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Sample records for 3d segmentation method

  1. 3D model retrieval method based on mesh segmentation

    NASA Astrophysics Data System (ADS)

    Gan, Yuanchao; Tang, Yan; Zhang, Qingchen

    2012-04-01

    In the process of feature description and extraction, current 3D model retrieval algorithms focus on the global features of 3D models but ignore the combination of global and local features of the model. For this reason, they show less effective performance to the models with similar global shape and different local shape. This paper proposes a novel algorithm for 3D model retrieval based on mesh segmentation. The key idea is to exact the structure feature and the local shape feature of 3D models, and then to compares the similarities of the two characteristics and the total similarity between the models. A system that realizes this approach was built and tested on a database of 200 objects and achieves expected results. The results show that the proposed algorithm improves the precision and the recall rate effectively.

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

    NASA Astrophysics Data System (ADS)

    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).

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

    PubMed

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

    2016-07-01

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

  4. A 3-D liver segmentation method with parallel computing for selective internal radiation therapy.

    PubMed

    Goryawala, Mohammed; Guillen, Magno R; Cabrerizo, Mercedes; Barreto, Armando; Gulec, Seza; Barot, Tushar C; Suthar, Rekha R; Bhatt, Ruchir N; Mcgoron, Anthony; Adjouadi, Malek

    2012-01-01

    This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a special localized contouring algorithm. In the segmentation process, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction. Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to 442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.

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

    SciTech Connect

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

    2012-03-15

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

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

    PubMed Central

    Ortiz, Andrés; Palacio, Antonio A.; Górriz, Juan M.; Ramírez, Javier; Salas-González, Diego

    2013-01-01

    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). PMID:23762192

  7. Segmentation of 3D cell membrane images by PDE methods and its applications.

    PubMed

    Mikula, K; Peyriéras, N; Remešíková, M; Stašová, O

    2011-06-01

    We present a set of techniques that enable us to segment objects from 3D cell membrane images. Particularly, we propose methods for detection of approximate cell nuclei centers, extraction of the inner cell boundaries, the surface of the organism and the intercellular borders--the so called intercellular skeleton. All methods are based on numerical solution of partial differential equations. The center detection problem is represented by a level set equation for advective motion in normal direction with curvature term. In case of the inner cell boundaries and the global surface, we use the generalized subjective surface model. The intercellular borders are segmented by the advective level set equation where the velocity field is given by the gradient of the signed distance function to the segmented inner cell boundaries. The distance function is computed by solving the time relaxed eikonal equation. We describe the mathematical models, explain their numerical approximation and finally we present various possible practical applications on the images of zebrafish embryogenesis--computation of important quantitative characteristics, evaluation of the cell shape, detection of cell divisions and others.

  8. Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images.

    PubMed

    Rebouças Filho, Pedro Pedrosa; Cortez, Paulo César; da Silva Barros, Antônio C; C Albuquerque, Victor Hugo; R S Tavares, João Manuel

    2017-01-01

    The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images.

  9. 3D Segmentation with an application of level set-method using MRI volumes for image guided surgery.

    PubMed

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

    2007-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  11. Freehand 3D ultrasound breast tumor segmentation

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

  12. NCC-RANSAC: A Fast Plane Extraction Method for 3-D Range Data Segmentation

    PubMed Central

    Qian, Xiangfei; Ye, Cang

    2015-01-01

    This paper presents a new plane extraction (PE) method based on the random sample consensus (RANSAC) approach. The generic RANSAC-based PE algorithm may over-extract a plane, and it may fail in case of a multistep scene where the RANSAC procedure results in multiple inlier patches that form a slant plane straddling the steps. The CC-RANSAC PE algorithm successfully overcomes the latter limitation if the inlier patches are separate. However, it fails if the inlier patches are connected. A typical scenario is a stairway with a stair wall where the RANSAC plane-fitting procedure results in inliers patches in the tread, riser, and stair wall planes. They connect together and form a plane. The proposed method, called normal-coherence CC-RANSAC (NCC-RANSAC), performs a normal coherence check to all data points of the inlier patches and removes the data points whose normal directions are contradictory to that of the fitted plane. This process results in separate inlier patches, each of which is treated as a candidate plane. A recursive plane clustering process is then executed to grow each of the candidate planes until all planes are extracted in their entireties. The RANSAC plane-fitting and the recursive plane clustering processes are repeated until no more planes are found. A probabilistic model is introduced to predict the success probability of the NCC-RANSAC algorithm and validated with real data of a 3-D time-of-flight camera–SwissRanger SR4000. Experimental results demonstrate that the proposed method extracts more accurate planes with less computational time than the existing RANSAC-based methods. PMID:24771605

  13. Active segmentation of 3D axonal images.

    PubMed

    Muralidhar, Gautam S; Gopinath, Ajay; Bovik, Alan C; Ben-Yakar, Adela

    2012-01-01

    We present an active contour framework for segmenting neuronal axons on 3D confocal microscopy data. Our work is motivated by the need to conduct high throughput experiments involving microfluidic devices and femtosecond lasers to study the genetic mechanisms behind nerve regeneration and repair. While most of the applications for active contours have focused on segmenting closed regions in 2D medical and natural images, there haven't been many applications that have focused on segmenting open-ended curvilinear structures in 2D or higher dimensions. The active contour framework we present here ties together a well known 2D active contour model [5] along with the physics of projection imaging geometry to yield a segmented axon in 3D. Qualitative results illustrate the promise of our approach for segmenting neruonal axons on 3D confocal microscopy data.

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

    PubMed

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

    2006-08-01

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

  15. A hybrid framework for 3D medical image segmentation.

    PubMed

    Chen, Ting; Metaxas, Dimitris

    2005-12-01

    In this paper we propose a novel hybrid 3D segmentation framework which combines Gibbs models, marching cubes and deformable models. In the framework, first we construct a new Gibbs model whose energy function is defined on a high order clique system. The new model includes both region and boundary information during segmentation. Next we improve the original marching cubes method to construct 3D meshes from Gibbs models' output. The 3D mesh serves as the initial geometry of the deformable model. Then we deform the deformable model using external image forces so that the model converges to the object surface. We run the Gibbs model and the deformable model recursively by updating the Gibbs model's parameters using the region and boundary information in the deformable model segmentation result. In our approach, the hybrid combination of region-based methods and boundary-based methods results in improved segmentations of complex structures. The benefit of the methodology is that it produces high quality segmentations of 3D structures using little prior information and minimal user intervention. The modules in this segmentation methodology are developed within the context of the Insight ToolKit (ITK). We present experimental segmentation results of brain tumors and evaluate our method by comparing experimental results with expert manual segmentations. The evaluation results show that the methodology achieves high quality segmentation results with computational efficiency. We also present segmentation results of other clinical objects to illustrate the strength of the methodology as a generic segmentation framework.

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

    PubMed

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

    2014-03-01

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

  17. Segmentation of 3D objects using live wire

    NASA Astrophysics Data System (ADS)

    Falcao, Alexandre X.; Udupa, Jayaram K.

    1997-04-01

    We have been developing user-steered image segmentation methods for situations which require considerable user assistance in object definition. In such situations, our segmentation methods aim (1) to provide effective control to the user on the segmentation process while it is being executed and (2) to minimize the total user's time required in the process. In the past, we have presented two paradigms, referred to as live wire and live lane, for segmenting 3D/4D object boundaries in a slice-by-slice fashion. In this paper, we introduce a 3D extension of the live wire approach which can further reduce the time spent by the user in the segmentation process. In 2D live wire, given a slice, for two specified points (pixel vertices) on the boundary of the object, the best boundary segment (as a set of oriented pixel edges) is the minimum-cost path between the two points. This segment is found via dynamic programming in real time as the user anchors the first point and moves the cursor to indicate the second point. A complete 2D boundary in this slice is identified as a set of consecutive boundary segments forming a 'closed,' 'connected,' 'oriented' contour. The strategy of the 3D extension is that, first, users specify contours via live- wiring on a few orthogonal slices. If these slices are selected strategically, then we have a sufficient number of points on the 3D boundary of the object to do live-wiring automatically on all axial slices of the 3D scene. Based on several validation studies involving segmentation of the bones of the foot in MR images, we found that the 3D extension of live wire is statistically significantly (p less than 0.0001) more repeatable and 2 - 6 times faster (p less than 0.01) than the 2D live wire method and 3 - 15 times faster than manual tracing.

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

    NASA Astrophysics Data System (ADS)

    Wang, Jiahui; Engelmann, Roger; Li, Qiang

    2008-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1993-07-01

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

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

    SciTech Connect

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

    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

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

    PubMed

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

    2011-01-01

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

  2. An accurate multimodal 3-D vessel segmentation method based on brightness variations on OCT layers and curvelet domain fundus image analysis.

    PubMed

    Kafieh, Raheleh; Rabbani, Hossein; Hajizadeh, Fedra; Ommani, Mohammadreza

    2013-10-01

    This paper proposes a multimodal approach for vessel segmentation of macular optical coherence tomography (OCT) slices along with the fundus image. The method is comprised of two separate stages; the first step is 2-D segmentation of blood vessels in curvelet domain, enhanced by taking advantage of vessel information in crossing OCT slices (named feedback procedure), and improved by suppressing the false positives around the optic nerve head. The proposed method for vessel localization of OCT slices is also enhanced utilizing the fact that retinal nerve fiber layer becomes thicker in the presence of the blood vessels. The second stage of this method is axial localization of the vessels in OCT slices and 3-D reconstruction of the blood vessels. Twenty-four macular spectral 3-D OCT scans of 16 normal subjects were acquired using a Heidelberg HRA OCT scanner. Each dataset consisted of a scanning laser ophthalmoscopy (SLO) image and limited number of OCT scans with size of 496 × 512 (namely, for a data with 19 selected OCT slices, the whole data size was 496 × 512 × 19). The method is developed with least complicated algorithms and the results show considerable improvement in accuracy of vessel segmentation over similar methods to produce a local accuracy of 0.9632 in area of SLO, covered with OCT slices, and the overall accuracy of 0.9467 in the whole SLO image. The results are also demonstrative of a direct relation between the overall accuracy and percentage of SLO coverage by OCT slices.

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

    PubMed

    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.

  4. ZipperDB: Predictions of Fibril-forming Segments within Proteins Identified by the 3D Profile Method (from the UCLA-DOE Institute for Genomics and Proteomics)

    DOE Data Explorer

    Goldschmidt, L.; Teng, P. K.; Riek, R.; Eisenberg, D.

    ZipperDB contains predictions of fibril-forming segments within proteins identified by the 3D Profile Method. The UCLA-DOE Institute for Genomics and Proteomics has analyzed over 20,000 putative protein sequences for segments with high fibrillation propensity that could form a "steric zipper"ùtwo self-complementary beta sheets, giving rise to the spine of an amyloid fibril. The approach is unique in that structural information is used to evaluate the likelihood that a particular sequence can form fibrils. [copied with edits from http://www.doe-mbi.ucla.edu/]. In addition to searching the database, academic and non-profit users may also submit their protein sequences to the database.

  5. 3D dento-maxillary osteolytic lesion and active contour segmentation pilot study in CBCT: semi-automatic vs manual methods

    PubMed Central

    Kacem, A; Legoux, H; Le Tenier, M; Hamitouche, C; Arbab-Chirani, R

    2015-01-01

    Objectives: This study was designed to evaluate the reliability of a semi-automatic segmentation tool for dento-maxillary osteolytic image analysis compared with manually defined segmentation in CBCT scans. Methods: Five CBCT scans were selected from patients for whom periapical radiolucency images were available. All images were obtained using a ProMax® 3D Mid Planmeca (Planmeca Oy, Helsinki, Finland) and were acquired with 200-μm voxel size. Two clinicians performed the manual segmentations. Four operators applied three different semi-automatic procedures. The volumes of the lesions were measured. An analysis of dispersion was made for each procedure and each case. An ANOVA was used to evaluate the operator effect. Non-paired t-tests were used to compare semi-automatic procedures with the manual procedure. Statistical significance was set at α = 0.01. Results: The coefficients of variation for the manual procedure were 2.5–3.5% on average. There was no statistical difference between the two operators. The results of manual procedures can be used as a reference. For the semi-automatic procedures, the dispersion around the mean can be elevated depending on the operator and case. ANOVA revealed significant differences between the operators for the three techniques according to cases. Conclusions: Region-based segmentation was only comparable with the manual procedure for delineating a circumscribed osteolytic dento-maxillary lesion. The semi-automatic segmentations tested are interesting but are limited to complex surface structures. A methodology that combines the strengths of both methods could be of interest and should be tested. The improvement in the image analysis that is possible through the segmentation procedure and CBCT image quality could be of value. PMID:25996572

  6. Dynamic deformable models for 3D MRI heart segmentation

    NASA Astrophysics Data System (ADS)

    Zhukov, Leonid; Bao, Zhaosheng; Gusikov, Igor; Wood, John; Breen, David E.

    2002-05-01

    Automated or semiautomated segmentation of medical images decreases interstudy variation, observer bias, and postprocessing time as well as providing clincally-relevant quantitative data. In this paper we present a new dynamic deformable modeling approach to 3D segmentation. It utilizes recently developed dynamic remeshing techniques and curvature estimation methods to produce high-quality meshes. The approach has been implemented in an interactive environment that allows a user to specify an initial model and identify key features in the data. These features act as hard constraints that the model must not pass through as it deforms. We have employed the method to perform semi-automatic segmentation of heart structures from cine MRI data.

  7. A rapid and efficient 2D/3D nuclear segmentation method for analysis of early mouse embryo and stem cell image data.

    PubMed

    Lou, Xinghua; Kang, Minjung; Xenopoulos, Panagiotis; Muñoz-Descalzo, Silvia; Hadjantonakis, Anna-Katerina

    2014-03-11

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

    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. Automated 3D renal segmentation based on image partitioning

    NASA Astrophysics Data System (ADS)

    Yeghiazaryan, Varduhi; Voiculescu, Irina D.

    2016-03-01

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

  11. 3D Building Models Segmentation Based on K-Means++ Cluster Analysis

    NASA Astrophysics Data System (ADS)

    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.

  12. 3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed

    PubMed Central

    Atta-Fosu, Thomas; Guo, Weihong; Jeter, Dana; Mizutani, Claudia M.; Stopczynski, Nathan; Sousa-Neves, Rui

    2017-01-01

    Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. In this paper, we present a segmentation approach based on the geometric morphological properties of the ‘landscape’ using curvatures. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. We compare with some popular approaches and show the advantage of the proposed method. PMID:28280723

  13. Automated 3D vascular segmentation in CT hepatic venography

    NASA Astrophysics Data System (ADS)

    Fetita, Catalin; Lucidarme, Olivier; Preteux, Francoise

    2005-08-01

    In the framework of preoperative evaluation of the hepatic venous anatomy in living-donor liver transplantation or oncologic rejections, this paper proposes an automated approach for the 3D segmentation of the liver vascular structure from 3D CT hepatic venography data. The developed segmentation approach takes into account the specificities of anatomical structures in terms of spatial location, connectivity and morphometric properties. It implements basic and advanced morphological operators (closing, geodesic dilation, gray-level reconstruction, sup-constrained connection cost) in mono- and multi-resolution filtering schemes in order to achieve an automated 3D reconstruction of the opacified hepatic vessels. A thorough investigation of the venous anatomy including morphometric parameter estimation is then possible via computer-vision 3D rendering, interaction and navigation capabilities.

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

    PubMed Central

    Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C.

    2016-01-01

    Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets. PMID:27332860

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

    PubMed

    Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C

    2016-01-01

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

  16. Improving segmentation of 3D touching cell nuclei using flow tracking on surface meshes.

    PubMed

    Li, Gang; Guo, Lei

    2012-01-01

    Automatic segmentation of touching cell nuclei in 3D microscopy images is of great importance in bioimage informatics and computational biology. This paper presents a novel method for improving 3D touching cell nuclei segmentation. Given binary touching nuclei by the method in Li et al. (2007), our method herein consists of several steps: surface mesh reconstruction and curvature information estimation; direction field diffusion on surface meshes; flow tracking on surface meshes; and projection of surface mesh segmentation to volumetric images. The method is validated on both synthesised and real 3D touching cell nuclei images, demonstrating its validity and effectiveness.

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

    PubMed

    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.

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

    PubMed Central

    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. PMID:26599505

  19. Vessel segmentation in 3D spectral OCT scans of the retina

    NASA Astrophysics Data System (ADS)

    Niemeijer, Meindert; Garvin, Mona K.; van Ginneken, Bram; Sonka, Milan; Abràmoff, Michael D.

    2008-03-01

    The latest generation of spectral optical coherence tomography (OCT) scanners is able to image 3D cross-sectional volumes of the retina at a high resolution and high speed. These scans offer a detailed view of the structure of the retina. Automated segmentation of the vessels in these volumes may lead to more objective diagnosis of retinal vascular disease including hypertensive retinopathy, retinopathy of prematurity. Additionally, vessel segmentation can allow color fundus images to be registered to these 3D volumes, possibly leading to a better understanding of the structure and localization of retinal structures and lesions. In this paper we present a method for automatically segmenting the vessels in a 3D OCT volume. First, the retina is automatically segmented into multiple layers, using simultaneous segmentation of their boundary surfaces in 3D. Next, a 2D projection of the vessels is produced by only using information from certain segmented layers. Finally, a supervised, pixel classification based vessel segmentation approach is applied to the projection image. We compared the influence of two methods for the projection on the performance of the vessel segmentation on 10 optic nerve head centered 3D OCT scans. The method was trained on 5 independent scans. Using ROC analysis, our proposed vessel segmentation system obtains an area under the curve of 0.970 when compared with the segmentation of a human observer.

  20. Ultrafast superpixel segmentation of large 3D medical datasets

    NASA Astrophysics Data System (ADS)

    Leblond, Antoine; Kauffmann, Claude

    2016-03-01

    Even with recent hardware improvements, superpixel segmentation of large 3D medical images at interactive speed (<500 ms) remains a challenge. We will describe methods to achieve such performances using a GPU based hybrid framework implementing wavefront propagation and cellular automata resolution. Tasks will be scheduled in blocks (work units) using a wavefront propagation strategy, therefore allowing sparse scheduling. Because work units has been designed as spatially cohesive, the fast Thread Group Shared Memory can be used and reused through a Gauss-Seidel like acceleration. The work unit partitioning scheme will however vary on odd- and even-numbered iterations to reduce convergence barriers. Synchronization will be ensured by an 8-step 3D variant of the traditional Red Black Ordering scheme. An attack model and early termination will also be described and implemented as additional acceleration techniques. Using our hybrid framework and typical operating parameters, we were able to compute the superpixels of a high-resolution 512x512x512 aortic angioCT scan in 283 ms using a AMD R9 290X GPU. We achieved a 22.3X speed-up factor compared to the published reference GPU implementation.

  1. Segmentation of 3D microPET images of the rat brain via the hybrid gaussian mixture method with kernel density estimation.

    PubMed

    Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing

    2012-01-01

    Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method.

  2. 3D CT spine data segmentation and analysis of vertebrae bone lesions.

    PubMed

    Peter, R; Malinsky, M; Ourednicek, P; Jan, J

    2013-01-01

    A method is presented aiming at detecting and classifying bone lesions in 3D CT data of human spine, via Bayesian approach utilizing Markov random fields. A developed algorithm for necessary segmentation of individual possibly heavily distorted vertebrae based on 3D intensity modeling of vertebra types is presented as well.

  3. Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans.

    PubMed

    Freeborough, P A; Fox, N C; Kitney, R I

    1997-05-01

    Interactive algorithms are an attractive approach to the accurate segmentation of 3D brain scans as they potentially improve the reliability of fully automated segmentation while avoiding the labour intensiveness and inaccuracies of manual segmentation. We present a 3D image analysis package (MIDAS) with a novel architecture enabling highly interactive segmentation algorithms to be implemented as add on modules. Interactive methods based on intensity thresholding, region growing and the constrained application of morphological operators are also presented. The methods involve the application of constraints and freedoms on the algorithms coupled with real time visualisation of the effect. This methodology has been applied to the segmentation, visualisation and measurement of the whole brain and a small irregular neuroanatomical structure, the hippocampus. We demonstrate reproducible and anatomically accurate segmentations of these structures. The efficacy of one method in measuring volume loss (atrophy) of the hippocampus in Alzheimer's disease is shown and is compared to conventional methods.

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

    PubMed

    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.

  5. Random walk based segmentation for the prostate on 3D transrectal ultrasound images

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

    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.

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

    PubMed Central

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

    2016-01-01

    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. PMID:27660383

  7. Segmentation of brain blood vessels using projections in 3-D CT angiography images.

    PubMed

    Babin, Danilo; Vansteenkiste, Ewout; Pizurica, Aleksandra; Philips, Wilfried

    2011-01-01

    Segmenting cerebral blood vessels is of great importance in diagnostic and clinical applications, especially in quantitative diagnostics and surgery on aneurysms and arteriovenous malformations (AVM). Segmentation of CT angiography images requires algorithms robust to high intensity noise, while being able to segment low-contrast vessels. Because of this, most of the existing methods require user intervention. In this work we propose an automatic algorithm for efficient segmentation of 3-D CT angiography images of cerebral blood vessels. Our method is robust to high intensity noise and is able to accurately segment blood vessels with high range of luminance values, as well as low-contrast vessels.

  8. Volumetric CT-based segmentation of NSCLC using 3D-Slicer

    PubMed Central

    Velazquez, Emmanuel Rios; Parmar, Chintan; Jermoumi, Mohammed; Mak, Raymond H.; van Baardwijk, Angela; Fennessy, Fiona M.; Lewis, John H.; De Ruysscher, Dirk; Kikinis, Ron; Lambin, Philippe; Aerts, Hugo J. W. L.

    2013-01-01

    Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck. PMID:24346241

  9. Volumetric CT-based segmentation of NSCLC using 3D-Slicer

    NASA Astrophysics Data System (ADS)

    Velazquez, Emmanuel Rios; Parmar, Chintan; Jermoumi, Mohammed; Mak, Raymond H.; van Baardwijk, Angela; Fennessy, Fiona M.; Lewis, John H.; de Ruysscher, Dirk; Kikinis, Ron; Lambin, Philippe; Aerts, Hugo J. W. L.

    2013-12-01

    Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the ``gold standard''. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81-0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck.

  10. Hybrid atlas-based and image-based approach for segmenting 3D brain MRIs

    NASA Astrophysics Data System (ADS)

    Bueno, Gloria; Musse, Olivier; Heitz, Fabrice; Armspach, Jean-Paul

    2001-07-01

    This work is a contribution to the problem of localizing key cerebral structures in 3D MRIs and its quantitative evaluation. In pursuing it, the cooperation between an image-based segmentation method and a hierarchical deformable registration approach has been considered. The segmentation relies on two main processes: homotopy modification and contour decision. The first one is achieved by a marker extraction stage where homogeneous 3D regions of an image, I(s), from the data set are identified. These regions, M(I), are obtained combining information from deformable atlas, achieved by the warping of eight previous labeled maps on I(s). Then, the goal of the decision stage is to precisely locate the contours of the 3D regions set by the markers. This contour decision is performed by a 3D extension of the watershed transform. The anatomical structures taken into consideration and embedded into the atlas are brain, ventricles, corpus callosum, cerebellum, right and left hippocampus, medulla and midbrain. The hybrid method operates fully automatically and in 3D, successfully providing segmented brain structures. The quality of the segmentation has been studied in terms of the detected volume ratio by using kappa statistic and ROC analysis. Results of the method are shown and validated on a 3D MRI phantom. This study forms part of an on-going long term research aiming at the creation of a 3D probabilistic multi-purpose anatomical brain atlas.

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  12. An automated image-based method of 3D subject-specific body segment parameter estimation for kinetic analyses of rapid movements.

    PubMed

    Sheets, Alison L; Corazza, Stefano; Andriacchi, Thomas P

    2010-01-01

    Accurate subject-specific body segment parameters (BSPs) are necessary to perform kinetic analyses of human movements with large accelerations, or no external contact forces or moments. A new automated topographical image-based method of estimating segment mass, center of mass (CM) position, and moments of inertia is presented. Body geometry and volume were measured using a laser scanner, then an automated pose and shape registration algorithm segmented the scanned body surface, and identified joint center (JC) positions. Assuming the constant segment densities of Dempster, thigh and shank masses, CM locations, and moments of inertia were estimated for four male subjects with body mass indexes (BMIs) of 19.7-38.2. The subject-specific BSP were compared with those determined using Dempster and Clauser regression equations. The influence of BSP and BMI differences on knee and hip net forces and moments during a running swing phase were quantified for the subjects with the smallest and largest BMIs. Subject-specific BSP for 15 body segments were quickly calculated using the image-based method, and total subject masses were overestimated by 1.7-2.9%.When compared with the Dempster and Clauser methods, image-based and regression estimated thigh BSP varied more than the shank parameters. Thigh masses and hip JC to thigh CM distances were consistently larger, and each transverse moment of inertia was smaller using the image-based method. Because the shank had larger linear and angular accelerations than the thigh during the running swing phase, shank BSP differences had a larger effect on calculated intersegmental forces and moments at the knee joint than thigh BSP differences did at the hip. It was the net knee kinetic differences caused by the shank BSP differences that were the largest contributors to the hip variations. Finally, BSP differences produced larger kinetic differences for the subject with larger segment masses, suggesting that parameter accuracy is more

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

    PubMed

    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

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

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

    PubMed

    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.

  16. Automated 3D Ultrasound Image Segmentation to Aid Breast Cancer Image Interpretation

    PubMed Central

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

    2015-01-01

    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. PMID:26547117

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

    NASA Astrophysics Data System (ADS)

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

    2004-05-01

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

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

    PubMed

    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 < 0.05), regardless of the acquisition technique. There were no significant differences between Groups 1 and 3. The ratings for Raters 1 and 2 had good correlation for overall quality (ICC = 0.63) and excellent correlation for the total number of vessels visualized (ICC = 0.77). The intra-rater reliability was good for Rater A (ICC = 0.65). Three models were successfully printed

  19. Automatic 3D kidney segmentation based on shape constrained GC-OAAM

    NASA Astrophysics Data System (ADS)

    Chen, Xinjian; Summers, Ronald M.; Yao, Jianhua

    2011-03-01

    The kidney can be classified into three main tissue types: renal cortex, renal medulla and renal pelvis (or collecting system). Dysfunction of different renal tissue types may cause different kidney diseases. Therefore, accurate and efficient segmentation of kidney into different tissue types plays a very important role in clinical research. In this paper, we propose an automatic 3D kidney segmentation method which segments the kidney into the three different tissue types: renal cortex, medulla and pelvis. The proposed method synergistically combines active appearance model (AAM), live wire (LW) and graph cut (GC) methods, GC-OAAM for short. Our method consists of two main steps. First, a pseudo 3D segmentation method is employed for kidney initialization in which the segmentation is performed slice-by-slice via a multi-object oriented active appearance model (OAAM) method. An improved iterative model refinement algorithm is proposed for the AAM optimization, which synergistically combines the AAM and LW method. Multi-object strategy is applied to help the object initialization. The 3D model constraints are applied to the initialization result. Second, the object shape information generated from the initialization step is integrated into the GC cost computation. A multi-label GC method is used to segment the kidney into cortex, medulla and pelvis. The proposed method was tested on 19 clinical arterial phase CT data sets. The preliminary results showed the feasibility and efficiency of the proposed method.

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

    NASA Astrophysics Data System (ADS)

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

    2004-05-01

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

  1. Supervised recursive segmentation of volumetric CT images for 3D reconstruction of lung and vessel tree.

    PubMed

    Li, Xuanping; Wang, Xue; Dai, Yixiang; Zhang, Pengbo

    2015-12-01

    Three dimensional reconstruction of lung and vessel tree has great significance to 3D observation and quantitative analysis for lung diseases. This paper presents non-sheltered 3D models of lung and vessel tree based on a supervised semi-3D lung tissues segmentation method. A recursive strategy based on geometric active contour is proposed instead of the "coarse-to-fine" framework in existing literature to extract lung tissues from the volumetric CT slices. In this model, the segmentation of the current slice is supervised by the result of the previous one slice due to the slight changes between adjacent slice of lung tissues. Through this mechanism, lung tissues in all the slices are segmented fast and accurately. The serious problems of left and right lungs fusion, caused by partial volume effects, and segmentation of pleural nodules can be settled meanwhile during the semi-3D process. The proposed scheme is evaluated by fifteen scans, from eight healthy participants and seven participants suffering from early-stage lung tumors. The results validate the good performance of the proposed method compared with the "coarse-to-fine" framework. The segmented datasets are utilized to reconstruct the non-sheltered 3D models of lung and vessel tree.

  2. 3D TEM reconstruction and segmentation process of laminar bio-nanocomposites

    SciTech Connect

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

    2015-03-30

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

  3. Alignment, segmentation and 3-D reconstruction of serial sections based on automated algorithm

    NASA Astrophysics Data System (ADS)

    Bian, Weiguo; Tang, Shaojie; Xu, Qiong; Lian, Qin; Wang, Jin; Li, Dichen

    2012-12-01

    A well-defined three-dimensional (3-D) reconstruction of bone-cartilage transitional structures is crucial for the osteochondral restoration. This paper presents an accurate, computationally efficient and fully-automated algorithm for the alignment and segmentation of two-dimensional (2-D) serial to construct the 3-D model of bone-cartilage transitional structures. Entire system includes the following five components: (1) image harvest, (2) image registration, (3) image segmentation, (4) 3-D reconstruction and visualization, and (5) evaluation. A computer program was developed in the environment of Matlab for the automatic alignment and segmentation of serial sections. Automatic alignment algorithm based on the position's cross-correlation of the anatomical characteristic feature points of two sequential sections. A method combining an automatic segmentation and an image threshold processing was applied to capture the regions and structures of interest. SEM micrograph and 3-D model reconstructed directly in digital microscope were used to evaluate the reliability and accuracy of this strategy. The morphology of 3-D model constructed by serial sections is consistent with the results of SEM micrograph and 3-D model of digital microscope.

  4. 3D watershed-based segmentation of internal structures within MR brain images

    NASA Astrophysics Data System (ADS)

    Bueno, Gloria; Musse, Olivier; Heitz, Fabrice; Armspach, Jean-Paul

    2000-06-01

    In this paper an image-based method founded on mathematical morphology is presented in order to facilitate the segmentation of cerebral structures on 3D magnetic resonance images (MRIs). The segmentation is described as an immersion simulation, applied to the modified gradient image, modeled by a generated 3D region adjacency graph (RAG). The segmentation relies on two main processes: homotopy modification and contour decision. The first one is achieved by a marker extraction stage where homogeneous 3D regions are identified in order to attribute an influence zone only to relevant minima of the image. This stage uses contrasted regions from morphological reconstruction and labeled flat regions constrained by the RAG. The goal of the decision stage is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a 3D extension of the watershed transform. Upon completion of the segmentation, the outcome of the preceding process is presented to the user for manual selection of the structures of interest (SOI). Results of this approach are described and illustrated with examples of segmented 3D MRIs of the human head.

  5. 3D Brain Segmentation Using Dual-Front Active Contours with Optional User Interaction

    PubMed Central

    Yezzi, Anthony; Cohen, Laurent D.

    2006-01-01

    Important attributes of 3D brain cortex segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours which minimize image-based energies in a manner that yields flexibly global minimizers based on active regions. Region-based information and boundary-based information may be combined flexibly in the evolution potentials for accurate segmentation results. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the flexibly global nature of the dual-front evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods. PMID:23165037

  6. Image segmentation to inspect 3-D object sizes

    NASA Astrophysics Data System (ADS)

    Hsu, Jui-Pin; Fuh, Chiou-Shann

    1996-01-01

    Object size inspection is an important task and has various applications in computer vision. For example, the automatic control of stone-breaking machines, which perform better if the sizes of the stones to be broken can be predicted. An algorithm is proposed for image segmentation in size inspection for almost round stones with high or low texture. Although our experiments are focused on stones, the algorithm can be applied to other 3-D objects. We use one fixed camera and four light sources at four different positions one at a time, to take four images. Then we compute the image differences and binarize them to extract edges. We explain, step by step, the photographing, the edge extraction, the noise removal, and the edge gap filling. Experimental results are presented.

  7. Segmentation and length measurement of the abdominal blood vessels in 3-D MRI images.

    PubMed

    Babin, Danilo; Vansteenkiste, Ewout; Pizurica, Aleksandra; Philips, Wilfried

    2009-01-01

    In diagnosing diseases and planning surgeries the structure and length of blood vessels is of great importance. In this research we develop a novel method for the segmentation of 2-D and 3-D images with an application to blood vessel length measurements in 3-D abdominal MRI images. Our approach is robust to noise and does not require contrast-enhanced images for segmentation. We use an effective algorithm for skeletonization, graph construction and shortest path estimation to measure the length of blood vessels of interest.

  8. Methods for comparing 3D surface attributes

    NASA Astrophysics Data System (ADS)

    Pang, Alex; Freeman, Adam

    1996-03-01

    A common task in data analysis is to compare two or more sets of data, statistics, presentations, etc. A predominant method in use is side-by-side visual comparison of images. While straightforward, it burdens the user with the task of discerning the differences between the two images. The user if further taxed when the images are of 3D scenes. This paper presents several methods for analyzing the extent, magnitude, and manner in which surfaces in 3D differ in their attributes. The surface geometry are assumed to be identical and only the surface attributes (color, texture, etc.) are variable. As a case in point, we examine the differences obtained when a 3D scene is rendered progressively using radiosity with different form factor calculation methods. The comparison methods include extensions of simple methods such as mapping difference information to color or transparency, and more recent methods including the use of surface texture, perturbation, and adaptive placements of error glyphs.

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

    PubMed

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

    2015-05-01

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

  10. 3D segmentations of neuronal nuclei from confocal microscope image stacks.

    PubMed

    Latorre, Antonio; Alonso-Nanclares, Lidia; Muelas, Santiago; Peña, José-María; Defelipe, Javier

    2013-01-01

    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.

  11. 3D segmentations of neuronal nuclei from confocal microscope image stacks

    PubMed Central

    LaTorre, Antonio; Alonso-Nanclares, Lidia; Muelas, Santiago; Peña, José-María; DeFelipe, Javier

    2013-01-01

    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. PMID:24409123

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

    PubMed Central

    Yan, Yiming; Gao, Fengjiao; Deng, Shupei; Su, Nan

    2017-01-01

    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. PMID:28125018

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

    PubMed

    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.

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

    PubMed

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

    2010-01-01

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

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    Lu, Kongkuo; Higgins, William E

    2011-09-01

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

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

    PubMed

    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.

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

    PubMed

    Rathke, Fabian; Schmidt, Stefan; Schnörr, Christoph

    2014-07-01

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

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

    PubMed

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

    2014-12-01

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

  20. Segmentation of whole cells and cell nuclei from 3-D optical microscope images using dynamic programming.

    PubMed

    McCullough, D P; Gudla, P R; Harris, B S; Collins, J A; Meaburn, K J; Nakaya, M A; Yamaguchi, T P; Misteli, T; Lockett, S J

    2008-05-01

    Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection.

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  3. 3-D segmentation of articular cartilages by graph cuts using knee MR images from osteoarthritis initiative

    NASA Astrophysics Data System (ADS)

    Shim, Hackjoon; Lee, Soochan; Kim, Bohyeong; Tao, Cheng; Chang, Samuel; Yun, Il Dong; Lee, Sang Uk; Kwoh, Kent; Bae, Kyongtae

    2008-03-01

    Knee osteoarthritis is the most common debilitating health condition affecting elderly population. MR imaging of the knee is highly sensitive for diagnosis and evaluation of the extent of knee osteoarthritis. Quantitative analysis of the progression of osteoarthritis is commonly based on segmentation and measurement of articular cartilage from knee MR images. Segmentation of the knee articular cartilage, however, is extremely laborious and technically demanding, because the cartilage is of complex geometry and thin and small in size. To improve precision and efficiency of the segmentation of the cartilage, we have applied a semi-automated segmentation method that is based on an s/t graph cut algorithm. The cost function was defined integrating regional and boundary cues. While regional cues can encode any intensity distributions of two regions, "object" (cartilage) and "background" (the rest), boundary cues are based on the intensity differences between neighboring pixels. For three-dimensional (3-D) segmentation, hard constraints are also specified in 3-D way facilitating user interaction. When our proposed semi-automated method was tested on clinical patients' MR images (160 slices, 0.7 mm slice thickness), a considerable amount of segmentation time was saved with improved efficiency, compared to a manual segmentation approach.

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

    NASA Technical Reports Server (NTRS)

    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.

  5. Multiscale 3-D shape representation and segmentation using spherical wavelets.

    PubMed

    Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

    2007-04-01

    This paper presents a novel multiscale shape representation and segmentation algorithm based on the spherical wavelet transform. This work is motivated by the need to compactly and accurately encode variations at multiple scales in the shape representation in order to drive the segmentation and shape analysis of deep brain structures, such as the caudate nucleus or the hippocampus. Our proposed shape representation can be optimized to compactly encode shape variations in a population at the needed scale and spatial locations, enabling the construction of more descriptive, nonglobal, nonuniform shape probability priors to be included in the segmentation and shape analysis framework. In particular, this representation addresses the shortcomings of techniques that learn a global shape prior at a single scale of analysis and cannot represent fine, local variations in a population of shapes in the presence of a limited dataset. Specifically, our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to two different brain structures, the caudate nucleus and the hippocampus, of interest in the study of schizophrenia. We show: 1) a reconstruction task of a test set to validate the expressiveness of

  6. 3D geometric split-merge segmentation of brain MRI datasets.

    PubMed

    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.

  7. 3D segmentation of lung CT data with graph-cuts: analysis of parameter sensitivities

    NASA Astrophysics Data System (ADS)

    Cha, Jung won; Dunlap, Neal; Wang, Brian; Amini, Amir

    2016-03-01

    Lung boundary image segmentation is important for many tasks including for example in development of radiation treatment plans for subjects with thoracic malignancies. In this paper, we describe a method and parameter settings for accurate 3D lung boundary segmentation based on graph-cuts from X-ray CT data1. Even though previously several researchers have used graph-cuts for image segmentation, to date, no systematic studies have been performed regarding the range of parameter that give accurate results. The energy function in the graph-cuts algorithm requires 3 suitable parameter settings: K, a large constant for assigning seed points, c, the similarity coefficient for n-links, and λ, the terminal coefficient for t-links. We analyzed the parameter sensitivity with four lung data sets from subjects with lung cancer using error metrics. Large values of K created artifacts on segmented images, and relatively much larger value of c than the value of λ influenced the balance between the boundary term and the data term in the energy function, leading to unacceptable segmentation results. For a range of parameter settings, we performed 3D image segmentation, and in each case compared the results with the expert-delineated lung boundaries. We used simple 6-neighborhood systems for n-link in 3D. The 3D image segmentation took 10 minutes for a 512x512x118 ~ 512x512x190 lung CT image volume. Our results indicate that the graph-cuts algorithm was more sensitive to the K and λ parameter settings than to the C parameter and furthermore that amongst the range of parameters tested, K=5 and λ=0.5 yielded good results.

  8. 3D Materials image segmentation by 2D propagation: a graph-cut approach considering homomorphism.

    PubMed

    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. Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras

    PubMed Central

    Morris, Mark; Sellers, William I.

    2015-01-01

    Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints. PMID:25780778

  10. Segmentation of the common carotid artery with active shape models from 3D ultrasound images

    NASA Astrophysics Data System (ADS)

    Yang, Xin; Jin, Jiaoying; He, Wanji; Yuchi, Ming; Ding, Mingyue

    2012-03-01

    Carotid atherosclerosis is a major cause of stroke, a leading cause of death and disability. In this paper, we develop and evaluate a new segmentation method for outlining both lumen and adventitia (inner and outer walls) of common carotid artery (CCA) from three-dimensional ultrasound (3D US) images for carotid atherosclerosis diagnosis and evaluation. The data set consists of sixty-eight, 17× 2× 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80mg atorvastain and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. We investigate the use of Active Shape Models (ASMs) to segment CCA inner and outer walls after statin therapy. The proposed method was evaluated with respect to expert manually outlined boundaries as a surrogate for ground truth. For the lumen and adventitia segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 93.6%+/- 2.6%, 91.8%+/- 3.5%, mean absolute distances (MAD) of 0.28+/- 0.17mm and 0.34 +/- 0.19mm, maximum absolute distances (MAXD) of 0.87 +/- 0.37mm and 0.74 +/- 0.49mm. The proposed algorithm took 4.4 +/- 0.6min to segment a single 3D US images, compared to 11.7+/-1.2min for manual segmentation. Therefore, the method would promote the translation of carotid 3D US to clinical care for the fast, safety and economical monitoring of the atherosclerotic disease progression and regression during therapy.

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

    PubMed

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

    2017-03-08

    Digital reconstruction, or tracing, of 3-dimensional (3D) 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 work, we proposed to use 3D 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 3D microscopy images from different organisms. Results showed that the proposed methods improved the tracing performance significantly when combined with different reconstruction algorithms.

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

    PubMed

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

    2010-04-01

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

  13. A shape prior-based MRF model for 3D masseter muscle segmentation

    NASA Astrophysics Data System (ADS)

    Majeed, Tahir; Fundana, Ketut; Lüthi, Marcel; Beinemann, Jörg; Cattin, Philippe

    2012-02-01

    Medical image segmentation is generally an ill-posed problem that can only be solved by incorporating prior knowledge. The ambiguities arise due to the presence of noise, weak edges, imaging artifacts, inhomogeneous interior and adjacent anatomical structures having similar intensity profile as the target structure. In this paper we propose a novel approach to segment the masseter muscle using the graph-cut incorporating additional 3D shape priors in CT datasets, which is robust to noise; artifacts; and shape deformations. The main contribution of this paper is in translating the 3D shape knowledge into both unary and pairwise potentials of the Markov Random Field (MRF). The segmentation task is casted as a Maximum-A-Posteriori (MAP) estimation of the MRF. Graph-cut is then used to obtain the global minimum which results in the segmentation of the masseter muscle. The method is tested on 21 CT datasets of the masseter muscle, which are noisy with almost all possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. the very common dental fillings and dental implants. We show that the proposed technique produces clinically acceptable results to the challenging problem of muscle segmentation, and further provide a quantitative and qualitative comparison with other methods. We statistically show that adding additional shape prior into both unary and pairwise potentials can increase the robustness of the proposed method in noisy datasets.

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

    PubMed

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

    2012-12-12

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

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

    PubMed Central

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

    2012-01-01

    Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances. PMID:23235454

  16. Automatic 2D and 3D segmentation of liver from Computerised Tomography

    NASA Astrophysics Data System (ADS)

    Evans, Alun

    As part of the diagnosis of liver disease, a Computerised Tomography (CT) scan is taken of the patient, which the clinician then uses for assistance in determining the presence and extent of the disease. This thesis presents the background, methodology, results and future work of a project that employs automated methods to segment liver tissue. The clinical motivation behind this work is the desire to facilitate the diagnosis of liver disease such as cirrhosis or cancer, assist in volume determination for liver transplantation, and possibly assist in measuring the effect of any treatment given to the liver. Previous attempts at automatic segmentation of liver tissue have relied on 2D, low-level segmentation techniques, such as thresholding and mathematical morphology, to obtain the basic liver structure. The derived boundary can then be smoothed or refined using more advanced methods. The 2D results presented in this thesis improve greatly on this previous work by using a topology adaptive active contour model to accurately segment liver tissue from CT images. The use of conventional snakes for liver segmentation is difficult due to the presence of other organs closely surrounding the liver this new technique avoids this problem by adding an inflationary force to the basic snake equation, and initialising the snake inside the liver. The concepts underlying the 2D technique are extended to 3D, and results of full 3D segmentation of the liver are presented. The 3D technique makes use of an inflationary active surface model which is adaptively reparameterised, according to its size and local curvature, in order that it may more accurately segment the organ. Statistical analysis of the accuracy of the segmentation is presented for 18 healthy liver datasets, and results of the segmentation of unhealthy livers are also shown. The novel work developed during the course of this project has possibilities for use in other areas of medical imaging research, for example the

  17. 3D Mesh Segmentation Based on Markov Random Fields and Graph Cuts

    NASA Astrophysics Data System (ADS)

    Shi, Zhenfeng; Le, Dan; Yu, Liyang; Niu, Xiamu

    3D Mesh segmentation has become an important research field in computer graphics during the past few decades. Many geometry based and semantic oriented approaches for 3D mesh segmentation has been presented. However, only a few algorithms based on Markov Random Field (MRF) has been presented for 3D object segmentation. In this letter, we present a definition of mesh segmentation according to the labeling problem. Inspired by the capability of MRF combining the geometric information and the topology information of a 3D mesh, we propose a novel 3D mesh segmentation model based on MRF and Graph Cuts. Experimental results show that our MRF-based schema achieves an effective segmentation.

  18. Depth map coding using residual segmentation for 3D video system

    NASA Astrophysics Data System (ADS)

    Lee, Cheon; Ho, Yo-Sung

    2013-06-01

    Advanced 3D video systems employ multi-view video-plus-depth data to support the free-viewpoint navigation and comfortable 3D viewing; thus efficient depth map coding becomes an important issue. Unlike the color image, the depth map has a property that depth values of the inner part of an object are monotonic, but those of object boundaries change abruptly. Therefore, residual data generated by prediction errors around object boundaries consume many bits in depth map coding. Representing them with segment data can be better than the use of the conventional transformation around the boundary regions. In this paper, we propose an efficient depth map coding method using a residual segmentation instead of using transformation. The proposed residual segmentation divides residual data into two regions with a segment map and two mean values. If the encoder selects the proposed method in terms of rates, two quantized mean values and an index of the segment map are transmitted. Simulation results show significant gains of up to 10 dB compared to the state-of-the-art coders, such as JPEG2000 and H.264/AVC. [Figure not available: see fulltext.

  19. A universal approach for automatic organ segmentations on 3D CT images based on organ localization and 3D GrabCut

    NASA Astrophysics Data System (ADS)

    Zhou, Xiangrong; Ito, Takaaki; Zhou, Xinxin; Chen, Huayue; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Hoshi, Hiroaki; Fujita, Hiroshi

    2014-03-01

    This paper describes a universal approach to automatic segmentation of different internal organ and tissue regions in three-dimensional (3D) computerized tomography (CT) scans. The proposed approach combines object localization, a probabilistic atlas, and 3D GrabCut techniques to achieve automatic and quick segmentation. The proposed method first detects a tight 3D bounding box that contains the target organ region in CT images and then estimates the prior of each pixel inside the bounding box belonging to the organ region or background based on a dynamically generated probabilistic atlas. Finally, the target organ region is separated from the background by using an improved 3D GrabCut algorithm. A machine-learning method is used to train a detector to localize the 3D bounding box of the target organ using template matching on a selected feature space. A content-based image retrieval method is used for online generation of a patient-specific probabilistic atlas for the target organ based on a database. A 3D GrabCut algorithm is used for final organ segmentation by iteratively estimating the CT number distributions of the target organ and backgrounds using a graph-cuts algorithm. We applied this approach to localize and segment twelve major organ and tissue regions independently based on a database that includes 1300 torso CT scans. In our experiments, we randomly selected numerous CT scans and manually input nine principal types of inner organ regions for performance evaluation. Preliminary results showed the feasibility and efficiency of the proposed approach for addressing automatic organ segmentation issues on CT images.

  20. 3D/2D registration and segmentation of scoliotic vertebrae using statistical models.

    PubMed

    Benameur, Said; Mignotte, Max; Parent, Stefan; Labelle, Hubert; Skalli, Wafa; de Guise, Jacques

    2003-01-01

    We propose a new 3D/2D registration method for vertebrae of the scoliotic spine, using two conventional radiographic views (postero-anterior and lateral), and a priori global knowledge of the geometric structure of each vertebra. This geometric knowledge is efficiently captured by a statistical deformable template integrating a set of admissible deformations, expressed by the first modes of variation in Karhunen-Loeve expansion, of the pathological deformations observed on a representative scoliotic vertebra population. The proposed registration method consists of fitting the projections of this deformable template with the preliminary segmented contours of the corresponding vertebra on the two radiographic views. The 3D/2D registration problem is stated as the minimization of a cost function for each vertebra and solved with a gradient descent technique. Registration of the spine is then done vertebra by vertebra. The proposed method efficiently provides accurate 3D reconstruction of each scoliotic vertebra and, consequently, it also provides accurate knowledge of the 3D structure of the whole scoliotic spine. This registration method has been successfully tested on several biplanar radiographic images and validated on 57 scoliotic vertebrae. The validation results reported in this paper demonstrate that the proposed statistical scheme performs better than other conventional 3D reconstruction methods.

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

    PubMed Central

    Robinson, Sean; Guyon, Laurent; Nevalainen, Jaakko; Toriseva, Mervi

    2015-01-01

    Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy. PMID:26630674

  2. Biview Learning for Human Posture Segmentation from 3D Points Cloud

    PubMed Central

    Qiao, Maoying; Cheng, Jun; Bian, Wei; Tao, Dacheng

    2014-01-01

    Posture segmentation plays an essential role in human motion analysis. The state-of-the-art method extracts sufficiently high-dimensional features from 3D depth images for each 3D point and learns an efficient body part classifier. However, high-dimensional features are memory-consuming and difficult to handle on large-scale training dataset. In this paper, we propose an efficient two-stage dimension reduction scheme, termed biview learning, to encode two independent views which are depth-difference features (DDF) and relative position features (RPF). Biview learning explores the complementary property of DDF and RPF, and uses two stages to learn a compact yet comprehensive low-dimensional feature space for posture segmentation. In the first stage, discriminative locality alignment (DLA) is applied to the high-dimensional DDF to learn a discriminative low-dimensional representation. In the second stage, canonical correlation analysis (CCA) is used to explore the complementary property of RPF and the dimensionality reduced DDF. Finally, we train a support vector machine (SVM) over the output of CCA. We carefully validate the effectiveness of DLA and CCA utilized in the two-stage scheme on our 3D human points cloud dataset. Experimental results show that the proposed biview learning scheme significantly outperforms the state-of-the-art method for human posture segmentation. PMID:24465721

  3. Biview learning for human posture segmentation from 3D points cloud.

    PubMed

    Qiao, Maoying; Cheng, Jun; Bian, Wei; Tao, Dacheng

    2014-01-01

    Posture segmentation plays an essential role in human motion analysis. The state-of-the-art method extracts sufficiently high-dimensional features from 3D depth images for each 3D point and learns an efficient body part classifier. However, high-dimensional features are memory-consuming and difficult to handle on large-scale training dataset. In this paper, we propose an efficient two-stage dimension reduction scheme, termed biview learning, to encode two independent views which are depth-difference features (DDF) and relative position features (RPF). Biview learning explores the complementary property of DDF and RPF, and uses two stages to learn a compact yet comprehensive low-dimensional feature space for posture segmentation. In the first stage, discriminative locality alignment (DLA) is applied to the high-dimensional DDF to learn a discriminative low-dimensional representation. In the second stage, canonical correlation analysis (CCA) is used to explore the complementary property of RPF and the dimensionality reduced DDF. Finally, we train a support vector machine (SVM) over the output of CCA. We carefully validate the effectiveness of DLA and CCA utilized in the two-stage scheme on our 3D human points cloud dataset. Experimental results show that the proposed biview learning scheme significantly outperforms the state-of-the-art method for human posture segmentation.

  4. 3D ultrasound image segmentation using multiple incomplete feature sets

    NASA Astrophysics Data System (ADS)

    Fan, Liexiang; Herrington, David M.; Santago, Peter, II

    1999-05-01

    We use three features, the intensity, texture and motion to obtain robust results for segmentation of intracoronary ultrasound images. Using a parameterized equation to describe the lumen-plaque and media-adventitia boundaries, we formulate the segmentation as a parameter estimation through a cost functional based on the posterior probability, which can handle the incompleteness of the features in ultrasound images by employing outlier detection.

  5. Segmentation of Skin Tumors in High-Frequency 3-D Ultrasound Images.

    PubMed

    Sciolla, Bruno; Cowell, Lester; Dambry, Thibaut; Guibert, Benoît; Delachartre, Philippe

    2017-01-01

    High-frequency 3-D ultrasound imaging is an informative tool for diagnosis, surgery planning and skin lesion examination. The purpose of this article was to describe a semi-automated segmentation tool providing easy access to the extent, shape and volume of a lesion. We propose an adaptive log-likelihood level-set segmentation procedure using non-parametric estimates of the intensity distribution. The algorithm has a single parameter to control the smoothness of the contour, and we describe how a fixed value yields satisfactory segmentation results with an average Dice coefficient of D = 0.76. The algorithm is implemented on a grid, which increases the speed by a factor of 100 compared with a standard pixelwise segmentation. We compare the method with parametric methods making the hypothesis of Rayleigh or Nakagami distributed signals, and illustrate that our method has greater robustness with similar computational speed. Benchmarks are made on realistic synthetic ultrasound images and a data set of nine clinical 3-D images acquired with a 50-MHz imaging system. The proposed algorithm is suitable for use in a clinical context as a post-processing tool.

  6. Parallel graph search: application to intraretinal layer segmentation of 3D macular OCT scans

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

    Image segmentation is of paramount importance for quantitative analysis of medical image data. Recently, a 3-D graph search method which can detect globally optimal interacting surfaces with respect to the cost function of volumetric images has been introduced, and its utility demonstrated in several application areas. Although the method provides excellent segmentation accuracy, its limitation is a slow processing speed when many surfaces are simultaneously segmented in large volumetric datasets. Here, we propose a novel method of parallel graph search, which overcomes the limitation and allows the quick detection of multiple surfaces. To demonstrate the obtained performance with respect to segmentation accuracy and processing speedup, the new approach was applied to retinal optical coherence tomography (OCT) image data and compared with the performance of the former non-parallel method. Our parallel graph search methods for single and double surface detection are approximately 267 and 181 times faster than the original graph search approach in 5 macular OCT volumes (200 x 5 x 1024 voxels) acquired from the right eyes of 5 normal subjects. The resulting segmentation differences were small as demonstrated by the mean unsigned differences between the non-parallel and parallel methods of 0.0 +/- 0.0 voxels (0.0 +/- 0.0 μm) and 0.27 +/- 0.34 voxels (0.53 +/- 0.66 μm) for the single- and dual-surface approaches, respectively.

  7. A 3D Contact Smoothing Method

    SciTech Connect

    Puso, M A; Laursen, T A

    2002-05-02

    Smoothing of contact surfaces can be used to eliminate the chatter typically seen with node on facet contact and give a better representation of the actual contact surface. The latter affect is well demonstrated for problems with interference fits. In this work we present two methods for the smoothing of contact surfaces for 3D finite element contact. In the first method, we employ Gregory patches to smooth the faceted surface in a node on facet implementation. In the second method, we employ a Bezier interpolation of the faceted surface in a mortar method implementation of contact. As is well known, node on facet approaches can exhibit locking due to the failure of the Babuska-Brezzi condition and in some instances fail the patch test. The mortar method implementation is stable and provides optimal convergence in the energy of error. In the this work we demonstrate the superiority of the smoothed versus the non-smoothed node on facet implementations. We also show where the node on facet method fails and some results from the smoothed mortar method implementation.

  8. Method for 3D Airway Topology Extraction

    PubMed Central

    Grothausmann, Roman; Kellner, Manuela; Heidrich, Marko; Lorbeer, Raoul-Amadeus; Ripken, Tammo; Meyer, Heiko; Kuehnel, Mark P.; Ochs, Matthias; Rosenhahn, Bodo

    2015-01-01

    In lungs the number of conducting airway generations as well as bifurcation patterns varies across species and shows specific characteristics relating to illnesses or gene variations. A method to characterize the topology of the mouse airway tree using scanning laser optical tomography (SLOT) tomograms is presented in this paper. It is used to test discrimination between two types of mice based on detected differences in their conducting airway pattern. Based on segmentations of the airways in these tomograms, the main spanning tree of the volume skeleton is computed. The resulting graph structure is used to distinguish between wild type and surfactant protein (SP-D) deficient knock-out mice. PMID:25767561

  9. Comparative Local Quality Assessment of 3D Medical Image Segmentations with Focus on Statistical Shape Model-Based Algorithms.

    PubMed

    Landesberger, Tatiana von; Basgier, Dennis; Becker, Meike

    2016-12-01

    The quality of automatic 3D medical segmentation algorithms needs to be assessed on test datasets comprising several 3D images (i.e., instances of an organ). The experts need to compare the segmentation quality across the dataset in order to detect systematic segmentation problems. However, such comparative evaluation is not supported well by current methods. We present a novel system for assessing and comparing segmentation quality in a dataset with multiple 3D images. The data is analyzed and visualized in several views. We detect and show regions with systematic segmentation quality characteristics. For this purpose, we extended a hierarchical clustering algorithm with a connectivity criterion. We combine quality values across the dataset for determining regions with characteristic segmentation quality across instances. Using our system, the experts can also identify 3D segmentations with extraordinary quality characteristics. While we focus on algorithms based on statistical shape models, our approach can also be applied to cases, where landmark correspondences among instances can be established. We applied our approach to three real datasets: liver, cochlea and facial nerve. The segmentation experts were able to identify organ regions with systematic segmentation characteristics as well as to detect outlier instances.

  10. Segmentation and interpretation of 3D protein images

    SciTech Connect

    Leherte, L.; Baxter, K.; Glasgow, J.; Fortier, S.

    1994-12-31

    The segmentation and interpretation of three-dimensional images of proteins is considered. A topological approach is used to represent a protein structure as a spanning tree of critical points, where each critical point corresponds to a residue or the connectivity between residues. The critical points are subsequently analyzed to recognize secondary structure motifs within the protein. Results of applying the approach to ideal and experimental images of proteins at medium resolution are presented.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  12. Computerized Liver Volumetry on MRI by Using 3D Geodesic Active Contour Segmentation

    PubMed Central

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

    2014-01-01

    OBJECTIVE Our purpose was to develop an accurate automated 3D liver segmentation scheme for measuring liver volumes on MRI. SUBJECTS AND METHODS Our scheme for MRI liver volumetry consisted of three main stages. First, the preprocessing stage was applied to T1-weighted MRI of the liver in the portal venous phase to reduce noise and produce the boundary-enhanced image. This boundary-enhanced image was used as a speed function for a 3D fast-marching algorithm to generate an initial surface that roughly approximated the shape of the liver. A 3D geodesic-active-contour segmentation algorithm refined the initial surface to precisely determine the liver boundaries. The liver volumes determined by our scheme were compared with those manually traced by a radiologist, used as the reference standard. RESULTS The two volumetric methods reached excellent agreement (intraclass correlation coefficient, 0.98) without statistical significance (p = 0.42). The average (± SD) accuracy was 99.4% ± 0.14%, and the average Dice overlap coefficient was 93.6% ± 1.7%. The mean processing time for our automated scheme was 1.03 ± 0.13 minutes, whereas that for manual volumetry was 24.0 ± 4.4 minutes (p < 0.001). CONCLUSION The MRI liver volumetry based on our automated scheme agreed excellently with reference-standard volumetry, and it required substantially less completion time. PMID:24370139

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

    PubMed

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

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

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

  15. Diffusive smoothing of 3D segmented medical data

    PubMed Central

    Patané, Giuseppe

    2014-01-01

    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. PMID:26257940

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    SciTech Connect

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

    2014-06-15

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

  18. Using 3-D shape models to guide segmentation of MR brain images.

    PubMed Central

    Hinshaw, K. P.; Brinkley, J. F.

    1997-01-01

    Accurate segmentation of medical images poses one of the major challenges in computer vision. Approaches that rely solely on intensity information frequently fail because similar intensity values appear in multiple structures. This paper presents a method for using shape knowledge to guide the segmentation process, applying it to the task of finding the surface of the brain. A 3-D model that includes local shape constraints is fitted to an MR volume dataset. The resulting low-resolution surface is used to mask out regions far from the cortical surface, enabling an isosurface extraction algorithm to isolate a more detailed surface boundary. The surfaces generated by this technique are comparable to those achieved by other methods, without requiring user adjustment of a large number of ad hoc parameters. Images Figure 1 Figure 2 Figure 3 Figure 4 PMID:9357670

  19. 3D liver segmentation using multiple region appearances and graph cuts

    SciTech Connect

    Peng, Jialin Zhang, Hongbo; Hu, Peijun; Lu, Fang; Kong, Dexing; Peng, Zhiyi

    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

  20. Lung lobe segmentation by graph search with 3D shape constraints

    NASA Astrophysics Data System (ADS)

    Zhang, Li; Hoffman, Eric A.; Reinhardt, Joseph M.

    2001-05-01

    The lung lobes are natural units for reporting image-based measurements of the respiratory system. Lobar segmentation can also be used in pulmonary image processing to guide registration and drive additional segmentation. We have developed a 3D shape-constrained lobar segmentation technique for volumetric pulmonary CT images. The method consists of a search engine and shape constraints that work together to detect lobar fissures using gray level information and anatomic shape characteristics in two steps: (1) a coarse localization step, (2) a fine tuning step. An error detecting mechanism using shape constraints is used in our method to correct erroneous search results. Our method has been tested in four subjects, and the results are compared to manually traced results. The average RMS difference between the manual results and shape-constrained segmentation results is 2.23 mm. We further validated our method by evaluating the repeatability of lobar volumes measured from repeat scans of the same subject. We compared lobar air and tissue volume variations to show that most of the lobar volume variations are due to difference in air volume scan to scan.

  1. Automatic segmentation and analysis of fibrin networks in 3D confocal microscopy images

    NASA Astrophysics Data System (ADS)

    Liu, Xiaomin; Mu, Jian; Machlus, Kellie R.; Wolberg, Alisa S.; Rosen, Elliot D.; Xu, Zhiliang; Alber, Mark S.; Chen, Danny Z.

    2012-02-01

    Fibrin networks are a major component of blood clots that provides structural support to the formation of growing clots. Abnormal fibrin networks that are too rigid or too unstable can promote cardiovascular problems and/or bleeding. However, current biological studies of fibrin networks rarely perform quantitative analysis of their structural properties (e.g., the density of branch points) due to the massive branching structures of the networks. In this paper, we present a new approach for segmenting and analyzing fibrin networks in 3D confocal microscopy images. We first identify the target fibrin network by applying the 3D region growing method with global thresholding. We then produce a one-voxel wide centerline for each fiber segment along which the branch points and other structural information of the network can be obtained. Branch points are identified by a novel approach based on the outer medial axis. Cells within the fibrin network are segmented by a new algorithm that combines cluster detection and surface reconstruction based on the α-shape approach. Our algorithm has been evaluated on computer phantom images of fibrin networks for identifying branch points. Experiments on z-stack images of different types of fibrin networks yielded results that are consistent with biological observations.

  2. 3D segmentation and image annotation for quantitative diagnosis in lung CT images with pulmonary lesions

    NASA Astrophysics Data System (ADS)

    Li, Suo; Zhu, Yanjie; Sun, Jianyong; Zhang, Jianguo

    2013-03-01

    Pulmonary nodules and ground glass opacities are highly significant findings in high-resolution computed tomography (HRCT) of patients with pulmonary lesion. The appearances of pulmonary nodules and ground glass opacities show a relationship with different lung diseases. According to corresponding characteristic of lesion, pertinent segment methods and quantitative analysis are helpful for control and treat diseases at an earlier and potentially more curable stage. Currently, most of the studies have focused on two-dimensional quantitative analysis of these kinds of deceases. Compared to two-dimensional images, three-dimensional quantitative analysis can take full advantage of isotropic image data acquired by using thin slicing HRCT in space and has better quantitative precision for clinical diagnosis. This presentation designs a computer-aided diagnosis component to segment 3D disease areas of nodules and ground glass opacities in lung CT images, and use AIML (Annotation and image makeup language) to annotate the segmented 3D pulmonary lesions with information of quantitative measurement which may provide more features and information to the radiologists in clinical diagnosis.

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

    PubMed

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

    2014-04-01

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

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

    PubMed Central

    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. PMID:26689833

  5. A Segmentation Algorithm for X-ray 3D Angiography and Vessel Catheterization

    SciTech Connect

    Franchi, Danilo; Rosa, Luigi; Placidi, Giuseppe

    2008-11-06

    Vessel Catheterization is a clinical procedure usually performed by a specialist by means of X-ray fluoroscopic guide with contrast-media. In the present paper, we present a simple and efficient algorithm for vessel segmentation which allows vessel separation and extraction from the background (noise and signal coming from other organs). This would reduce the number of projections (X-ray scans) to reconstruct a complete and accurate 3D vascular model and the radiological risk, in particular for the patient. In what follows, the algorithm is described and some preliminary experimental results are reported illustrating the behaviour of the proposed method.

  6. Image intensity standardization in 3D rotational angiography and its application to vascular segmentation

    NASA Astrophysics Data System (ADS)

    Bogunović, Hrvoje; Radaelli, Alessandro G.; De Craene, Mathieu; Delgado, David; Frangi, Alejandro F.

    2008-03-01

    Knowledge-based vascular segmentation methods typically rely on a pre-built training set of segmented images, which is used to estimate the probability of each voxel to belong to a particular tissue. In 3D Rotational Angiography (3DRA) the same tissue can correspond to different intensity ranges depending on the imaging device, settings and contrast injection protocol. As a result, pre-built training sets do not apply to all images and the best segmentation results are often obtained when the training set is built specifically for each individual image. We present an Image Intensity Standardization (IIS) method designed to ensure a correspondence between specific tissues and intensity ranges common to every image that undergoes the standardization process. The method applies a piecewise linear transformation to the image that aligns the intensity histogram to the histogram taken as reference. The reference histogram has been selected from a high quality image not containing artificial objects such as coils or stents. This is a pre-processing step that allows employing a training set built on a limited number of standardized images for the segmentation of standardized images which were not part of the training set. The effectiveness of the presented IIS technique in combination with a well-validated knowledge-based vasculature segmentation method is quantified on a variety of 3DRA images depicting cerebral arteries and intracranial aneurysms. The proposed IIS method offers a solution to the standardization of tissue classes in routine medical images and effectively improves automation and usability of knowledge-based vascular segmentation algorithms.

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

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

    Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation.

  8. 3-D segmentation of retinal blood vessels in spectral-domain OCT volumes of the optic nerve head

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  9. 3D variational brain tumor segmentation on a clustered feature set

    NASA Astrophysics Data System (ADS)

    Popuri, Karteek; Cobzas, Dana; Jagersand, Martin; Shah, Sirish L.; Murtha, Albert

    2009-02-01

    Tumor segmentation from MRI data is a particularly challenging and time consuming 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. Our work addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multi-dimensional feature set. Further, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this paper is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to the previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned inside and outside region voxel probabilities 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 in the ventricles to be in the tumor and hence better disambiguate the tumor from brain tissue. We show the performance of our method on real MRI scans. The experimental dataset includes MRI scans, from patients with difficult instances, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Our method shows good results on these test cases.

  10. 3-D Ultrasound Segmentation of the Placenta Using the Random Walker Algorithm: Reliability and Agreement.

    PubMed

    Stevenson, Gordon N; Collins, Sally L; Ding, Jane; Impey, Lawrence; Noble, J Alison

    2015-12-01

    Volumetric segmentation of the placenta using 3-D ultrasound is currently performed clinically to investigate correlation between organ volume and fetal outcome or pathology. Previously, interpolative or semi-automatic contour-based methodologies were used to provide volumetric results. We describe the validation of an original random walker (RW)-based algorithm against manual segmentation and an existing semi-automated method, virtual organ computer-aided analysis (VOCAL), using initialization time, inter- and intra-observer variability of volumetric measurements and quantification accuracy (with respect to manual segmentation) as metrics of success. Both semi-automatic methods require initialization. Therefore, the first experiment compared initialization times. Initialization was timed by one observer using 20 subjects. This revealed significant differences (p < 0.001) in time taken to initialize the VOCAL method compared with the RW method. In the second experiment, 10 subjects were used to analyze intra-/inter-observer variability between two observers. Bland-Altman plots were used to analyze variability combined with intra- and inter-observer variability measured by intra-class correlation coefficients, which were reported for all three methods. Intra-class correlation coefficient values for intra-observer variability were higher for the RW method than for VOCAL, and both were similar to manual segmentation. Inter-observer variability was 0.94 (0.88, 0.97), 0.91 (0.81, 0.95) and 0.80 (0.61, 0.90) for manual, RW and VOCAL, respectively. Finally, a third observer with no prior ultrasound experience was introduced and volumetric differences from manual segmentation were reported. Dice similarity coefficients for observers 1, 2 and 3 were respectively 0.84 ± 0.12, 0.94 ± 0.08 and 0.84 ± 0.11, and the mean was 0.87 ± 0.13. The RW algorithm was found to provide results concordant with those for manual segmentation and to outperform VOCAL in aspects of observer

  11. Layout consistent segmentation of 3-D meshes via conditional random fields and spatial ordering constraints.

    PubMed

    Zouhar, Alexander; Baloch, Sajjad; Tsin, Yanghai; Fang, Tong; Fuchs, Siegfried

    2010-01-01

    We address the problem of 3-D Mesh segmentation for categories of objects with known part structure. Part labels are derived from a semantic interpretation of non-overlapping subsurfaces. Our approach models the label distribution using a Conditional Random Field (CRF) that imposes constraints on the relative spatial arrangement of neighboring labels, thereby ensuring semantic consistency. To this end, each label variable is associated with a rich shape descriptor that is intrinsic to the surface. Randomized decision trees and cross validation are employed for learning the model, which is eventually applied using graph cuts. The method is flexible enough for segmenting even geometrically less structured regions and is robust to local and global shape variations.

  12. Surface modeling and segmentation of the 3D airway wall in MSCT

    NASA Astrophysics Data System (ADS)

    Ortner, Margarete; Fetita, Catalin; Brillet, Pierre-Yves; Pr"teux, Françoise; Grenier, Philippe

    2011-03-01

    Airway wall remodeling in asthma and chronic obstructive pulmonary disease (COPD) is a well-known indicator of the pathology. In this context, current clinical studies aim for establishing the relationship between the airway morphological structure and its function. Multislice computed tomography (MSCT) allows morphometric assessment of airways, but requires dedicated segmentation tools for clinical exploitation. While most of the existing tools are limited to cross-section measurements, this paper develops a fully 3D approach for airway wall segmentation. Such approach relies on a deformable model which is built up as a patient-specific surface model at the level of the airway lumen and deformed to reach the outer surface of the airway wall. The deformation dynamics obey a force equilibrium in a Lagrangian framework constrained by a vector field which avoids model self-intersections. The segmentation result allows a dense quantitative investigation of the airway wall thickness with a deeper insight at bronchus subdivisions than classic cross-section methods. The developed approach has been assessed both by visual inspection of 2D cross-sections, performed by two experienced radiologists on clinical data obtained with various protocols, and by using a simulated ground truth (pulmonary CT image model). The results confirmed a robust segmentation in intra-pulmonary regions with an error in the range of the MSCT image resolution and underlined the interest of the volumetric approach versus purely 2D methods.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  14. Three dimensional level set based semiautomatic segmentation of atherosclerotic carotid artery wall volume using 3D ultrasound imaging

    NASA Astrophysics Data System (ADS)

    Hossain, Md. Murad; AlMuhanna, Khalid; Zhao, Limin; Lal, Brajesh K.; Sikdar, Siddhartha

    2014-03-01

    3D segmentation of carotid plaque from ultrasound (US) images is challenging due to image artifacts and poor boundary definition. Semiautomatic segmentation algorithms for calculating vessel wall volume (VWV) have been proposed for the common carotid artery (CCA) but they have not been applied on plaques in the internal carotid artery (ICA). In this work, we describe a 3D segmentation algorithm that is robust to shadowing and missing boundaries. Our algorithm uses distance regularized level set method with edge and region based energy to segment the adventitial wall boundary (AWB) and lumen-intima boundary (LIB) of plaques in the CCA, ICA and external carotid artery (ECA). The algorithm is initialized by manually placing points on the boundary of a subset of transverse slices with an interslice distance of 4mm. We propose a novel user defined stopping surface based energy to prevent leaking of evolving surface across poorly defined boundaries. Validation was performed against manual segmentation using 3D US volumes acquired from five asymptomatic patients with carotid stenosis using a linear 4D probe. A pseudo gold-standard boundary was formed from manual segmentation by three observers. The Dice similarity coefficient (DSC), Hausdor distance (HD) and modified HD (MHD) were used to compare the algorithm results against the pseudo gold-standard on 1205 cross sectional slices of 5 3D US image sets. The algorithm showed good agreement with the pseudo gold standard boundary with mean DSC of 93.3% (AWB) and 89.82% (LIB); mean MHD of 0.34 mm (AWB) and 0.24 mm (LIB); mean HD of 1.27 mm (AWB) and 0.72 mm (LIB). The proposed 3D semiautomatic segmentation is the first step towards full characterization of 3D plaque progression and longitudinal monitoring.

  15. Automatic 3D segmentation of spinal cord MRI using propagated deformable models

    NASA Astrophysics Data System (ADS)

    De Leener, B.; Cohen-Adad, J.; Kadoury, S.

    2014-03-01

    Spinal cord diseases or injuries can cause dysfunction of the sensory and locomotor systems. Segmentation of the spinal cord provides measures of atrophy and allows group analysis of multi-parametric MRI via inter-subject registration to a template. All these measures were shown to improve diagnostic and surgical intervention. We developed a framework to automatically segment the spinal cord on T2-weighted MR images, based on the propagation of a deformable model. The algorithm is divided into three parts: first, an initialization step detects the spinal cord position and orientation by using the elliptical Hough transform on multiple adjacent axial slices to produce an initial tubular mesh. Second, a low-resolution deformable model is iteratively propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a contrast adaptation at each iteration. Third, a refinement process and a global deformation are applied on the low-resolution mesh to provide an accurate segmentation of the spinal cord. Our method was evaluated against a semi-automatic edge-based snake method implemented in ITK-SNAP (with heavy manual adjustment) by computing the 3D Dice coefficient, mean and maximum distance errors. Accuracy and robustness were assessed from 8 healthy subjects. Each subject had two volumes: one at the cervical and one at the thoracolumbar region. Results show a precision of 0.30 +/- 0.05 mm (mean absolute distance error) in the cervical region and 0.27 +/- 0.06 mm in the thoracolumbar region. The 3D Dice coefficient was of 0.93 for both regions.

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

    PubMed

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

    2006-07-01

    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

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

    PubMed Central

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

    2009-01-01

    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 (Az) 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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  19. Elastic wave modelling in 3D heterogeneous media: 3D grid method

    NASA Astrophysics Data System (ADS)

    Jianfeng, Zhang; Tielin, Liu

    2002-09-01

    We present a new numerical technique for elastic wave modelling in 3D heterogeneous media with surface topography, which is called the 3D grid method in this paper. This work is an extension of the 2D grid method that models P-SV wave propagation in 2D heterogeneous media. Similar to the finite-element method in the discretization of a numerical mesh, the proposed scheme is flexible in incorporating surface topography and curved interfaces; moreover it satisfies the free-surface boundary conditions of 3D topography naturally. The algorithm, developed from a parsimonious staggered-grid scheme, solves the problem using integral equilibrium around each node, instead of satisfying elastodynamic differential equations at each node as in the conventional finite-difference method. The computational cost and memory requirements for the proposed scheme are approximately the same as those used by the same order finite-difference method. In this paper, a mixed tetrahedral and parallelepiped grid method is presented; and the numerical dispersion and stability criteria on the tetrahedral grid method and parallelepiped grid method are discussed in detail. The proposed scheme is successfully tested against an analytical solution for the 3D Lamb problem and a solution of the boundary method for the diffraction of a hemispherical crater. Moreover, examples of surface-wave propagation in an elastic half-space with a semi-cylindrical trench on the surface and 3D plane-layered model are presented.

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

    SciTech Connect

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

    2014-07-15

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  2. Effect of segmentation errors on 3D-to-2D registration of implant models in X-ray images.

    PubMed

    Mahfouz, Mohamed R; Hoff, William A; Komistek, Richard D; Dennis, Douglas A

    2005-02-01

    In many biomedical applications, it is desirable to estimate the three-dimensional (3D) position and orientation (pose) of a metallic rigid object (such as a knee or hip implant) from its projection in a two-dimensional (2D) X-ray image. If the geometry of the object is known, as well as the details of the image formation process, then the pose of the object with respect to the sensor can be determined. A common method for 3D-to-2D registration is to first segment the silhouette contour from the X-ray image; that is, identify all points in the image that belong to the 2D silhouette and not to the background. This segmentation step is then followed by a search for the 3D pose that will best match the observed contour with a predicted contour. Although the silhouette of a metallic object is often clearly visible in an X-ray image, adjacent tissue and occlusions can make the exact location of the silhouette contour difficult to determine in places. Occlusion can occur when another object (such as another implant component) partially blocks the view of the object of interest. In this paper, we argue that common methods for segmentation can produce errors in the location of the 2D contour, and hence errors in the resulting 3D estimate of the pose. We show, on a typical fluoroscopy image of a knee implant component, that interactive and automatic methods for segmentation result in segmented contours that vary significantly. We show how the variability in the 2D contours (quantified by two different metrics) corresponds to variability in the 3D poses. Finally, we illustrate how traditional segmentation methods can fail completely in the (not uncommon) cases of images with occlusion.

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

    PubMed

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

    2007-12-01

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

  4. Segmentation of Blood Vessels and 3D Representation of CMR Image

    NASA Astrophysics Data System (ADS)

    Jiji, G. W.

    2013-06-01

    Current cardiac magnetic resonance imaging (CMR) technology allows the determination of patient-individual coronary tree structure, detection of infarctions, and assessment of myocardial perfusion. The purpose of this work is to segment heart blood vessels and visualize it in 3D. In this work, 3D visualisation of vessel was performed into four phases. The first step is to detect the tubular structures using multiscale medialness function, which distinguishes tube-like structures from and other structures. Second step is to extract the centrelines of the tubes. From the centreline radius the cylindrical tube model is constructed. The third step is segmentation of the tubular structures. The cylindrical tube model is used in segmentation process. Fourth step is to 3D representation of the tubular structure using Volume . The proposed approach is applied to 10 datasets of patients from the clinical routine and tested the results with radiologists.

  5. Fast and memory-efficient LOGISMOS graph search for intraretinal layer segmentation of 3D macular OCT scans

    NASA Astrophysics Data System (ADS)

    Lee, Kyungmoo; Zhang, Li; Abramoff, Michael D.; Sonka, Milan

    2015-03-01

    Image segmentation is important for quantitative analysis of medical image data. Recently, our research group has introduced a 3-D graph search method which can simultaneously segment optimal interacting surfaces with respect to the cost function in volumetric images. Although it provides excellent segmentation accuracy, it is computationally demanding (both CPU and memory) to simultaneously segment multiple surfaces from large volumetric images. Therefore, we propose a new, fast, and memory-efficient graph search method for intraretinal layer segmentation of 3-D macular optical coherence tomograpy (OCT) scans. The key idea is to reduce the size of a graph by combining the nodes with high costs based on the multiscale approach. The new approach requires significantly less memory and achieves significantly faster processing speeds (p < 0.01) with only small segmentation differences compared to the original graph search method. This paper discusses sub-optimality of this approach and assesses trade-off relationships between decreasing processing speed and increasing segmentation differences from that of the original method as a function of employed scale of the underlying graph construction.

  6. A perceptual preprocess method for 3D-HEVC

    NASA Astrophysics Data System (ADS)

    Shi, Yawen; Wang, Yongfang; Wang, Yubing

    2015-08-01

    A perceptual preprocessing method for 3D-HEVC coding is proposed in the paper. Firstly we proposed a new JND model, which accounts for luminance contrast masking effect, spatial masking effect, and temporal masking effect, saliency characteristic as well as depth information. We utilize spectral residual approach to obtain the saliency map and built a visual saliency factor based on saliency map. In order to distinguish the sensitivity of objects in different depth. We segment each texture frame into foreground and background by a automatic threshold selection algorithm using corresponding depth information, and then built a depth weighting factor. A JND modulation factor is built with a linear combined with visual saliency factor and depth weighting factor to adjust the JND threshold. Then, we applied the proposed JND model to 3D-HEVC for residual filtering and distortion coefficient processing. The filtering process is that the residual value will be set to zero if the JND threshold is greater than residual value, or directly subtract the JND threshold from residual value if JND threshold is less than residual value. Experiment results demonstrate that the proposed method can achieve average bit rate reduction of 15.11%, compared to the original coding scheme with HTM12.1, while maintains the same subjective quality.

  7. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    SciTech Connect

    Chen, Xinjian; Bagci, Ulas

    2011-08-15

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

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

    PubMed

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

    2016-02-01

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

  9. 3D Face modeling using the multi-deformable method.

    PubMed

    Hwang, Jinkyu; Yu, Sunjin; Kim, Joongrock; Lee, Sangyoun

    2012-09-25

    In this paper, we focus on the problem of the accuracy performance of 3D face modeling techniques using corresponding features in multiple views, which is quite sensitive to feature extraction errors. To solve the problem, we adopt a statistical model-based 3D face modeling approach in a mirror system consisting of two mirrors and a camera. The overall procedure of our 3D facial modeling method has two primary steps: 3D facial shape estimation using a multiple 3D face deformable model and texture mapping using seamless cloning that is a type of gradient-domain blending. To evaluate our method's performance, we generate 3D faces of 30 individuals and then carry out two tests: accuracy test and robustness test. Our method shows not only highly accurate 3D face shape results when compared with the ground truth, but also robustness to feature extraction errors. Moreover, 3D face rendering results intuitively show that our method is more robust to feature extraction errors than other 3D face modeling methods. An additional contribution of our method is that a wide range of face textures can be acquired by the mirror system. By using this texture map, we generate realistic 3D face for individuals at the end of the paper.

  10. Correlation-based discrimination between cardiac tissue and blood for segmentation of 3D echocardiographic images

    NASA Astrophysics Data System (ADS)

    Saris, Anne E. C. M.; Nillesen, Maartje M.; Lopata, Richard G. P.; de Korte, Chris L.

    2013-03-01

    Automated segmentation of 3D echocardiographic images in patients with congenital heart disease is challenging, because the boundary between blood and cardiac tissue is poorly defined in some regions. Cardiologists mentally incorporate movement of the heart, using temporal coherence of structures to resolve ambiguities. Therefore, we investigated the merit of temporal cross-correlation for automated segmentation over the entire cardiac cycle. Optimal settings for maximum cross-correlation (MCC) calculation, based on a 3D cross-correlation based displacement estimation algorithm, were determined to obtain the best contrast between blood and myocardial tissue over the entire cardiac cycle. Resulting envelope-based as well as RF-based MCC values were used as additional external force in a deformable model approach, to segment the left-ventricular cavity in entire systolic phase. MCC values were tested against, and combined with, adaptive filtered, demodulated RF-data. Segmentation results were compared with manually segmented volumes using a 3D Dice Similarity Index (3DSI). Results in 3D pediatric echocardiographic images sequences (n = 4) demonstrate that incorporation of temporal information improves segmentation. The use of MCC values, either alone or in combination with adaptive filtered, demodulated RF-data, resulted in an increase of the 3DSI in 75% of the cases (average 3DSI increase: 0.71 to 0.82). Results might be further improved by optimizing MCC-contrast locally, in regions with low blood-tissue contrast. Reducing underestimation of the endocardial volume due to MCC processing scheme (choice of window size) and consequential border-misalignment, could also lead to more accurate segmentations. Furthermore, increasing the frame rate will also increase MCC-contrast and thus improve segmentation.

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

    PubMed

    Azimifar, Zohreh; Mohaddesi, Mahsa

    2015-04-01

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

  12. 3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilation

    NASA Astrophysics Data System (ADS)

    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.

  13. Segmentation of complex objects with non-spherical topologies from volumetric medical images using 3D livewire

    NASA Astrophysics Data System (ADS)

    Poon, Kelvin; Hamarneh, Ghassan; Abugharbieh, Rafeef

    2007-03-01

    Segmentation of 3D data is one of the most challenging tasks in medical image analysis. While reliable automatic methods are typically preferred, their success is often hindered by poor image quality and significant variations in anatomy. Recent years have thus seen an increasing interest in the development of semi-automated segmentation methods that combine computational tools with intuitive, minimal user interaction. In an earlier work, we introduced a highly-automated technique for medical image segmentation, where a 3D extension of the traditional 2D Livewire was proposed. In this paper, we present an enhanced and more powerful 3D Livewire-based segmentation approach with new features designed to primarily enable the handling of complex object topologies that are common in biological structures. The point ordering algorithm we proposed earlier, which automatically pairs up seedpoints in 3D, is improved in this work such that multiple sets of points are allowed to simultaneously exist. Point sets can now be automatically merged and split to accommodate for the presence of concavities, protrusions, and non-spherical topologies. The robustness of the method is further improved by extending the 'turtle algorithm', presented earlier, by using a turtle-path pruning step. Tests on both synthetic and real medical images demonstrate the efficiency, reproducibility, accuracy, and robustness of the proposed approach. Among the examples illustrated is the segmentation of the left and right ventricles from a T1-weighted MRI scan, where an average task time reduction of 84.7% was achieved when compared to a user performing 2D Livewire segmentation on every slice.

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

    PubMed Central

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

    2015-01-01

    In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions. PMID:26295395

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

    PubMed Central

    Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Sim, Sungdae

    2014-01-01

    A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame. PMID:25093204

  16. The iterative image foresting transform and its application to user-steered 3D segmentation

    NASA Astrophysics Data System (ADS)

    Falcao, Alexandre X.; Bergo, Felipe P. G.

    2003-05-01

    Segmentation and 3D visualization at interactive speeds are highly desirable for routine use in clinical settings. We circumvent this problem in the framework of the image foresting transform (IFT) - a graph-based approach to the design of image processing operators. In this paper we introduce the iterative image foresting transform (IFT+), which computes sequences of IFTs in a differencial way, present the general IFT+ algorithm, and instantiate it to be a watershed transform. The IFT+-watershed transform is evaluated in the context of interactive segmentation, where the user makes corrections by adding/removing scene regions with mouse clicks. The IFT+-watershed requires time proportional to the number of voxels in the modified regions, while the conventional algorithm computes one watershed transform over the entire scene for each iteration. The IFT+-watershed is 5.75 times faster than the watershed and considerably reduces from 17.7 to 3.16 seconds the user's waiting time in segmentation with 3D visualization. These results were obtained in an 1.5GHz Pentium-IV PC over 10 MR scenes of the head, requiring 12 to 28 corrections to segment cerebellum, pons-medulla, ventricle, and the rest of the brain, simultaneously. These results indicate that the IFT+ is a significant contribution toward interactive segmentation and 3D visualization.

  17. Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

    NASA Astrophysics Data System (ADS)

    Hajiesmaeili, Maryam; Dehmeshki, Jamshid; Bagheri Nakhjavanlo, Bashir; Ellis, Tim

    2014-04-01

    Shrinkage of the hippocampus is a primary biomarker for Alzheimer's disease and can be measured through accurate segmentation of brain MR images. The paper will describe the problem of initialisation of a 3D level set algorithm for hippocampus segmentation that must cope with the some challenging characteristics, such as small size, wide range of intensities, narrow width, and shape variation. In addition, MR images require bias correction, to account for additional inhomogeneity associated with the scanner technology. Due to these inhomogeneities, using a single initialisation seed region inside the hippocampus is prone to failure. Alternative initialisation strategies are explored, such as using multiple initialisations in different sections (such as the head, body and tail) of the hippocampus. The Dice metric is used to validate our segmentation results with respect to ground truth for a dataset of 25 MR images. Experimental results indicate significant improvement in segmentation performance using the multiple initialisations techniques, yielding more accurate segmentation results for the hippocampus.

  18. a Fast Method for Measuring the Similarity Between 3d Model and 3d Point Cloud

    NASA Astrophysics Data System (ADS)

    Zhang, Zongliang; Li, Jonathan; Li, Xin; Lin, Yangbin; Zhang, Shanxin; Wang, Cheng

    2016-06-01

    This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.

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

    PubMed

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

    2013-12-01

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

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

    PubMed Central

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

    2015-01-01

    Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semiautomated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases. PMID:25728595

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

    PubMed

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

    2015-07-01

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

  2. Framework for quantitative evaluation of 3D vessel segmentation approaches using vascular phantoms in conjunction with 3D landmark localization and registration

    NASA Astrophysics Data System (ADS)

    Wörz, Stefan; Hoegen, Philipp; Liao, Wei; Müller-Eschner, Matthias; Kauczor, Hans-Ulrich; von Tengg-Kobligk, Hendrik; Rohr, Karl

    2016-03-01

    We introduce a framework for quantitative evaluation of 3D vessel segmentation approaches using vascular phantoms. Phantoms are designed using a CAD system and created with a 3D printer, and comprise realistic shapes including branches and pathologies such as abdominal aortic aneurysms (AAA). To transfer ground truth information to the 3D image coordinate system, we use a landmark-based registration scheme utilizing fiducial markers integrated in the phantom design. For accurate 3D localization of the markers we developed a novel 3D parametric intensity model that is directly fitted to the markers in the images. We also performed a quantitative evaluation of different vessel segmentation approaches for a phantom of an AAA.

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

    PubMed

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

    2015-02-01

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

  4. Nerves of Steel: a Low-Cost Method for 3D Printing the Cranial Nerves.

    PubMed

    Javan, Ramin; Davidson, Duncan; Javan, Afshin

    2017-02-21

    Steady-state free precession (SSFP) magnetic resonance imaging (MRI) can demonstrate details down to the cranial nerve (CN) level. High-resolution three-dimensional (3D) visualization can now quickly be performed at the workstation. However, we are still limited by visualization on flat screens. The emerging technologies in rapid prototyping or 3D printing overcome this limitation. It comprises a variety of automated manufacturing techniques, which use virtual 3D data sets to fabricate solid forms in a layer-by-layer technique. The complex neuroanatomy of the CNs may be better understood and depicted by the use of highly customizable advanced 3D printed models. In this technical note, after manually perfecting the segmentation of each CN and brain stem on each SSFP-MRI image, initial 3D reconstruction was performed. The bony skull base was also reconstructed from computed tomography (CT) data. Autodesk 3D Studio Max, available through freeware student/educator license, was used to three-dimensionally trace the 3D reconstructed CNs in order to create smooth graphically designed CNs and to assure proper fitting of the CNs into their respective neural foramina and fissures. This model was then 3D printed with polyamide through a commercial online service. Two different methods are discussed for the key segmentation and 3D reconstruction steps, by either using professional commercial software, i.e., Materialise Mimics, or utilizing a combination of the widely available software Adobe Photoshop, as well as a freeware software, OsiriX Lite.

  5. Segmentation of bone structures in 3D CT images based on continuous max-flow optimization

    NASA Astrophysics Data System (ADS)

    Pérez-Carrasco, J. A.; Acha-Piñero, B.; Serrano, C.

    2015-03-01

    In this paper an algorithm to carry out the automatic segmentation of bone structures in 3D CT images has been implemented. Automatic segmentation of bone structures is of special interest for radiologists and surgeons to analyze bone diseases or to plan some surgical interventions. This task is very complicated as bones usually present intensities overlapping with those of surrounding tissues. This overlapping is mainly due to the composition of bones and to the presence of some diseases such as Osteoarthritis, Osteoporosis, etc. Moreover, segmentation of bone structures is a very time-consuming task due to the 3D essence of the bones. Usually, this segmentation is implemented manually or with algorithms using simple techniques such as thresholding and thus providing bad results. In this paper gray information and 3D statistical information have been combined to be used as input to a continuous max-flow algorithm. Twenty CT images have been tested and different coefficients have been computed to assess the performance of our implementation. Dice and Sensitivity values above 0.91 and 0.97 respectively were obtained. A comparison with Level Sets and thresholding techniques has been carried out and our results outperformed them in terms of accuracy.

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

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

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

    PubMed

    Eslami, Abouzar; Yigitsoy, Mehmet; Navab, Nassir

    2011-01-01

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

  8. Dynamic 3D MR Visualization and Detection of Upper Airway Obstruction during Sleep using Region Growing Segmentation

    PubMed Central

    Kim, Yoon-Chul; Khoo, Michael C.K.; Davidson Ward, Sally L.; Nayak, Krishna S.

    2016-01-01

    Goal We demonstrate a novel and robust approach for visualization of upper airway dynamics and detection of obstructive events from dynamic 3D magnetic resonance imaging (MRI) scans of the pharyngeal airway. Methods This approach uses 3D region growing, where the operator selects a region of interest that includes the pharyngeal airway, places two seeds in the patent airway, and determines a threshold for the first frame. Results This approach required 5 sec/frame of CPU time compared to 10 min/frame of operator time for manual segmentation. It compared well with manual segmentation, resulting in Dice Coefficients of 0.84 to 0.94, whereas the Dice Coefficients for two manual segmentations by the same observer were 0.89 to 0.97. It was also able to automatically detect 83% of collapse events. Conclusion Use of this simple semi-automated segmentation approach improves the workflow of novel dynamic MRI studies of the pharyngeal airway and enables visualization and detection of obstructive events. Significance Obstructive sleep apnea is a significant public health issue affecting 4-9% of adults and 2% of children. Recently, 3D dynamic MRI of the upper airway has been demonstrated during natural sleep, with sufficient spatio-temporal resolution to non-invasively study patterns of airway obstruction in young adults with OSA. This work makes it practical to analyze these long scans and visualize important factors in an MRI sleep study, such as the time, site, and extent of airway collapse. PMID:26258929

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  10. Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching.

    PubMed

    Xia, Ying; Chandra, Shekhar S; Engstrom, Craig; Strudwick, Mark W; Crozier, Stuart; Fripp, Jurgen

    2014-12-07

    Accurate segmentation of hip joint cartilage from magnetic resonance (MR) images offers opportunities for quantitative investigations of pathoanatomical conditions such as osteoarthritis. In this paper, we present a fully automatic scheme for the segmentation of the individual femoral and acetabular cartilage plates in the human hip joint from high-resolution 3D MR images. The developed scheme uses an improved optimal multi-object multi-surface graph search framework with an arc-weighted graph representation that incorporates prior morphological knowledge as a basis for segmentation of the individual femoral and acetabular cartilage plates despite weak or incomplete boundary interfaces. This automated scheme was validated against manual segmentations from 3D true fast imaging with steady-state precession (TrueFISP) MR examinations of the right hip joints in 52 asymptomatic volunteers. Compared with expert manual segmentations of the combined, femoral and acetabular cartilage volumes, the automatic scheme obtained mean (± standard deviation) Dice's similarity coefficients of 0.81 (± 0.03), 0.79 (± 0.03) and 0.72 (± 0.05). The corresponding mean absolute volume difference errors were 8.44% (± 6.36), 9.44% (± 7.19) and 9.05% (± 8.02). The mean absolute differences between manual and automated measures of cartilage thickness for femoral and acetabular cartilage plates were 0.13 mm (± 0.12) and 0.11 mm (± 0.11), respectively.

  11. SU-E-T-356: Efficient Segmentation of Flattening Filter Free Photon Beamsfor 3D-Conformal SBRT Treatment Planning

    SciTech Connect

    Barbiere, J; Beninati, G; Ndlovu, A

    2015-06-15

    Purpose: It has been argued that a 3D-conformal technique (3DCRT) is suitable for SBRT due to its simplicity for non-coplanar planning and delivery. It has also been hypothesized that a high dose delivered in a short time can enhance indirect cell death due to vascular damage as well as limiting intrafraction motion. Flattening Filter Free (FFF) photon beams are ideal for high dose rate treatment but their conical profiles are not ideal for 3DCRT. The purpose of our work is to present a method to efficiently segment an FFF beam for standard 3DCRT planning. Methods: A 10×10 cm Varian True Beam 6X FFF beam profile was analyzed using segmentation theory to determine the optimum segmentation intensity required to create an 8 cm uniform dose profile. Two segments were automatically created in sequence with a Varian Eclipse treatment planning system by converting isodoses corresponding to the calculated segmentation intensity to contours and applying the “fit and shield” tool. All segments were then added to the FFF beam to create a single merged field. Field blocking can be incorporated but was not used for clarity. Results: Calculation of the segmentation intensity using an algorithm originally proposed by Xia and Verhey indicated that each segment should extend to the 92% isodose. The original FFF beam with 100% at the isocenter at a depth of 10 cm was reduced to 80% at 4cm from the isocenter; the segmented beam had +/−2.5 % uniformity up to 4.4cm from the isocenter. An additional benefit of our method is a 50% decrease in the 80%-20% penumbra of 0.6cm compared to 1.2cm in the original FFF beam. Conclusion: Creation of two optimum segments can flatten a FFF beam and also reduce its penumbra for clinical 3DCRT SBRT treatment.

  12. A 3D Level Set Method for Microwave Breast Imaging

    PubMed Central

    Colgan, Timothy J.; Hagness, Susan C.; Van Veen, Barry D.

    2015-01-01

    Objective Conventional inverse-scattering algorithms for microwave breast imaging result in moderate resolution images with blurred boundaries between tissues. Recent 2D numerical microwave imaging studies demonstrate that the use of a level set method preserves dielectric boundaries, resulting in a more accurate, higher resolution reconstruction of the dielectric properties distribution. Previously proposed level set algorithms are computationally expensive and thus impractical in 3D. In this paper we present a computationally tractable 3D microwave imaging algorithm based on level sets. Methods We reduce the computational cost of the level set method using a Jacobian matrix, rather than an adjoint method, to calculate Frechet derivatives. We demonstrate the feasibility of 3D imaging using simulated array measurements from 3D numerical breast phantoms. We evaluate performance by comparing full 3D reconstructions to those from a conventional microwave imaging technique. We also quantitatively assess the efficacy of our algorithm in evaluating breast density. Results Our reconstructions of 3D numerical breast phantoms improve upon those of a conventional microwave imaging technique. The density estimates from our level set algorithm are more accurate than those of conventional microwave imaging, and the accuracy is greater than that reported for mammographic density estimation. Conclusion Our level set method leads to a feasible level of computational complexity for full 3D imaging, and reconstructs the heterogeneous dielectric properties distribution of the breast more accurately than conventional microwave imaging methods. Significance 3D microwave breast imaging using a level set method is a promising low-cost, non-ionizing alternative to current breast imaging techniques. PMID:26011863

  13. 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models

    PubMed Central

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

    2017-01-01

    Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images' inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels' appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach. PMID:28280519

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

    PubMed

    Badura, P; Pietka, E

    2014-10-01

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

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

    PubMed

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

    2015-04-01

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

  16. Streaming level set algorithm for 3D segmentation of confocal microscopy images.

    PubMed

    Gouaillard, Alexandre; Mosaliganti, Kishore; Gelas, Arnaud; Souhait, Lydie; Obholzer, Nikolaus; Megason, Sean

    2009-01-01

    We present a high performance variant of the popular geodesic active contours which are used for splitting cell clusters in microscopy images. Previously, we implemented a linear pipelined version that incorporates as many cues as possible into developing a suitable level-set speed function so that an evolving contour exactly segments a cell/nuclei blob. We use image gradients, distance maps, multiple channel information and a shape model to drive the evolution. We also developed a dedicated seeding strategy that uses the spatial coherency of the data to generate an over complete set of seeds along with a quality metric which is further used to sort out which seed should be used for a given cell. However, the computational performance of any level-set methodology is quite poor when applied to thousands of 3D data-sets each containing thousands of cells. Those data-sets are common in confocal microscopy. In this work, we explore methods to stream the algorithm in shared memory, multi-core environments. By partitioning the input and output using spatial data structures we insure the spatial coherency needed by our seeding algorithm as well as improve drastically the speed without memory overhead. Our results show speed-ups up to a factor of six.

  17. Segmentation and Tracking of Adherens Junctions in 3D for the Analysis of Epithelial Tissue Morphogenesis

    PubMed Central

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

    2015-01-01

    Epithelial morphogenesis generates the shape of tissues, organs and embryos and is fundamental for their proper function. It is a dynamic process that occurs at multiple spatial scales from macromolecular dynamics, to cell deformations, mitosis and apoptosis, to coordinated cell rearrangements that lead to global changes of tissue shape. Using time lapse imaging, it is possible to observe these events at a system level. However, to investigate morphogenetic events it is necessary to develop computational tools to extract quantitative information from the time lapse data. Toward this goal, we developed an image-based computational pipeline to preprocess, segment and track epithelial cells in 4D confocal microscopy data. The computational pipeline we developed, for the first time, detects the adherens junctions of epithelial cells in 3D, without the need to first detect cell nuclei. We accentuate and detect cell outlines in a series of steps, symbolically describe the cells and their connectivity, and employ this information to track the cells. We validated the performance of the pipeline for its ability to detect vertices and cell-cell contacts, track cells, and identify mitosis and apoptosis in surface epithelia of Drosophila imaginal discs. We demonstrate the utility of the pipeline to extract key quantitative features of cell behavior with which to elucidate the dynamics and biomechanical control of epithelial tissue morphogenesis. We have made our methods and data available as an open-source multiplatform software tool called TTT (http://github.com/morganrcu/TTT) PMID:25884654

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

    PubMed

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

    2016-12-01

    Three-dimensional (3D) photonic scanning is an emerging technique to acquire accurate body segment parameter data. This study established the repeated reliability of segmental centres of mass when using 3D photonic scanning (3DPS). Seventeen male participants were scanned twice by a 3D whole-body laser scanner. The same operators conducted the reconstruction and segmentation processes to obtain segmental meshes for calculating the segmental centres of mass. The segmental centres of mass obtained from repeated 3DPS were compared by relative technical error of measurement (TEM). Hypothesis tests were conducted to determine the size of change required for each segment to be determined a true variation. The relative TEMs for all segments were less than 5%. The relative changes in centres of mass at ±1.5% for most segments can be detected (p < 0.05). The arm segments which are difficult to keep in the same scanning pose generated more error than other segments. Practitioner Summary: Three-dimensional photonic scanning is an emerging technique to acquire body segment parameter data. This study established the repeated reliability of segmental centres of mass when using 3D photonic scanning and emphasised that the error for arm segments need to be considered while using this technique to acquire centres of mass.

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

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  20. Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data

    PubMed Central

    Kajić, Vedran; Esmaeelpour, Marieh; Glittenberg, Carl; Kraus, Martin F.; Honegger, Joachim; Othara, Richu; Binder, Susanne; Fujimoto, James G.; Drexler, Wolfgang

    2012-01-01

    A fully automated, robust vessel segmentation algorithm has been developed for choroidal OCT, employing multiscale 3D edge filtering and projection of “probability cones” to determine the vessel “core”, even in the tomograms with low signal-to-noise ratio (SNR). Based on the ideal vessel response after registration and multiscale filtering, with computed depth related SNR, the vessel core estimate is dilated to quantify the full vessel diameter. As a consequence, various statistics can be computed using the 3D choroidal vessel information, such as ratios of inner (smaller) to outer (larger) choroidal vessels or the absolute/relative volume of choroid vessels. Choroidal vessel quantification can be displayed in various forms, focused and averaged within a special region of interest, or analyzed as the function of image depth. In this way, the proposed algorithm enables unique visualization of choroidal watershed zones, as well as the vessel size reduction when investigating the choroid from the sclera towards the retinal pigment epithelium (RPE). To the best of our knowledge, this is the first time that an automatic choroidal vessel segmentation algorithm is successfully applied to 1060 nm 3D OCT of healthy and diseased eyes. PMID:23304653

  1. Automated segmentation of 3-D spectral OCT retinal blood vessels by neural canal opening false positive suppression.

    PubMed

    Hu, Zhihong; Niemeijer, Meindert; Abràmoft, Michael D; Lee, Kyungmoo; Garvin, Mona K

    2010-01-01

    We present a method for automatically segmenting the blood vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes, with a focus on the ability to segment the vessels in the region near the neural canal opening (NCO). The algorithm first pre-segments the NCO using a graph-theoretic approach. Oriented Gabor wavelets rotated around the center of the NCO are applied to extract features in a 2-D vessel-aimed projection image. Corresponding oriented NCO-based templates are utilized to help suppress the false positive tendency near the NCO boundary. The vessels are identified in a vessel-aimed projection image using a pixel classification algorithm. Based on the 2-D vessel profiles, 3-D vessel segmentation is performed by a triangular-mesh-based graph search approach in the SD-OCT volume. The segmentation method is trained on 5 and is tested on 10 randomly chosen independent ONH-centered SD-OCT volumes from 15 subjects with glaucoma. Using ROC analysis, for the 2-D vessel segmentation, we demonstrate an improvement over the closest previous work with an area under the curve (AUC) of 0.81 (0.72 for previously reported approach) for the region around the NCO and 0.84 for the region outside the NCO (0.81 for previously reported approach).

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    Rouhani, Mohammad; Lafarge, Florent; Alliez, Pierre

    2017-01-01

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

  4. Improving automated 3D reconstruction methods via vision metrology

    NASA Astrophysics Data System (ADS)

    Toschi, Isabella; Nocerino, Erica; Hess, Mona; Menna, Fabio; Sargeant, Ben; MacDonald, Lindsay; Remondino, Fabio; Robson, Stuart

    2015-05-01

    This paper aims to provide a procedure for improving automated 3D reconstruction methods via vision metrology. The 3D reconstruction problem is generally addressed using two different approaches. On the one hand, vision metrology (VM) systems try to accurately derive 3D coordinates of few sparse object points for industrial measurement and inspection applications; on the other, recent dense image matching (DIM) algorithms are designed to produce dense point clouds for surface representations and analyses. This paper strives to demonstrate a step towards narrowing the gap between traditional VM and DIM approaches. Efforts are therefore intended to (i) test the metric performance of the automated photogrammetric 3D reconstruction procedure, (ii) enhance the accuracy of the final results and (iii) obtain statistical indicators of the quality achieved in the orientation step. VM tools are exploited to integrate their main functionalities (centroid measurement, photogrammetric network adjustment, precision assessment, etc.) into the pipeline of 3D dense reconstruction. Finally, geometric analyses and accuracy evaluations are performed on the raw output of the matching (i.e. the point clouds) by adopting a metrological approach. The latter is based on the use of known geometric shapes and quality parameters derived from VDI/VDE guidelines. Tests are carried out by imaging the calibrated Portable Metric Test Object, designed and built at University College London (UCL), UK. It allows assessment of the performance of the image orientation and matching procedures within a typical industrial scenario, characterised by poor texture and known 3D/2D shapes.

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

    PubMed

    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.

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

    NASA Technical Reports Server (NTRS)

    Kaul, Upender K.; Nguyen, Nhan T.

    2015-01-01

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

  7. Bone canalicular network segmentation in 3D nano-CT images through geodesic voting and image tessellation

    NASA Astrophysics Data System (ADS)

    Zuluaga, Maria A.; Orkisz, Maciej; Dong, Pei; Pacureanu, Alexandra; Gouttenoire, Pierre-Jean; Peyrin, Françoise

    2014-05-01

    Recent studies emphasized the role of the bone lacuno-canalicular network (LCN) in the understanding of bone diseases such as osteoporosis. However, suitable methods to investigate this structure are lacking. The aim of this paper is to introduce a methodology to segment the LCN from three-dimensional (3D) synchrotron radiation nano-CT images. Segmentation of such structures is challenging due to several factors such as limited contrast and signal-to-noise ratio, partial volume effects and huge number of data that needs to be processed, which restrains user interaction. We use an approach based on minimum-cost paths and geodesic voting, for which we propose a fully automatic initialization scheme based on a tessellation of the image domain. The centroids of pre-segmented lacunæ are used as Voronoi-tessellation seeds and as start-points of a fast-marching front propagation, whereas the end-points are distributed in the vicinity of each Voronoi-region boundary. This initialization scheme was devised to cope with complex biological structures involving cells interconnected by multiple thread-like, branching processes, while the seminal geodesic-voting method only copes with tree-like structures. Our method has been assessed quantitatively on phantom data and qualitatively on real datasets, demonstrating its feasibility. To the best of our knowledge, presented 3D renderings of lacunæ interconnected by their canaliculi were achieved for the first time.

  8. Improving Nearest Neighbour Search in 3d Spatial Access Method

    NASA Astrophysics Data System (ADS)

    Suhaibaha, A.; Rahman, A. A.; Uznir, U.; Anton, F.; Mioc, D.

    2016-10-01

    Nearest Neighbour (NN) is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO) operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usually large due to its detail geometry and other attached information. In this research, a clustered 3D hierarchical structure is introduced as a 3D spatial access method structure. The structure is expected to improve the retrieval of Nearest Neighbour information for 3D objects. Several tests are performed in answering Single Nearest Neighbour search and k Nearest Neighbour (kNN) search. The tests indicate that clustered hierarchical structure is efficient in handling Nearest Neighbour query compared to its competitor. From the results, clustered hierarchical structure reduced the repetitive data entry and the accessed page. The proposed structure also produced minimal Input/Output operation. The query response time is also outperformed compared to the other competitor. For future outlook of this research several possible applications are discussed and summarized.

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  10. A method to fabricate disconnected silver nanostructures in 3D.

    PubMed

    Vora, Kevin; Kang, SeungYeon; Mazur, Eric

    2012-11-27

    The standard nanofabrication toolkit includes techniques primarily aimed at creating 2D patterns in dielectric media. Creating metal patterns on a submicron scale requires a combination of nanofabrication tools and several material processing steps. For example, steps to create planar metal structures using ultraviolet photolithography and electron-beam lithography can include sample exposure, sample development, metal deposition, and metal liftoff. To create 3D metal structures, the sequence is repeated multiple times. The complexity and difficulty of stacking and aligning multiple layers limits practical implementations of 3D metal structuring using standard nanofabrication tools. Femtosecond-laser direct-writing has emerged as a pre-eminent technique for 3D nanofabrication.(1,2) Femtosecond lasers are frequently used to create 3D patterns in polymers and glasses.(3-7) However, 3D metal direct-writing remains a challenge. Here, we describe a method to fabricate silver nanostructures embedded inside a polymer matrix using a femtosecond laser centered at 800 nm. The method enables the fabrication of patterns not feasible using other techniques, such as 3D arrays of disconnected silver voxels.(8) Disconnected 3D metal patterns are useful for metamaterials where unit cells are not in contact with each other,(9) such as coupled metal dot(10,11)or coupled metal rod(12,13) resonators. Potential applications include negative index metamaterials, invisibility cloaks, and perfect lenses. In femtosecond-laser direct-writing, the laser wavelength is chosen such that photons are not linearly absorbed in the target medium. When the laser pulse duration is compressed to the femtosecond time scale and the radiation is tightly focused inside the target, the extremely high intensity induces nonlinear absorption. Multiple photons are absorbed simultaneously to cause electronic transitions that lead to material modification within the focused region. Using this approach, one can

  11. A Method to Fabricate Disconnected Silver Nanostructures in 3D

    PubMed Central

    Vora, Kevin; Kang, SeungYeon; Mazur, Eric

    2012-01-01

    The standard nanofabrication toolkit includes techniques primarily aimed at creating 2D patterns in dielectric media. Creating metal patterns on a submicron scale requires a combination of nanofabrication tools and several material processing steps. For example, steps to create planar metal structures using ultraviolet photolithography and electron-beam lithography can include sample exposure, sample development, metal deposition, and metal liftoff. To create 3D metal structures, the sequence is repeated multiple times. The complexity and difficulty of stacking and aligning multiple layers limits practical implementations of 3D metal structuring using standard nanofabrication tools. Femtosecond-laser direct-writing has emerged as a pre-eminent technique for 3D nanofabrication.1,2 Femtosecond lasers are frequently used to create 3D patterns in polymers and glasses.3-7 However, 3D metal direct-writing remains a challenge. Here, we describe a method to fabricate silver nanostructures embedded inside a polymer matrix using a femtosecond laser centered at 800 nm. The method enables the fabrication of patterns not feasible using other techniques, such as 3D arrays of disconnected silver voxels.8 Disconnected 3D metal patterns are useful for metamaterials where unit cells are not in contact with each other,9 such as coupled metal dot10,11or coupled metal rod12,13 resonators. Potential applications include negative index metamaterials, invisibility cloaks, and perfect lenses. In femtosecond-laser direct-writing, the laser wavelength is chosen such that photons are not linearly absorbed in the target medium. When the laser pulse duration is compressed to the femtosecond time scale and the radiation is tightly focused inside the target, the extremely high intensity induces nonlinear absorption. Multiple photons are absorbed simultaneously to cause electronic transitions that lead to material modification within the focused region. Using this approach, one can form structures

  12. Comparing 3D virtual methods for hemimandibular body reconstruction.

    PubMed

    Benazzi, Stefano; Fiorenza, Luca; Kozakowski, Stephanie; Kullmer, Ottmar

    2011-07-01

    Reconstruction of fractured, distorted, or missing parts in human skeleton presents an equal challenge in the fields of paleoanthropology, bioarcheology, forensics, and medicine. This is particularly important within the disciplines such as orthodontics and surgery, when dealing with mandibular defects due to tumors, developmental abnormalities, or trauma. In such cases, proper restorations of both form (for esthetic purposes) and function (restoration of articulation, occlusion, and mastication) are required. Several digital approaches based on three-dimensional (3D) digital modeling, computer-aided design (CAD)/computer-aided manufacturing techniques, and more recently geometric morphometric methods have been used to solve this problem. Nevertheless, comparisons among their outcomes are rarely provided. In this contribution, three methods for hemimandibular body reconstruction have been tested. Two bone defects were virtually simulated in a 3D digital model of a human hemimandible. Accordingly, 3D digital scaffolds were obtained using the mirror copy of the unaffected hemimandible (Method 1), the thin plate spline (TPS) interpolation (Method 2), and the combination between TPS and CAD techniques (Method 3). The mirror copy of the unaffected hemimandible does not provide a suitable solution for bone restoration. The combination between TPS interpolation and CAD techniques (Method 3) produces an almost perfect-fitting 3D digital model that can be used for biocompatible custom-made scaffolds generated by rapid prototyping technologies.

  13. Free segmentation in rendered 3D images through synthetic impulse response in integral imaging

    NASA Astrophysics Data System (ADS)

    Martínez-Corral, M.; Llavador, A.; Sánchez-Ortiga, E.; Saavedra, G.; Javidi, B.

    2016-06-01

    Integral Imaging is a technique that has the capability of providing not only the spatial, but also the angular information of three-dimensional (3D) scenes. Some important applications are the 3D display and digital post-processing as for example, depth-reconstruction from integral images. In this contribution we propose a new reconstruction method that takes into account the integral image and a simplified version of the impulse response function (IRF) of the integral imaging (InI) system to perform a two-dimensional (2D) deconvolution. The IRF of an InI system has a periodic structure that depends directly on the axial position of the object. Considering different periods of the IRFs we recover by deconvolution the depth information of the 3D scene. An advantage of our method is that it is possible to obtain nonconventional reconstructions by considering alternative synthetic impulse responses. Our experiments show the feasibility of the proposed method.

  14. Elastic model-based segmentation of 3-D neuroradiological data sets.

    PubMed

    Kelemen, A; Székely, G; Gerig, G

    1999-10-01

    This paper presents a new technique for the automatic model-based segmentation of three-dimensional (3-D) objects from volumetric image data. The development closely follows the seminal work of Taylor and Cootes on active shape models, but is based on a hierarchical parametric object description rather than a point distribution model. The segmentation system includes both the building of statistical models and the automatic segmentation of new image data sets via a restricted elastic deformation of shape models. Geometric models are derived from a sample set of image data which have been segmented by experts. The surfaces of these binary objects are converted into parametric surface representations, which are normalized to get an invariant object-centered coordinate system. Surface representations are expanded into series of spherical harmonics which provide parametric descriptions of object shapes. It is shown that invariant object surface parametrization provides a good approximation to automatically determine object homology in terms of sets of corresponding sets of surface points. Gray-level information near object boundaries is represented by 1-D intensity profiles normal to the surface. Considering automatic segmentation of brain structures as our driving application, our choice of coordinates for object alignment was the well-accepted stereotactic coordinate system. Major variation of object shapes around the mean shape, also referred to as shape eigenmodes, are calculated in shape parameter space rather than the feature space of point coordinates. Segmentation makes use of the object shape statistics by restricting possible elastic deformations into the range of the training shapes. The mean shapes are initialized in a new data set by specifying the landmarks of the stereotactic coordinate system. The model elastically deforms, driven by the displacement forces across the object's surface, which are generated by matching local intensity profiles. Elastic

  15. Episcopic 3D Imaging Methods: Tools for Researching Gene Function

    PubMed Central

    Weninger, Wolfgang J; Geyer, Stefan H

    2008-01-01

    This work aims at describing episcopic 3D imaging methods and at discussing how these methods can contribute to researching the genetic mechanisms driving embryogenesis and tissue remodelling, and the genesis of pathologies. Several episcopic 3D imaging methods exist. The most advanced are capable of generating high-resolution volume data (voxel sizes from 0.5x0.5x1 µm upwards) of small to large embryos of model organisms and tissue samples. Beside anatomy and tissue architecture, gene expression and gene product patterns can be three dimensionally analyzed in their precise anatomical and histological context with the aid of whole mount in situ hybridization or whole mount immunohistochemical staining techniques. Episcopic 3D imaging techniques were and are employed for analyzing the precise morphological phenotype of experimentally malformed, randomly produced, or genetically engineered embryos of biomedical model organisms. It has been shown that episcopic 3D imaging also fits for describing the spatial distribution of genes and gene products during embryogenesis, and that it can be used for analyzing tissue samples of adult model animals and humans. The latter offers the possibility to use episcopic 3D imaging techniques for researching the causality and treatment of pathologies or for staging cancer. Such applications, however, are not yet routine and currently only preliminary results are available. We conclude that, although episcopic 3D imaging is in its very beginnings, it represents an upcoming methodology, which in short terms will become an indispensable tool for researching the genetic regulation of embryo development as well as the genesis of malformations and diseases. PMID:19452045

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

    PubMed

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

    2014-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  18. Spline-based deforming ellipsoids for interactive 3D bioimage segmentation.

    PubMed

    Delgado-Gonzalo, Ricard; Chenouard, Nicolas; Unser, Michael

    2013-10-01

    We present a new fast active-contour model (a.k.a. snake) for image segmentation in 3D microscopy. We introduce a parametric design that relies on exponential B-spline bases and allows us to build snakes that are able to reproduce ellipsoids. We design our bases to have the shortest-possible support, subject to some constraints. Thus, computational efficiency is maximized. The proposed 3D snake can approximate blob-like objects with good accuracy and can perfectly reproduce spheres and ellipsoids, irrespective of their position and orientation. The optimization process is remarkably fast due to the use of Gauss' theorem within our energy computation scheme. Our technique yields successful segmentation results, even for challenging data where object contours are not well defined. This is due to our parametric approach that allows one to favor prior shapes. In addition, this paper provides a software that gives full control over the snakes via an intuitive manipulation of few control points.

  19. Combining Population and Patient-Specific Characteristics for Prostate Segmentation on 3D CT Images.

    PubMed

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

    2016-02-27

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  1. Combining Population and Patient-Specific Characteristics for Prostate Segmentation on 3D CT Images

    PubMed Central

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

    2016-01-01

    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. PMID:27660382

  2. A new visualization method for 3D head MRA data

    NASA Astrophysics Data System (ADS)

    Ohashi, Satoshi; Hatanaka, Masahiko

    2008-03-01

    In this paper, we propose a new visualization method for head MRA data which supports the user to easily determine the positioning of MPR images and/or MIP images based on the blood vessel network structure (the anatomic location of blood vessels). This visualization method has following features: (a) the blood vessel (cerebral artery) network structure in 3D head MRA data is portrayed the 3D line structure; (b) the MPR or MIP images are combined with the blood vessel network structure and displayed in a 3D visualization space; (c) the positioning of MPR or MIP is decided based on the anatomic location of blood vessels; (d) The image processing and drawing can be operated at real-time without a special hardware accelerator. As a result, we believe that our method is available to position MPR images or MIP images related to the blood vessel network structure. Moreover, we think that the user using this method can obtain the 3D information (position, angle, direction) of both these images and the blood vessel network structure.

  3. Data-driven interactive 3D medical image segmentation based on structured patch model.

    PubMed

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

    2013-01-01

    In this paper, we present a novel three dimensional interactive medical image segmentation method based on high level knowledge of training set. Since the interactive system should provide intermediate results to an user quickly, insufficient low level models are used for most of previous methods. To exploit the high level knowledge within a short time, we construct a structured patch model that consists of multiple corresponding patch sets. The structured patch model includes the spatial relationships between neighboring patch sets and the prior knowledge of the corresponding patch set on each local region. The spatial relationships accelerate the search of corresponding patch in test time, while the prior knowledge improves the segmentation accuracy. The proposed framework provides not only fast editing tool, but the incremental learning system through adding the segmentation result to the training set. Experiments demonstrate that the proposed method is useful for fast and accurate segmentation of target objects from the multiple medical images.

  4. Novel 3D Compression Methods for Geometry, Connectivity and Texture

    NASA Astrophysics Data System (ADS)

    Siddeq, M. M.; Rodrigues, M. A.

    2016-06-01

    A large number of applications in medical visualization, games, engineering design, entertainment, heritage, e-commerce and so on require the transmission of 3D models over the Internet or over local networks. 3D data compression is an important requirement for fast data storage, access and transmission within bandwidth limitations. The Wavefront OBJ (object) file format is commonly used to share models due to its clear simple design. Normally each OBJ file contains a large amount of data (e.g. vertices and triangulated faces, normals, texture coordinates and other parameters) describing the mesh surface. In this paper we introduce a new method to compress geometry, connectivity and texture coordinates by a novel Geometry Minimization Algorithm (GM-Algorithm) in connection with arithmetic coding. First, each vertex ( x, y, z) coordinates are encoded to a single value by the GM-Algorithm. Second, triangle faces are encoded by computing the differences between two adjacent vertex locations, which are compressed by arithmetic coding together with texture coordinates. We demonstrate the method on large data sets achieving compression ratios between 87 and 99 % without reduction in the number of reconstructed vertices and triangle faces. The decompression step is based on a Parallel Fast Matching Search Algorithm (Parallel-FMS) to recover the structure of the 3D mesh. A comparative analysis of compression ratios is provided with a number of commonly used 3D file formats such as VRML, OpenCTM and STL highlighting the performance and effectiveness of the proposed method.

  5. A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours

    PubMed Central

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

    2012-01-01

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

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

    PubMed

    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.

  7. Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach

    NASA Astrophysics Data System (ADS)

    Kirisli, Hortense A.; Schaap, Michiel; Klein, Stefan; Neefjes, Lisan A.; Weustink, Annick C.; Van Walsum, Theo; Niessen, Wiro J.

    2010-03-01

    Computed tomography angiography (CTA), a non-invasive imaging technique, is becoming increasingly popular for cardiac examination, mainly due to its superior spatial resolution compared to MRI. This imaging modality is currently widely used for the diagnosis of coronary artery disease (CAD) but it is not commonly used for the diagnosis of ventricular and atrial function. In this paper, we present a fully automatic method for segmenting the whole heart (i.e. the outer surface of the myocardium) and cardiac chambers from CTA datasets. Cardiac chamber segmentation is particularly valuable for the extraction of ventricular and atrial functional information, such as stroke volume and ejection fraction. With our approach, we aim to improve the diagnosis of CAD by providing functional information extracted from the same CTA data, thus not requiring additional scanning. In addition, the whole heart segmentation method we propose can be used for visualization of the coronary arteries and for obtaining a region of interest for subsequent segmentation of the coronaries, ventricles and atria. Our approach is based on multi-atlas segmentation, and performed within a non-rigid registration framework. A leave-one-out quantitative validation was carried out on 8 images. The method showed a high accuracy, which is reflected in both a mean segmentation error of 1.05+/-1.30 mm and an average Dice coefficient of 0.93. The robustness of the method is demonstrated by successfully applying the method to 243 additional datasets, without any significant failure.

  8. Automated multilayer segmentation and characterization in 3D spectral-domain optical coherence tomography images

    NASA Astrophysics Data System (ADS)

    Hu, Zhihong; Wu, Xiaodong; Hariri, Amirhossein; Sadda, SriniVas R.

    2013-03-01

    Spectral-domain optical coherence tomography (SD-OCT) is a 3-D imaging technique, allowing direct visualization of retinal morphology and architecture. The various layers of the retina may be affected differentially by various diseases. In this study, an automated graph-based multilayer approach was developed to sequentially segment eleven retinal surfaces including the inner retinal bands to the outer retinal bands in normal SD-OCT volume scans at three different stages. For stage 1, the four most detectable and/or distinct surfaces were identified in the four-times-downsampled images and were used as a priori positional information to limit the graph search for other surfaces at stage 2. Eleven surfaces were then detected in the two-times-downsampled images at stage 2, and refined in the original image space at stage 3 using the graph search integrating the estimated morphological shape models. Twenty macular SD-OCT (Heidelberg Spectralis) volume scans from 20 normal subjects (one eye per subject) were used in this study. The overall mean and absolute mean differences in border positions between the automated and manual segmentation for all 11 segmented surfaces were -0.20 +/- 0.53 voxels (-0.76 +/- 2.06 μm) and 0.82 +/- 0.64 voxels (3.19 +/- 2.46 μm). Intensity and thickness properties in the resultant retinal layers were investigated. This investigation in normal subjects may provide a comparative reference for subsequent investigations in eyes with disease.

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    Wan, Weibing; Shi, Pengfei; Li, Shuguang

    2009-10-01

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

  11. Effective classification of 3D image data using partitioning methods

    NASA Astrophysics Data System (ADS)

    Megalooikonomou, Vasileios; Pokrajac, Dragoljub; Lazarevic, Aleksandar; Obradovic, Zoran

    2002-03-01

    We propose partitioning-based methods to facilitate the classification of 3-D binary image data sets of regions of interest (ROIs) with highly non-uniform distributions. The first method is based on recursive dynamic partitioning of a 3-D volume into a number of 3-D hyper-rectangles. For each hyper-rectangle, we consider, as a potential attribute, the number of voxels (volume elements) that belong to ROIs. A hyper-rectangle is partitioned only if the corresponding attribute does not have high discriminative power, determined by statistical tests, but it is still sufficiently large for further splitting. The final discriminative hyper-rectangles form new attributes that are further employed in neural network classification models. The second method is based on maximum likelihood employing non-spatial (k-means) and spatial DBSCAN clustering algorithms to estimate the parameters of the underlying distributions. The proposed methods were experimentally evaluated on mixtures of Gaussian distributions, on realistic lesion-deficit data generated by a simulator conforming to a clinical study, and on synthetic fractal data. Both proposed methods have provided good classification on Gaussian mixtures and on realistic data. However, the experimental results on fractal data indicated that the clustering-based methods were only slightly better than random guess, while the recursive partitioning provided significantly better classification accuracy.

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

    PubMed

    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.

  13. Brain tumor segmentation in 3D MRIs using an improved Markov random field model

    NASA Astrophysics Data System (ADS)

    Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

    2011-10-01

    Markov Random Field (MRF) models have been recently suggested for MRI brain segmentation by a large number of researchers. By employing Markovianity, which represents the local property, MRF models are able to solve a global optimization problem locally. But they still have a heavy computation burden, especially when they use stochastic relaxation schemes such as Simulated Annealing (SA). In this paper, a new 3D-MRF model is put forward to raise the speed of the convergence. Although, search procedure of SA is fairly localized and prevents from exploring the same diversity of solutions, it suffers from several limitations. In comparison, Genetic Algorithm (GA) has a good capability of global researching but it is weak in hill climbing. Our proposed algorithm combines SA and an improved GA (IGA) to optimize the solution which speeds up the computation time. What is more, this proposed algorithm outperforms the traditional 2D-MRF in quality of the solution.

  14. A method for building 3D models of barchan dunes

    NASA Astrophysics Data System (ADS)

    Nai, Yang; Li-lan, Su; Lin, Wan; Jie, Yang; Shi-yi, Chen; Wei-lu, Hu

    2016-01-01

    The distributions of barchan dunes are usually represented by digital terrain models (DTMs) overlaid with digital orthophoto maps. Given that most regions with barchan dues have low relief, a 3D map obtained from a DTM may ineffectively show the stereoscopic shape of each dune. The method of building 3D models of barchan dunes using existing modeling software seldom considers the geographical environment. As a result, barchan dune models are often inconsistent with actual DTMs and incompletely express the morphological characteristics of dunes. Manual construction of barchan dune models is also costly and time consuming. Considering these problems, the morphological characteristics of barchan dunes and the mathematical relationships between the morphological parameters of the dunes, such as length, height, and width, are analyzed in this study. The methods of extracting the morphological feature points of barchan dunes, calculating their morphological parameters and building dune outlines and skeleton lines based on the medial axes, are also presented. The dune outlines, skeleton lines, and part of the medial axes of dunes are used to construct a constrained triangulated irregular network. C# and ArcEngine are employed to build 3D models of barchan dunes automatically. Experimental results of a study conducted in Tengger Desert show that the method can be used to approximate the morphological characteristics of barchan dunes and is less time consuming than manual methods.

  15. Application of 3D reflection seismic methods to mineral exploration

    NASA Astrophysics Data System (ADS)

    Urosevic, Milovan

    2013-04-01

    Seismic exploration for mineral deposits is often tested by excessively complex structures, regolith heterogeneity, intrinsically low signal to noise ratio, ground relief and accessibility. In brown fields, where the majority of the seismic surveys have been conducted, existing infrastructure, old pits and tailings, heavy machinery in operation, mine drainage and other mine related activities are further challenging the application of seismic methods and furthermore increasing its cost. It is therefore not surprising that the mining industry has been reluctant to use seismic methods, particularly 3D for mineral exploration, primarily due to the high cost, but also because of variable performance, and in some cases ambiguous interpretation results. However, shallow mineral reserves are becoming depleted and exploration is moving towards deeper targets. Seismic methods will be more important for deeper investigations and may become the primary exploration tool in the near future. The big issue is if we have an appropriate seismic "strategy" for exploration of deep, complex mineral reserves. From the existing case histories worldwide we know that massive ore deposits (VMS, VHMS) constitute the best case scenario for the application of 3D seismic. Direct targeting of massive ore bodies from seismic has been documented in several case histories. Sediment hosted deposits could, in some cases, can also produce a detectable seismic signature. Other deposit types such as IOCG and skarn are much more challenging for the application of seismic methods. The complexity of these deposits requires new thinking. Several 3D surveys acquired over different deposit types will be presented and discussed.

  16. Simultaneous segmentation of the bone and cartilage surfaces of a knee joint in 3D

    NASA Astrophysics Data System (ADS)

    Yin, Y.; Zhang, X.; Anderson, D. D.; Brown, T. D.; Hofwegen, C. Van; Sonka, M.

    2009-02-01

    We present a novel framework for the simultaneous segmentation of multiple interacting surfaces belonging to multiple mutually interacting objects. The method is a non-trivial extension of our previously reported optimal multi-surface segmentation. Considering an example application of knee-cartilage segmentation, the framework consists of the following main steps: 1) Shape model construction: Building a mean shape for each bone of the joint (femur, tibia, patella) from interactively segmented volumetric datasets. Using the resulting mean-shape model - identification of cartilage, non-cartilage, and transition areas on the mean-shape bone model surfaces. 2) Presegmentation: Employment of iterative optimal surface detection method to achieve approximate segmentation of individual bone surfaces. 3) Cross-object surface mapping: Detection of inter-bone equidistant separating sheets to help identify corresponding vertex pairs for all interacting surfaces. 4) Multi-object, multi-surface graph construction and final segmentation: Construction of a single multi-bone, multi-surface graph so that two surfaces (bone and cartilage) with zero and non-zero intervening distances can be detected for each bone of the joint, according to whether or not cartilage can be locally absent or present on the bone. To define inter-object relationships, corresponding vertex pairs identified using the separating sheets were interlinked in the graph. The graph optimization algorithm acted on the entire multiobject, multi-surface graph to yield a globally optimal solution. The segmentation framework was tested on 16 MR-DESS knee-joint datasets from the Osteoarthritis Initiative database. The average signed surface positioning error for the 6 detected surfaces ranged from 0.00 to 0.12 mm. When independently initialized, the signed reproducibility error of bone and cartilage segmentation ranged from 0.00 to 0.26 mm. The results showed that this framework provides robust, accurate, and

  17. Breast tumour visualization using 3D quantitative ultrasound methods

    NASA Astrophysics Data System (ADS)

    Gangeh, Mehrdad J.; Raheem, Abdul; Tadayyon, Hadi; Liu, Simon; Hadizad, Farnoosh; Czarnota, Gregory J.

    2016-04-01

    Breast cancer is one of the most common cancer types accounting for 29% of all cancer cases. Early detection and treatment has a crucial impact on improving the survival of affected patients. Ultrasound (US) is non-ionizing, portable, inexpensive, and real-time imaging modality for screening and quantifying breast cancer. Due to these attractive attributes, the last decade has witnessed many studies on using quantitative ultrasound (QUS) methods in tissue characterization. However, these studies have mainly been limited to 2-D QUS methods using hand-held US (HHUS) scanners. With the availability of automated breast ultrasound (ABUS) technology, this study is the first to develop 3-D QUS methods for the ABUS visualization of breast tumours. Using an ABUS system, unlike the manual 2-D HHUS device, the whole patient's breast was scanned in an automated manner. The acquired frames were subsequently examined and a region of interest (ROI) was selected in each frame where tumour was identified. Standard 2-D QUS methods were used to compute spectral and backscatter coefficient (BSC) parametric maps on the selected ROIs. Next, the computed 2-D parameters were mapped to a Cartesian 3-D space, interpolated, and rendered to provide a transparent color-coded visualization of the entire breast tumour. Such 3-D visualization can potentially be used for further analysis of the breast tumours in terms of their size and extension. Moreover, the 3-D volumetric scans can be used for tissue characterization and the categorization of breast tumours as benign or malignant by quantifying the computed parametric maps over the whole tumour volume.

  18. Optical Sensors and Methods for Underwater 3D Reconstruction

    PubMed Central

    Massot-Campos, Miquel; Oliver-Codina, Gabriel

    2015-01-01

    This paper presents a survey on optical sensors and methods for 3D reconstruction in underwater environments. The techniques to obtain range data have been listed and explained, together with the different sensor hardware that makes them possible. The literature has been reviewed, and a classification has been proposed for the existing solutions. New developments, commercial solutions and previous reviews in this topic have also been gathered and considered. PMID:26694389

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

    NASA Astrophysics Data System (ADS)

    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

  20. 3D X-ray imaging methods in support catheter ablations of cardiac arrhythmias.

    PubMed

    Stárek, Zdeněk; Lehar, František; Jež, Jiří; Wolf, Jiří; Novák, Miroslav

    2014-10-01

    Cardiac arrhythmias are a very frequent illness. Pharmacotherapy is not very effective in persistent arrhythmias and brings along a number of risks. Catheter ablation has became an effective and curative treatment method over the past 20 years. To support complex arrhythmia ablations, the 3D X-ray cardiac cavities imaging is used, most frequently the 3D reconstruction of CT images. The 3D cardiac rotational angiography (3DRA) represents a modern method enabling to create CT like 3D images on a standard X-ray machine equipped with special software. Its advantage lies in the possibility to obtain images during the procedure, decreased radiation dose and reduction of amount of the contrast agent. The left atrium model is the one most frequently used for complex atrial arrhythmia ablations, particularly for atrial fibrillation. CT data allow for creation and segmentation of 3D models of all cardiac cavities. Recently, a research has been made proving the use of 3DRA to create 3D models of other cardiac (right ventricle, left ventricle, aorta) and non-cardiac structures (oesophagus). They can be used during catheter ablation of complex arrhythmias to improve orientation during the construction of 3D electroanatomic maps, directly fused with 3D electroanatomic systems and/or fused with fluoroscopy. An intensive development in the 3D model creation and use has taken place over the past years and they became routinely used during catheter ablations of arrhythmias, mainly atrial fibrillation ablation procedures. Further development may be anticipated in the future in both the creation and use of these models.

  1. A new method of 3D scene recognition from still images

    NASA Astrophysics Data System (ADS)

    Zheng, Li-ming; Wang, Xing-song

    2014-04-01

    Most methods of monocular visual three dimensional (3D) scene recognition involve supervised machine learning. However, these methods often rely on prior knowledge. Specifically, they learn the image scene as part of a training dataset. For this reason, when the sampling equipment or scene is changed, monocular visual 3D scene recognition may fail. To cope with this problem, a new method of unsupervised learning for monocular visual 3D scene recognition is here proposed. First, the image is made using superpixel segmentation based on the CIELAB color space values L, a, and b and on the coordinate values x and y of pixels, forming a superpixel image with a specific density. Second, a spectral clustering algorithm based on the superpixels' color characteristics and neighboring relationships was used to reduce the dimensions of the superpixel image. Third, the fuzzy distribution density functions representing sky, ground, and façade are multiplied with the segment pixels, where the expectations of these segments are obtained. A preliminary classification of sky, ground, and façade is generated in this way. Fourth, the most accurate classification images of sky, ground, and façade were extracted through the tier-1 wavelet sampling and Manhattan direction feature. Finally, a depth perception map is generated based on the pinhole imaging model and the linear perspective information of ground surface. Here, 400 images of Make3D Image data from the Cornell University website were used to test the algorithm. The experimental results showed that this unsupervised learning method provides a more effective monocular visual 3D scene recognition model than other methods.

  2. Discrete Method of Images for 3D Radio Propagation Modeling

    NASA Astrophysics Data System (ADS)

    Novak, Roman

    2016-09-01

    Discretization by rasterization is introduced into the method of images (MI) in the context of 3D deterministic radio propagation modeling as a way to exploit spatial coherence of electromagnetic propagation for fine-grained parallelism. Traditional algebraic treatment of bounding regions and surfaces is replaced by computer graphics rendering of 3D reflections and double refractions while building the image tree. The visibility of reception points and surfaces is also resolved by shader programs. The proposed rasterization is shown to be of comparable run time to that of the fundamentally parallel shooting and bouncing rays. The rasterization does not affect the signal evaluation backtracking step, thus preserving its advantage over the brute force ray-tracing methods in terms of accuracy. Moreover, the rendering resolution may be scaled back for a given level of scenario detail with only marginal impact on the image tree size. This allows selection of scene optimized execution parameters for faster execution, giving the method a competitive edge. The proposed variant of MI can be run on any GPU that supports real-time 3D graphics.

  3. Method for modeling post-mortem biometric 3D fingerprints

    NASA Astrophysics Data System (ADS)

    Rajeev, Srijith; Shreyas, Kamath K. M.; Agaian, Sos S.

    2016-05-01

    Despite the advancements of fingerprint recognition in 2-D and 3-D domain, authenticating deformed/post-mortem fingerprints continue to be an important challenge. Prior cleansing and reconditioning of the deceased finger is required before acquisition of the fingerprint. The victim's finger needs to be precisely and carefully operated by a medium to record the fingerprint impression. This process may damage the structure of the finger, which subsequently leads to higher false rejection rates. This paper proposes a non-invasive method to perform 3-D deformed/post-mortem finger modeling, which produces a 2-D rolled equivalent fingerprint for automated verification. The presented novel modeling method involves masking, filtering, and unrolling. Computer simulations were conducted on finger models with different depth variations obtained from Flashscan3D LLC. Results illustrate that the modeling scheme provides a viable 2-D fingerprint of deformed models for automated verification. The quality and adaptability of the obtained unrolled 2-D fingerprints were analyzed using NIST fingerprint software. Eventually, the presented method could be extended to other biometric traits such as palm, foot, tongue etc. for security and administrative applications.

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

    PubMed Central

    Barbier, Michaël; Jaensch, Steffen; Cornelissen, Frans; Vidic, Suzana; Gjerde, Kjersti; de Hoogt, Ronald; Graeser, Ralph; Gustin, Emmanuel; Chong, Yolanda T.

    2016-01-01

    In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation. PMID:27303813

  5. Mesenteric Vasculature-guided Small Bowel Segmentation on 3D CT

    PubMed Central

    Zhang, Weidong; Liu, Jiamin; Yao, Jianhua; Louie, Adeline; Nguyen, Tan B.; Wank, Stephen; Nowinski, Wieslaw L.; Summers, Ronald M.

    2014-01-01

    Due to its importance and possible applications in visualization, tumor detection and pre-operative planning, automatic small bowel segmentation is essential for computer-aided diagnosis of small bowel pathology. However, segmenting the small bowel directly on CT scans is very difficult because of the low image contrast on CT scans and high tortuosity of the small bowel and its close proximity to other abdominal organs. Motivated by the intensity characteristics of abdominal CT images, the anatomic relationship between the mesenteric vasculature and the small bowel, and potential usefulness of the mesenteric vasculature for establishing the path of the small bowel, we propose a novel mesenteric vasculature map-guided method for small bowel segmentation on high-resolution CT angiography scans. The major mesenteric arteries are first segmented using a vessel tracing method based on multi-linear subspace vessel model and Bayesian inference. Second, multi-view, multi-scale vesselness enhancement filters are used to segment small vessels, and vessels directly or indirectly connecting to the superior mesenteric artery are classified as mesenteric vessels. Third, a mesenteric vasculature map is built by linking vessel bifurcation points, and the small bowel is segmented by employing the mesenteric vessel map and fuzzy connectness. The method was evaluated on 11 abdominal CT scans of patients suspected of having carcinoid tumors with manually labeled reference standard. The result, 82.5% volume overlap accuracy compared with the reference standard, shows it is feasible to segment the small bowel on CT scans using the mesenteric vasculature as a roadmap. PMID:23807437

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

    PubMed

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

    2017-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2004-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  9. Reconstruction 3D des structures adjacentes de l'articulation de la hanche par une segmentation multi-structures a l'aide des maillages surfaciques triangulaires

    NASA Astrophysics Data System (ADS)

    Meghoufel, Brahim

    A new 3D reconstruction technique of the two adjacent structures forming the hip joint from the 3D CT-scans images has been developed. The femoral head and the acetabulum are reconstructed using a 3D multi-structure segmentation method for the adjacent surfaces which is based on the use of a 3D triangular surface meshes. This method begins with a preliminary hierarchical segmentation of the two structures, using one triangular mesh for each structure. The two resulting 3D meshes of the hierarchical segmentation are deployed into two planar 2D surfaces. We have used the umbrella deployment to deploy the femoral head mesh, and the parameterization 3D/2D to deploy the acetabulum mesh. The two planar generated surfaces are used to deploy the CT-scan volume around each structure. The surface of each structure is nearly planar in the corresponding deployed volume. The iterative method of minimal surfaces ensures the optimal identification of both sought surfaces from the deployed volumes. The last step of the 3D reconstruction method aims at detecting and correcting the overlap between the two structures. This 3D reconstruction method has been validated using a data base of 10 3D CT-scan images. The results of the 3D reconstructions seem satisfactory. The precision errors of these 3D reconstructions have been quantified by comparing the 3D reconstructions with an available manual gold standard. The errors resulting from the quantification are better than those available in the literature; the mean of those errors is 0,83 +/- 0,25 mm for acetabulum and 0,70 +/- 0,17 mm for the femoral head. The mean execution time of the 3D reconstruction of the two structures forming the hip joint has been estimated at approximately 3,0 +/- 0,3 min . The proposed method shows the potential of the solution which the image processing can provide to the surgeons in order to achieve their routine tasks. Such a method can be applied to every imaging modality.

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

    SciTech Connect

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

    1996-12-31

    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.

  11. A Review of Failure Analysis Methods for Advanced 3D Microelectronic Packages

    NASA Astrophysics Data System (ADS)

    Li, Yan; Srinath, Purushotham Kaushik Muthur; Goyal, Deepak

    2016-01-01

    Advanced three dimensional (3D) packaging is a key enabler in driving form factor reduction, performance benefits, and package cost reduction, especially in the fast paced mobility and ultraportable consumer electronics segments. The high level of functional integration and the complex package architecture pose a significant challenge for conventional fault isolation (FI) and failure analysis (FA) methods. Innovative FI/FA tools and techniques are required to tackle the technical and throughput challenges. In this paper, the applications of FI and FA techniques such as Electro Optic Terahertz Pulse Reflectometry, 3D x-ray computed tomography, lock-in thermography, and novel physical sample preparation methods to 3D packages with package on package and stacked die with through silicon via configurations are reviewed, along with the key FI and FA challenges.

  12. 3D segmentation of abdominal aorta from CT-scan and MR images.

    PubMed

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

    2012-06-01

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

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

    PubMed

    Oh, Sejong; Choe, Yoonsuck

    2007-08-01

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

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

    PubMed

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

    2011-09-01

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

  15. Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

    NASA Astrophysics Data System (ADS)

    Sundareswaran, Kartik S.; Frakes, David H.; Yoganathan, Ajit P.

    2008-02-01

    Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

  16. Development of a piecewise linear omnidirectional 3D image registration method.

    PubMed

    Bae, Hyunsoo; Kang, Wonjin; Lee, SukGyu; Kim, Youngwoo

    2016-12-01

    This paper proposes a new piecewise linear omnidirectional image registration method. The proposed method segments an image captured by multiple cameras into 2D segments defined by feature points of the image and then stitches each segment geometrically by considering the inclination of the segment in the 3D space. Depending on the intended use of image registration, the proposed method can be used to improve image registration accuracy or reduce the computation time in image registration because the trade-off between the computation time and image registration accuracy can be controlled for. In general, nonlinear image registration methods have been used in 3D omnidirectional image registration processes to reduce image distortion by camera lenses. The proposed method depends on a linear transformation process for omnidirectional image registration, and therefore it can enhance the effectiveness of the geometry recognition process, increase image registration accuracy by increasing the number of cameras or feature points of each image, increase the image registration speed by reducing the number of cameras or feature points of each image, and provide simultaneous information on shapes and colors of captured objects.

  17. Development of a piecewise linear omnidirectional 3D image registration method

    NASA Astrophysics Data System (ADS)

    Bae, Hyunsoo; Kang, Wonjin; Lee, SukGyu; Kim, Youngwoo

    2016-12-01

    This paper proposes a new piecewise linear omnidirectional image registration method. The proposed method segments an image captured by multiple cameras into 2D segments defined by feature points of the image and then stitches each segment geometrically by considering the inclination of the segment in the 3D space. Depending on the intended use of image registration, the proposed method can be used to improve image registration accuracy or reduce the computation time in image registration because the trade-off between the computation time and image registration accuracy can be controlled for. In general, nonlinear image registration methods have been used in 3D omnidirectional image registration processes to reduce image distortion by camera lenses. The proposed method depends on a linear transformation process for omnidirectional image registration, and therefore it can enhance the effectiveness of the geometry recognition process, increase image registration accuracy by increasing the number of cameras or feature points of each image, increase the image registration speed by reducing the number of cameras or feature points of each image, and provide simultaneous information on shapes and colors of captured objects.

  18. Method and simulation to study 3D crosstalk perception

    NASA Astrophysics Data System (ADS)

    Khaustova, Dar'ya; Blondé, Laurent; Huynh-Thu, Quan; Vienne, Cyril; Doyen, Didier

    2012-03-01

    To various degrees, all modern 3DTV displays suffer from crosstalk, which can lead to a decrease of both visual quality and visual comfort, and also affect perception of depth. In the absence of a perfect 3D display technology, crosstalk has to be taken into account when studying perception of 3D stereoscopic content. In order to improve 3D presentation systems and understand how to efficiently eliminate crosstalk, it is necessary to understand its impact on human perception. In this paper, we present a practical method to study the perception of crosstalk. The approach consists of four steps: (1) physical measurements of a 3DTV, (2) building of a crosstalk surface based on those measurements and representing specifically the behavior of that 3TV, (3) manipulation of the crosstalk function and application on reference images to produce test images degraded by crosstalk in various ways, and (4) psychophysical tests. Our approach allows both a realistic representation of the behavior of a 3DTV and the easy manipulation of its resulting crosstalk in order to conduct psycho-visual experiments. Our approach can be used in all studies requiring the understanding of how crosstalk affects perception of stereoscopic content and how it can be corrected efficiently.

  19. System and method for 3D printing of aerogels

    DOEpatents

    Worsley, Marcus A.; Duoss, Eric; Kuntz, Joshua; Spadaccini, Christopher; Zhu, Cheng

    2016-03-08

    A method of forming an aerogel. The method may involve providing a graphene oxide powder and mixing the graphene oxide powder with a solution to form an ink. A 3D printing technique may be used to write the ink into a catalytic solution that is contained in a fluid containment member to form a wet part. The wet part may then be cured in a sealed container for a predetermined period of time at a predetermined temperature. The cured wet part may then be dried to form a finished aerogel part.

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

    NASA Astrophysics Data System (ADS)

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

    2005-04-01

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

  1. Segmentation and quantitative evaluation of brain MRI data with a multiphase 3D implicit deformable model

    NASA Astrophysics Data System (ADS)

    Angelini, Elsa D.; Song, Ting; Mensh, Brett D.; Laine, Andrew

    2004-05-01

    Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative evaluation of a four-phase three-dimensional active contour implemented with a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours.

  2. 3D cerebral MR image segmentation using multiple-classifier system.

    PubMed

    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. 3D reconstruction methods of coronal structures by radio observations

    NASA Technical Reports Server (NTRS)

    Aschwanden, Markus J.; Bastian, T. S.; White, Stephen M.

    1992-01-01

    The ability to carry out the three dimensional (3D) reconstruction of structures in the solar corona would represent a major advance in the study of the physical properties in active regions and in flares. Methods which allow a geometric reconstruction of quasistationary coronal structures (for example active region loops) or dynamic structures (for example flaring loops) are described: stereoscopy of multi-day imaging observations by the VLA (Very Large Array); tomography of optically thin emission (in radio or soft x-rays); multifrequency band imaging by the VLA; and tracing of magnetic field lines by propagating electron beams.

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

    PubMed

    Gargiulo, Paolo; Helgason, Thordur; Ramon, Ceon; Jr, Halldór Jónsson; Carraro, Ugo

    2014-03-31

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

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

    PubMed

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

    2015-01-01

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

  6. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  7. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images.

    PubMed

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

    2014-12-21

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

  8. Hybrid 3D-2D printing of bone scaffolds Hybrid 3D-2D printing methods for bone scaffolds fabrication.

    PubMed

    Prinz, V Ya; Seleznev, Vladimir

    2016-12-13

    It is a well-known fact that bone scaffold topography on micro- and nanometer scale influences the cellular behavior. Nano-scale surface modification of scaffolds allows the modulation of biological activity for enhanced cell differentiation. To date, there has been only a limited success in printing scaffolds with micro- and nano-scale features exposed on the surface. To improve on the currently available imperfect technologies, in our paper we introduce new hybrid technologies based on a combination of 2D (nano imprint) and 3D printing methods. The first method is based on using light projection 3D printing and simultaneous 2D nanostructuring of each of the layers during the formation of the 3D structure. The second method is based on the sequential integration of preliminarily created 2D nanostructured films into a 3D printed structure. The capabilities of the developed hybrid technologies are demonstrated with the example of forming 3D bone scaffolds. The proposed technologies can be used to fabricate complex 3D micro- and nanostructured products for various fields.

  9. The development of a 3D risk analysis method.

    PubMed

    I, Yet-Pole; Cheng, Te-Lung

    2008-05-01

    Much attention has been paid to the quantitative risk analysis (QRA) research in recent years due to more and more severe disasters that have happened in the process industries. Owing to its calculation complexity, very few software, such as SAFETI, can really make the risk presentation meet the practice requirements. However, the traditional risk presentation method, like the individual risk contour in SAFETI, is mainly based on the consequence analysis results of dispersion modeling, which usually assumes that the vapor cloud disperses over a constant ground roughness on a flat terrain with no obstructions and concentration fluctuations, which is quite different from the real situations of a chemical process plant. All these models usually over-predict the hazardous regions in order to maintain their conservativeness, which also increases the uncertainty of the simulation results. On the other hand, a more rigorous model such as the computational fluid dynamics (CFD) model can resolve the previous limitations; however, it cannot resolve the complexity of risk calculations. In this research, a conceptual three-dimensional (3D) risk calculation method was proposed via the combination of results of a series of CFD simulations with some post-processing procedures to obtain the 3D individual risk iso-surfaces. It is believed that such technique will not only be limited to risk analysis at ground level, but also be extended into aerial, submarine, or space risk analyses in the near future.

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

    PubMed

    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.

  11. Robust method for extracting the pulmonary vascular trees from 3D MDCT images

    NASA Astrophysics Data System (ADS)

    Taeprasartsit, Pinyo; Higgins, William E.

    2011-03-01

    Segmentation of pulmonary blood vessels from three-dimensional (3D) multi-detector CT (MDCT) images is important for pulmonary applications. This work presents a method for extracting the vascular trees of the pulmonary arteries and veins, applicable to both contrast-enhanced and unenhanced 3D MDCT image data. The method finds 2D elliptical cross-sections and evaluates agreement of these cross-sections in consecutive slices to find likely cross-sections. It next employs morphological multiscale analysis to separate vessels from adjoining airway walls. The method then tracks the center of the likely cross-sections to connect them to the pulmonary vessels in the mediastinum and forms connected vascular trees spanning both lungs. A ground-truth study indicates that the method was able to detect on the order of 98% of the vessel branches having diameter >= 3.0 mm. The extracted vascular trees can be utilized for the guidance of safe bronchoscopic biopsy.

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

    PubMed

    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.

  13. Image segmentation and registration for the analysis of joint motion from 3D MRI

    NASA Astrophysics Data System (ADS)

    Hu, Yangqiu; Haynor, David R.; Fassbind, Michael; Rohr, Eric; Ledoux, William

    2006-03-01

    We report an image segmentation and registration method for studying joint morphology and kinematics from in vivo MRI scans and its application to the analysis of ankle joint motion. Using an MR-compatible loading device, a foot was scanned in a single neutral and seven dynamic positions including maximal flexion, rotation and inversion/eversion. A segmentation method combining graph cuts and level sets was developed which allows a user to interactively delineate 14 bones in the neutral position volume in less than 30 minutes total, including less than 10 minutes of user interaction. In the subsequent registration step, a separate rigid body transformation for each bone is obtained by registering the neutral position dataset to each of the dynamic ones, which produces an accurate description of the motion between them. We have processed six datasets, including 3 normal and 3 pathological feet. For validation our results were compared with those obtained from 3DViewnix, a semi-automatic segmentation program, and achieved good agreement in volume overlap ratios (mean: 91.57%, standard deviation: 3.58%) for all bones. Our tool requires only 1/50 and 1/150 of the user interaction time required by 3DViewnix and NIH Image Plus, respectively, an improvement that has the potential to make joint motion analysis from MRI practical in research and clinical applications.

  14. The COMET method in 3-D hexagonal geometry

    SciTech Connect

    Connolly, K. J.; Rahnema, F.

    2012-07-01

    The hybrid stochastic-deterministic coarse mesh radiation transport (COMET) method developed at Georgia Tech now solves reactor core problems in 3-D hexagonal geometry. In this paper, the method is used to solve three preliminary test problems designed to challenge the method with steep flux gradients, high leakage, and strong asymmetry and heterogeneity in the core. The test problems are composed of blocks taken from a high temperature test reactor benchmark problem. As the method is still in development, these problems and their results are strictly preliminary. Results are compared to whole core Monte Carlo reference solutions in order to verify the method. Relative errors are on the order of 50 pcm in core eigenvalue, and mean relative error in pin fission density calculations is less than 1% in these difficult test cores. The method requires the one-time pre-computation of a response expansion coefficient library, which may be compiled in a comparable amount of time to a single whole core Monte Carlo calculation. After the library has been computed, COMET may solve any number of core configurations on the order of an hour, representing a significant gain in efficiency over other methods for whole core transport calculations. (authors)

  15. Comparison of bootstrap resampling methods for 3-D PET imaging.

    PubMed

    Lartizien, C; Aubin, J-B; Buvat, I

    2010-07-01

    Two groups of bootstrap methods have been proposed to estimate the statistical properties of positron emission tomography (PET) images by generating multiple statistically equivalent data sets from few data samples. The first group generates resampled data based on a parametric approach assuming that data from which resampling is performed follows a Poisson distribution while the second group consists of nonparametric approaches. These methods either require a unique original sample or a series of statistically equivalent data that can be list-mode files or sinograms. Previous reports regarding these bootstrap approaches suggest different results. This work compares the accuracy of three of these bootstrap methods for 3-D PET imaging based on simulated data. Two methods are based on a unique file, namely a list-mode based nonparametric (LMNP) method and a sinogram based parametric (SP) method. The third method is a sinogram-based nonparametric (SNP) method. Another original method (extended LMNP) was also investigated, which is an extension of the LMNP methods based on deriving a resampled list-mode file by drawings events from multiple original list-mode files. Our comparison is based on the analysis of the statistical moments estimated on the repeated and resampled data. This includes the probability density function and the moments of order 1 and 2. Results show that the two methods based on multiple original data (SNP and extended LMNP) are the only methods that correctly estimate the statistical parameters. Performances of the LMNP and SP methods are variable. Simulated data used in this study were characterized by a high noise level. Differences among the tested strategies might be reduced with clinical data sets with lower noise.

  16. On 3D inelastic analysis methods for hot section components

    NASA Technical Reports Server (NTRS)

    Mcknight, R. L.; Chen, P. C.; Dame, L. T.; Holt, R. V.; Huang, H.; Hartle, M.; Gellin, S.; Allen, D. H.; Haisler, W. E.

    1986-01-01

    Accomplishments are described for the 2-year program, to develop advanced 3-D inelastic structural stress analysis methods and solution strategies for more accurate and cost effective analysis of combustors, turbine blades and vanes. The approach was to develop a matrix of formulation elements and constitutive models. Three constitutive models were developed in conjunction with optimized iterating techniques, accelerators, and convergence criteria within a framework of dynamic time incrementing. Three formulations models were developed; an eight-noded mid-surface shell element, a nine-noded mid-surface shell element and a twenty-noded isoparametric solid element. A separate computer program was developed for each combination of constitutive model-formulation model. Each program provides a functional stand alone capability for performing cyclic nonlinear structural analysis. In addition, the analysis capabilities incorporated into each program can be abstracted in subroutine form for incorporation into other codes or to form new combinations.

  17. The 3D inelastic analysis methods for hot section components

    NASA Technical Reports Server (NTRS)

    Mcknight, R. L.; Maffeo, R. J.; Tipton, M. T.; Weber, G.

    1992-01-01

    A two-year program to develop advanced 3D inelastic structural stress analysis methods and solution strategies for more accurate and cost effective analysis of combustors, turbine blades, and vanes is described. The approach was to develop a matrix of formulation elements and constitutive models. Three constitutive models were developed in conjunction with optimized iterating techniques, accelerators, and convergence criteria within a framework of dynamic time incrementing. Three formulation models were developed: an eight-noded midsurface shell element; a nine-noded midsurface shell element; and a twenty-noded isoparametric solid element. A separate computer program has been developed for each combination of constitutive model-formulation model. Each program provides a functional stand alone capability for performing cyclic nonlinear structural analysis. In addition, the analysis capabilities incorporated into each program can be abstracted in subroutine form for incorporation into other codes or to form new combinations.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  19. Automated torso organ segmentation from 3D CT images using conditional random field

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

    This paper presents a segmentation method for torso organs using conditional random field (CRF) from medical images. A lot of methods have been proposed to enable automated extraction of organ regions from volumetric medical images. However, it is necessary to adjust empirical parameters of them to obtain precise organ regions. In this paper, we propose an organ segmentation method using structured output learning which is based on probabilistic graphical model. The proposed method utilizes CRF on three-dimensional grids as probabilistic graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weight parameters of the CRF using stochastic gradient descent algorithm and estimate organ labels for a given image by maximum a posteriori (MAP) estimation. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 6.6%. The DICE coefficients of right lung, left lung, heart, liver, spleen, right kidney, and left kidney are 0.94, 0.92, 0.65, 0.67, 0.36, 0.38, and 0.37, respectively.

  20. Evaluation of Methods for Coregistration and Fusion of Rpas-Based 3d Point Clouds and Thermal Infrared Images

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

    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.

  1. Lattice Boltzmann Method for 3-D Flows with Curved Boundary

    NASA Technical Reports Server (NTRS)

    Mei, Renwei; Shyy, Wei; Yu, Dazhi; Luo, Li-Shi

    2002-01-01

    In this work, we investigate two issues that are important to computational efficiency and reliability in fluid dynamics applications of the lattice, Boltzmann equation (LBE): (1) Computational stability and accuracy of different lattice Boltzmann models and (2) the treatment of the boundary conditions on curved solid boundaries and their 3-D implementations. Three athermal 3-D LBE models (D3QI5, D3Ql9, and D3Q27) are studied and compared in terms of efficiency, accuracy, and robustness. The boundary treatment recently developed by Filippova and Hanel and Met et al. in 2-D is extended to and implemented for 3-D. The convergence, stability, and computational efficiency of the 3-D LBE models with the boundary treatment for curved boundaries were tested in simulations of four 3-D flows: (1) Fully developed flows in a square duct, (2) flow in a 3-D lid-driven cavity, (3) fully developed flows in a circular pipe, and (4) a uniform flow over a sphere. We found that while the fifteen-velocity 3-D (D3Ql5) model is more prone to numerical instability and the D3Q27 is more computationally intensive, the 63Q19 model provides a balance between computational reliability and efficiency. Through numerical simulations, we demonstrated that the boundary treatment for 3-D arbitrary curved geometry has second-order accuracy and possesses satisfactory stability characteristics.

  2. 3D Wavelet-Based Filter and Method

    DOEpatents

    Moss, William C.; Haase, Sebastian; Sedat, John W.

    2008-08-12

    A 3D wavelet-based filter for visualizing and locating structural features of a user-specified linear size in 2D or 3D image data. The only input parameter is a characteristic linear size of the feature of interest, and the filter output contains only those regions that are correlated with the characteristic size, thus denoising the image.

  3. Automatic segmentation of solitary pulmonary nodules based on local intensity structure analysis and 3D neighborhood features in 3D chest CT images

    NASA Astrophysics Data System (ADS)

    Chen, Bin; Kitasaka, Takayuki; Honma, Hirotoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi; Mori, Kensaku

    2012-03-01

    This paper presents a solitary pulmonary nodule (SPN) segmentation method based on local intensity structure analysis and neighborhood feature analysis in chest CT images. Automated segmentation of SPNs is desirable for a chest computer-aided detection/diagnosis (CAS) system since a SPN may indicate early stage of lung cancer. Due to the similar intensities of SPNs and other chest structures such as blood vessels, many false positives (FPs) are generated by nodule detection methods. To reduce such FPs, we introduce two features that analyze the relation between each segmented nodule candidate and it neighborhood region. The proposed method utilizes a blob-like structure enhancement (BSE) filter based on Hessian analysis to augment the blob-like structures as initial nodule candidates. Then a fine segmentation is performed to segment much more accurate region of each nodule candidate. FP reduction is mainly addressed by investigating two neighborhood features based on volume ratio and eigenvector of Hessian that are calculates from the neighborhood region of each nodule candidate. We evaluated the proposed method by using 40 chest CT images, include 20 standard-dose CT images that we randomly chosen from a local database and 20 low-dose CT images that were randomly chosen from a public database: LIDC. The experimental results revealed that the average TP rate of proposed method was 93.6% with 12.3 FPs/case.

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

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

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

  5. Methods for Geometric Data Validation of 3d City Models

    NASA Astrophysics Data System (ADS)

    Wagner, D.; Alam, N.; Wewetzer, M.; Pries, M.; Coors, V.

    2015-12-01

    Geometric quality of 3D city models is crucial for data analysis and simulation tasks, which are part of modern applications of the data (e.g. potential heating energy consumption of city quarters, solar potential, etc.). Geometric quality in these contexts is however a different concept as it is for 2D maps. In the latter case, aspects such as positional or temporal accuracy and correctness represent typical quality metrics of the data. They are defined in ISO 19157 and should be mentioned as part of the metadata. 3D data has a far wider range of aspects which influence their quality, plus the idea of quality itself is application dependent. Thus, concepts for definition of quality are needed, including methods to validate these definitions. Quality on this sense means internal validation and detection of inconsistent or wrong geometry according to a predefined set of rules. A useful starting point would be to have correct geometry in accordance with ISO 19107. A valid solid should consist of planar faces which touch their neighbours exclusively in defined corner points and edges. No gaps between them are allowed, and the whole feature must be 2-manifold. In this paper, we present methods to validate common geometric requirements for building geometry. Different checks based on several algorithms have been implemented to validate a set of rules derived from the solid definition mentioned above (e.g. water tightness of the solid or planarity of its polygons), as they were developed for the software tool CityDoctor. The method of each check is specified, with a special focus on the discussion of tolerance values where they are necessary. The checks include polygon level checks to validate the correctness of each polygon, i.e. closeness of the bounding linear ring and planarity. On the solid level, which is only validated if the polygons have passed validation, correct polygon orientation is checked, after self-intersections outside of defined corner points and edges

  6. 3D face recognition by projection-based methods

    NASA Astrophysics Data System (ADS)

    Dutagaci, Helin; Sankur, Bülent; Yemez, Yücel

    2006-02-01

    In this paper, we investigate recognition performances of various projection-based features applied on registered 3D scans of faces. Some features are data driven, such as ICA-based features or NNMF-based features. Other features are obtained using DFT or DCT-based schemes. We apply the feature extraction techniques to three different representations of registered faces, namely, 3D point clouds, 2D depth images and 3D voxel. We consider both global and local features. Global features are extracted from the whole face data, whereas local features are computed over the blocks partitioned from 2D depth images. The block-based local features are fused both at feature level and at decision level. The resulting feature vectors are matched using Linear Discriminant Analysis. Experiments using different combinations of representation types and feature vectors are conducted on the 3D-RMA dataset.

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  9. A region-appearance-based adaptive variational model for 3D liver segmentation

    SciTech Connect

    Peng, Jialin; Dong, Fangfang; Chen, Yunmei; Kong, Dexing

    2014-04-15

    Purpose: Liver segmentation from computed tomography images is a challenging task owing to pixel intensity overlapping, ambiguous edges, and complex backgrounds. The authors address this problem with a novel active surface scheme, which minimizes an energy functional combining both edge- and region-based information. Methods: In this semiautomatic method, the evolving surface is principally attracted to strong edges but is facilitated by the region-based information where edge information is missing. As avoiding oversegmentation is the primary challenge, the authors take into account multiple features and appearance context information. Discriminative cues, such as multilayer consecutiveness and local organ deformation are also implicitly incorporated. Case-specific intensity and appearance constraints are included to cope with the typically large appearance variations over multiple images. Spatially adaptive balancing weights are employed to handle the nonuniformity of image features. Results: Comparisons and validations on difficult cases showed that the authors’ model can effectively discriminate the liver from adhering background tissues. Boundaries weak in gradient or with no local evidence (e.g., small edge gaps or parts with similar intensity to the background) were delineated without additional user constraint. With an average surface distance of 0.9 mm and an average volume overlap of 93.9% on the MICCAI data set, the authors’ model outperformed most state-of-the-art methods. Validations on eight volumes with different initial conditions had segmentation score variances mostly less than unity. Conclusions: The proposed model can efficiently delineate ambiguous liver edges from complex tissue backgrounds with reproducibility. Quantitative validations and comparative results demonstrate the accuracy and efficacy of the model.

  10. Unlocking the scientific potential of complex 3D point cloud dataset : new classification and 3D comparison methods

    NASA Astrophysics Data System (ADS)

    Lague, D.; Brodu, N.; Leroux, J.

    2012-12-01

    Ground based lidar and photogrammetric techniques are increasingly used to track the evolution of natural surfaces in 3D at an unprecedented resolution and precision. The range of applications encompass many type of natural surfaces with different geometries and roughness characteristics (landslides, cliff erosion, river beds, bank erosion,....). Unravelling surface change in these contexts requires to compare large point clouds in 2D or 3D. The most commonly used method in geomorphology is based on a 2D difference of the gridded point clouds. Yet this is hardly adapted to many 3D natural environments such as rivers (with horizontal beds and vertical banks), while gridding complex rough surfaces is a complex task. On the other hand, tools allowing to perform 3D comparison are scarce and may require to mesh the point clouds which is difficult on rough natural surfaces. Moreover, existing 3D comparison tools do not provide an explicit calculation of confidence intervals that would factor in registration errors, roughness effects and instrument related position uncertainties. To unlock this problem, we developed the first algorithm combining a 3D measurement of surface change directly on point clouds with an estimate of spatially variable confidence intervals (called M3C2). The method has two steps : (1) surface normal estimation and orientation in 3D at a scale consistent with the local roughness ; (2) measurement of mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing 3D methods based on a closest-point calculation demonstrates the higher precision of the M3C2 method when mm changes needs to be detected. The M3C2 method is also simple to use as it does not require surface meshing or gridding, and is not sensitive to missing data or change in point density. We also present a 3D classification tool (CANUPO) for vegetation removal based on a new geometrical measure: the multi

  11. Methods For Electronic 3-D Moving Pictures Without Glasses

    NASA Astrophysics Data System (ADS)

    Collender, Robert B.

    1987-06-01

    This paper describes implementation approaches in image acquisition and playback for 3-D computer graphics, 3-D television and 3-D theatre movies without special glasses. Projection lamps, spatial light modulators, CRT's and dynamic scanning are all eliminated by the application of an active image array, all static components and a semi-specular screen. The resulting picture shows horizontal parallax with a wide horizontal view field (up to 360 de-grees) giving a holographic appearance in full color with smooth continuous viewing without speckle. Static component systems are compared with dynamic component systems using both linear and circular arrays. Implementation of computer graphic systems are shown that allow complex shaded color images to extend from the viewer's eyes to infinity. Large screen systems visible by hundreds of people are feasible by the use of low f-stops and high gain screens in projection. Screen geometries and special screen properties are shown. Viewing characteristics offer no restrictions in view-position over the entire view-field and have a "look-around" feature for all the categories of computer graphics, television and movies. Standard video cassettes and optical discs can also interface the system to generate a 3-D window viewable without glasses. A prognosis is given for technology application to 3-D pictures without glasses that replicate the daily viewing experience. Super-position of computer graphics on real-world pictures is shown feasible.

  12. Automated 3D Segmentation of Intraretinal Surfaces in SD-OCT Volumes in Normal and Diabetic Mice

    PubMed Central

    Antony, Bhavna J.; Jeong, Woojin; Abràmoff, Michael D.; Vance, Joseph; Sohn, Elliott H.; Garvin, Mona K.

    2014-01-01

    Purpose To describe an adaptation of an existing graph-theoretic method (initially developed for human optical coherence tomography [OCT] images) for the three-dimensional (3D) automated segmentation of 10 intraretinal surfaces in mice scans, and assess the accuracy of the method and the reproducibility of thickness measurements. Methods Ten intraretinal surfaces were segmented in repeat spectral domain (SD)-OCT volumetric images acquired from normal (n = 8) and diabetic (n = 10) mice. The accuracy of the method was assessed by computing the border position errors of the automated segmentation with respect to manual tracings obtained from two experts. The reproducibility was statistically assessed for four retinal layers within eight predefined regions using the mean and SD of the differences in retinal thickness measured in the repeat scans, the coefficient of variation (CV) and the intraclass correlation coefficients (ICC; with 95% confidence intervals [CIs]). Results The overall mean unsigned border position error for the 10 surfaces computed over 97 B-scans (10 scans, 10 normal mice) was 3.16 ± 0.91 μm. The overall mean differences in retinal thicknesses computed from the normal and diabetic mice were 1.86 ± 0.95 and 2.15 ± 0.86 μm, respectively. The CV of the retinal thicknesses for all the measured layers ranged from 1.04% to 5%. The ICCs for the total retinal thickness in the normal and diabetic mice were 0.78 [0.10, 0.92] and 0.83 [0.31, 0.96], respectively. Conclusion The presented method (publicly available as part of the Iowa Reference Algorithms) has acceptable accuracy and reproducibility and is expected to be useful in the quantitative study of intraretinal layers in mice. Translational Relevance The presented method, initially developed for human OCT, has been adapted for mice, with the potential to be adapted for other animals as well. Quantitative in vivo assessment of the retina in mice allows changes to be measured longitudinally, decreasing

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  15. MO-G-17A-03: MR-Based Cortical Bone Segmentation for PET Attenuation Correction with a Non-UTE 3D Fast GRE Sequence

    SciTech Connect

    Ai, H; Pan, T; Hwang, K

    2014-06-15

    Purpose: To determine the feasibility of identifying cortical bone on MR images with a short-TE 3D fast-GRE sequence for attenuation correction of PET data in PET/MR. Methods: A water-fat-bone phantom was constructed with two pieces of beef shank. MR scans were performed on a 3T MR scanner (GE Discovery™ MR750). A 3D GRE sequence was first employed to measure the level of residual signal in cortical bone (TE{sub 1}/TE{sub 2}/TE{sub 3}=2.2/4.4/6.6ms, TR=20ms, flip angle=25°). For cortical bone segmentation, a 3D fast-GRE sequence (TE/TR=0.7/1.9ms, acquisition voxel size=2.5×2.5×3mm{sup 3}) was implemented along with a 3D Dixon sequence (TE{sub 1}/TE{sub 2}/TR=1.2/2.3/4.0ms, acquisition voxel size=1.25×1.25×3mm{sup 3}) for water/fat imaging. Flip angle (10°), acquisition bandwidth (250kHz), FOV (480×480×144mm{sup 3}) and reconstructed voxel size (0.94×0.94×1.5mm{sup 3}) were kept the same for both sequences. Soft tissue and fat tissue were first segmented on the reconstructed water/fat image. A tissue mask was created by combining the segmented water/fat masks, which was then applied on the fast-GRE image (MRFGRE). A second mask was created to remove the Gibbs artifacts present in regions in close vicinity to the phantom. MRFGRE data was smoothed with a 3D anisotropic diffusion filter for noise reduction, after which cortical bone and air was separated using a threshold determined from the histogram. Results: There is signal in the cortical bone region in the 3D GRE images, indicating the possibility of separating cortical bone and air based on signal intensity from short-TE MR image. The acquisition time for the 3D fast-GRE sequence was 17s, which can be reduced to less than 10s with parallel imaging. The attenuation image created from water-fat-bone segmentation is visually similar compared to reference CT. Conclusion: Cortical bone and air can be separated based on intensity in MR image with a short-TE 3D fast-GRE sequence. Further research is required

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

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

    We developed, tested, and evaluated a 3D segmentation and analysis system for in vivo MRI examinations of the human vocal tract during phonation. For this purpose, six professionally trained speakers, age 22-34y, were examined using a standardized MRI protocol (1.5 T, T1w FLASH, ST 4mm, 23 slices, acq. time 21s). The volunteers performed a prolonged (>=21s) emission of sounds of the German phonemic inventory. Simultaneous audio tape recording was obtained to control correct utterance. Scans were made in axial, coronal, and sagittal planes each. Computer-aided quantitative 3D evaluation included (i) automated registration of the phoneme-specific data acquired in different slice orientations, (ii) semi-automated segmentation of oropharyngeal structures, (iii) computation of a curvilinear vocal tract midline in 3D by nonlinear PCA, (iv) computation of cross-sectional areas of the vocal tract perpendicular to this midline. For the vowels /a/,/e/,/i/,/o/,/ø/,/u/,/y/, the extracted area functions were used to synthesize phoneme sounds based on an articulatory-acoustic model. For quantitative analysis, recorded and synthesized phonemes were compared, where area functions extracted from 2D midsagittal slices were used as a reference. All vowels could be identified correctly based on the synthesized phoneme sounds. The comparison between synthesized and recorded vowel phonemes revealed that the quality of phoneme sound synthesis was improved for phonemes /a/ and /y/, if 3D instead of 2D data were used, as measured by the average relative frequency shift between recorded and synthesized vowel formants (p<0.05, one-sided Wilcoxon rank sum test). In summary, the combination of fast MRI followed by subsequent 3D segmentation and analysis is a novel approach to examine human phonation in vivo. It unveils functional anatomical findings that may be essential for realistic modelling of the human vocal tract during speech production.

  17. Systolic and diastolic assessment by 3D-ASM segmentation of gated-SPECT Studies: a comparison with MRI

    NASA Astrophysics Data System (ADS)

    Tobon-Gomez, C.; Bijnens, B. H.; Huguet, M.; Sukno, F.; Moragas, G.; Frangi, A. F.

    2009-02-01

    Gated single photon emission tomography (gSPECT) is a well-established technique used routinely in clinical practice. It can be employed to evaluate global left ventricular (LV) function of a patient. The purpose of this study is to assess LV systolic and diastolic function from gSPECT datasets in comparison with cardiac magnetic resonance imaging (CMR) measurements. This is achieved by applying our recently implemented 3D active shape model (3D-ASM) segmentation approach for gSPECT studies. This methodology allows for generation of 3D LV meshes for all cardiac phases, providing volume time curves and filling rate curves. Both systolic and diastolic functional parameters can be derived from these curves for an assessment of patient condition even at early stages of LV dysfunction. Agreement of functional parameters, with respect to CMR measurements, were analyzed by means of Bland-Altman plots. The analysis included subjects presenting either LV hypertrophy, dilation or myocardial infarction.

  18. From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    PubMed Central

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

    2014-01-01

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

  19. Scientific rotoscoping: a morphology-based method of 3-D motion analysis and visualization.

    PubMed

    Gatesy, Stephen M; Baier, David B; Jenkins, Farish A; Dial, Kenneth P

    2010-06-01

    Three-dimensional skeletal movement is often impossible to accurately quantify from external markers. X-ray imaging more directly visualizes moving bones, but extracting 3-D kinematic data is notoriously difficult from a single perspective. Stereophotogrammetry is extremely powerful if bi-planar fluoroscopy is available, yet implantation of three radio-opaque markers in each segment of interest may be impractical. Herein we introduce scientific rotoscoping (SR), a new method of motion analysis that uses articulated bone models to simultaneously animate and quantify moving skeletons without markers. The three-step process is described using examples from our work on pigeon flight and alligator walking. First, the experimental scene is reconstructed in 3-D using commercial animation software so that frames of undistorted fluoroscopic and standard video can be viewed in their correct spatial context through calibrated virtual cameras. Second, polygonal models of relevant bones are created from CT or laser scans and rearticulated into a hierarchical marionette controlled by virtual joints. Third, the marionette is registered to video images by adjusting each of its degrees of freedom over a sequence of frames. SR outputs high-resolution 3-D kinematic data for multiple, unmarked bones and anatomically accurate animations that can be rendered from any perspective. Rather than generating moving stick figures abstracted from the coordinates of independent surface points, SR is a morphology-based method of motion analysis deeply rooted in osteological and arthrological data.

  20. Object-oriented urban 3D spatial data model organization method

    NASA Astrophysics Data System (ADS)

    Li, Jing-wen; Li, Wen-qing; Lv, Nan; Su, Tao

    2015-12-01

    This paper combined the 3d data model with object-oriented organization method, put forward the model of 3d data based on object-oriented method, implemented the city 3d model to quickly build logical semantic expression and model, solved the city 3d spatial information representation problem of the same location with multiple property and the same property with multiple locations, designed the space object structure of point, line, polygon, body for city of 3d spatial database, and provided a new thought and method for the city 3d GIS model and organization management.

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

    PubMed

    Markel, Daniel; Caldwell, Curtis; Alasti, Hamideh; Soliman, Hany; Ung, Yee; Lee, Justin; Sun, Alexander

    2013-01-01

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

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

    PubMed Central

    Markel, Daniel; Caldwell, Curtis; Alasti, Hamideh; Soliman, Hany; Ung, Yee; Lee, Justin; Sun, Alexander

    2013-01-01

    Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET). First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT) with K-nearest neighbours (KNN) classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians. PMID:23533750

  3. An automated 3D reconstruction method of UAV images

    NASA Astrophysics Data System (ADS)

    Liu, Jun; Wang, He; Liu, Xiaoyang; Li, Feng; Sun, Guangtong; Song, Ping

    2015-10-01

    In this paper a novel fully automated 3D reconstruction approach based on low-altitude unmanned aerial vehicle system (UAVs) images will be presented, which does not require previous camera calibration or any other external prior knowledge. Dense 3D point clouds are generated by integrating orderly feature extraction, image matching, structure from motion (SfM) and multi-view stereo (MVS) algorithms, overcoming many of the cost, time limitations of rigorous photogrammetry techniques. An image topology analysis strategy is introduced to speed up large scene reconstruction by taking advantage of the flight-control data acquired by UAV. Image topology map can significantly reduce the running time of feature matching by limiting the combination of images. A high-resolution digital surface model of the study area is produced base on UAV point clouds by constructing the triangular irregular network. Experimental results show that the proposed approach is robust and feasible for automatic 3D reconstruction of low-altitude UAV images, and has great potential for the acquisition of spatial information at large scales mapping, especially suitable for rapid response and precise modelling in disaster emergency.

  4. Deployment of a 3D tag tracking method utilising RFID

    NASA Astrophysics Data System (ADS)

    Wasif Reza, Ahmed; Yun, Teoh Wei; Dimyati, Kaharudin; Geok Tan, Kim; Ariffin Noordin, Kamarul

    2012-04-01

    Recent trend shows that one of the crucial problems faced while using radio frequency to track the objects is the inconsistency of the signal strength reception, which can be mainly due to the environmental factors and the blockage, which always have the most impact on the tracking accuracy. Besides, three dimensions are more relevant to a warehouse scanning. Therefore, this study proposes a highly accurate and new three-dimensional (3D) radio frequency identification-based indoor tracking system with the consideration of different attenuation factors and obstacles. The obtained results show that the proposed system yields high-quality performance with an average error as low as 0.27 m (without obstacles and attenuation effects). The obtained results also show that the proposed tracking technique can achieve relatively lower errors (0.4 and 0.36 m, respectively) even in the presence of the highest attenuation effect, e = 3.3 or when the environment is largely affected by 50% of the obstacles. Furthermore, the superiority of the proposed 3D tracking system has been proved by comparing with other existing approaches. The 3D tracking system proposed in this study can be applicable to a warehouse scanning.

  5. 3D-FIESTA Magnetic Resonance Angiography Fusion Imaging of Distal Segment of Occluded Middle Cerebral Artery.

    PubMed

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

    2015-01-01

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

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

    PubMed

    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 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.

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

    PubMed Central

    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 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. PMID:27597960

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

    SciTech Connect

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

    2015-06-15

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

  9. Segment-interaction in sprint start: Analysis of 3D angular velocity and kinetic energy in elite sprinters.

    PubMed

    Slawinski, J; Bonnefoy, A; Ontanon, G; Leveque, J M; Miller, C; Riquet, A; Chèze, L; Dumas, R

    2010-05-28

    The aim of the present study was to measure during a sprint start the joint angular velocity and the kinetic energy of the different segments in elite sprinters. This was performed using a 3D kinematic analysis of the whole body. Eight elite sprinters (10.30+/-0.14s 100 m time), equipped with 63 passive reflective markers, realised four maximal 10 m sprints start on an indoor track. An opto-electronic Motion Analysis system consisting of 12 digital cameras (250 Hz) was used to collect the 3D marker trajectories. During the pushing phase on the blocks, the 3D angular velocity vector and its norm were calculated for each joint. The kinetic energy of 16 segments of the lower and upper limbs and of the total body was calculated. The 3D kinematic analysis of the whole body demonstrated that joints such as shoulders, thoracic or hips did not reach their maximal angular velocity with a movement of flexion-extension, but with a combination of flexion-extension, abduction-adduction and internal-external rotation. The maximal kinetic energy of the total body was reached before clearing block (respectively, 537+/-59.3 J vs. 514.9+/-66.0 J; p< or =0.01). These results suggested that a better synchronization between the upper and lower limbs could increase the efficiency of pushing phase on the blocks. Besides, to understand low interindividual variances in the sprint start performance in elite athletes, a 3D complete body kinematic analysis shall be used.

  10. Atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control.

    PubMed

    Ciofolo, Cybèle; Barillot, Christian

    2009-06-01

    We propose a novel approach for the simultaneous segmentation of multiple structures with competitive level sets driven by fuzzy control. To this end, several contours evolve simultaneously toward previously defined anatomical targets. A fuzzy decision system combines the a priori knowledge provided by an anatomical atlas with the intensity distribution of the image and the relative position of the contours. This combination automatically determines the directional term of the evolution equation of each level set. This leads to a local expansion or contraction of the contours, in order to match the boundaries of their respective targets. Two applications are presented: the segmentation of the brain hemispheres and the cerebellum, and the segmentation of deep internal structures. Experimental results on real magnetic resonance (MR) images are presented, quantitatively assessed and discussed.

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

    NASA Astrophysics Data System (ADS)

    Harizanov, S.; Georgiev, I.

    2016-10-01

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

  12. 3D full tensor gradient method improves subsalt interpretation

    SciTech Connect

    Coburn, G.W.

    1998-09-14

    Imagine you`re working the deepwater Gulf of Mexico, looking for potential subsalt prospects to guide your company`s bidding in an upcoming lease sale. There are no speculative 3D surveys in the area, just 2D seismic and a few well logs. So you obtain some regional 2D lines across a number of promising salt features and begin your initial structural interpretation. The top of salt is pretty easy to pick. But, not surprisingly, the base is fuzzy in many areas. Large shadow zones wipe out the image, making it difficult to tell how thick the salt may be and whether sediments continue beneath the salt or truncate at the edges. With the limited data you have available, you could pick the base of salt in several different places, all of them reasonable. How do you decide? One option is an expensive reprocessing job. But it would be nice to have another choice--a way to independently test your salt interpretation against high-quality data not derived from seismic. Three-dimensional full tensor gradient (FTG) data can provide such an alternative. This article focuses on an actual test study done on a regional 2D seismic line across the Green Canyon area of the Gulf of Mexico. The purpose of the study was to determine how well 3D FTG data could identify the base of salt, where standard seismic interpretation was ambiguous.

  13. Accurate and Fully Automatic Hippocampus Segmentation Using Subject-Specific 3D Optimal Local Maps Into a Hybrid Active Contour Model

    PubMed Central

    Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

    2014-01-01

    Assessing the structural integrity of the hippocampus (HC) is an essential step toward prevention, diagnosis, and follow-up of various brain disorders due to the implication of the structural changes of the HC in those disorders. In this respect, the development of automatic segmentation methods that can accurately, reliably, and reproducibly segment the HC has attracted considerable attention over the past decades. This paper presents an innovative 3-D fully automatic method to be used on top of the multiatlas concept for the HC segmentation. The method is based on a subject-specific set of 3-D optimal local maps (OLMs) that locally control the influence of each energy term of a hybrid active contour model (ACM). The complete set of the OLMs for a set of training images is defined simultaneously via an optimization scheme. At the same time, the optimal ACM parameters are also calculated. Therefore, heuristic parameter fine-tuning is not required. Training OLMs are subsequently combined, by applying an extended multiatlas concept, to produce the OLMs that are anatomically more suitable to the test image. The proposed algorithm was tested on three different and publicly available data sets. Its accuracy was compared with that of state-of-the-art methods demonstrating the efficacy and robustness of the proposed method. PMID:27170866

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  15. MOM3D method of moments code theory manual

    NASA Technical Reports Server (NTRS)

    Shaeffer, John F.

    1992-01-01

    MOM3D is a FORTRAN algorithm that solves Maxwell's equations as expressed via the electric field integral equation for the electromagnetic response of open or closed three dimensional surfaces modeled with triangle patches. Two joined triangles (couples) form the vector current unknowns for the surface. Boundary conditions are for perfectly conducting or resistive surfaces. The impedance matrix represents the fundamental electromagnetic interaction of the body with itself. A variety of electromagnetic analysis options are possible once the impedance matrix is computed including backscatter radar cross section (RCS), bistatic RCS, antenna pattern prediction for user specified body voltage excitation ports, RCS image projection showing RCS scattering center locations, surface currents excited on the body as induced by specified plane wave excitation, and near field computation for the electric field on or near the body.

  16. 3D sensitivity of 6-electrode Focused Impedance Method (FIM)

    NASA Astrophysics Data System (ADS)

    Masum Iquebal, A. H.; Siddique-e Rabbani, K.

    2010-04-01

    The present work was taken up to have an understanding of the depth sensitivity of the 6 electrode FIM developed by our laboratory earlier, so that it may be applied judiciously for the measurement of organs in 3D, with electrodes on the skin surface. For a fixed electrode geometry sensitivity is expected to depend on the depth, size and conductivity of the target object. With current electrodes 18 cm apart and potential electrodes 5 cm apart, depth sensitivity of spherical conductors, insulators and of pieces of potato of different diameters were measured. The sensitivity dropped sharply with depth gradually leveling off to background, and objects could be sensed down to a depth of about twice their diameters. The sensitivity at a certain depth increases almost linearly with volume for objects with the same conductivity. Thus these results increase confidence in the use of FIM for studying organs at depths of the body.

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

    DTIC Science & Technology

    2014-04-26

    while still quickly and conveniently allowing manual addition and removal of segments in real -time, (2) multiple extensions to the interactive tools...inside the region, and – The mean intensity inside the region. These properties can be computed quickly, which fits well with the real -time...10), 1731–1744 (2000) 14. Cortes , C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995) 15. Django Software Foundation: Django

  18. ROI-preserving 3D video compression method utilizing depth information

    NASA Astrophysics Data System (ADS)

    Ti, Chunli; Xu, Guodong; Guan, Yudong; Teng, Yidan

    2015-09-01

    Efficiently transmitting the extra information of three dimensional (3D) video is becoming a key issue of the development of 3DTV. 2D plus depth format not only occupies the smaller bandwidth and is compatible transmission under the condition of the existing channel, but also can provide technique support for advanced 3D video compression in some extend. This paper proposes an ROI-preserving compression scheme to further improve the visual quality at a limited bit rate. According to the connection between the focus of Human Visual System (HVS) and depth information, region of interest (ROI) can be automatically selected via depth map progressing. The main improvement from common method is that a meanshift based segmentation is executed to the depth map before foreground ROI selection to keep the integrity of scene. Besides, the sensitive areas along the edges are also protected. The Spatio-temporal filtering adapting to H.264 is used to the non-ROI of both 2D video and depth map before compression. Experiments indicate that, the ROI extracted by this method is more undamaged and according with subjective feeling, and the proposed method can keep the key high-frequency information more effectively while the bit rate is reduced.

  19. New color segmentation method and its applications

    NASA Astrophysics Data System (ADS)

    Wang, Jian

    1999-01-01

    Segmentation is an important step in the early stage of image analysis. Color or multi-spectral image segmentation usually involves search and clustering techniques in a three or higher dimensional spectral space - an exercise which is considered computationally expensive. This paper presents a new color segmentation method for color image analysis with its application to plant leaf area measurement. A 3D histogram for an RGB color image is established basing on an octree data structure. The histogram represents the color distribution of the image in the RGB color space on which a 3D Gaussian filter is applied to smooth out small maxima of this distribution. The color space is then searched to find out al the major maxima. Around each maxima, a covering cube with a controlled side width is established. These maxima and covering cubes are considered to be potential color classes. Each cube may expand according to the value of surrounding neighbors. Once enough modes and their cover cubes have been found, a k-means clustering algorithm is used to classify these maxima into a predetermined number of classes. Then, the classified modes and the color covered by the cubes are used as training samples for a Bayes classifier which can be used to classify all the pixels in the image. A statistical relaxation method is then sued as a find segmentation. This method can either be supervised or unsupervised, depending on the different requirements of specific applications. The octree data structure significantly reduces the color space to be searched and consequently reduces computational cost. An extension of this method can also be applied to multi-spectral image analysis.

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

    NASA Astrophysics Data System (ADS)

    Kim, Hyeonjin; Kim, Hannah; Hong, Helen

    2015-03-01

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

  1. Development of 3-D Ice Accretion Measurement Method

    NASA Technical Reports Server (NTRS)

    Lee, Sam; Broeren, Andy P.; Addy, Harold E., Jr.; Sills, Robert; Pifer, Ellen M.

    2012-01-01

    A research plan is currently being implemented by NASA to develop and validate the use of a commercial laser scanner to record and archive fully three-dimensional (3-D) ice shapes from an icing wind tunnel. The plan focused specifically upon measuring ice accreted in the NASA Icing Research Tunnel (IRT). The plan was divided into two phases. The first phase was the identification and selection of the laser scanning system and the post-processing software to purchase and develop further. The second phase was the implementation and validation of the selected system through a series of icing and aerodynamic tests. Phase I of the research plan has been completed. It consisted of evaluating several scanning hardware and software systems against an established selection criteria through demonstrations in the IRT. The results of Phase I showed that all of the scanning systems that were evaluated were equally capable of scanning ice shapes. The factors that differentiated the scanners were ease of use and the ability to operate in a wide range of IRT environmental conditions.

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

    NASA Astrophysics Data System (ADS)

    Orme, Haydn; Bell, Rebecca; Jackson, Christopher

    2016-04-01

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

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

    NASA Astrophysics Data System (ADS)

    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.

  4. Computer-aided mesenteric small vessel segmentation on high-resolution 3D contrast-enhanced CT angiography scans

    NASA Astrophysics Data System (ADS)

    Zhang, Weidong; Liu, Jiamin; Yao, Jianhua; Nguyen, Tan; Louie, Adeline; Wank, Stephen; Summers, Ronald M.

    2012-03-01

    Segmentation of the mesenteric vasculature has important applications for evaluation of the small bowel. In particular, it may be useful for small bowel path reconstruction and precise localization of small bowel tumors such as carcinoid. Segmentation of the mesenteric vasculature is very challenging, even for manual labeling, because of the low contrast and tortuosity of the small blood vessels. Many vessel segmentation methods have been proposed. However, most of them are designed for segmenting large vessels. We propose a semi-automated method to extract the mesenteric vasculature on contrast-enhanced abdominal CT scans. First, the internal abdominal region of the body is automatically identified. Second, the major vascular branches are segmented using a multi-linear vessel tracing method. Third, small mesenteric vessels are segmented using multi-view multi-scale vesselness enhancement filters. The method is insensitive to image contrast, variations of vessel shape and small occlusions due to overlapping. The method could automatically detect mesenteric vessels with diameters as small as 1 mm. Compared with the standard-of-reference manually labeled by an expert radiologist, the segmentation accuracy (recall rate) for the whole mesenteric vasculature was 82.3% with a 3.6% false positive rate.

  5. Building a 3D Virtual Liver: Methods for Simulating Blood Flow and Hepatic Clearance on 3D Structures

    PubMed Central

    Rezania, Vahid; Tuszynski, Jack

    2016-01-01

    In this paper, we develop a spatio-temporal modeling approach to describe blood and drug flow, as well as drug uptake and elimination, on an approximation of the liver. Extending on previously developed computational approaches, we generate an approximation of a liver, which consists of a portal and hepatic vein vasculature structure, embedded in the surrounding liver tissue. The vasculature is generated via constrained constructive optimization, and then converted to a spatial grid of a selected grid size. Estimates for surrounding upscaled lobule tissue properties are then presented appropriate to the same grid size. Simulation of fluid flow and drug metabolism (hepatic clearance) are completed using discretized forms of the relevant convective-diffusive-reactive partial differential equations for these processes. This results in a single stage, uniformly consistent method to simulate equations for blood and drug flow, as well as drug metabolism, on a 3D structure representative of a liver. PMID:27649537

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

    SciTech Connect

    Karakaya, Mahmut; Kerekes, Ryan A; Gleason, Shaun Scott; Martins, Rodrigo; Dyer, Michael

    2011-01-01

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

  7. A line segment based registration method for Terrestrial Laser Scanning point cloud data

    NASA Astrophysics Data System (ADS)

    Cheng, Jun; Cheng, Ming; Lin, Yangbin; Wang, Cheng

    2016-03-01

    This paper proposed a 3d line segment based registration method for terrestrial laser scanning (TLS) data. The 3D line segment is adopted to describe the point cloud data and reduce geometric complexity. After that, we introduce a framework for registration. We demonstrate the accuracy of our method for rigid transformations in the presence of terrestrial laser scanning point cloud.

  8. A Computational Method for 3D Anisotropic Travel-time Tomography of Rocks in the Laboratory

    NASA Astrophysics Data System (ADS)

    Ghofranitabari, Mehdi; Young, R. Paul

    2013-04-01

    True triaxial loading in the laboratory applies three principal stresses on a cubic rock specimen. Elliptical anisotropy and distributed heterogeneities are introduced in the rock due to closure and opening of the pre-existing cracks and creation and growth of the new aligned cracks. The rock sample is tested in a Geophysical Imaging Cell that is armed with an Acoustic Emission monitoring system which can perform transducer to transducer velocity surveys to image velocity structure of the sample during the experiment. Ultrasonic travel-time tomography as a non-destructive method outfits a map of wave propagation velocity in the sample in order to detect the uniformly distributed or localised heterogeneities and provide the spatial variation and temporal evolution of induced damages in rocks at various stages of loading. The rock sample is partitioned into cubic grid cells as model space. Ray-based tomography method measuring body wave travel time along ray paths between pairs of emitting and receiving transducers is used to calculate isotropic ray-path segment matrix elements (Gij) which contain segment lengths of the ith ray in the jth cell in three dimensions. Synthetic P wave travel times are computed between pairs of transducers in a hypothetical isotropic heterogeneous cubic sample as data space along with an error due to precision of measurement. 3D strain of the squeezed rock and the consequent geometrical deformation is also included in computations for further accuracy. Singular Value Decomposition method is used for the inversion from data space to model space. In the next step, the anisotropic ray-path segment matrix and the corresponded data space are computed for hypothetical anisotropic heterogeneous samples based on the elliptical anisotropic model of velocity which is obtained from the real laboratory experimental data. The method is examined for several different synthetic heterogeneous models. An "Inaccuracy factor" is utilized to inquire the

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

    PubMed

    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.

  10. Structural Stereo Matching Of Laplacian-Of-Gaussian Contour Segments For 3D Perception

    NASA Astrophysics Data System (ADS)

    Boyer, K. L.; Sotak, G. E.

    1989-03-01

    We solve the stereo correspondence problem using Lapla-cian of Gaussian (LoG) zero-crossing contours as a source of primitives for structural stereopsis, as opposed to traditional point-based algorithms. For each image in the stereo pair, we apply the LoG operator, extract and link zero crossing points, filter and segment the contours into meaningful primitives, and compute a parametric structural description over the resulting primitive set. We then apply a variant of the inexact structural matching technique of Boyer and Kak Ill to recover the optimal interprimitive mapping (correspon-dence) function. Since an extended image feature conveys more information than a single point, its spatial and photometric behavior may be exploited to advantage; there are also fewer features to match, resulting in a smaller combinatorial problem. The structural approach allows greater use of spatial relational constraints, which allows us to eliminate (or reduce) the coarse-to-fine tracking of most point-based algorithms. Solving the correspondence problem at this level requires only an approximate probabilistic characterization of the image-to-image structural distortion, and does not require detailed knowledge of the epipolar geometry.

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

    PubMed

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

    2014-12-01

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

  12. Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room.

    PubMed

    Lo Presti, Giuseppe; Carbone, Marina; Ciriaci, Damiano; Aramini, Daniele; Ferrari, Mauro; Ferrari, Vincenzo

    2015-10-01

    Patient-specific 3D models obtained by the segmentation of volumetric diagnostic images play an increasingly important role in surgical planning. Surgeons use the virtual models reconstructed through segmentation to plan challenging surgeries. Many solutions exist for the different anatomical districts and surgical interventions. The possibility to bring the 3D virtual reconstructions with native radiological images in the operating room is essential for fostering the use of intraoperative planning. To the best of our knowledge, current DICOM viewers are not able to simultaneously connect to the picture archiving and communication system (PACS) and import 3D models generated by external platforms to allow a straight integration in the operating room. A total of 26 DICOM viewers were evaluated: 22 open source and four commercial. Two DICOM viewers can connect to PACS and import segmentations achieved by other applications: Synapse 3D® by Fujifilm and OsiriX by University of Geneva. We developed a software network that converts diffuse visual tool kit (VTK) format 3D model segmentations, obtained by any software platform, to a DICOM format that can be displayed using OsiriX or Synapse 3D. Both OsiriX and Synapse 3D were suitable for our purposes and had comparable performance. Although Synapse 3D loads native images and segmentations faster, the main benefits of OsiriX are its user-friendly loading of elaborated images and it being both free of charge and open source.

  13. Segmentation of densely populated cell nuclei from confocal image stacks using 3D non-parametric shape priors.

    PubMed

    Ong, Lee-Ling S; Wang, Mengmeng; Dauwels, Justin; Asada, H Harry

    2014-01-01

    An approach to jointly estimate 3D shapes and poses of stained nuclei from confocal microscopy images, using statistical prior information, is presented. Extracting nuclei boundaries from our experimental images of cell migration is challenging due to clustered nuclei and variations in their shapes. This issue is formulated as a maximum a posteriori estimation problem. By incorporating statistical prior models of 3D nuclei shapes into level set functions, the active contour evolutions applied on the images is constrained. A 3D alignment algorithm is developed to build the training databases and to match contours obtained from the images to them. To address the issue of aligning the model over multiple clustered nuclei, a watershed-like technique is used to detect and separate clustered regions prior to active contour evolution. Our method is tested on confocal images of endothelial cells in microfluidic devices, compared with existing approaches.

  14. Multi-Surface and Multi-Field Co-Segmentation of 3-D Retinal Optical Coherence Tomography

    PubMed Central

    Sonka, Milan; Kwon, Young H.; Kemp, Pavlina; Abràmoff, Michael D.; Wu, Xiaodong

    2015-01-01

    When segmenting intraretinal layers from multiple optical coherence tomography (OCT) images forming a mosaic or a set of repeated scans, it is attractive to exploit the additional information from the overlapping areas rather than discarding it as redundant, especially in low contrast and noisy images. However, it is currently not clear how to effectively combine the multiple information sources available in the areas of overlap. In this paper, we propose a novel graph-theoretic method for multi-surface multi-field co-segmentation of intraretinal layers, assuring consistent segmentation of the fields across the overlapped areas. After 2-D en-face alignment, all the fields are segmented simultaneously, imposing a priori soft interfield-intrasurface constraints for each pair of overlapping fields. The constraints penalize deviations from the expected surface height differences, taken to be the depth-axis shifts that produce the maximum cross-correlation of pairwise-overlapped areas. The method’s accuracy and reproducibility are evaluated qualitatively and quantitatively on 212 OCT images (20 nine-field, 32 single-field acquisitions) from 26 patients with glaucoma. Qualitatively, the obtained thickness maps show no stitching artifacts, compared to pronounced stitches when the fields are segmented independently. Quantitatively, two ophthalmologists manually traced four intraretinal layers on 10 patients, and the average error (4.58±1.46 μm) was comparable to the average difference between the observers (5.86±1.72 μm). Furthermore, we show the benefit of the proposed approach in co-segmenting longitudinal scans. As opposed to segmenting layers in each of the fields independently, the proposed co-segmentation method obtains consistent segmentations across the overlapped areas, producing accurate, reproducible, and artifact-free results. PMID:25020067

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

    PubMed

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

    2017-02-07

    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.

  16. Educational Material for 3D Visualization of Spine Procedures: Methods for Creation and Dissemination.

    PubMed

    Cramer, Justin; Quigley, Edward; Hutchins, Troy; Shah, Lubdha

    2017-01-12

    Spine anatomy can be difficult to master and is essential for performing spine procedures. We sought to utilize the rapidly expanding field of 3D technology to create freely available, interactive educational materials for spine procedures. Our secondary goal was to convey lessons learned about 3D modeling and printing. This project involved two parallel processes: the creation of 3D-printed physical models and interactive digital models. We segmented illustrative CT studies of the lumbar and cervical spine to create 3D models and then printed them using a consumer 3D printer and a professional 3D printing service. We also included downloadable versions of the models in an interactive eBook and platform-independent web viewer. We then provided these educational materials to residents with a pretest and posttest to assess efficacy. The "Spine Procedures in 3D" eBook has been downloaded 71 times as of October 5, 2016. All models used in the book are available for download and printing. Regarding test results, the mean exam score improved from 70 to 86%, with the most dramatic improvement seen in the least experienced trainees. Participants reported increased confidence in performing lumbar punctures after exposure to the material. We demonstrate the value of 3D models, both digital and printed, in learning spine procedures. Moreover, 3D printing and modeling is a rapidly expanding field with a large potential role for radiologists. We have detailed our process for creating and sharing 3D educational materials in the hopes of motivating and enabling similar projects.

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

    PubMed Central

    Osman, Onur; Ucan, Osman N.

    2008-01-01

    Objective The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each 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. Results 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. Conclusion 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. PMID:18253070

  18. Comparison of 3D-OP-OSEM and 3D-FBP reconstruction algorithms for High-Resolution Research Tomograph studies: effects of randoms estimation methods

    NASA Astrophysics Data System (ADS)

    van Velden, Floris H. P.; Kloet, Reina W.; van Berckel, Bart N. M.; Wolfensberger, Saskia P. A.; Lammertsma, Adriaan A.; Boellaard, Ronald

    2008-06-01

    The High-Resolution Research Tomograph (HRRT) is a dedicated human brain positron emission tomography (PET) scanner. Recently, a 3D filtered backprojection (3D-FBP) reconstruction method has been implemented to reduce bias in short duration frames, currently observed in 3D ordinary Poisson OSEM (3D-OP-OSEM) reconstructions. Further improvements might be expected using a new method of variance reduction on randoms (VRR) based on coincidence histograms instead of using the delayed window technique (DW) to estimate randoms. The goal of this study was to evaluate VRR in combination with 3D-OP-OSEM and 3D-FBP reconstruction techniques. To this end, several phantom studies and a human brain study were performed. For most phantom studies, 3D-OP-OSEM showed higher accuracy of observed activity concentrations with VRR than with DW. However, both positive and negative deviations in reconstructed activity concentrations and large biases of grey to white matter contrast ratio (up to 88%) were still observed as a function of scan statistics. Moreover 3D-OP-OSEM+VRR also showed bias up to 64% in clinical data, i.e. in some pharmacokinetic parameters as compared with those obtained with 3D-FBP+VRR. In the case of 3D-FBP, VRR showed similar results as DW for both phantom and clinical data, except that VRR showed a better standard deviation of 6-10%. Therefore, VRR should be used to correct for randoms in HRRT PET studies.

  19. Filtering method for 3D laser scanning point cloud

    NASA Astrophysics Data System (ADS)

    Liu, Da; Wang, Li; Hao, Yuncai; Zhang, Jun

    2015-10-01

    In recent years, with the rapid development of the hardware and software of the three-dimensional model acquisition, three-dimensional laser scanning technology is utilized in various aspects, especially in space exploration. The point cloud filter is very important before using the data. In the paper, considering both the processing quality and computing speed, an improved mean-shift point cloud filter method is proposed. Firstly, by analyze the relevance of the normal vector between the upcoming processing point and the near points, the iterative neighborhood of the mean-shift is selected dynamically, then the high frequency noise is constrained. Secondly, considering the normal vector of the processing point, the normal vector is updated. Finally, updated position is calculated for each point, then each point is moved in the normal vector according to the updated position. The experimental results show that the large features are retained, at the same time, the small sharp features are also existed for different size and shape of objects, so the target feature information is protected precisely. The computational complexity of the proposed method is not high, it can bring high precision results with fast speed, so it is very suitable for space application. It can also be utilized in civil, such as large object measurement, industrial measurement, car navigation etc. In the future, filter with the help of point strength will be further exploited.

  20. GPU Accelerated Spectral Element Methods: 3D Euler equations

    NASA Astrophysics Data System (ADS)

    Abdi, D. S.; Wilcox, L.; Giraldo, F.; Warburton, T.

    2015-12-01

    A GPU accelerated nodal discontinuous Galerkin method for the solution of three dimensional Euler equations is presented. The Euler equations are nonlinear hyperbolic equations that are widely used in Numerical Weather Prediction (NWP). Therefore, acceleration of the method plays an important practical role in not only getting daily forecasts faster but also in obtaining more accurate (high resolution) results. The equation sets used in our atomospheric model NUMA (non-hydrostatic unified model of the atmosphere) take into consideration non-hydrostatic effects that become more important with high resolution. We use algorithms suitable for the single instruction multiple thread (SIMT) architecture of GPUs to accelerate solution by an order of magnitude (20x) relative to CPU implementation. For portability to heterogeneous computing environment, we use a new programming language OCCA, which can be cross-compiled to either OpenCL, CUDA or OpenMP at runtime. Finally, the accuracy and performance of our GPU implementations are veried using several benchmark problems representative of different scales of atmospheric dynamics.

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

    SciTech Connect

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

    2015-07-15

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

  2. A three-stage method for the 3D reconstruction of the tracheobronchial tree from CT scans.

    PubMed

    Rosell, Jan; Cabras, Paolo

    2013-01-01

    This paper proposes a method for segmenting the airways from CT scans of the chest to obtain a 3D model that can be used in the virtual bronchoscopy for the exploration and the planning of paths to the lesions. The method is composed of 3 stages: a gross segmentation that reconstructs the main airway tree using adaptive region growing, a finer segmentation that identifies any potential airway region based on a 2D process that enhances bronchi walls using local information, and a final process to connect any isolated bronchus to the main airways using a morphologic reconstruction process and a path planning technique. The paper includes two examples for the evaluation and discussion of the proposal.

  3. Multi-crosswell profile 3D imaging and method

    DOEpatents

    Washbourne, John K.; Rector, III, James W.; Bube, Kenneth P.

    2002-01-01

    Characterizing the value of a particular property, for example, seismic velocity, of a subsurface region of ground is described. In one aspect, the value of the particular property is represented using at least one continuous analytic function such as a Chebychev polynomial. The seismic data may include data derived from at least one crosswell dataset for the subsurface region of interest and may also include other data. In either instance, data may simultaneously be used from a first crosswell dataset in conjunction with one or more other crosswell datasets and/or with the other data. In another aspect, the value of the property is characterized in three dimensions throughout the region of interest using crosswell and/or other data. In still another aspect, crosswell datasets for highly deviated or horizontal boreholes are inherently useful. The method is performed, in part, by fitting a set of vertically spaced layer boundaries, represented by an analytic function such as a Chebychev polynomial, within and across the region encompassing the boreholes such that a series of layers is defined between the layer boundaries. Initial values of the particular property are then established between the layer boundaries and across the subterranean region using a series of continuous analytic functions. The continuous analytic functions are then adjusted to more closely match the value of the particular property across the subterranean region of ground to determine the value of the particular property for any selected point within the region.

  4. An adaptive 3D region growing algorithm to automatically segment and identify thoracic aorta and its centerline using computed tomography angiography scans

    NASA Astrophysics Data System (ADS)

    Ferreira, F.; Dehmeshki, J.; Amin, H.; Dehkordi, M. E.; Belli, A.; Jouannic, A.; Qanadli, S.

    2010-03-01

    Thoracic Aortic Aneurysm (TAA) is a localized swelling of the thoracic aorta. The progressive growth of an aneurysm may eventually cause a rupture if not diagnosed or treated. This necessitates the need for an accurate measurement which in turn calls for the accurate segmentation of the aneurysm regions. Computer Aided Detection (CAD) is a tool to automatically detect and segment the TAA in the Computer tomography angiography (CTA) images. The fundamental major step of developing such a system is to develop a robust method for the detection of main vessel and measuring its diameters. In this paper we propose a novel adaptive method to simultaneously segment the thoracic aorta and to indentify its center line. For this purpose, an adaptive parametric 3D region growing is proposed in which its seed will be automatically selected through the detection of the celiac artery and the parameters of the method will be re-estimated while the region is growing thorough the aorta. At each phase of region growing the initial center line of aorta will also be identified and modified through the process. Thus the proposed method simultaneously detect aorta and identify its centerline. The method has been applied on CT images from 20 patients with good agreement with the visual assessment by two radiologists.

  5. Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancement

    SciTech Connect

    Munbodh, Reshma; Jaffray, David A.; Moseley, Douglas J.; Chen Zhe; Knisely, Jonathan P.S.; Cathier, Pascal; Duncan, James S.

    2006-05-15

    The objective of this study was to develop a fully automated two-dimensional (2D)-three-dimensional (3D) registration framework to quantify setup deviations in prostate radiation therapy from cone beam CT (CBCT) data and a single AP radiograph. A kilovoltage CBCT image and kilovoltage AP radiograph of an anthropomorphic phantom of the pelvis were acquired at 14 accurately known positions. The shifts in the phantom position were subsequently estimated by registering digitally reconstructed radiographs (DRRs) from the 3D CBCT scan to the AP radiographs through the correlation of enhanced linear image features mainly representing bony ridges. Linear features were enhanced by filtering the images with ''sticks,'' short line segments which are varied in orientation to achieve the maximum projection value at every pixel in the image. The mean (and standard deviations) of the absolute errors in estimating translations along the three orthogonal axes in millimeters were 0.134 (0.096) AP(out-of-plane), 0.021 (0.023) ML and 0.020 (0.020) SI. The corresponding errors for rotations in degrees were 0.011 (0.009) AP, 0.029 (0.016) ML (out-of-plane), and 0.030 (0.028) SI (out-of-plane). Preliminary results with megavoltage patient data have also been reported. The results suggest that it may be possible to enhance anatomic features that are common to DRRs from a CBCT image and a single AP radiography of the pelvis for use in a completely automated and accurate 2D-3D registration framework for setup verification in prostate radiotherapy. This technique is theoretically applicable to other rigid bony structures such as the cranial vault or skull base and piecewise rigid structures such as the spine.

  6. 3D-2D registration of cerebral angiograms: a method and evaluation on clinical images.

    PubMed

    Mitrovic, Uroš; Špiclin, Žiga; Likar, Boštjan; Pernuš, Franjo

    2013-08-01

    Endovascular image-guided interventions (EIGI) involve navigation of a catheter through the vasculature followed by application of treatment at the site of anomaly using live 2D projection images for guidance. 3D images acquired prior to EIGI are used to quantify the vascular anomaly and plan the intervention. If fused with the information of live 2D images they can also facilitate navigation and treatment. For this purpose 3D-2D image registration is required. Although several 3D-2D registration methods for EIGI achieve registration accuracy below 1 mm, their clinical application is still limited by insufficient robustness or reliability. In this paper, we propose a 3D-2D registration method based on matching a 3D vasculature model to intensity gradients of live 2D images. To objectively validate 3D-2D registration methods, we acquired a clinical image database of 10 patients undergoing cerebral EIGI and established "gold standard" registrations by aligning fiducial markers in 3D and 2D images. The proposed method had mean registration accuracy below 0.65 mm, which was comparable to tested state-of-the-art methods, and execution time below 1 s. With the highest rate of successful registrations and the highest capture range the proposed method was the most robust and thus a good candidate for application in EIGI.

  7. Convergence of the point vortex method for the 3-D Euler equations

    NASA Astrophysics Data System (ADS)

    Hou, Thomas Y.; Lowengrub, John

    1990-11-01

    Consistency, stability, and convergence of a point vortex approximation to the 3-D incompressible Euler equations with smooth solutions. The 3-D algorithm considered is similar to the corresponding 3-D vortex are proved blob algorithm introduced by Beale and Majda; The discretization error is second-order accurate. Then the method is stable in l sup p norm for the particle trajectories and in w sup -1,p norm for discrete vorticity. Consequently, the method converges up to any time for which the Euler equations have a smooth solution. One immediate application of the convergence result is that the vortex filament method without smoothing also converges.

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

    PubMed

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

    2008-08-01

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

  9. FGG-NUFFT-Based Method for Near-Field 3-D Imaging Using Millimeter Waves

    PubMed Central

    Kan, Yingzhi; Zhu, Yongfeng; Tang, Liang; Fu, Qiang; Pei, Hucheng

    2016-01-01

    In this paper, to deal with the concealed target detection problem, an accurate and efficient algorithm for near-field millimeter wave three-dimensional (3-D) imaging is proposed that uses a two-dimensional (2-D) plane antenna array. First, a two-dimensional fast Fourier transform (FFT) is performed on the scattered data along the antenna array plane. Then, a phase shift is performed to compensate for the spherical wave effect. Finally, fast Gaussian gridding based nonuniform FFT (FGG-NUFFT) combined with 2-D inverse FFT (IFFT) is performed on the nonuniform 3-D spatial spectrum in the frequency wavenumber domain to achieve 3-D imaging. The conventional method for near-field 3-D imaging uses Stolt interpolation to obtain uniform spatial spectrum samples and performs 3-D IFFT to reconstruct a 3-D image. Compared with the conventional method, our FGG-NUFFT based method is comparable in both efficiency and accuracy in the full sampled case and can obtain more accurate images with less clutter and fewer noisy artifacts in the down-sampled case, which are good properties for practical applications. Both simulation and experimental results demonstrate that the FGG-NUFFT-based near-field 3-D imaging algorithm can have better imaging performance than the conventional method for down-sampled measurements. PMID:27657066

  10. FGG-NUFFT-Based Method for Near-Field 3-D Imaging Using Millimeter Waves.

    PubMed

    Kan, Yingzhi; Zhu, Yongfeng; Tang, Liang; Fu, Qiang; Pei, Hucheng

    2016-09-19

    In this paper, to deal with the concealed target detection problem, an accurate and efficient algorithm for near-field millimeter wave three-dimensional (3-D) imaging is proposed that uses a two-dimensional (2-D) plane antenna array. First, a two-dimensional fast Fourier transform (FFT) is performed on the scattered data along the antenna array plane. Then, a phase shift is performed to compensate for the spherical wave effect. Finally, fast Gaussian gridding based nonuniform FFT (FGG-NUFFT) combined with 2-D inverse FFT (IFFT) is performed on the nonuniform 3-D spatial spectrum in the frequency wavenumber domain to achieve 3-D imaging. The conventional method for near-field 3-D imaging uses Stolt interpolation to obtain uniform spatial spectrum samples and performs 3-D IFFT to reconstruct a 3-D image. Compared with the conventional method, our FGG-NUFFT based method is comparable in both efficiency and accuracy in the full sampled case and can obtain more accurate images with less clutter and fewer noisy artifacts in the down-sampled case, which are good properties for practical applications. Both simulation and experimental results demonstrate that the FGG-NUFFT-based near-field 3-D imaging algorithm can have better imaging performance than the conventional method for down-sampled measurements.

  11. A review of 3D/2D registration methods for image-guided interventions.

    PubMed

    Markelj, P; Tomaževič, D; Likar, B; Pernuš, F

    2012-04-01

    Registration of pre- and intra-interventional data is one of the key technologies for image-guided radiation therapy, radiosurgery, minimally invasive surgery, endoscopy, and interventional radiology. In this paper, we survey those 3D/2D data registration methods that utilize 3D computer tomography or magnetic resonance images as the pre-interventional data and 2D X-ray projection images as the intra-interventional data. The 3D/2D registration methods are reviewed with respect to image modality, image dimensionality, registration basis, geometric transformation, user interaction, optimization procedure, subject, and object of registration.

  12. 3D segmentation and quantification of magnetic resonance data: application to the osteonecrosis of the femoral head

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

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

    2008-04-01

    An automatic method for delineating the prostate (including the seminal vesicles) in three-dimensional magnetic resonance scans is presented. The method is based on nonrigid registration of a set of prelabeled atlas images. Each atlas image is nonrigidly registered with the target patient image. Subsequently, the deformed atlas label images are fused to yield a single segmentation of the patient image. The proposed method is evaluated on 50 clinical scans, which were manually segmented by three experts. The Dice similarity coefficient (DSC) is used to quantify the overlap between the automatic and manual segmentations. We investigate the impact of several factors on the performance of the segmentation method. For the registration, two similarity measures are compared: Mutual information and a localized version of mutual information. The latter turns out to be superior (median DeltaDSC approximately equal 0.02, p < 0.01 with a paired two-sided Wilcoxon test) and comes at no added computational cost, thanks to the use of a novel stochastic optimization scheme. For the atlas fusion step we consider a majority voting rule and the "simultaneous truth and performance level estimation" algorithm, both with and without a preceding atlas selection stage. The differences between the various fusion methods appear to be small and mostly not statistically significant (p > 0.05). To assess the influence of the atlas composition, two atlas sets are compared. The first set consists of 38 scans of healthy volunteers. The second set is constructed by a leave-one-out approach using the 50 clinical scans that are used for evaluation. The second atlas set gives substantially better performance (DeltaDSC=0.04, p < 0.01), stressing the importance of a careful atlas definition. With the best settings, a median DSC of around 0.85 is achieved, which is close to the median interobserver DSC of 0.87. The segmentation quality is especially good at the prostate-rectum interface, where the

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

    NASA Astrophysics Data System (ADS)

    Poręba, M.; Goulette, F.

    2014-12-01

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

  15. A 3D terrain reconstruction method of stereo vision based quadruped robot navigation system

    NASA Astrophysics Data System (ADS)

    Ge, Zhuo; Zhu, Ying; Liang, Guanhao

    2017-01-01

    To provide 3D environment information for the quadruped robot autonomous navigation system during walking through rough terrain, based on the stereo vision, a novel 3D terrain reconstruction method is presented. In order to solve the problem that images collected by stereo sensors have large regions with similar grayscale and the problem that image matching is poor at real-time performance, watershed algorithm and fuzzy c-means clustering algorithm are combined for contour extraction. Aiming at the problem of error matching, duel constraint with region matching and pixel matching is established for matching optimization. Using the stereo matching edge pixel pairs, the 3D coordinate algorithm is estimated according to the binocular stereo vision imaging model. Experimental results show that the proposed method can yield high stereo matching ratio and reconstruct 3D scene quickly and efficiently.

  16. A method for 3D scene recognition using shadow information and a single fixed viewpoint

    NASA Astrophysics Data System (ADS)

    Bamber, David C.; Rogers, Jeremy D.; Page, Scott F.

    2012-05-01

    The ability to passively reconstruct a scene in 3D provides significant benefit to Situational Awareness systems employed in security and surveillance applications. Traditionally, passive 3D scene modelling techniques, such as Shape from Silhouette, require images from multiple sensor viewpoints, acquired either through the motion of a single sensor or from multiple sensors. As a result, the application of these techniques often attracts high costs, and presents numerous practical challenges. This paper presents a 3D scene reconstruction approach based on exploiting scene shadows, which only requires information from a single static sensor. This paper demonstrates that a large amount of 3D information about a scene can be interpreted from shadows; shadows reveal the shape of objects as viewed from a solar perspective and additional perspectives are gained as the sun arcs across the sky. The approach has been tested on synthetic and real data and is shown to be capable of reconstructing 3D scene objects where traditional 3D imaging methods fail. Providing the shadows within a scene are discernible, the proposed technique is able to reconstruct 3D objects that are camouflaged, obscured or even outside of the sensor's Field of View. The proposed approach can be applied in a range of applications, for example urban surveillance, checkpoint and border control, critical infrastructure protection and for identifying concealed or suspicious objects or persons which would normally be hidden from the sensor viewpoint.

  17. Accurate compressed look up table method for CGH in 3D holographic display.

    PubMed

    Gao, Chuan; Liu, Juan; Li, Xin; Xue, Gaolei; Jia, Jia; Wang, Yongtian

    2015-12-28

    Computer generated hologram (CGH) should be obtained with high accuracy and high speed in 3D holographic display, and most researches focus on the high speed. In this paper, a simple and effective computation method for CGH is proposed based on Fresnel diffraction theory and look up table. Numerical simulations and optical experiments are performed to demonstrate its feasibility. The proposed method can obtain more accurate reconstructed images with lower memory usage compared with split look up table method and compressed look up table method without sacrificing the computational speed in holograms generation, so it is called accurate compressed look up table method (AC-LUT). It is believed that AC-LUT method is an effective method to calculate the CGH of 3D objects for real-time 3D holographic display where the huge information data is required, and it could provide fast and accurate digital transmission in various dynamic optical fields in the future.

  18. A multimaterial bioink method for 3D printing tunable, cell-compatible hydrogels.

    PubMed

    Rutz, Alexandra L; Hyland, Kelly E; Jakus, Adam E; Burghardt, Wesley R; Shah, Ramille N

    2015-03-04

    A multimaterial bio-ink method using polyethylene glycol crosslinking is presented for expanding the biomaterial palette required for 3D bioprinting of more mimetic and customizable tissue and organ constructs. Lightly crosslinked, soft hydrogels are produced from precursor solutions of various materials and 3D printed. Rheological and biological characterizations are presented, and the promise of this new bio-ink synthesis strategy is discussed.

  19. A Hybrid Method for Endocardial Contour Extraction of Right Ventricle in 4-Slices from 3D Echocardiography Dataset.

    PubMed

    Dawood, Faten A; Rahmat, Rahmita W; Kadiman, Suhaini B; Abdullah, Lili N; Zamrin, Mohd D

    2014-01-01

    This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours.

  20. Novel scanning electron microscopy methods for analyzing the 3D structure of the Golgi apparatus.

    PubMed

    Koga, Daisuke; Ushiki, Tatsuo; Watanabe, Tsuyoshi

    2017-01-01

    The structure of the Golgi apparatus has been extensively examined by light and electron microscopy, but details of its three-dimensional (3D) structure have remained unclear because of the technical limitations of conventional microscopy techniques. To overcome this problem, we have developed several novel scanning electron microscopy (SEM) methods for observing the 3D structure of subcellular organelles including the Golgi apparatus: (1) an osmium maceration method that facilitates SEM observation of membranous organelles, including the Golgi apparatus, by selectively removing soluble cytoplasmic proteins, (2) an osmium impregnation/maceration method that combines an osmium impregnation method with the osmium maceration method to determine the polarity of the Golgi apparatus by SEM, (3) a correlative light and SEM method that combines a cryosectioning technique with the osmium maceration method to enable correlation of the immunocytochemical distribution of molecules with the 3D ultrastructure of the Golgi apparatus, and (4) array tomography based on the systematic collection and integration of SEM images of serial ultrathin sections on glass slides for revealing the 3D ultrastructure of the entire Golgi apparatus. Together, the novel SEM techniques listed above can reveal the complete 3D structure of the Golgi apparatus in different cell types.

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

    NASA Astrophysics Data System (ADS)

    Price, Gareth; Moore, Chris

    2007-03-01

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

  2. Local motion-compensated method for high-quality 3D coronary artery reconstruction

    PubMed Central

    Liu, Bo; Bai, Xiangzhi; Zhou, Fugen

    2016-01-01

    The 3D reconstruction of coronary artery from X-ray angiograms rotationally acquired on C-arm has great clinical value. While cardiac-gated reconstruction has shown promising results, it suffers from the problem of residual motion. This work proposed a new local motion-compensated reconstruction method to handle this issue. An initial image was firstly reconstructed using a regularized iterative reconstruction method. Then a 3D/2D registration method was proposed to estimate the residual vessel motion. Finally, the residual motion was compensated in the final reconstruction using the extended iterative reconstruction method. Through quantitative evaluation, it was found that high-quality 3D reconstruction could be obtained and the result was comparable to state-of-the-art method. PMID:28018741

  3. Local motion-compensated method for high-quality 3D coronary artery reconstruction.

    PubMed

    Liu, Bo; Bai, Xiangzhi; Zhou, Fugen

    2016-12-01

    The 3D reconstruction of coronary artery from X-ray angiograms rotationally acquired on C-arm has great clinical value. While cardiac-gated reconstruction has shown promising results, it suffers from the problem of residual motion. This work proposed a new local motion-compensated reconstruction method to handle this issue. An initial image was firstly reconstructed using a regularized iterative reconstruction method. Then a 3D/2D registration method was proposed to estimate the residual vessel motion. Finally, the residual motion was compensated in the final reconstruction using the extended iterative reconstruction method. Through quantitative evaluation, it was found that high-quality 3D reconstruction could be obtained and the result was comparable to state-of-the-art method.

  4. Isolation, growth, and characterization of human renal epithelial cells using traditional and 3D methods.

    PubMed

    Gildea, John J; McGrath, Helen E; Van Sciver, Robert E; Wang, Dora Bigler; Felder, Robin A

    2013-01-01

    The kidney is a highly heterogeneous organ that is responsible for fluid and electrolyte balance. Much interest is focused on determining the function of specific renal epithelial cells in humans, which can only be accomplished through the isolation and growth of nephron segment-specific epithelial cells. However, human renal epithelial cells are notoriously difficult to maintain in culture. This chapter describes the isolation, growth, immortalization, and characterization of the human renal proximal tubule cell. In addition, we describe new paradigms in 3D cell culture which allow the cells to maintain more in vivo-like morphology and function.

  5. Implementation of algebraic stress models in a general 3-D Navier-Stokes method (PAB3D)

    NASA Technical Reports Server (NTRS)

    Abdol-Hamid, Khaled S.

    1995-01-01

    A three-dimensional multiblock Navier-Stokes code, PAB3D, which was developed for propulsion integration and general aerodynamic analysis, has been used extensively by NASA Langley and other organizations to perform both internal (exhaust) and external flow analysis of complex aircraft configurations. This code was designed to solve the simplified Reynolds Averaged Navier-Stokes equations. A two-equation k-epsilon turbulence model has been used with considerable success, especially for attached flows. Accurate predicting of transonic shock wave location and pressure recovery in separated flow regions has been more difficult. Two algebraic Reynolds stress models (ASM) have been recently implemented in the code that greatly improved the code's ability to predict these difficult flow conditions. Good agreement with Direct Numerical Simulation (DNS) for a subsonic flat plate was achieved with ASM's developed by Shih, Zhu, and Lumley and Gatski and Speziale. Good predictions were also achieved at subsonic and transonic Mach numbers for shock location and trailing edge boattail pressure recovery on a single-engine afterbody/nozzle model.

  6. Theoretical assessment of 3-D magnetotelluric method for oil and gas exploration: Synthetic examples

    NASA Astrophysics Data System (ADS)

    Zhang, Kun; Wei, Wenbo; Lu, Qingtian; Dong, Hao; Li, Yanqing

    2014-07-01

    In petroleum explorations, seismic reflection technique has been almost always the preferred method for its high exploration depth and resolution. However, with the development of three dimensional (3D) inversion and interpretation schemes, much potential has been shown in MT method dealing with complex geological structures as in oil and gas exploration. In this study, synthetic geophysical models of petroleum reservoir structures are modeled and utilized to demonstrate that feasibility of 3-D MT technique for hydrocarbon exploration. A series of typical reservoir structure models are constructed and used to generate synthetic MT and seismic data to test the capabilities of 2-D/3-D MT and 2-D seismic inversion techniques. According to the inversion comparison, in addition to correctly retrieve the original forward model, the 3-D MT method also has some advantages over the reflective seismology method, which suffered from the lack of reflection wave and multiple wave problems. With the presented 3-D high resolution MT inversion method, MT techniques should be employed as one of the first choices for petroleum explorations.

  7. A measurement method for micro 3D shape based on grids-processing and stereovision technology

    NASA Astrophysics Data System (ADS)

    Li, Chuanwei; Liu, Zhanwei; Xie, Huimin

    2013-04-01

    An integrated measurement method for micro 3D surface shape by a combination of stereovision technology in a scanning electron microscope (SEM) and grids-processing methodology is proposed. The principle of the proposed method is introduced in detail. By capturing two images of the tested specimen with grids on the surface at different tilt angles in an SEM, the 3D surface shape of the specimen can be obtained. Numerical simulation is applied to analyze the feasibility of the proposed method. A validation experiment is performed here. The surface shape of the metal-wire/polymer-membrane structures with thermal deformation is reconstructed. By processing the surface grids of the specimen, the out-of-plane displacement field of the specimen surface is also obtained. Compared with the measurement results obtained by a 3D digital microscope, the experimental error of the proposed method is discussed

  8. Numerical Investigation of 3D multichannel analysis of surface wave method

    NASA Astrophysics Data System (ADS)

    Wang, Limin; Xu, Yixian; Luo, Yinhe

    2015-08-01

    Multichannel analysis of surface wave (MASW) method is an efficient tool to obtain near-surface S-wave velocity, and it has gained popularity in engineering practice. Up to now, most examples of using the MASW technique are focused on 2D models or data from a 1D linear receiver spread. We propose a 3D MASW scheme. A finite-difference (FD) method is used to investigate the method using linear and fan-shaped receiver spreads. Results show that the 3D topography strongly affects propagation of Rayleigh waves. The energy concentration of dispersion image is distorted and bifurcated because of the influence of free-surface topography. These effects are reduced with the 3D MASW method. Lastly we investigate the relation between the array size and the resolution of dispersion measurement.

  9. Tunable nonuniform sampling method for fast calculation and intensity modulation in 3D dynamic holographic display.

    PubMed

    Zhang, Zhao; Liu, Juan; Jia, Jia; Li, Xin; Han, Jian; Hu, Bin; Wang, Yongtian

    2013-08-01

    Heavy computational load of computer-generated hologram (CGH) and imprecise intensity modulation of 3D images are crucial problems in dynamic holographic display. The nonuniform sampling method is proposed to speed up CGH generation and precisely modulate the reconstructed intensities of phase-only CGH. The proposed method can eliminate the redundant information properly, where 70% reduction in the storage amount can be reached when it is combined with the novel lookup table method. Multigrayscale modulation of reconstructed 3D images can be achieved successfully. Numerical simulations and optical experiments are performed, and both are in good agreement. It is believed that the proposed method can be used in 3D dynamic holographic display.

  10. Efficient fabrication method of nano-grating for 3D holographic display with full parallax views.

    PubMed

    Wan, Wenqiang; Qiao, Wen; Huang, Wenbin; Zhu, Ming; Fang, Zongbao; Pu, Donglin; Ye, Yan; Liu, Yanhua; Chen, Linsen

    2016-03-21

    Without any special glasses, multiview 3D displays based on the diffractive optics can present high resolution, full-parallax 3D images in an ultra-wide viewing angle. The enabling optical component, namely the phase plate, can produce arbitrarily distributed view zones by carefully designing the orientation and the period of each nano-grating pixel. However, such 3D display screen is restricted to a limited size due to the time-consuming fabricating process of nano-gratings on the phase plate. In this paper, we proposed and developed a lithography system that can fabricate the phase plate efficiently. Here we made two phase plates with full nano-grating pixel coverage at a speed of 20 mm2/mins, a 500 fold increment in the efficiency when compared to the method of E-beam lithography. One 2.5-inch phase plate generated 9-view 3D images with horizontal-parallax, while the other 6-inch phase plate produced 64-view 3D images with full-parallax. The angular divergence in horizontal axis and vertical axis was 1.5 degrees, and 1.25 degrees, respectively, slightly larger than the simulated value of 1.2 degrees by Finite Difference Time Domain (FDTD). The intensity variation was less than 10% for each viewpoint, in consistency with the simulation results. On top of each phase plate, a high-resolution binary masking pattern containing amplitude information of all viewing zone was well aligned. We achieved a resolution of 400 pixels/inch and a viewing angle of 40 degrees for 9-view 3D images with horizontal parallax. In another prototype, the resolution of each view was 160 pixels/inch and the view angle was 50 degrees for 64-view 3D images with full parallax. As demonstrated in the experiments, the homemade lithography system provided the key fabricating technology for multiview 3D holographic display.

  11. Receptor-based 3D-QSAR in Drug Design: Methods and Applications in Kinase Studies.

    PubMed

    Fang, Cheng; Xiao, Zhiyan

    2016-01-01

    Receptor-based 3D-QSAR strategy represents a superior integration of structure-based drug design (SBDD) and three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. It combines the accurate prediction of ligand poses by the SBDD approach with the good predictability and interpretability of statistical models derived from the 3D-QSAR approach. Extensive efforts have been devoted to the development of receptor-based 3D-QSAR methods and two alternative approaches have been exploited. One associates with computing the binding interactions between a receptor and a ligand to generate structure-based descriptors for QSAR analyses. The other concerns the application of various docking protocols to generate optimal ligand poses so as to provide reliable molecular alignments for the conventional 3D-QSAR operations. This review highlights new concepts and methodologies recently developed in the field of receptorbased 3D-QSAR, and in particular, covers its application in kinase studies.

  12. a Method of 3d Measurement and Reconstruction for Cultural Relics in Museums

    NASA Astrophysics Data System (ADS)

    Zheng, S.; Zhou, Y.; Huang, R.; Zhou, L.; Xu, X.; Wang, C.

    2012-07-01

    Three-dimensional measurement and reconstruction during conservation and restoration of cultural relics have become an essential part of a modem museum regular work. Although many kinds of methods including laser scanning, computer vision and close-range photogrammetry have been put forward, but problems still exist, such as contradiction between cost and good result, time and fine effect. Aimed at these problems, this paper proposed a structure-light based method for 3D measurement and reconstruction of cultural relics in museums. Firstly, based on structure-light principle, digitalization hardware has been built and with its help, dense point cloud of cultural relics' surface can be easily acquired. To produce accurate 3D geometry model from point cloud data, multi processing algorithms have been developed and corresponding software has been implemented whose functions include blunder detection and removal, point cloud alignment and merge, 3D mesh construction and simplification. Finally, high-resolution images are captured and the alignment of these images and 3D geometry model is conducted and realistic, accurate 3D model is constructed. Based on such method, a complete system including hardware and software are built. Multi-kinds of cultural relics have been used to test this method and results prove its own feature such as high efficiency, high accuracy, easy operation and so on.

  13. 2D-3D hybrid stabilized finite element method for tsunami runup simulations

    NASA Astrophysics Data System (ADS)

    Takase, S.; Moriguchi, S.; Terada, K.; Kato, J.; Kyoya, T.; Kashiyama, K.; Kotani, T.

    2016-09-01

    This paper presents a two-dimensional (2D)-three-dimensional (3D) hybrid stabilized finite element method that enables us to predict a propagation process of tsunami generated in a hypocentral region, which ranges from offshore propagation to runup to urban areas, with high accuracy and relatively low computational costs. To be more specific, the 2D shallow water equation is employed to simulate the propagation of offshore waves, while the 3D Navier-Stokes equation is employed for the runup in urban areas. The stabilized finite element method is utilized for numerical simulations for both of the 2D and 3D domains that are independently discretized with unstructured meshes. The multi-point constraint and transmission methods are applied to satisfy the continuity of flow velocities and pressures at the interface between the resulting 2D and 3D meshes, since neither their spatial dimensions nor node arrangements are consistent. Numerical examples are presented to demonstrate the performance of the proposed hybrid method to simulate tsunami behavior, including offshore propagation and runup to urban areas, with substantially lower computation costs in comparison with full 3D computations.

  14. a Method of 3d Freeform Fabrication Using a Curing of Photopolymer Resin

    NASA Astrophysics Data System (ADS)

    Kim, Jung Su; Kim, Dong Soo; Lee, Min Cheol; Lee, Won Hee

    Recently, Study of 3D freeform fabrication method was working in the various applications. For example, in the powder base, it's laminated using a binding method or laser sintering method. However, the demerits of these methods are to take long time for post process and not enough to keep high strength of manufacturing part. The binding method needs the post process and the time for post process needs longer time than a manufacturing time. The sintering method has huge size of system with module of the laser. In this paper, we introduce a method of 3D freeform fabrication using a curing of photopolymer resin. A photopolymer curing method has simply fabrication process and high strength of manufacturing part. So, we are configuration the system with compact type module for the office environment and experiment a UV curing test with photopolymer resin in the 3D freeform fabrication method. In the conclusion, we fabricate the 3D freeform part, which is suitable to the office environment using a photopolymer curing method.

  15. A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

    PubMed Central

    Yang, Qiyao; Wang, Zhiguo; Zhang, Guoxu

    2017-01-01

    The PET and CT fusion image, combining the anatomical and functional information, has important clinical meaning. An effective registration of PET and CT images is the basis of image fusion. This paper presents a multithread registration method based on contour point cloud for 3D whole-body PET and CT images. Firstly, a geometric feature-based segmentation (GFS) method and a dynamic threshold denoising (DTD) method are creatively proposed to preprocess CT and PET images, respectively. Next, a new automated trunk slices extraction method is presented for extracting feature point clouds. Finally, the multithread Iterative Closet Point is adopted to drive an affine transform. We compare our method with a multiresolution registration method based on Mattes Mutual Information on 13 pairs (246~286 slices per pair) of 3D whole-body PET and CT data. Experimental results demonstrate the registration effectiveness of our method with lower negative normalization correlation (NC = −0.933) on feature images and less Euclidean distance error (ED = 2.826) on landmark points, outperforming the source data (NC = −0.496, ED = 25.847) and the compared method (NC = −0.614, ED = 16.085). Moreover, our method is about ten times faster than the compared one. PMID:28316979

  16. Numerical non-LTE 3D radiative transfer using a multigrid method

    NASA Astrophysics Data System (ADS)

    Bjørgen, Johan P.; Leenaarts, Jorrit

    2017-03-01

    Context. 3D non-LTE radiative transfer problems are computationally demanding, and this sets limits on the size of the problems that can be solved. So far, multilevel accelerated lambda iteration (MALI) has been the method of choice to perform high-resolution computations in multidimensional problems. The disadvantage of MALI is that its computing time scales as O(n2), with n the number of grid points. When the grid becomes finer, the computational cost increases quadratically. Aims: We aim to develop a 3D non-LTE radiative transfer code that is more efficient than MALI. Methods: We implement a non-linear multigrid, fast approximation storage scheme, into the existing Multi3D radiative transfer code. We verify our multigrid implementation by comparing with MALI computations. We show that multigrid can be employed in realistic problems with snapshots from 3D radiative magnetohydrodynamics (MHD) simulations as input atmospheres. Results: With multigrid, we obtain a factor 3.3-4.5 speed-up compared to MALI. With full-multigrid, the speed-up increases to a factor 6. The speed-up is expected to increase for input atmospheres with more grid points and finer grid spacing. Conclusions: Solving 3D non-LTE radiative transfer problems using non-linear multigrid methods can be applied to realistic atmospheres with a substantial increase in speed.

  17. Voxel-coding method for quantification of vascular structure from 3D images

    NASA Astrophysics Data System (ADS)

    Soltanian-Zadeh, Hamid; Shahrokni, Ali; Zoroofi, Reza A.

    2001-05-01

    This paper presents an image processing method for information extraction from 3D images of vasculature. It automates the study of vascular structures by extracting quantitative information such as skeleton, length, diameter, and vessel-to- tissue ratio for different vessels as well as their branches. Furthermore, it generates 3D visualization of vessels based on desired anatomical characteristics such as vessel diameter or 3D connectivity. Steps of the proposed approach are as follows. (1) Preprocessing, in which intensity adjustment, optimal thresholding, and median filtering are done. (2) 3D thinning, in which medial axis and skeleton of the vessels are found. (3) Branch labeling, in which different branches are identified and each voxel is assigned to the corresponding branch. (4) Quantitation, in which length of each branch is estimated, based on the number of voxels assigned to it, and its diameter is calculated using the medial axis direction. (5) Visualization, in which vascular structure is shown in 3D, using color coding and surface rendering methods. We have tested and evaluated the proposed algorithms using simulated images of multi-branch vessels and real confocal microscopic images of the vessels in rat brains. Experimental results illustrate performance of the methods and usefulness of the results for medical image analysis applications.

  18. Analysis of corner cracks at hole by a 3-D weight function method with stresses from finite element method

    NASA Technical Reports Server (NTRS)

    Zhao, W.; Newman, J. C., Jr.; Sutton, M. A.; Wu, X. R.; Shivakumar, K. N.

    1995-01-01

    Stress intensity factors for quarter-elliptical corner cracks emanating from a circular hole are determined using a 3-D weight function method combined with a 3-D finite element method. The 3-D finite element method is used to analyze uncracked configuration and provide stress distribution in the region where crack is to occur. Using this stress distribution as input, the 3-D weight function method is used to determine stress intensity factors. Three different loading conditions, i.e. remote tension, remote bending and wedge loading, are considered for a wide range in geometrical parameters. The significance in using 3-D uncracked stress distribution and the difference between single and double corner cracks are studied. Typical crack opening displacements are also provided. Comparisons are made with solutions available in the literature.

  19. 3D FEM-BEM-coupling method to solve magnetostatic Maxwell equations

    NASA Astrophysics Data System (ADS)

    Bruckner, Florian; Vogler, Christoph; Feischl, Michael; Praetorius, Dirk; Bergmair, Bernhard; Huber, Thomas; Fuger, Markus; Suess, Dieter

    2012-05-01

    3D magnetostatic Maxwell equations are solved using the direct Johnson-Nédélec FEM-BEM coupling method and a reduced scalar potential approach. The occurring BEM matrices are calculated analytically and approximated by H-matrices using the ACA+ algorithm. In addition a proper preconditioning method is suggested that allows to solve large-scale problems using iterative solvers.

  20. Detecting and estimating errors in 3D restoration methods using analog models.

    NASA Astrophysics Data System (ADS)

    José Ramón, Ma; Pueyo, Emilio L.; Briz, José Luis

    2015-04-01

    Some geological scenarios may be important for a number of socio-economic reasons, such as water or energy resources, but the available underground information is often limited, scarce and heterogeneous. A truly 3D reconstruction, which is still necessary during the decision-making process, may have important social and economic implications. For this reason, restoration methods were developed. By honoring some geometric or mechanical laws, they help build a reliable image of the subsurface. Pioneer methods were firstly applied in 2D (balanced and restored cross-sections) during the sixties and seventies. Later on, and due to the improvements of computational capabilities, they were extended to 3D. Currently, there are some academic and commercial restoration solutions; Unfold by the Université de Grenoble, Move by Midland Valley Exploration, Kine3D (on gOcad code) by Paradigm, Dynel3D by igeoss-Schlumberger. We have developed our own restoration method, Pmag3Drest (IGME-Universidad de Zaragoza), which is designed to tackle complex geometrical scenarios using paleomagnetic vectors as a pseudo-3D indicator of deformation. However, all these methods have limitations based on the assumptions they need to establish. For this reason, detecting and estimating uncertainty in 3D restoration methods is of key importance to trust the reconstructions. Checking the reliability and the internal consistency of every method, as well as to compare the results among restoration tools, is a critical issue never tackled so far because of the impossibility to test out the results in Nature. To overcome this problem we have developed a technique using analog models. We built complex geometric models inspired in real cases of superposed and/or conical folding at laboratory scale. The stratigraphic volumes were modeled using EVA sheets (ethylene vinyl acetate). Their rheology (tensile and tear strength, elongation, density etc) and thickness can be chosen among a large number of values

  1. A new 3D reconstruction method of small solar system bodies

    NASA Astrophysics Data System (ADS)

    Capanna, C.; Jorda, L.; Lamy, P.; Gesquiere, G.

    2011-10-01

    The 3D reconstruction of small solar system bodies consitutes an essential step toward understanding and interpreting their physical and geological properties. We propose a new reconstruction method by photoclinometry based on the minimization of the chisquare difference between observed and synthetic images by deformation of a 3D triangular mesh. This method has been tested on images of the two asteroids (2867) Steins and (21) Lutetia observed during ESA's ROSETTA mission, and it will be applied to elaborate digital terrain models from images of the asteroid (4) Vesta, the target of NASA's DAWN spacecraft.

  2. Streaming video-based 3D reconstruction method compatible with existing monoscopic and stereoscopic endoscopy systems

    NASA Astrophysics Data System (ADS)

    Bouma, Henri; van der Mark, Wannes; Eendebak, Pieter T.; Landsmeer, Sander H.; van Eekeren, Adam W. M.; ter Haar, Frank B.; Wieringa, F. Pieter; van Basten, Jean-Paul

    2012-06-01

    Compared to open surgery, minimal invasive surgery offers reduced trauma and faster recovery. However, lack of direct view limits space perception. Stereo-endoscopy improves depth perception, but is still restricted to the direct endoscopic field-of-view. We describe a novel technology that reconstructs 3D-panoramas from endoscopic video streams providing a much wider cumulative overview. The method is compatible with any endoscope. We demonstrate that it is possible to generate photorealistic 3D-environments from mono- and stereoscopic endoscopy. The resulting 3D-reconstructions can be directly applied in simulators and e-learning. Extended to real-time processing, the method looks promising for telesurgery or other remote vision-guided tasks.

  3. Simulations of Coulomb systems with slab geometry using an efficient 3D Ewald summation method.

    PubMed

    dos Santos, Alexandre P; Girotto, Matheus; Levin, Yan

    2016-04-14

    We present a new approach to efficiently simulate electrolytes confined between infinite charged walls using a 3d Ewald summation method. The optimal performance is achieved by separating the electrostatic potential produced by the charged walls from the electrostatic potential of electrolyte. The electric field produced by the 3d periodic images of the walls is constant inside the simulation cell, with the field produced by the transverse images of the charged plates canceling out. The non-neutral confined electrolyte in an external potential can be simulated using 3d Ewald summation with a suitable renormalization of the electrostatic energy, to remove a divergence, and a correction that accounts for the conditional convergence of the resulting lattice sum. The new algorithm is at least an order of magnitude more rapid than the usual simulation methods for the slab geometry and can be further sped up by adopting a particle-particle particle-mesh approach.

  4. Multiplexing encoding method for full-color dynamic 3D holographic display.

    PubMed

    Xue, Gaolei; Liu, Juan; Li, Xin; Jia, Jia; Zhang, Zhao; Hu, Bin; Wang, Yongtian

    2014-07-28

    The multiplexing encoding method is proposed and demonstrated for reconstructing colorful images accurately by using single phase-only spatial light modulator (SLM). It will encode the light waves at different wavelengths into one pure-phase hologram at the same time based on the analytic formulas. The three-dimensional (3D) images can be reconstructed clearly when the light waves at different wavelengths are incident into the encoding hologram. Numerical simulations and optical experiments for 2D and 3D colorful images are performed. The results show that the colorful reconstructed images with high quality are achieved successfully. The proposed multiplexing method is a simple and fast encoding approach and the size of the system is small and compact. It is expected to be used for realizing full-color 3D holographic display in future.

  5. 3D reconstruction method from biplanar radiography using non-stereocorresponding points and elastic deformable meshes.

    PubMed

    Mitton, D; Landry, C; Véron, S; Skalli, W; Lavaste, F; De Guise, J A

    2000-03-01

    Standard 3D reconstruction of bones using stereoradiography is limited by the number of anatomical landmarks visible in more than one projection. The proposed technique enables the 3D reconstruction of additional landmarks that can be identified in only one of the radiographs. The principle of this method is the deformation of an elastic object that respects stereocorresponding and non-stereocorresponding observations available in different projections. This technique is based on the principle that any non-stereocorresponding point belongs to a line joining the X-ray source and the projection of the point in one view. The aim is to determine the 3D position of these points on their line of projection when submitted to geometrical and topological constraints. This technique is used to obtain the 3D geometry of 18 cadaveric upper cervical vertebrae. The reconstructed geometry obtained is compared with direct measurements using a magnetic digitiser. The order of precision determined with the point-to-surface distance between the reconstruction obtained with that technique and reference measurements is about 1 mm, depending on the vertebrae studied. Comparison results indicate that the obtained reconstruction is close to the actual vertebral geometry. This method can therefore be proposed to obtain the 3D geometry of vertebrae.

  6. Device and methods for "gold standard" registration of clinical 3D and 2D cerebral angiograms

    NASA Astrophysics Data System (ADS)

    Madan, Hennadii; Likar, Boštjan; Pernuš, Franjo; Å piclin, Žiga

    2015-03-01

    Translation of any novel and existing 3D-2D image registration methods into clinical image-guidance systems is limited due to lack of their objective validation on clinical image datasets. The main reason is that, besides the calibration of the 2D imaging system, a reference or "gold standard" registration is very difficult to obtain on clinical image datasets. In the context of cerebral endovascular image-guided interventions (EIGIs), we present a calibration device in the form of a headband with integrated fiducial markers and, secondly, propose an automated pipeline comprising 3D and 2D image processing, analysis and annotation steps, the result of which is a retrospective calibration of the 2D imaging system and an optimal, i.e., "gold standard" registration of 3D and 2D images. The device and methods were used to create the "gold standard" on 15 datasets of 3D and 2D cerebral angiograms, whereas each dataset was acquired on a patient undergoing EIGI for either aneurysm coiling or embolization of arteriovenous malformation. The use of the device integrated seamlessly in the clinical workflow of EIGI. While the automated pipeline eliminated all manual input or interactive image processing, analysis or annotation. In this way, the time to obtain the "gold standard" was reduced from 30 to less than one minute and the "gold standard" of 3D-2D registration on all 15 datasets of cerebral angiograms was obtained with a sub-0.1 mm accuracy.

  7. Investigation of Presage 3D Dosimetry as a Method of Clinically Intuitive Quality Assurance and Comparison to a Semi-3D Delta4 System

    NASA Astrophysics Data System (ADS)

    Crockett, Ethan Van

    The need for clinically intuitive metrics for patient-specific quality assurance in radiation therapy has been well-documented (Zhen, Nelms et al. 2011). A novel transform method has shown to be effective at converting full-density 3D dose measurements made in a phantom to dose values in the patient geometry, enabling comparisons using clinically intuitive metrics such as dose-volume histograms (Oldham et al. 2011). This work investigates the transform method and compares its calculated dose-volume histograms (DVHs) to DVH values calculated by a Delta4 QA device (Scandidos), marking the first comparison of a true 3D system to a semi-3D device using clinical metrics. Measurements were made using Presage 3D dosimeters, which were readout by an in-house optical-CT scanner. Three patient cases were chosen for the study: one head-and-neck VMAT treatment and two spine IMRT treatments. The transform method showed good agreement with the planned dose values for all three cases. Furthermore, the transformed DVHs adhered to the planned dose with more accuracy than the Delta4 DVHs. The similarity between the Delta4 DVHs and the transformed DVHs, however, was greater for one of the spine cases than it was for the head-and-neck case, implying that the accuracy of the Delta4 Anatomy software may vary from one treatment site to another. Overall, the transform method, which incorporates data from full-density 3D dose measurements, provides clinically intuitive results that are more accurate and consistent than the corresponding results from a semi-3D Delta 4 system.

  8. Methods of constructing a 3D geological model from scatter data

    SciTech Connect

    Horsman, J.; Bethel, W.

    1995-04-01

    Most geoscience applications, such as assessment of an oil reservoir or hazardous waste site, require geological characterization of the site. Geological characterization involves analysis of spatial distributions of lithology, porosity, etc. Because of the complexity of the spatial relationships, the authors find that a 3-D model of geology is better suited for integration of many different types of data and provides a better representation of a site than a 2-D one. A 3-D model of geology is constructed from sample data obtained from field measurements, which are usually scattered. To create a volume model from scattered data, interpolation between points is required. The interpolation can be computed using one of several computational algorithms. Alternatively, a manual method may be employed, in which an interactive graphics device is used to input by hand the information that lies between the data points. For example, a mouse can be used to draw lines connecting data points with equal values. The combination of these two methods presents yet another approach. In this study, the authors will compare selected methods of 3-D geological modeling, They used a flow-based, modular visualization environment (AVS) to construct the geological models computationally. Within this system, they used three modules, scat{_}3d, trivar and scatter{_}to{_}ucd, as examples of computational methods. They compare these methods to the combined manual and computational approach. Because there are no tools readily available in AVS for this type of construction, they used a geological modeling system to demonstrate this method.

  9. A 3-D aerodynamic method for the analysis of isolated horizontal-axis wind turbines

    SciTech Connect

    Ammara, I.; Masson, C.; Paraschivoiu, I.

    1997-12-31

    In most existing performance-analysis methods, wind turbines are considered isolated so that interference effects caused by other rotors or by the site topography are neglected. The main objective of this paper is to propose a practical 3-D method suitable for the study of these effects, in order to optimize the arrangement and the positioning of Horizontal-Axis Wind Turbines (HAWTs) in a wind farm. In the proposed methodology, the flow field around isolated HAWTs is predicted by solving the 3-D, time-averaged, steady-state, incompressible, Navier-Stokes equations in which the turbines are represented by distributions of momentum sources. The resulting governing equations are solved using a Control-Volume Finite Element Method (CVFEM). The fundamental aspects related to the development of a practical 3-D method are discussed in this paper, with an emphasis on some of the challenges that arose during its implementation. The current implementation is limited to the analysis of isolated HAWTs. Preliminary results have indicated that, the proposed 3-D method reaches the same level of accuracy, in terms of performance predictions, that the previously developed 2-D axisymmetric model and the well-known momentum-strip theory, while still using reasonable computers resources. It can be considered as a useful tool for the design of HAWTs. Its main advantages, however, are its intrinsic capacity to predict the details of the flow in the wake, and its capabilities of modelling arbitrary wind-turbine arrangements and including ground effects.

  10. Efficient methods to model the scattering of ultrasonic guided waves in 3D

    NASA Astrophysics Data System (ADS)

    Moreau, L.; Velichko, A.; Wilcox, P. D.

    2010-03-01

    The propagation of ultrasonic guided waves and their interaction with a defect is of interest to the nondestructive testing community. There is no general solution to the scattering problem and it is still an ongoing research topic. Due to the complexity of guided wave scattering problems, most existing models are related to the 2D case. However, thanks to the increase in computer calculation power, specific 3D problems can also be studied, with the help of numerical or semi-analytical methods. This paper describes two efficient methods aimed at modeling 3D scattering problems. The first method is the use of the Huygens' principle to reduce the size of finite element models. This principle allows the area of interest to be restricted to the very near field of the defect, for both the generation of the incident field and the modal decomposition of the scattered field. The second method consists of separating the 3D problem into two 2D problems for which the solutions are calculated and used to approximate the 3D solution. This can be used at low frequency-thickness products, where Lamb waves have a similar behavior to bulk waves. These two methods are presented briefly and compared on simple scattering cases.

  11. An improved 3-D Look--Locker imaging method for T(1) parameter estimation.

    PubMed

    Nkongchu, Ken; Santyr, Giles

    2005-09-01

    The 3-D Look-Locker (LL) imaging method has been shown to be a highly efficient and accurate method for the volumetric mapping of the spin lattice relaxation time T(1). However, conventional 3-D LL imaging schemes are typically limited to small tip angle RF pulses (3-D LL imaging method that incorporates an additional and variable delay time between recovery samples is described, which permits the use of larger tip angles (>5 degrees ), thereby improving the SNR and the accuracy of the method. In phantom studies, a mean T(1) measurement accuracy of less than 2% (0.2-3.1%) using a tip angle of 10 degrees was obtained for a range of T(1) from approximately 300 to 1,700 ms with a measurement time increase of only 15%. This accuracy compares favorably with the conventional 3-D LL method that provided an accuracy between 2.2% and 7.3% using a 5 degrees flip angle.

  12. Accident or homicide--virtual crime scene reconstruction using 3D methods.

    PubMed

    Buck, Ursula; Naether, Silvio; Räss, Beat; Jackowski, Christian; Thali, Michael J

    2013-02-10

    The analysis and reconstruction of forensically relevant events, such as traffic accidents, criminal assaults and homicides are based on external and internal morphological findings of the injured or deceased person. For this approach high-tech methods are gaining increasing importance in forensic investigations. The non-contact optical 3D digitising system GOM ATOS is applied as a suitable tool for whole body surface and wound documentation and analysis in order to identify injury-causing instruments and to reconstruct the course of event. In addition to the surface documentation, cross-sectional imaging methods deliver medical internal findings of the body. These 3D data are fused into a whole body model of the deceased. Additional to the findings of the bodies, the injury inflicting instruments and incident scene is documented in 3D. The 3D data of the incident scene, generated by 3D laser scanning and photogrammetry, is also included into the reconstruction. Two cases illustrate the methods. In the fist case a man was shot in his bedroom and the main question was, if the offender shot the man intentionally or accidentally, as he declared. In the second case a woman was hit by a car, driving backwards into a garage. It was unclear if the driver drove backwards once or twice, which would indicate that he willingly injured and killed the woman. With this work, we demonstrate how 3D documentation, data merging and animation enable to answer reconstructive questions regarding the dynamic development of patterned injuries, and how this leads to a real data based reconstruction of the course of event.

  13. On the evaluation of photogrammetric methods for dense 3D surface reconstruction in a metrological context

    NASA Astrophysics Data System (ADS)

    Toschi, I.; Capra, A.; De Luca, L.; Beraldin, J.-A.; Cournoyer, L.

    2014-05-01

    This paper discusses a methodology to evaluate the accuracy of recently developed image-based 3D modelling techniques. So far, the emergence of these novel methods has not been supported by the definition of an internationally recognized standard which is fundamental for user confidence and market growth. In order to provide an element of reflection and solution to the different communities involved in 3D imaging, a promising approach is presented in this paper for the assessment of both metric quality and limitations of an open-source suite of tools (Apero/MicMac), developed for the extraction of dense 3D point clouds from a set of unordered 2D images. The proposed procedural workflow is performed within a metrological context, through inter-comparisons with "reference" data acquired with two hemispherical laser scanners, one total station, and one laser tracker. The methodology is applied to two case studies, designed in order to analyse the software performances in dealing with both outdoor and environmentally controlled conditions, i.e. the main entrance of Cathédrale de la Major (Marseille, France) and a custom-made scene located at National Research Council of Canada 3D imaging Metrology Laboratory (Ottawa). Comparative data and accuracy evidence produced for both tests allow the study of some key factors affecting 3D model accuracy.

  14. Method of Individual Adjustment for 3D CT Analysis: Linear Measurement.

    PubMed

    Kim, Dong Kyu; Choi, Dong Hun; Lee, Jeong Woo; Yang, Jung Dug; Chung, Ho Yun; Cho, Byung Chae; Choi, Kang Young

    2016-01-01

    Introduction. We aim to regularize measurement values in three-dimensional (3D) computed tomography (CT) reconstructed images for higher-precision 3D analysis, focusing on length-based 3D cephalometric examinations. Methods. We measure the linear distances between points on different skull models using Vernier calipers (real values). We use 10 differently tilted CT scans for 3D CT reconstruction of the models and measure the same linear distances from the picture archiving and communication system (PACS). In both cases, each measurement is performed three times by three doctors, yielding nine measurements. The real values are compared with the PACS values. Each PACS measurement is revised based on the display field of view (DFOV) values and compared with the real values. Results. The real values and the PACS measurement changes according to tilt value have no significant correlations (p > 0.05). However, significant correlations appear between the real values and DFOV-adjusted PACS measurements (p < 0.001). Hence, we obtain a correlation expression that can yield real physical values from PACS measurements. The DFOV value intervals for various age groups are also verified. Conclusion. Precise confirmation of individual preoperative length and precise analysis of postoperative improvements through 3D analysis is possible, which is helpful for facial-bone-surgery symmetry correction.

  15. Method of Individual Adjustment for 3D CT Analysis: Linear Measurement

    PubMed Central

    Choi, Dong Hun; Lee, Jeong Woo; Yang, Jung Dug; Chung, Ho Yun; Cho, Byung Chae

    2016-01-01

    Introduction. We aim to regularize measurement values in three-dimensional (3D) computed tomography (CT) reconstructed images for higher-precision 3D analysis, focusing on length-based 3D cephalometric examinations. Methods. We measure the linear distances between points on different skull models using Vernier calipers (real values). We use 10 differently tilted CT scans for 3D CT reconstruction of the models and measure the same linear distances from the picture archiving and communication system (PACS). In both cases, each measurement is performed three times by three doctors, yielding nine measurements. The real values are compared with the PACS values. Each PACS measurement is revised based on the display field of view (DFOV) values and compared with the real values. Results. The real values and the PACS measurement changes according to tilt value have no significant correlations (p > 0.05). However, significant correlations appear between the real values and DFOV-adjusted PACS measurements (p < 0.001). Hence, we obtain a correlation expression that can yield real physical values from PACS measurements. The DFOV value intervals for various age groups are also verified. Conclusion. Precise confirmation of individual preoperative length and precise analysis of postoperative improvements through 3D analysis is possible, which is helpful for facial-bone-surgery symmetry correction. PMID:28070517

  16. 3D unstructured mesh ALE hydrodynamics with the upwind discontinuous galerkin method

    SciTech Connect

    Kershaw, D S; Milovich, J L; Prasad, M K; Shaw, M J; Shestakov, A I

    1999-05-07

    The authors describe a numerical scheme to solve 3D Arbitrary Lagrangian-Eulerian (ALE) hydrodynamics on an unstructured mesh using a discontinuous Galerkin method (DGM) and an explicit Runge-Kutta time discretization. Upwinding is achieved through Roe's linearized Riemann solver with the Harten-Hyman entropy fix. For stabilization, a 3D quadratic programming generalization of van Leer's 1D minmod slope limiter is used along with a Lapidus type artificial viscosity. This DGM scheme has been tested on a variety of hydrodynamic test problems and appears to be robust making it the basis for the integrated 3D inertial confinement fusion modeling code (ICF3D). For efficient code development, they use C++ object oriented programming to easily separate the complexities of an unstructured mesh from the basic physics modules. ICF3D is fully parallelized using domain decomposition and the MPI message passing library. It is fully portable. It runs on uniprocessor workstations and massively parallel platforms with distributed and shared memory.

  17. Solving Dirac equations on a 3D lattice with inverse Hamiltonian and spectral methods

    NASA Astrophysics Data System (ADS)

    Ren, Z. X.; Zhang, S. Q.; Meng, J.

    2017-02-01

    A new method to solve the Dirac equation on a 3D lattice is proposed, in which the variational collapse problem is avoided by the inverse Hamiltonian method and the fermion doubling problem is avoided by performing spatial derivatives in momentum space with the help of the discrete Fourier transform, i.e., the spectral method. This method is demonstrated in solving the Dirac equation for a given spherical potential in a 3D lattice space. In comparison with the results obtained by the shooting method, the differences in single-particle energy are smaller than 10-4 MeV, and the densities are almost identical, which demonstrates the high accuracy of the present method. The results obtained by applying this method without any modification to solve the Dirac equations for an axial-deformed, nonaxial-deformed, and octupole-deformed potential are provided and discussed.

  18. A fast and accurate method to predict 2D and 3D aerodynamic boundary layer flows

    NASA Astrophysics Data System (ADS)

    Bijleveld, H. A.; Veldman, A. E. P.

    2014-12-01

    A quasi-simultaneous interaction method is applied to predict 2D and 3D aerodynamic flows. This method is suitable for offshore wind turbine design software as it is a very accurate and computationally reasonably cheap method. This study shows the results for a NACA 0012 airfoil. The two applied solvers converge to the experimental values when the grid is refined. We also show that in separation the eigenvalues remain positive thus avoiding the Goldstein singularity at separation. In 3D we show a flow over a dent in which separation occurs. A rotating flat plat is used to show the applicability of the method for rotating flows. The shown capabilities of the method indicate that the quasi-simultaneous interaction method is suitable for design methods for offshore wind turbine blades.

  19. Fabrication of light, flexible and multifunctional graphene nanoribbon fibers via a 3D solution printing method.

    PubMed

    Wang, Mingqiang; Zhang, Shuai; Song, Yuanjun; Dong, Jidong; Wei, Huawei; Xie, Huaquan; Fang, Xiaojiao; Shao, Lu; Huang, Yudong; Jiang, Zaixing

    2016-11-18

    Graphene oxide nanoribbons (GONRs) are one of the most promising carbon based materials. The integration of 2D GONR sheets into macroscopic materials, such as continuous fibers or film, leads the way in translating the good properties of individual GONR sheets into macroscopic and ordered materials for future applications. In this study, we first report the fabrication of GONR fibers utilizing GONR sheets as the raw material without any supporting surfactant or polymer. The method of fabricating fibers is referred to as '3D solution printing'. GONR fibers exhibit good mechanical and electrical properties, whose tensile strength and electrical conductivity could reach up to 95 MPa and 680 S cm(-1), respectively. Hence, the fabricated 3D integrated circuits are lighter and smaller compared to traditional metal circuits, and with high electrical properties. The 3D integrated circuits, therefore, have a bright future prospect.

  20. Fabrication of light, flexible and multifunctional graphene nanoribbon fibers via a 3D solution printing method

    NASA Astrophysics Data System (ADS)

    Wang, Mingqiang; Zhang, Shuai; Song, Yuanjun; Dong, Jidong; Wei, Huawei; Xie, Huaquan; Fang, Xiaojiao; Shao, Lu; Huang, Yudong; Jiang, Zaixing

    2016-11-01

    Graphene oxide nanoribbons (GONRs) are one of the most promising carbon based materials. The integration of 2D GONR sheets into macroscopic materials, such as continuous fibers or film, leads the way in translating the good properties of individual GONR sheets into macroscopic and ordered materials for future applications. In this study, we first report the fabrication of GONR fibers utilizing GONR sheets as the raw material without any supporting surfactant or polymer. The method of fabricating fibers is referred to as ‘3D solution printing’. GONR fibers exhibit good mechanical and electrical properties, whose tensile strength and electrical conductivity could reach up to 95 MPa and 680 S cm-1, respectively. Hence, the fabricated 3D integrated circuits are lighter and smaller compared to traditional metal circuits, and with high electrical properties. The 3D integrated circuits, therefore, have a bright future prospect.

  1. An efficient calibration method for freehand 3-D ultrasound imaging systems.

    PubMed

    Leotta, Daniel F

    2004-07-01

    A phantom has been developed to quickly calibrate a freehand 3-D ultrasound (US) imaging system. Calibration defines the spatial relationship between the US image plane and an external tracking device attached to the scanhead. The phantom consists of a planar array of strings and beads, and a set of out-of-plane strings that guide the user to proper scanhead orientation for imaging. When an US image plane is coincident with the plane defined by the strings, the calibration parameters are calculated by matching of homologous points in the image and phantom. The resulting precision and accuracy of the 3-D imaging system are similar to those achieved with a more complex calibration procedure. The 3-D reconstruction performance of the calibrated system is demonstrated with a magnetic tracking system, but the method could be applied to other tracking devices.

  2. "Gold standard" data for evaluation and comparison of 3D/2D registration methods.

    PubMed

    Tomazevic, Dejan; Likar, Bostjan; Pernus, Franjo

    2004-01-01

    Evaluation and comparison of registration techniques for image-guided surgery is an important problem that has received little attention in the literature. In this paper we address the challenging problem of generating reliable "gold standard" data for use in evaluating the accuracy of 3D/2D registrations. We have devised a cadaveric lumbar spine phantom with fiducial markers and established highly accurate correspondences between 3D CT and MR images and 18 2D X-ray images. The expected target registration errors for target points on the pedicles are less than 0.26 mm for CT-to-X-ray registration and less than 0.42 mm for MR-to-X-ray registration. As such, the "gold standard" data, which has been made publicly available on the Internet (http://lit.fe.uni-lj.si/Downloads/downloads.asp), is useful for evaluation and comparison of 3D/2D image registration methods.

  3. A 3D AgCl hierarchical superstructure synthesized by a wet chemical oxidation method.

    PubMed

    Lou, Zaizhu; Huang, Baibiao; Ma, Xiangchao; Zhang, Xiaoyang; Qin, Xiaoyan; Wang, Zeyan; Dai, Ying; Liu, Yuanyuan

    2012-12-07

    A novel 3D AgCl hierarchical superstructure, with fast growth along the 〈111〉 directions of cubic seeds, is synthesized by using a wet chemical oxidation method. The morphological structures and the growth process are investigated by scanning electron microscopy and X-ray diffraction. The crystal structures are analyzed by their crystallographic orientations. The surface energy of AgCl facets {100}, {110}, and {111} with absorbance of Cl(-) ions is studied by density functional theory calculations. Based on the experimental and computational results, a plausible mechanism is proposed to illustrate the formation of the 3D AgCl hierarchical superstructures. With more active sites, the photocatalytic activity of the 3D AgCl hierarchical superstructures is better than those of concave and cubic ones in oxygen evolution under irradiation by visible light.

  4. Application of fuzzy connectedness in 3D blood vessel extraction.

    PubMed

    Lv, Xinrong; Zou, Hua

    2010-01-01

    Three-dimensional (3D) segmentation of blood vessels plays a very important role in solving some practical problems such as diagnosis of vessels diseases. Because of the effective segmentation for 2D images, the fuzzy connectedness segmentation method is introduced to extract vascular structures from 3D blood vessel volume dataset. In the experiments, three segmentation methods including thresholding method, region growing method and fuzzy connectedness method are all used to extract the vascular structures, and their results are compared. The results indicate that fuzzy connectedness method is better than thresholding method in connectivity of segmentation results, and better than region growing method in precision of segmentation results.

  5. 2D and 3D Method of Characteristic Tools for Complex Nozzle Development

    NASA Technical Reports Server (NTRS)

    Rice, Tharen

    2003-01-01

    This report details the development of a 2D and 3D Method of Characteristic (MOC) tool for the design of complex nozzle geometries. These tools are GUI driven and can be run on most Windows-based platforms. The report provides a user's manual for these tools as well as explains the mathematical algorithms used in the MOC solutions.

  6. 3-D Localization Method for a Magnetically Actuated Soft Capsule Endoscope and Its Applications

    PubMed Central

    Yim, Sehyuk; Sitti, Metin

    2014-01-01

    In this paper, we present a 3-D localization method for a magnetically actuated soft capsule endoscope (MASCE). The proposed localization scheme consists of three steps. First, MASCE is oriented to be coaxially aligned with an external permanent magnet (EPM). Second, MASCE is axially contracted by the enhanced magnetic attraction of the approaching EPM. Third, MASCE recovers its initial shape by the retracting EPM as the magnetic attraction weakens. The combination of the estimated direction in the coaxial alignment step and the estimated distance in the shape deformation (recovery) step provides the position of MASCE in 3-D. It is experimentally shown that the proposed localization method could provide 2.0–3.7 mm of distance error in 3-D. This study also introduces two new applications of the proposed localization method. First, based on the trace of contact points between the MASCE and the surface of the stomach, the 3-D geometrical model of a synthetic stomach was reconstructed. Next, the relative tissue compliance at each local contact point in the stomach was characterized by measuring the local tissue deformation at each point due to the preloading force. Finally, the characterized relative tissue compliance parameter was mapped onto the geometrical model of the stomach toward future use in disease diagnosis. PMID:25383064

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

    PubMed

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

    2016-10-05

    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 with

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

    PubMed

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

    2017-01-01

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

  9. Gap-filling methods for 3D PlanTIS data

    NASA Astrophysics Data System (ADS)

    Loukiala, A.; Tuna, U.; Beer, S.; Jahnke, S.; Ruotsalainen, U.

    2010-10-01

    The range of positron emitters and their labeled compounds have led to high-resolution PET scanners becoming widely used, not only in clinical and pre-clinical studies but also in plant studies. A high-resolution PET scanner, plant tomographic imaging system (PlanTIS), was designed to study metabolic and physiological functions of plants noninvasively. The gantry of the PlanTIS scanner has detector-free regions. Even when the gantry of the PlanTIS is rotated during the scan, these regions result in missing sinogram bins in the acquired data. Missing data need to be estimated prior to the analytical image reconstructions in order to avoid artifacts in the final reconstructed images. In this study, we propose three gap-filling methods for estimation of the unique gaps existing in the 3D PlanTIS sinogram data. The 3D sinogram data were gap-filled either by linear interpolation in the transaxial planes or by the bicubic interpolation method (proposed for the ECAT high-resolution research tomograph) in the transradial planes or by the inpainting method in the transangular planes. Each gap-filling method independently compensates for slices in one of three orthogonal sinogram planes (transaxial, transradial and transangular planes). A 3D numerical Shepp-Logan phantom and the NEMA image quality phantom were used to evaluate the methods. The gap-filled sinograms were reconstructed using the analytical 3D reprojection (3DRP) method. The NEMA phantom sinograms were also reconstructed by the iterative reconstruction method, ordered subsets maximum a posteriori one step late (OSMAPOSL), to compare the results of gap filling followed by 3DRP with the results of OSMAPOSL reconstruction without gap filling. The three methods were evaluated quantitatively (by mean square error and coefficients of variation) over the selected regions of the 3D numerical Shepp-Logan phantom at eight different Poisson noise levels. Moreover, the NEMA phantom scan data were used in visual assessments

  10. An extension of the Saltykov method to quantify 3D grain size distributions in mylonites

    NASA Astrophysics Data System (ADS)

    Lopez-Sanchez, Marco A.; Llana-Fúnez, Sergio

    2016-12-01

    The estimation of 3D grain size distributions (GSDs) in mylonites is key to understanding the rheological properties of crystalline aggregates and to constraining dynamic recrystallization models. This paper investigates whether a common stereological method, the Saltykov method, is appropriate for the study of GSDs in mylonites. In addition, we present a new stereological method, named the two-step method, which estimates a lognormal probability density function describing the 3D GSD. Both methods are tested for reproducibility and accuracy using natural and synthetic data sets. The main conclusion is that both methods are accurate and simple enough to be systematically used in recrystallized aggregates with near-equant grains. The Saltykov method is particularly suitable for estimating the volume percentage of particular grain-size fractions with an absolute uncertainty of ±5 in the estimates. The two-step method is suitable for quantifying the shape of the actual 3D GSD in recrystallized rocks using a single value, the multiplicative standard deviation (MSD) parameter, and providing a precision in the estimate typically better than 5%. The novel method provides a MSD value in recrystallized quartz that differs from previous estimates based on apparent 2D GSDs, highlighting the inconvenience of using apparent GSDs for such tasks.

  11. Computational methods for constructing protein structure models from 3D electron microscopy maps.

    PubMed

    Esquivel-Rodríguez, Juan; Kihara, Daisuke

    2013-10-01

    Protein structure determination by cryo-electron microscopy (EM) has made significant progress in the past decades. Resolutions of EM maps have been improving as evidenced by recently reported structures that are solved at high resolutions close to 3Å. Computational methods play a key role in interpreting EM data. Among many computational procedures applied to an EM map to obtain protein structure information, in this article we focus on reviewing computational methods that model protein three-dimensional (3D) structures from a 3D EM density map that is constructed from two-dimensional (2D) maps. The computational methods we discuss range from de novo methods, which identify structural elements in an EM map, to structure fitting methods, where known high resolution structures are fit into a low-resolution EM map. A list of available computational tools is also provided.

  12. Efficient calculation method for realistic deep 3D scene hologram using orthographic projection

    NASA Astrophysics Data System (ADS)

    Igarashi, Shunsuke; Nakamura, Tomoya; Matsushima, Kyoji; Yamaguchi, Masahiro

    2016-03-01

    We propose a fast calculation method to synthesize a computer-generated hologram (CGH) of realistic deep three-dimensional (3D) scene. In our previous study, we have proposed a calculation method of CGH for reproducing such scene called ray-sampling-plane (RSP) method, in which light-ray information of a scene is converted to wavefront, and the wavefront is numerically propagated based on diffraction theory. In this paper, we introduce orthographic projection to the RSP method for accelerating calculation time. By numerical experiments, we verified the accelerated calculation with the ratio of 28-times compared to the conventional RSP method. The calculated CGH was fabricated by the printing system using laser lithography and demonstrated deep 3D image reconstruction in 52mm×52mm with realistic appearance effect such as gloss and translucent effect.

  13. Importance of a 3D forward modeling tool for surface wave analysis methods

    NASA Astrophysics Data System (ADS)

    Pageot, Damien; Le Feuvre, Mathieu; Donatienne, Leparoux; Philippe, Côte; Yann, Capdeville

    2016-04-01

    Since a few years, seismic surface waves analysis methods (SWM) have been widely developed and tested in the context of subsurface characterization and have demonstrated their effectiveness for sounding and monitoring purposes, e.g., high-resolution tomography of the principal geological units of California or real time monitoring of the Piton de la Fournaise volcano. Historically, these methods are mostly developed under the assumption of semi-infinite 1D layered medium without topography. The forward modeling is generally based on Thomson-Haskell matrix based modeling algorithm and the inversion is driven by Monte-Carlo sampling. Given their efficiency, SWM have been transfered to several scale of which civil engineering structures in order to, e.g., determine the so-called V s30 parameter or assess other critical constructional parameters in pavement engineering. However, at this scale, many structures may often exhibit 3D surface variations which drastically limit the efficiency of SWM application. Indeed, even in the case of an homogeneous structure, 3D geometry can bias the dispersion diagram of Rayleigh waves up to obtain discontinuous phase velocity curves which drastically impact the 1D mean velocity model obtained from dispersion inversion. Taking advantages of high-performance computing center accessibility and wave propagation modeling algorithm development, it is now possible to consider the use of a 3D elastic forward modeling algorithm instead of Thomson-Haskell method in the SWM inversion process. We use a parallelized 3D elastic modeling code based on the spectral element method which allows to obtain accurate synthetic data with very low numerical dispersion and a reasonable numerical cost. In this study, we choose dike embankments as an illustrative example. We first show that their longitudinal geometry may have a significant effect on dispersion diagrams of Rayleigh waves. Then, we demonstrate the necessity of 3D elastic modeling as a forward

  14. TU-CD-BRA-01: A Novel 3D Registration Method for Multiparametric Radiological Images

    SciTech Connect

    Akhbardeh, A; Parekth, VS; Jacobs, MA

    2015-06-15

    Purpose: Multiparametric and multimodality radiological imaging methods, such as, magnetic resonance imaging(MRI), computed tomography(CT), and positron emission tomography(PET), provide multiple types of tissue contrast and anatomical information for clinical diagnosis. However, these radiological modalities are acquired using very different technical parameters, e.g.,field of view(FOV), matrix size, and scan planes, which, can lead to challenges in registering the different data sets. Therefore, we developed a hybrid registration method based on 3D wavelet transformation and 3D interpolations that performs 3D resampling and rotation of the target radiological images without loss of information Methods: T1-weighted, T2-weighted, diffusion-weighted-imaging(DWI), dynamic-contrast-enhanced(DCE) MRI and PET/CT were used in the registration algorithm from breast and prostate data at 3T MRI and multimodality(PET/CT) cases. The hybrid registration scheme consists of several steps to reslice and match each modality using a combination of 3D wavelets, interpolations, and affine registration steps. First, orthogonal reslicing is performed to equalize FOV, matrix sizes and the number of slices using wavelet transformation. Second, angular resampling of the target data is performed to match the reference data. Finally, using optimized angles from resampling, 3D registration is performed using similarity transformation(scaling and translation) between the reference and resliced target volume is performed. After registration, the mean-square-error(MSE) and Dice Similarity(DS) between the reference and registered target volumes were calculated. Results: The 3D registration method registered synthetic and clinical data with significant improvement(p<0.05) of overlap between anatomical structures. After transforming and deforming the synthetic data, the MSE and Dice similarity were 0.12 and 0.99. The average improvement of the MSE in breast was 62%(0.27 to 0.10) and prostate was

  15. New data-driven method from 3D confocal microscopy for calculating phytoplankton cell biovolume.

    PubMed

    Roselli, L; Paparella, F; Stanca, E; Basset, A

    2015-06-01

    Confocal laser scanner microscopy coupled with an image analysis system was used to directly determine the shape and calculate the biovolume of phytoplankton organisms by constructing 3D models of cells. The study was performed on Biceratium furca (Ehrenberg) Vanhoeffen, which is one of the most complex-shaped phytoplankton. Traditionally, biovolume is obtained from a standardized set of geometric models based on linear dimensions measured by light microscopy. However, especially in the case of complex-shaped cells, biovolume is affected by very large errors associated with the numerous manual measurements that this entails. We evaluate the accuracy of these traditional methods by comparing the results obtained using geometric models with direct biovolume measurement by image analysis. Our results show cell biovolume measurement based on decomposition into simple geometrical shapes can be highly inaccurate. Although we assume that the most accurate cell shape is obtained by 3D direct biovolume measurement, which is based on voxel counting, the intrinsic uncertainty of this method is explored and assessed. Finally, we implement a data-driven formula-based approach to the calculation of biovolume of this complex-shaped organism. On one hand, the model is obtained from 3D direct calculation. On the other hand, it is based on just two linear dimensions which can easily be measured by hand. This approach has already been used for investigating the complexities of morphology and for determining the 3D structure of cells. It could also represent a novel way to generalize scaling laws for biovolume calculation.

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

    NASA Astrophysics Data System (ADS)

    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.

  17. A correction method of color projection fringes in 3D contour measurement

    NASA Astrophysics Data System (ADS)

    Song, Li-mei; Li, Zong-yan; Chen, Chang-man; Xi, Jiang-tao; Guo, Qing-hua; Li, Xiao-jie

    2015-07-01

    In the three-dimensional (3D) contour measurement, the phase shift profilometry (PSP) method is the most widely used one. However, the measurement speed of PSP is very low because of the multiple projections. In order to improve the measurement speed, color grating stripes are used for measurement in this paper. During the measurement, only one color sinusoidal fringe is projected on the measured object. Therefore, the measurement speed is greatly improved. Since there is coupling or interference phenomenon between the adjacent color grating stripes, a color correction method is used to improve the measurement results. A method for correcting nonlinear error of measurement system is proposed in this paper, and the sinusoidal property of acquired image after correction is better than that before correction. Experimental results show that with these correction methods, the measurement errors can be reduced. Therefore, it can support a good foundation for the high-precision 3D reconstruction.

  18. Development of direct-inverse 3-D methods for applied aerodynamic design and analysis

    NASA Technical Reports Server (NTRS)

    Carlson, Leland A.

    1988-01-01

    Several inverse methods have been compared and initial results indicate that differences in results are primarily due to coordinate systems and fuselage representations and not to design procedures. Further, results from a direct-inverse method that includes 3-D wing boundary layer effects, wake curvature, and wake displacement are presented. These results show that boundary layer displacements must be included in the design process for accurate results.

  19. Combination of photogrammetric and geoelectric methods to assess 3d structures associated to natural hazards

    NASA Astrophysics Data System (ADS)

    Fargier, Yannick; Dore, Ludovic; Antoine, Raphael; Palma Lopes, Sérgio; Fauchard, Cyrille

    2016-04-01

    The extraction of subsurface materials is a key element for the economy of a nation. However, natural degradation of underground quarries is a major issue from an economic and public safety point of view. Consequently, the quarries stakeholders require relevant tools to define hazards associated to these structures. Safety assessment methods of underground quarries are recent and mainly based on rock physical properties. This kind of method leads to a certain homogeneity assumption of pillar internal properties that can cause an underestimation of the risk. Electrical Resistivity Imaging (ERI) is a widely used method that possesses two advantages to overcome this limitation. The first is to provide a qualitative understanding for the detection and monitoring of anomalies in the pillar body (e.g. faults). The second is to provide a quantitative description of the electrical resistivity distribution inside the pillar. This quantitative description can be interpreted with constitutive laws to help decision support (water content decreases the mechanical resistance of a chalk). However, conventional 2D and 3D Imaging techniques are usually applied to flat surface surveys or to surfaces with moderate topography. A 3D inversion of more complex media (case of the pillar) requires a full consideration of the geometry that was never taken into account before. The Photogrammetric technique presents a cost effective solution to obtain an accurate description of the external geometry of a complex media. However, this method has never been fully coupled with a geophysical method to enhance/improve the inversion process. Consequently we developed a complete procedure showing that photogrammetric and ERI tools can be efficiently combined to assess a complex 3D structure. This procedure includes in a first part a photogrammetric survey, a processing stage with an open source software and a post-processing stage finalizing a 3D surface model. The second part necessitates the

  20. 3D hierarchical interface-enriched finite element method: Implementation and applications

    NASA Astrophysics Data System (ADS)

    Soghrati, Soheil; Ahmadian, Hossein

    2015-10-01

    A hierarchical interface-enriched finite element method (HIFEM) is proposed for the mesh-independent treatment of 3D problems with intricate morphologies. The HIFEM implements a recursive algorithm for creating enrichment functions that capture gradient discontinuities in nonconforming finite elements cut by arbitrary number and configuration of materials interfaces. The method enables the mesh-independent simulation of multiphase problems with materials interfaces that are in close proximity or contact while providing a straightforward general approach for evaluating the enrichments. In this manuscript, we present a detailed discussion on the implementation issues and required computational geometry considerations associated with the HIFEM approximation of thermal and mechanical responses of 3D problems. A convergence study is provided to investigate the accuracy and convergence rate of the HIFEM and compare them with standard FEM benchmark solutions. We will also demonstrate the application of this mesh-independent method for simulating the thermal and mechanical responses of two composite materials systems with complex microstructures.

  1. A Method for Sectioning and Immunohistochemical Analysis of Stem Cell-Derived 3-D Organoids.

    PubMed

    Wiley, Luke A; Beebe, David C; Mullins, Robert F; Stone, Edwin M; Tucker, Budd A

    2016-05-12

    This unit describes a protocol for embedding, sectioning, and immunocytochemical analysis of pluripotent stem cell-derived 3-D organoids. Specifically, we describe a method to embed iPSC-derived retinal cups in low-melt agarose, acquire thick sections using a vibratome tissue slicer, and perform immunohistochemical analysis. This method includes an approach for antibody labeling that minimizes the amount of antibody needed for individual experiments and that utilizes large-volume washing to increase the signal-to-noise ratio, allowing for clean, high-resolution imaging of developing cell types. The universal methods described can be employed regardless of the type of pluripotent stem cell used and 3-D organoid generated. © 2016 by John Wiley & Sons, Inc.

  2. A novel adaptive 3D medical image interpolation method based on shape

    NASA Astrophysics Data System (ADS)

    Chen, Jiaxin; Ma, Wei

    2013-03-01

    Image interpolation of cross-sections is one of the key steps of medical visualization. Aiming at the problem of fuzzy boundaries and large amount of calculation, which are brought by the traditional interpolation, a novel adaptive 3-D medical image interpolation method is proposed in this paper. Firstly, the contour is obtained by the edge interpolation, and the corresponding points are found according to the relation of the contour and points on the original images. Secondly, this algorithm utilizes volume relativity to get the best point-pair with the adaptive methods. Finally, the grey value of interpolation pixel is got by the matching point interpolation. The experimental results show that the method presented in the paper not only can meet the requirements of interpolation accuracy, but also can be used effectively in medical image 3D reconstruction.

  3. Finite volume and finite element methods applied to 3D laminar and turbulent channel flows

    SciTech Connect

    Louda, Petr; Příhoda, Jaromír; Sváček, Petr; Kozel, Karel

    2014-12-10

    The work deals with numerical simulations of incompressible flow in channels with rectangular cross section. The rectangular cross section itself leads to development of various secondary flow patterns, where accuracy of simulation is influenced by numerical viscosity of the scheme and by turbulence modeling. In this work some developments of stabilized finite element method are presented. Its results are compared with those of an implicit finite volume method also described, in laminar and turbulent flows. It is shown that numerical viscosity can cause errors of same magnitude as different turbulence models. The finite volume method is also applied to 3D turbulent flow around backward facing step and good agreement with 3D experimental results is obtained.

  4. Standardization based on human factors for 3D display: performance characteristics and measurement methods

    NASA Astrophysics Data System (ADS)

    Uehara, Shin-ichi; Ujike, Hiroyasu; Hamagishi, Goro; Taira, Kazuki; Koike, Takafumi; Kato, Chiaki; Nomura, Toshio; Horikoshi, Tsutomu; Mashitani, Ken; Yuuki, Akimasa; Izumi, Kuniaki; Hisatake, Yuzo; Watanabe, Naoko; Umezu, Naoaki; Nakano, Yoshihiko

    2010-02-01

    We are engaged in international standardization activities for 3D displays. We consider that for a sound development of 3D displays' market, the standards should be based on not only mechanism of 3D displays, but also human factors for stereopsis. However, we think that there is no common understanding on what the 3D display should be and that the situation makes developing the standards difficult. In this paper, to understand the mechanism and human factors, we focus on a double image, which occurs in some conditions on an autostereoscopic display. Although the double image is generally considered as an unwanted effect, we consider that whether the double image is unwanted or not depends on the situation and that there are some allowable double images. We tried to classify the double images into the unwanted and the allowable in terms of the display mechanism and visual ergonomics for stereopsis. The issues associated with the double image are closely related to performance characteristics for the autostereoscopic display. We also propose performance characteristics, measurement and analysis methods to represent interocular crosstalk and motion parallax.

  5. MR diffusion-weighted imaging-based subcutaneous tumour volumetry in a xenografted nude mouse model using 3D Slicer: an accurate and repeatable method

    PubMed Central

    Ma, Zelan; Chen, Xin; Huang, Yanqi; He, Lan; Liang, Cuishan; Liang, Changhong; Liu, Zaiyi

    2015-01-01

    Accurate and repeatable measurement of the gross tumour volume(GTV) of subcutaneous xenografts is crucial in the evaluation of anti-tumour therapy. Formula and image-based manual segmentation methods are commonly used for GTV measurement but are hindered by low accuracy and reproducibility. 3D Slicer is open-source software that provides semiautomatic segmentation for GTV measurements. In our study, subcutaneous GTVs from nude mouse xenografts were measured by semiautomatic segmentation with 3D Slicer based on morphological magnetic resonance imaging(mMRI) or diffusion-weighted imaging(DWI)(b = 0,20,800 s/mm2) . These GTVs were then compared with those obtained via the formula and image-based manual segmentation methods with ITK software using the true tumour volume as the standard reference. The effects of tumour size and shape on GTVs measurements were also investigated. Our results showed that, when compared with the true tumour volume, segmentation for DWI(P = 0.060–0.671) resulted in better accuracy than that mMRI(P < 0.001) and the formula method(P < 0.001). Furthermore, semiautomatic segmentation for DWI(intraclass correlation coefficient, ICC = 0.9999) resulted in higher reliability than manual segmentation(ICC = 0.9996–0.9998). Tumour size and shape had no effects on GTV measurement across all methods. Therefore, DWI-based semiautomatic segmentation, which is accurate and reproducible and also provides biological information, is the optimal GTV measurement method in the assessment of anti-tumour treatments. PMID:26489359

  6. A novel method for the 3-D reconstruction of scoliotic ribs from frontal and lateral radiographs.

    PubMed

    Seoud, Lama; Cheriet, Farida; Labelle, Hubert; Dansereau, Jean

    2011-05-01

    Among the external manifestations of scoliosis, the rib hump, which is associated with the ribs' deformities and rotations, constitutes the most disturbing aspect of the scoliotic deformity for patients. A personalized 3-D model of the rib cage is important for a better evaluation of the deformity, and hence, a better treatment planning. A novel method for the 3-D reconstruction of the rib cage, based only on two standard radiographs, is proposed in this paper. For each rib, two points are extrapolated from the reconstructed spine, and three points are reconstructed by stereo radiography. The reconstruction is then refined using a surface approximation. The method was evaluated using clinical data of 13 patients with scoliosis. A comparison was conducted between the reconstructions obtained with the proposed method and those obtained by using a previous reconstruction method based on two frontal radiographs. A first comparison criterion was the distances between the reconstructed ribs and the surface topography of the trunk, considered as the reference modality. The correlation between ribs axial rotation and back surface rotation was also evaluated. The proposed method successfully reconstructed the ribs of the 6th-12th thoracic levels. The evaluation results showed that the 3-D configuration of the new rib reconstructions is more consistent with the surface topography and provides more accurate measurements of ribs axial rotation.

  7. 3D Imaging of Rapidly Spinning Space Targets Based on a Factorization Method

    PubMed Central

    Bi, Yanxian; Wei, Shaoming; Wang, Jun; Mao, Shiyi

    2017-01-01

    Three-dimensional (3D) imaging of space targets can provide crucial information about the target shape and size, which are significant supports for the application of automatic target classification and recognition. In this paper, a new 3D imaging of space spinning targets via a factorization method is proposed. Firstly, after the translational compensation, the scattering centers two-dimensional (2D) range and range-rate sequence induced by the target spinning is extracted using a high resolution spectral estimation technique. Secondly, measurement data association is implemented to obtain the scattering center trajectory matrix by using a range-Doppler tracker. Then, we use an initial coarse angular velocity to generate the projection matrix, which consists of the scattering centers range and cross-range, and a factorization method is applied iteratively to the projection matrix to estimate the accurate angular velocity. Finally, we use the accurate estimate spinning angular velocity to rescale the projection matrix and the well-scaled target 3D geometry is reconstructed. Compared to the previous literature methods, ambiguity in the spatial axes can be removed by this method. Simulation results have demonstrated the effectiveness and robustness of the proposed method. PMID:28216588

  8. Earthquake source tensor inversion with the gCAP method and 3D Green's functions

    NASA Astrophysics Data System (ADS)

    Zheng, J.; Ben-Zion, Y.; Zhu, L.; Ross, Z.

    2013-12-01

    We develop and apply a method to invert earthquake seismograms for source properties using a general tensor representation and 3D Green's functions. The method employs (i) a general representation of earthquake potency/moment tensors with double couple (DC), compensated linear vector dipole (CLVD), and isotropic (ISO) components, and (ii) a corresponding generalized CAP (gCap) scheme where the continuous wave trains are broken into Pnl and surface waves (Zhu & Ben-Zion, 2013). For comparison, we also use the waveform inversion method of Zheng & Chen (2012) and Ammon et al. (1998). Sets of 3D Green's functions are calculated on a grid of 1 km3 using the 3-D community velocity model CVM-4 (Kohler et al. 2003). A bootstrap technique is adopted to establish robustness of the inversion results using the gCap method (Ross & Ben-Zion, 2013). Synthetic tests with 1-D and 3-D waveform calculations show that the source tensor inversion procedure is reasonably reliable and robust. As initial application, the method is used to investigate source properties of the March 11, 2013, Mw=4.7 earthquake on the San Jacinto fault using recordings of ~45 stations up to ~0.2Hz. Both the best fitting and most probable solutions include ISO component of ~1% and CLVD component of ~0%. The obtained ISO component, while small, is found to be a non-negligible positive value that can have significant implications for the physics of the failure process. Work on using higher frequency data for this and other earthquakes is in progress.

  9. A Quality Assurance Method that Utilizes 3D Dosimetry and Facilitates Clinical Interpretation

    SciTech Connect

    Oldham, Mark; Thomas, Andrew; O'Daniel, Jennifer; Juang, Titania; Ibbott, Geoffrey; Adamovics, John; Kirkpatrick, John P.

    2012-10-01

    Purpose: To demonstrate a new three-dimensional (3D) quality assurance (QA) method that provides comprehensive dosimetry verification and facilitates evaluation of the clinical significance of QA data acquired in a phantom. Also to apply the method to investigate the dosimetric efficacy of base-of-skull (BOS) intensity-modulated radiotherapy (IMRT) treatment. Methods and Materials: Two types of IMRT QA verification plans were created for 6 patients who received BOS IMRT. The first plan enabled conventional 2D planar IMRT QA using the Varian portal dosimetry system. The second plan enabled 3D verification using an anthropomorphic head phantom. In the latter, the 3D dose distribution was measured using the DLOS/Presage dosimetry system (DLOS = Duke Large-field-of-view Optical-CT System, Presage Heuris Pharma, Skillman, NJ), which yielded isotropic 2-mm data throughout the treated volume. In a novel step, measured 3D dose distributions were transformed back to the patient's CT to enable calculation of dose-volume histograms (DVH) and dose overlays. Measured and planned patient DVHs were compared to investigate clinical significance. Results: Close agreement between measured and calculated dose distributions was observed for all 6 cases. For gamma criteria of 3%, 2 mm, the mean passing rate for portal dosimetry was 96.8% (range, 92.0%-98.9%), compared to 94.9% (range, 90.1%-98.9%) for 3D. There was no clear correlation between 2D and 3D passing rates. Planned and measured dose distributions were evaluated on the patient's anatomy, using DVH and dose overlays. Minor deviations were detected, and the clinical significance of these are presented and discussed. Conclusions: Two advantages accrue to the methods presented here. First, treatment accuracy is evaluated throughout the whole treated volume, yielding comprehensive verification. Second, the clinical significance of any deviations can be assessed through the generation of DVH curves and dose overlays on the patient

  10. Enhanced Rgb-D Mapping Method for Detailed 3d Modeling of Large Indoor Environments

    NASA Astrophysics Data System (ADS)

    Tang, Shengjun; Zhu, Qing; Chen, Wu; Darwish, Walid; Wu, Bo; Hu, Han; Chen, Min

    2016-06-01

    RGB-D sensors are novel sensing systems that capture RGB images along with pixel-wise depth information. Although they are widely used in various applications, RGB-D sensors have significant drawbacks with respect to 3D dense mapping of indoor environments. First, they only allow a measurement range with a limited distance (e.g., within 3 m) and a limited field of view. Second, the error of the depth measurement increases with increasing distance to the sensor. In this paper, we propose an enhanced RGB-D mapping method for detailed 3D modeling of large indoor environments by combining RGB image-based modeling and depth-based modeling. The scale ambiguity problem during the pose estimation with RGB image sequences can be resolved by integrating the information from the depth and visual information provided by the proposed system. A robust rigid-transformation recovery method is developed to register the RGB image-based and depth-based 3D models together. The proposed method is examined with two datasets collected in indoor environments for which the experimental results demonstrate the feasibility and robustness of the proposed method

  11. 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

    NASA Astrophysics Data System (ADS)

    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.

  12. Generic precise augmented reality guiding system and its calibration method based on 3D virtual model.

    PubMed

    Liu, Miao; Yang, Shourui; Wang, Zhangying; Huang, Shujun; Liu, Yue; Niu, Zhenqi; Zhang, Xiaoxuan; Zhu, Jigui; Zhang, Zonghua

    2016-05-30

    Augmented reality system can be applied to provide precise guidance for various kinds of manual works. The adaptability and guiding accuracy of such systems are decided by the computational model and the corresponding calibration method. In this paper, a novel type of augmented reality guiding system and the corresponding designing scheme are proposed. Guided by external positioning equipment, the proposed system can achieve high relative indication accuracy in a large working space. Meanwhile, the proposed system is realized with a digital projector and the general back projection model is derived with geometry relationship between digitized 3D model and the projector in free space. The corresponding calibration method is also designed for the proposed system to obtain the parameters of projector. To validate the proposed back projection model, the coordinate data collected by a 3D positioning equipment is used to calculate and optimize the extrinsic parameters. The final projecting indication accuracy of the system is verified with subpixel pattern projecting technique.

  13. A novel 3D constellation-masked method for physical security in hierarchical OFDMA system.

    PubMed

    Zhang, Lijia; Liu, Bo; Xin, Xiangjun; Liu, Deming

    2013-07-01

    This paper proposes a novel 3D constellation-masked method to ensure the physical security in hierarchical optical orthogonal frequency division multiplexing access (OFDMA) system. The 3D constellation masking is executed on the two levels of hierarchical modulation and among different OFDM subcarriers, which is realized by the masking vectors. The Lorenz chaotic model is adopted for the generation of masking vectors in the proposed scheme. A 9.85 Gb/s encrypted hierarchical QAM OFDM signal is successfully demonstrated in the experiment. The performance of illegal optical network unit (ONU) with different masking vectors is also investigated. The proposed method is demonstrated to be secure and efficient against the commonly known attacks in the experiment.

  14. Investigating the Bag-of-Words Method for 3D Shape Retrieval

    NASA Astrophysics Data System (ADS)

    Li, Xiaolan; Godil, Afzal

    2010-12-01

    This paper investigates the capabilities of the Bag-of-Words (BWs) method in the 3D shape retrieval field. The contributions of this paper are (1) the 3D shape retrieval task is categorized from different points of view: specific versus generic, partial-to-global retrieval (PGR) versus global-to-global retrieval (GGR), and articulated versus nonarticulated (2) the spatial information, represented as concentric spheres, is integrated into the framework to improve the discriminative ability (3) the analysis of the experimental results on Purdue Engineering Benchmark (PEB) reveals that some properties of the BW approach make it perform better on the PGR task than the GGR task (4) the BW approach is evaluated on nonarticulated database PEB and articulated database McGill Shape Benchmark (MSB) and compared to other methods.

  15. Comparison of parabolic filtration methods for 3D filtered back projection in pulsed EPR imaging

    NASA Astrophysics Data System (ADS)

    Qiao, Zhiwei; Redler, Gage; Epel, Boris; Halpern, Howard J.

    2014-11-01

    Pulse electron paramagnetic resonance imaging (Pulse EPRI) is a robust method for noninvasively measuring local oxygen concentrations in vivo. For 3D tomographic EPRI, the most commonly used reconstruction algorithm is filtered back projection (FBP), in which the parabolic filtration process strongly influences image quality. In this work, we designed and compared 7 parabolic filtration methods to reconstruct both simulated and real phantoms. To evaluate these methods, we designed 3 error criteria and 1 spatial resolution criterion. It was determined that the 2 point derivative filtration method and the two-ramp-filter method have unavoidable negative effects resulting in diminished spatial resolution and increased artifacts respectively. For the noiseless phantom the rectangular-window parabolic filtration method and sinc-window parabolic filtration method were found to be optimal, providing high spatial resolution and small errors. In the presence of noise, the 3 point derivative method and Hamming-window parabolic filtration method resulted in the best compromise between low image noise and high spatial resolution. The 3 point derivative method is faster than Hamming-window parabolic filtration method, so we conclude that the 3 point derivative method is optimal for 3D FBP.

  16. CONTINUOUS-ENERGY MONTE CARLO METHODS FOR CALCULATING GENERALIZED RESPONSE SENSITIVITIES USING TSUNAMI-3D

    SciTech Connect

    Perfetti, Christopher M; Rearden, Bradley T

    2014-01-01

    This work introduces a new approach for calculating sensitivity coefficients for generalized neutronic responses to nuclear data uncertainties using continuous-energy Monte Carlo methods. The approach presented in this paper, known as the GEAR-MC method, allows for the calculation of generalized sensitivity coefficients for multiple responses in a single Monte Carlo calculation with no nuclear data perturbations or knowledge of nuclear covariance data. The theory behind the GEAR-MC method is presented here, and proof of principle is demonstrated by using the GEAR-MC method to calculate sensitivity coefficients for responses in several 3D, continuous-energy Monte Carlo applications.

  17. A Method for 3D Histopathology Reconstruction Supporting Mouse Microvasculature Analysis

    PubMed Central

    Xu, Yiwen; Pickering, J. Geoffrey; Nong, Zengxuan; Gibson, Eli; Arpino, John-Michael; Yin, Hao; Ward, Aaron D.

    2015-01-01

    Structural abnormalities of the microvasculature can impair perfusion and function. Conventional histology provides good spatial resolution with which to evaluate the microvascular structure but affords no 3-dimensional information; this limitation could lead to misinterpretations of the complex microvessel network in health and disease. The objective of this study was to develop and evaluate an accurate, fully automated 3D histology reconstruction method to visualize the arterioles and venules within the mouse hind-limb. Sections of the tibialis anterior muscle from C57BL/J6 mice (both normal and subjected to femoral artery excision) were reconstructed using pairwise rigid and affine registrations of 5 µm-thick, paraffin-embedded serial sections digitized at 0.25 µm/pixel. Low-resolution intensity-based rigid registration was used to initialize the nucleus landmark-based registration, and conventional high-resolution intensity-based registration method. The affine nucleus landmark-based registration was developed in this work and was compared to the conventional affine high-resolution intensity-based registration method. Target registration errors were measured between adjacent tissue sections (pairwise error), as well as with respect to a 3D reference reconstruction (accumulated error, to capture propagation of error through the stack of sections). Accumulated error measures were lower (p<0.01) for the nucleus landmark technique and superior vasculature continuity was observed. These findings indicate that registration based on automatic extraction and correspondence of small, homologous landmarks may support accurate 3D histology reconstruction. This technique avoids the otherwise problematic “banana-into-cylinder” effect observed using conventional methods that optimize the pairwise alignment of salient structures, forcing them to be section-orthogonal. This approach will provide a valuable tool for high-accuracy 3D histology tissue reconstructions for

  18. Ultrasonic 3-D Vector Flow Method for Quantitative In Vivo Peak Velocity and Flow Rate Estimation.

    PubMed

    Holbek, Simon; Ewertsen, Caroline; Bouzari, Hamed; Pihl, Michael Johannes; Hansen, Kristoffer Lindskov; Stuart, Matthias Bo; Thomsen, Carsten; Nielsen, Michael Bachmann; Jensen, Jorgen Arendt

    2017-03-01

    Current clinical ultrasound (US) systems are limited to show blood flow movement in either 1-D or 2-D. In this paper, a method for estimating 3-D vector velocities in a plane using the transverse oscillation method, a 32×32 element matrix array, and the experimental US scanner SARUS is presented. The aim of this paper is to estimate precise flow rates and peak velocities derived from 3-D vector flow estimates. The emission sequence provides 3-D vector flow estimates at up to 1.145 frames/s in a plane, and was used to estimate 3-D vector flow in a cross-sectional image plane. The method is validated in two phantom studies, where flow rates are measured in a flow-rig, providing a constant parabolic flow, and in a straight-vessel phantom ( ∅=8 mm) connected to a flow pump capable of generating time varying waveforms. Flow rates are estimated to be 82.1 ± 2.8 L/min in the flow-rig compared with the expected 79.8 L/min, and to 2.68 ± 0.04 mL/stroke in the pulsating environment compared with the expected 2.57 ± 0.08 mL/stroke. Flow rates estimated in the common carotid artery of a healthy volunteer are compared with magnetic resonance imaging (MRI) measured flow rates using a 1-D through-plane velocity sequence. Mean flow rates were 333 ± 31 mL/min for the presented method and 346 ± 2 mL/min for the MRI measurements.

  19. A harmonic polynomial cell (HPC) method for 3D Laplace equation with application in marine hydrodynamics

    SciTech Connect

    Shao, Yan-Lin Faltinsen, Odd M.

    2014-10-01

    We propose a new efficient and accurate numerical method based on harmonic polynomials to solve boundary value problems governed by 3D Laplace equation. The computational domain is discretized by overlapping cells. Within each cell, the velocity potential is represented by the linear superposition of a complete set of harmonic polynomials, which are the elementary solutions of Laplace equation. By its definition, the method is named as Harmonic Polynomial Cell (HPC) method. The characteristics of the accuracy and efficiency of the HPC method are demonstrated by studying analytical cases. Comparisons will be made with some other existing boundary element based methods, e.g. Quadratic Boundary Element Method (QBEM) and the Fast Multipole Accelerated QBEM (FMA-QBEM) and a fourth order Finite Difference Method (FDM). To demonstrate the applications of the method, it is applied to some studies relevant for marine hydrodynamics. Sloshing in 3D rectangular tanks, a fully-nonlinear numerical wave tank, fully-nonlinear wave focusing on a semi-circular shoal, and the nonlinear wave diffraction of a bottom-mounted cylinder in regular waves are studied. The comparisons with the experimental results and other numerical results are all in satisfactory agreement, indicating that the present HPC method is a promising method in solving potential-flow problems. The underlying procedure of the HPC method could also be useful in other fields than marine hydrodynamics involved with solving Laplace equation.

  20. Topographical surveys: Classical method versus 3D laser scanning. Case study - An application in civil engineering

    NASA Astrophysics Data System (ADS)

    Grigoraş, I.-R.; Covăsnianu, A.; Pleşu, G.; Benedict, B.

    2009-04-01

    The paper describes an experiment which took place in Iasi town, Romania, consisted in two different topographical survey techniques applied for one and the same objective placed in a block within the city (western part) - a thermal power station. The purpose was to compare those methods and to determine which one is proper to be used in this domain in terms of fastness, optimization and speed of data processing. First technique applied for our survey was the classical one, with a total station. Using the CAD technique, we obtained a final product (a dwg file) and a list of coordinates (a text file). The second method, which we focused our attention more, was the measurement with a very precise 3D laser scanstation, also very suitable in archeology. The data obtained were processed with special software. Result was a 3D model of the thermal power plant composed of measurable cloud point data. Finally, analyzing the advantages and disadvantages of each method, we came to the conclusion that the 3D laser scanning which we used matches well the application, in this case civil engineering, but the future of accepting and implementing this technique is in the hands of Romanian authorities.

  1. A new combined prior based reconstruction method for compressed sensing in 3D ultrasound imaging

    NASA Astrophysics Data System (ADS)

    Uddin, Muhammad S.; Islam, Rafiqul; Tahtali, Murat; Lambert, Andrew J.; Pickering, Mark R.

    2015-03-01

    Ultrasound (US) imaging is one of the most popular medical imaging modalities, with 3D US imaging gaining popularity recently due to its considerable advantages over 2D US imaging. However, as it is limited by long acquisition times and the huge amount of data processing it requires, methods for reducing these factors have attracted considerable research interest. Compressed sensing (CS) is one of the best candidates for accelerating the acquisition rate and reducing the data processing time without degrading image quality. However, CS is prone to introduce noise-like artefacts due to random under-sampling. To address this issue, we propose a combined prior-based reconstruction method for 3D US imaging. A Laplacian mixture model (LMM) constraint in the wavelet domain is combined with a total variation (TV) constraint to create a new regularization regularization prior. An experimental evaluation conducted to validate our method using synthetic 3D US images shows that it performs better than other approaches in terms of both qualitative and quantitative measures.

  2. Embedded 3D shape measurement system based on a novel spatio-temporal coding method

    NASA Astrophysics Data System (ADS)

    Xu, Bin; Tian, Jindong; Tian, Yong; Li, Dong

    2016-11-01

    Structured light measurement has been wildly used since 1970s in industrial component detection, reverse engineering, 3D molding, robot navigation, medical and many other fields. In order to satisfy the demand for high speed, high precision and high resolution 3-D measurement for embedded system, a new patterns combining binary and gray coding principle in space are designed and projected onto the object surface orderly. Each pixel corresponds to the designed sequence of gray values in time - domain, which is treated as a feature vector. The unique gray vector is then dimensionally reduced to a scalar which could be used as characteristic information for binocular matching. In this method, the number of projected structured light patterns is reduced, and the time-consuming phase unwrapping in traditional phase shift methods is avoided. This algorithm is eventually implemented on DM3730 embedded system for 3-D measuring, which consists of an ARM and a DSP core and has a strong capability of digital signal processing. Experimental results demonstrated the feasibility of the proposed method.

  3. A variable flip angle-based method for reducing blurring in 3D GRASE ASL

    NASA Astrophysics Data System (ADS)

    Liang, Xiaoyun; Connelly, Alan; Tournier, Jacques-Donald; Calamante, Fernando

    2014-09-01

    Arterial Spin Labeling (ASL) is an MRI technique to measure cerebral blood flow directly and noninvasively, and thus provides a more direct quantitative correlate of neural activity than blood-oxygen-level-dependent fMRI. A 3D gradient and spin-echo (GRASE) sequence is capable of enhancing signal-to-noise ratio, and has been shown to be a very useful readout module for ASL sequences. Nonetheless, the introduction of significant blurring in its single-shot version, due to T2 decay along the partition dimension, compromises the achievable spatial resolution, limiting the potential of this technique for whole-brain coverage. To address this issue, a method for reducing blurring based on a variable flip angle (VFA) scheme is proposed in this study for 3D GRASE ASL perfusion. Numerical simulations show that the proposed method is capable of reducing the blurring significantly compared to the standard constant flip angle approach; this result was further confirmed using in vivo data. The proposed VFA method should therefore be of significance to 3D GRASE ASL fMRI studies, since it is able to reduce blurring without sacrificing temporal resolution.

  4. Validation of 3D Seismic Velocity Models Using the Spectral Element Method

    NASA Astrophysics Data System (ADS)

    Maceira, M.; Larmat, C. S.; Porritt, R. W.; Higdon, D.; Allen, R. M.

    2012-12-01

    For over a decade now, many research institutions have been focusing on addressing the Earth's 3D heterogeneities and complexities by improving tomographic methods. Utilizing dense array datasets, these efforts have led to unprecedented 3D seismic images, but little is done in terms of model validation or to provide any absolute assessment of model uncertainty. Furthermore, the question of "How good is a 3D geophysical model at representing the Earth's true physics? " remains largely not addressed in a time when 3D Earth models are used for societal and energy security. In the last few years, new horizons have opened up in earth structure imaging, with the advent of new numerical and mathematical methods in computational seismology and statistical sciences. We use these methods to tackle the question of model validation taking advantage of unique and extensive High Performance Computing resources available at Los Alamos National Laboratory. We present results from a study focused on validating 3D models for the Western USA generated using both ray-theoretical and finite-frequency approximations. In this manner we do not validate just the model but also the imaging technique. For this test case, we utilize the Dynamic North America (DNA) model family of UC Berkeley, as they are readily available in both formulations. We evaluate model performances by comparing observed and synthetic seismograms generated using the Spectral Element Method. Results show that both, finite-frequency and ray-theoretical DNA09 models, predict the observations well. Waveform cross-correlation coefficients show a difference in performance between models obtained with the finite-frequency or ray-theory limited to smallest periods (<15s), with no perceptible difference at longer periods (50-200s). At those shortest periods, and based on statistical analyses on S-wave phase delay measurements, finite-frequency shows an improvement over ray theory. We are also investigating the breakdown of ray

  5. A Mortar Segment-to-Segment Frictional Contact Method for Large Deformations

    SciTech Connect

    Puso, M; Laursen, T

    2003-10-29

    Contact modeling is still one of the most difficult aspects of nonlinear implicit structural analysis. Most 3D contact algorithms employed today use node-on-segment approaches for contacting dissimilar meshes. Two pass node-on-segment contact approaches have the well known deficiency of locking due to over constraint. Furthermore, node-on-segment approaches suffer when individual nodes slide out of contact at contact surface boundaries or when contacting nodes slide from facet to facet. This causes jumps in the contact forces due to the discrete nature of the constraint enforcement and difficulties in convergence for implicit solution techniques. In a previous work, we developed a segment-to-segment contact approach based on the mortar method that was applicable to large deformation mechanics. The approach proved extremely robust since it eliminated the overconstraint which caused ''locking'' and provided smooth force variations in large sliding. Here, we extend this previous approach in to treat frictional contact problems. The proposed approach is then applied to several challenging frictional contact problems which demonstrate its effectiveness.

  6. A 3D front tracking method on a CPU/GPU system

    SciTech Connect

    Bo, Wurigen; Grove, John

    2011-01-21

    We describe the method to port a sequential 3D interface tracking code to a GPU with CUDA. The interface is represented as a triangular mesh. Interface geometry properties and point propagation are performed on a GPU. Interface mesh adaptation is performed on a CPU. The convergence of the method is assessed from the test problems with given velocity fields. Performance results show overall speedups from 11 to 14 for the test problems under mesh refinement. We also briefly describe our ongoing work to couple the interface tracking method with a hydro solver.

  7. Wound Measurement Techniques: Comparing the Use of Ruler Method, 2D Imaging and 3D Scanner.

    PubMed

    Shah, Aj; Wollak, C; Shah, J B

    2013-12-01

    The statistics on the growing number of non-healing wounds is alarming. In the United States, chronic wounds affect 6.5 million patients. An estimated US $25 billion is spent annually on treatment of chronic wounds and the burden is rapidly growing due to increasing health care costs, an aging population and a sharp rise in the incidence of diabetes and obesity worldwide.(1) Accurate wound measurement techniques will help health care personnel to monitor the wounds which will indirectly help improving care.(7,9) The clinical practice of measuring wounds has not improved even today.(2,3) A common method like the ruler method to measure wounds has poor interrater and intrarater reliability.(2,3) Measuring the greatest length by the greatest width perpendicular to the greatest length, the perpendicular method, is more valid and reliable than other ruler based methods.(2) Another common method like acetate tracing is more accurate than the ruler method but still has its disadvantages. These common measurement techniques are time consuming with variable inaccuracies. In this study, volumetric measurements taken with a non-contact 3-D scanner are benchmarked against the common ruler method, acetate grid tracing, and 2-D image planimetry volumetric measurement technique. A liquid volumetric fill method is used as the control volume. Results support the hypothesis that the 3-D scanner consistently shows accurate volumetric measurements in comparison to standard volumetric measurements obtained by the waterfill technique (average difference of 11%). The 3-D scanner measurement technique was found more reliable and valid compared to other three techniques, the ruler method (average difference of 75%), acetate grid tracing (average difference of 41%), and 2D planimetric measurements (average difference of 52%). Acetate tracing showed more accurate measurements compared to the ruler method (average difference of 41% (acetate tracing) compared to 75% (ruler method)). Improving

  8. Wound Measurement Techniques: Comparing the Use of Ruler Method, 2D Imaging and 3D Scanner

    PubMed Central

    Shah, Aj; Wollak, C.; Shah, J.B.

    2015-01-01

    The statistics on the growing number of non-healing wounds is alarming. In the United States, chronic wounds affect 6.5 million patients. An estimated US $25 billion is spent annually on treatment of chronic wounds and the burden is rapidly growing due to increasing health care costs, an aging population and a sharp rise in the incidence of diabetes and obesity worldwide.1 Accurate wound measurement techniques will help health care personnel to monitor the wounds which will indirectly help improving care.7,9 The clinical practice of measuring wounds has not improved even today.2,3 A common method like the ruler method to measure wounds has poor interrater and intrarater reliability.2,3 Measuring the greatest length by the greatest width perpendicular to the greatest length, the perpendicular method, is more valid and reliable than other ruler based methods.2 Another common method like acetate tracing is more accurate than the ruler method but still has its disadvantages. These common measurement techniques are time consuming with variable inaccuracies. In this study, volumetric measurements taken with a non-contact 3-D scanner are benchmarked against the common ruler method, acetate grid tracing, and 2-D image planimetry volumetric measurement technique. A liquid volumetric fill method is used as the control volume. Results support the hypothesis that the 3-D scanner consistently shows accurate volumetric measurements in comparison to standard volumetric measurements obtained by the waterfill technique (average difference of 11%). The 3-D scanner measurement technique was found more reliable and valid compared to other three techniques, the ruler method (average difference of 75%), acetate grid tracing (average difference of 41%), and 2D planimetric measurements (average difference of 52%). Acetate tracing showed more accurate measurements compared to the ruler method (average difference of 41% (acetate tracing) compared to 75% (ruler method)). Improving the

  9. On 3-D inelastic analysis methods for hot section components. Volume 1: Special finite element models

    NASA Technical Reports Server (NTRS)

    Nakazawa, S.

    1988-01-01

    This annual status report presents the results of work performed during the fourth year of the 3-D Inelastic Analysis Methods for Hot Section Components program (NASA Contract NAS3-23697). The objective of the program is to produce a series of new computer codes permitting more accurate and efficient 3-D analysis of selected hot section components, i.e., combustor liners, turbine blades and turbine vanes. The computer codes embody a progression of math models and are streamlined to take advantage of geometrical features, loading conditions, and forms of material response that distinguish each group of selected components. Volume 1 of this report discusses the special finite element models developed during the fourth year of the contract.

  10. OPTIMIZATION OF 3-D IMAGE-GUIDED NEAR INFRARED SPECTROSCOPY USING BOUNDARY ELEMENT METHOD

    PubMed Central

    Srinivasan, Subhadra; Carpenter, Colin; Pogue, Brian W.; Paulsen, Keith D.

    2010-01-01

    Multimodality imaging systems combining optical techniques with MRI/CT provide high-resolution functional characterization of tissue by imaging molecular and vascular biomarkers. To optimize these hybrid systems for clinical use, faster and automatable algorithms are required for 3-D imaging. Towards this end, a boundary element model was used to incorporate tissue boundaries from MRI/CT into image formation process. This method uses surface rendering to describe light propagation in 3-D using diffusion equation. Parallel computing provided speedup of up to 54% in time of computation. Simulations showed that location of NIRS probe was crucial for quantitatively accurate estimation of tumor response. A change of up to 61% was seen between cycles 1 and 3 in monitoring tissue response to neoadjuvant chemotherapy. PMID:20523751

  11. A Novel 2D-to-3D Video Conversion Method Using Time-Coherent Depth Maps

    PubMed Central

    Yin, Shouyi; Dong, Hao; Jiang, Guangli; Liu, Leibo; Wei, Shaojun

    2015-01-01

    In this paper, we propose a novel 2D-to-3D video conversion method for 3D entertainment applications. 3D entertainment is getting more and more popular and can be found in many contexts, such as TV and home gaming equipment. 3D image sensors are a new method to produce stereoscopic video content conveniently and at a low cost, and can thus meet the urgent demand for 3D videos in the 3D entertaiment market. Generally, 2D image sensor and 2D-to-3D conversion chip can compose a 3D image sensor. Our study presents a novel 2D-to-3D video conversion algorithm which can be adopted in a 3D image sensor. In our algorithm, a depth map is generated by combining global depth gradient and local depth refinement for each frame of 2D video input. Global depth gradient is computed according to image type while local depth refinement is related to color information. As input 2D video content consists of a number of video shots, the proposed algorithm reuses the global depth gradient of frames within the same video shot to generate time-coherent depth maps. The experimental results prove that this novel method can adapt to different image types, reduce computational complexity and improve the temporal smoothness of generated 3D video. PMID:26131674

  12. A compact robotic apparatus and method for 3-D ultrasound guided prostate therapy

    NASA Astrophysics Data System (ADS)

    Bax, Jeffrey; Gardi, Lori; Montreuil, Jacques; Smith, David; Fenster, Aaron

    2007-03-01

    Ultrasound imaging has revolutionized the treatment of prostate cancer by producing increasingly accurate models of the prostate and influencing sophisticated targeting procedures for the insertion of radioactive seeds during brachytherapy. Three-dimensional (3D) ultrasound imaging, which allows 3D models of the prostate to be constructed from a series of two-dimensional images, helps to accurately target and implant seeds into the prostate. We have developed a compact robotic apparatus, as well as an effective method for guiding and controlling the insertion of transperineal needles into the prostate. This device has been designed to accurately guide a needle in 3D space so that the needle can be inserted into the prostate at an angle that does not interfere with the pubic arch. The physician can adjust manually or automatically the position of the apparatus in order to place several radioactive seeds into the prostate at designated target locations. Because many physicians are wary of conducting robotic surgical procedures, the apparatus has been developed so that the physician can position the needle for manual insertion and apply a method for manually releasing the needle without damaging the apparatus or endangering the patient.

  13. 3D In Vitro Model for Breast Cancer Research Using Magnetic Levitation and Bioprinting Method.

    PubMed

    Leonard, Fransisca; Godin, Biana

    2016-01-01

    Tumor microenvironment composition and architecture are known as a major factor in orchestrating the tumor growth and its response to various therapies. In this context, in vivo studies are necessary to evaluate the responses. However, while tumor cells can be of human origin, tumor microenvironment in the in vivo models is host-based. On the other hand, in vitro studies in a flat monoculture of tumor cells (the most frequently used in vitro tumor model) are unable to recapitulate the complexity of tumor microenvironment. Three-dimensional (3D) in vitro cell cultures of tumor cells have been proven to be an important experimental tool in understanding mechanisms of tumor growth, response to therapeutics, and transport of nutrients/drugs. We have recently described a novel tool to create 3D co-cultures of tumor cells and cells in the tumor microenvironment. Our method utilizes magnetic manipulation/levitation of the specific ratios of tumor cells and cells in the tumor microenvironment (from human or animal origin) aiding in the formation of tumor spheres with defined cellular composition and density, as quickly as within 24 h. This chapter describes the experimental protocols developed to model the 3D structure of the cancer environment using the above method.

  14. 3D in vitro model for breast cancer research using magnetic levitation and bioprinting method

    PubMed Central

    Leonard, Fransisca; Godin, Biana

    2016-01-01

    Summary Tumor microenvironment composition and architecture are known as a major factor in orchestrating the tumor growth and its response to various therapies. In this context, in vivo studies are necessary to evaluate the responses. However, while tumor cells can be of human origin, tumor microenvironment in the in vivo models is host-based. On the other hand, in vitro studies in a flat monoculture of tumor cells (the most frequently used in vitro tumor model) are unable to recapitulate the complexity of tumor microenvironment. Three-dimensional (3D) in vitro cell cultures of tumor cells have been proven to be an important experimental tool in understanding mechanisms of tumor growth, response to therapeutics and transport of nutrients/drugs. We have recently described a novel tool to create 3D co-cultures of tumor cells and cells in the tumor microenvironment. Our method utilizes magnetic manipulation/levitation of the specific ratios of tumor cells and cells in the tumor microenvironment (from human or animal origin) aiding in the formation of tumor spheres with defined cellular composition and density, as quickly as within 24 hours. This chapter describes the experimental protocols developed to model the 3D structure of the cancer environment using the above method. PMID:26820961

  15. Comparing a novel automatic 3D method for LGE-CMR quantification of scar size with established methods.

    PubMed

    Woie, Leik; Måløy, Frode; Eftestøl, Trygve; Engan, Kjersti; Edvardsen, Thor; Kvaløy, Jan Terje; Ørn, Stein

    2014-02-01

    Current methods for the estimation of infarct size by late-enhanced cardiac magnetic imaging are based upon 2D analysis that first determines the size of the infarction in each slice, and thereafter adds the infarct sizes from each slice to generate a volume. We present a novel, automatic 3D method that estimates infarct size by a simultaneous analysis of all pixels from all slices. In a population of 54 patients with ischemic scars, the infarct size estimated by the automatic 3D method was compared with four established 2D methods. The new 3D method defined scar as the sum of all pixels with signal intensity (SI) ≥35 % of max SI from the complete myocardium, border zone: SI 35-50 % of max SI and core as SI ≥50 % of max SI. The 3D method yielded smaller infarct size (-2.8 ± 2.3 %) and core size (-3.0 ± 1.7 %) than the 2D method most similar to ours. There was no difference in the size of the border zone (0.2 ± 1.4 %). The 3D method demonstrated stronger correlations between scar size and left ventricular (LV) remodelling parameters (LV ejection fraction: r = -0.71, p < 0.0005, LV end-diastolic index: r = 0.54, p < 0.0005, and LV end-systolic index: r = 0.59, p < 0.0005) compared with conventional 2D methods. Infarct size estimation by our novel 3D automatic method is without the need for manual demarcation of the scar; it is less time-consuming and has a stronger correlation with remodelling parameters compared with existing methods.

  16. A Cost-Effective Method to Assemble Biomimetic 3D Cell Culture Platforms

    PubMed Central

    Khalil, Sabreen; El-Badri, Nagwa; El-Mokhtaar, Mohamed; Al-Mofty, Saif; Farghaly, Mohamed; Ayman, Radwa; Habib, Dina; Mousa, Noha

    2016-01-01

    Developing effective stem cell based therapies requires the design of complex in vitro culture systems for more accurate representation of the stem cell niche. Attempts to improve conventional cell culture platforms include the use of biomaterial coated culture plates, sphere culture, microfluidic systems and bioreactors. Most of these platforms are not cost-effective, require industrial technical expertise to fabricate, and remain too simplistic compared to the physiological cell niche. The human amniotic membrane (hAM) has been used successfully in clinical grafting applications due to its unique biological composition and regenerative properties. In this study, we present a combinatorial platform that integrates the hAM with biomolecular, topographic and mechanical cues in one versatile model. Methods We utilized the hAM to provide the biological and the three dimensional (3D) topographic components of the prototype. The 3D nano-roughness of the hAM was characterized using surface electron microscopy and surface image analysis (ImageJ and SurfaceJ). We developed additional macro-scale and micro-scale versions of the platform which provided additional shear stress factors to simulate the fluid dynamics of the in vivo extracellular fluids. Results Three models of varying complexities of the prototype were assembled. A well-defined 3D surface modulation of the hAM in comparable to commercial 3D biomaterial culture substrates was achieved without complex fabrication and with significantly lower cost. Performance of the prototype was demonstrated through culture of primary human umbilical cord mononuclear blood cells (MNCs), human bone marrow mesenchymal stem cell line (hBMSC), and human breast cancer tissue. Conclusion This study presents methods of assembling an integrated, flexible and low cost biomimetic cell culture platform for diverse cell culture applications. PMID:27935982

  17. 2D and 3D visualization methods of endoscopic panoramic bladder images

    NASA Astrophysics Data System (ADS)

    Behrens, Alexander; Heisterklaus, Iris; Müller, Yannick; Stehle, Thomas; Gross, Sebastian; Aach, Til

    2011-03-01

    While several mosaicking algorithms have been developed to compose endoscopic images of the internal urinary bladder wall into panoramic images, the quantitative evaluation of these output images in terms of geometrical distortions have often not been discussed. However, the visualization of the distortion level is highly desired for an objective image-based medical diagnosis. Thus, we present in this paper a method to create quality maps from the characteristics of transformation parameters, which were applied to the endoscopic images during the registration process of the mosaicking algorithm. For a global first view impression, the quality maps are laid over the panoramic image and highlight image regions in pseudo-colors according to their local distortions. This illustration supports then surgeons to identify geometrically distorted structures easily in the panoramic image, which allow more objective medical interpretations of tumor tissue in shape and size. Aside from introducing quality maps in 2-D, we also discuss a visualization method to map panoramic images onto a 3-D spherical bladder model. Reference points are manually selected by the surgeon in the panoramic image and the 3-D model. Then the panoramic image is mapped by the Hammer-Aitoff equal-area projection onto the 3-D surface using texture mapping. Finally the textured bladder model can be freely moved in a virtual environment for inspection. Using a two-hemisphere bladder representation, references between panoramic image regions and their corresponding space coordinates within the bladder model are reconstructed. This additional spatial 3-D information thus assists the surgeon in navigation, documentation, as well as surgical planning.

  18. 3D multi-object segmentation of cardiac MSCT imaging by using a multi-agent approach.

    PubMed

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

    2007-01-01

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

  19. 3D Multi-Object Segmentation of Cardiac MSCT Imaging by using a Multi-Agent Approach

    PubMed Central

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

    2007-01-01

    We propose a new technique for general purpose, semi-interactive and multi-object segmentation in N-dimensional images, applied to the extraction of cardiac structures in MultiSlice Computed Tomography (MSCT) imaging. The proposed approach makes use of a multi-agent scheme combined with a supervised classification methodology allowing the introduction of a priori information and presenting fast computing times. The multi-agent system is organised around a communicating agent which manages a population of situated agents which segment the image through cooperative and competitive interactions. The proposed technique has been tested on several patient data sets. Some typical results are finally presented and discussed. PMID:18003382

  20. Reconstruction of three-dimensional grain structure in polycrystalline iron via an interactive segmentation method

    NASA Astrophysics Data System (ADS)

    Feng, Min-nan; Wang, Yu-cong; Wang, Hao; Liu, Guo-quan; Xue, Wei-hua

    2017-03-01

    Using a total of 297 segmented sections, we reconstructed the three-dimensional (3D) structure of pure iron and obtained the largest dataset of 16254 3D complete grains reported to date. The mean values of equivalent sphere radius and face number of pure iron were observed to be consistent with those of Monte Carlo simulated grains, phase-field simulated grains, Ti-alloy grains, and Ni-based super alloy grains. In this work, by finding a balance between automatic methods and manual refinement, we developed an interactive segmentation method to segment serial sections accurately in the reconstruction of the 3D microstructure; this approach can save time as well as substantially eliminate errors. The segmentation process comprises four operations: image preprocessing, breakpoint detection based on mathematical morphology analysis, optimized automatic connection of the breakpoints, and manual refinement by artificial evaluation.

  1. 3-D surface profilometry based on modulation measurement by applying wavelet transform method

    NASA Astrophysics Data System (ADS)

    Zhong, Min; Chen, Feng; Xiao, Chao; Wei, Yongchao

    2017-01-01

    A new analysis of 3-D surface profilometry based on modulation measurement technique by the application of Wavelet Transform method is proposed. As a tool excelling for its multi-resolution and localization in the time and frequency domains, Wavelet Transform method with good localized time-frequency analysis ability and effective de-noizing capacity can extract the modulation distribution more accurately than Fourier Transform method. Especially for the analysis of complex object, more details of the measured object can be well remained. In this paper, the theoretical derivation of Wavelet Transform method that obtains the modulation values from a captured fringe pattern is given. Both computer simulati