comparative study to 3D IVUS and 3D QCA.

    Science.gov (United States)

    Toutouzas, Konstantinos; Chatzizisis, Yiannis S; Riga, Maria; Giannopoulos, Andreas; Antoniadis, Antonios P; Tu, Shengxian; Fujino, Yusuke; Mitsouras, Dimitrios; Doulaverakis, Charalampos; Tsampoulatidis, Ioannis; Koutkias, Vassilis G; Bouki, Konstantina; Li, Yingguang; Chouvarda, Ioanna; Cheimariotis, Grigorios; Maglaveras, Nicos; Kompatsiaris, Ioannis; Nakamura, Sunao; Reiber, Johan H C; Rybicki, Frank; Karvounis, Haralambos; Stefanadis, Christodoulos; Tousoulis, Dimitris; Giannoglou, George D

    2015-06-01

    Geometrically-correct 3D OCT is a new imaging modality with the potential to investigate the association of local hemodynamic microenvironment with OCT-derived high-risk features. We aimed to describe the methodology of 3D OCT and investigate the accuracy, inter- and intra-observer agreement of 3D OCT in reconstructing coronary arteries and calculating ESS, using 3D IVUS and 3D QCA as references. 35 coronary artery segments derived from 30 patients were reconstructed in 3D space using 3D OCT. 3D OCT was validated against 3D IVUS and 3D QCA. The agreement in artery reconstruction among 3D OCT, 3D IVUS and 3D QCA was assessed in 3-mm-long subsegments using lumen morphometry and ESS parameters. The inter- and intra-observer agreement of 3D OCT, 3D IVUS and 3D QCA were assessed in a representative sample of 61 subsegments (n = 5 arteries). The data processing times for each reconstruction methodology were also calculated. There was a very high agreement between 3D OCT vs. 3D IVUS and 3D OCT vs. 3D QCA in terms of total reconstructed artery length and volume, as well as in terms of segmental morphometric and ESS metrics with mean differences close to zero and narrow limits of agreement (Bland-Altman analysis). 3D OCT exhibited excellent inter- and intra-observer agreement. The analysis time with 3D OCT was significantly lower compared to 3D IVUS. Geometrically-correct 3D OCT is a feasible, accurate and reproducible 3D reconstruction technique that can perform reliable ESS calculations in coronary arteries. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  5. Sealing Clay Text Segmentation Based on Radon-Like Features and Adaptive Enhancement Filters

    Directory of Open Access Journals (Sweden)

    Xia Zheng

    2015-01-01

    Full Text Available Text extraction is a key issue in sealing clay research. The traditional method based on rubbings increases the risk of sealing clay damage and is unfavorable to sealing clay protection. Therefore, using digital image of sealing clay, a new method for text segmentation based on Radon-like features and adaptive enhancement filters is proposed in this paper. First, adaptive enhancement LM filter bank is used to get the maximum energy image; second, the edge image of the maximum energy image is calculated; finally, Radon-like feature images are generated by combining maximum energy image and its edge image. The average image of Radon-like feature images is segmented by the image thresholding method. Compared with 2D Otsu, GA, and FastFCM, the experiment result shows that this method can perform better in terms of accuracy and completeness of the text.

  6. Interpolation of 3D slice volume data for 3D printing

    Science.gov (United States)

    Littley, Samuel; Voiculescu, Irina

    2017-03-01

    Medical imaging from CT and MRI scans has become essential to clinicians for diagnosis, treatment planning and even prevention of a wide array of conditions. The presentation of image data volumes as 2D slice series provides some challenges with visualising internal structures. 3D reconstructions of organs and other tissue samples from data with low scan resolution leads to a `stepped' appearance. This paper demonstrates how to improve 3D visualisation of features and automated preparation for 3D printing from such low resolution data, using novel techniques for morphing from one slice to the next. The boundary of the starting contour is grown until it matches the boundary of the ending contour by adapting a variant of the Fast Marching Method (FMM). Our spoke based approach generates scalar speed field for FMM by estimating distances to boundaries with line segments connecting the two boundaries. These can be regularly spaced radial spokes or spokes at radial extrema. We introduce clamped FMM by running the algorithm outwards from the smaller boundary and inwards from the larger boundary and combining the two runs to achieve FMM growth stability near the two region boundaries. Our method inserts a series of uniformly distributed intermediate contours between each pair of consecutive slices from the scan volume thus creating smoother feature boundaries. Whilst hard to quantify, our overall results give clinicians an evidently improved tangible and tactile representation of the tissues, that they can examine more easily and even handle.

  7. A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation

    Science.gov (United States)

    Botter Martins, Samuel; Vallin Spina, Thiago; Yasuda, Clarissa; Falcão, Alexandre X.

    2017-02-01

    Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

  8. 3D future internet media

    CERN Document Server

    Dagiuklas, Tasos

    2014-01-01

    This book describes recent innovations in 3D media and technologies, with coverage of 3D media capturing, processing, encoding, and adaptation, networking aspects for 3D Media, and quality of user experience (QoE). The main contributions are based on the results of the FP7 European Projects ROMEO, which focus on new methods for the compression and delivery of 3D multi-view video and spatial audio, as well as the optimization of networking and compression jointly across the Future Internet (www.ict-romeo.eu). The delivery of 3D media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics and user terminal requirements, as well as the user’s context such as their preferences and location. As the number of visual views increases, current systems will struggle to meet the demanding requirements in terms of delivery of constant video quality to both fixed and mobile users. ROMEO will design and develop hybrid-networking solutions that co...

  9. Novel 3D media technologies

    CERN Document Server

    Dagiuklas, Tasos

    2015-01-01

    This book describes recent innovations in 3D media and technologies, with coverage of 3D media capturing, processing, encoding, and adaptation, networking aspects for 3D Media, and quality of user experience (QoE). The contributions are based on the results of the FP7 European Project ROMEO, which focuses on new methods for the compression and delivery of 3D multi-view video and spatial audio, as well as the optimization of networking and compression jointly across the future Internet. The delivery of 3D media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics and user terminal requirements, as well as the user’s context such as their preferences and location. As the number of visual views increases, current systems will struggle to meet the demanding requirements in terms of delivery of consistent video quality to fixed and mobile users. ROMEO will present hybrid networking solutions that combine the DVB-T2 and DVB-NGH broadcas...

  10. 3D virtuel udstilling

    DEFF Research Database (Denmark)

    Tournay, Bruno; Rüdiger, Bjarne

    2006-01-01

    3d digital model af Arkitektskolens gård med virtuel udstilling af afgangsprojekter fra afgangen sommer 2006. 10 s.......3d digital model af Arkitektskolens gård med virtuel udstilling af afgangsprojekter fra afgangen sommer 2006. 10 s....

  11. 3D Ground Mapping

    OpenAIRE

    Poverud, Therese Tokle; Christensen, Ole Petter; Jacop, Asadullah; Mpoyi, Giresse Kadima; Mann, Harjit Laly Singh; Albert, Ngenzi; Dalset, Bjørnar

    2015-01-01

    Utført i samarbeid med Cube AS Cube AS wants a system for 3D mapping of terrain using an UAV (unmanned aerial vehicle). We chose to equip the UAV with a camera to take aerial photos that are processed through image processing software to produce detailed, digital 3D maps.

  12. Local adaptive approach toward segmentation of microscopic images of activated sludge flocs

    Science.gov (United States)

    Khan, Muhammad Burhan; Nisar, Humaira; Ng, Choon Aun; Lo, Po Kim; Yap, Vooi Voon

    2015-11-01

    Activated sludge process is a widely used method to treat domestic and industrial effluents. The conditions of activated sludge wastewater treatment plant (AS-WWTP) are related to the morphological properties of flocs (microbial aggregates) and filaments, and are required to be monitored for normal operation of the plant. Image processing and analysis is a potential time-efficient monitoring tool for AS-WWTPs. Local adaptive segmentation algorithms are proposed for bright-field microscopic images of activated sludge flocs. Two basic modules are suggested for Otsu thresholding-based local adaptive algorithms with irregular illumination compensation. The performance of the algorithms has been compared with state-of-the-art local adaptive algorithms of Sauvola, Bradley, Feng, and c-mean. The comparisons are done using a number of region- and nonregion-based metrics at different microscopic magnifications and quantification of flocs. The performance metrics show that the proposed algorithms performed better and, in some cases, were comparable to the state-of the-art algorithms. The performance metrics were also assessed subjectively for their suitability for segmentations of activated sludge images. The region-based metrics such as false negative ratio, sensitivity, and negative predictive value gave inconsistent results as compared to other segmentation assessment metrics.

  13. Underwater 3D filming

    Directory of Open Access Journals (Sweden)

    Roberto Rinaldi

    2014-12-01

    Full Text Available After an experimental phase of many years, 3D filming is now effective and successful. Improvements are still possible, but the film industry achieved memorable success on 3D movie’s box offices due to the overall quality of its products. Special environments such as space (“Gravity” and the underwater realm look perfect to be reproduced in 3D. “Filming in space” was possible in “Gravity” using special effects and computer graphic. The underwater realm is still difficult to be handled. Underwater filming in 3D was not that easy and effective as filming in 2D, since not long ago. After almost 3 years of research, a French, Austrian and Italian team realized a perfect tool to film underwater, in 3D, without any constrains. This allows filmmakers to bring the audience deep inside an environment where they most probably will never have the chance to be.

  14. Dimensional accuracy of 3D printed vertebra

    Science.gov (United States)

    Ogden, Kent; Ordway, Nathaniel; Diallo, Dalanda; Tillapaugh-Fay, Gwen; Aslan, Can

    2014-03-01

    3D printer applications in the biomedical sciences and medical imaging are expanding and will have an increasing impact on the practice of medicine. Orthopedic and reconstructive surgery has been an obvious area for development of 3D printer applications as the segmentation of bony anatomy to generate printable models is relatively straightforward. There are important issues that should be addressed when using 3D printed models for applications that may affect patient care; in particular the dimensional accuracy of the printed parts needs to be high to avoid poor decisions being made prior to surgery or therapeutic procedures. In this work, the dimensional accuracy of 3D printed vertebral bodies derived from CT data for a cadaver spine is compared with direct measurements on the ex-vivo vertebra and with measurements made on the 3D rendered vertebra using commercial 3D image processing software. The vertebra was printed on a consumer grade 3D printer using an additive print process using PLA (polylactic acid) filament. Measurements were made for 15 different anatomic features of the vertebral body, including vertebral body height, endplate width and depth, pedicle height and width, and spinal canal width and depth, among others. It is shown that for the segmentation and printing process used, the results of measurements made on the 3D printed vertebral body are substantially the same as those produced by direct measurement on the vertebra and measurements made on the 3D rendered vertebra.

  15. Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing

    Science.gov (United States)

    He, Yufan; Sun, Yankui; Chen, Min; Zheng, Yuanjie; Liu, Hui; Leon, Cecilia; Beltran, William; Gee, James C.

    2016-03-01

    In recent years, several studies have shown that the canine retina model offers important insight for our understanding of human retinal diseases. Several therapies developed to treat blindness in such models have already moved onto human clinical trials, with more currently under development [1]. Optical coherence tomography (OCT) offers a high resolution imaging modality for performing in-vivo analysis of the retinal layers. However, existing algorithms for automatically segmenting and analyzing such data have been mostly focused on the human retina. As a result, canine retinal images are often still being analyzed using manual segmentations, which is a slow and laborious task. In this work, we propose a method for automatically segmenting 5 boundaries in canine retinal OCT. The algorithm employs the position relationships between different boundaries to adaptively enhance the gradient map. A region growing algorithm is then used on the enhanced gradient maps to find the five boundaries separately. The automatic segmentation was compared against manual segmentations showing an average absolute error of 5.82 +/- 4.02 microns.

  16. An Adaptive Window-setting Scheme for Segmentation of Bladder Tumor Surface via MR Cystography

    Science.gov (United States)

    Duan, Chaijie; Liu, Fanghua; Xiao, Ping; Lv, Guoqing

    2012-01-01

    This paper proposes an adaptive window-setting scheme for non-invasive detection and segmentation of bladder tumor surface in T1-weighted magnetic resonance (MR) images. The inner border of the bladder wall is firstly covered by a group of ball-shaped detecting windows with different radii. By extracting the candidate tumor windows and excluding the false positive (FP) candidates, the entire bladder tumor surface is detected and segmented by the remaining windows. Different from previous bladder tumor detection methods which are mostly focusing on the existence of a tumor, this paper emphasizes segmenting the entire tumor surface in addition to detecting the presence of the tumor. The presented scheme was validated by 10 clinical T1-weighted MR image datasets (5 volunteers and 5 patients). The bladder tumor surfaces and the normal bladder wall inner borders in the ten datasets were covered by 223 and 10491 windows, respectively. Such large number of the detecting windows makes the validation statistically meaningful. In the FP reduction step, the best feature combination was obtained by using receiver operating characteristics or ROC analysis. The validation results demonstrated the potential of this presented scheme in segmenting the entire tumor surface with high sensitivity and low FP rate. This work inherits our previous results of automatic segmentation of the bladder wall and will be an important element in our MR-based virtual cystoscopy or MR cystography system. PMID:22645274

  17. Blender 3D cookbook

    CERN Document Server

    Valenza, Enrico

    2015-01-01

    This book is aimed at the professionals that already have good 3D CGI experience with commercial packages and have now decided to try the open source Blender and want to experiment with something more complex than the average tutorials on the web. However, it's also aimed at the intermediate Blender users who simply want to go some steps further.It's taken for granted that you already know how to move inside the Blender interface, that you already have 3D modeling knowledge, and also that of basic 3D modeling and rendering concepts, for example, edge-loops, n-gons, or samples. In any case, it'

  18. TISKALNIK 3D DLP

    OpenAIRE

    Debeljak, Mitja

    2016-01-01

    Diplomska naloga opisuje različne vrste 3D tiskalnikov in izdelavo 3D tiskalnika s tehnologijo digitalnega svetlobnega procesa (Digital Light Processing ali DLP) za potrebe podjetja Doorson d.o.o. V nalogi je opisan postopek izdelave, deljen na mehanski, elektro in programski del ter podrobnejša predstavitev komponent, med katere kot pomembnejše sodijo Raspberry Pi in koračni motor. Prikazana je primerjava obstoječega 3D tiskalnika s tehnologijo ciljnega nalaganja (Fused Deposition Modeling a...

  19. Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

    Science.gov (United States)

    Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

    2012-01-01

    Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. DELTA 3D PRINTER

    Directory of Open Access Journals (Sweden)

    ȘOVĂILĂ Florin

    2016-07-01

    Full Text Available 3D printing is a very used process in industry, the generic name being “rapid prototyping”. The essential advantage of a 3D printer is that it allows the designers to produce a prototype in a very short time, which is tested and quickly remodeled, considerably reducing the required time to get from the prototype phase to the final product. At the same time, through this technique we can achieve components with very precise forms, complex pieces that, through classical methods, could have been accomplished only in a large amount of time. In this paper, there are presented the stages of a 3D model execution, also the physical achievement after of a Delta 3D printer after the model.

  1. In situ repair of bone and cartilage defects using 3D scanning and 3D printing

    OpenAIRE

    Li, Lan; Yu, Fei; Shi, Jianping; Shen, Sheng; Teng, Huajian; Yang, Jiquan; Wang, Xingsong; Jiang, Qing

    2017-01-01

    Three-dimensional (3D) printing is a rapidly emerging technology that promises to transform tissue engineering into a commercially successful biomedical industry. However, the use of robotic bioprinters alone is not sufficient for disease treatment. This study aimed to report the combined application of 3D scanning and 3D printing for treating bone and cartilage defects. Three different kinds of defect models were created to mimic three orthopedic diseases: large segmental defects of long bon...

  2. Applications of deformable image registration: Automatic segmentation and adaptive radiation therapy

    Science.gov (United States)

    Morcos, Marc

    The contents of this thesis are best divided into two components: (i) evaluation of atlas-based segmentation and deformable contour propagation and (ii) adaptive radiation therapy using deformable electron density mapping. The first component of this thesis involves the evaluation of two commercial deformable registration systems with respect to automatic segmentation techniques. Overall, the techniques revealed that manual modifications would be required if the structures were to be used for treatment planning. The automatic segmentation methods utilized by both commercial products serve as an excellent starting point for contouring process and also reduce inter- and intra-physician variability when contouring. In the second component, we developed a framework for dose accumulation adaptive radiation therapy. By registering the planning computed tomography (CT) images to the weekly cone-beam computed tomography (CBCT) images, we were able to produce modified CBCT images which possessed CT Hounsfield units; this was achieved by using deformable image registration. Dose distributions were recalculated onto the modified CBCT images and then compared to the planned dose distributions. Results indicated that deformable electron density mapping is a feasible technique to allow dose distributions to be recalculated on pre-treatment CBCT scans.

  3. Reactive power and voltage control strategy based on dynamic and adaptive segment for DG inverter

    Science.gov (United States)

    Zhai, Jianwei; Lin, Xiaoming; Zhang, Yongjun

    2018-03-01

    The inverter of distributed generation (DG) can support reactive power to help solve the problem of out-of-limit voltage in active distribution network (ADN). Therefore, a reactive voltage control strategy based on dynamic and adaptive segment for DG inverter is put forward to actively control voltage in this paper. The proposed strategy adjusts the segmented voltage threshold of Q(U) droop curve dynamically and adaptively according to the voltage of grid-connected point and the power direction of adjacent downstream line. And then the reactive power reference of DG inverter can be got through modified Q(U) control strategy. The reactive power of inverter is controlled to trace the reference value. The proposed control strategy can not only control the local voltage of grid-connected point but also help to maintain voltage within qualified range considering the terminal voltage of distribution feeder and the reactive support for adjacent downstream DG. The scheme using the proposed strategy is compared with the scheme without the reactive support of DG inverter and the scheme using the Q(U) control strategy with constant segmented voltage threshold. The simulation results suggest that the proposed method has a significant improvement on solving the problem of out-of-limit voltage, restraining voltage variation and improving voltage quality.

  4. Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

    2017-07-01

    The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial. This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system. The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset. The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069. The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. An adaptation of astronomical image processing enables characterization and functional 3D mapping of individual sites of excitation-contraction coupling in rat cardiac muscle.

    Science.gov (United States)

    Tian, Qinghai; Kaestner, Lars; Schröder, Laura; Guo, Jia; Lipp, Peter

    2017-11-14

    In beating cardiomyocytes, synchronized localized Ca 2+ transients from thousands of active excitation-contraction coupling sites (ECC couplons) comprising plasma and sarcoplasmic reticulum membrane calcium channels are important determinants of the heart's performance. Nevertheless, our knowledge about the properties of ECC couplons is limited by the lack of appropriate experimental and analysis strategies. We designed CaCLEAN to untangle the fundamental characteristics of ECC couplons by combining the astronomer's CLEAN algorithm with known properties of calcium diffusion. CaCLEAN empowers the investigation of fundamental properties of ECC couplons in beating cardiomyocytes without pharmacological interventions. Upon examining individual ECC couplons at the nanoscopic level, we reveal their roles in the negative amplitude-frequency relationship and in β-adrenergic stimulation, including decreasing and increasing firing reliability, respectively. CaCLEAN combined with 3D confocal imaging of beating cardiomyocytes provides a functional 3D map of active ECC couplons (on average, 17,000 per myocyte). CaCLEAN will further enlighten the ECC-couplon-remodelling processes that underlie cardiac diseases.

  6. Adaptation Measurement of CAD/CAM Dental Crowns with X-Ray Micro-CT: Metrological Chain Standardization and 3D Gap Size Distribution

    Directory of Open Access Journals (Sweden)

    L. Tapie

    2016-01-01

    Full Text Available Computer-Aided Design and Manufacturing systems are increasingly used to produce dental prostheses, but the parts produced suffer from a lack of evaluation, especially concerning the internal gap of the final assembly, that is, the space between the prepared tooth and the prosthesis. X-ray micro-Computed Tomography (micro-CT is a noninvasive imaging technique enabling the internal inspection of the assembly. It has proved to be an efficient tool for measuring the gap. In this study, a critical review of the protocols using micro-CT to quantify the gap is proposed as an introduction to a new protocol aimed at minimizing errors and enabling comparison between CAD/CAM systems. To compare different systems, a standardized protocol is proposed including two reference geometries. Micro-CT is used to acquire the reference geometries. A new 3D method is then proposed and a new indicator is defined (Gap Size Distribution (GSD. In addition, the usual 2D measurements are described and discussed. The 3D gap measurement method proposed can be used in clinical case geometries and has the considerable advantage of minimizing the data processing steps before performing the measurements.

  7. Radiochromic 3D Detectors

    Science.gov (United States)

    Oldham, Mark

    2015-01-01

    Radiochromic materials exhibit a colour change when exposed to ionising radiation. Radiochromic film has been used for clinical dosimetry for many years and increasingly so recently, as films of higher sensitivities have become available. The two principle advantages of radiochromic dosimetry include greater tissue equivalence (radiologically) and the lack of requirement for development of the colour change. In a radiochromic material, the colour change arises direct from ionising interactions affecting dye molecules, without requiring any latent chemical, optical or thermal development, with important implications for increased accuracy and convenience. It is only relatively recently however, that 3D radiochromic dosimetry has become possible. In this article we review recent developments and the current state-of-the-art of 3D radiochromic dosimetry, and the potential for a more comprehensive solution for the verification of complex radiation therapy treatments, and 3D dose measurement in general.

  8. Impresora 3D

    OpenAIRE

    Moreno, Iveth; Serracín, Pilar

    2017-01-01

    La idea de llevar a la realidad un objeto dibujado en un software de diseño asistido por computadora, da lugar a la creación de las impresoras 3D. Los orígenes de la impresión 3D se dieron para los años 80, y desde aquella época hasta hoy en día, este tipo de impresión ha contribuido en diversos campos del saber, que van desde la ingeniería hasta la medicina. En sus inicios las impresoras 3D además de costosas, eran de uso exclusivo para la creación de piezas de prototipado con una fuerte apl...

  9. 3D Digital Modelling

    DEFF Research Database (Denmark)

    Hundebøl, Jesper

    wave of new building information modelling tools demands further investigation, not least because of industry representatives' somewhat coarse parlance: Now the word is spreading -3D digital modelling is nothing less than a revolution, a shift of paradigm, a new alphabet... Research qeustions. Based...... on empirical probes (interviews, observations, written inscriptions) within the Danish construction industry this paper explores the organizational and managerial dynamics of 3D Digital Modelling. The paper intends to - Illustrate how the network of (non-)human actors engaged in the promotion (and arrest) of 3...... important to appreciate the analysis. Before turning to the presentation of preliminary findings and a discussion of 3D digital modelling, it begins, however, with an outline of industry specific ICT strategic issues. Paper type. Multi-site field study...

  10. 3D Projection Installations

    DEFF Research Database (Denmark)

    Halskov, Kim; Johansen, Stine Liv; Bach Mikkelsen, Michelle

    2014-01-01

    contributes to the understanding of the distinctive characteristics of such a new medium, and identifies three strategies for designing 3-D projection installations: establishing space; interplay between the digital and the physical; and transformation of materiality. The principal empirical case, From......Three-dimensional projection installations are particular kinds of augmented spaces in which a digital 3-D model is projected onto a physical three-dimensional object, thereby fusing the digital content and the physical object. Based on interaction design research and media studies, this article...

  11. Herramientas SIG 3D

    Directory of Open Access Journals (Sweden)

    Francisco R. Feito Higueruela

    2010-04-01

    Full Text Available Applications of Geographical Information Systems on several Archeology fields have been increasing during the last years. Recent avances in these technologies make possible to work with more realistic 3D models. In this paper we introduce a new paradigm for this system, the GIS Thetrahedron, in which we define the fundamental elements of GIS, in order to provide a better understanding of their capabilities. At the same time the basic 3D characteristics of some comercial and open source software are described, as well as the application to some samples on archeological researchs

  12. DELTA 3D PRINTER

    OpenAIRE

    ȘOVĂILĂ Florin; ȘOVĂILĂ Claudiu; BAROIU Nicuşor

    2016-01-01

    3D printing is a very used process in industry, the generic name being “rapid prototyping”. The essential advantage of a 3D printer is that it allows the designers to produce a prototype in a very short time, which is tested and quickly remodeled, considerably reducing the required time to get from the prototype phase to the final product. At the same time, through this technique we can achieve components with very precise forms, complex pieces that, through classical methods, could have been...

  13. Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues.

    Science.gov (United States)

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

    2010-02-01

    Biological specimens are rife with optical inhomogeneities that seriously degrade imaging performance under all but the most ideal conditions. Measuring and then correcting for these inhomogeneities is the province of adaptive optics. Here we introduce an approach to adaptive optics in microscopy wherein the rear pupil of an objective lens is segmented into subregions, and light is directed individually to each subregion to measure, by image shift, the deflection faced by each group of rays as they emerge from the objective and travel through the specimen toward the focus. Applying our method to two-photon microscopy, we could recover near-diffraction-limited performance from a variety of biological and nonbiological samples exhibiting aberrations large or small and smoothly varying or abruptly changing. In particular, results from fixed mouse cortical slices illustrate our ability to improve signal and resolution to depths of 400 microm.

  14. Development of a tomographic system adapted to 3D measurement of contaminated wounds based on the Cacao concept (Computer aided collimation Gamma Camera)

    International Nuclear Information System (INIS)

    Douiri, A.

    2002-03-01

    The computer aided collimation gamma camera (CACAO in French) is a gamma camera using a collimator with large holes, a supplementary linear scanning motion during the acquisition and a dedicated reconstruction program taking full account of the source depth. The CACAO system was introduced to improve both the sensitivity and the resolution in nuclear medicine. This thesis focuses on the design of a fast and robust reconstruction algorithm in the CACAO project. We start by an overview of tomographic imaging techniques in nuclear medicine. After modelling the physical CACAO system, we present the complete reconstruction program which involves three steps: 1) shift and sum 2) deconvolution and filtering 3) rotation and sum. The deconvolution is the critical step that decreases the signal to noise ratio of the reconstructed images. We propose a regularized multi-channel algorithm to solve the deconvolution problem. We also present a fast algorithm based on Splines functions and preserving the high quality of the reconstructed images for the shift and the rotation steps. Comparisons of simulated reconstructed images in 2D and 3D for the conventional system (CPHC) and CACAO demonstrate the ability of CACAO system to increase the quality of the SPECT images. Finally, this study concludes with an experimental approach with a pixellated detector conceived for a 3D measurement of contaminated wounds. This experimentation proves the possible advantages of coupling the CACAO project with pixellated detectors. Moreover, a variety of applications could fully benefit from the CACAO system, such as low activity imaging, the use of high-energy gamma isotopes and the visualization of deep organs. Moreover the combination of the CACAO system with a pixels detector may open up further possibilities for the future of nuclear medicine. (author)

  15. Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

    Directory of Open Access Journals (Sweden)

    Luis Daniel Lledó

    2015-03-01

    Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.

  16. Tangible 3D Modelling

    DEFF Research Database (Denmark)

    Hejlesen, Aske K.; Ovesen, Nis

    2012-01-01

    This paper presents an experimental approach to teaching 3D modelling techniques in an Industrial Design programme. The approach includes the use of tangible free form models as tools for improving the overall learning. The paper is based on lecturer and student experiences obtained through...

  17. Robust semi-automatic segmentation of single- and multichannel MRI volumes through adaptable class-specific representation

    Science.gov (United States)

    Nielsen, Casper F.; Passmore, Peter J.

    2002-05-01

    Segmentation of MRI volumes is complicated by noise, inhomogeneity and partial volume artefacts. Fully or semi-automatic methods often require time consuming or unintuitive initialization. Adaptable Class-Specific Representation (ACSR) is a semi-automatic segmentation framework implemented by the Path Growing Algorithm (PGA), which reduces artefacts near segment boundaries. The user visually defines the desired segment classes through the selection of class templates and the following segmentation process is fully automatic. Good results have previously been achieved with color cryo section segmentation and ACSR has been developed further for the MRI modality. In this paper we present two optimizations for robust ACSR segmentation of MRI volumes. Automatic template creation based on an initial segmentation step using Learning Vector Quantization is applied for higher robustness to noise. Inhomogeneity correction is added as a pre-processing step, comparing the EQ and N3 algorithms. Results based on simulated T1-weighed and multispectral (T1 and T2) MRI data from the BrainWeb database and real data from the Internet Brain Segmentation Repository are presented. We show that ACSR segmentation compares favorably to previously published results on the same volumes and discuss the pros and cons of using quantitative ground truth evaluation compared to qualitative visual assessment.

  18. Adaptive output-based command shaping for sway control of a 3D overhead crane with payload hoisting and wind disturbance

    Science.gov (United States)

    Abdullahi, Auwalu M.; Mohamed, Z.; Selamat, H.; Pota, Hemanshu R.; Zainal Abidin, M. S.; Ismail, F. S.; Haruna, A.

    2018-01-01

    Payload hoisting and wind disturbance during crane operations are among the challenging factors that affect a payload sway and thus, affect the crane's performance. This paper proposes a new online adaptive output-based command shaping (AOCS) technique for an effective payload sway reduction of an overhead crane under the influence of those effects. This technique enhances the previously developed output-based command shaping (OCS) which was effective only for a fixed system and without external disturbances. Unlike the conventional input shaping design technique which requires the system's natural frequency and damping ratio, the proposed technique is designed by using the output signal and thus, an online adaptive algorithm can be formulated. To test the effectiveness of the AOCS, experiments are carried out using a laboratory overhead crane with a payload hoisting in the presence of wind, and with different payloads. The superiority of the method is confirmed by 82% and 29% reductions in the overall sway and the maximum transient sway respectively, when compared to the OCS, and two robust input shapers namely Zero Vibration Derivative-Derivative and Extra-Insensitive shapers. Furthermore, the method demonstrates a uniform crane's performance under all conditions. It is envisaged that the proposed method can be very useful in designing an effective controller for a crane system with an unknown payload and under the influence of external disturbances.

  19. Uncovering the true nature of deformation microstructures using 3D analysis methods

    International Nuclear Information System (INIS)

    Ferry, M; Quadir, M Z; Afrin, N; Xu, W; Loeb, A; Soe, B; McMahon, C; George, C; Bassman, L

    2015-01-01

    Three-dimensional electron backscatter diffraction (3D EBSD) has emerged as a powerful technique for generating 3D crystallographic information in reasonably large volumes of a microstructure. The technique uses a focused ion beam (FIB) as a high precision serial sectioning device for generating consecutive ion milled surfaces of a material, with each milled surface subsequently mapped by EBSD. The successive EBSD maps are combined using a suitable post-processing method to generate a crystallographic volume of the microstructure. The first part of this paper shows the usefulness of 3D EBSD for understanding the origin of various structural features associated with the plastic deformation of metals. The second part describes a new method for automatically identifying the various types of low and high angle boundaries found in deformed and annealed metals, particularly those associated with grains exhibiting subtle and gradual variations in orientation. We have adapted a 2D image segmentation technique, fast multiscale clustering, to 3D EBSD data using a novel variance function to accommodate quaternion data. This adaptation is capable of segmenting based on subtle and gradual variation as well as on sharp boundaries within the data. We demonstrate the excellent capabilities of this technique with application to 3D EBSD data sets generated from a range of cold rolled and annealed metals described in the paper. (paper)

  20. Twin Peaks - 3D

    Science.gov (United States)

    1997-01-01

    The two hills in the distance, approximately one to two kilometers away, have been dubbed the 'Twin Peaks' and are of great interest to Pathfinder scientists as objects of future study. 3D glasses are necessary to identify surface detail. The white areas on the left hill, called the 'Ski Run' by scientists, may have been formed by hydrologic processes.The IMP is a stereo imaging system with color capability provided by 24 selectable filters -- twelve filters per 'eye.Click below to see the left and right views individually. [figure removed for brevity, see original site] Left [figure removed for brevity, see original site] Right

  1. Shaping 3-D boxes

    DEFF Research Database (Denmark)

    Stenholt, Rasmus; Madsen, Claus B.

    2011-01-01

    Enabling users to shape 3-D boxes in immersive virtual environments is a non-trivial problem. In this paper, a new family of techniques for creating rectangular boxes of arbitrary position, orientation, and size is presented and evaluated. These new techniques are based solely on position data......F) docking experiment against an existing technique, which requires the user to perform the rotation and scaling of the box explicitly. The precision of the users' box construction is evaluated by a novel error metric measuring the difference between two boxes. The results of the experiment strongly indicate...

  2. 3D Audio System

    Science.gov (United States)

    1992-01-01

    Ames Research Center research into virtual reality led to the development of the Convolvotron, a high speed digital audio processing system that delivers three-dimensional sound over headphones. It consists of a two-card set designed for use with a personal computer. The Convolvotron's primary application is presentation of 3D audio signals over headphones. Four independent sound sources are filtered with large time-varying filters that compensate for motion. The perceived location of the sound remains constant. Possible applications are in air traffic control towers or airplane cockpits, hearing and perception research and virtual reality development.

  3. In situ repair of bone and cartilage defects using 3D scanning and 3D printing.

    Science.gov (United States)

    Li, Lan; Yu, Fei; Shi, Jianping; Shen, Sheng; Teng, Huajian; Yang, Jiquan; Wang, Xingsong; Jiang, Qing

    2017-08-25

    Three-dimensional (3D) printing is a rapidly emerging technology that promises to transform tissue engineering into a commercially successful biomedical industry. However, the use of robotic bioprinters alone is not sufficient for disease treatment. This study aimed to report the combined application of 3D scanning and 3D printing for treating bone and cartilage defects. Three different kinds of defect models were created to mimic three orthopedic diseases: large segmental defects of long bones, free-form fracture of femoral condyle, and International Cartilage Repair Society grade IV chondral lesion. Feasibility of in situ 3D bioprinting for these diseases was explored. The 3D digital models of samples with defects and corresponding healthy parts were obtained using high-resolution 3D scanning. The Boolean operation was used to achieve the shape of the defects, and then the target geometries were imported in a 3D bioprinter. Two kinds of photopolymerized hydrogels were synthesized as bioinks. Finally, the defects of bone and cartilage were restored perfectly in situ using 3D bioprinting. The results of this study suggested that 3D scanning and 3D bioprinting could provide another strategy for tissue engineering and regenerative medicine.

  4. EVALUATION OF METHODS FOR COREGISTRATION AND FUSION OF RPAS-BASED 3D POINT CLOUDS AND THERMAL INFRARED IMAGES

    Directory of Open Access Journals (Sweden)

    L. Hoegner

    2016-06-01

    Full Text Available This paper discusses the automatic coregistration and fusion of 3d point clouds generated from aerial image sequences and corresponding thermal infrared (TIR images. Both RGB and TIR images have been taken from a RPAS platform with a predefined flight path where every RGB image has a corresponding TIR image taken from the same position and with the same orientation with respect to the accuracy of the RPAS system and the inertial measurement unit. To remove remaining differences in the exterior orientation, different strategies for coregistering RGB and TIR images are discussed: (i coregistration based on 2D line segments for every single TIR image and the corresponding RGB image. This method implies a mainly planar scene to avoid mismatches; (ii coregistration of both the dense 3D point clouds from RGB images and from TIR images by coregistering 2D image projections of both point clouds; (iii coregistration based on 2D line segments in every single TIR image and 3D line segments extracted from intersections of planes fitted in the segmented dense 3D point cloud; (iv coregistration of both the dense 3D point clouds from RGB images and from TIR images using both ICP and an adapted version based on corresponding segmented planes; (v coregistration of both image sets based on point features. The quality is measured by comparing the differences of the back projection of homologous points in both corrected RGB and TIR images.

  5. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET

    Science.gov (United States)

    Hatt, Mathieu; Cheze-Lerest, Catherine; Turzo, Alexandre; Roux, Christian; Visvikis, Dimitris

    2009-01-01

    Accurate volume estimation in PET is crucial for different oncology applications. The objective of our study was to develop a new fuzzy locally adaptive Bayesian (FLAB) segmentation for automatic lesion volume delineation. FLAB was compared with a threshold approach as well as the previously proposed fuzzy hidden Markov chains (FHMC) and the Fuzzy C-Means (FCM) algorithms. The performance of the algorithms was assessed on acquired datasets of the IEC phantom, covering a range of spherical lesion sizes (10–37mm), contrast ratios (4:1 and 8:1), noise levels (1, 2 and 5 min acquisitions) and voxel sizes (8mm3 and 64mm3). In addition, the performance of the FLAB model was assessed on realistic non-uniform and non-spherical volumes simulated from patient lesions. Results show that FLAB performs better than the other methodologies, particularly for smaller objects. The volume error was 5%–15% for the different sphere sizes (down to 13mm), contrast and image qualities considered, with a high reproducibility (variation <4%). By comparison, the thresholding results were greatly dependent on image contrast and noise, whereas FCM results were less dependent on noise but consistently failed to segment lesions <2cm. In addition, FLAB performed consistently better for lesions <2cm in comparison to the FHMC algorithm. Finally the FLAB model provided errors less than 10% for non-spherical lesions with inhomogeneous activity distributions. Future developments will concentrate on an extension of FLAB in order to allow the segmentation of separate activity distribution regions within the same functional volume as well as a robustness study with respect to different scanners and reconstruction algorithms. PMID:19150782

  6. From medical imaging data to 3D printed anatomical models.

    Science.gov (United States)

    Bücking, Thore M; Hill, Emma R; Robertson, James L; Maneas, Efthymios; Plumb, Andrew A; Nikitichev, Daniil I

    2017-01-01

    Anatomical models are important training and teaching tools in the clinical environment and are routinely used in medical imaging research. Advances in segmentation algorithms and increased availability of three-dimensional (3D) printers have made it possible to create cost-efficient patient-specific models without expert knowledge. We introduce a general workflow that can be used to convert volumetric medical imaging data (as generated by Computer Tomography (CT)) to 3D printed physical models. This process is broken up into three steps: image segmentation, mesh refinement and 3D printing. To lower the barrier to entry and provide the best options when aiming to 3D print an anatomical model from medical images, we provide an overview of relevant free and open-source image segmentation tools as well as 3D printing technologies. We demonstrate the utility of this streamlined workflow by creating models of ribs, liver, and lung using a Fused Deposition Modelling 3D printer.

  7. FUN3D Manual: 12.9

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2016-01-01

    This manual describes the installation and execution of FUN3D version 12.9, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  8. FUN3D Manual: 12.5

    Science.gov (United States)

    Biedron, Robert T.; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, William L.; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; Rumsey, Christopher L.; hide

    2014-01-01

    This manual describes the installation and execution of FUN3D version 12.5, including optional dependent packages. FUN3D is a suite of computational uid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables ecient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  9. FUN3D Manual: 12.4

    Science.gov (United States)

    Biedron, Robert T.; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; Rumsey, Christopher L.; hide

    2014-01-01

    This manual describes the installation and execution of FUN3D version 12.4, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixedelement unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  10. FUN3D Manual: 13.3

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2018-01-01

    This manual describes the installation and execution of FUN3D version 13.3, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  11. FUN3D Manual: 13.0

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bill; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2016-01-01

    This manual describes the installation and execution of FUN3D version 13.0, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  12. FUN3D Manual: 12.6

    Science.gov (United States)

    Biedron, Robert T.; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, William L.; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; Rumsey, Christopher L.; hide

    2015-01-01

    This manual describes the installation and execution of FUN3D version 12.6, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  13. FUN3D Manual: 12.8

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2015-01-01

    This manual describes the installation and execution of FUN3D version 12.8, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  14. FUN3D Manual: 12.7

    Science.gov (United States)

    Biedron, Robert T.; Carlson, Jan-Renee; Derlaga, Joseph M.; Gnoffo, Peter A.; Hammond, Dana P.; Jones, William T.; Kleb, Bil; Lee-Rausch, Elizabeth M.; Nielsen, Eric J.; Park, Michael A.; hide

    2015-01-01

    This manual describes the installation and execution of FUN3D version 12.7, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

  15. 3D ADAPTIVE MESH REFINEMENT SIMULATIONS OF THE GAS CLOUD G2 BORN WITHIN THE DISKS OF YOUNG STARS IN THE GALACTIC CENTER

    Energy Technology Data Exchange (ETDEWEB)

    Schartmann, M.; Ballone, A.; Burkert, A. [Universitäts-Sternwarte München, Scheinerstraße 1, D-81679 München (Germany); Gillessen, S.; Genzel, R.; Pfuhl, O.; Eisenhauer, F.; Plewa, P. M.; Ott, T.; George, E. M.; Habibi, M., E-mail: mschartmann@swin.edu.au [Max-Planck-Institut für extraterrestrische Physik, Postfach 1312, Giessenbachstr., D-85741 Garching (Germany)

    2015-10-01

    The dusty, ionized gas cloud G2 is currently passing the massive black hole in the Galactic Center at a distance of roughly 2400 Schwarzschild radii. We explore the possibility of a starting point of the cloud within the disks of young stars. We make use of the large amount of new observations in order to put constraints on G2's origin. Interpreting the observations as a diffuse cloud of gas, we employ three-dimensional hydrodynamical adaptive mesh refinement (AMR) simulations with the PLUTO code and do a detailed comparison with observational data. The simulations presented in this work update our previously obtained results in multiple ways: (1) high resolution three-dimensional hydrodynamical AMR simulations are used, (2) the cloud follows the updated orbit based on the Brackett-γ data, (3) a detailed comparison to the observed high-quality position–velocity (PV) diagrams and the evolution of the total Brackett-γ luminosity is done. We concentrate on two unsolved problems of the diffuse cloud scenario: the unphysical formation epoch only shortly before the first detection and the too steep Brackett-γ light curve obtained in simulations, whereas the observations indicate a constant Brackett-γ luminosity between 2004 and 2013. For a given atmosphere and cloud mass, we find a consistent model that can explain both, the observed Brackett-γ light curve and the PV diagrams of all epochs. Assuming initial pressure equilibrium with the atmosphere, this can be reached for a starting date earlier than roughly 1900, which is close to apo-center and well within the disks of young stars.

  16. 3D Wire 2015

    DEFF Research Database (Denmark)

    Jordi, Moréton; F, Escribano; J. L., Farias

    , therefore, we’ve drawn conclusions and recommendations for future editions of the event, also generalizable to other experiences of gamification especially in events. This report details the methodology and working elements from the design phase, human resources and organization of production......This document is a general report on the implementation of gamification in 3D Wire 2015 event. As the second gamification experience in this event, we have delved deeply in the previous objectives (attracting public areas less frequented exhibition in previous years and enhance networking) and have...... proposed new ones (viralization of the event on social networks and improvement of the integration of international attendees). On the other hand we defined a set of research objectives related to the study of gamification in an eminently social place like an event. Most of the goals have been met and...

  17. Image segmentation for uranium isotopic analysis by SIMS: Combined adaptive thresholding and marker controlled watershed approach

    Energy Technology Data Exchange (ETDEWEB)

    Willingham, David G.; Naes, Benjamin E.; Heasler, Patrick G.; Zimmer, Mindy M.; Barrett, Christopher A.; Addleman, Raymond S.

    2016-05-31

    A novel approach to particle identification and particle isotope ratio determination has been developed for nuclear safeguard applications. This particle search approach combines an adaptive thresholding algorithm and marker-controlled watershed segmentation (MCWS) transform, which improves the secondary ion mass spectrometry (SIMS) isotopic analysis of uranium containing particle populations for nuclear safeguards applications. The Niblack assisted MCWS approach (a.k.a. SEEKER) developed for this work has improved the identification of isotopically unique uranium particles under conditions that have historically presented significant challenges for SIMS image data processing techniques. Particles obtained from five NIST uranium certified reference materials (CRM U129A, U015, U150, U500 and U850) were successfully identified in regions of SIMS image data 1) where a high variability in image intensity existed, 2) where particles were touching or were in close proximity to one another and/or 3) where the magnitude of ion signal for a given region was count limited. Analysis of the isotopic distributions of uranium containing particles identified by SEEKER showed four distinct, accurately identified 235U enrichment distributions, corresponding to the NIST certified 235U/238U isotope ratios for CRM U129A/U015 (not statistically differentiated), U150, U500 and U850. Additionally, comparison of the minor uranium isotope (234U, 235U and 236U) atom percent values verified that, even in the absence of high precision isotope ratio measurements, SEEKER could be used to segment isotopically unique uranium particles from SIMS image data. Although demonstrated specifically for SIMS analysis of uranium containing particles for nuclear safeguards, SEEKER has application in addressing a broad set of image processing challenges.

  18. 3D Printing and 3D Bioprinting in Pediatrics

    OpenAIRE

    Vijayavenkataraman, Sanjairaj; Fuh, Jerry Y H; Lu, Wen Feng

    2017-01-01

    Additive manufacturing, commonly referred to as 3D printing, is a technology that builds three-dimensional structures and components layer by layer. Bioprinting is the use of 3D printing technology to fabricate tissue constructs for regenerative medicine from cell-laden bio-inks. 3D printing and bioprinting have huge potential in revolutionizing the field of tissue engineering and regenerative medicine. This paper reviews the application of 3D printing and bioprinting in the field of pediatrics.

  19. 3D Printing and 3D Bioprinting in Pediatrics.

    Science.gov (United States)

    Vijayavenkataraman, Sanjairaj; Fuh, Jerry Y H; Lu, Wen Feng

    2017-07-13

    Additive manufacturing, commonly referred to as 3D printing, is a technology that builds three-dimensional structures and components layer by layer. Bioprinting is the use of 3D printing technology to fabricate tissue constructs for regenerative medicine from cell-laden bio-inks. 3D printing and bioprinting have huge potential in revolutionizing the field of tissue engineering and regenerative medicine. This paper reviews the application of 3D printing and bioprinting in the field of pediatrics.

  20. 3D printing for dummies

    CERN Document Server

    Hausman, Kalani Kirk

    2014-01-01

    Get started printing out 3D objects quickly and inexpensively! 3D printing is no longer just a figment of your imagination. This remarkable technology is coming to the masses with the growing availability of 3D printers. 3D printers create 3-dimensional layered models and they allow users to create prototypes that use multiple materials and colors.  This friendly-but-straightforward guide examines each type of 3D printing technology available today and gives artists, entrepreneurs, engineers, and hobbyists insight into the amazing things 3D printing has to offer. You'll discover methods for

  1. Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer

    NARCIS (Netherlands)

    L. Bondar (Luiza); M.S. Hoogeman (Mischa); W. Schillemans; B.J.M. Heijmen (Ben)

    2013-01-01

    textabstractFor online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and

  2. Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function.

    Science.gov (United States)

    Shrestha, Ravi; Mohammed, Shahed K; Hasan, Md Mehedi; Zhang, Xuechao; Wahid, Khan A

    2016-08-01

    Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal (GI) diseases by capturing images of human small intestine. Accurate diagnosis of endoscopic images depends heavily on the quality of captured images. Along with image and frame rate, brightness of the image is an important parameter that influences the image quality which leads to the design of an efficient illumination system. Such design involves the choice and placement of proper light source and its ability to illuminate GI surface with proper brightness. Light emitting diodes (LEDs) are normally used as sources where modulated pulses are used to control LED's brightness. In practice, instances like under- and over-illumination are very common in WCE, where the former provides dark images and the later provides bright images with high power consumption. In this paper, we propose a low-power and efficient illumination system that is based on an automated brightness algorithm. The scheme is adaptive in nature, i.e., the brightness level is controlled automatically in real-time while the images are being captured. The captured images are segmented into four equal regions and the brightness level of each region is calculated. Then an adaptive sigmoid function is used to find the optimized brightness level and accordingly a new value of duty cycle of the modulated pulse is generated to capture future images. The algorithm is fully implemented in a capsule prototype and tested with endoscopic images. Commercial capsules like Pillcam and Mirocam were also used in the experiment. The results show that the proposed algorithm works well in controlling the brightness level accordingly to the environmental condition, and as a result, good quality images are captured with an average of 40% brightness level that saves power consumption of the capsule.

  3. An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images.

    Science.gov (United States)

    Feng, Yuan; Dong, Fenglin; Xia, Xiaolong; Hu, Chun-Hong; Fan, Qianmin; Hu, Yanle; Gao, Mingyuan; Mutic, Sasa

    2017-07-01

    Ultrasound (US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis (CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means (FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 ± 5.33%, 97.83 ± 2.17%, 86.38 ± 5.80%, and 92.58 ± 3.68%, respectively. The region-based metrics of average symmetric surface distance (ASSD), root mean square symmetric distance (RMSD), and maximum symmetric surface distance (MSSD) were 0.03 ± 0.04 mm, 0.04 ± 0.03 mm, and 1.18 ± 1.01 mm, respectively. All the metrics except sensitivity were better than that of the non-adaptive algorithm and the conventional FCM. Only three region-based metrics were better than that of the kernel-induced distance based FCM with spatial constraints. Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the

  4. 3D visualisation of underground pipelines : Best strategy for 3D scene creation

    NARCIS (Netherlands)

    Guerrero, J.M.; Zlatanova, S.; Meijers, B.M.

    2013-01-01

    Underground pipelines pose numerous challenges to 3D visualization. Pipes and cables are conceptually simple and narrow objects with clearly defined shapes, spanned over large geographical areas and made of multiple segments. Pipes are usually maintained as linear objects in the databases. However,

  5. 3D game environments create professional 3D game worlds

    CERN Document Server

    Ahearn, Luke

    2008-01-01

    The ultimate resource to help you create triple-A quality art for a variety of game worlds; 3D Game Environments offers detailed tutorials on creating 3D models, applying 2D art to 3D models, and clear concise advice on issues of efficiency and optimization for a 3D game engine. Using Photoshop and 3ds Max as his primary tools, Luke Ahearn explains how to create realistic textures from photo source and uses a variety of techniques to portray dynamic and believable game worlds.From a modern city to a steamy jungle, learn about the planning and technological considerations for 3D modelin

  6. Lunaserv Global Explorer, 3D

    Science.gov (United States)

    Miconi, C. E.; Estes, N. M.; Bowman-Cisneros, E.; Robinson, M. S.

    2015-06-01

    Lunaserv Global Explorer 3D is a platform independent, planetary data visualization application, which serves high resolution base-map imagery and terrain from web map service data sources, and displays it on a 3D spinning-globe interface.

  7. Adaptive multi-cluster fuzzy C-means segmentation of breast parenchymal tissue in digital mammography.

    Science.gov (United States)

    Keller, Brad; Nathan, Diane; Wang, Yan; Zheng, Yuanjie; Gee, James; Conant, Emily; Kontos, Despina

    2011-01-01

    The relative fibroglandular tissue content in the breast, commonly referred to as breast density, has been shown to be the most significant risk factor for breast cancer after age. Currently, the most common approaches to quantify density are based on either semi-automated methods or visual assessment, both of which are highly subjective. This work presents a novel multi-class fuzzy c-means (FCM) algorithm for fully-automated identification and quantification of breast density, optimized for the imaging characteristics of digital mammography. The proposed algorithm involves adaptive FCM clustering based on an optimal number of clusters derived by the tissue properties of the specific mammogram, followed by generation of a final segmentation through cluster agglomeration using linear discriminant analysis. When evaluated on 80 bilateral screening digital mammograms, a strong correlation was observed between algorithm-estimated PD% and radiological ground-truth of r=0.83 (pspatial similarity coefficient of 0.62. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner.

  8. The Future Is 3D

    Science.gov (United States)

    Carter, Luke

    2015-01-01

    3D printers are a way of producing a 3D model of an item from a digital file. The model builds up in successive layers of material placed by the printer controlled by the information in the computer file. In this article the author argues that 3D printers are one of the greatest technological advances of recent times. He discusses practical uses…

  9. The 3D additivist cookbook

    NARCIS (Netherlands)

    Allahyari, Morehshin; Rourke, Daniel; Rasch, Miriam

    The 3D Additivist Cookbook, devised and edited by Morehshin Allahyari & Daniel Rourke, is a free compendium of imaginative, provocative works from over 100 world-leading artists, activists and theorists. The 3D Additivist Cookbook contains .obj and .stl files for the 3D printer, as well as critical

  10. A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning.

    Science.gov (United States)

    Ghose, Soumya; Holloway, Lois; Lim, Karen; Chan, Philip; Veera, Jacqueline; Vinod, Shalini K; Liney, Gary; Greer, Peter B; Dowling, Jason

    2015-06-01

    Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. 3-D contextual Bayesian classifiers

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    distribution for the pixel values as well as a prior distribution for the configuration of class variables within the cross that is made of a pixel and its four nearest neighbours. We will extend these algorithms to 3-D, i.e. we will specify a simultaneous Gaussian distribution for a pixel and its 6 nearest 3......-D neighbours, and generalise the class variable configuration distributions within the 3-D cross given in 2-D algorithms. The new 3-D algorithms are tested on a synthetic 3-D multivariate dataset....

  12. Automatic Segmentation and Online virtualCT in Head-and-Neck Adaptive Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Peroni, Marta, E-mail: marta.peroni@mail.polimi.it [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Ciardo, Delia [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Spadea, Maria Francesca [Department of Experimental and Clinical Medicine, Universita degli Studi Magna Graecia, Catanzaro (Italy); Riboldi, Marco [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy); Comi, Stefania; Alterio, Daniela [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Baroni, Guido [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy); Orecchia, Roberto [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Universita degli Studi di Milano, Milano (Italy); Medical Department, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy)

    2012-11-01

    Purpose: The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. Method: We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. Results: Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. Conclusion: The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.

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

    Science.gov (United States)

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

    2015-01-01

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

  14. 3D Structure of Tillage Soils

    Science.gov (United States)

    González-Torre, Iván; Losada, Juan Carlos; Falconer, Ruth; Hapca, Simona; Tarquis, Ana M.

    2015-04-01

    application of multifractal analysis methods in images for the study of soil structure. Master thesis, UPM, 2014. Houston, A.N.; S. Schmidt, A.M. Tarquis, W. Otten, P.C. Baveye, S.M. Hapca. Effect of scanning and image reconstruction settings in X-ray computed tomography on soil image quality and segmentation performance. Geoderma, 207-208, 154-165, 2013a. Houston, A, Otten, W., Baveye, Ph., Hapca, S. Adaptive-Window Indicator Kriging: A Thresholding Method for Computed Tomography, Computers & Geosciences, 54, 239-248, 2013b. Tarquis, A.M., R.J. Heck, D. Andina, A. Alvarez and J.M. Antón. Multifractal analysis and thresholding of 3D soil images. Ecological Complexity, 6, 230-239, 2009. Tarquis, A.M.; D. Giménez, A. Saa, M.C. Díaz. and J.M. Gascó. Scaling and Multiscaling of Soil Pore Systems Determined by Image Analysis. Scaling Methods in Soil Systems. Pachepsky, Radcliffe and Selim Eds., 19-33, 2003. CRC Press, Boca Ratón, Florida. Acknowledgements First author acknowledges the financial support obtained from Soil Imaging Laboratory (University of Gueplh, Canada) in 2014.

  15. Adaptive local multi-atlas segmentation: application to the heart and the caudate nucleus.

    NARCIS (Netherlands)

    Rikxoort, E.M. van; Isgum, I.; Arzhaeva, Y.; Staring, M.; Klein, S.; Viergever, M.A.; Pluim, J.P.; Ginneken, B. van

    2010-01-01

    Atlas-based segmentation is a powerful generic technique for automatic delineation of structures in volumetric images. Several studies have shown that multi-atlas segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on volumetric data is

  16. Adaptive segmentation of nuclei in H&S stained tendon microscopy

    Science.gov (United States)

    Chuang, Bo-I.; Wu, Po-Ting; Hsu, Jian-Han; Jou, I.-Ming; Su, Fong-Chin; Sun, Yung-Nien

    2015-12-01

    Tendiopathy is a popular clinical issue in recent years. In most cases like trigger finger or tennis elbow, the pathology change can be observed under H and E stained tendon microscopy. However, the qualitative analysis is too subjective and thus the results heavily depend on the observers. We develop an automatic segmentation procedure which segments and counts the nuclei in H and E stained tendon microscopy fast and precisely. This procedure first determines the complexity of images and then segments the nuclei from the image. For the complex images, the proposed method adopts sampling-based thresholding to segment the nuclei. While for the simple images, the Laplacian-based thresholding is employed to re-segment the nuclei more accurately. In the experiments, the proposed method is compared with the experts outlined results. The nuclei number of proposed method is closed to the experts counted, and the processing time of proposed method is much faster than the experts'.

  17. Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models

    DEFF Research Database (Denmark)

    Puonti, Oula; Van Leemput, Koen

    2016-01-01

    In this paper we propose a new generative model for simultaneous brain parcellation and white matter lesion segmentation from multi-contrast magnetic resonance images. The method combines an existing whole-brain segmentation technique with a novel spatial lesion model based on a convolutional...... in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations....... restricted Boltzmann machine. Unlike current state-of-the-art lesion detection techniques based on discriminative modeling, the proposed method is not tuned to one specific scanner or imaging protocol, and simultaneously segments dozens of neuroanatomical structures. Experiments on a public benchmark dataset...

  18. Using 3D in Visualization

    DEFF Research Database (Denmark)

    Wood, Jo; Kirschenbauer, Sabine; Döllner, Jürgen

    2005-01-01

    to display 3D imagery. The extra cartographic degree of freedom offered by using 3D is explored and offered as a motivation for employing 3D in visualization. The use of VR and the construction of virtual environments exploit navigational and behavioral realism, but become most usefil when combined...... with abstracted representations embedded in a 3D space. The interactions between development of geovisualization, the technology used to implement it and the theory surrounding cartographic representation are explored. The dominance of computing technologies, driven particularly by the gaming industry...

  19. 3D for Graphic Designers

    CERN Document Server

    Connell, Ellery

    2011-01-01

    Helping graphic designers expand their 2D skills into the 3D space The trend in graphic design is towards 3D, with the demand for motion graphics, animation, photorealism, and interactivity rapidly increasing. And with the meteoric rise of iPads, smartphones, and other interactive devices, the design landscape is changing faster than ever.2D digital artists who need a quick and efficient way to join this brave new world will want 3D for Graphic Designers. Readers get hands-on basic training in working in the 3D space, including product design, industrial design and visualization, modeling, ani

  20. 3-D printers for libraries

    CERN Document Server

    Griffey, Jason

    2014-01-01

    As the maker movement continues to grow and 3-D printers become more affordable, an expanding group of hobbyists is keen to explore this new technology. In the time-honored tradition of introducing new technologies, many libraries are considering purchasing a 3-D printer. Jason Griffey, an early enthusiast of 3-D printing, has researched the marketplace and seen several systems first hand at the Consumer Electronics Show. In this report he introduces readers to the 3-D printing marketplace, covering such topics asHow fused deposition modeling (FDM) printing workBasic terminology such as build

  1. 3D Bayesian contextual classifiers

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    2000-01-01

    We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....

  2. A 3-D Contextual Classifier

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    1997-01-01

    . This includes the specification of a Gaussian distribution for the pixel values as well as a prior distribution for the configuration of class variables within the cross that is m ade of a pixel and its four nearest neighbours. We will extend this algorithm to 3-D, i.e. we will specify a simultaneous Gaussian...... distr ibution for a pixel and its 6 nearest 3-D neighbours, and generalise the class variable configuration distribution within the 3-D cross. The algorithm is tested on a synthetic 3-D multivariate dataset....

  3. Using 3D in Visualization

    DEFF Research Database (Denmark)

    Wood, Jo; Kirschenbauer, Sabine; Döllner, Jürgen

    2005-01-01

    The notion of three-dimensionality is applied to five stages of the visualization pipeline. While 3D visulization is most often associated with the visual mapping and representation of data, this chapter also identifies its role in the management and assembly of data, and in the media used...... to display 3D imagery. The extra cartographic degree of freedom offered by using 3D is explored and offered as a motivation for employing 3D in visualization. The use of VR and the construction of virtual environments exploit navigational and behavioral realism, but become most usefil when combined...

  4. Qademah Fault 3D Survey

    KAUST Repository

    Hanafy, Sherif M.

    2014-01-01

    Objective: Collect 3D seismic data at Qademah Fault location to 1. 3D traveltime tomography 2. 3D surface wave migration 3. 3D phase velocity 4. Possible reflection processing Acquisition Date: 26 – 28 September 2014 Acquisition Team: Sherif, Kai, Mrinal, Bowen, Ahmed Acquisition Layout: We used 288 receiver arranged in 12 parallel lines, each line has 24 receiver. Inline offset is 5 m and crossline offset is 10 m. One shot is fired at each receiver location. We use the 40 kgm weight drop as seismic source, with 8 to 15 stacks at each shot location.

  5. Shaping 3D Root System Architecture.

    Science.gov (United States)

    Morris, Emily C; Griffiths, Marcus; Golebiowska, Agata; Mairhofer, Stefan; Burr-Hersey, Jasmine; Goh, Tatsuaki; von Wangenheim, Daniel; Atkinson, Brian; Sturrock, Craig J; Lynch, Jonathan P; Vissenberg, Kris; Ritz, Karl; Wells, Darren M; Mooney, Sacha J; Bennett, Malcolm J

    2017-09-11

    Plants are sessile organisms rooted in one place. The soil resources that plants require are often distributed in a highly heterogeneous pattern. To aid foraging, plants have evolved roots whose growth and development are highly responsive to soil signals. As a result, 3D root architecture is shaped by myriad environmental signals to ensure resource capture is optimised and unfavourable environments are avoided. The first signals sensed by newly germinating seeds - gravity and light - direct root growth into the soil to aid seedling establishment. Heterogeneous soil resources, such as water, nitrogen and phosphate, also act as signals that shape 3D root growth to optimise uptake. Root architecture is also modified through biotic interactions that include soil fungi and neighbouring plants. This developmental plasticity results in a 'custom-made' 3D root system that is best adapted to forage for resources in each soil environment that a plant colonises. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. 3D Viewer Platform of Cloud Clustering Management System: Google Map 3D

    Science.gov (United States)

    Choi, Sung-Ja; Lee, Gang-Soo

    The new management system of framework for cloud envrionemnt is needed by the platfrom of convergence according to computing environments of changes. A ISV and small business model is hard to adapt management system of platform which is offered from super business. This article suggest the clustering management system of cloud computing envirionments for ISV and a man of enterprise in small business model. It applies the 3D viewer adapt from map3D & earth of google. It is called 3DV_CCMS as expand the CCMS[1].

  7. Referenceless Phase Holography for 3D Imaging

    Science.gov (United States)

    Kreis, Thomas

    2017-12-01

    Referenceless phase holography generates the full (amplitude and phase) optical field if intensity and phase of this field are given as numerical data. It is based on the interference of two pure phase fields which are produced by reflection of two mutually coherent plane waves at two phase modulating spatial light modulators of the liquid crystal on silicon type. Thus any optical field of any real or artificial 3D scene can be displayed. This means that referenceless phase holography is a promising method for future 3D imaging, e. g. in 3D television. The paper introduces the theory of the method and presents three possible interferometer arrangements, for the first time the Mach-Zehnder and the grating interferometer adapted to this application. The possibilities and problems in calculating the diffraction fields of given 3D scenes are worked out, the best choice and modifications of the algorithms are given. Several novelty experimental examples are given proving the 3D cues depth of field, occlusion and parallax. The benefits and advantages over other holographic approaches are pointed out, open problems and necessary technological developments as well as possibilities and future prospects are discussed.

  8. Market study: 3-D eyetracker

    Science.gov (United States)

    1977-01-01

    A market study of a proposed version of a 3-D eyetracker for initial use at NASA's Ames Research Center was made. The commercialization potential of a simplified, less expensive 3-D eyetracker was ascertained. Primary focus on present and potential users of eyetrackers, as well as present and potential manufacturers has provided an effective means of analyzing the prospects for commercialization.

  9. Euro3D Science Conference

    Science.gov (United States)

    Walsh, J. R.

    2004-02-01

    The Euro3D RTN is an EU funded Research Training Network to foster the exploitation of 3D spectroscopy in Europe. 3D spectroscopy is a general term for spectroscopy of an area of the sky and derives its name from its two spatial + one spectral dimensions. There are an increasing number of instruments which use integral field devices to achieve spectroscopy of an area of the sky, either using lens arrays, optical fibres or image slicers, to pack spectra of multiple pixels on the sky (``spaxels'') onto a 2D detector. On account of the large volume of data and the special methods required to reduce and analyse 3D data, there are only a few centres of expertise and these are mostly involved with instrument developments. There is a perceived lack of expertise in 3D spectroscopy spread though the astronomical community and its use in the armoury of the observational astronomer is viewed as being highly specialised. For precisely this reason the Euro3D RTN was proposed to train young researchers in this area and develop user tools to widen the experience with this particular type of data in Europe. The Euro3D RTN is coordinated by Martin M. Roth (Astrophysikalisches Institut Potsdam) and has been running since July 2002. The first Euro3D science conference was held in Cambridge, UK from 22 to 23 May 2003. The main emphasis of the conference was, in keeping with the RTN, to expose the work of the young post-docs who are funded by the RTN. In addition the team members from the eleven European institutes involved in Euro3D also presented instrumental and observational developments. The conference was organized by Andy Bunker and held at the Institute of Astronomy. There were over thirty participants and 26 talks covered the whole range of application of 3D techniques. The science ranged from Galactic planetary nebulae and globular clusters to kinematics of nearby galaxies out to objects at high redshift. Several talks were devoted to reporting recent observations with newly

  10. 3D printing in dentistry.