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Sample records for 3d segmentation algorithm

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

    PubMed

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

    2011-10-01

    Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling. PMID:21908904

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

    NASA Astrophysics Data System (ADS)

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

    2011-10-01

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

  3. 3D MRI brain image segmentation based on region restricted EM algorithm

    NASA Astrophysics Data System (ADS)

    Li, Zhong; Fan, Jianping

    2008-03-01

    This paper presents a novel algorithm of 3D human brain tissue segmentation and classification in magnetic resonance image (MRI) based on region restricted EM algorithm (RREM). The RREM is a level set segmentation method while the evolution of the contours was driven by the force field composed by the probability density functions of the Gaussian models. Each tissue is modeled by one or more Gaussian models restricted by free shaped contour so that the Gaussian models are adaptive to the local intensities. The RREM is guaranteed to be convergency and achieving the local minimum. The segmentation avoids to be trapped in the local minimum by the split and merge operation. A fuzzy rule based classifier finally groups the regions belonging to the same tissue and forms the segmented 3D image of white matter (WM) and gray matter (GM) which are of major interest in numerous applications. The presented method can be extended to segment brain images with tumor or the images having part of the brain removed with the adjusted classifier.

  4. 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.

  5. Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.

    PubMed

    Bernard, Olivier; Bosch, Johan G; Heyde, Brecht; Alessandrini, Martino; Barbosa, Daniel; Camarasu-Pop, Sorina; Cervenansky, Frederic; Valette, Sebastien; Mirea, Oana; Bernier, Michel; Jodoin, Pierre-Marc; Domingos, Jaime Santo; Stebbing, Richard V; Keraudren, Kevin; Oktay, Ozan; Caballero, Jose; Shi, Wei; Rueckert, Daniel; Milletari, Fausto; Ahmadi, Seyed-Ahmad; Smistad, Erik; Lindseth, Frank; van Stralen, Maartje; Wang, Chen; Smedby, Orjan; Donal, Erwan; Monaghan, Mark; Papachristidis, Alex; Geleijnse, Marcel L; Galli, Elena; D'hooge, Jan

    2016-04-01

    Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions. PMID:26625409

  6. Simulation of 3D MRI brain images for quantitative evaluation of image segmentation algorithms

    NASA Astrophysics Data System (ADS)

    Wagenknecht, Gudrun; Kaiser, Hans-Juergen; Obladen, Thorsten; Sabri, Osama; Buell, Udalrich

    2000-06-01

    To model the true shape of MRI brain images, automatically classified T1-weighted 3D MRI images (gray matter, white matter, cerebrospinal fluid, scalp/bone and background) are utilized for simulation of grayscale data and imaging artifacts. For each class, Gaussian distribution of grayscale values is assumed, and mean and variance are computed from grayscale images. A random generator fills up the class images with Gauss-distributed grayscale values. Since grayscale values of neighboring voxels are not correlated, a Gaussian low-pass filtering is done, preserving class region borders. To simulate anatomical variability, a Gaussian distribution in space with user-defined mean and variance can be added at any user-defined position. Several imaging artifacts can be added: (1) to simulate partial volume effects, every voxel is averaged with neighboring voxels if they have a different class label; (2) a linear or quadratic bias field can be added with user-defined strength and orientation; (3) additional background noise can be added; and (4) artifacts left over after spoiling can be simulated by adding a band with increasing/decreasing grayscale values. With this method, realistic-looking simulated MRI images can be produced to test classification and segmentation algorithms regarding accuracy and robustness even in the presence of artifacts.

  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. 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. PMID:27597960

  9. 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.

  10. Automatic needle segmentation in 3D ultrasound images using 3D Hough transform

    NASA Astrophysics Data System (ADS)

    Zhou, Hua; Qiu, Wu; Ding, Mingyue; Zhang, Songgeng

    2007-12-01

    3D ultrasound (US) is a new technology that can be used for a variety of diagnostic applications, such as obstetrical, vascular, and urological imaging, and has been explored greatly potential in the applications of image-guided surgery and therapy. Uterine adenoma and uterine bleeding are the two most prevalent diseases in Chinese woman, and a minimally invasive ablation system using an RF button electrode which is needle-like is being used to destroy tumor cells or stop bleeding currently. Now a 3D US guidance system has been developed to avoid accidents or death of the patient by inaccurate localizations of the electrode and the tumor position during treatment. In this paper, we described two automated techniques, the 3D Hough Transform (3DHT) and the 3D Randomized Hough Transform (3DRHT), which is potentially fast, accurate, and robust to provide needle segmentation in 3D US image for use of 3D US imaging guidance. Based on the representation (Φ , θ , ρ , α ) of straight lines in 3D space, we used the 3DHT algorithm to segment needles successfully assumed that the approximate needle position and orientation are known in priori. The 3DRHT algorithm was developed to detect needles quickly without any information of the 3D US images. The needle segmentation techniques were evaluated using the 3D US images acquired by scanning water phantoms. The experiments demonstrated the feasibility of two 3D needle segmentation algorithms described in this paper.

  11. Breast Tissue 3D Segmentation and Visualization on MRI

    PubMed Central

    Cui, Xiangfei; Sun, Feifei

    2013-01-01

    Tissue segmentation and visualization are useful for breast lesion detection and quantitative analysis. In this paper, a 3D segmentation algorithm based on Kernel-based Fuzzy C-Means (KFCM) is proposed to separate the breast MR images into different tissues. Then, an improved volume rendering algorithm based on a new transfer function model is applied to implement 3D breast visualization. Experimental results have been shown visually and have achieved reasonable consistency. PMID:23983676

  12. 3D surface analysis and classification in neuroimaging segmentation.

    PubMed

    Zagar, Martin; Mlinarić, Hrvoje; Knezović, Josip

    2011-06-01

    This work emphasizes new algorithms for 3D edge and corner detection used in surface extraction and new concept of image segmentation in neuroimaging based on multidimensional shape analysis and classification. We propose using of NifTI standard for describing input data which enables interoperability and enhancement of existing computing tools used widely in neuroimaging research. In methods section we present our newly developed algorithm for 3D edge and corner detection, together with the algorithm for estimating local 3D shape. Surface of estimated shape is analyzed and segmented according to kernel shapes. PMID:21755723

  13. Concurrent 3-D motion segmentation and 3-D interpretation of temporal sequences of monocular images.

    PubMed

    Sekkati, Hicham; Mitiche, Amar

    2006-03-01

    The purpose of this study is to investigate a variational method for joint multiregion three-dimensional (3-D) motion segmentation and 3-D interpretation of temporal sequences of monocular images. Interpretation consists of dense recovery of 3-D structure and motion from the image sequence spatiotemporal variations due to short-range image motion. The method is direct insomuch as it does not require prior computation of image motion. It allows movement of both viewing system and multiple independently moving objects. The problem is formulated following a variational statement with a functional containing three terms. One term measures the conformity of the interpretation within each region of 3-D motion segmentation to the image sequence spatiotemporal variations. The second term is of regularization of depth. The assumption that environmental objects are rigid accounts automatically for the regularity of 3-D motion within each region of segmentation. The third and last term is for the regularity of segmentation boundaries. Minimization of the functional follows the corresponding Euler-Lagrange equations. This results in iterated concurrent computation of 3-D motion segmentation by curve evolution, depth by gradient descent, and 3-D motion by least squares within each region of segmentation. Curve evolution is implemented via level sets for topology independence and numerical stability. This algorithm and its implementation are verified on synthetic and real image sequences. Viewers presented with anaglyphs of stereoscopic images constructed from the algorithm's output reported a strong perception of depth. PMID:16519351

  14. 3-D segmentation of human sternum in lung MDCT images.

    PubMed

    Pazokifard, Banafsheh; Sowmya, Arcot

    2013-01-01

    A fully automatic novel algorithm is presented for accurate 3-D segmentation of the human sternum in lung multi detector computed tomography (MDCT) images. The segmentation result is refined by employing active contours to remove calcified costal cartilage that is attached to the sternum. For each dataset, costal notches (sternocostal joints) are localized in 3-D by using a sternum mask and positions of the costal notches on it as reference. The proposed algorithm for sternum segmentation was tested on 16 complete lung MDCT datasets and comparison of the segmentation results to the reference delineation provided by a radiologist, shows high sensitivity (92.49%) and specificity (99.51%) and small mean distance (dmean=1.07 mm). Total average of the Euclidean distance error for costal notches positioning in 3-D is 4.2 mm. PMID:24110446

  15. Automatic needle segmentation in 3D ultrasound images using 3D improved Hough transform

    NASA Astrophysics Data System (ADS)

    Zhou, Hua; Qiu, Wu; Ding, Mingyue; Zhang, Songgen

    2008-03-01

    3D ultrasound (US) is a new technology that can be used for a variety of diagnostic applications, such as obstetrical, vascular, and urological imaging, and has been explored greatly potential in the applications of image-guided surgery and therapy. Uterine adenoma and uterine bleeding are the two most prevalent diseases in Chinese woman, and a minimally invasive ablation system using a needle-like RF button electrode is widely used to destroy tumor cells or stop bleeding. To avoid accidents or death of the patient by inaccurate localizations of the electrode and the tumor position during treatment, 3D US guidance system was developed. In this paper, a new automated technique, the 3D Improved Hough Transform (3DIHT) algorithm, which is potentially fast, accurate, and robust to provide needle segmentation in 3D US image for use of 3D US imaging guidance, was presented. Based on the coarse-fine search strategy and a four parameter representation of lines in 3D space, 3DIHT algorithm can segment needles quickly, accurately and robustly. The technique was evaluated using the 3D US images acquired by scanning a water phantom. The segmentation position deviation of the line was less than 2mm and angular deviation was much less than 2°. The average computational time measured on a Pentium IV 2.80GHz PC computer with a 381×381×250 image was less than 2s.

  16. Volume rendering for interactive 3D segmentation

    NASA Astrophysics Data System (ADS)

    Toennies, Klaus D.; Derz, Claus

    1997-05-01

    Combined emission/absorption and reflection/transmission volume rendering is able to display poorly segmented structures from 3D medical image sequences. Visual cues such as shading and color let the user distinguish structures in the 3D display that are incompletely extracted by threshold segmentation. In order to be truly helpful, analyzed information needs to be quantified and transferred back into the data. We extend our previously presented scheme for such display be establishing a communication between visual analysis and the display process. The main tool is a selective 3D picking device. For being useful on a rather rough segmentation, the device itself and the display offer facilities for object selection. Selective intersection planes let the user discard information prior to choosing a tissue of interest. Subsequently, a picking is carried out on the 2D display by casting a ray into the volume. The picking device is made pre-selective using already existing segmentation information. Thus, objects can be picked that are visible behind semi-transparent surfaces of other structures. Information generated by a later connected- component analysis can then be integrated into the data. Data examination is continued on an improved display letting the user actively participate in the analysis process. Results of this display-and-interaction scheme proved to be very effective. The viewer's ability to extract relevant information form a complex scene is combined with the computer's ability to quantify this information. The approach introduces 3D computer graphics methods into user- guided image analysis creating an analysis-synthesis cycle for interactive 3D segmentation.

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. An algorithm for segmenting range imagery

    SciTech Connect

    Roberts, R.S.

    1997-03-01

    This report describes the technical accomplishments of the FY96 Cross Cutting and Advanced Technology (CC&AT) project at Los Alamos National Laboratory. The project focused on developing algorithms for segmenting range images. The image segmentation algorithm developed during the project is described here. In addition to segmenting range images, the algorithm can fuse multiple range images thereby providing true 3D scene models. The algorithm has been incorporated into the Rapid World Modelling System at Sandia National Laboratory.

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

    PubMed

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

    2015-12-01

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

  3. 3D Model Segmentation and Representation with Implicit Polynomials

    NASA Astrophysics Data System (ADS)

    Zheng, Bo; Takamatsu, Jun; Ikeuchi, Katsushi

    When large-scale and complex 3D objects are obtained by range finders, it is often necessary to represent them by algebraic surfaces for such purposes as data compression, multi-resolution, noise elimination, and 3D recognition. Representing the 3D data with algebraic surfaces of an implicit polynomial (IP) has proved to offer the advantages that IP representation is capable of encoding geometric properties easily with desired smoothness, few parameters, algebraic/geometric invariants, and robustness to noise and missing data. Unfortunately, generating a high-degree IP surface for a whole complex 3D shape is impossible because of high computational cost and numerical instability. In this paper we propose a 3D segmentation method based on a cut-and-merge approach. Two cutting procedures adopt low-degree IPs to divide and fit the surface segments simultaneously, while avoiding generating high-curved segments. A merging procedure merges the similar adjacent segments to avoid over-segmentation. To prove the effectiveness of this segmentation method, we open up some new vistas for 3D applications such as 3D matching, recognition, and registration.

  4. Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model.

    PubMed

    Gutiérrez-Becker, Benjamín; Arámbula Cosío, Fernando; Guzmán Huerta, Mario E; Benavides-Serralde, Jesús Andrés; Camargo-Marín, Lisbeth; Medina Bañuelos, Verónica

    2013-09-01

    Previous work has shown that the segmentation of anatomical structures on 3D ultrasound data sets provides an important tool for the assessment of the fetal health. In this work, we present an algorithm based on a 3D statistical shape model to segment the fetal cerebellum on 3D ultrasound volumes. This model is adjusted using an ad hoc objective function which is in turn optimized using the Nelder-Mead simplex algorithm. Our algorithm was tested on ultrasound volumes of the fetal brain taken from 20 pregnant women, between 18 and 24 gestational weeks. An intraclass correlation coefficient of 0.8528 and a mean Dice coefficient of 0.8 between cerebellar volumes measured using manual techniques and the volumes calculated using our algorithm were obtained. As far as we know, this is the first effort to automatically segment fetal intracranial structures on 3D ultrasound data. PMID:23686392

  5. 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. PMID:24110203

  6. Hybrid segmentation framework for 3D medical image analysis

    NASA Astrophysics Data System (ADS)

    Chen, Ting; Metaxas, Dimitri N.

    2003-05-01

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

  7. Computational efficient segmentation of cell nuclei in 2D and 3D fluorescent micrographs

    NASA Astrophysics Data System (ADS)

    De Vylder, Jonas; Philips, Wilfried

    2011-02-01

    This paper proposes a new segmentation technique developed for the segmentation of cell nuclei in both 2D and 3D fluorescent micrographs. The proposed method can deal with both blurred edges as with touching nuclei. Using a dual scan line algorithm its both memory as computational efficient, making it interesting for the analysis of images coming from high throughput systems or the analysis of 3D microscopic images. Experiments show good results, i.e. recall of over 0.98.

  8. 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.

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

    NASA Astrophysics Data System (ADS)

    Xie, Yiting; Reeves, Anthony P.

    2014-03-01

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

  10. 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.

  11. 3D ultrasound image segmentation using wavelet support vector machines

    PubMed Central

    Akbari, Hamed; Fei, Baowei

    2012-01-01

    Purpose: Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy. Methods: This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method. Results: The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%. Conclusions: The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate. PMID:22755682

  12. 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

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

    PubMed

    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

  14. 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. PMID:23273891

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

    PubMed

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

    2013-07-01

    An integrated segmentation method for 3D ultrasound carotid artery was proposed. 3D ultrasound image was sliced into transverse, coronal and sagittal 2D images on the carotid bifurcation point. Then, the three images were processed respectively, and the carotid artery contours and thickness were obtained finally. This paper tries to overcome the disadvantages of current computer aided diagnosis method, such as high computational complexity, easily introduced subjective errors et al. The proposed method could get the carotid artery overall information rapidly, accurately and completely. It could be transplanted into clinical usage for atherosclerosis diagnosis and prevention. PMID:24195385

  16. Accuracy evaluation of segmentation for high resolution imagery and 3D laser point cloud data

    NASA Astrophysics Data System (ADS)

    Ni, Nina; Chen, Ninghua; Chen, Jianyu

    2014-09-01

    High resolution satellite imagery and 3D laser point cloud data provide precise geometry, rich spectral information and clear texture of feature. The segmentation of high resolution remote sensing images and 3D laser point cloud is the basis of object-oriented remote sensing image analysis, for the segmentation results will directly influence the accuracy of subsequent analysis and discrimination. Currently, there still lacks a common segmentation theory to support these algorithms. So when we face a specific problem, we should determine applicability of the segmentation method through segmentation accuracy assessment, and then determine an optimal segmentation. To today, the most common method for evaluating the effectiveness of a segmentation method is subjective evaluation and supervised evaluation. For providing a more objective evaluation result, we have carried out following work. Analysis and comparison previous proposed image segmentation accuracy evaluation methods, which are area-based metrics, location-based metrics and combinations metrics. 3D point cloud data, which was gathered by Reigl VZ1000, was used to make two-dimensional transformation of point cloud data. The object-oriented segmentation result of aquaculture farm, building and farmland polygons were used as test object and adopted to evaluate segmentation accuracy.

  17. Volume quantization of the mouse cerebellum by semiautomatic 3D segmentation of magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Sijbers, Jan; Van der Linden, Anne-Marie; Scheunders, Paul; Van Audekerke, Johan; Van Dyck, Dirk; Raman, Erik R.

    1996-04-01

    The aim of this work is the development of a non-invasive technique for efficient and accurate volume quantization of the cerebellum of mice. This enables an in-vivo study on the development of the cerebellum in order to define possible alterations in cerebellum volume of transgenic mice. We concentrate on a semi-automatic segmentation procedure to extract the cerebellum from 3D magnetic resonance data. The proposed technique uses a 3D variant of Vincent and Soille's immersion based watershed algorithm which is applied to the gradient magnitude of the MR data. The algorithm results in a partitioning of the data in volume primitives. The known drawback of the watershed algorithm, over-segmentation, is strongly reduced by a priori application of an adaptive anisotropic diffusion filter on the gradient magnitude data. In addition, over-segmentation is a posteriori contingently reduced by properly merging volume primitives, based on the minimum description length principle. The outcome of the preceding image processing step is presented to the user for manual segmentation. The first slice which contains the object of interest is quickly segmented by the user through selection of basic image regions. In the sequel, the subsequent slices are automatically segmented. The segmentation results are contingently manually corrected. The technique is tested on phantom objects, where segmentation errors less than 2% were observed. Three-dimensional reconstructions of the segmented data are shown for the mouse cerebellum and the mouse brains in toto.

  18. A high capacity 3D steganography algorithm.

    PubMed

    Chao, Min-Wen; Lin, Chao-hung; Yu, Cheng-Wei; Lee, Tong-Yee

    2009-01-01

    In this paper, we present a very high-capacity and low-distortion 3D steganography scheme. Our steganography approach is based on a novel multilayered embedding scheme to hide secret messages in the vertices of 3D polygon models. Experimental results show that the cover model distortion is very small as the number of hiding layers ranges from 7 to 13 layers. To the best of our knowledge, this novel approach can provide much higher hiding capacity than other state-of-the-art approaches, while obeying the low distortion and security basic requirements for steganography on 3D models. PMID:19147891

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

    PubMed Central

    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. PMID:22319297

  20. 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. PMID:22319297

  1. 3D graph segmentation for target detection in FOPEN LiDAR data

    NASA Astrophysics Data System (ADS)

    Shorter, Nicholas; Locke, Judson; Smith, O'Neil; Keating, Emma; Smith, Philip

    2013-05-01

    A novel use of Felzenszwalb's graph based efficient image segmentation algorithm* is proposed for segmenting 3D volumetric foliage penetrating (FOPEN) Light Detection and Ranging (LiDAR) data for automated target detection. The authors propose using an approximate nearest neighbors algorithm to establish neighbors of points in 3D and thus form the graph for segmentation. Following graph formation, the angular difference in the points' estimated normal vectors is proposed for the graph edge weights. Then the LiDAR data is segmented, in 3D, and metrics are calculated from the segments to determine their geometrical characteristics and thus likelihood of being a target. Finally, the bare earth within the scene is automatically identified to avoid confusion of flat bare earth with flat targets. The segmentation, the calculated metrics, and the bare earth all culminate in a target detection system deployed for FOPEN LiDAR. General purpose graphics processing units (GPGPUs) are leveraged to reduce processing times for the approximate nearest neighbors and point normal estimation algorithms such that the application can be run in near real time. Results are presented on several data sets.

  2. 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.

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

    PubMed

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

    2016-08-01

    Automatic 3D liver segmentation is a fundamental step in the liver disease diagnosis and surgery planning. This paper presents a novel fully automatic algorithm for 3D liver segmentation in clinical 3D computed tomography (CT) images. Based on image features, we propose a new Mahalanobis distance cost function using an active shape model (ASM). We call our method MD-ASM. Unlike the standard active shape model (ST-ASM), the proposed method introduces a new feature-constrained Mahalanobis distance cost function to measure the distance between the generated shape during the iterative step and the mean shape model. The proposed Mahalanobis distance function is learned from a public database of liver segmentation challenge (MICCAI-SLiver07). As a refinement step, we propose the use of a 3D graph-cut segmentation. Foreground and background labels are automatically selected using texture features of the learned Mahalanobis distance. Quantitatively, the proposed method is evaluated using two clinical 3D CT scan databases (MICCAI-SLiver07 and MIDAS). The evaluation of the MICCAI-SLiver07 database is obtained by the challenge organizers using five different metric scores. The experimental results demonstrate the availability of the proposed method by achieving an accurate liver segmentation compared to the state-of-the-art methods. PMID:26501155

  4. 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.

  5. 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. PMID:26547117

  6. Automated segmentation of breast in 3-D MR images using a robust atlas.

    PubMed

    Khalvati, Farzad; Gallego-Ortiz, Cristina; Balasingham, Sharmila; Martel, Anne L

    2015-01-01

    This paper presents a robust atlas-based segmentation (ABS) algorithm for segmentation of the breast boundary in 3-D MR images. The proposed algorithm combines the well-known methodologies of ABS namely probabilistic atlas and atlas selection approaches into a single framework where two configurations are realized. The algorithm uses phase congruency maps to create an atlas which is robust to intensity variations. This allows an atlas derived from images acquired with one MR imaging sequence to be used to segment images acquired with a different MR imaging sequence and eliminates the need for intensity-based registration. Images acquired using a Dixon sequence were used to create an atlas which was used to segment both Dixon images (intra-sequence) and T1-weighted images (inter-sequence). In both cases, highly accurate results were achieved with the median Dice similarity coefficient values of 94% ±4% and 87 ±6.5%, respectively. PMID:25137725

  7. 3D segmentation and reconstruction of endobronchial ultrasound

    NASA Astrophysics Data System (ADS)

    Zang, Xiaonan; Breslav, Mikhail; Higgins, William E.

    2013-03-01

    State-of-the-art practice for lung-cancer staging bronchoscopy often draws upon a combination of endobronchial ultrasound (EBUS) and multidetector computed-tomography (MDCT) imaging. While EBUS offers real-time in vivo imaging of suspicious lesions and lymph nodes, its low signal-to-noise ratio and tendency to exhibit missing region-of-interest (ROI) boundaries complicate diagnostic tasks. Furthermore, past efforts did not incorporate automated analysis of EBUS images and a subsequent fusion of the EBUS and MDCT data. To address these issues, we propose near real-time automated methods for three-dimensional (3D) EBUS segmentation and reconstruction that generate a 3D ROI model along with ROI measurements. Results derived from phantom data and lung-cancer patients show the promise of the methods. In addition, we present a preliminary image-guided intervention (IGI) system example, whereby EBUS imagery is registered to a patient's MDCT chest scan.

  8. Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images

    PubMed Central

    Nunez-Iglesias, Juan; Kennedy, Ryan; Parag, Toufiq; Shi, Jianbo; Chklovskii, Dmitri B.

    2013-01-01

    We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images. PMID:23977123

  9. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2004-04-01

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

  11. 3D segmentation of prostate ultrasound images using wavelet transform

    NASA Astrophysics Data System (ADS)

    Akbari, Hamed; Yang, Xiaofeng; Halig, Luma V.; Fei, Baowei

    2011-03-01

    The current definitive diagnosis of prostate cancer is transrectal ultrasound (TRUS) guided biopsy. However, the current procedure is limited by using 2D biopsy tools to target 3D biopsy locations. This paper presents a new method for automatic segmentation of the prostate in three-dimensional transrectal ultrasound images, by extracting texture features and by statistically matching geometrical shape of the prostate. A set of Wavelet-based support vector machines (WSVMs) are located and trained at different regions of the prostate surface. The WSVMs capture texture priors of ultrasound images for classification of the prostate and non-prostate tissues in different zones around the prostate boundary. In the segmentation procedure, these W-SVMs are trained in three sagittal, coronal, and transverse planes. The pre-trained W-SVMs are employed to tentatively label each voxel around the surface of the model as a prostate or non-prostate voxel by the texture matching. The labeled voxels in three planes after post-processing is overlaid on a prostate probability model. The probability prostate model is created using 10 segmented prostate data. Consequently, each voxel has four labels: sagittal, coronal, and transverse planes and one probability label. By defining a weight function for each labeling in each region, each voxel is labeled as a prostate or non-prostate voxel. Experimental results by using real patient data show the good performance of the proposed model in segmenting the prostate from ultrasound images.

  12. An Automatic Registration Algorithm for 3D Maxillofacial Model

    NASA Astrophysics Data System (ADS)

    Qiu, Luwen; Zhou, Zhongwei; Guo, Jixiang; Lv, Jiancheng

    2016-09-01

    3D image registration aims at aligning two 3D data sets in a common coordinate system, which has been widely used in computer vision, pattern recognition and computer assisted surgery. One challenging problem in 3D registration is that point-wise correspondences between two point sets are often unknown apriori. In this work, we develop an automatic algorithm for 3D maxillofacial models registration including facial surface model and skull model. Our proposed registration algorithm can achieve a good alignment result between partial and whole maxillofacial model in spite of ambiguous matching, which has a potential application in the oral and maxillofacial reparative and reconstructive surgery. The proposed algorithm includes three steps: (1) 3D-SIFT features extraction and FPFH descriptors construction; (2) feature matching using SAC-IA; (3) coarse rigid alignment and refinement by ICP. Experiments on facial surfaces and mandible skull models demonstrate the efficiency and robustness of our algorithm.

  13. Segmentation of Whole Cells and Cell Nuclei From 3-D Optical Microscope Images Using Dynamic Programming

    PubMed Central

    McCullough, Dean P.; Gudla, Prabhakar R.; Harris, Bradley S.; Collins, Jason A.; Meaburn, Karen J.; Nakaya, Masa-Aki; Yamaguchi, Terry P.; Misteli, Tom; Lockett, Stephen J.

    2009-01-01

    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. PMID:18450544

  14. 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.

  15. Automatic needle segmentation in 3D ultrasound images

    NASA Astrophysics Data System (ADS)

    Ding, Mingyue; Cardinal, H. Neale; Guan, Weiguang; Fenster, Aaron

    2002-05-01

    In this paper, we propose to use 2D image projections to automatically segment a needle in a 3D ultrasound image. This approach is motivated by the twin observations that the needle is more conspicuous in a projected image, and its projected area is a minimum when the rays are cast parallel to the needle direction. To avoid the computational burden of an exhaustive 2D search for the needle direction, a faster 1D search procedure is proposed. First, a plane which contains the needle direction is determined by the initial projection direction and the (estimated) direction of the needle in the corresponding projection image. Subsequently, an adaptive 1D search technique is used to adjust the projection direction iteratively until the projected needle area is minimized. In order to remove noise and complex background structure from the projection images, a priori information about the needle position and orientation is used to crop the 3D volume, and the cropped volume is rendered with Gaussian transfer functions. We have evaluated this approach experimentally using agar and turkey breast phantoms. The results show that it can find the 3D needle orientation within 1 degree, in about 1 to 3 seconds on a 500 MHz computer.

  16. 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.

  17. 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.

  18. 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.

  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. PMID:25155697

  20. Improved 3-D turbomachinery CFD algorithm

    NASA Technical Reports Server (NTRS)

    Janus, J. Mark; Whitfield, David L.

    1988-01-01

    The building blocks of a computer algorithm developed for the time-accurate flow analysis of rotating machines are described. The flow model is a finite volume method utilizing a high resolution approximate Riemann solver for interface flux definitions. This block LU implicit numerical scheme possesses apparent unconditional stability. Multi-block composite gridding is used to orderly partition the field into a specified arrangement. Block interfaces, including dynamic interfaces, are treated such as to mimic interior block communication. Special attention is given to the reduction of in-core memory requirements by placing the burden on secondary storage media. Broad applicability is implied, although the results presented are restricted to that of an even blade count configuration. Several other configurations are presently under investigation, the results of which will appear in subsequent publications.

  1. 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.

  2. 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.

  3. Segmentation and reconstruction of cerebral vessels from 3D rotational angiography for AVM embolization planning.

    PubMed

    Li, Fan; Chenoune, Yasmina; Ouenniche, Meriem; Blanc, Raphaël; Petit, Eric

    2014-01-01

    Diagnosis and computer-guided therapy of cerebral Arterio-Venous Malformations (AVM) require an accurate understanding of the cerebral vascular network both from structural and biomechanical point of view. We propose to obtain such information by analyzing three Dimensional Rotational Angiography (3DRA) images. In this paper, we describe a two-step process allowing 1) the 3D automatic segmentation of cerebral vessels from 3DRA images using a region-growing based algorithm and 2) the reconstruction of the segmented vessels using the 3D constrained Delaunay Triangulation method. The proposed algorithm was successfully applied to reconstruct cerebral blood vessels from ten datasets of 3DRA images. This software allows the neuroradiologist to separately analyze cerebral vessels for pre-operative interventions planning and therapeutic decision making. PMID:25571245

  4. Differential and relaxed image foresting transform for graph-cut segmentation of multiple 3D objects.

    PubMed

    Moya, Nikolas; Falcão, Alexandre X; Ciesielski, Krzysztof C; Udupa, Jayaram K

    2014-01-01

    Graph-cut algorithms have been extensively investigated for interactive binary segmentation, when the simultaneous delineation of multiple objects can save considerable user's time. We present an algorithm (named DRIFT) for 3D multiple object segmentation based on seed voxels and Differential Image Foresting Transforms (DIFTs) with relaxation. DRIFT stands behind efficient implementations of some state-of-the-art methods. The user can add/remove markers (seed voxels) along a sequence of executions of the DRIFT algorithm to improve segmentation. Its first execution takes linear time with the image's size, while the subsequent executions for corrections take sublinear time in practice. At each execution, DRIFT first runs the DIFT algorithm, then it applies diffusion filtering to smooth boundaries between objects (and background) and, finally, it corrects possible objects' disconnection occurrences with respect to their seeds. We evaluate DRIFT in 3D CT-images of the thorax for segmenting the arterial system, esophagus, left pleural cavity, right pleural cavity, trachea and bronchi, and the venous system. PMID:25333179

  5. Automatic classification of 3D segmented CT data using data fusion and support vector machine

    NASA Astrophysics Data System (ADS)

    Osman, Ahmad; Kaftandjian, Valérie; Hassler, Ulf

    2011-07-01

    The three dimensional X-ray computed tomography (3D-CT) has proved its successful usage as inspection method in non destructive testing. The generated 3D volume using high efficiency reconstruction algorithms contains all the inner structures of the inspected part. Segmentation of this volume reveals suspicious regions which need to be classified into defects or false alarms. This paper deals with the classification step using data fusion theory and support vector machine. Results achieved are very promising and prove the effectiveness of the data fusion theory as a method to build stronger classifier.

  6. 3D prostate boundary segmentation from ultrasound images using 2D active shape models.

    PubMed

    Hodge, Adam C; Ladak, Hanif M

    2006-01-01

    Boundary outlining, or segmentation, of the prostate is an important task in diagnosis and treatment planning for prostate cancer. This paper describes an algorithm for semi-automatic, three-dimensional (3D) segmentation of the prostate boundary from ultrasound images based on two-dimensional (2D) active shape models (ASM) and rotation-based slicing. Evaluation of the algorithm used distance- and volume-based error metrics to compare algorithm generated boundary outlines to gold standard (manually generated) boundary outlines. The mean absolute distance between the algorithm and gold standard boundaries was 1.09+/-0.49 mm, the average percent absolute volume difference was 3.28+/-3.16%, and a 5x speed increase as compared manual planimetry was achieved. PMID:17946106

  7. 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.

  8. Rule-based automatic segmentation for 3-D coronary arteriography

    NASA Astrophysics Data System (ADS)

    Sarwal, Alok; Truitt, Paul; Ozguner, Fusun; Zhang, Qian; Parker, Dennis L.

    1992-03-01

    Coronary arteriography is a technique used for evaluating the state of coronary arteries and assessing the need for bypass surgery and angioplasty. The present clinical application of this technology is based on the use of a contrast medium for manual radiographic visualization. This method is inaccurate due to varying interpretation of the visual results. Coronary arteriography based quantitations are impractical in a clinical setting without the use of automatic techniques applied to the 3-D reconstruction of the arterial tree. Such a system will provide an easily reproducible method for following the temporal changes in coronary morphology. The labeling of the arteries and establishing of the correspondence between multiple views is necessary for all subsequent processing required for 3-D reconstruction. This work represents a rule based expert system utilized for automatic labeling and segmentation of the arterial branches across multiple views. X-ray data of two and three views of human subjects and a pig arterial cast have been used for this research.

  9. Sloped terrain segmentation for autonomous drive using sparse 3D point cloud.

    PubMed

    Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Jeong, Young-Sik; Um, Kyhyun; 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

  10. 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

  11. Model based 3D segmentation and OCT image undistortion of percutaneous implants.

    PubMed

    Müller, Oliver; Donner, Sabine; Klinder, Tobias; Dragon, Ralf; Bartsch, Ivonne; Witte, Frank; Krüger, Alexander; Heisterkamp, Alexander; Rosenhahn, Bodo

    2011-01-01

    Optical Coherence Tomography (OCT) is a noninvasive imaging technique which is used here for in vivo biocompatibility studies of percutaneous implants. A prerequisite for a morphometric analysis of the OCT images is the correction of optical distortions caused by the index of refraction in the tissue. We propose a fully automatic approach for 3D segmentation of percutaneous implants using Markov random fields. Refraction correction is done by using the subcutaneous implant base as a prior for model based estimation of the refractive index using a generalized Hough transform. Experiments show the competitiveness of our algorithm towards manual segmentations done by experts. PMID:22003731

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

    SciTech Connect

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

    2005-09-15

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

  13. 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.

  14. 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.

  15. Demonstration of a 3D vision algorithm for space applications

    NASA Technical Reports Server (NTRS)

    Defigueiredo, Rui J. P. (Editor)

    1987-01-01

    This paper reports an extension of the MIAG algorithm for recognition and motion parameter determination of general 3-D polyhedral objects based on model matching techniques and using movement invariants as features of object representation. Results of tests conducted on the algorithm under conditions simulating space conditions are presented.

  16. 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.

  17. 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

  18. 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. PMID:27037463

  19. 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.

  20. 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.

  1. GPS 3-D cockpit displays: Sensors, algorithms, and flight testing

    NASA Astrophysics Data System (ADS)

    Barrows, Andrew Kevin

    Tunnel-in-the-Sky 3-D flight displays have been investigated for several decades as a means of enhancing aircraft safety and utility. However, high costs have prevented commercial development and seriously hindered research into their operational benefits. The rapid development of Differential Global Positioning Systems (DGPS), inexpensive computing power, and ruggedized displays is now changing this situation. A low-cost prototype system was built and flight tested to investigate implementation and operational issues. The display provided an "out the window" 3-D perspective view of the world, letting the pilot see the horizon, runway, and desired flight path even in instrument flight conditions. The flight path was depicted as a tunnel through which the pilot flew the airplane, while predictor symbology provided guidance to minimize path-following errors. Positioning data was supplied, by various DGPS sources including the Stanford Wide Area Augmentation System (WAAS) testbed. A combination of GPS and low-cost inertial sensors provided vehicle heading, pitch, and roll information. Architectural and sensor fusion tradeoffs made during system implementation are discussed. Computational algorithms used to provide guidance on curved paths over the earth geoid are outlined along with display system design issues. It was found that current technology enables low-cost Tunnel-in-the-Sky display systems with a target cost of $20,000 for large-scale commercialization. Extensive testing on Piper Dakota and Beechcraft Queen Air aircraft demonstrated enhanced accuracy and operational flexibility on a variety of complex flight trajectories. These included curved and segmented approaches, traffic patterns flown on instruments, and skywriting by instrument reference. Overlays to existing instrument approaches at airports in California and Alaska were flown and compared with current instrument procedures. These overlays demonstrated improved utility and situational awareness for

  2. 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

  3. Volume rendering segmented data using 3D textures: a practical approach for intra-operative visualization

    NASA Astrophysics Data System (ADS)

    Subramanian, Navneeth; Mullick, Rakesh; Vaidya, Vivek

    2006-03-01

    Volume rendering has high utility in visualization of segmented datasets. However, volume rendering of the segmented labels along with the original data causes undesirable intermixing/bleeding artifacts arising from interpolation at the sharp boundaries. This issue is further amplified in 3D textures based volume rendering due to the inaccessibility of the interpolation stage. We present an approach which helps minimize intermixing artifacts while maintaining the high performance of 3D texture based volume rendering - both of which are critical for intra-operative visualization. Our approach uses a 2D transfer function based classification scheme where label distinction is achieved through an encoding that generates unique gradient values for labels. This helps ensure that labelled voxels always map to distinct regions in the 2D transfer function, irrespective of interpolation. In contrast to previously reported algorithms, our algorithm does not require multiple passes for rendering and supports greater than 4 masks. It also allows for real-time modification of the colors/opacities of the segmented structures along with the original data. Additionally, these capabilities are available with minimal texture memory requirements amongst comparable algorithms. Results are presented on clinical and phantom data.

  4. A 3D Frictional Segment-to-Segment Contact Method for Large Deformations and Quadratic Elements

    SciTech Connect

    Puso, M; Laursen, T; Solberg, J

    2004-04-01

    Node-on-segment contact is the most common form of contact used today but has many deficiencies ranging from potential locking to non-smooth behavior with large sliding. Furthermore, node-on-segment approaches are not at all applicable to higher order discretizations (e.g. quadratic elements). In a previous work, [3, 4] we developed a segment-to-segment contact approach for eight node hexahedral elements based on the mortar method that was applicable to large deformation mechanics. The approach proved extremely robust since it eliminated the over-constraint that caused 'locking' and provided smooth force variations in large sliding. Here, we extend this previous approach to treat frictional contact problems. In addition, the method is extended to 3D quadratic tetrahedrals and hexahedrals. The proposed approach is then applied to several challenging frictional contact problems that demonstrate its effectiveness.

  5. Parallel algorithm for computing 3-D reachable workspaces

    NASA Astrophysics Data System (ADS)

    Alameldin, Tarek K.; Sobh, Tarek M.

    1992-03-01

    The problem of computing the 3-D workspace for redundant articulated chains has applications in a variety of fields such as robotics, computer aided design, and computer graphics. The computational complexity of the workspace problem is at least NP-hard. The recent advent of parallel computers has made practical solutions for the workspace problem possible. Parallel algorithms for computing the 3-D workspace for redundant articulated chains with joint limits are presented. The first phase of these algorithms computes workspace points in parallel. The second phase uses workspace points that are computed in the first phase and fits a 3-D surface around the volume that encompasses the workspace points. The second phase also maps the 3- D points into slices, uses region filling to detect the holes and voids in the workspace, extracts the workspace boundary points by testing the neighboring cells, and tiles the consecutive contours with triangles. The proposed algorithms are efficient for computing the 3-D reachable workspace for articulated linkages, not only those with redundant degrees of freedom but also those with joint limits.

  6. 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).

  7. 3D video sequence reconstruction algorithms implemented on a DSP

    NASA Astrophysics Data System (ADS)

    Ponomaryov, V. I.; Ramos-Diaz, E.

    2011-03-01

    A novel approach for 3D image and video reconstruction is proposed and implemented. This is based on the wavelet atomic functions (WAF) that have demonstrated better approximation properties in different processing problems in comparison with classical wavelets. Disparity maps using WAF are formed, and then they are employed in order to present 3D visualization using color anaglyphs. Additionally, the compression via Pth law is performed to improve the disparity map quality. Other approaches such as optical flow and stereo matching algorithm are also implemented as the comparative approaches. Numerous simulation results have justified the efficiency of the novel framework. The implementation of the proposed algorithm on the Texas Instruments DSP TMS320DM642 permits to demonstrate possible real time processing mode during 3D video reconstruction for images and video sequences.

  8. Multiple footprint stereo algorithms for 3D display content generation

    NASA Astrophysics Data System (ADS)

    Boughorbel, Faysal

    2007-02-01

    This research focuses on the conversion of stereoscopic video material into an image + depth format which is suitable for rendering on the multiview auto-stereoscopic displays of Philips. The recent interest shown in the movie industry for 3D significantly increased the availability of stereo material. In this context the conversion from stereo to the input formats of 3D displays becomes an important task. In this paper we present a stereo algorithm that uses multiple footprints generating several depth candidates for each image pixel. We characterize the various matching windows and we devise a robust strategy for extracting high quality estimates from the resulting depth candidates. The proposed algorithm is based on a surface filtering method that employs simultaneously the available depth estimates in a small local neighborhood while ensuring correct depth discontinuities by the inclusion of image constraints. The resulting highquality image-aligned depth maps proved an excellent match with our 3D displays.

  9. Improved hybrid optimization algorithm for 3D protein structure prediction.

    PubMed

    Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

    2014-07-01

    A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins. PMID:25069136

  10. Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor.

    PubMed

    Pearlman, P C; Tagare, H D; Lin, B A; Sinusas, A J; Duncan, J S

    2012-02-01

    This paper presents an algorithm for segmenting left ventricular endocardial boundaries from RF ultrasound. Our method incorporates a computationally efficient linear predictor that exploits short-term spatio-temporal coherence in the RF data. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model that relates neighboring frames in the image sequence. Segmentation using the RF data overcomes challenges due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from six canine studies. PMID:22078842

  11. A new algorithm for 3D reconstruction from support functions.

    PubMed

    Gardner, Richard J; Kiderlen, Markus

    2009-03-01

    We introduce a new algorithm for reconstructing an unknown shape from a finite number of noisy measurements of its support function. The algorithm, based on a least squares procedure, is very easy to program in standard software such as Matlab, and it works for both 2D and 3D reconstructions (in fact, in principle, in any dimension). Reconstructions may be obtained without any pre- or post-processing steps and with no restriction on the sets of measurement directions except their number, a limitation dictated only by computing time. An algorithm due to Prince and Willsky was implemented earlier for 2D reconstructions, and we compare the performance of their algorithm and ours. But our algorithm is the first that works for 3D reconstructions with the freedom stated in the previous paragraph. Moreover, under mild conditions, theory guarantees that outputs of the new algorithm will converge to the input shape as the number of measurements increases. In addition we offer a linear program version of the new algorithm that is much faster and better, or at least comparable, in performance at low levels of noise and reasonably small numbers of measurements. Another modification of the algorithm, suitable for use in a "focus of attention" scheme, is also described. PMID:19147881

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

    PubMed

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

    2016-01-01

    As the right ventricle's (RV) role in cardiovascular diseases is being more widely recognized, interest in RV imaging, function and quantification is growing. However, there are currently few RV quantification methods for 3D echocardiography presented in the literature or commercially available. In this paper we propose an automated RV segmentation method for 3D echocardiographic images. We represent the RV geometry by a Doo-Sabin subdivision surface with deformation modes derived from a training set of manual segmentations. The segmentation is then represented as a state estimation problem and solved with an extended Kalman filter by combining the RV geometry with a motion model and edge detection. Validation was performed by comparing surface-surface distances, volumes and ejection fractions in 17 patients with aortic insufficiency between the proposed method, magnetic resonance imaging (MRI), and a manual echocardiographic reference. The algorithm was efficient with a mean computation time of 2.0 s. The mean absolute distances between the proposed and manual segmentations were 3.6 ± 0.7 mm. Good agreements of end diastolic volume, end systolic volume and ejection fraction with respect to MRI ( -26±24 mL , -16±26 mL and 0 ± 10%, respectively) and a manual echocardiographic reference (7 ± 30 mL, 13 ± 17 mL and -5±7% , respectively) were observed. PMID:26168434

  13. Fast phase-added stereogram algorithm for generation of photorealistic 3D content.

    PubMed

    Kang, Hoonjong; Stoykova, Elena; Yoshikawa, Hiroshi

    2016-01-20

    A new phase-added stereogram algorithm for accelerated computation of holograms from a point cloud model is proposed. The algorithm relies on the hologram segmentation, sampling of directional information, and usage of the fast Fourier transform with a finer grid in the spatial frequency domain than is provided by the segment size. The algorithm gives improved quality of reconstruction due to new phase compensation introduced in the segment fringe patterns. The result is finer beam steering leading to high peak intensity and a large peak signal-to-noise ratio in reconstruction. The feasibility of the algorithm is checked by the generation of 3D contents for a color wavefront printer. PMID:26835945

  14. An efficient memetic algorithm for 3D shape matching problems

    NASA Astrophysics Data System (ADS)

    Sharif Khan, Mohammad; Mohamad Ayob, Ahmad F.; Ray, Tapabrata

    2014-05-01

    Shape representation plays a vital role in any shape optimization exercise. The ability to identify a shape with good functional properties is dependent on the underlying shape representation scheme, the morphing mechanism and the efficiency of the optimization algorithm. This article presents a novel and efficient methodology for morphing 3D shapes via smart repair of control points. The repaired sequence of control points are subsequently used to define the 3D object using a B-spline surface representation. The control points are evolved within the framework of a memetic algorithm for greater efficiency. While the authors have already proposed an approach for 2D shape matching, this article extends it further to deal with 3D shape matching problems. Three 3D examples and a real customized 3D earplug design have been used as examples to illustrate the performance of the proposed approach and the effectiveness of the repair scheme. Complete details of the problems are presented for future work in this direction.

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

    PubMed

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

    2012-08-01

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

  16. 3D-design exploration of CNN algorithms

    NASA Astrophysics Data System (ADS)

    Spaanenburg, Lambert; Malki, Suleyman

    2011-05-01

    Multi-dimensional algorithms are hard to implement on classical platforms. Pipelining may exploit instruction-level parallelism, but not in the presence of simultaneous data; threads optimize only within the given restrictions. Tiled architectures do add a dimension to the solution space. With locally a large register store, data parallelism is handled, but only to a dimension. 3-D technologies are meant to add a dimension in the realization. Applied on the device level, it makes each computational node smaller. The interconnections become shorter and hence the network will be condensed. Such advantages will be easily lost at higher implementation levels unless 3-D technologies as multi-cores or chip stacking are also introduced. 3-D technologies scale in space, where (partial) reconfiguration scales in time. The optimal selection over the various implementation levels is algorithm dependent. The paper discusses such principles while applied on the scaling of cellular neural networks (CNN). It illustrates how stacking of reconfigurable chips supports many algorithmic requirements in a defect-insensitive manner. Further the paper explores the potential of chip stacking for multi-modal implementations in a reconfigurable approach to heterogeneous architectures for algorithm domains.

  17. Conservative Patch Algorithm and Mesh Sequencing for PAB3D

    NASA Technical Reports Server (NTRS)

    Pao, S. P.; Abdol-Hamid, K. S.

    2005-01-01

    A mesh-sequencing algorithm and a conservative patched-grid-interface algorithm (hereafter Patch Algorithm ) have been incorporated into the PAB3D code, which is a computer program that solves the Navier-Stokes equations for the simulation of subsonic, transonic, or supersonic flows surrounding an aircraft or other complex aerodynamic shapes. These algorithms are efficient, flexible, and have added tremendously to the capabilities of PAB3D. The mesh-sequencing algorithm makes it possible to perform preliminary computations using only a fraction of the grid cells (provided the original cell count is divisible by an integer) along any grid coordinate axis, independently of the other axes. The patch algorithm addresses another critical need in multi-block grid situation where the cell faces of adjacent grid blocks may not coincide, leading to errors in calculating fluxes of conserved physical quantities across interfaces between the blocks. The patch algorithm, based on the Stokes integral formulation of the applicable conservation laws, effectively matches each of the interfacial cells on one side of the block interface to the corresponding fractional cell area pieces on the other side. This approach is comprehensive and unified such that all interface topology is automatically processed without user intervention. This algorithm is implemented in a preprocessing code that creates a cell-by-cell database that will maintain flux conservation at any level of full or reduced grid density as the user may choose by way of the mesh-sequencing algorithm. These two algorithms have enhanced the numerical accuracy of the code, reduced the time and effort for grid preprocessing, and provided users with the flexibility of performing computations at any desired full or reduced grid resolution to suit their specific computational requirements.

  18. Algorithms for Haptic Rendering of 3D Objects

    NASA Technical Reports Server (NTRS)

    Basdogan, Cagatay; Ho, Chih-Hao; Srinavasan, Mandayam

    2003-01-01

    Algorithms have been developed to provide haptic rendering of three-dimensional (3D) objects in virtual (that is, computationally simulated) environments. The goal of haptic rendering is to generate tactual displays of the shapes, hardnesses, surface textures, and frictional properties of 3D objects in real time. Haptic rendering is a major element of the emerging field of computer haptics, which invites comparison with computer graphics. We have already seen various applications of computer haptics in the areas of medicine (surgical simulation, telemedicine, haptic user interfaces for blind people, and rehabilitation of patients with neurological disorders), entertainment (3D painting, character animation, morphing, and sculpting), mechanical design (path planning and assembly sequencing), and scientific visualization (geophysical data analysis and molecular manipulation).

  19. 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.

  20. SIFT algorithm-based 3D pose estimation of femur.

    PubMed

    Zhang, Xuehe; Zhu, Yanhe; Li, Changle; Zhao, Jie; Li, Ge

    2014-01-01

    To address the lack of 3D space information in the digital radiography of a patient femur, a pose estimation method based on 2D-3D rigid registration is proposed in this study. The method uses two digital radiography images to realize the preoperative 3D visualization of a fractured femur. Compared with the pure Digital Radiography or Computed Tomography imaging diagnostic methods, the proposed method has the advantages of low cost, high precision, and minimal harmful radiation. First, stable matching point pairs in the frontal and lateral images of the patient femur and the universal femur are obtained by using the Scale Invariant Feature Transform method. Then, the 3D pose estimation registration parameters of the femur are calculated by using the Iterative Closest Point (ICP) algorithm. Finally, based on the deviation between the six degrees freedom parameter calculated by the proposed method, preset posture parameters are calculated to evaluate registration accuracy. After registration, the rotation error is less than l.5°, and the translation error is less than 1.2 mm, which indicate that the proposed method has high precision and robustness. The proposed method provides 3D image information for effective preoperative orthopedic diagnosis and surgery planning. PMID:25226990

  1. 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

  2. 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

  3. Segmentation of 3D RF echocardiography using a multiframe spatio-temporal predictor.

    PubMed

    Pearlman, Paul C; Tagare, Hemant D; Lin, Ben A; Sinusas, Albert J; Duncan, James S

    2011-01-01

    We present an approach for segmenting left ventricular endocardial boundaries from RF ultrasound. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model. The conditional model relates neighboring frames in the image sequence by means of a computationally efficient linear predictor that exploits spatio-temporal coherence in the data. Segmentation using the RF data overcomes problems due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from 6 canine studies. PMID:21761644

  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. Segmentation of 3D holographic images using bivariate jointly distributed region snake

    NASA Astrophysics Data System (ADS)

    Daneshpanah, Mehdi; Javidi, Bahram

    2006-06-01

    In this paper, we describe the bivariate jointly distributed region snake method in segmentation of microorganisms in Single Exposure On- Line (SEOL) holographic microscopy images. 3D images of the microorganisms are digitally reconstructed and numerically focused from any arbitrary depth from a single recorded digital hologram without mechanical scanning. Living organisms are non-rigid and they vary in shape and size. Moreover, they often do not exhibit clear edges in digitally reconstructed SEOL holographic images. Thus, conventional segmentation techniques based on the edge map may fail to segment these images. However, SEOL holographic microscopy provides both magnitude and phase information of the sample specimen, which could be helpful in the segmentation process. In this paper, we present a statistical framework based on the joint probability distribution of magnitude and phase information of SEOL holographic microscopy images and maximum likelihood estimation of image probability density function parameters. An optimization criterion is computed by maximizing the likelihood function of the target support hypothesis. In addition, a simple stochastic algorithm has been adapted for carrying out the optimization, while several boosting techniques have been employed to enhance its performance. Finally, the proposed method is applied for segmentation of biological microorganisms in SEOL holographic images and the experimental results are presented.

  6. Implementation of wireless 3D stereo image capture system and 3D exaggeration algorithm for the region of interest

    NASA Astrophysics Data System (ADS)

    Ham, Woonchul; Song, Chulgyu; Lee, Kangsan; Badarch, Luubaatar

    2015-05-01

    In this paper, we introduce the mobile embedded system implemented for capturing stereo image based on two CMOS camera module. We use WinCE as an operating system and capture the stereo image by using device driver for CMOS camera interface and Direct Draw API functions. We aslo comments on the GPU hardware and CUDA programming for implementation of 3D exaggeraion algorithm for ROI by adjusting and synthesizing the disparity value of ROI (region of interest) in real time. We comment on the pattern of aperture for deblurring of CMOS camera module based on the Kirchhoff diffraction formula and clarify the reason why we can get more sharp and clear image by blocking some portion of aperture or geometric sampling. Synthesized stereo image is real time monitored on the shutter glass type three-dimensional LCD monitor and disparity values of each segment are analyzed to prove the validness of emphasizing effect of ROI.

  7. 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.

  8. An algorithm for studying rigidity in disordered 3D networks

    NASA Astrophysics Data System (ADS)

    Chubynsky, M. V.; Thorpe, M. F.

    2004-03-01

    Some physical systems, such as covalent glasses and proteins, can be modeled as elastic networks, by dividing the interactions between particles into strong and weak, representing the former as constraints and neglecting the latter. For low enough connectivities, motions maintaining the constraints and thus having zero energy cost are possible. The goal of rigidity analysis is finding the number of such zero energy modes, the rigid clusters and flexible joints between them, as well as stressed bonds. For a certain class of networks there is a very fast graph-theoretical algorithm (the Pebble Game) for doing this analysis, but for more general networks, there are known counterexamples. While generalizing the Pebble Game is the ultimate goal, we propose a slower algorithm capable of doing all the same analyses as the Pebble Game but applicable to any networks. We discuss the applications of this algorithm to specific examples of 3D networks, such as diluted central force lattices, colloidal glasses and proteins.

  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. PMID:23836390

  10. A parallel algorithm for solving the 3d Schroedinger equation

    SciTech Connect

    Strickland, Michael; Yager-Elorriaga, David

    2010-08-20

    We describe a parallel algorithm for solving the time-independent 3d Schroedinger equation using the finite difference time domain (FDTD) method. We introduce an optimized parallelization scheme that reduces communication overhead between computational nodes. We demonstrate that the compute time, t, scales inversely with the number of computational nodes as t {proportional_to} (N{sub nodes}){sup -0.95} {sup {+-} 0.04}. This makes it possible to solve the 3d Schroedinger equation on extremely large spatial lattices using a small computing cluster. In addition, we present a new method for precisely determining the energy eigenvalues and wavefunctions of quantum states based on a symmetry constraint on the FDTD initial condition. Finally, we discuss the usage of multi-resolution techniques in order to speed up convergence on extremely large lattices.

  11. Prostate boundary segmentation from ultrasound images using 2D active shape models: optimisation and extension to 3D.

    PubMed

    Hodge, Adam C; Fenster, Aaron; Downey, Dónal B; Ladak, Hanif M

    2006-12-01

    Boundary outlining, or segmentation, of the prostate is an important task in diagnosis and treatment planning for prostate cancer. This paper describes an algorithm based on two-dimensional (2D) active shape models (ASM) for semi-automatic segmentation of the prostate boundary from ultrasound images. Optimisation of the 2D ASM for prostatic ultrasound was done first by examining ASM construction and image search parameters. Extension of the algorithm to three-dimensional (3D) segmentation was then done using rotational-based slicing. Evaluation of the 3D segmentation algorithm used distance- and volume-based error metrics to compare algorithm generated boundary outlines to gold standard (manually generated) boundary outlines. Minimum description length landmark placement for ASM construction, and specific values for constraints and image search were found to be optimal. Evaluation of the algorithm versus gold standard boundaries found an average mean absolute distance of 1.09+/-0.49 mm, an average percent absolute volume difference of 3.28+/-3.16%, and a 5x speed increase versus manual segmentation. PMID:16930764

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

    PubMed

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

    2013-10-21

    Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation. PMID:24077264

  13. 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.

  14. Secure 3D watermarking algorithm based on point set projection

    NASA Astrophysics Data System (ADS)

    Liu, Quan; Zhang, Xiaomei

    2007-11-01

    3D digital models greatly facilitate the distribution and storage of information. While its copyright protection problems attract more and more research interests. A novel secure digital watermarking algorithm for 3D models is proposed in this paper. In order to survive most attacks like rotation, cropping, smoothing, adding noise, etc, the projection of the model's point set is chosen as the carrier of the watermark in the presented algorithm, in which contains the copyright information as logos, text, and so on. Then projection of the model's point set onto x, y and z plane are calculated respectively. Before watermark embedding process, the original watermark is scrambled by a key. Each projection is singular value decomposed, and the scrambled watermark is embedded into the SVD(singular value decomposed) domain of the above x, y and z plane respectively. After that we use the watermarked x, y and z plane to recover the vertices of the model and the watermarked model is attained. Only the legal user can remove the watermark from the watermarked models using the private key. Experiments are presented in the paper to show that the proposed algorithm has good performance on various malicious attacks.

  15. 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.

  16. 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.

  17. 3D Hail Size Distribution Interpolation/Extrapolation Algorithm

    NASA Technical Reports Server (NTRS)

    Lane, John

    2013-01-01

    Radar data can usually detect hail; however, it is difficult for present day radar to accurately discriminate between hail and rain. Local ground-based hail sensors are much better at detecting hail against a rain background, and when incorporated with radar data, provide a much better local picture of a severe rain or hail event. The previous disdrometer interpolation/ extrapolation algorithm described a method to interpolate horizontally between multiple ground sensors (a minimum of three) and extrapolate vertically. This work is a modification to that approach that generates a purely extrapolated 3D spatial distribution when using a single sensor.

  18. 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.

  19. 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. PMID:24710155

  20. 3D Filament Network Segmentation with Multiple Active Contours

    NASA Astrophysics Data System (ADS)

    Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

    2014-03-01

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

  1. Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images

    PubMed Central

    Jang, Yeonggul; Jung, Ho Yub; Hong, Youngtaek; Cho, Iksung; Shim, Hackjoon; Chang, Hyuk-Jae

    2016-01-01

    This paper presents a method for the automatic 3D segmentation of the ascending aorta from coronary computed tomography angiography (CCTA). The segmentation is performed in three steps. First, the initial seed points are selected by minimizing a newly proposed energy function across the Hough circles. Second, the ascending aorta is segmented by geodesic distance transformation. Third, the seed points are effectively transferred through the next axial slice by a novel transfer function. Experiments are performed using a database composed of 10 patients' CCTA images. For the experiment, the ground truths are annotated manually on the axial image slices by a medical expert. A comparative evaluation with state-of-the-art commercial aorta segmentation algorithms shows that our approach is computationally more efficient and accurate under the DSC (Dice Similarity Coefficient) measurements. PMID:26904151

  2. 2D/3D registration algorithm for lung brachytherapy

    SciTech Connect

    Zvonarev, P. S.; Farrell, T. J.; Hunter, R.; Wierzbicki, M.; Hayward, J. E.; Sur, R. K.

    2013-02-15

    Purpose: A 2D/3D registration algorithm is proposed for registering orthogonal x-ray images with a diagnostic CT volume for high dose rate (HDR) lung brachytherapy. Methods: The algorithm utilizes a rigid registration model based on a pixel/voxel intensity matching approach. To achieve accurate registration, a robust similarity measure combining normalized mutual information, image gradient, and intensity difference was developed. The algorithm was validated using a simple body and anthropomorphic phantoms. Transfer catheters were placed inside the phantoms to simulate the unique image features observed during treatment. The algorithm sensitivity to various degrees of initial misregistration and to the presence of foreign objects, such as ECG leads, was evaluated. Results: The mean registration error was 2.2 and 1.9 mm for the simple body and anthropomorphic phantoms, respectively. The error was comparable to the interoperator catheter digitization error of 1.6 mm. Preliminary analysis of data acquired from four patients indicated a mean registration error of 4.2 mm. Conclusions: Results obtained using the proposed algorithm are clinically acceptable especially considering the complications normally encountered when imaging during lung HDR brachytherapy.

  3. A Neurocomputational Method for Fully Automated 3D Dendritic Spine Detection and Segmentation of Medium-sized Spiny Neurons

    PubMed Central

    Zhang, Yong; Chen, Kun; Baron, Matthew; Teylan, Merilee A.; Kim, Yong; Song, Zhihuan; Greengard, Paul

    2010-01-01

    Acquisition and quantitative analysis of high resolution images of dendritic spines are challenging tasks but are necessary for the study of animal models of neurological and psychiatric diseases. Currently available methods for automated dendritic spine detection are for the most part customized for 2D image slices, not volumetric 3D images. In this work, a fully automated method is proposed to detect and segment dendritic spines from 3D confocal microscopy images of medium-sized spiny neurons (MSNs). MSNs constitute a major neuronal population in striatum, and abnormalities in their function are associated with several neurological and psychiatric diseases. Such automated detection is critical for the development of new 3D neuronal assays which can be used for the screening of drugs and the studies of their therapeutic effects. The proposed method utilizes a generalized gradient vector flow (GGVF) with a new smoothing constraint and then detects feature points near the central regions of dendrites and spines. Then, the central regions are refined and separated based on eigen-analysis and multiple shape measurements. Finally, the spines are segmented in 3D space using the fast marching algorithm, taking the detected central regions of spines as initial points. The proposed method is compared with three popular existing methods for centerline extraction and also with manual results for dendritic spine detection in 3D space. The experimental results and comparisons show that the proposed method is able to automatically and accurately detect, segment, and quantitate dendritic spines in 3D images of MSNs. PMID:20100579

  4. Automatic 3D segmentation of ultrasound images using atlas registration and statistical texture prior

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  5. 3D sensor algorithms for spacecraft pose determination

    NASA Astrophysics Data System (ADS)

    Trenkle, John M.; Tchoryk, Peter, Jr.; Ritter, Greg A.; Pavlich, Jane C.; Hickerson, Aaron S.

    2006-05-01

    Researchers at the Michigan Aerospace Corporation have developed accurate and robust 3-D algorithms for pose determination (position and orientation) of satellites as part of an on-going effort supporting autonomous rendezvous, docking and space situational awareness activities. 3-D range data from a LAser Detection And Ranging (LADAR) sensor is the expected input; however, the approach is unique in that the algorithms are designed to be sensor independent. Parameterized inputs allow the algorithms to be readily adapted to any sensor of opportunity. The cornerstone of our approach is the ability to simulate realistic range data that may be tailored to the specifications of any sensor. We were able to modify an open-source raytracing package to produce point cloud information from which high-fidelity simulated range images are generated. The assumptions made in our experimentation are as follows: 1) we have access to a CAD model of the target including information about the surface scattering and reflection characteristics of the components; 2) the satellite of interest may appear at any 3-D attitude; 3) the target is not necessarily rigid, but does have a limited number of configurations; and, 4) the target is not obscured in any way and is the only object in the field of view of the sensor. Our pose estimation approach then involves rendering a large number of exemplars (100k to 5M), extracting 2-D (silhouette- and projection-based) and 3-D (surface-based) features, and then training ensembles of decision trees to predict: a) the 4-D regions on a unit hypersphere into which the unit quaternion that represents the vehicle [Q X, Q Y, Q Z, Q W] is pointing, and, b) the components of that unit quaternion. Results have been quite promising and the tools and simulation environment developed for this application may also be applied to non-cooperative spacecraft operations, Autonomous Hazard Detection and Avoidance (AHDA) for landing craft, terrain mapping, vehicle

  6. 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.

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

    PubMed

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

    2013-01-01

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

  8. 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.

  9. 3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest.

    PubMed

    Jin, Chao; Shi, Fei; Xiang, Dehui; Jiang, Xueqing; Zhang, Bin; Wang, Ximing; Zhu, Weifang; Gao, Enting; Chen, Xinjian

    2016-06-01

    In this paper, a fully automatic method is proposed to segment the kidney into multiple components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT abdominal images. The proposed fast automatic segmentation method of kidney consists of two main parts: localization of renal cortex and segmentation of kidney components. In the localization of renal cortex phase, a method which fully combines 3D Generalized Hough Transform (GHT) and 3D Active Appearance Models (AAM) is applied to localize the renal cortex. In the segmentation of kidney components phase, a modified Random Forests (RF) method is proposed to segment the kidney into four components based on the result from localization phase. During the implementation, a multithreading technology is applied to speed up the segmentation process. The proposed method was evaluated on a clinical abdomen CT data set, including 37 contrast-enhanced volume data using leave-one-out strategy. The overall true-positive volume fraction and false-positive volume fraction were 93.15%, 0.37% for renal cortex segmentation; 83.09%, 0.97% for renal column segmentation; 81.92%, 0.55% for renal medulla segmentation; and 80.28%, 0.30% for renal pelvis segmentation, respectively. The average computational time of segmenting kidney into four components took 20 seconds. PMID:26742124

  10. Fusion of ultrasound B-mode and vibro-elastography images for automatic 3D segmentation of the prostate.

    PubMed

    Mahdavi, S Sara; Moradi, Mehdi; Morris, William J; Goldenberg, S Larry; Salcudean, Septimiu E

    2012-11-01

    Prostate segmentation in B-mode images is a challenging task even when done manually by experts. In this paper we propose a 3D automatic prostate segmentation algorithm which makes use of information from both ultrasound B-mode and vibro-elastography data.We exploit the high contrast to noise ratio of vibro-elastography images of the prostate, in addition to the commonly used B-mode images, to implement a 2D Active Shape Model (ASM)-based segmentation algorithm on the midgland image. The prostate model is deformed by a combination of two measures: the gray level similarity and the continuity of the prostate edge in both image types. The automatically obtained mid-gland contour is then used to initialize a 3D segmentation algorithm which models the prostate as a tapered and warped ellipsoid. Vibro-elastography images are used in addition to ultrasound images to improve boundary detection.We report a Dice similarity coefficient of 0.87±0.07 and 0.87±0.08 comparing the 2D automatic contours with manual contours of two observers on 61 images. For 11 cases, a whole gland volume error of 10.2±2.2% and 13.5±4.1% and whole gland volume difference of -7.2±9.1% and -13.3±12.6% between 3D automatic and manual surfaces of two observers is obtained. This is the first validated work showing the fusion of B-mode and vibro-elastography data for automatic 3D segmentation of the prostate. PMID:22829391

  11. Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

    Multi-chamber heart segmentation is a prerequisite for quantification of the cardiac function. In this paper, we propose an automatic heart chamber segmentation system. There are two closely related tasks to develop such a system: heart modeling and automatic model fitting to an unseen volume. The heart is a complicated non-rigid organ with four chambers and several major vessel trunks attached. A flexible and accurate model is necessary to capture the heart chamber shape at an appropriate level of details. In our four-chamber surface mesh model, the following two factors are considered and traded-off: 1) accuracy in anatomy and 2) easiness for both annotation and automatic detection. Important landmarks such as valves and cusp points on the interventricular septum are explicitly represented in our model. These landmarks can be detected reliably to guide the automatic model fitting process. We also propose two mechanisms, the rotation-axis based and parallel-slice based resampling methods, to establish mesh point correspondence, which is necessary to build a statistical shape model to enforce priori shape constraints in the model fitting procedure. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3D computed tomography (CT) volumes. Our approach is based on recent advances in learning discriminative object models and we exploit a large database of annotated CT volumes. We formulate the segmentation as a two step learning problem: anatomical structure localization and boundary delineation. A novel algorithm, Marginal Space Learning (MSL), is introduced to solve the 9-dimensional similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. Extensive experiments demonstrate the efficiency and robustness of the proposed approach, comparing favorably to the state-of-the-art. This

  12. Automatic 3D image registration using voxel similarity measurements based on a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Huang, Wei; Sullivan, John M., Jr.; Kulkarni, Praveen; Murugavel, Murali

    2006-03-01

    An automatic 3D non-rigid body registration system based upon the genetic algorithm (GA) process is presented. The system has been successfully applied to 2D and 3D situations using both rigid-body and affine transformations. Conventional optimization techniques and gradient search strategies generally require a good initial start location. The GA approach avoids the local minima/maxima traps of conventional optimization techniques. Based on the principles of Darwinian natural selection (survival of the fittest), the genetic algorithm has two basic steps: 1. Randomly generate an initial population. 2. Repeated application of the natural selection operation until a termination measure is satisfied. The natural selection process selects individuals based on their fitness to participate in the genetic operations; and it creates new individuals by inheritance from both parents, genetic recombination (crossover) and mutation. Once the termination criteria are satisfied, the optimum is selected from the population. The algorithm was applied on 2D and 3D magnetic resonance images (MRI). It does not require any preprocessing such as threshold, smoothing, segmentation, or definition of base points or edges. To evaluate the performance of the GA registration, the results were compared with results of the Automatic Image Registration technique (AIR) and manual registration which was used as the gold standard. Results showed that our GA implementation was a robust algorithm and gives very close results to the gold standard. A pre-cropping strategy was also discussed as an efficient preprocessing step to enhance the registration accuracy.

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

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  14. 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

  15. New Algorithms for Large-scale 3D Radiation Transport

    NASA Astrophysics Data System (ADS)

    Lentz, Eric J.

    2009-05-01

    Radiation transport is critical not only for analysis of astrophysical objects but also for the dynamical transport of energy within. Increased fidelity and dimensionality of the other components of such models requires a similar improvement in the radiation transport. Modern astrophysical simulations can be large enough that the values for a single variable for the entire computational domain cannot be stored on a single compute node. The natural solution is to decompose the physical domain into pieces with each node responsible for a single sub-domain. Using localized plus "ghost" zone data works well for problems like explicit hydrodynamics or nuclear reaction networks with modest impact from inter-process communication. Unfortunately, radiation transport is an inherently non-local process that couples the entire model domain together and efficient algorithms are needed to conquer this problem. In this poster, I present the early development of a new parallel, 3-D transport code using ray tracing to formally solve the transport equation across numerically decomposed domains. The algorithm model takes advantage of one-sided communication to develop a scalable, parallel formal solver. Other aspects and future direction of the parallel code development such as scalability and the inclusion of scattering will also be discussed.

  16. Split Bregman's algorithm for three-dimensional mesh segmentation

    NASA Astrophysics Data System (ADS)

    Habiba, Nabi; Ali, Douik

    2016-05-01

    Variational methods have attracted a lot of attention in the literature, especially for image and mesh segmentation. The methods aim at minimizing the energy to optimize both edge and region detections. We propose a spectral mesh decomposition algorithm to obtain disjoint but meaningful regions of an input mesh. The related optimization problem is nonconvex, and it is very difficult to find a good approximation or global optimum, which represents a challenge in computer vision. We propose an alternating split Bregman algorithm for mesh segmentation, where we extended the image-dedicated model to a three-dimensional (3-D) mesh one. By applying our scheme to 3-D mesh segmentation, we obtain fast solvers that can outperform various conventional ones, such as graph-cut and primal dual methods. A consistent evaluation of the proposed method on various public domain 3-D databases for different metrics is elaborated, and a comparison with the state-of-the-art is performed.

  17. 3-D Multiphase Segmentation of X-Ray Micro Computed Tomography Data of Geologic Materials

    NASA Astrophysics Data System (ADS)

    Tuller, M.; Kulkarni, R.; Fink, W.

    2011-12-01

    Advancements of noninvasive imaging methods such as X-Ray Computed Tomography (CT) led to a recent surge of applications in Geoscience. While substantial efforts and resources have been devoted to advance CT technology and micro-scale analysis, the development of a stable 3-D multiphase image segmentation method applicable to large datasets is lacking. To eliminate the need for wet/dry or dual energy scans, image alignment, and subtraction analysis, commonly applied in synchrotron X-Ray micro CT, a segmentation method based on a Bayesian Markov Random Field (MRF) framework amenable to true 3-D multiphase processing was developed and evaluated. Furthermore, several heuristic and deterministic combinatorial optimization schemes required to solve the labeling problem of the MRF image model were implemented and tested for computational efficiency and their impact on segmentation results. Test results for natural and artificial porous media datasets demonstrate great potential of the MRF image model for 3-D multiphase segmentation.

  18. 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.

  19. 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.

  20. Reliability measure for segmenting algorithms

    NASA Astrophysics Data System (ADS)

    Alvarez, Robert E.

    2004-05-01

    Segmenting is a key initial step in many computer-aided detection (CAD) systems. Our purpose is to develop a method to estimate the reliability of segmenting algorithm results. We use a statistical shape model computed using principal component analysis. The model retains a small number of eigenvectors, or modes, that represent a large fraction of the variance. The residuals between the segmenting result and its projection into the space of retained modes are computed. The sum of the squares of residuals is transformed to a zero-mean, unit standard deviation Gaussian random variable. We also use the standardized scale parameter. The reliability measure is the probability that the transformed residuals and scale parameter are greater than the absolute value of the observed values. We tested the reliability measure with thirty chest x-ray images with "leave-out-one" testing. The Gaussian assumption was verified using normal probability plots. For each image, a statistical shape model was computed from the hand-digitized data of the rest of the images in the training set. The residuals and scale parameter with automated segment results for the image were used to compute the reliability measure in each case. The reliability measure was significantly lower for two images in the training set with unusual lung fields or processing errors. The data and Matlab scripts for reproducing the figures are at http://www.aprendtech.com/papers/relmsr.zip Errors detected by the new reliability measure can be used to adjust processing or warn the user.

  1. Algorithms for Accurate and Fast Plotting of Contour Surfaces in 3D Using Hexahedral Elements

    NASA Astrophysics Data System (ADS)

    Singh, Chandan; Saini, Jaswinder Singh

    2016-07-01

    In the present study, Fast and accurate algorithms for the generation of contour surfaces in 3D are described using hexahedral elements which are popular in finite element analysis. The contour surfaces are described in the form of groups of boundaries of contour segments and their interior points are derived using the contour equation. The locations of contour boundaries and the interior points on contour surfaces are as accurate as the interpolation results obtained by hexahedral elements and thus there are no discrepancies between the analysis and visualization results.

  2. Algorithms for Accurate and Fast Plotting of Contour Surfaces in 3D Using Hexahedral Elements

    NASA Astrophysics Data System (ADS)

    Singh, Chandan; Saini, Jaswinder Singh

    2016-05-01

    In the present study, Fast and accurate algorithms for the generation of contour surfaces in 3D are described using hexahedral elements which are popular in finite element analysis. The contour surfaces are described in the form of groups of boundaries of contour segments and their interior points are derived using the contour equation. The locations of contour boundaries and the interior points on contour surfaces are as accurate as the interpolation results obtained by hexahedral elements and thus there are no discrepancies between the analysis and visualization results.

  3. 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.

  4. Potential of ILRIS3D Intensity Data for Planar Surfaces Segmentation

    PubMed Central

    Wang, Chi-Kuei; Lu, Yao-Yu

    2009-01-01

    Intensity value based point cloud segmentation has received less attention because the intensity value of the terrestrial laser scanner is usually altered by receiving optics/hardware or the internal propriety software, which is unavailable to the end user. We offer a solution by assuming the terrestrial laser scanners are stable and the behavior of the intensity value can be characterized. Then, it is possible to use the intensity value for segmentation by observing its behavior, i.e., intensity value variation, pattern and presence of location of intensity values, etc. In this study, experiment results for characterizing the intensity data of planar surfaces collected by ILRIS3D, a terrestrial laser scanner, are reported. Two intensity formats, grey and raw, are employed by ILRIS3D. It is found from the experiment results that the grey intensity has less variation; hence it is preferable for point cloud segmentation. A warm-up time of approximate 1.5 hours is suggested for more stable intensity data. A segmentation method based on the visual cues of the intensity images sequence, which contains consecutive intensity images, is proposed in order to segment the 3D laser points of ILRIS3D. This method is unique to ILRIS3D data and does not require radiometric calibration. PMID:22346726

  5. Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning

    NASA Astrophysics Data System (ADS)

    Serna, Andrés; Marcotegui, Beatriz

    2014-07-01

    We propose an automatic and robust approach to detect, segment and classify urban objects from 3D point clouds. Processing is carried out using elevation images and the result is reprojected onto the 3D point cloud. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM with geometrical and contextual features. Our methodology is evaluated on databases from Ohio (USA) and Paris (France). In the former, our method detects 98% of the objects, 78% of them are correctly segmented and 82% of the well-segmented objects are correctly classified. In the latter, our method leads to an improvement of about 15% on the classification step with respect to previous works. Quantitative results prove that our method not only provides a good performance but is also faster than other works reported in the literature.

  6. Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging.

    PubMed

    Cohen, Laurent D; Deschamps, Thomas

    2007-08-01

    We present a new fast approach for segmentation of thin branching structures, like vascular trees, based on Fast-Marching (FM) and Level Set (LS) methods. FM allows segmentation of tubular structures by inflating a "long balloon" from a user given single point. However, when the tubular shape is rather long, the front propagation may blow up through the boundary of the desired shape close to the starting point. Our contribution is focused on a method to propagate only the useful part of the front while freezing the rest of it. We demonstrate its ability to segment quickly and accurately tubular and tree-like structures. We also develop a useful stopping criterion for the causal front propagation. We finally derive an efficient algorithm for extracting an underlying 1D skeleton of the branching objects, with minimal path techniques. Each branch being represented by its centerline, we automatically detect the bifurcations, leading to the "Minimal Tree" representation. This so-called "Minimal Tree" is very useful for visualization and quantification of the pathologies in our anatomical data sets. We illustrate our algorithms by applying it to several arteries datasets. PMID:17671862

  7. 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.

  8. Weakly supervised automatic segmentation and 3D modeling of the knee joint from MR images

    NASA Astrophysics Data System (ADS)

    Amami, Amal; Ben Azouz, Zouhour

    2013-12-01

    Automatic segmentation and 3D modeling of the knee joint from MR images, is a challenging task. Most of the existing techniques require the tedious manual segmentation of a training set of MRIs. We present an approach that necessitates the manual segmentation of one MR image. It is based on a volumetric active appearance model. First, a dense tetrahedral mesh is automatically created on a reference MR image that is arbitrary selected. Second, a pairwise non-rigid registration between each MRI from a training set and the reference MRI is computed. The non-rigid registration is based on a piece-wise affine deformation using the created tetrahedral mesh. The minimum description length is then used to bring all the MR images into a correspondence. An average image and tetrahedral mesh, as well as a set of main modes of variations, are generated using the established correspondence. Any manual segmentation of the average MRI can be mapped to other MR images using the AAM. The proposed approach has the advantage of simultaneously generating 3D reconstructions of the surface as well as a 3D solid model of the knee joint. The generated surfaces and tetrahedral meshes present the interesting property of fulfilling a correspondence between different MR images. This paper shows preliminary results of the proposed approach. It demonstrates the automatic segmentation and 3D reconstruction of a knee joint obtained by mapping a manual segmentation of a reference image.

  9. A parallel algorithm for 3D dislocation dynamics

    NASA Astrophysics Data System (ADS)

    Wang, Zhiqiang; Ghoniem, Nasr; Swaminarayan, Sriram; LeSar, Richard

    2006-12-01

    Dislocation dynamics (DD), a discrete dynamic simulation method in which dislocations are the fundamental entities, is a powerful tool for investigation of plasticity, deformation and fracture of materials at the micron length scale. However, severe computational difficulties arising from complex, long-range interactions between these curvilinear line defects limit the application of DD in the study of large-scale plastic deformation. We present here the development of a parallel algorithm for accelerated computer simulations of DD. By representing dislocations as a 3D set of dislocation particles, we show here that the problem of an interacting ensemble of dislocations can be converted to a problem of a particle ensemble, interacting with a long-range force field. A grid using binary space partitioning is constructed to keep track of node connectivity across domains. We demonstrate the computational efficiency of the parallel micro-plasticity code and discuss how O(N) methods map naturally onto the parallel data structure. Finally, we present results from applications of the parallel code to deformation in single crystal fcc metals.

  10. 3D Segmentation of the Left Ventricle Combining Long- and Shortaxis Views

    NASA Astrophysics Data System (ADS)

    Relan, Jatin; Säring, Dennis; Groth, Michael; Müllerleile, Kai; Handels, Heinz

    Segmentation of the left ventricle (LV) is required to quantify LV remodeling after myocardial infarction. Therefore spatiotemporal Cine MR sequences including longaxis and shortaxis images are acquired. In this paper a new segmentation method for fast and robust segmentation of the left ventricle is presented. The new approach considers the position of the mitral valve and the apex as well as the longaxis contours to generate a 3D LV surface model. The segmentation result can be checked and adjusted in the shortaxis images. Finally quantitative parameters were extracted. For evaluation the LV was segmented in eight datasets of the same subject by two medical experts using a contour drawing tool and the new segmentation tool. The results of both methods were compared concerning interaction time and intra- and interobserver variance. The presented segmentation method proved to be fast. The intra- and interobserver variance is decreased for all extracted parameters.

  11. 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.

  12. 3D segmentation of masses in DCE-MRI images using FCM and adaptive MRF

    NASA Astrophysics Data System (ADS)

    Zhang, Chengjie; Li, Lihua

    2014-03-01

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

  13. 3-D surface rendering of myocardial SPECT images segmented by level set technique.

    PubMed

    Lee, Hwun-Jae; Lee, Sangbock

    2012-06-01

    SPECT(single photon emission computed tomography) myocardial imaging is a diagnosis technique that images the region of interest and examines any change induced by disease using a computer after injects intravenously a radiopharmaceutical drug emitting gamma ray and the drug has dispersed evenly in the heart . Myocardial perfusion imaging, which contains functional information, is useful for non-invasive diagnosis of myocardial disease but noises caused by physical factors and low resolution give difficulty in reading the images. In order to help reading myocardial images, this study proposed a method that segments myocardial images and reconstructs the segmented region into a 3D image. To resolve difficulty in reading, we segmented the left ventricle, the region of interest, using a level set and modeled the segmented region into a 3D image. PMID:20839037

  14. Phase grouping-based needle segmentation in 3-D trans-rectal ultrasound-guided prostate trans-perineal therapy.

    PubMed

    Qiu, Wu; Yuchi, Ming; Ding, Mingyue

    2014-04-01

    A robust and efficient needle segmentation method used to localize and track the needle in 3-D trans-rectal ultrasound (TRUS)-guided prostate therapy is proposed. The algorithmic procedure begins by cropping the 3-D US image containing a needle; then all voxels in the cropped 3-D image are grouped into different line support regions (LSRs) based on the outer product of the adjacent voxels' gradient vector. Two different needle axis extraction methods in the candidate LSR are presented: least-squares fitting and 3-D randomized Hough transform. Subsequent local optimization refines the position of the needle axis. Finally, the needle endpoint is localized by finding an intensity drop along the needle axis. The proposed methods were validated with 3-D TRUS tissue-mimicking agar phantom images, chicken breast phantom images and patient images obtained during prostate cryotherapy. The results of the in vivo test indicate that our method can localize the needle accurately and robustly with a needle endpoint localization accuracy <1.43 mm and detection accuracy >84%, which are favorable for 3-D TRUS-guided prostate trans-perineal therapy. PMID:24462163

  15. 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.

  16. Image enhancement and segmentation of fluid-filled structures in 3D ultrasound images

    NASA Astrophysics Data System (ADS)

    Chalana, Vikram; Dudycha, Stephen; McMorrow, Gerald

    2003-05-01

    Segmentation of fluid-filled structures, such as the urinary bladder, from three-dimensional ultrasound images is necessary for measuring their volume. This paper describes a system for image enhancement, segmentation and volume measurement of fluid-filled structures on 3D ultrasound images. The system was applied for the measurement of urinary bladder volume. Results show an average error of less than 10% in the estimation of the total bladder volume.

  17. 3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning

    NASA Astrophysics Data System (ADS)

    Yang, Xiaofeng; Fei, Baowei

    2012-02-01

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

  18. 2D segmented large inkjet printhead for high speed 3D printers

    NASA Astrophysics Data System (ADS)

    Einat, Moshe; Bar-Levav, Elkana

    2015-05-01

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

  19. Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search

    PubMed Central

    Abràmoff, Michael D.; Kardon, Randy; Russell, Stephen R.; Wu, Xiaodong; Sonka, Milan

    2008-01-01

    Current techniques for segmenting macular optical coherence tomography (OCT) images have been 2-D in nature. Furthermore, commercially available OCT systems have only focused on segmenting a single layer of the retina, even though each intraretinal layer may be affected differently by disease. We report an automated approach for segmenting (anisotropic) 3-D macular OCT scans into five layers. Each macular OCT dataset consisted of six linear radial scans centered at the fovea. The six surfaces defining the five layers were identified on each 3-D composite image by transforming the segmentation task into that of finding a minimum-cost closed set in a geometric graph constructed from edge/regional information and a priori determined surface smoothness and interaction constraints. The method was applied to the macular OCT scans of 12 patients (24 3-D composite image datasets) with unilateral anterior ischemic optic neuropathy (AION). Using the average of three experts’ tracings as a reference standard resulted in an overall mean unsigned border positioning error of 6.1 ± 2.9 µm, a result comparable to the interobserver variability (6.9 ± 3.3 µm). Our quantitative analysis of the automated segmentation results from AION subject data revealed that the inner retinal layer thickness for the affected eye was 24.1 µm (21%) smaller on average than for the unaffected eye (P < 0.001), supporting the need for segmenting the layers separately. PMID:18815101

  20. Automated Lung Segmentation and Image Quality Assessment for Clinical 3-D/4-D-Computed Tomography

    PubMed Central

    Li, Guang

    2014-01-01

    4-D-computed tomography (4DCT) provides not only a new dimension of patient-specific information for radiation therapy planning and treatment, but also a challenging scale of data volume to process and analyze. Manual analysis using existing 3-D tools is unable to keep up with vastly increased 4-D data volume, automated processing and analysis are thus needed to process 4DCT data effectively and efficiently. In this paper, we applied ideas and algorithms from image/signal processing, computer vision, and machine learning to 4DCT lung data so that lungs can be reliably segmented in a fully automated manner, lung features can be visualized and measured on the fly via user interactions, and data quality classifications can be computed in a robust manner. Comparisons of our results with an established treatment planning system and calculation by experts demonstrated negligible discrepancies (within ±2%) for volume assessment but one to two orders of magnitude performance enhancement. An empirical Fourier-analysis-based quality measure-delivered performances closely emulating human experts. Three machine learners are inspected to justify the viability of machine learning techniques used to robustly identify data quality of 4DCT images in the scalable manner. The resultant system provides a toolkit that speeds up 4-D tasks in the clinic and facilitates clinical research to improve current clinical practice. PMID:25621194

  1. 3D segmentation of the true and false lumens on CT aortic dissection images

    NASA Astrophysics Data System (ADS)

    Fetnaci, Nawel; Łubniewski, Paweł; Miguel, Bruno; Lohou, Christophe

    2013-03-01

    Our works are related to aortic dissections which are a medical emergency and can quickly lead to death. In this paper, we want to retrieve in CT images the false and the true lumens which are aortic dissection features. Our aim is to provide a 3D view of the lumens that we can difficultly obtain either by volume rendering or by another visualization tool which only directly gives the outer contour of the aorta; or by other segmentation methods because they mainly directly segment either only the outer contour of the aorta or other connected arteries and organs both. In our work, we need to segment the two lumens separately; this segmentation will allow us to: distinguish them automatically, facilitate the landing of the aortic prosthesis, propose a virtual 3d navigation and do quantitative analysis. We chose to segment these data by using a deformable model based on the fast marching method. In the classical fast marching approach, a speed function is used to control the front propagation of a deforming curve. The speed function is only based on the image gradient. In our CT images, due to the low resolution, with the fast marching the front propagates from a lumen to the other; therefore, the gradient data is insufficient to have accurate segmentation results. In the paper, we have adapted the fast marching method more particularly by modifying the speed function and we succeed in segmenting the two lumens separately.

  2. 3D active surfaces for liver segmentation in multisequence MRI images.

    PubMed

    Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro

    2016-08-01

    Biopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59. PMID:27282235

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    Accurate and objective evaluation of vertebral deformations is of significant importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is focused on three-dimensional (3D) computed tomography (CT) and magnetic resonance (MR) imaging techniques, the established methods for evaluation of vertebral deformations are limited to measuring deformations in two-dimensional (2D) x-ray images. In this paper, we propose a method for quantitative description of vertebral body deformations by efficient modelling and segmentation of vertebral bodies in 3D. The deformations are evaluated from the parameters of a 3D superquadric model, which is initialized as an elliptical cylinder and then gradually deformed by introducing transformations that yield a more detailed representation of the vertebral body shape. After modelling the vertebral body shape with 25 clinically meaningful parameters and the vertebral body pose with six rigid body parameters, the 3D model is aligned to the observed vertebral body in the 3D image. The performance of the method was evaluated on 75 vertebrae from CT and 75 vertebrae from T2-weighted MR spine images, extracted from the thoracolumbar part of normal and pathological spines. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images, as the proposed 3D model is able to describe both normal and pathological vertebral body deformations. The method may therefore be used for initialization of whole vertebra segmentation or for quantitative measurement of vertebral body deformations.

  4. HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images.

    PubMed

    Wang, Qingzhu; Kang, Wanjun; Hu, Haihui; Wang, Bin

    2016-07-01

    An Active Appearance Model (AAM) is a computer vision model which can be used to effectively segment lung fields in CT images. However, the fitting result is often inadequate when the lungs are affected by high-density pathologies. To overcome this problem, we propose a Higher-order Singular Value Decomposition (HOSVD)-based Three-dimensional (3D) AAM. An evaluation was performed on 310 diseased lungs form the Lung Image Database Consortium Image Collection. Other contemporary AAMs operate directly on patterns represented by vectors, i.e., before applying the AAM to a 3D lung volume,it has to be vectorized first into a vector pattern by some technique like concatenation. However, some implicit structural or local contextual information may be lost in this transformation. According to the nature of the 3D lung volume, HOSVD is introduced to represent and process the lung in tensor space. Our method can not only directly operate on the original 3D tensor patterns, but also efficiently reduce the computer memory usage. The evaluation resulted in an average Dice coefficient of 97.0 % ± 0.59 %, a mean absolute surface distance error of 1.0403 ± 0.5716 mm, a mean border positioning errors of 0.9187 ± 0.5381 pixel, and a Hausdorff Distance of 20.4064 ± 4.3855, respectively. Experimental results showed that our methods delivered significant and better segmentation results, compared with the three other model-based lung segmentation approaches, namely 3D Snake, 3D ASM and 3D AAM. PMID:27277277

  5. 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

  6. Automatic pulmonary vessel segmentation in 3D computed tomographic pulmonary angiographic (CTPA) images

    NASA Astrophysics Data System (ADS)

    Zhou, Chuan; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Patel, Smita; Cascade, Philip N.; Sahiner, Berkman; Wei, Jun; Ge, Jun; Kazerooni, Ella A.

    2006-03-01

    Automatic and accurate segmentation of the pulmonary vessels in 3D computed tomographic angiographic images (CTPA) is an essential step for computerized detection of pulmonary embolism (PE) because PEs only occur inside the pulmonary arteries. We are developing an automated method to segment the pulmonary vessels in 3D CTPA images. The lung region is first extracted using thresholding and morphological operations. 3D multiscale filters in combination with a newly developed response function derived from the eigenvalues of Hessian matrices are used to enhance all vascular structures including the vessel bifurcations and suppress non-vessel structures such as the lymphoid tissues surrounding the vessels. At each scale, a volume of interest (VOI) containing the response function value at each voxel is defined. The voxels with a high response indicate that there is an enhanced vessel whose size matches the given filter scale. A hierarchical expectation-maximization (EM) estimation is then applied to the VOI to segment the vessel by extracting the high response voxels at this single scale. The vessel tree is finally reconstructed by combining the segmented vessels at all scales based on a "connected component" analysis. Two experienced thoracic radiologists provided the gold standard of pulmonary arteries by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. Two CTPA cases containing PEs were used to evaluate the performance. One of these two cases also contained other lung diseases. The accuracy of vessel tree segmentation was evaluated by the percentage of the "gold standard" vessel center points overlapping with the segmented vessels. The result shows that 97.3% (1868/1920) and 92.0% (2277/2476) of the manually marked center points overlapped with the segmented vessels for the cases without and with other lung disease, respectively. The results demonstrate that vessel segmentation using our method is

  7. 3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

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

    PubMed

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

    2015-06-01

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

  9. 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

  10. Semi-automatic 3D segmentation of carotid lumen in contrast-enhanced computed tomography angiography images.

    PubMed

    Hemmati, Hamidreza; Kamli-Asl, Alireza; Talebpour, Alireza; Shirani, Shapour

    2015-12-01

    The atherosclerosis disease is one of the major causes of the death in the world. Atherosclerosis refers to the hardening and narrowing of the arteries by plaques. Carotid stenosis is a narrowing or constriction of carotid artery lumen usually caused by atherosclerosis. Carotid artery stenosis can increase risk of brain stroke. Contrast-enhanced Computed Tomography Angiography (CTA) is a minimally invasive method for imaging and quantification of the carotid plaques. Manual segmentation of carotid lumen in CTA images is a tedious and time consuming procedure which is subjected to observer variability. As a result, there is a strong and growing demand for developing computer-aided carotid segmentation procedures. In this study, a novel method is presented for carotid artery lumen segmentation in CTA data. First, the mean shift smoothing is used for uniformity enhancement of gray levels. Then with the help of three seed points, the centerlines of the arteries are extracted by a 3D Hessian based fast marching shortest path algorithm. Finally, a 3D Level set function is performed for segmentation. Results on 14 CTA volumes data show 85% of Dice similarity and 0.42 mm of mean absolute surface distance measures. Evaluation shows that the proposed method requires minimal user intervention, low dependence to gray levels changes in artery path, resistance to extreme changes in carotid diameter and carotid branch locations. The proposed method has high accuracy and can be used in qualitative and quantitative evaluation. PMID:26429385

  11. Validation of bone segmentation and improved 3-D registration using contour coherency in CT data.

    PubMed

    Wang, Liping Ingrid; Greenspan, Michael; Ellis, Randy

    2006-03-01

    A method is presented to validate the segmentation of computed tomography (CT) image sequences, and improve the accuracy and efficiency of the subsequent registration of the three-dimensional surfaces that are reconstructed from the segmented slices. The method compares the shapes of contours extracted from neighborhoods of slices in CT stacks of tibias. The bone is first segmented by an automatic segmentation technique, and the bone contour for each slice is parameterized as a one-dimensional function of normalized arc length versus inscribed angle. These functions are represented as vectors within a K-dimensional space comprising the first K amplitude coefficients of their Fourier Descriptors. The similarity or coherency of neighboring contours is measured by comparing statistical properties of their vector representations within this space. Experimentation has demonstrated this technique to be very effective at identifying low-coherency segmentations. Compared with experienced human operators, in a set of 23 CT stacks (1,633 slices), the method correctly detected 87.5% and 80% of the low-coherency and 97.7% and 95.5% of the high coherency segmentations, respectively from two different automatic segmentation techniques. Removal of the automatically detected low-coherency segmentations also significantly improved the accuracy and time efficiency of the registration of 3-D bone surface models. The registration error was reduced by over 500% (i.e., a factor of 5) and 280%, and the computational performance was improved by 540% and 791% for the two respective segmentation methods. PMID:16524088

  12. 3D Visualization of Machine Learning Algorithms with Astronomical Data

    NASA Astrophysics Data System (ADS)

    Kent, Brian R.

    2016-01-01

    We present innovative machine learning (ML) methods using unsupervised clustering with minimum spanning trees (MSTs) to study 3D astronomical catalogs. Utilizing Python code to build trees based on galaxy catalogs, we can render the results with the visualization suite Blender to produce interactive 360 degree panoramic videos. The catalogs and their ML results can be explored in a 3D space using mobile devices, tablets or desktop browsers. We compare the statistics of the MST results to a number of machine learning methods relating to optimization and efficiency.

  13. 2D and 3D shape based segmentation using deformable models.

    PubMed

    El-Baz, Ayman; Yuksel, Seniha E; Shi, Hongjian; Farag, Aly A; El-Ghar, Mohamed A; Eldiasty, Tarek; Ghoneim, Mohamed A

    2005-01-01

    A novel shape based segmentation approach is proposed by modifying the external energy component of a deformable model. The proposed external energy component depends not only on the gray level of the images but also on the shape information which is obtained from the signed distance maps of objects in a given data set. The gray level distribution and the signed distance map of the points inside and outside the object of interest are accurately estimated by modelling the empirical density function with a linear combination of discrete Gaussians (LCDG) with positive and negative components. Experimental results on the segmentation of the kidneys from low-contrast DCE-MRI and on the segmentation of the ventricles from brain MRI's show how the approach is accurate in segmenting 2-D and 3-D data sets. The 2D results for the kidney segmentation have been validated by a radiologist and the 3D results of the ventricle segmentation have been validated with a geometrical phantom. PMID:16686036

  14. 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.

  15. Bone segmentation and fracture detection in ultrasound using 3D local phase features.

    PubMed

    Hacihaliloglu, Ilker; Abugharbieh, Rafeef; Hodgson, Antony; Rohling, Robert

    2008-01-01

    3D ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted orthopaedic surgery (CAOS) applications. Automatic bone segmentation from US images, however, remains a challenge due to speckle noise and various other artifacts inherent to US. In this paper, we present intensity invariant three dimensional (3D) local image phase features, obtained using 3D Log-Gabor filter banks, for extracting ridge-like features similar to those that occur at soft tissue/bone interfaces. Our contributions include the novel extension of 2D phase symmetry features to 3D and their use in automatic extraction of bone surfaces and fractured fragments in 3D US. We validate our technique using phantom, in vitro, and in vivo experiments. Qualitative and quantitative results demonstrate remarkably clear segmentations results of bone surfaces with a localization accuracy of better than 0.62 mm and mean errors in estimating fracture displacements below 0.65 mm, which will likely be of strong clinical utility. PMID:18979759

  16. Automated segmentation of necrotic femoral head from 3D MR data.

    PubMed

    Zoroofi, Reza A; Sato, Yoshinobu; Nishii, Takashi; Sugano, Nobuhiko; Yoshikawa, Hideki; Tamura, Shinichi

    2004-07-01

    Segmentation of diseased organs is an important topic in computer assisted medical image analysis. In particular, automatic segmentation of necrotic femoral head is of importance for various corresponding clinical tasks including visualization, quantitative assessment, early diagnosis and adequate management of patients suffering from avascular necrosis of the femoral head (ANFH). Early diagnosis and treatment of ANFH is crucial since the disease occurs in relatively young individuals with an average age of 20-50, and since treatment options for more advanced disease are frequently unsuccessful. The present paper describes several new techniques and software for automatic segmentation of necrotic femoral head based on clinically obtained multi-slice T1-weighted MR data. In vivo MR data sets of 50 actual patients are used in the study. An automatic method built up to manage the segmentation task according to image intensity of bone tissues, shape of the femoral head, and other characters. The processing scheme consisted of the following five steps. (1) Rough segmentation of non-necrotic lesions of the femur by applying a 3D gray morphological operation and a 3D region growing technique. (2) Fitting a 3D ellipse to the femoral head by a new approach utilizing the constraint of the shape of the femur, and employing a principle component analysis and a simulated annealing technique. (3) Estimating the femoral neck location, and also femoral head axis by integrating anatomical information of the femur and boundary of estimated 3D ellipse. (4) Removal of non-bony tissues around the femoral neck and femoral head ligament by utilizing the estimated femoral neck axis. (5) Classification of necrotic lesions inside the estimated femoral head by a k-means technique. The above method was implemented in a Microsoft Windows software package. The feasibility of this method was tested on the data sets of 50 clinical cases (3000 MR images). PMID:15249072

  17. 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

  18. A 3D mosaic algorithm using disparity map

    NASA Astrophysics Data System (ADS)

    Yu, Bo; Kakeya, Hideki

    2015-03-01

    Conventionally there exist two major methods to create mosaics in 3D videos. One is to duplicate the area of mosaics from the image of one viewpoint (the left view or the right view) to that of the other viewpoint. This method, which is not capable of expressing depth, cannot give viewers a natural perception in 3D. The other method is to create the mosaics separately in the left view and the right view. With this method the depth is expressed in the area of mosaics, but 3D perception is not natural enough. To overcome these problems, we propose a method to create mosaics by using a disparity map. In the proposed method the mosaic of the image from one viewpoint is made with the conventional method, while the mosaic of the image from the other viewpoint is made based on the data of the disparity map so that the mosaic patterns of the two images can give proper depth perception to the viewer. We confirm that the proposed mosaic pattern using a disparity map gives more natural depth perception of the viewer by subjective experiments using a static image and two videos.

  19. 3D+t brain MRI segmentation using robust 4D Hidden Markov Chain.

    PubMed

    Lavigne, François; Collet, Christophe; Armspach, Jean-Paul

    2014-01-01

    In recent years many automatic methods have been developed to help physicians diagnose brain disorders, but the problem remains complex. In this paper we propose a method to segment brain structures on two 3D multi-modal MR images taken at different times (longitudinal acquisition). A bias field correction is performed with an adaptation of the Hidden Markov Chain (HMC) allowing us to take into account the temporal correlation in addition to spatial neighbourhood information. To improve the robustness of the segmentation of the principal brain structures and to detect Multiple Sclerosis Lesions as outliers the Trimmed Likelihood Estimator (TLE) is used during the process. The method is validated on 3D+t brain MR images. PMID:25571045

  20. A novel Hessian based algorithm for rat kidney glomerulus detection in 3D MRI

    NASA Astrophysics Data System (ADS)

    Zhang, Min; Wu, Teresa; Bennett, Kevin M.

    2015-03-01

    The glomeruli of the kidney perform the key role of blood filtration and the number of glomeruli in a kidney is correlated with susceptibility to chronic kidney disease and chronic cardiovascular disease. This motivates the development of new technology using magnetic resonance imaging (MRI) to measure the number of glomeruli and nephrons in vivo. However, there is currently a lack of computationally efficient techniques to perform fast, reliable and accurate counts of glomeruli in MR images due to the issues inherent in MRI, such as acquisition noise, partial volume effects (the mixture of several tissue signals in a voxel) and bias field (spatial intensity inhomogeneity). Such challenges are particularly severe because the glomeruli are very small, (in our case, a MRI image is ~16 million voxels, each glomerulus is in the size of 8~20 voxels), and the number of glomeruli is very large. To address this, we have developed an efficient Hessian based Difference of Gaussians (HDoG) detector to identify the glomeruli on 3D rat MR images. The image is first smoothed via DoG followed by the Hessian process to pre-segment and delineate the boundary of the glomerulus candidates. This then provides a basis to extract regional features used in an unsupervised clustering algorithm, completing segmentation by removing the false identifications occurred in the pre-segmentation. The experimental results show that Hessian based DoG has the potential to automatically detect glomeruli,from MRI in 3D, enabling new measurements of renal microstructure and pathology in preclinical and clinical studies.

  1. 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. PMID:24465721

  2. 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

  3. Segmentation of 3D EBSD data for subgrain boundary identification and feature characterization.

    PubMed

    Loeb, Andrew; Ferry, Michael; Bassman, Lori

    2016-02-01

    Subgrain structures formed during plastic deformation of metals can be observed by electron backscatter diffraction (EBSD) but are challenging to identify automatically. We have adapted a 2D image segmentation technique, fast multiscale clustering (FMC), to 3D EBSD data using a novel variance function to accommodate quaternion data. This adaptation, which has been incorporated into the free open source texture analysis software package MTEX, is capable of segmenting based on subtle and gradual variation as well as on sharp boundaries within the data. FMC has been further modified to group the resulting closed 3D segment boundaries into distinct coherent surfaces based on local normals of a triangulated surface. We demonstrate the excellent capabilities of this technique with application to 3D EBSD data sets generated from cold rolled aluminum containing well-defined microbands, cold rolled and partly recrystallized extra low carbon steel microstructure containing three magnitudes of boundary misorientations, and channel-die plane strain compressed Goss-oriented nickel crystal containing microbands with very subtle changes in orientation. PMID:26630071

  4. 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

    2012-01-01

    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

  5. 3D radiative transfer in colliding wind binaries: Application of the SimpleX algorithm to 3D SPH simulations

    NASA Astrophysics Data System (ADS)

    Madura, Thomas; Clementel, Nicola; Kruip, Chael; Icke, Vincent; Gull, Theodore

    2014-09-01

    We present the first results of full 3D radiative transfer simulations of the colliding stellar winds in a massive binary system. We accomplish this by applying the SIMPLEX algorithm for 3D radiative transfer on an unstructured Delaunay grid to recent 3D smoothed particle hydrodynamics (SPH) simulations of the colliding winds in the binary system η Carinae. We use SIMPLEX to obtain detailed ionization fractions of hydrogen and helium, in 3D, at the resolution of the original SPH simulations. We show how the SIMPLEX simulations can be used to generate synthetic spectral data cubes for comparison to data obtained with the Hubble Space Telescope (HST)/Space Telescope Imaging Spectrograph as part of a multi-cycle program to map changes in η Car's extended interacting wind structures across one binary cycle. Comparison of the HST observations to the SIMPLEX models can help lead to more accurate constraints on the orbital, stellar, and wind parameters of the η Car system, such as the primary's mass-loss rate and the companion's temperature and luminosity. While we initially focus specifically on the η Car binary, the numerical methods employed can be applied to numerous other colliding wind (WR140, WR137, WR19) and dusty 'pinwheel' (WR104, WR98a) binary systems. One of the biggest remaining mysteries is how dust can form and survive in such systems that contain a hot, luminous O star. Coupled with 3D hydrodynamical simulations, SIMPLEX simulations have the potential to help determine the regions where dust can form and survive in these unique objects.

  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. Vector algorithms for geometrically nonlinear 3D finite element analysis

    NASA Technical Reports Server (NTRS)

    Whitcomb, John D.

    1989-01-01

    Algorithms for geometrically nonlinear finite element analysis are presented which exploit the vector processing capability of the VPS-32, which is closely related to the CYBER 205. By manipulating vectors (which are long lists of numbers) rather than individual numbers, very high processing speeds are obtained. Long vector lengths are obtained without extensive replication or reordering by storage of intermediate results in strategic patterns at all stages of the computations. Comparisons of execution times with those from programs using either scalar or other vector programming techniques indicate that the algorithms presented are quite efficient.

  8. Fast algorithm of 3D median filter for medical image despeckling

    NASA Astrophysics Data System (ADS)

    Xiong, Chengyi; Hou, Jianhua; Gao, Zhirong; He, Xiang; Chen, Shaoping

    2007-12-01

    Three-dimensional (3-D) median filtering is very useful to eliminate speckle noise from a medical imaging source, such as functional magnetic resonance imaging (fMRI) and ultrasonic imaging. 3-D median filtering is characterized by its higher computation complexity. N 3(N 3-1)/2 comparison operations would be required for 3-D median filtering with N×N×N window if the conventional bubble-sorting algorithm is adopted. In this paper, an efficient fast algorithm for 3-D median filtering was presented, which considerably reduced the computation complexity for extracting the median of a 3-D data array. Compared to the state-of-the-art, the proposed method could reduce the computation complexity of 3-D median filtering by 33%. It results in efficiently reducing the system delay of the 3-D median filter by software implementation, and the system cost and power consumption by hardware implementation.

  9. Multivariate statistical analysis as a tool for the segmentation of 3D spectral data.

    PubMed

    Lucas, G; Burdet, P; Cantoni, M; Hébert, C

    2013-01-01

    Acquisition of three-dimensional (3D) spectral data is nowadays common using many different microanalytical techniques. In order to proceed to the 3D reconstruction, data processing is necessary not only to deal with noisy acquisitions but also to segment the data in term of chemical composition. In this article, we demonstrate the value of multivariate statistical analysis (MSA) methods for this purpose, allowing fast and reliable results. Using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) coupled with a focused ion beam (FIB), a stack of spectrum images have been acquired on a sample produced by laser welding of a nickel-titanium wire and a stainless steel wire presenting a complex microstructure. These data have been analyzed using principal component analysis (PCA) and factor rotations. PCA allows to significantly improve the overall quality of the data, but produces abstract components. Here it is shown that rotated components can be used without prior knowledge of the sample to help the interpretation of the data, obtaining quickly qualitative mappings representative of elements or compounds found in the material. Such abundance maps can then be used to plot scatter diagrams and interactively identify the different domains in presence by defining clusters of voxels having similar compositions. Identified voxels are advantageously overlaid on secondary electron (SE) images with higher resolution in order to refine the segmentation. The 3D reconstruction can then be performed using available commercial softwares on the basis of the provided segmentation. To asses the quality of the segmentation, the results have been compared to an EDX quantification performed on the same data. PMID:24035679

  10. Level set algorithms comparison for multi-slice CT left ventricle segmentation

    NASA Astrophysics Data System (ADS)

    Medina, Ruben; La Cruz, Alexandra; Ordoñes, Andrés.; Pesántez, Daniel; Morocho, Villie; Vanegas, Pablo

    2015-12-01

    The comparison of several Level Set algorithms is performed with respect to 2D left ventricle segmentation in Multi-Slice CT images. Five algorithms are compared by calculating the Dice coefficient between the resulting segmentation contour and a reference contour traced by a cardiologist. The algorithms are also tested on images contaminated with Gaussian noise for several values of PSNR. Additionally an algorithm for providing the initialization shape is proposed. This algorithm is based on a combination of mathematical morphology tools with watershed and region growing algorithms. Results on the set of test images are promising and suggest the extension to 3{D MSCT database segmentation.

  11. An algorithm for segmenting polarimetric SAR imagery

    NASA Astrophysics Data System (ADS)

    Geaga, Jorge V.

    2015-05-01

    We have developed an algorithm for segmenting fully polarimetric single look TerraSAR-X, multilook SIR-C and 7 band Landsat 5 imagery using neural nets. The algorithm uses a feedforward neural net with one hidden layer to segment different surface classes. The weights are refined through an iterative filtering process characteristic of a relaxation process. Features selected from studies of fully polarimetric complex single look TerraSAR-X data and multilook SIR-C data are used as input to the net. The seven bands from Landsat 5 data are used as input for the Landsat neural net. The Cloude-Pottier incoherent decomposition is used to investigate the physical basis of the polarimetric SAR data segmentation. The segmentation of a SIR-C ocean surface scene into four classes is presented. This segmentation algorithm could be a very useful tool for investigating complex polarimetric SAR phenomena.

  12. Novel multiresolution mammographic density segmentation using pseudo 3D features and adaptive cluster merging

    NASA Astrophysics Data System (ADS)

    He, Wenda; Juette, Arne; Denton, Erica R. E.; Zwiggelaar, Reyer

    2015-03-01

    Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

  13. A 3D neurovascular bundles segmentation method based on MR-TRUS deformable registration

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

    In this paper, we propose a 3D neurovascular bundles (NVB) segmentation method for ultrasound (US) image by integrating MR and transrectal ultrasound (TRUS) images through MR-TRUS deformable registration. First, 3D NVB was contoured by a physician in MR images, and the 3D MRdefined NVB was then transformed into US images using a MR-TRUS registration method, which models the prostate tissue as an elastic material, and jointly estimates the boundary deformation and the volumetric deformations under the elastic constraint. This technique was validated with a clinical study of 6 patients undergoing radiation therapy (RT) treatment for prostate cancer. The accuracy of our approach was assessed through the locations of landmarks, as well as previous ultrasound Doppler images of patients. MR-TRUS registration was successfully performed for all patients. The mean displacement of the landmarks between the post-registration MR and TRUS images was less than 2 mm, and the average NVB volume Dice Overlap Coefficient was over 89%. This NVB segmentation technique could be a useful tool as we try to spare the NVB in prostate RT, monitor NVB response to RT, and potentially improve post-RT potency outcomes.

  14. Diaphragm dome surface segmentation in CT data sets: a 3D active appearance model approach

    NASA Astrophysics Data System (ADS)

    Beichel, Reinhard; Gotschuli, Georg; Sorantin, Erich; Leberl, Franz W.; Sonka, Milan

    2002-05-01

    Knowledge about the location of the diaphragm dome surface, which separates the lungs and the heart from the abdominal cavity, is of vital importance for applications like automated segmentation of adjacent organs (e.g., liver) or functional analysis of the respiratory cycle. We present a new 3D Active Appearance Model (AAM) approach to segmentation of the top layer of the diaphragm dome. The 3D AAM consists of three parts: a 2D closed curve (reference curve), an elevation image and texture layers. The first two parts combined represent 3D shape information and the third part image intensity of the diaphragm dome and the surrounding layers. Differences in height between dome voxels and a reference plane are stored in the elevation image. The reference curve is generated by a parallel projection of the diaphragm dome outline in the axial direction. Landmark point placement is only done on the (2D) reference curve, which can be seen as the bounding curve of the elevation image. Matching is based on a gradient-descent optimization process and uses image intensity appearance around the actual dome shape. Results achieved in 60 computer generated phantom data sets show a high degree of accuracy (positioning error -0.07+/-1.29 mm). Validation using real CT data sets yielded a positioning error of -0.16+/-2.95 mm. Additional training and testing on in-vivo CT image data is ongoing.

  15. 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.

  16. Comparative study of diverse model building strategies for 3D-ASM segmentation of dynamic gated SPECT data

    NASA Astrophysics Data System (ADS)

    Tobon-Gomez, C.; Butakoff, C.; Ordas, S.; Aguade, S.; Frangi, A. F.

    2007-03-01

    Over the course of the last two decades, myocardial perfusion with Single Photon Emission Computed Tomography (SPECT) has emerged as an established and well-validated method for assessing myocardial ischemia, viability, and function. Gated-SPECT imaging integrates traditional perfusion information along with global left ventricular function. Despite of these advantages, inherent limitations of SPECT imaging yield a challenging segmentation problem, since an error of only one voxel along the chamber surface may generate a huge difference in volume calculation. In previous works we implemented a 3-D statistical model-based algorithm for Left Ventricle (LV) segmentation of in dynamic perfusion SPECT studies. The present work evaluates the relevance of training a different Active Shape Model (ASM) for each frame of the gated SPECT imaging acquisition in terms of their subsequent segmentation accuracy. Models are subsequently employed to segment the LV cavity of gated SPECT studies of a virtual population. The evaluation is accomplished by comparing point-to-surface (P2S) and volume errors, both against a proper Gold Standard. The dataset comprised 40 voxel phantoms (NCAT, Johns Hopkins, University of of North Carolina). Monte-Carlo simulations were generated with SIMIND (Lund University) and reconstructed to tomographic slices with ASPIRE (University of Michigan).

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  18. 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.

  19. Bayesian Segmentation of Atrium Wall Using Globally-Optimal Graph Cuts on 3D Meshes

    PubMed Central

    Veni, Gopalkrishna; Fu, Zhisong; Awate, Suyash P.; Whitaker, Ross T.

    2014-01-01

    Efficient segmentation of the left atrium (LA) wall from delayed enhancement MRI is challenging due to inconsistent contrast, combined with noise, and high variation in atrial shape and size. We present a surface-detection method that is capable of extracting the atrial wall by computing an optimal a-posteriori estimate. This estimation is done on a set of nested meshes, constructed from an ensemble of segmented training images, and graph cuts on an associated multi-column, proper-ordered graph. The graph/mesh is a part of a template/model that has an associated set of learned intensity features. When this mesh is overlaid onto a test image, it produces a set of costs which lead to an optimal segmentation. The 3D mesh has an associated weighted, directed multi-column graph with edges that encode smoothness and inter-surface penalties. Unlike previous graph-cut methods that impose hard constraints on the surface properties, the proposed method follows from a Bayesian formulation resulting in soft penalties on spatial variation of the cuts through the mesh. The novelty of this method also lies in the construction of proper-ordered graphs on complex shapes for choosing among distinct classes of base shapes for automatic LA segmentation. We evaluate the proposed segmentation framework on simulated and clinical cardiac MRI. PMID:24684007

  20. Preliminary results in large bone segmentation from 3D freehand ultrasound

    NASA Astrophysics Data System (ADS)

    Fanti, Zian; Torres, Fabian; Arámbula Cosío, Fernando

    2013-11-01

    Computer Assisted Orthopedic Surgery (CAOS) requires a correct registration between the patient in the operating room and the virtual models representing the patient in the computer. In order to increase the precision and accuracy of the registration a set of new techniques that eliminated the need to use fiducial markers have been developed. The majority of these newly developed registration systems are based on costly intraoperative imaging systems like Computed Tomography (CT scan) or Magnetic resonance imaging (MRI). An alternative to these methods is the use of an Ultrasound (US) imaging system for the implementation of a more cost efficient intraoperative registration solution. In order to develop the registration solution with the US imaging system, the bone surface is segmented in both preoperative and intraoperative images, and the registration is done using the acquire surface. In this paper, we present the a preliminary results of a new approach to segment bone surface from ultrasound volumes acquired by means 3D freehand ultrasound. The method is based on the enhancement of the voxels that belongs to surface and its posterior segmentation. The enhancement process is based on the information provided by eigenanalisis of the multiscale 3D Hessian matrix. The preliminary results shows that from the enhance volume the final bone surfaces can be extracted using a singular value thresholding.

  1. Algorithm of pulmonary emphysema extraction using thoracic 3D CT images

    NASA Astrophysics Data System (ADS)

    Saita, Shinsuke; Kubo, Mitsuru; Kawata, Yoshiki; Niki, Noboru; Nakano, Yasutaka; Ohmatsu, Hironobu; Tominaga, Keigo; Eguchi, Kenji; Moriyama, Noriyuki

    2007-03-01

    Recently, due to aging and smoking, emphysema patients are increasing. The restoration of alveolus which was destroyed by emphysema is not possible, thus early detection of emphysema is desired. We describe a quantitative algorithm for extracting emphysematous lesions and quantitatively evaluate their distribution patterns using low dose thoracic 3-D CT images. The algorithm identified lung anatomies, and extracted low attenuation area (LAA) as emphysematous lesion candidates. Applying the algorithm to thoracic 3-D CT images and then by follow-up 3-D CT images, we demonstrate its potential effectiveness to assist radiologists and physicians to quantitatively evaluate the emphysematous lesions distribution and their evolution in time interval changes.

  2. Algorithm of pulmonary emphysema extraction using low dose thoracic 3D CT images

    NASA Astrophysics Data System (ADS)

    Saita, S.; Kubo, M.; Kawata, Y.; Niki, N.; Nakano, Y.; Omatsu, H.; Tominaga, K.; Eguchi, K.; Moriyama, N.

    2006-03-01

    Recently, due to aging and smoking, emphysema patients are increasing. The restoration of alveolus which was destroyed by emphysema is not possible, thus early detection of emphysema is desired. We describe a quantitative algorithm for extracting emphysematous lesions and quantitatively evaluate their distribution patterns using low dose thoracic 3-D CT images. The algorithm identified lung anatomies, and extracted low attenuation area (LAA) as emphysematous lesion candidates. Applying the algorithm to 100 thoracic 3-D CT images and then by follow-up 3-D CT images, we demonstrate its potential effectiveness to assist radiologists and physicians to quantitatively evaluate the emphysematous lesions distribution and their evolution in time interval changes.

  3. 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.

  4. SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data.

    PubMed

    van Assen, Hans C; Danilouchkine, Mikhail G; Frangi, Alejandro F; Ordás, Sebastián; Westenberg, Jos J M; Reiber, Johan H C; Lelieveldt, Boudewijn P F

    2006-04-01

    A new technique (SPASM) based on a 3D-ASM is presented for automatic segmentation of cardiac MRI image data sets consisting of multiple planes with arbitrary orientations, and with large undersampled regions. Model landmark positions are updated in a two-stage iterative process. First, landmark positions close to intersections with images are updated. Second, the update information is propagated to the regions without image information, such that new locations for the whole set of the model landmarks are obtained. Feature point detection is performed by a fuzzy inference system, based on fuzzy C-means clustering. Model parameters were optimized on a computer cluster and the computational load distributed by grid computing. SPASM was applied to image data sets with an increasing sparsity (from 2 to 11 slices) comprising images with different orientations and stemming from different MRI acquisition protocols. Segmentation outcomes and calculated volumes were compared to manual segmentation on a dense short-axis data configuration in a 3D manner. For all data configurations, (sub-)pixel accuracy was achieved. Performance differences between data configurations were significantly different (p<0.05) for SA data sets with less than 6 slices, but not clinically relevant (volume differences<4 ml). Comparison to results from other 3D model-based methods showed that SPASM performs comparable to or better than these other methods, but SPASM uses considerably less image data. Sensitivity to initial model placement proved to be limited within a range of position perturbations of approximately 20 mm in all directions. PMID:16439182

  5. 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

  6. Automatic segmentation of bladder and prostate using coupled 3D deformable models.

    PubMed

    Costa, María Jimena; Delingette, Hervé; Novellas, Sébastien; Ayache, Nicholas

    2007-01-01

    In this paper, we propose a fully automatic method for the coupled 3D localization and segmentation of lower abdomen structures. We apply it to the joint segmentation of the prostate and bladder in a database of CT scans of the lower abdomen of male patients. A flexible approach on the bladder allows the process to easily adapt to high shape variation and to intensity inhomogeneities that would be hard to characterize (due, for example, to the level of contrast agent that is present). On the other hand, a statistical shape prior is enforced on the prostate. We also propose an adaptive non-overlapping constraint that arbitrates the evolution of both structures based on the availability of strong image data at their common boundary. The method has been tested on a database of 16 volumetric images, and the validation process includes an assessment of inter-expert variability in prostate delineation, with promising results. PMID:18051066

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

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

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

  8. Volume analysis of treatment response of head and neck lesions using 3D level set segmentation

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

    A computerized system for segmenting lesions in head and neck CT scans was developed to assist radiologists in estimation of the response to treatment of malignant lesions. The system performs 3D segmentations based on a level set model and uses as input an approximate bounding box for the lesion of interest. In this preliminary study, CT scans from a pre-treatment exam and a post one-cycle chemotherapy exam of 13 patients containing head and neck neoplasms were used. A radiologist marked 35 temporal pairs of lesions. 13 pairs were primary site cancers and 22 pairs were metastatic lymph nodes. For all lesions, a radiologist outlined a contour on the best slice on both the pre- and post treatment scans. For the 13 primary lesion pairs, full 3D contours were also extracted by a radiologist. The average pre- and post-treatment areas on the best slices for all lesions were 4.5 and 2.1 cm2, respectively. For the 13 primary site pairs the average pre- and post-treatment primary lesions volumes were 15.4 and 6.7 cm 3 respectively. The correlation between the automatic and manual estimates for the pre-to-post-treatment change in area for all 35 pairs was r=0.97, while the correlation for the percent change in area was r=0.80. The correlation for the change in volume for the 13 primary site pairs was r=0.89, while the correlation for the percent change in volume was r=0.79. The average signed percent error between the automatic and manual areas for all 70 lesions was 11.0+/-20.6%. The average signed percent error between the automatic and manual volumes for all 26 primary lesions was 37.8+/-42.1%. The preliminary results indicate that the automated segmentation system can reliably estimate tumor size change in response to treatment relative to radiologist's hand segmentation.

  9. A 3D Cloud-Construction Algorithm for the EarthCARE Satellite Mission

    NASA Technical Reports Server (NTRS)

    Barker, H. W.; Jerg, M. P.; Wehr, T.; Kato, S.; Donovan, D. P.; Hogan, R. J.

    2011-01-01

    This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data.

  10. 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.

  11. Fuzzy watershed segmentation algorithm: an enhanced algorithm for 2D gel electrophoresis image segmentation.

    PubMed

    Rashwan, Shaheera; Sarhan, Amany; Faheem, Muhamed Talaat; Youssef, Bayumy A

    2015-01-01

    Detection and quantification of protein spots is an important issue in the analysis of two-dimensional electrophoresis images. However, there is a main challenge in the segmentation of 2DGE images which is to separate overlapping protein spots correctly and to find the weak protein spots. In this paper, we describe a new robust technique to segment and model the different spots present in the gels. The watershed segmentation algorithm is modified to handle the problem of over-segmentation by initially partitioning the image to mosaic regions using the composition of fuzzy relations. The experimental results showed the effectiveness of the proposed algorithm to overcome the over segmentation problem associated with the available algorithm. We also use a wavelet denoising function to enhance the quality of the segmented image. The results of using a denoising function before the proposed fuzzy watershed segmentation algorithm is promising as they are better than those without denoising. PMID:26510287

  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. 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.

  14. 3D robust Chan-Vese model for industrial computed tomography volume data segmentation

    NASA Astrophysics Data System (ADS)

    Liu, Linghui; Zeng, Li; Luan, Xiao

    2013-11-01

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

  15. 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

  16. Semiautomated segmentation and 3D reconstruction of coronary trees: biplane angiography and intravascular ultrasound data fusion

    NASA Astrophysics Data System (ADS)

    Prause, Guido P. M.; DeJong, Steven C.; McKay, Charles R.; Sonka, Milan

    1996-04-01

    In this paper, we describe an approach to 3D reconstruction of the coronary tree based on combined use of biplane coronary angiography and intravascular ultrasound (IVUS). Shortly before the start of a constant-speed IVUS pullback, radiopaque dye is injected into the examined coronary tree and the heart is imaged with a calibrated biplane X-ray system. The 3D centerline of the coronary tree is reconstructed from the geometrically corrected biplane angiograms using an automated segmentation method and manual matching of corresponding branching points. The borders of vessel wall and plaque are automatically detected in the acquired pullback images and the IVUS cross sections are mapped perpendicular to the previously reconstructed 3D vessel centerline. In addition, the twist of the IVUS probe due to the curvature of the coronary artery is calculated for a torsion-free catheter and the whole vessel reconstruction is rotationally adjusted using available anatomic landmarks. The accuracy of the biplane reconstruction procedure is validated by means of a left coronary tree phantom. The feasibility of the entire approach is demonstrated in a cadaveric pig heart.

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

    PubMed

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

    2012-07-01

    Repeatability studies on 3D multi-segment foot models (3DMFMs) have mainly considered healthy participants which contrasts with the widespread application of these models to evaluate foot pathologies. The current study aimed at establishing the repeatability of the 3DMFM described by Leardini et al. in presence of foot deformities. Foot kinematics of eight adult participants were analyzed using a repeated-measures design including two therapists with different levels of experience. The inter-trial variability was higher compared to the kinematics of healthy subjects. Consideration of relative angles resulted in the lowest inter-session variability. The absolute 3D rotations between the Sha-Cal and Cal-Met seem to have the lowest variability in both therapists. A general trend towards higher σ(sess)/σ(trial) ratios was observed when the midfoot was involved. The current study indicates that not only relative 3D rotations and planar angles can be measured consistently in patients, also a number of absolute parameters can be consistently measured serving as basis for the decision making process. PMID:22591792

  18. Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.

    PubMed

    Zhou, Changjun; Hou, Caixia; Zhang, Qiang; Wei, Xiaopeng

    2013-09-01

    The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences. PMID:23824509

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  20. 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.

  1. 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.

  2. Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

    One widely accepted classification of a prostate is by a central gland (CG) and a peripheral zone (PZ). In some clinical applications, separating CG and PZ from the whole prostate is useful. For instance, in prostate cancer detection, radiologist wants to know in which zone the cancer occurs. Another application is for multiparametric MR tissue characterization. In prostate T2 MR images, due to the high intensity variation between CG and PZ, automated differentiation of CG and PZ is difficult. Previously, we developed an automated prostate boundary segmentation system, which tested on large datasets and showed good performance. Using the results of the pre-segmented prostate boundary, in this paper, we proposed an automated CG segmentation algorithm based on Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces (LOGISMOS). The designed LOGISMOS model contained both shape and topology information during deformation. We generated graph cost by training classifiers and used coarse-to-fine search. The LOGISMOS framework guarantees optimal solution regarding to cost and shape constraint. A five-fold cross-validation approach was applied to training dataset containing 261 images to optimize the system performance and compare with a voxel classification based reference approach. After the best parameter settings were found, the system was tested on a dataset containing another 261 images. The mean DSC of 0.81 for the test set indicates that our approach is promising for automated CG segmentation. Running time for the system is about 15 seconds.

  3. A modular hierarchical approach to 3D electron microscopy image segmentation.

    PubMed

    Liu, Ting; Jones, Cory; Seyedhosseini, Mojtaba; Tasdizen, Tolga

    2014-04-15

    The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy. PMID:24491638

  4. 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.

  5. Medical image segmentation using genetic algorithms.

    PubMed

    Maulik, Ujjwal

    2009-03-01

    Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation. PMID:19272859

  6. 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

  7. 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.

  8. Learning structured models for segmentation of 2-D and 3-D imagery.

    PubMed

    Lucchi, Aurelien; Marquez-Neila, Pablo; Becker, Carlos; Li, Yunpeng; Smith, Kevin; Knott, Graham; Fua, Pascal

    2015-05-01

    Efficient and accurate segmentation of cellular structures in microscopic data is an essential task in medical imaging. Many state-of-the-art approaches to image segmentation use structured models whose parameters must be carefully chosen for optimal performance. A popular choice is to learn them using a large-margin framework and more specifically structured support vector machines (SSVM). Although SSVMs are appealing, they suffer from certain limitations. First, they are restricted in practice to linear kernels because the more powerful nonlinear kernels cause the learning to become prohibitively expensive. Second, they require iteratively finding the most violated constraints, which is often intractable for the loopy graphical models used in image segmentation. This requires approximation that can lead to reduced quality of learning. In this paper, we propose three novel techniques to overcome these limitations. We first introduce a method to "kernelize" the features so that a linear SSVM framework can leverage the power of nonlinear kernels without incurring much additional computational cost. Moreover, we employ a working set of constraints to increase the reliability of approximate subgradient methods and introduce a new way to select a suitable step size at each iteration. We demonstrate the strength of our approach on both 2-D and 3-D electron microscopic (EM) image data and show consistent performance improvement over state-of-the-art approaches. PMID:25438309

  9. 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.

  10. Parallel contact detection algorithm for transient solid dynamics simulations using PRONTO3D

    SciTech Connect

    Attaway, S.W.; Hendrickson, B.A.; Plimpton, S.J.

    1996-09-01

    An efficient, scalable, parallel algorithm for treating material surface contacts in solid mechanics finite element programs has been implemented in a modular way for MIMD parallel computers. The serial contact detection algorithm that was developed previously for the transient dynamics finite element code PRONTO3D has been extended for use in parallel computation by devising a dynamic (adaptive) processor load balancing scheme.

  11. A moving mesh algorithm for 3-D regional groundwater flow with water table and seepage face

    NASA Astrophysics Data System (ADS)

    Knupp, P.

    A numerical algorithm is described for solving the free-surface groundwater flow equations in 3-D large-scale unconfined aquifers with strongly heterogeneous conductivity and surface recharge. The algorithm uses a moving mesh to track the water-table as it evolves according to kinematic and seepage face boundary conditions. Both steady-state and transient algorithms are implemented in the SECO-Flow 3-D code and demonstrated on stratigraphy based on the Delaware Basin of south-eastern New Mexico.

  12. Robust Adaptive 3-D Segmentation of Vessel Laminae From Fluorescence Confocal Microscope Images and Parallel GPU Implementation

    PubMed Central

    Narayanaswamy, Arunachalam; Dwarakapuram, Saritha; Bjornsson, Christopher S.; Cutler, Barbara M.; Shain, William

    2010-01-01

    This paper presents robust 3-D algorithms to segment vasculature that is imaged by labeling laminae, rather than the lumenal volume. The signal is weak, sparse, noisy, nonuniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding and Hessian filtering based methods are not effective. The structure deviates from a tubular geometry, so tracing algorithms are not effective. We propose a four step approach. The first step detects candidate voxels using a robust hypothesis test based on a model that assumes Poisson noise and locally planar geometry. The second step performs an adaptive region growth to extract weakly labeled and fine vessels while rejecting spectral artifacts. To enable interactive visualization and estimation of features such as statistical confidence, local curvature, local thickness, and local normal, we perform the third step. In the third step, we construct an accurate mesh representation using marching tetrahedra, volume-preserving smoothing, and adaptive decimation algorithms. To enable topological analysis and efficient validation, we describe a method to estimate vessel centerlines using a ray casting and vote accumulation algorithm which forms the final step of our algorithm. Our algorithm lends itself to parallel processing, and yielded an 8× speedup on a graphics processor (GPU). On synthetic data, our meshes had average error per face (EPF) values of (0.1–1.6) voxels per mesh face for peak signal-to-noise ratios from (110–28 dB). Separately, the error from decimating the mesh to less than 1% of its original size, the EPF was less than 1 voxel/face. When validated on real datasets, the average recall and precision values were found to be 94.66% and 94.84%, respectively. PMID:20199906

  13. Sensitivity Analysis of the Scattering-Based SARBM3D Despeckling Algorithm.

    PubMed

    Di Simone, Alessio

    2016-01-01

    Synthetic Aperture Radar (SAR) imagery greatly suffers from multiplicative speckle noise, typical of coherent image acquisition sensors, such as SAR systems. Therefore, a proper and accurate despeckling preprocessing step is almost mandatory to aid the interpretation and processing of SAR data by human users and computer algorithms, respectively. Very recently, a scattering-oriented version of the popular SAR Block-Matching 3D (SARBM3D) despeckling filter, named Scattering-Based (SB)-SARBM3D, was proposed. The new filter is based on the a priori knowledge of the local topography of the scene. In this paper, an experimental sensitivity analysis of the above-mentioned despeckling algorithm is carried out, and the main results are shown and discussed. In particular, the role of both electromagnetic and geometrical parameters of the surface and the impact of its scattering behavior are investigated. Furthermore, a comprehensive sensitivity analysis of the SB-SARBM3D filter against the Digital Elevation Model (DEM) resolution and the SAR image-DEM coregistration step is also provided. The sensitivity analysis shows a significant robustness of the algorithm against most of the surface parameters, while the DEM resolution plays a key role in the despeckling process. Furthermore, the SB-SARBM3D algorithm outperforms the original SARBM3D in the presence of the most realistic scattering behaviors of the surface. An actual scenario is also presented to assess the DEM role in real-life conditions. PMID:27347971

  14. Sensitivity Analysis of the Scattering-Based SARBM3D Despeckling Algorithm

    PubMed Central

    Di Simone, Alessio

    2016-01-01

    Synthetic Aperture Radar (SAR) imagery greatly suffers from multiplicative speckle noise, typical of coherent image acquisition sensors, such as SAR systems. Therefore, a proper and accurate despeckling preprocessing step is almost mandatory to aid the interpretation and processing of SAR data by human users and computer algorithms, respectively. Very recently, a scattering-oriented version of the popular SAR Block-Matching 3D (SARBM3D) despeckling filter, named Scattering-Based (SB)-SARBM3D, was proposed. The new filter is based on the a priori knowledge of the local topography of the scene. In this paper, an experimental sensitivity analysis of the above-mentioned despeckling algorithm is carried out, and the main results are shown and discussed. In particular, the role of both electromagnetic and geometrical parameters of the surface and the impact of its scattering behavior are investigated. Furthermore, a comprehensive sensitivity analysis of the SB-SARBM3D filter against the Digital Elevation Model (DEM) resolution and the SAR image-DEM coregistration step is also provided. The sensitivity analysis shows a significant robustness of the algorithm against most of the surface parameters, while the DEM resolution plays a key role in the despeckling process. Furthermore, the SB-SARBM3D algorithm outperforms the original SARBM3D in the presence of the most realistic scattering behaviors of the surface. An actual scenario is also presented to assess the DEM role in real-life conditions. PMID:27347971

  15. A deformable model for hippocampus segmentation: Improvements and extension to 3D

    SciTech Connect

    Ghanei, A.; Soltanian-Zadeh, H. |; Windham, J.P.

    1996-12-31

    In this work, the application of a deformable model to the segmentation of hippocampus in brain MRI has been investigated. Common problems of the model in this case and similar cases have been discussed and solved. A new method for extracting discontinuous boundaries of an object with multiple unwanted edges has been developed. This method is based on detecting and following the edge by external forces. For improving the contour stability, its movement has been limited. Also, adaptive values for internal force weights have been used. In the next step, the model has been extended to 3D which is a Deformable Surface Model. A geometric structure used for this purpose. This helps in definition of normal vectors and internal forces. Finally, a method for generating the initial volume from individual initial polygons has been developed.

  16. 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.

  17. 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.

  18. Optical flow 3D segmentation and interpretation: a variational method with active curve evolution and level sets.

    PubMed

    Mitiche, Amar; Sekkati, Hicham

    2006-11-01

    This study investigates a variational, active curve evolution method for dense three-dimentional (3D) segmentation and interpretation of optical flow in an image sequence of a scene containing moving rigid objects viewed by a possibly moving camera. This method jointly performs 3D motion segmentation, 3D interpretation (recovery of 3D structure and motion), and optical flow estimation. The objective functional contains two data terms for each segmentation region, one based on the motion-only equation which relates the essential parameters of 3D rigid body motion to optical flow, and the other on the Horn and Schunck optical flow constraint. It also contains two regularization terms for each region, one for optical flow, the other for the region boundary. The necessary conditions for a minimum of the functional result in concurrent 3D-motion segmentation, by active curve evolution via level sets, and linear estimation of each region essential parameters and optical flow. Subsequently, the screw of 3D motion and regularized relative depth are recovered analytically for each region from the estimated essential parameters and optical flow. Examples are provided which verify the method and its implementation. PMID:17063686

  19. 3D shape descriptors for face segmentation and fiducial points detection: an anatomical-based analysis

    NASA Astrophysics Data System (ADS)

    Salazar, Augusto E.; Cerón, Alexander; Prieto, Flavio A.

    2011-03-01

    The behavior of nine 3D shape descriptors which were computed on the surface of 3D face models, is studied. The set of descriptors includes six curvature-based ones, SPIN images, Folded SPIN Images, and Finger prints. Instead of defining clusters of vertices based on the value of a given primitive surface feature, a face template composed by 28 anatomical regions, is used to segment the models and to extract the location of different landmarks and fiducial points. Vertices are grouped by: region, region boundaries, and subsampled versions of them. The aim of this study is to analyze the discriminant capacity of each descriptor to characterize regions and to identify key points on the facial surface. The experiment includes testing with data from neutral faces and faces showing expressions. Also, in order to see the usefulness of the bending-invariant canonical form (BICF) to handle variations due to facial expressions, the descriptors are computed directly from the surface and also from its BICF. In the results: the values, distributions, and relevance indexes of each set of vertices, were analyzed.

  20. A simple backprojection algorithm for 3D in vivo EPID dosimetry of IMRT treatments

    SciTech Connect

    Wendling, Markus; McDermott, Leah N.; Mans, Anton; Sonke, Jan-Jakob; Herk, Marcel van; Mijnheer, Ben J.

    2009-07-15

    Treatment plans are usually designed, optimized, and evaluated based on the total 3D dose distribution, motivating a total 3D dose verification. The purpose of this study was to develop a 2D transmission-dosimetry method using an electronic portal imaging device (EPID) into a simple 3D method that provides 3D dose information. In the new method, the dose is reconstructed within the patient volume in multiple planes parallel to the EPID for each gantry angle. By summing the 3D dose grids of all beams, the 3D dose distribution for the total treatment fraction is obtained. The algorithm uses patient contours from the planning CT scan but does not include tissue inhomogeneity corrections. The 3D EPID dosimetry method was tested for IMRT fractions of a prostate, a rectum, and a head-and-neck cancer patient. Planned and in vivo-measured dose distributions were within 2% at the dose prescription point. Within the 50% isodose surface of the prescribed dose, at least 97% of points were in agreement, evaluated with a 3D {gamma} method with criteria of 3% of the prescribed dose and 0.3 cm. Full 3D dose reconstruction on a 0.1x0.1x0.1 cm{sup 3} grid and 3D {gamma} evaluation took less than 15 min for one fraction on a standard PC. The method allows in vivo determination of 3D dose-volume parameters that are common in clinical practice. The authors conclude that their EPID dosimetry method is an accurate and fast tool for in vivo dose verification of IMRT plans in 3D. Their approach is independent of the treatment planning system and provides a practical safety net for radiotherapy.

  1. An image encryption algorithm based on 3D cellular automata and chaotic maps

    NASA Astrophysics Data System (ADS)

    Del Rey, A. Martín; Sánchez, G. Rodríguez

    2015-05-01

    A novel encryption algorithm to cipher digital images is presented in this work. The digital image is rendering into a three-dimensional (3D) lattice and the protocol consists of two phases: the confusion phase where 24 chaotic Cat maps are applied and the diffusion phase where a 3D cellular automata is evolved. The encryption method is shown to be secure against the most important cryptanalytic attacks.

  2. Effects of using a 3D model on the performance of vision algorithms

    NASA Astrophysics Data System (ADS)

    Benjamin, D. Paul; Lyons, Damian; Lynch, Robert

    2015-05-01

    In previous work, we have shown how a 3D model can be built in real time and synchronized with the environment. This world model permits a robot to predict dynamics in its environment and classify behaviors. In this paper we evaluate the effect of such a 3D model on the accuracy and speed of various computer vision algorithms, including tracking, optical flow and stereo disparity. We report results based on the KITTI database and on our own videos.

  3. Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions.

    PubMed

    Shuang, Bo; Wang, Wenxiao; Shen, Hao; Tauzin, Lawrence J; Flatebo, Charlotte; Chen, Jianbo; Moringo, Nicholas A; Bishop, Logan D C; Kelly, Kevin F; Landes, Christy F

    2016-01-01

    Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions. PMID:27488312

  4. Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions

    NASA Astrophysics Data System (ADS)

    Shuang, Bo; Wang, Wenxiao; Shen, Hao; Tauzin, Lawrence J.; Flatebo, Charlotte; Chen, Jianbo; Moringo, Nicholas A.; Bishop, Logan D. C.; Kelly, Kevin F.; Landes, Christy F.

    2016-08-01

    Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions.

  5. Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions

    PubMed Central

    Shuang, Bo; Wang, Wenxiao; Shen, Hao; Tauzin, Lawrence J.; Flatebo, Charlotte; Chen, Jianbo; Moringo, Nicholas A.; Bishop, Logan D. C.; Kelly, Kevin F.; Landes, Christy F.

    2016-01-01

    Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions. PMID:27488312

  6. Near-infrared optical imaging of human brain based on the semi-3D reconstruction algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Ming; Meng, Wei; Qin, Zhuanping; Zhou, Xiaoqing; Zhao, Huijuan; Gao, Feng

    2013-03-01

    In the non-invasive brain imaging with near-infrared light, precise head model is of great significance to the forward model and the image reconstruction. To deal with the individual difference of human head tissues and the problem of the irregular curvature, in this paper, we extracted head structure with Mimics software from the MRI image of a volunteer. This scheme makes it possible to assign the optical parameters to every layer of the head tissues reasonably and solve the diffusion equation with the finite-element analysis. During the solution of the inverse problem, a semi-3D reconstruction algorithm is adopted to trade off the computation cost and accuracy between the full 3-D and the 2-D reconstructions. In this scheme, the changes in the optical properties of the inclusions are assumed either axially invariable or confined to the imaging plane, while the 3-D nature of the photon migration is still retained. This therefore leads to a 2-D inverse issue with the matched 3-D forward model. Simulation results show that comparing to the 3-D reconstruction algorithm, the Semi-3D reconstruction algorithm cut 27% the calculation time consumption.

  7. Automated multimodality concurrent classification for segmenting vessels in 3D spectral OCT and color fundus images

    NASA Astrophysics Data System (ADS)

    Hu, Zhihong; Abràmoff, Michael D.; Niemeijer, Meindert; Garvin, Mona K.

    2011-03-01

    Segmenting vessels in spectral-domain optical coherence tomography (SD-OCT) volumes is particularly challenging in the region near and inside the neural canal opening (NCO). Furthermore, accurately segmenting them in color fundus photographs also presents a challenge near the projected NCO. However, both modalities also provide complementary information to help indicate vessels, such as a better NCO contrast from the NCO-aimed OCT projection image and a better vessel contrast inside the NCO from fundus photographs. We thus present a novel multimodal automated classification approach for simultaneously segmenting vessels in SD-OCT volumes and fundus photographs, with a particular focus on better segmenting vessels near and inside the NCO by using a combination of their complementary features. In particular, in each SD-OCT volume, the algorithm pre-segments the NCO using a graph-theoretic approach and then applies oriented Gabor wavelets with oriented NCO-based templates to generate OCT image features. After fundus-to-OCT registration, the fundus image features are computed using Gaussian filter banks and combined with OCT image features. A k-NN classifier is trained on 5 and tested on 10 randomly chosen independent image pairs of SD-OCT volumes and fundus images from 15 subjects with glaucoma. Using ROC analysis, we demonstrate an improvement over two closest previous works performed in single modal SD-OCT volumes with an area under the curve (AUC) of 0.87 (0.81 for our and 0.72 for Niemeijer's single modal approach) in the region around the NCO and 0.90 outside the NCO (0.84 for our and 0.81 for Niemeijer's single modal approach).

  8. Image segmentation using an improved differential algorithm

    NASA Astrophysics Data System (ADS)

    Gao, Hao; Shi, Yujiao; Wu, Dongmei

    2014-10-01

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

  9. Feature-Based Quality Evaluation of 3d Point Clouds - Study of the Performance of 3d Registration Algorithms

    NASA Astrophysics Data System (ADS)

    Ridene, T.; Goulette, F.; Chendeb, S.

    2013-08-01

    The production of realistic 3D map databases is continuously growing. We studied an approach of 3D mapping database producing based on the fusion of heterogeneous 3D data. In this term, a rigid registration process was performed. Before starting the modeling process, we need to validate the quality of the registration results, and this is one of the most difficult and open research problems. In this paper, we suggest a new method of evaluation of 3D point clouds based on feature extraction and comparison with a 2D reference model. This method is based on tow metrics: binary and fuzzy.

  10. 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).

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

    PubMed

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

    2015-12-01

    Three-dimensional (3D) measurements of peripheral arterial disease (PAD) plaque burden extracted from fast black-blood magnetic resonance (MR) images have shown to be more predictive of clinical outcomes than PAD stenosis measurements. To this end, accurate segmentation of the femoral artery lumen and outer wall is required for generating volumetric measurements of PAD plaque burden. Here, we propose a semi-automated algorithm to jointly segment the femoral artery lumen and outer wall surfaces from 3D black-blood MR images, which are reoriented and reconstructed along the medial axis of the femoral artery to obtain improved spatial coherence between slices of the long, thin femoral artery and to reduce computation time. The developed segmentation algorithm enforces two priors in a global optimization manner: the spatial consistency between the adjacent 2D slices and the anatomical region order between the femoral artery lumen and outer wall surfaces. The formulated combinatorial optimization problem for segmentation is solved globally and exactly by means of convex relaxation using a coupled continuous max-flow (CCMF) model, which is a dual formulation to the convex relaxed optimization problem. In addition, the CCMF model directly derives an efficient duality-based algorithm based on the modern multiplier augmented optimization scheme, which has been implemented on a GPU for fast computation. The computed segmentations from the developed algorithm were compared to manual delineations from experts using 20 black-blood MR images. The developed algorithm yielded both high accuracy (Dice similarity coefficients ≥ 87% for both the lumen and outer wall surfaces) and high reproducibility (intra-class correlation coefficient of 0.95 for generating vessel wall area), while outperforming the state-of-the-art method in terms of computational time by a factor of ≈ 20. PMID:26387053

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

    PubMed Central

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

    2013-01-01

    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. PMID:23837966

  13. Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm

    PubMed Central

    2015-01-01

    Background Organ segmentation is an important step in computer-aided diagnosis and pathology detection. Accurate kidney segmentation in abdominal computed tomography (CT) sequences is an essential and crucial task for surgical planning and navigation in kidney tumor ablation. However, kidney segmentation in CT is a substantially challenging work because the intensity values of kidney parenchyma are similar to those of adjacent structures. Results In this paper, a coarse-to-fine method was applied to segment kidney from CT images, which consists two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is implemented with improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint into fuzzy c-means clustering (FCM) algorithm. The IGC algorithm makes good use of the continuity of CT sequences in space which can automatically generate the seed labels and improve the efficiency of segmentation. The experimental results performed on the whole dataset of abdominal CT images have shown that the proposed method is accurate and efficient. The method provides a sensitivity of 95.46% with specificity of 99.82% and performs better than other related methods. Conclusions Our method achieves high accuracy in kidney segmentation and considerably reduces the time and labor required for contour delineation. In addition, the method can be expanded to 3D segmentation directly without modification. PMID:26356850

  14. Maximum likelihood reconstruction in fully 3D PET via the SAGE algorithm

    SciTech Connect

    Ollinger, J.M.; Goggin, A.S.

    1996-12-31

    The SAGE and ordered subsets algorithms have been proposed as fast methods to compute penalized maximum likelihood estimates in PET. We have implemented both for use in fully 3D PET and completed a preliminary evaluation. The technique used to compute the transition matrix is fully described. The evaluation suggests that the ordered subsets algorithm converges much faster than SAGE, but that it stops short of the optimal solution.

  15. An accurate 3D inspection system using heterodyne multiple frequency phase-shifting algorithm

    NASA Astrophysics Data System (ADS)

    Xiao, Zhenzhong; Chee, Oichoo; Asundi, Anand

    This paper presents an accurate 3D inspection system for industrial applications, which uses digital fringe projection technology. The system consists of two CCD cameras and a DLP projector. The mathematical model of the 3D inspection system with 10 distortion parameters for each camera is proposed. A heterodyne multiple frequency phase-shifting algorithm is employed for overcoming the unwrapping problem of phase functions and for a reliable unwrapping procedure. The redundant phase information is used to increase the accuracy of the 3D reconstruction. To demonstrate the effectiveness of our system, a standard sphere was used for testing. The verification test for the 3D inspection systems are based on the VDI standard 2634. The result shows the proposed system can be used for industrial quality inspection with high measurement precision.

  16. Feature edge extraction from 3D triangular meshes using a thinning algorithm

    NASA Astrophysics Data System (ADS)

    Nomura, Masaru; Hamada, Nozomu

    2001-11-01

    Highly detailed geometric models, which are represented as dense triangular meshes are becoming popular in computer graphics. Since such 3D meshes often have huge information, we require some methods to treat them efficiently in the 3D mesh processing such as, surface simplification, subdivision surface, curved surface approximation and morphing. In these applications, we often extract features of 3D meshes such as feature vertices and feature edges in preprocessing step. An automatic extraction method of feature edges is treated in this study. In order to realize the feature edge extraction method, we first introduce the concavity and convexity evaluation value. Then the histogram of the concavity and convexity evaluation value is used to separate the feature edge region. We apply a thinning algorithm, which is used in 2D binary image processing. It is shown that the proposed method can extract appropriate feature edges from 3D meshes.

  17. An N-body Tree Algorithm for the Cray T3D

    NASA Technical Reports Server (NTRS)

    Olson, Kevin M.; Packer, Charles V.

    1996-01-01

    We describe in this paper an algorithm for solving the gravitational N-body problem using tree data structures on the Cray T3D parallel supercomputer. This implementation is an adaptation of previous work where this problem was solved using an SIMD, fine-grained parallel computer. We show here that this approach lends itself, with small modifications, to more coarse-grained parallelism as well. We also show that the performance of the algorithm on the Cray T3D parallel architecture scales adequately with the number of processors (up to 256). Specific levels to be reached using the Cray T3D parallel architecture. A peak performance level of 9.6 Gflop/s is reached on 256 processors for the time critical gravity computation.

  18. An N-body Tree Algorithm for the Cray T3D

    NASA Astrophysics Data System (ADS)

    Olson, Kevin M.; Packer, Charles V.

    1996-05-01

    We describe in this paper an algorithm for solving the gravitational N-body problem using tree data structures on the Cray T3D parallel supercomputer. This implementation is an adaptation of previous work where this problem was solved using an SIMD, fine-grained parallel computer. We show here that this approach lends itself, with small modifications, to more coarse-grained parallelism as well. We also show that the performance of the algorithm on the Cray T3D parallel architecture scales adequately with the number of processors (up to 256). Specific levels to be reached using the Cray T3D parallel architecture. A peak performance level of 9.6 Gflop/s is reached on 256 processors for the time critical gravity computation.

  19. A new algorithm for evaluating 3D curvature and curvature gradient for improved fracture detection

    NASA Astrophysics Data System (ADS)

    Di, Haibin; Gao, Dengliang

    2014-09-01

    In 3D seismic interpretation, both curvature and curvature gradient are useful seismic attributes for structure characterization and fault detection in the subsurface. However, the existing algorithms are computationally intensive and limited by the lateral resolution for steeply-dipping formations. This study presents new and robust volume-based algorithms that evaluate both curvature and curvature gradient attributes more accurately and effectively. The algorithms first instantaneously fit a local surface to seismic data and then compute attributes using the spatial derivatives of the built surface. Specifically, the curvature algorithm constructs a quadratic surface by using a rectangle 9-node grid cell, whereas the curvature gradient algorithm builds a cubic surface by using a diamond 13-node grid cell. A dip-steering approach based on 3D complex seismic trace analysis is implemented to enhance the accuracy of surface construction and to reduce computational time. Applications to two 3D seismic surveys demonstrate the accuracy and efficiency of the new curvature and curvature gradient algorithms for characterizing faults and fractures in fractured reservoirs.

  20. 3D image reconstruction algorithms for cryo-electron-microscopy images of virus particles

    NASA Astrophysics Data System (ADS)

    Doerschuk, Peter C.; Johnson, John E.

    2000-11-01

    A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are computed using the expectation maximization algorithm and an example based on Cowpea mosaic virus is provided.

  1. A fast rebinning algorithm for 3D positron emission tomography using John's equation

    NASA Astrophysics Data System (ADS)

    Defrise, Michel; Liu, Xuan

    1999-08-01

    Volume imaging in positron emission tomography (PET) requires the inversion of the three-dimensional (3D) x-ray transform. The usual solution to this problem is based on 3D filtered-backprojection (FBP), but is slow. Alternative methods have been proposed which factor the 3D data into independent 2D data sets corresponding to the 2D Radon transforms of a stack of parallel slices. Each slice is then reconstructed using 2D FBP. These so-called rebinning methods are numerically efficient but are approximate. In this paper a new exact rebinning method is derived by exploiting the fact that the 3D x-ray transform of a function is the solution to the second-order partial differential equation first studied by John. The method is proposed for two sampling schemes, one corresponding to a pair of infinite plane detectors and another one corresponding to a cylindrical multi-ring PET scanner. The new FORE-J algorithm has been implemented for this latter geometry and was compared with the approximate Fourier rebinning algorithm FORE and with another exact rebinning algorithm, FOREX. Results with simulated data demonstrate a significant improvement in accuracy compared to FORE, while the reconstruction time is doubled. Compared to FOREX, the FORE-J algorithm is slightly less accurate but more than three times faster.

  2. 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

  3. 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

  4. 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

  5. 3D polygonal representation of dense point clouds by triangulation, segmentation, and texture projection

    NASA Astrophysics Data System (ADS)

    Tajbakhsh, Touraj

    2010-02-01

    A basic concern of computer graphic is the modeling and realistic representation of three-dimensional objects. In this paper we present our reconstruction framework which determines a polygonal surface from a set of dense points such those typically obtained from laser scanners. We deploy the concept of adaptive blobs to achieve a first volumetric representation of the object. In the next step we estimate a coarse surface using the marching cubes method. We propose to deploy a depth-first search segmentation algorithm traversing a graph representation of the obtained polygonal mesh in order to identify all connected components. A so called supervised triangulation maps the coarse surfaces onto the dense point cloud. We optimize the mesh topology using edge exchange operations. For photo-realistic visualization of objects we finally synthesize optimal low-loss textures from available scene captures of different projections. We evaluate our framework on artificial data as well as real sensed data.

  6. Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm

    PubMed Central

    2012-01-01

    Background Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space. Results We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples. Conclusions Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data. PMID:22475802

  7. Athena3D: Flux-conservative Godunov-type algorithm for compressible magnetohydrodynamics

    NASA Astrophysics Data System (ADS)

    Hawley, John; Simon, Jake; Stone, James; Gardiner, Thomas; Teuben, Peter

    2015-05-01

    Written in FORTRAN, Athena3D, based on Athena (ascl:1010.014), is an implementation of a flux-conservative Godunov-type algorithm for compressible magnetohydrodynamics. Features of the Athena3D code include compressible hydrodynamics and ideal MHD in one, two or three spatial dimensions in Cartesian coordinates; adiabatic and isothermal equations of state; 1st, 2nd or 3rd order reconstruction using the characteristic variables; and numerical fluxes computed using the Roe scheme. In addition, it offers the ability to add source terms to the equations and is parallelized based on MPI.

  8. A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina

    PubMed Central

    Kafieh, Raheleh; Rabbani, Hossein; Kermani, Saeed

    2013-01-01

    Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues. OCT image segmentation is mostly introduced on retinal OCT to localize the intra-retinal boundaries. Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Current researches in OCT segmentation are mostly based on improving the accuracy and precision, and on reducing the required processing time. There is no doubt that current 3-D imaging modalities are now moving the research projects toward volume segmentation along with 3-D rendering and visualization. It is also important to develop robust methods capable of dealing with pathologic cases in OCT imaging. PMID:24083137

  9. 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

  10. Performance comparison of accelerometer calibration algorithms based on 3D-ellipsoid fitting methods.

    PubMed

    Gietzelt, Matthias; Wolf, Klaus-Hendrik; Marschollek, Michael; Haux, Reinhold

    2013-07-01

    Calibration of accelerometers can be reduced to 3D-ellipsoid fitting problems. Changing extrinsic factors like temperature, pressure or humidity, as well as intrinsic factors like the battery status, demand to calibrate the measurements permanently. Thus, there is a need for fast calibration algorithms, e.g. for online analyses. The primary aim of this paper is to propose a non-iterative calibration algorithm for accelerometers with the focus on minimal execution time and low memory consumption. The secondary aim is to benchmark existing calibration algorithms based on 3D-ellipsoid fitting methods. We compared the algorithms regarding the calibration quality and the execution time as well as the number of quasi-static measurements needed for a stable calibration. As evaluation criterion for the calibration, both the norm of calibrated real-life measurements during inactivity and simulation data was used. The algorithms showed a high calibration quality, but the execution time differed significantly. The calibration method proposed in this paper showed the shortest execution time and a very good performance regarding the number of measurements needed to produce stable results. Furthermore, this algorithm was successfully implemented on a sensor node and calibrates the measured data on-the-fly while continuously storing the measured data to a microSD-card. PMID:23566707

  11. 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.

  12. Meta-Model Based Optimisation Algorithms for Robust Optimization of 3D Forging Sequences

    SciTech Connect

    Fourment, Lionel

    2007-04-07

    In order to handle costly and complex 3D metal forming optimization problems, we develop a new optimization algorithm that allows finding satisfactory solutions within less than 50 iterations (/function evaluation) in the presence of local extrema. It is based on the sequential approximation of the problem objective function by the Meshless Finite Difference Method (MFDM). This changing meta-model allows taking into account the gradient information, if available, or not. It can be easily extended to take into account uncertainties on the optimization parameters. This new algorithm is first evaluated on analytic functions, before being applied to a 3D forging benchmark, the preform tool shape optimization that allows minimizing the potential of fold formation during the two-stepped forging sequence.

  13. 3D Motion Planning Algorithms for Steerable Needles Using Inverse Kinematics

    PubMed Central

    Duindam, Vincent; Xu, Jijie; Alterovitz, Ron; Sastry, Shankar; Goldberg, Ken

    2010-01-01

    Steerable needles can be used in medical applications to reach targets behind sensitive or impenetrable areas. The kinematics of a steerable needle are nonholonomic and, in 2D, equivalent to a Dubins car with constant radius of curvature. In 3D, the needle can be interpreted as an airplane with constant speed and pitch rate, zero yaw, and controllable roll angle. We present a constant-time motion planning algorithm for steerable needles based on explicit geometric inverse kinematics similar to the classic Paden-Kahan subproblems. Reachability and path competitivity are analyzed using analytic comparisons with shortest path solutions for the Dubins car (for 2D) and numerical simulations (for 3D). We also present an algorithm for local path adaptation using null-space results from redundant manipulator theory. Finally, we discuss several ways to use and extend the inverse kinematics solution to generate needle paths that avoid obstacles. PMID:21359051

  14. Meanie3D - a mean-shift based, multivariate, multi-scale clustering and tracking algorithm

    NASA Astrophysics Data System (ADS)

    Simon, Jürgen-Lorenz; Malte, Diederich; Silke, Troemel

    2014-05-01

    Project OASE is the one of 5 work groups at the HErZ (Hans Ertel Centre for Weather Research), an ongoing effort by the German weather service (DWD) to further research at Universities concerning weather prediction. The goal of project OASE is to gain an object-based perspective on convective events by identifying them early in the onset of convective initiation and follow then through the entire lifecycle. The ability to follow objects in this fashion requires new ways of object definition and tracking, which incorporate all the available data sets of interest, such as Satellite imagery, weather Radar or lightning counts. The Meanie3D algorithm provides the necessary tool for this purpose. Core features of this new approach to clustering (object identification) and tracking are the ability to identify objects using the mean-shift algorithm applied to a multitude of variables (multivariate), as well as the ability to detect objects on various scales (multi-scale) using elements of Scale-Space theory. The algorithm works in 2D as well as 3D without modifications. It is an extension of a method well known from the field of computer vision and image processing, which has been tailored to serve the needs of the meteorological community. In spite of the special application to be demonstrated here (like convective initiation), the algorithm is easily tailored to provide clustering and tracking for a wide class of data sets and problems. In this talk, the demonstration is carried out on two of the OASE group's own composite sets. One is a 2D nationwide composite of Germany including C-Band Radar (2D) and Satellite information, the other a 3D local composite of the Bonn/Jülich area containing a high-resolution 3D X-Band Radar composite.

  15. 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.

  16. Axial magnetic anomalies over slow-spreading ridge segments: insights from numerical 3-D thermal and physical modelling

    NASA Astrophysics Data System (ADS)

    Gac, Sébastien; Dyment, Jérôme; Tisseau, Chantal; Goslin, Jean

    2003-09-01

    The axial magnetic anomaly amplitude along Mid-Atlantic Ridge segments is systematically twice as high at segment ends compared with segment centres. Various processes have been proposed to account for such observations, either directly or indirectly related to the thermal structure of the segments: (1) shallower Curie isotherm at segment centres, (2) higher Fe-Ti content at segment ends, (3) serpentinized peridotites at segment ends or (4) a combination of these processes. In this paper the contribution of each of these processes to the axial magnetic anomaly amplitude is quantitatively evaluated by achieving a 3-D numerical modelling of the magnetization distribution and a magnetic anomaly over a medium-sized, 50 km long segment. The magnetization distribution depends on the thermal structure and thermal evolution of the lithosphere. The thermal structure is calculated considering the presence of a permanent hot zone beneath the segment centre. The `best-fitting' thermal structure is determined by adjusting the parameters (shape, size, depth, etc.) of this hot zone, to fit the modelled geophysical outputs (Mantle Bouguer anomaly, maximum earthquake depths and crustal thickness) to the observations. Both the thermoremanent magnetization, acquired during the thermal evolution, and the induced magnetization, which depends on the present thermal structure, are modelled. The resulting magnetic anomalies are then computed and compared with the observed ones. This modelling exercise suggests that, in the case of aligned and slightly offset segments, a combination of higher Fe-Ti content and the presence of serpentinized peridotites at segment ends will produce the observed higher axial magnetic anomaly amplitudes over the segment ends. In the case of greater offsets, the presence of serpentinized peridotites at segment ends is sufficient to account for the observations.

  17. A new algorithm of laser 3D visualization based on space-slice

    NASA Astrophysics Data System (ADS)

    Yang, Hui; Song, Yanfeng; Song, Yong; Cao, Jie; Hao, Qun

    2013-12-01

    Traditional visualization algorithms based on three-dimensional (3D) laser point cloud data consist of two steps: stripe point cloud data into different target objects and establish the 3D surface models of the target objects to realize visualization using interpolation point or surface fitting method. However, some disadvantages, such as low efficiency, loss of image details, exist in most of these algorithms. In order to cope with these problems, a 3D visualization algorithm based on space-slice is proposed in this paper, which includes two steps: data classification and image reconstruction. In the first step, edge detection method is used to check the parametric continuity and extract edges to classify data into different target regions preliminarily. In the second stage, the divided data is split further into space-slice according to coordinates. Based on space-slice of the point cloud data, one-dimensional interpolation methods is adopted to get the curves connected by each group of cloud point data smoother. In the end, these interpolation points obtained from each group are made by the use of getting the fitting surface. As expected, visual morphology of the objects is obtained. The simulation experiment results compared with real scenes show that the final visual images have explicit details and the overall visual result is natural.

  18. Algorithms for extraction of structural attitudes from 3D outcrop models

    NASA Astrophysics Data System (ADS)

    Duelis Viana, Camila; Endlein, Arthur; Ademar da Cruz Campanha, Ginaldo; Henrique Grohmann, Carlos

    2016-05-01

    The acquisition of geological attitudes on rock cuts using traditional field compass survey can be a time consuming, dangerous, or even impossible task depending on the conditions and location of outcrops. The importance of this type of data in rock-mass classifications and structural geology has led to the development of new techniques, in which the application of photogrammetric 3D digital models has had an increasing use. In this paper we present two algorithms for extraction of attitudes of geological discontinuities from virtual outcrop models: ply2atti and scanline, implemented with the Python programming language. The ply2atti algorithm allows for the virtual sampling of planar discontinuities appearing on the 3D model as individual exposed surfaces, while the scanline algorithm allows the sampling of discontinuities (surfaces and traces) along a virtual scanline. Application to digital models of a simplified test setup and a rock cut demonstrated a good correlation between the surveys undertaken using traditional field compass reading and virtual sampling on 3D digital models.

  19. Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution.

    PubMed

    Dey, Nicolas; Blanc-Feraud, Laure; Zimmer, Christophe; Roux, Pascal; Kam, Zvi; Olivo-Marin, Jean-Christophe; Zerubia, Josiane

    2006-04-01

    Confocal laser scanning microscopy is a powerful and popular technique for 3D imaging of biological specimens. Although confocal microscopy images are much sharper than standard epifluorescence ones, they are still degraded by residual out-of-focus light and by Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy iterative algorithm, which computes maximum likelihood estimation adapted to Poisson statistics. As this algorithm tends to amplify noise, regularization constraints based on some prior knowledge on the data have to be applied to stabilize the solution. Here, we propose to combine the Richardson-Lucy algorithm with a regularization constraint based on Total Variation, which suppresses unstable oscillations while preserving object edges. We show on simulated and real images that this constraint improves the deconvolution results as compared with the unregularized Richardson-Lucy algorithm, both visually and quantitatively. PMID:16586486

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

    PubMed

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

    2016-01-01

    We propose a novel hybrid approach for automatic 3D segmentation and quantification of high-resolution 7 Tesla magnetic resonance angiography (MRA) images of the human cerebral vasculature. Our approach consists of two main steps. First, a 3D model-based approach is used to segment and quantify thick vessels and most parts of thin vessels. Second, remaining vessel gaps of the first step in low-contrast and noisy regions are completed using a 3D minimal path approach, which exploits directional information. We present two novel minimal path approaches. The first is an explicit approach based on energy minimization using probabilistic sampling, and the second is an implicit approach based on fast marching with anisotropic directional prior. We conducted an extensive evaluation with over 2300 3D synthetic images and 40 real 3D 7 Tesla MRA images. Quantitative and qualitative evaluation shows that our approach achieves superior results compared with a previous minimal path approach. Furthermore, our approach was successfully used in two clinical studies on stroke and vascular dementia. PMID:26571526

  1. Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science

    PubMed Central

    Egger, Jan

    2014-01-01

    In this contribution, a semi-automatic segmentation algorithm for (medical) image analysis is presented. More precise, the approach belongs to the category of interactive contouring algorithms, which provide real-time feedback of the segmentation result. However, even with interactive real-time contouring approaches there are always cases where the user cannot find a satisfying segmentation, e.g. due to homogeneous appearances between the object and the background, or noise inside the object. For these difficult cases the algorithm still needs additional user support. However, this additional user support should be intuitive and rapid integrated into the segmentation process, without breaking the interactive real-time segmentation feedback. I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases. These additional seed(s) restrict(s) the calculation of the segmentation for the algorithm, but at the same time, still enable to continue with the interactive real-time feedback segmentation. For a practical and genuine application in translational science, the approach has been tested on medical data from the clinical routine in 2D and 3D. PMID:24893650

  2. A hybrid framework of multiple active appearance models and global registration for 3D prostate segmentation in MRI

    NASA Astrophysics Data System (ADS)

    Ghose, Soumya; Oliver, Arnau; Martí, Robert; Lladó, Xavier; Freixenet, Jordi; Mitra, Jhimli; Vilanova, Joan C.; Meriaudeau, Fabrice

    2012-02-01

    Real-time fusion of Magnetic Resonance (MR) and Trans Rectal Ultra Sound (TRUS) images aid in the localization of malignant tissues in TRUS guided prostate biopsy. Registration performed on segmented contours of the prostate reduces computational complexity and improves the multimodal registration accuracy. However, accurate and computationally efficient 3D segmentation of the prostate in MR images could be a challenging task due to inter-patient shape and intensity variability of the prostate gland. In this work, we propose to use multiple statistical shape and appearance models to segment the prostate in 2D and a global registration framework to impose shape restriction in 3D. Multiple mean parametric models of the shape and appearance corresponding to the apex, central and base regions of the prostate gland are derived from principal component analysis (PCA) of prior shape and intensity information of the prostate from the training data. The estimated parameters are then modified with the prior knowledge of the optimization space to achieve segmentation in 2D. The 2D segmented slices are then rigidly registered with the average 3D model produced by affine registration of the ground truth of the training datasets to minimize pose variations and impose 3D shape restriction. The proposed method achieves a mean Dice similarity coefficient (DSC) value of 0.88+/-0.11, and mean Hausdorff distance (HD) of 3.38+/-2.81 mm when validated with 15 prostate volumes of a public dataset in leave-one-out validation framework. The results achieved are better compared to some of the works in the literature.

  3. 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).

  4. Development of an algorithm to measure defect geometry using a 3D laser scanner

    NASA Astrophysics Data System (ADS)

    Kilambi, S.; Tipton, S. M.

    2012-08-01

    Current fatigue life prediction models for coiled tubing (CT) require accurate measurements of the defect geometry. Three-dimensional (3D) laser imaging has shown promise toward becoming a nondestructive, non-contacting method of surface defect characterization. Laser imaging provides a detailed photographic image of a flaw, in addition to a detailed 3D surface map from which its critical dimensions can be measured. This paper describes algorithms to determine defect characteristics, specifically depth, width, length and projected cross-sectional area. Curve-fitting methods were compared and implicit algebraic fits have higher probability of convergence compared to explicit geometric fits. Among the algebraic fits, the Taubin circle fit has the least error. The algorithm was able to extract the dimensions of the flaw geometry from the scanned data of CT to within a tolerance of about 0.127 mm, close to the tolerance specified for the laser scanner itself, compared to measurements made using traveling microscopes. The algorithm computes the projected surface area of the flaw, which could previously only be estimated from the dimension measurements and the assumptions made about cutter shape. Although shadows compromised the accuracy of the shape characterization, especially for deep and narrow flaws, the results indicate that the algorithm with laser scanner can be used for non-destructive evaluation of CT in the oil field industry. Further work is needed to improve accuracy, to eliminate shadow effects and to reduce radial deviation.

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  6. Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings.

    PubMed

    Alessandrini, Martino; Heyde, Brecht; Queiros, Sandro; Cygan, Szymon; Zontak, Maria; Somphone, Oudom; Bernard, Olivier; Sermesant, Maxime; Delingette, Herve; Barbosa, Daniel; De Craene, Mathieu; ODonnell, Matthew; Dhooge, Jan

    2016-08-01

    A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchrony. PMID:26960220

  7. Crowdsourcing the creation of image segmentation algorithms for connectomics

    PubMed Central

    Arganda-Carreras, Ignacio; Turaga, Srinivas C.; Berger, Daniel R.; Cireşan, Dan; Giusti, Alessandro; Gambardella, Luca M.; Schmidhuber, Jürgen; Laptev, Dmitry; Dwivedi, Sarvesh; Buhmann, Joachim M.; Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga; Kamentsky, Lee; Burget, Radim; Uher, Vaclav; Tan, Xiao; Sun, Changming; Pham, Tuan D.; Bas, Erhan; Uzunbas, Mustafa G.; Cardona, Albert; Schindelin, Johannes; Seung, H. Sebastian

    2015-01-01

    To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This “deep learning” approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge. PMID:26594156

  8. Crowdsourcing the creation of image segmentation algorithms for connectomics.

    PubMed

    Arganda-Carreras, Ignacio; Turaga, Srinivas C; Berger, Daniel R; Cireşan, Dan; Giusti, Alessandro; Gambardella, Luca M; Schmidhuber, Jürgen; Laptev, Dmitry; Dwivedi, Sarvesh; Buhmann, Joachim M; Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga; Kamentsky, Lee; Burget, Radim; Uher, Vaclav; Tan, Xiao; Sun, Changming; Pham, Tuan D; Bas, Erhan; Uzunbas, Mustafa G; Cardona, Albert; Schindelin, Johannes; Seung, H Sebastian

    2015-01-01

    To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge. PMID:26594156

  9. Simultaneous Multi-Structure Segmentation and 3D Nonrigid Pose Estimation in Image-Guided Robotic Surgery.

    PubMed

    Nosrati, Masoud S; Abugharbieh, Rafeef; Peyrat, Jean-Marc; Abinahed, Julien; Al-Alao, Osama; Al-Ansari, Abdulla; Hamarneh, Ghassan

    2016-01-01

    In image-guided robotic surgery, segmenting the endoscopic video stream into meaningful parts provides important contextual information that surgeons can exploit to enhance their perception of the surgical scene. This information provides surgeons with real-time decision-making guidance before initiating critical tasks such as tissue cutting. Segmenting endoscopic video is a challenging problem due to a variety of complications including significant noise attributed to bleeding and smoke from cutting, poor appearance contrast between different tissue types, occluding surgical tools, and limited visibility of the objects' geometries on the projected camera views. In this paper, we propose a multi-modal approach to segmentation where preoperative 3D computed tomography scans and intraoperative stereo-endoscopic video data are jointly analyzed. The idea is to segment multiple poorly visible structures in the stereo/multichannel endoscopic videos by fusing reliable prior knowledge captured from the preoperative 3D scans. More specifically, we estimate and track the pose of the preoperative models in 3D and consider the models' non-rigid deformations to match with corresponding visual cues in multi-channel endoscopic video and segment the objects of interest. Further, contrary to most augmented reality frameworks in endoscopic surgery that assume known camera parameters, an assumption that is often violated during surgery due to non-optimal camera calibration and changes in camera focus/zoom, our method embeds these parameters into the optimization hence correcting the calibration parameters within the segmentation process. We evaluate our technique on synthetic data, ex vivo lamb kidney datasets, and in vivo clinical partial nephrectomy surgery with results demonstrating high accuracy and robustness. PMID:26151933

  10. Detectability limitations with 3-D point reconstruction algorithms using digital radiography

    SciTech Connect

    Lindgren, Erik

    2015-03-31

    The estimated impact of pores in clusters on component fatigue will be highly conservative when based on 2-D rather than 3-D pore positions. To 3-D position and size defects using digital radiography and 3-D point reconstruction algorithms in general require a lower inspection time and in some cases work better with planar geometries than X-ray computed tomography. However, the increase in prior assumptions about the object and the defects will increase the intrinsic uncertainty in the resulting nondestructive evaluation output. In this paper this uncertainty arising when detecting pore defect clusters with point reconstruction algorithms is quantified using simulations. The simulation model is compared to and mapped to experimental data. The main issue with the uncertainty is the possible masking (detectability zero) of smaller defects around some other slightly larger defect. In addition, the uncertainty is explored in connection to the expected effects on the component fatigue life and for different amount of prior object-defect assumptions made.

  11. An Automatic 3D Facial Landmarking Algorithm Using 2D Gabor Wavelets.

    PubMed

    de Jong, Markus A; Wollstein, Andreas; Ruff, Clifford; Dunaway, David; Hysi, Pirro; Spector, Tim; Fan Liu; Niessen, Wiro; Koudstaal, Maarten J; Kayser, Manfred; Wolvius, Eppo B; Bohringer, Stefan

    2016-02-01

    In this paper, we present a novel approach to automatic 3D facial landmarking using 2D Gabor wavelets. Our algorithm considers the face to be a surface and uses map projections to derive 2D features from raw data. Extracted features include texture, relief map, and transformations thereof. We extend an established 2D landmarking method for simultaneous evaluation of these data. The method is validated by performing landmarking experiments on two data sets using 21 landmarks and compared with an active shape model implementation. On average, landmarking error for our method was 1.9 mm, whereas the active shape model resulted in an average landmarking error of 2.3 mm. A second study investigating facial shape heritability in related individuals concludes that automatic landmarking is on par with manual landmarking for some landmarks. Our algorithm can be trained in 30 min to automatically landmark 3D facial data sets of any size, and allows for fast and robust landmarking of 3D faces. PMID:26540684

  12. Calibration algorithm in robotic remanufacturing measurement system based on 3D laser scanner

    NASA Astrophysics Data System (ADS)

    Shen, C. D.; Zhu, S.; Li, C.; Liang, Y. Y.

    2009-07-01

    In robotic remanufacturing measurement system, the 3D laser scanner is arranged by the robot and the object scanned is mounted on a turntable. This paper deals with the algorithm of calibrating the relationship between the scanner coordinate and the robot Tool0, and furthermore locating the center axis of the turntable. The data of Tool0 can be directly obtained denoting its relationship with the robot base coordinate. So, the coordinate transformation problems are effectively solved and the measuring data which relative to the robot base coordinate could be congruously saved. This paper detailed explains the basic algorithm theory, computing method and the result data analysis, and etc. The calibration algorithm is deduced under the orthogonal coordinate.

  13. Architectures and algorithms for all-optical 3D signal processing

    NASA Astrophysics Data System (ADS)

    Giglmayr, Josef

    1999-07-01

    All-optical signal processing by >= 2D lightwave circuits (LCs) is (i) aimed to allow the (later) inclusion of the frequency domain and is (ii) subject to photonic integration and thus the architectural and algorithmic framework has to be prepared carefully. Much work has been done in >= 2D algebraic system theory/modern control theory which has been applied in the electronic field of signal and image processing. For the application to modeling, analysis and design of the proposed 3D lightwave circuits (LCs) some elements are needed to describe and evalute the system efficiency as the number of system states of 3D LCs increases dramatically with regard to the number of i/o. Several problems, arising throughput such an attempt, are made transparent and solutions are proposed.

  14. Nodes Localization in 3D Wireless Sensor Networks Based on Multidimensional Scaling Algorithm

    PubMed Central

    2014-01-01

    In the recent years, there has been a huge advancement in wireless sensor computing technology. Today, wireless sensor network (WSN) has become a key technology for different types of smart environment. Nodes localization in WSN has arisen as a very challenging problem in the research community. Most of the applications for WSN are not useful without a priory known nodes positions. Adding GPS receivers to each node is an expensive solution and inapplicable for indoor environments. In this paper, we implemented and evaluated an algorithm based on multidimensional scaling (MDS) technique for three-dimensional (3D) nodes localization in WSN using improved heuristic method for distance calculation. Using extensive simulations we investigated our approach regarding various network parameters. We compared the results from the simulations with other approaches for 3D-WSN localization and showed that our approach outperforms other techniques in terms of accuracy.

  15. 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.

  16. 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.

  17. 3D Drop Size Distribution Extrapolation Algorithm Using a Single Disdrometer

    NASA Technical Reports Server (NTRS)

    Lane, John

    2012-01-01

    Determining the Z-R relationship (where Z is the radar reflectivity factor and R is rainfall rate) from disdrometer data has been and is a common goal of cloud physicists and radar meteorology researchers. The usefulness of this quantity has traditionally been limited since radar represents a volume measurement, while a disdrometer corresponds to a point measurement. To solve that problem, a 3D-DSD (drop-size distribution) method of determining an equivalent 3D Z-R was developed at the University of Central Florida and tested at the Kennedy Space Center, FL. Unfortunately, that method required a minimum of three disdrometers clustered together within a microscale network (.1-km separation). Since most commercial disdrometers used by the radar meteorology/cloud physics community are high-cost instruments, three disdrometers located within a microscale area is generally not a practical strategy due to the limitations of these kinds of research budgets. A relatively simple modification to the 3D-DSD algorithm provides an estimate of the 3D-DSD and therefore, a 3D Z-R measurement using a single disdrometer. The basis of the horizontal extrapolation is mass conservation of a drop size increment, employing the mass conservation equation. For vertical extrapolation, convolution of a drop size increment using raindrop terminal velocity is used. Together, these two independent extrapolation techniques provide a complete 3DDSD estimate in a volume around and above a single disdrometer. The estimation error is lowest along a vertical plane intersecting the disdrometer position in the direction of wind advection. This work demonstrates that multiple sensors are not required for successful implementation of the 3D interpolation/extrapolation algorithm. This is a great benefit since it is seldom that multiple sensors in the required spatial arrangement are available for this type of analysis. The original software (developed at the University of Central Florida, 1998.- 2000) has

  18. Object-constrained meshless deformable algorithm for high speed 3D nonrigid registration between CT and CBCT

    SciTech Connect

    Chen Ting; Kim, Sung; Goyal, Sharad; Jabbour, Salma; Zhou Jinghao; Rajagopal, Gunaretnum; Haffty, Bruce; Yue Ning

    2010-01-15

    Purpose: High-speed nonrigid registration between the planning CT and the treatment CBCT data is critical for real time image guided radiotherapy (IGRT) to improve the dose distribution and to reduce the toxicity to adjacent organs. The authors propose a new fully automatic 3D registration framework that integrates object-based global and seed constraints with the grayscale-based ''demons'' algorithm. Methods: Clinical objects were segmented on the planning CT images and were utilized as meshless deformable models during the nonrigid registration process. The meshless models reinforced a global constraint in addition to the grayscale difference between CT and CBCT in order to maintain the shape and the volume of geometrically complex 3D objects during the registration. To expedite the registration process, the framework was stratified into hierarchies, and the authors used a frequency domain formulation to diffuse the displacement between the reference and the target in each hierarchy. Also during the registration of pelvis images, they replaced the air region inside the rectum with estimated pixel values from the surrounding rectal wall and introduced an additional seed constraint to robustly track and match the seeds implanted into the prostate. The proposed registration framework and algorithm were evaluated on 15 real prostate cancer patients. For each patient, prostate gland, seminal vesicle, bladder, and rectum were first segmented by a radiation oncologist on planning CT images for radiotherapy planning purpose. The same radiation oncologist also manually delineated the tumor volumes and critical anatomical structures in the corresponding CBCT images acquired at treatment. These delineated structures on the CBCT were only used as the ground truth for the quantitative validation, while structures on the planning CT were used both as the input to the registration method and the ground truth in validation. By registering the planning CT to the CBCT, a

  19. Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: Application for mammography

    SciTech Connect

    Bliznakova, K.; Suryanarayanan, S.; Karellas, A.; Pallikarakis, N.

    2010-11-15

    Purpose: This work presents an improved algorithm for the generation of 3D breast software phantoms and its evaluation for mammography. Methods: The improved methodology has evolved from a previously presented 3D noncompressed breast modeling method used for the creation of breast models of different size, shape, and composition. The breast phantom is composed of breast surface, duct system and terminal ductal lobular units, Cooper's ligaments, lymphatic and blood vessel systems, pectoral muscle, skin, 3D mammographic background texture, and breast abnormalities. The key improvement is the development of a new algorithm for 3D mammographic texture generation. Simulated images of the enhanced 3D breast model without lesions were produced by simulating mammographic image acquisition and were evaluated subjectively and quantitatively. For evaluation purposes, a database with regions of interest taken from simulated and real mammograms was created. Four experienced radiologists participated in a visual subjective evaluation trial, as they judged the quality of the simulated mammograms, using the new algorithm compared to mammograms, obtained with the old modeling approach. In addition, extensive quantitative evaluation included power spectral analysis and calculation of fractal dimension, skewness, and kurtosis of simulated and real mammograms from the database. Results: The results from the subjective evaluation strongly suggest that the new methodology for mammographic breast texture creates improved breast models compared to the old approach. Calculated parameters on simulated images such as {beta} exponent deducted from the power law spectral analysis and fractal dimension are similar to those calculated on real mammograms. The results for the kurtosis and skewness are also in good coincidence with those calculated from clinical images. Comparison with similar calculations published in the literature showed good agreement in the majority of cases. Conclusions: The

  20. 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

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

    PubMed

    Zarpalas, Dimitrios; 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

  2. 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.

  3. Ultrafast vectorized multispin coding algorithm for the Monte Carlo simulation of the 3D Ising model

    NASA Astrophysics Data System (ADS)

    Wansleben, Stephan

    1987-02-01

    A new Monte Carlo algorithm for the 3D Ising model and its implementation on a CDC CYBER 205 is presented. This approach is applicable to lattices with sizes between 3·3·3 and 192·192·192 with periodic boundary conditions, and is adjustable to various kinetic models. It simulates a canonical ensemble at given temperature generating a new random number for each spin flip. For the Metropolis transition probability the speed is 27 ns per updates on a two-pipe CDC Cyber 205 with 2 million words physical memory, i.e. 1.35 times the cycle time per update or 38 million updates per second.

  4. Robust 3D object localization and pose estimation for random bin picking with the 3DMaMa algorithm

    NASA Astrophysics Data System (ADS)

    Skotheim, Øystein; Thielemann, Jens T.; Berge, Asbjørn; Sommerfelt, Arne

    2010-02-01

    Enabling robots to automatically locate and pick up randomly placed and oriented objects from a bin is an important challenge in factory automation, replacing tedious and heavy manual labor. A system should be able to recognize and locate objects with a predefined shape and estimate the position with the precision necessary for a gripping robot to pick it up. We describe a system that consists of a structured light instrument for capturing 3D data and a robust approach for object location and pose estimation. The method does not depend on segmentation of range images, but instead searches through pairs of 2D manifolds to localize candidates for object match. This leads to an algorithm that is not very sensitive to scene complexity or the number of objects in the scene. Furthermore, the strategy for candidate search is easily reconfigurable to arbitrary objects. Experiments reported in this paper show the utility of the method on a general random bin picking problem, in this paper exemplified by localization of car parts with random position and orientation. Full pose estimation is done in less than 380 ms per image. We believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D objects in a scene is needed.

  5. Segmentation of Hypocenters and 3-D Velocity Structure around the Kii Peninsula Revealed by Onshore and Offshore Seismic Observations

    NASA Astrophysics Data System (ADS)

    Akuhara, T.; Mochizuki, K.; Nakahigashi, K.; Yamada, T.; Shinohara, M.; Sakai, S.; Kanazawa, T.; Uehira, K.; Shimizu, H.

    2013-12-01

    The Philippine Sea Plate subducts beneath the Eurasian Plate at a rate of ~4 cm/year along the Nankai Trough, southwest of Japan. Around the Kii Peninsula, the rupture boundary of the historical Tonankai and Nankai large earthquakes is located, and previous researches have revealed along-strike segmentation of hypocenters [Mochizuki et al., 2010], P-wave anisotropy [Ishise et al., 2009], low frequency earthquake (LFE) distribution [e.g., Obara, 2010] and subduction depth of the Philippine Sea (PHS) Plate, or there may exist a split in the PHS Plate [Ide et al., 2010]. To investigate such segmentation, in our previous work we determined 3-D velocity structure and hypocenters using P- and S-wave arrival times of earthquakes recorded by both ocean bottom seismometers (OBSs) that were deployed from 2003 to 2007 and on-land stations [Akuhara et al., 2013]. As a result, it was discovered that Vp/Vs ratio is also segmented within the oceanic crust and at the bottom of the overriding plate, which coincides with the LFE distribution: segment A is located along the Kii Channel, segment B around the western Kii Peninsula, and segment C around the eastern Kii Peninsula. In segment B, Vp/Vs ratio is low within the oceanic crust and LFE cluster characterized by an anomalously small amount of cumulative slip, compared to the other LFE clusters around the Kii Peninsula, is located [Obara, 2010]. The difference of Vp/Vs ratio and LFE activity among segments were interpreted as difference of pore fluid pressure. In fact, similar segmentation can be seen in hypocenters: Segment A with concentrated seismicity in the oceanic mantle, segment B with that in the oceanic crust, and segment C with little seismicity. To derive characteristic patterns of the hypocenters, we conducted a cluster analysis of earthquakes based on waveform similarity represented by cross-correlation coefficients (CCs) [e.g., Cattaneo, 1999], in which we took varying structural site effects among the OBS stations

  6. 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

  7. Intra-chain 3D segment swapping spawns the evolution of new multidomain protein architectures.

    PubMed

    Szilágyi, András; Zhang, Yang; Závodszky, Péter

    2012-01-01

    Multidomain proteins form in evolution through the concatenation of domains, but structural domains may comprise multiple segments of the chain. In this work, we demonstrate that new multidomain architectures can evolve by an apparent three-dimensional swap of segments between structurally similar domains within a single-chain monomer. By a comprehensive structural search of the current Protein Data Bank (PDB), we identified 32 well-defined segment-swapped proteins (SSPs) belonging to 18 structural families. Nearly 13% of all multidomain proteins in the PDB may have a segment-swapped evolutionary precursor as estimated by more permissive searching criteria. The formation of SSPs can be explained by two principal evolutionary mechanisms: (i) domain swapping and fusion (DSF) and (ii) circular permutation (CP). By large-scale comparative analyses using structural alignment and hidden Markov model methods, it was found that the majority of SSPs have evolved via the DSF mechanism, and a much smaller fraction, via CP. Functional analyses further revealed that segment swapping, which results in two linkers connecting the domains, may impart directed flexibility to multidomain proteins and contributes to the development of new functions. Thus, inter-domain segment swapping represents a novel general mechanism by which new protein folds and multidomain architectures arise in evolution, and SSPs have structural and functional properties that make them worth defining as a separate group. PMID:22079367

  8. 3D Radiative Transfer in Eta Carinae: Application of the SimpleX Algorithm to 3D SPH Simulations of Binary Colliding Winds

    NASA Technical Reports Server (NTRS)

    Clementel, N.; Madura, T. I.; Kruip, C. J. H.; Icke, V.; Gull, T. R.

    2014-01-01

    Eta Carinae is an ideal astrophysical laboratory for studying massive binary interactions and evolution, and stellar wind-wind collisions. Recent three-dimensional (3D) simulations set the stage for understanding the highly complex 3D flows in Eta Car. Observations of different broad high- and low-ionization forbidden emission lines provide an excellent tool to constrain the orientation of the system, the primary's mass-loss rate, and the ionizing flux of the hot secondary. In this work we present the first steps towards generating synthetic observations to compare with available and future HST/STIS data. We present initial results from full 3D radiative transfer simulations of the interacting winds in Eta Car. We use the SimpleX algorithm to post-process the output from 3D SPH simulations and obtain the ionization fractions of hydrogen and helium assuming three different mass-loss rates for the primary star. The resultant ionization maps of both species constrain the regions where the observed forbidden emission lines can form. Including collisional ionization is necessary to achieve a better description of the ionization states, especially in the areas shielded from the secondary's radiation. We find that reducing the primary's mass-loss rate increases the volume of ionized gas, creating larger areas where the forbidden emission lines can form. We conclude that post processing 3D SPH data with SimpleX is a viable tool to create ionization maps for Eta Car.

  9. 3D Radiative Transfer in Eta Carinae: Application of the SimpleX Algorithm to 3D SPH Simulations of Binary Colliding Winds

    NASA Technical Reports Server (NTRS)

    Clementel, N.; Madura, T. I.; Kruip, C.J.H.; Icke, V.; Gull, T. R.

    2014-01-01

    Eta Carinae is an ideal astrophysical laboratory for studying massive binary interactions and evolution, and stellar wind-wind collisions. Recent three-dimensional (3D) simulations set the stage for understanding the highly complex 3D flows in eta Car. Observations of different broad high- and low-ionization forbidden emission lines provide an excellent tool to constrain the orientation of the system, the primary's mass-loss rate, and the ionizing flux of the hot secondary. In this work we present the first steps towards generating synthetic observations to compare with available and future HST/STIS data. We present initial results from full 3D radiative transfer simulations of the interacting winds in eta Car.We use the SimpleX algorithm to post-process the output from 3D SPH simulations and obtain the ionization fractions of hydrogen and helium assuming three different mass-loss rates for the primary star. The resultant ionization maps of both species constrain the regions where the observed forbidden emission lines can form. Including collisional ionization is necessary to achieve a better description of the ionization states, especially in the areas shielded from the secondary's radiation. We find that reducing the primary's mass-loss rate increases the volume of ionized gas, creating larger areas where the forbidden emission lines can form.We conclude that post processing 3D SPH data with SimpleX is a viable tool to create ionization maps for eta Car.

  10. Cardiac Multi-detector CT Segmentation Based on Multiscale Directional Edge Detector and 3D Level Set.

    PubMed

    Antunes, Sofia; Esposito, Antonio; Palmisano, Anna; Colantoni, Caterina; Cerutti, Sergio; Rizzo, Giovanna

    2016-05-01

    Extraction of the cardiac surfaces of interest from multi-detector computed tomographic (MDCT) data is a pre-requisite step for cardiac analysis, as well as for image guidance procedures. Most of the existing methods need manual corrections, which is time-consuming. We present a fully automatic segmentation technique for the extraction of the right ventricle, left ventricular endocardium and epicardium from MDCT images. The method consists in a 3D level set surface evolution approach coupled to a new stopping function based on a multiscale directional second derivative Gaussian filter, which is able to stop propagation precisely on the real boundary of the structures of interest. We validated the segmentation method on 18 MDCT volumes from healthy and pathologic subjects using manual segmentation performed by a team of expert radiologists as gold standard. Segmentation errors were assessed for each structure resulting in a surface-to-surface mean error below 0.5 mm and a percentage of surface distance with errors less than 1 mm above 80%. Moreover, in comparison to other segmentation approaches, already proposed in previous work, our method presented an improved accuracy (with surface distance errors less than 1 mm increased of 8-20% for all structures). The obtained results suggest that our approach is accurate and effective for the segmentation of ventricular cavities and myocardium from MDCT images. PMID:26319010

  11. Assessment of next-best-view algorithms performance with various 3D scanners and manipulator

    NASA Astrophysics Data System (ADS)

    Karaszewski, M.; Adamczyk, M.; Sitnik, R.

    2016-09-01

    The problem of calculating three dimensional (3D) sensor position (and orientation) during the digitization of real-world objects (called next best view planning or NBV) has been an active topic of research for over 20 years. While many solutions have been developed, it is hard to compare their quality based only on the exemplary results presented in papers. We implemented 13 of the most popular NBV algorithms and evaluated their performance by digitizing five objects of various properties, using three measurement heads with different working volumes mounted on a 6-axis robot with a rotating table for placing objects. The results obtained for the 13 algorithms were then compared based on four criteria: the number of directional measurements, digitization time, total positioning distance, and surface coverage required to digitize test objects with available measurement heads.

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

    NASA Astrophysics Data System (ADS)

    Akbari, Hamed; Fei, Baowei

    2012-02-01

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

  13. 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. PMID:20226465

  14. IMPROVEMENTS TO THE TIME STEPPING ALGORITHM OF RELAP5-3D

    SciTech Connect

    Cumberland, R.; Mesina, G.

    2009-01-01

    The RELAP5-3D time step method is used to perform thermo-hydraulic and neutronic simulations of nuclear reactors and other devices. It discretizes time and space by numerically solving several differential equations. Previously, time step size was controlled by halving or doubling the size of a previous time step. This process caused the code to run slower than it potentially could. In this research project, the RELAP5-3D time step method was modifi ed to allow a new method of changing time steps to improve execution speed and to control error. The new RELAP5-3D time step method being studied involves making the time step proportional to the material courant limit (MCL), while insuring that the time step does not increase by more than a factor of two between advancements. As before, if a step fails or mass error is excessive, the time step is cut in half. To examine performance of the new method, a measure of run time and a measure of error were plotted against a changing MCL proportionality constant (m) in seven test cases. The removal of the upper time step limit produced a small increase in error, but a large decrease in execution time. The best value of m was found to be 0.9. The new algorithm is capable of producing a signifi cant increase in execution speed, with a relatively small increase in mass error. The improvements made are now under consideration for inclusion as a special option in the RELAP5-3D production code.

  15. 3D Segmentation of Rodent Brain Structures Using Hierarchical Shape Priors and Deformable Models

    PubMed Central

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

    2016-01-01

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

  16. 3D segmentation of rodent brain structures using hierarchical shape priors and deformable models.

    PubMed

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

    2011-01-01

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

  17. An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images

    PubMed Central

    Sun, Zhuli; Chen, Haoyu; Shi, Fei; Wang, Lirong; Zhu, Weifang; Xiang, Dehui; Yan, Chenglin; Li, Liang; Chen, Xinjian

    2016-01-01

    Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorioretinal diseases, which can cause loss of central vision. In this paper, an automated framework is proposed to segment serous PED in SD-OCT images. The proposed framework consists of four main steps: first, a multi-scale graph search method is applied to segment abnormal retinal layers; second, an effective AdaBoost method is applied to refine the initial segmented regions based on 62 extracted features; third, a shape-constrained graph cut method is applied to segment serous PED, in which the foreground and background seeds are obtained automatically; finally, an adaptive structure elements based morphology method is applied to remove false positive segmented regions. The proposed framework was tested on 25 SD-OCT volumes from 25 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 90.08%, 0.22%, 91.20% and 92.62%, respectively. The proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED region, which can assist clinical diagnosis and treatment. PMID:26899236

  18. Dynamic 3D scanning as a markerless method to calculate multi-segment foot kinematics during stance phase: methodology and first application.

    PubMed

    Van den Herrewegen, Inge; Cuppens, Kris; Broeckx, Mario; Barisch-Fritz, Bettina; Vander Sloten, Jos; Leardini, Alberto; Peeraer, Louis

    2014-08-22

    Multi-segmental foot kinematics have been analyzed by means of optical marker-sets or by means of inertial sensors, but never by markerless dynamic 3D scanning (D3DScanning). The use of D3DScans implies a radically different approach for the construction of the multi-segment foot model: the foot anatomy is identified via the surface shape instead of distinct landmark points. We propose a 4-segment foot model consisting of the shank (Sha), calcaneus (Cal), metatarsus (Met) and hallux (Hal). These segments are manually selected on a static scan. To track the segments in the dynamic scan, the segments of the static scan are matched on each frame of the dynamic scan using the iterative closest point (ICP) fitting algorithm. Joint rotations are calculated between Sha-Cal, Cal-Met, and Met-Hal. Due to the lower quality scans at heel strike and toe off, the first and last 10% of the stance phase is excluded. The application of the method to 5 healthy subjects, 6 trials each, shows a good repeatability (intra-subject standard deviations between 1° and 2.5°) for Sha-Cal and Cal-Met joints, and inferior results for the Met-Hal joint (>3°). The repeatability seems to be subject-dependent. For the validation, a qualitative comparison with joint kinematics from a corresponding established marker-based multi-segment foot model is made. This shows very consistent patterns of rotation. The ease of subject preparation and also the effective and easy to interpret visual output, make the present technique very attractive for functional analysis of the foot, enhancing usability in clinical practice. PMID:24998032

  19. 3D structural analysis of proteins using electrostatic surfaces based on image segmentation

    PubMed Central

    Vlachakis, Dimitrios; Champeris Tsaniras, Spyridon; Tsiliki, Georgia; Megalooikonomou, Vasileios; Kossida, Sophia

    2016-01-01

    Herein, we present a novel strategy to analyse and characterize proteins using protein molecular electro-static surfaces. Our approach starts by calculating a series of distinct molecular surfaces for each protein that are subsequently flattened out, thus reducing 3D information noise. RGB images are appropriately scaled by means of standard image processing techniques whilst retaining the weight information of each protein’s molecular electrostatic surface. Then homogeneous areas in the protein surface are estimated based on unsupervised clustering of the 3D images, while performing similarity searches. This is a computationally fast approach, which efficiently highlights interesting structural areas among a group of proteins. Multiple protein electrostatic surfaces can be combined together and in conjunction with their processed images, they can provide the starting material for protein structural similarity and molecular docking experiments.

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

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  1. 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.

  2. 3D segmentation of non-isolated pulmonary nodules in high resolution CT images

    NASA Astrophysics Data System (ADS)

    Zhang, Xiangwei; McLennan, Geoffrey; Hoffman, Eric A.; Sonka, Milan

    2005-04-01

    The purpose of this study is to develop a computer-aided diagnosis (CAD) system to segment small size non-isolated pulmonary nodules in high resolution helical CT scans. A new automated method of segmenting juxtapleural nodules was proposed, in which a quadric surface fitting procedure was used to create a boundary between a juxtapleural nodule and its neighboring pleural surface. Experiments on some real CT nodule data showed that this method was able to yield results that reflect the local shape of the pleural surface. Additionally, a scheme based on parametrically deformable geometric model was developed to deal with the problem of segmenting nodules attached to vessels. A vessel segment connected to a nodule was modeled using superquadrics with parametric deformations. The boundary between a vascularized nodule and the attached vessels can be recovered by finding the deformed superquadrics which approximates the vessels. Gradient descent scheme was utilized to optimize the parameters of the superquadrics. Simple experiments on synthetic data showed this scheme is promising.

  3. Accuracy of a Mitral Valve Segmentation Method Using J-Splines for Real-Time 3D Echocardiography Data

    PubMed Central

    Siefert, Andrew W.; Icenogle, David A.; Rabbah, Jean-Pierre; Saikrishnan, Neelakantan; Rossignac, Jarek; Lerakis, Stamatios; Yoganathan, Ajit P.

    2013-01-01

    Patient-specific models of the heart’s mitral valve (MV) exhibit potential for surgical planning. While advances in 3D echocardiography (3DE) have provided adequate resolution to extract MV leaflet geometry, no study has quantitatively assessed the accuracy of their modeled leaflets versus a ground-truth standard for temporal frames beyond systolic closure or for differing valvular dysfunctions. The accuracy of a 3DE-based segmentation methodology based on J-splines was assessed for porcine MVs with known 4D leaflet coordinates within a pulsatile simulator during closure, peak closure, and opening for a control, prolapsed, and billowing MV model. For all time points, the mean distance error between the segmented models and ground-truth data were 0.40±0.32 mm, 0.52±0.51 mm, and 0.74±0.69 mm for the control, flail, and billowing models. For all models and temporal frames, 95% of the distance errors were below 1.64 mm. When applied to a patient data set, segmentation was able to confirm a regurgitant orifice and post-operative improvements in coaptation. This study provides an experimental platform for assessing the accuracy of an MV segmentation methodology at phases beyond systolic closure and for differing MV dysfunctions. Results demonstrate the accuracy of a MV segmentation methodology for the development of future surgical planning tools. PMID:23460042

  4. 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.

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

    PubMed

    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. PMID:24710691

  6. Improved document image segmentation algorithm using multiresolution morphology

    NASA Astrophysics Data System (ADS)

    Bukhari, Syed Saqib; Shafait, Faisal; Breuel, Thomas M.

    2011-01-01

    Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,1 which is also available in his open-source Leptonica library.2The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.

  7. Research of the multimodal brain-tumor segmentation algorithm

    NASA Astrophysics Data System (ADS)

    Lu, Yisu; Chen, Wufan

    2015-12-01

    It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. A new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain tumor images, we developed the algorithm to segment multimodal brain tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated and compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance.

  8. Adaptive model based pulmonary artery segmentation in 3D chest CT

    NASA Astrophysics Data System (ADS)

    Feuerstein, Marco; Kitasaka, Takayuki; Mori, Kensaku

    2010-03-01

    The extraction and analysis of the pulmonary artery in computed tomography (CT) of the chest can be an important, but time-consuming step for the diagnosis and treatment of lung disease, in particular in non-contrast data, where the pulmonary artery has low contrast and frequently merges with adjacent tissue of similar intensity. We here present a new method for the automatic segmentation of the pulmonary artery based on an adaptive model, Hough and Euclidean distance transforms, and spline fitting, which works equally well on non-contrast and contrast enhanced data. An evaluation on 40 patient data sets and a comparison to manual segmentations in terms of Jaccard index, sensitivity, specificity, and minimum mean distance shows its overall robustness.

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

    PubMed

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

    2012-07-01

    Training active shape models requires collecting manual ground-truth meshes in a large image database. While shape information can be reused across multiple imaging modalities, intensity information needs to be imaging modality and protocol specific. In this context, this study has two main purposes: (1) to test the potential of using intensity models learned from MRI simulated datasets and (2) to test the potential of including a measure of reliability during the matching process to increase robustness. We used a population of 400 virtual subjects (XCAT phantom), and two clinical populations of 40 and 45 subjects. Virtual subjects were used to generate simulated datasets (MRISIM simulator). Intensity models were trained both on simulated and real datasets. The trained models were used to segment the left ventricle (LV) and right ventricle (RV) from real datasets. Segmentations were also obtained with and without reliability information. Performance was evaluated with point-to-surface and volume errors. Simulated intensity models obtained average accuracy comparable to inter-observer variability for LV segmentation. The inclusion of reliability information reduced volume errors in hypertrophic patients (EF errors from 17 ± 57% to 10 ± 18%; LV MASS errors from -27 ± 22 g to -14 ± 25 g), and in heart failure patients (EF errors from -8 ± 42% to -5 ± 14%). The RV model of the simulated images needs further improvement to better resemble image intensities around the myocardial edges. Both for real and simulated models, reliability information increased segmentation robustness without penalizing accuracy. PMID:22683992

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

  11. Automatic histogram-based segmentation of white matter hyperintensities using 3D FLAIR images

    NASA Astrophysics Data System (ADS)

    Simões, Rita; Slump, Cornelis; Moenninghoff, Christoph; Wanke, Isabel; Dlugaj, Martha; Weimar, Christian

    2012-03-01

    White matter hyperintensities are known to play a role in the cognitive decline experienced by patients suffering from neurological diseases. Therefore, accurately detecting and monitoring these lesions is of importance. Automatic methods for segmenting white matter lesions typically use multimodal MRI data. Furthermore, many methods use a training set to perform a classification task or to determine necessary parameters. In this work, we describe and evaluate an unsupervised segmentation method that is based solely on the histogram of FLAIR images. It approximates the histogram by a mixture of three Gaussians in order to find an appropriate threshold for white matter hyperintensities. We use a context-sensitive Expectation-Maximization method to determine the Gaussian mixture parameters. The segmentation is subsequently corrected for false positives using the knowledge of the location of typical FLAIR artifacts. A preliminary validation with the ground truth on 6 patients revealed a Similarity Index of 0.73 +/- 0.10, indicating that the method is comparable to others in the literature which require multimodal MRI and/or a preliminary training step.

  12. 3D reconstruction for sinusoidal motion based on different feature detection algorithms

    NASA Astrophysics Data System (ADS)

    Zhang, Peng; Zhang, Jin; Deng, Huaxia; Yu, Liandong

    2015-02-01

    The dynamic testing of structures and components is an important area of research. Extensive researches on the methods of using sensors for vibration parameters have been studied for years. With the rapid development of industrial high-speed camera and computer hardware, the method of using stereo vision for dynamic testing has been the focus of the research since the advantages of non-contact, full-field, high resolution and high accuracy. But in the country there is not much research about the dynamic testing based on stereo vision, and yet few people publish articles about the three-dimensional (3D) reconstruction of feature points in the case of dynamic. It is essential to the following analysis whether it can obtain accurate movement of target objects. In this paper, an object with sinusoidal motion is detected by stereo vision and the accuracy with different feature detection algorithms is investigated. Three different marks including dot, square and circle are stuck on the object and the object is doing sinusoidal motion by vibration table. Then use feature detection algorithm speed-up robust feature (SURF) to detect point, detect square corners by Harris and position the center by Hough transform. After obtaining the pixel coordinate values of the feature point, the stereo calibration parameters are used to achieve three-dimensional reconstruction through triangulation principle. The trajectories of the specific direction according to the vibration frequency and the frequency camera acquisition are obtained. At last, the reconstruction accuracy of different feature detection algorithms is compared.

  13. Integration of Libration Point Orbit Dynamics into a Universal 3-D Autonomous Formation Flying Algorithm

    NASA Technical Reports Server (NTRS)

    Folta, David; Bauer, Frank H. (Technical Monitor)

    2001-01-01

    The autonomous formation flying control algorithm developed by the Goddard Space Flight Center (GSFC) for the New Millennium Program (NMP) Earth Observing-1 (EO-1) mission is investigated for applicability to libration point orbit formations. In the EO-1 formation-flying algorithm, control is accomplished via linearization about a reference transfer orbit with a state transition matrix (STM) computed from state inputs. The effect of libration point orbit dynamics on this algorithm architecture is explored via computation of STMs using the flight proven code, a monodromy matrix developed from a N-body model of a libration orbit, and a standard STM developed from the gravitational and coriolis effects as measured at the libration point. A comparison of formation flying Delta-Vs calculated from these methods is made to a standard linear quadratic regulator (LQR) method. The universal 3-D approach is optimal in the sense that it can be accommodated as an open-loop or closed-loop control using only state information.

  14. Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

    SciTech Connect

    Corso, N; Zakhor, A

    2013-12-03

    Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting of a variety of sensors. There are three novel contributions in our proposed mapping approach. First, we present an algorithm which automatically detects loop closure constraints from an occupancy grid map. In doing so, we ensure that constraints are detected only in locations that are well conditioned for scan matching. Secondly, we address the problem of scan matching with poor initial condition by presenting an outlier-resistant, genetic scan matching algorithm that accurately matches scans despite a poor initial condition. Third, we present two metrics based on the amount and complexity of overlapping geometry in order to vet the estimated loop closure constraints. By doing so, we automatically prevent erroneous loop closures from degrading the accuracy of the reconstructed trajectory. The proposed algorithms are experimentally verified using both controlled and real-world data. The end-to-end system performance is evaluated using 100 surveyed control points in an office environment and obtains a mean accuracy of 10 cm. Experimental results are also shown on three additional datasets from real world environments including a 1500 meter trajectory in a warehouse sized retail shopping center.

  15. The development of a scalable parallel 3-D CFD algorithm for turbomachinery. M.S. Thesis Final Report

    NASA Technical Reports Server (NTRS)

    Luke, Edward Allen

    1993-01-01

    Two algorithms capable of computing a transonic 3-D inviscid flow field about rotating machines are considered for parallel implementation. During the study of these algorithms, a significant new method of measuring the performance of parallel algorithms is developed. The theory that supports this new method creates an empirical definition of scalable parallel algorithms that is used to produce quantifiable evidence that a scalable parallel application was developed. The implementation of the parallel application and an automated domain decomposition tool are also discussed.

  16. Automatic registration and segmentation algorithm for multiple electrophoresis images

    NASA Astrophysics Data System (ADS)

    Baker, Matthew S.; Busse, Harald; Vogt, Martin

    2000-06-01

    We present an algorithm for registering, segmenting and quantifying multiple scanned electrophoresis images. (2D gel) Electrophoresis is a technique for separating proteins or other macromolecules in organic material according to net charge and molecular mass and results in scanned grayscale images with dark spots against a light background marking the presence of such macromolecules. The algorithm begins by registering each of the images using a non-rigid registration algorithm. The registered images are then jointly segmented using a Markov random field approach to obtain a single segmentation. By using multiple images, the effect of noise is greatly reduced. We demonstrate the algorithm on several sets of real data.

  17. Novel irregular mesh tagging algorithm for wound synthesis on a 3D face.

    PubMed

    Lee, Sangyong; Chin, Seongah

    2015-01-01

    Recently, advanced visualizing techniques in computer graphics have considerably enhanced the visual appearance of synthetic models. To realize enhanced visual graphics for synthetic medical effects, the first step followed by rendering techniques involves attaching albedo textures to the region where a certain graphic is to be rendered. For instance, in order to render wound textures efficiently, the first step is to recognize the area where the user wants to attach a wound. However, in general, face indices are not stored in sequential order, which makes sub-texturing difficult. In this paper, we present a novel mesh tagging algorithm that utilizes a task for mesh traversals and level extension in the general case of a wound sub-texture mapping and a selected region deformation in a three-dimensional (3D) model. This method works automatically on both regular and irregular mesh surfaces. The approach consists of mesh selection (MS), mesh leveling (ML), and mesh tagging (MT). To validate our approach, we performed experiments for synthesizing wounds on a 3D face model and on a simulated mesh. PMID:26405904

  18. Benchmarking of state-of-the-art needle detection algorithms in 3D ultrasound data volumes

    NASA Astrophysics Data System (ADS)

    Pourtaherian, Arash; Zinger, Svitlana; de With, Peter H. N.; Korsten, Hendrikus H. M.; Mihajlovic, Nenad

    2015-03-01

    Ultrasound-guided needle interventions are widely practiced in medical diagnostics and therapy, i.e. for biopsy guidance, regional anesthesia or for brachytherapy. Needle guidance using 2D ultrasound can be very challenging due to the poor needle visibility and the limited field of view. Since 3D ultrasound transducers are becoming more widely used, needle guidance can be improved and simplified with appropriate computer-aided analyses. In this paper, we compare two state-of-the-art 3D needle detection techniques: a technique based on line filtering from literature and a system employing Gabor transformation. Both algorithms utilize supervised classification to pre-select candidate needle voxels in the volume and then fit a model of the needle on the selected voxels. The major differences between the two approaches are in extracting the feature vectors for classification and selecting the criterion for fitting. We evaluate the performance of the two techniques using manually-annotated ground truth in several ex-vivo situations of different complexities, containing three different needle types with various insertion angles. This extensive evaluation provides better understanding on the limitations and advantages of each technique under different acquisition conditions, which is leading to the development of improved techniques for more reliable and accurate localization. Benchmarking results that the Gabor features are better capable of distinguishing the needle voxels in all datasets. Moreover, it is shown that the complete processing chain of the Gabor-based method outperforms the line filtering in accuracy and stability of the detection results.

  19. Enhanced 3-D-reconstruction algorithm for C-arm systems suitable for interventional procedures.

    PubMed

    Wiesent, K; Barth, K; Navab, N; Durlak, P; Brunner, T; Schuetz, O; Seissler, W

    2000-05-01

    Increasingly, three-dimensional (3-D) imaging technologies are used in medical diagnosis, for therapy planning, and during interventional procedures. We describe the possibilities of fast 3-D-reconstruction of high-contrast objects with high spatial resolution from only a small series of two-dimensional (2-D) planar radiographs. The special problems arising from the intended use of an open, mechanically unstable C-arm system are discussed. For the description of the irregular sampling geometry, homogeneous coordinates are used thoroughly. The well-known Feldkamp algorithm is modified to incorporate corresponding projection matrices without any decomposition into intrinsic and extrinsic parameters. Some approximations to speed up the whole reconstruction procedure and the tradeoff between image quality and computation time are also considered. Using standard hardware the reconstruction of a 256(3) cube is now possible within a few minutes, a time that is acceptable during interventions. Examples for cranial vessel imaging from some clinical test installations will be shown as well as promising results for bone imaging with a laboratory C-arm system. PMID:11021683

  20. Brightness-compensated 3-D optical flow algorithm for monitoring cochlear motion patterns

    NASA Astrophysics Data System (ADS)

    von Tiedemann, Miriam; Fridberger, Anders; Ulfendahl, Mats; de Monvel, Jacques Boutet

    2010-09-01

    A method for three-dimensional motion analysis designed for live cell imaging by fluorescence confocal microscopy is described. The approach is based on optical flow computation and takes into account brightness variations in the image scene that are not due to motion, such as photobleaching or fluorescence variations that may reflect changes in cellular physiology. The 3-D optical flow algorithm allowed almost perfect motion estimation on noise-free artificial sequences, and performed with a relative error of <10% on noisy images typical of real experiments. The method was applied to a series of 3-D confocal image stacks from an in vitro preparation of the guinea pig cochlea. The complex motions caused by slow pressure changes in the cochlear compartments were quantified. At the surface of the hearing organ, the largest motion component was the transverse one (normal to the surface), but significant radial and longitudinal displacements were also present. The outer hair cell displayed larger radial motion at their basolateral membrane than at their apical surface. These movements reflect mechanical interactions between different cellular structures, which may be important for communicating sound-evoked vibrations to the sensory cells. A better understanding of these interactions is important for testing realistic models of cochlear mechanics.

  1. 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.

  2. An automatic contact algorithm in DYNA3D for impact problems

    SciTech Connect

    Whirley, R.G.; Engelmann, B.E.

    1993-07-23

    This paper presents a new approach for the automatic definition and treatment of mechanical contact in explicit nonlinear finite element analysis. Automatic contact offers the benefits of significantly reduced model construction time and fewer opportunities for user error, but faces significant challenges in reliability and computational costs. Key aspects of the proposed new method include automatic identification of adjacent and opposite surfaces in the global search phase, and the use of a well-defined surface normal which allows a consistent treatment of shell intersection and corner contact conditions without a ad-hoc rules. The paper concludes with three examples which illustrate the performance of the newly proposed algorithm in the public DYNA3D code.

  3. LiDAR Segmentation using Suitable Seed Points for 3D Building Extraction

    NASA Astrophysics Data System (ADS)

    Abdullah, S. M.; Awrangjeb, M.; Lu, G.

    2014-08-01

    Effective building detection and roof reconstruction has an influential demand over the remote sensing research community. In this paper, we present a new automatic LiDAR point cloud segmentation method using suitable seed points for building detection and roof plane extraction. Firstly, the LiDAR point cloud is separated into "ground" and "non-ground" points based on the analysis of DEM with a height threshold. Each of the non-ground point is marked as coplanar or non-coplanar based on a coplanarity analysis. Commencing from the maximum LiDAR point height towards the minimum, all the LiDAR points on each height level are extracted and separated into several groups based on 2D distance. From each group, lines are extracted and a coplanar point which is the nearest to the midpoint of each line is considered as a seed point. This seed point and its neighbouring points are utilised to generate the plane equation. The plane is grown in a region growing fashion until no new points can be added. A robust rule-based tree removal method is applied subsequently to remove planar segments on trees. Four different rules are applied in this method. Finally, the boundary of each object is extracted from the segmented LiDAR point cloud. The method is evaluated with six different data sets consisting hilly and densely vegetated areas. The experimental results indicate that the proposed method offers a high building detection and roof plane extraction rates while compared to a recently proposed method.

  4. Performance analysis of different surface reconstruction algorithms for 3D reconstruction of outdoor objects from their digital images.

    PubMed

    Maiti, Abhik; Chakravarty, Debashish

    2016-01-01

    3D reconstruction of geo-objects from their digital images is a time-efficient and convenient way of studying the structural features of the object being modelled. This paper presents a 3D reconstruction methodology which can be used to generate photo-realistic 3D watertight surface of different irregular shaped objects, from digital image sequences of the objects. The 3D reconstruction approach described here is robust, simplistic and can be readily used in reconstructing watertight 3D surface of any object from its digital image sequence. Here, digital images of different objects are used to build sparse, followed by dense 3D point clouds of the objects. These image-obtained point clouds are then used for generation of photo-realistic 3D surfaces, using different surface reconstruction algorithms such as Poisson reconstruction and Ball-pivoting algorithm. Different control parameters of these algorithms are identified, which affect the quality and computation time of the reconstructed 3D surface. The effects of these control parameters in generation of 3D surface from point clouds of different density are studied. It is shown that the reconstructed surface quality of Poisson reconstruction depends on Samples per node (SN) significantly, greater SN values resulting in better quality surfaces. Also, the quality of the 3D surface generated using Ball-Pivoting algorithm is found to be highly depend upon Clustering radius and Angle threshold values. The results obtained from this study give the readers of the article a valuable insight into the effects of different control parameters on determining the reconstructed surface quality. PMID:27386376

  5. Correlation-based discrimination between cardiac tissue and blood for segmentation of the left ventricle in 3-D echocardiographic images.

    PubMed

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

    2014-03-01

    For automated segmentation of 3-D echocardiographic images, incorporation of temporal information may be helpful. In this study, optimal settings for calculation of temporal cross-correlations between subsequent time frames were determined, to obtain the maximum cross-correlation (MCC) values that provided the best contrast between blood and cardiac tissue over the entire cardiac cycle. Both contrast and boundary gradient quality measures were assessed to optimize MCC values with respect to signal choice (radiofrequency or envelope data) and axial window size. Optimal MCC values were incorporated into a deformable model to automatically segment the left ventricular cavity. MCC values were tested against, and combined with, filtered, demodulated radiofrequency data. Results reveal that using envelope data in combination with a relatively small axial window (0.7-1.25 mm) at fine scale results in optimal contrast and boundary gradient between the two tissues over the entire cardiac cycle. Preliminary segmentation results indicate that incorporation of MCC values has additional value for automated segmentation of the left ventricle. PMID:24412178

  6. 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.

  7. 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.

  8. 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

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

    PubMed

    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

  10. An analytical algorithm for 3D magnetic field mapping of a watt balance magnet

    NASA Astrophysics Data System (ADS)

    Fu, Zhuang; Zhang, Zhonghua; Li, Zhengkun; Zhao, Wei; Han, Bing; Lu, Yunfeng; Li, Shisong

    2016-04-01

    A yoke-based permanent magnet, which has been employed in many watt balances at national metrology institutes, is supposed to generate strong and uniform magnetic field in an air gap in the radial direction. However, in reality the fringe effect due to the finite height of the air gap will introduce an undesired vertical magnetic component to the air gap, which should either be measured or modeled towards some optimizations of the watt balance. A recent publication, i.e. Li et al (2015 Metrologia 52 445), presented a full field mapping method, which in theory will supply useful information for profile characterization and misalignment analysis. This article is an additional material of Li et al (2015 Metrologia 52 445), which develops a different analytical algorithm to represent the 3D magnetic field of a watt balance magnet based on only one measurement for the radial magnetic flux density along the vertical direction, B r (z). The new algorithm is based on the electromagnetic nature of the magnet, which has a much better accuracy.

  11. 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.

  12. An efficient parallel algorithm: Poststack and prestack Kirchhoff 3D depth migration using flexi-depth iterations

    NASA Astrophysics Data System (ADS)

    Rastogi, Richa; Srivastava, Abhishek; Khonde, Kiran; Sirasala, Kirannmayi M.; Londhe, Ashutosh; Chavhan, Hitesh

    2015-07-01

    This paper presents an efficient parallel 3D Kirchhoff depth migration algorithm suitable for current class of multicore architecture. The fundamental Kirchhoff depth migration algorithm exhibits inherent parallelism however, when it comes to 3D data migration, as the data size increases the resource requirement of the algorithm also increases. This challenges its practical implementation even on current generation high performance computing systems. Therefore a smart parallelization approach is essential to handle 3D data for migration. The most compute intensive part of Kirchhoff depth migration algorithm is the calculation of traveltime tables due to its resource requirements such as memory/storage and I/O. In the current research work, we target this area and develop a competent parallel algorithm for post and prestack 3D Kirchhoff depth migration, using hybrid MPI+OpenMP programming techniques. We introduce a concept of flexi-depth iterations while depth migrating data in parallel imaging space, using optimized traveltime table computations. This concept provides flexibility to the algorithm by migrating data in a number of depth iterations, which depends upon the available node memory and the size of data to be migrated during runtime. Furthermore, it minimizes the requirements of storage, I/O and inter-node communication, thus making it advantageous over the conventional parallelization approaches. The developed parallel algorithm is demonstrated and analysed on Yuva II, a PARAM series of supercomputers. Optimization, performance and scalability experiment results along with the migration outcome show the effectiveness of the parallel algorithm.

  13. 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.

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

    PubMed

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

    2007-03-01

    Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study. The methodology described in this work has four steps: a pre-processing of IVUS images, a segmentation of media-adventitia contour, a detection of intima and plaque and a 3D reconstruction of the vessel. Preprocessing is intended to remove noise from the images without blurring the edges. Segmentation of media-adventitia contour is achieved using active contours (snakes). In particular, we use the gradient vector flow (GVF) as external force for the snakes. The detection of lumen border is obtained taking into account gray-level information of the inner part of the previously detected contours. A knowledge-based approach is used to determine which level of gray corresponds statistically to the different regions of interest: intima, plaque and lumen. The catheter region is automatically discarded. An estimate of plaque type is also given. Finally, 3D reconstruction of all detected regions is made. The suitability of this methodology has been verified for the analysis and visualization of plaque length, stenosis severity, automatic detection of the most problematic regions, calculus of plaque volumes and a preliminary estimation of plaque type obtaining for automatic measures of lumen and vessel area an average error smaller than 1mm(2) (equivalent aproximately to 10% of the average measure), for calculus of plaque and lumen volume errors smaller than 0.5mm(3) (equivalent approximately to 20% of the average measure) and for plaque type estimates a mismatch of less than 8% in the analysed frames. PMID:17215103

  15. 3D Radiative Transfer in Eta Carinae: The SimpleX Radiative Transfer Algorithm Applied to 3D SPH Simulations of Eta Car's Colliding Winds

    NASA Astrophysics Data System (ADS)

    Clementel, N.; Madura, T. I.; Kruip, C. J. H.; Icke, V.; Gull, T. R.

    2014-04-01

    At the heart of the spectacular bipolar Homunculus nebula lies an extremely luminous (5*10^6 L_sun) colliding wind binary with a highly eccentric (e ~ 0.9), 5.54-year orbit and a total mass ~ 110 M_sun. Our closest (D ~ 2.3 kpc) and best example of a pre-hypernova environment, Eta Carinae is an ideal astrophysical laboratory for studying massive binary interactions, stellar wind-wind collisions, and massive star evolution. In order to improve our knowledge of the system, we need to generate synthetic observations and compare them with the already available and future HST/STIS data. We present initial results from full 3D radiative transfer post-processing of 3D SPH hydrodynamical simulations of the interacting winds of Eta Carinae. We use SimpleX algorithm to obtain the ionization fractions of hydrogen and helium, this results in ionization maps of both species that constrain the regions where these lines can form. These results will allow us to put constraints on the number of ionizing photons coming from the companion. This construction of synthetic observations allows us to obtain insight into the highly complex 3D flows in Eta, from the shape of the ionized volume and its resulting optical/spectral appearance.

  16. A meshless EFG-based algorithm for 3D deformable modeling of soft tissue in real-time.

    PubMed

    Abdi, Elahe; Farahmand, Farzam; Durali, Mohammad

    2012-01-01

    The meshless element-free Galerkin method was generalized and an algorithm was developed for 3D dynamic modeling of deformable bodies in real time. The efficacy of the algorithm was investigated in a 3D linear viscoelastic model of human spleen subjected to a time-varying compressive force exerted by a surgical grasper. The model remained stable in spite of the considerably large deformations occurred. There was a good agreement between the results and those of an equivalent finite element model. The computational cost, however, was much lower, enabling the proposed algorithm to be effectively used in real-time applications. PMID:22356947

  17. Structural stereo matching of Laplacian-of-Gaussian contour segments for 3D perception

    NASA Technical Reports Server (NTRS)

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

    1989-01-01

    The stereo correspondence problem is solved using Laplacian-of-Gaussian zero-crossing contours as a source of primitives for structural stereopsis, as opposed to traditional point-based algorithms. Up to 74 percent matching of candidate zero crossing points are being achieved on 240 x 246 images at small scales and large ranges of disparity, without coarse-to-fine tracking and without precise knowledge of the epipolar geometry. This approach should prove particularly useful in recovering the epipolar geometry automatically for stereo pairs for which it is unavailable a priori. Such situations occur in the extraction of terrain models from stereo aerial photographs.

  18. Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms

    PubMed Central

    2010-01-01

    Background Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models. Results We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU) code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture. Conclusions We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a starting point for modelers to

  19. Efficient Algorithms for Segmentation of Item-Set Time Series

    NASA Astrophysics Data System (ADS)

    Chundi, Parvathi; Rosenkrantz, Daniel J.

    We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

  20. Axial 3D region of interest reconstruction using weighted cone beam BPF/DBPF algorithm cascaded with adequately oriented orthogonal butterfly filtering

    NASA Astrophysics Data System (ADS)

    Tang, Shaojie; Tang, Xiangyang

    2016-03-01

    Axial cone beam (CB) computed tomography (CT) reconstruction is still the most desirable in clinical applications. As the potential candidates with analytic form for the task, the back projection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical and axial reconstruction from CB and fan beam projection data, respectively. These two algorithms have been heuristically extended for axial CB reconstruction via adoption of virtual PI-line segments. Unfortunately, however, streak artifacts are induced along the Hilbert filtering direction, since these algorithms are no longer accurate on the virtual PI-line segments. We have proposed to cascade the extended BPF/DBPF algorithm with orthogonal butterfly filtering for image reconstruction (namely axial CB-BPP/DBPF cascaded with orthogonal butterfly filtering), in which the orientation-specific artifacts caused by post-BP Hilbert transform can be eliminated, at a possible expense of losing the BPF/DBPF's capability of dealing with projection data truncation. Our preliminary results have shown that this is not the case in practice. Hence, in this work, we carry out an algorithmic analysis and experimental study to investigate the performance of the axial CB-BPP/DBPF cascaded with adequately oriented orthogonal butterfly filtering for three-dimensional (3D) reconstruction in region of interest (ROI).

  1. 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. PMID:25739346

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

    PubMed Central

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

    2014-01-01

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

  3. Rapid probabilistic source characterisation in 3D earth models using learning algorithms

    NASA Astrophysics Data System (ADS)

    Valentine, A. P.; Kaeufl, P.; Trampert, J.

    2015-12-01

    Characterising earthquake sources rapidly and robustly is an essential component of any earthquake early warning (EEW) procedure. Ideally, this characterisation should:(i) be probabilistic -- enabling appreciation of the full range of mechanisms compatible with available data, and taking observational and theoretical uncertainties into account; and(ii) operate in a physically-complete theoretical framework.However, implementing either of these ideals increases computational costs significantly, making it unfeasible to satisfy both in the short timescales necessary for EEW applications.The barrier here arises from the fact that conventional probabilistic inversion techniques involve running many thousands of forward simulations after data has been obtained---a procedure known as `posterior sampling'. Thus, for EEW, all computational costs must be incurred after the event time. Here, we demonstrate a new approach---based instead on `prior sampling'---which circumvents this problem and is feasible for EEW applications. All forward simulations are conducted in advance, and a learning algorithm is used to assimilate information about the relationship between model and data. Once observations from an earthquake become available, this information can be used to infer probability density functions (pdfs) for seismic source parameters, within milliseconds.We demonstrate this procedure using data from the 2008 Mw5.4 Chino Hills earthquake. We compute Green's functions for 150 randomly-chosen locations on the Whittier and Chino faults, using SPECFEM3D and a 3D model of the regional velocity structure. We then use these to train neural networks that map from seismic waveforms to pdfs on a point-source, moment-tensor representation of the event mechanism. We show that using local network data from the Chino Hills event, this system provides accurate information on magnitude, epicentral location and source half-duration using data available 6 seconds after the first station

  4. Automatic lung lobe segmentation in x-ray CT images by 3D watershed transform using anatomic information from the segmented airway tree

    NASA Astrophysics Data System (ADS)

    Ukil, Soumik; Hoffman, Eric A.; Reinhardt, Joseph M.

    2005-04-01

    The human lungs are divided into five distinct anatomic compartments called lobes. The physical boundaries between the lobes are called the lobar fissures. Detection of lobar fissure positions in pulmonary X-ray CT images is of increasing interest for the diagnosis of lung disease. We have developed an automatic method for segmentation of all five lung lobes simultaneously using a 3D watershed transform on the distance transform of a previously generated vessel mask, linearly combined with the original data. Due to the anatomically separate airway sub-trees for individual lobes, we can accurately and automatically place seed points for the watershed segmentation based on the airway tree anatomical description, due to the fact that lower generation airway and vascular tree segments are located near each other. This, along with seed point placement using information on the spatial location of the lobes, can give a close approximation to the actual lobar fissures. The accuracy of the lobar borders is assessed by comparing the automatic segmentation to manually traced lobar boundaries. Averaged over all volumes, the RMS distance errors for the left oblique fissure, right oblique fissure and right horizontal fissure are 3.720 mm, 0.713 mm and 1.109 mm respectively.

  5. Real-time 3D curved needle segmentation using combined B-mode and power Doppler ultrasound.

    PubMed

    Greer, Joseph D; Adebar, Troy K; Hwang, Gloria L; Okamura, Allison M

    2014-01-01

    This paper presents a real-time segmentation method for curved needles in biological tissue based on analysis of B-mode and power Doppler images from a tracked 2D ultrasound transducer. Mechanical vibration induced by an external voice coil results in a Doppler response along the needle shaft, which is centered around the needle section in the ultrasound image. First, B-mode image analysis is performed within regions of interest indicated by the Doppler response to create a segmentation of the needle section in the ultrasound image. Next, each needle section is decomposed into a sequence of points and transformed into a global coordinate system using the tracked transducer pose. Finally, the 3D shape is reconstructed from these points. The results of this method differ from manual segmentation by 0.71 ± 0.55 mm in needle tip location and 0.38 ± 0.27 mm along the needle shaft. This method is also fast, taking 5-10 ms to run on a standard PC, and is particularly advantageous in robotic needle steering, which involves thin, curved needles with poor echogenicity. PMID:25485402

  6. Optimized mean shift algorithm for color segmentation in image sequences

    NASA Astrophysics Data System (ADS)

    Bailer, Werner; Schallauer, Peter; Haraldsson, Harald B.; Rehatschek, Herwig

    2005-03-01

    The application of the mean shift algorithm to color image segmentation has been proposed in 1997 by Comaniciu and Meer. We apply the mean shift color segmentation to image sequences, as the first step of a moving object segmentation algorithm. Previous work has shown that it is well suited for this task, because it provides better temporal stability of the segmentation result than other approaches. The drawback is higher computational cost. For speed up of processing on image sequences we exploit the fact that subsequent frames are similar and use the cluster centers of previous frames as initial estimates, which also enhances spatial segmentation continuity. In contrast to other implementations we use the originally proposed CIE LUV color space to ensure high quality segmentation results. We show that moderate quantization of the input data before conversion to CIE LUV has little influence on the segmentation quality but results in significant speed up. We also propose changes in the post-processing step to increase the temporal stability of border pixels. We perform objective evaluation of the segmentation results to compare the original algorithm with our modified version. We show that our optimized algorithm reduces processing time and increases the temporal stability of the segmentation.

  7. Full Waveform 3D Synthetic Seismic Algorithm for 1D Layered Anelastic Models

    NASA Astrophysics Data System (ADS)

    Schwaiger, H. F.; Aldridge, D. F.; Haney, M. M.

    2007-12-01

    Numerical calculation of synthetic seismograms for 1D layered earth models remains a significant aspect of amplitude-offset investigations, surface wave studies, microseismic event location approaches, and reflection interpretation or inversion processes. Compared to 3D finite-difference algorithms, memory demand and execution time are greatly reduced, enabling rapid generation of seismic data within workstation or laptop computational environments. We have developed a frequency-wavenumber forward modeling algorithm adapted to realistic 1D geologic media, for the purpose of calculating seismograms accurately and efficiently. The earth model consists of N layers bounded by two halfspaces. Each layer/halfspace is a homogeneous and isotropic anelastic (attenuative and dispersive) solid, characterized by a rectangular relaxation spectrum of absorption mechanisms. Compressional and shear phase speeds and quality factors are specified at a particular reference frequency. Solution methodology involves 3D Fourier transforming the three coupled, second- order, integro-differential equations for particle displacements to the frequency-horizontal wavenumber domain. An analytic solution of the resulting ordinary differential system is obtained. Imposition of welded interface conditions (continuity of displacement and stress) at all interfaces, as well as radiation conditions in the two halfspaces, yields a system of 6(N+1) linear algebraic equations for the coefficients in the ODE solution. An optimized inverse 2D Fourier transform to the space domain gives the seismic wavefield on a horizontal plane. Finally, three-component seismograms are obtained by accumulating frequency spectra at designated receiver positions on this plane, followed by a 1D inverse FFT from angular frequency ω to time. Stress-free conditions may be applied at the top or bottom interfaces, and seismic waves are initiated by force or moment density sources. Examples reveal that including attenuation

  8. Finite-Difference Algorithm for Simulating 3D Electromagnetic Wavefields in Conductive Media

    NASA Astrophysics Data System (ADS)

    Aldridge, D. F.; Bartel, L. C.; Knox, H. A.

    2013-12-01

    Electromagnetic (EM) wavefields are routinely used in geophysical exploration for detection and characterization of subsurface geological formations of economic interest. Recorded EM signals depend strongly on the current conductivity of geologic media. Hence, they are particularly useful for inferring fluid content of saturated porous bodies. In order to enhance understanding of field-recorded data, we are developing a numerical algorithm for simulating three-dimensional (3D) EM wave propagation and diffusion in heterogeneous conductive materials. Maxwell's equations are combined with isotropic constitutive relations to obtain a set of six, coupled, first-order partial differential equations governing the electric and magnetic vectors. An advantage of this system is that it does not contain spatial derivatives of the three medium parameters electric permittivity, magnetic permeability, and current conductivity. Numerical solution methodology consists of explicit, time-domain finite-differencing on a 3D staggered rectangular grid. Temporal and spatial FD operators have order 2 and N, where N is user-selectable. We use an artificially-large electric permittivity to maximize the FD timestep, and thus reduce execution time. For the low frequencies typically used in geophysical exploration, accuracy is not unduly compromised. Grid boundary reflections are mitigated via convolutional perfectly matched layers (C-PMLs) imposed at the six grid flanks. A shared-memory-parallel code implementation via OpenMP directives enables rapid algorithm execution on a multi-thread computational platform. Good agreement is obtained in comparisons of numerically-generated data with reference solutions. EM wavefields are sourced via point current density and magnetic dipole vectors. Spatially-extended inductive sources (current carrying wire loops) are under development. We are particularly interested in accurate representation of high-conductivity sub-grid-scale features that are common

  9. Algorithms for improved 3-D reconstruction of live mammalian embryo vasculature from optical coherence tomography data

    PubMed Central

    Kulkarni, Prathamesh M.; Rey-Villamizar, Nicolas; Merouane, Amine; Sudheendran, Narendran; Wang, Shang; Garcia, Monica; Larina, Irina V.; Roysam, Badrinath

    2015-01-01

    Background Robust reconstructions of the three-dimensional network of blood vessels in developing embryos imaged by optical coherence tomography (OCT) are needed for quantifying the longitudinal development of vascular networks in live mammalian embryos, in support of developmental cardiovascular research. Past computational methods [such as speckle variance (SV)] have demonstrated the feasibility of vascular reconstruction, but multiple challenges remain including: the presence of vessel structures at multiple spatial scales, thin blood vessels with weak flow, and artifacts resulting from bulk tissue motion (BTM). Methods In order to overcome these challenges, this paper introduces a robust and scalable reconstruction algorithm based on a combination of anomaly detection algorithms and a parametric dictionary based sparse representation of blood vessels from structural OCT data. Results Validation results using confocal data as the baseline demonstrate that the proposed method enables the detection of vessel segments that are either partially missed or weakly reconstructed using the SV method. Finally, quantitative measurements of vessel reconstruction quality indicate an overall higher quality of vessel reconstruction with the proposed method. Conclusions Results suggest that sparsity-integrated speckle anomaly detection (SSAD) is potentially a valuable tool for performing accurate quantification of the progression of vascular development in the mammalian embryonic yolk sac as imaged using OCT. PMID:25694962

  10. A novel iris segmentation algorithm based on small eigenvalue analysis

    NASA Astrophysics Data System (ADS)

    Harish, B. S.; Aruna Kumar, S. V.; Guru, D. S.; Ngo, Minh Ngoc

    2015-12-01

    In this paper, a simple and robust algorithm is proposed for iris segmentation. The proposed method consists of two steps. In first step, iris and pupil is segmented using Robust Spatial Kernel FCM (RSKFCM) algorithm. RSKFCM is based on traditional Fuzzy-c-Means (FCM) algorithm, which incorporates spatial information and uses kernel metric as distance measure. In second step, small eigenvalue transformation is applied to localize iris boundary. The transformation is based on statistical and geometrical properties of the small eigenvalue of the covariance matrix of a set of edge pixels. Extensive experimentations are carried out on standard benchmark iris dataset (viz. CASIA-IrisV4 and UBIRIS.v2). We compared our proposed method with existing iris segmentation methods. Our proposed method has the least time complexity of O(n(i+p)) . The result of the experiments emphasizes that the proposed algorithm outperforms the existing iris segmentation methods.

  11. Exact and approximate Fourier rebinning algorithms for the solution of the data truncation problem in 3-D PET.

    PubMed

    Bouallègue, Fayçal Ben; Crouzet, Jean-François; Comtat, Claude; Fourcade, Marjolaine; Mohammadi, Bijan; Mariano-Goulart, Denis

    2007-07-01

    This paper presents an extended 3-D exact rebinning formula in the Fourier space that leads to an iterative reprojection algorithm (iterative FOREPROJ), which enables the estimation of unmeasured oblique projection data on the basis of the whole set of measured data. In first approximation, this analytical formula also leads to an extended Fourier rebinning equation that is the basis for an approximate reprojection algorithm (extended FORE). These algorithms were evaluated on numerically simulated 3-D positron emission tomography (PET) data for the solution of the truncation problem, i.e., the estimation of the missing portions in the oblique projection data, before the application of algorithms that require complete projection data such as some rebinning methods (FOREX) or 3-D reconstruction algorithms (3DRP or direct Fourier methods). By taking advantage of all the 3-D data statistics, the iterative FOREPROJ reprojection provides a reliable alternative to the classical FOREPROJ method, which only exploits the low-statistics nonoblique data. It significantly improves the quality of the external reconstructed slices without loss of spatial resolution. As for the approximate extended FORE algorithm, it clearly exhibits limitations due to axial interpolations, but will require clinical studies with more realistic measured data in order to decide on its pertinence. PMID:17649913

  12. Brain tumor segmentation in MR slices using improved GrowCut algorithm

    NASA Astrophysics Data System (ADS)

    Ji, Chunhong; Yu, Jinhua; Wang, Yuanyuan; Chen, Liang; Shi, Zhifeng; Mao, Ying

    2015-12-01

    The detection of brain tumor from MR images is very significant for medical diagnosis and treatment. However, the existing methods are mostly based on manual or semiautomatic segmentation which are awkward when dealing with a large amount of MR slices. In this paper, a new fully automatic method for the segmentation of brain tumors in MR slices is presented. Based on the hypothesis of the symmetric brain structure, the method improves the interactive GrowCut algorithm by further using the bounding box algorithm in the pre-processing step. More importantly, local reflectional symmetry is used to make up the deficiency of the bounding box method. After segmentation, 3D tumor image is reconstructed. We evaluate the accuracy of the proposed method on MR slices with synthetic tumors and actual clinical MR images. Result of the proposed method is compared with the actual position of simulated 3D tumor qualitatively and quantitatively. In addition, our automatic method produces equivalent performance as manual segmentation and the interactive GrowCut with manual interference while providing fully automatic segmentation.

  13. Feature measures for the segmentation of neuronal membrane using a machine learning algorithm

    NASA Astrophysics Data System (ADS)

    Iftikhar, Saadia; Godil, Afzal

    2013-12-01

    In this paper, we present a Support Vector Machine (SVM) based pixel classifier for a semi-automated segmentation algorithm to detect neuronal membrane structures in stacks of electron microscopy images of brain tissue samples. This algorithm uses high-dimensional feature spaces extracted from center-surrounded patches, and some distinct edge sensitive features for each pixel in the image, and a training dataset for the segmentation of neuronal membrane structures and background. Some threshold conditions are later applied to remove small regions, which are below a certain threshold criteria, and morphological operations, such as the filling of the detected objects, are done to get compactness in the objects. The performance of the segmentation method is calculated on the unseen data by using three distinct error measures: pixel error, wrapping error, and rand error, and also a pixel by pixel accuracy measure with their respective ground-truth. The trained SVM classifier achieves the best precision level in these three distinct errors at 0.23, 0.016 and 0.15, respectively; while the best accuracy using pixel by pixel measure reaches 77% on the given dataset. The results presented here are one step further towards exploring possible ways to solve these hard problems, such as segmentation in medical image analysis. In the future, we plan to extend it as a 3D segmentation approach for 3D datasets to not only retain the topological structures in the dataset but also for the ease of further analysis.

  14. Embedding SAS approach into conjugate gradient algorithms for asymmetric 3D elasticity problems

    SciTech Connect

    Chen, Hsin-Chu; Warsi, N.A.; Sameh, A.

    1996-12-31

    In this paper, we present two strategies to embed the SAS (symmetric-and-antisymmetric) scheme into conjugate gradient (CG) algorithms to make solving 3D elasticity problems, with or without global reflexive symmetry, more efficient. The SAS approach is physically a domain decomposition scheme that takes advantage of reflexive symmetry of discretized physical problems, and algebraically a matrix transformation method that exploits special reflexivity properties of the matrix resulting from discretization. In addition to offering large-grain parallelism, which is valuable in a multiprocessing environment, the SAS scheme also has the potential for reducing arithmetic operations in the numerical solution of a reasonably wide class of scientific and engineering problems. This approach can be applied directly to problems that have global reflexive symmetry, yielding smaller and independent subproblems to solve, or indirectly to problems with partial symmetry, resulting in loosely coupled subproblems. The decomposition is achieved by separating the reflexive subspace from the antireflexive one, possessed by a special class of matrices A, A {element_of} C{sup n x n} that satisfy the relation A = PAP where P is a reflection matrix (symmetric signed permutation matrix).

  15. Automated segment matching algorithm-theory, test, and evaluation

    NASA Technical Reports Server (NTRS)

    Kalcic, M. T. (Principal Investigator)

    1982-01-01

    Results to automate the U.S. Department of Agriculture's process of segment shifting and obtain results within one-half pixel accuracy are presented. Given an initial registration, the digitized segment is shifted until a more precise fit to the LANDSAT data is found. The algorithm automates the shifting process and performs certain tests for matching and accepting the computed shift numbers. Results indicate the algorithm can obtain results within one-half pixel accuracy.

  16. Segmentation of center brains and optic lobes in 3D confocal images of adult fruit fly brains.

    PubMed

    Lam, Shing Chun Benny; Ruan, Zongcai; Zhao, Ting; Long, Fuhui; Jenett, Arnim; Simpson, Julie; Myers, Eugene W; Peng, Hanchuan

    2010-02-01

    Automatic alignment (registration) of 3D images of adult fruit fly brains is often influenced by the significant displacement of the relative locations of the two optic lobes (OLs) and the center brain (CB). In one of our ongoing efforts to produce a better image alignment pipeline of adult fruit fly brains, we consider separating CB and OLs and align them independently. This paper reports our automatic method to segregate CB and OLs, in particular under conditions where the signal to noise ratio (SNR) is low, the variation of the image intensity is big, and the relative displacement of OLs and CB is substantial. We design an algorithm to find a minimum-cost 3D surface in a 3D image stack to best separate an OL (of one side, either left or right) from CB. This surface is defined as an aggregation of the respective minimum-cost curves detected in each individual 2D image slice. Each curve is defined by a list of control points that best segregate OL and CB. To obtain the locations of these control points, we derive an energy function that includes an image energy term defined by local pixel intensities and two internal energy terms that constrain the curve's smoothness and length. Gradient descent method is used to optimize this energy function. To improve both the speed and robustness of the method, for each stack, the locations of optimized control points in a slice are taken as the initialization prior for the next slice. We have tested this approach on simulated and real 3D fly brain image stacks and demonstrated that this method can reasonably segregate OLs from CBs despite the aforementioned difficulties. PMID:19698789

  17. Masseter segmentation using an improved watershed algorithm with unsupervised classification.

    PubMed

    Ng, H P; Ong, S H; Foong, K W C; Goh, P S; Nowinski, W L

    2008-02-01

    The watershed algorithm always produces a complete division of the image. However, it is susceptible to over-segmentation and sensitivity to false edges. In medical images this leads to unfavorable representations of the anatomy. We address these drawbacks by introducing automated thresholding and post-segmentation merging. The automated thresholding step is based on the histogram of the gradient magnitude map while post-segmentation merging is based on a criterion which measures the similarity in intensity values between two neighboring partitions. Our improved watershed algorithm is able to merge more than 90% of the initial partitions, which indicates that a large amount of over-segmentation has been reduced. To further improve the segmentation results, we make use of K-means clustering to provide an initial coarse segmentation of the highly textured image before the improved watershed algorithm is applied to it. When applied to the segmentation of the masseter from 60 magnetic resonance images of 10 subjects, the proposed algorithm achieved an overlap index (kappa) of 90.6%, and was able to merge 98% of the initial partitions on average. The segmentation results are comparable to those obtained using the gradient vector flow snake. PMID:17950265

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

    PubMed Central

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

    2014-01-01

    The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them. PMID:24772079

  19. 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.

  20. 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.

  1. Algorithms For Segmentation Of Complex-Amplitude SAR Data

    NASA Technical Reports Server (NTRS)

    Rignot, Eric J. M.; Chellappa, Ramalingam

    1993-01-01

    Several algorithms implement improved method of segmenting highly speckled, high-resolution, complex-amplitude synthetic-aperture-radar (SAR) digitized images into regions, within each backscattering characteristics similar or homogeneous from place to place. Method provides for approximate, deterministic solution by two alternative algorithms almost always converging to local minimums: one, Iterative Conditional Modes (ICM) algorithm, which locally maximizes posterior probability density of region labels; other, Maximum Posterior Marginal (MPM) algorithm, which maximizes posterior marginal density of region labels at each pixel location. ICM algorithm optimizes reconstruction of underlying scene. MPM algorithm minimizes expected number of misclassified pixels, possibly better in remote sensing of natural scenes.

  2. 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.

  3. An enhanced fast scanning algorithm for image segmentation

    NASA Astrophysics Data System (ADS)

    Ismael, Ahmed Naser; Yusof, Yuhanis binti

    2015-12-01

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

  4. A 2D to 3D ultrasound image registration algorithm for robotically assisted laparoscopic radical prostatectomy

    NASA Astrophysics Data System (ADS)

    Esteghamatian, Mehdi; Pautler, Stephen E.; McKenzie, Charles A.; Peters, Terry M.

    2011-03-01

    Robotically assisted laparoscopic radical prostatectomy (RARP) is an effective approach to resect the diseased organ, with stereoscopic views of the targeted tissue improving the dexterity of the surgeons. However, since the laparoscopic view acquires only the surface image of the tissue, the underlying distribution of the cancer within the organ is not observed, making it difficult to make informed decisions on surgical margins and sparing of neurovascular bundles. One option to address this problem is to exploit registration to integrate the laparoscopic view with images of pre-operatively acquired dynamic contrast enhanced (DCE) MRI that can demonstrate the regions of malignant tissue within the prostate. Such a view potentially allows the surgeon to visualize the location of the malignancy with respect to the surrounding neurovascular structures, permitting a tissue-sparing strategy to be formulated directly based on the observed tumour distribution. If the tumour is close to the capsule, it may be determined that the adjacent neurovascular bundle (NVB) needs to be sacrificed within the surgical margin to ensure that any erupted tumour was resected. On the other hand, if the cancer is sufficiently far from the capsule, one or both NVBs may be spared. However, in order to realize such image integration, the pre-operative image needs to be fused with the laparoscopic view of the prostate. During the initial stages of the operation, the prostate must be tracked in real time so that the pre-operative MR image remains aligned with patient coordinate system. In this study, we propose and investigate a novel 2D to 3D ultrasound image registration algorithm to track the prostate motion with an accuracy of 2.68+/-1.31mm.

  5. Tissue Probability Map Constrained 4-D Clustering Algorithm for Increased Accuracy and Robustness in Serial MR Brain Image Segmentation

    PubMed Central

    Xue, Zhong; Shen, Dinggang; Li, Hai; Wong, Stephen

    2010-01-01

    The traditional fuzzy clustering algorithm and its extensions have been successfully applied in medical image segmentation. However, because of the variability of tissues and anatomical structures, the clustering results might be biased by the tissue population and intensity differences. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue probability map constrained clustering algorithm and apply it to serial MR brain image segmentation, i.e., a series of 3-D MR brain images of the same subject at different time points. Using the new serial image segmentation algorithm in the framework of the CLASSIC framework, which iteratively segments the images and estimates the longitudinal deformations, we improved both accuracy and robustness for serial image computing, and at the mean time produced longitudinally consistent segmentation and stable measures. In the algorithm, the tissue probability maps consist of both the population-based and subject-specific segmentation priors. Experimental study using both simulated longitudinal MR brain data and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data confirmed that using both priors more accurate and robust segmentation results can be obtained. The proposed algorithm can be applied in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders. PMID:26566399

  6. Blind watermark algorithm on 3D motion model based on wavelet transform

    NASA Astrophysics Data System (ADS)

    Qi, Hu; Zhai, Lang

    2013-12-01

    With the continuous development of 3D vision technology, digital watermark technology, as the best choice for copyright protection, has fused with it gradually. This paper proposed a blind watermark plan of 3D motion model based on wavelet transform, and made it loaded into the Vega real-time visual simulation system. Firstly, put 3D model into affine transform, and take the distance from the center of gravity to the vertex of 3D object in order to generate a one-dimensional discrete signal; then make this signal into wavelet transform to change its frequency coefficients and embed watermark, finally generate 3D motion model with watermarking. In fixed affine space, achieve the robustness in translation, revolving and proportion transforms. The results show that this approach has better performances not only in robustness, but also in watermark- invisibility.

  7. Quantitative comparison of the performance of SAR segmentation algorithms.

    PubMed

    Caves, R; Quegan, S; White, R

    1998-01-01

    Methods to evaluate the performance of segmentation algorithms for synthetic aperture radar (SAR) images are developed, based on known properties of coherent speckle and a scene model in which areas of constant backscatter coefficient are separated by abrupt edges. Local and global measures of segmentation homogeneity are derived and applied to the outputs of two segmentation algorithms developed for SAR data, one based on iterative edge detection and segment growing, the other based on global maximum a posteriori (MAP) estimation using simulated annealing. The quantitative statistically based measures appear consistent with visual impressions of the relative quality of the segmentations produced by the two algorithms. On simulated data meeting algorithm assumptions, both algorithms performed well but MAP methods appeared visually and measurably better. On real data, MAP estimation was markedly the better method and retained performance comparable to that on simulated data, while the performance of the other algorithm deteriorated sharply. Improvements in the performance measures will require a more realistic scene model and techniques to recognize oversegmentation. PMID:18276219

  8. A segmentation algorithm for automated tracking of fast swimming unlabelled cells in three dimensions.

    PubMed

    Pimentel, J A; Carneiro, J; Darszon, A; Corkidi, G

    2012-01-01

    Recent advances in microscopy and cytolabelling methods enable the real time imaging of cells as they move and interact in their real physiological environment. Scenarios in which multiple cells move autonomously in all directions are not uncommon in biology. A remarkable example is the swimming of marine spermatozoa in search of the conspecific oocyte. Imaging cells in these scenarios, particularly when they move fast and are poorly labelled or even unlabelled requires very fast three-dimensional time-lapse (3D+t) imaging. This 3D+t imaging poses challenges not only to the acquisition systems but also to the image analysis algorithms. It is in this context that this work describes an original automated multiparticle segmentation method to analyse motile translucent cells in 3D microscopical volumes. The proposed segmentation technique takes advantage of the way the cell appearance changes with the distance to the focal plane position. The cells translucent properties and their interaction with light produce a specific pattern: when the cell is within or close to the focal plane, its two-dimensional (2D) appearance matches a bright spot surrounded by a dark ring, whereas when it is farther from the focal plane the cell contrast is inverted looking like a dark spot surrounded by a bright ring. The proposed method analyses the acquired video sequence frame-by-frame taking advantage of 2D image segmentation algorithms to identify and select candidate cellular sections. The crux of the method is in the sequential filtering of the candidate sections, first by template matching of the in-focus and out-of-focus templates and second by considering adjacent candidates sections in 3D. These sequential filters effectively narrow down the number of segmented candidate sections making the automatic tracking of cells in three dimensions a straightforward operation. PMID:21999166

  9. Theoretical and experimental study of DOA estimation using AML algorithm for an isotropic and non-isotropic 3D array

    NASA Astrophysics Data System (ADS)

    Asgari, Shadnaz; Ali, Andreas M.; Collier, Travis C.; Yao, Yuan; Hudson, Ralph E.; Yao, Kung; Taylor, Charles E.

    2007-09-01

    The focus of most direction-of-arrival (DOA) estimation problems has been based mainly on a two-dimensional (2D) scenario where we only need to estimate the azimuth angle. But in various practical situations we have to deal with a three-dimensional scenario. The importance of being able to estimate both azimuth and elevation angles with high accuracy and low complexity is of interest. We present the theoretical and the practical issues of DOA estimation using the Approximate-Maximum-Likelihood (AML) algorithm in a 3D scenario. We show that the performance of the proposed 3D AML algorithm converges to the Cramer-Rao Bound. We use the concept of an isotropic array to reduce the complexity of the proposed algorithm by advocating a decoupled 3D version. We also explore a modified version of the decoupled 3D AML algorithm which can be used for DOA estimation with non-isotropic arrays. Various numerical results are presented. We use two acoustic arrays each consisting of 8 microphones to do some field measurements. The processing of the measured data from the acoustic arrays for different azimuth and elevation angles confirms the effectiveness of the proposed methods.

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

    PubMed Central

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

    2014-01-01

    Summary 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. PMID:24672759

  11. Gap-Closing 3d Building Reconstruction by Aligning Boundaries of Roof Segments and Detecting Uncovered Details

    NASA Astrophysics Data System (ADS)

    Pohl, M.; Bulatov, D.

    2015-03-01

    We describe a work flow to border building faces which aims to obtain a detailed and closed building model. Initially, we use the estimated roof planes and the rasterized binary mask of the corresponding inlier set to generate bordering polygons. To close the gaps between the initial boundary polygons and between the polygons and the building ground outline, we introduce an algorithm to align boundaries which successfully works in 2.5D and 3D. To enhance the accuracy of the boundary alignment, we use additional reliable model entities such as cut lines and step lines between the initial estimated roof planes. All gaps that cannot be avoided by this procedure are afterwards covered by a method searching for uncovered details.

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

    PubMed Central

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

    2016-01-01

    Background Shortcomings in existing methods of image segmentation preclude the widespread adoption of patient-specific 3D printing as a routine decision-making tool in the care of those with congenital heart disease. We sought to determine the range of cardiovascular segmentation methods and how long each of these methods takes. Methods A systematic review of literature was undertaken. Medical imaging modality, segmentation methods, segmentation time, segmentation descriptive quality (SDQ) and segmentation software were recorded. Results Totally 136 studies met the inclusion criteria (1 clinical trial; 80 journal articles; 55 conference, technical and case reports). The most frequently used image segmentation methods were brightness thresholding, region growing and manual editing, as supported by the most popular piece of proprietary software: Mimics (Materialise NV, Leuven, Belgium, 1992–2015). The use of bespoke software developed by individual authors was not uncommon. SDQ indicated that reporting of image segmentation methods was generally poor with only one in three accounts providing sufficient detail for their procedure to be reproduced. Conclusions and implication of key findings Predominantly anecdotal and case reporting precluded rigorous assessment of risk of bias and strength of evidence. This review finds a reliance on manual and semi-automated segmentation methods which demand a high level of expertise and a significant time commitment on the part of the operator. In light of the findings, we have made recommendations regarding reporting of 3D printing studies. We anticipate that these findings will encourage the development of advanced image segmentation methods. PMID:27170842

  13. Performance evaluation of image segmentation algorithms on microscopic image data.

    PubMed

    Beneš, Miroslav; Zitová, Barbara

    2015-01-01

    In our paper, we present a performance evaluation of image segmentation algorithms on microscopic image data. In spite of the existence of many algorithms for image data partitioning, there is no universal and 'the best' method yet. Moreover, images of microscopic samples can be of various character and quality which can negatively influence the performance of image segmentation algorithms. Thus, the issue of selecting suitable method for a given set of image data is of big interest. We carried out a large number of experiments with a variety of segmentation methods to evaluate the behaviour of individual approaches on the testing set of microscopic images (cross-section images taken in three different modalities from the field of art restoration). The segmentation results were assessed by several indices used for measuring the output quality of image segmentation algorithms. In the end, the benefit of segmentation combination approach is studied and applicability of achieved results on another representatives of microscopic data category - biological samples - is shown. PMID:25233873

  14. Portable high-intensity focused ultrasound system with 3D electronic steering, real-time cavitation monitoring, and 3D image reconstruction algorithms: a preclinical study in pigs

    PubMed Central

    2014-01-01

    Purpose: The aim of this study was to evaluate the safety and accuracy of a new portable ultrasonography-guided high-intensity focused ultrasound (USg-HIFU) system with a 3-dimensional (3D) electronic steering transducer, a simultaneous ablation and imaging module, real-time cavitation monitoring, and 3D image reconstruction algorithms. Methods: To address the accuracy of the transducer, hydrophones in a water chamber were used to assess the generation of sonic fields. An animal study was also performed in five pigs by ablating in vivo thighs by single-point sonication (n=10) or volume sonication (n=10) and ex vivo kidneys by single-point sonication (n=10). Histological and statistical analyses were performed. Results: In the hydrophone study, peak voltages were detected within 1.0 mm from the targets on the y- and z-axes and within 2.0-mm intervals along the x-axis (z-axis, direction of ultrasound propagation; y- and x-axes, perpendicular to the direction of ultrasound propagation). Twenty-nine of 30 HIFU sessions successfully created ablations at the target. The in vivo porcine thigh study showed only a small discrepancy (width, 0.5-1.1 mm; length, 3.0 mm) between the planning ultrasonograms and the pathological specimens. Inordinate thermal damage was not observed in the adjacent tissues or sonic pathways in the in vivo thigh and ex vivo kidney studies. Conclusion: Our study suggests that this new USg-HIFU system may be a safe and accurate technique for ablating soft tissues and encapsulated organs. PMID:25038809

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  16. Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images.

    PubMed

    Wu, Jun; Wang, Yuanyuan; Yu, Jinhua; Shi, Xinling; Zhang, Junhua; Chen, Yue; Pang, Yun

    2015-02-01

    A novel 3-D filtering method is presented for speckle reduction and detail preservation in automated 3-D ultrasound images. First, texture features of an image are analyzed by using the improved quadtree (QT) decomposition. Then, the optimal homogeneous and the obvious heterogeneous regions are selected from QT decomposition results. Finally, diffusion parameters and diffusion process are automatically decided based on the properties of these two selected regions. The computing time needed for 2-D speckle reduction is very short. However, the computing time required for 3-D speckle reduction is often hundreds of times longer than 2-D speckle reduction. This may limit its potential application in practice. Because this new filter can adaptively adjust the time step of iteration, the computation time is reduced effectively. Both synthetic and real 3-D ultrasound images are used to evaluate the proposed filter. It is shown that this filter is superior to other methods in both practicality and efficiency. PMID:26366596

  17. A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation

    NASA Astrophysics Data System (ADS)

    Gu, Xuejun; Jelen, Urszula; Li, Jinsheng; Jia, Xun; Jiang, Steve B.

    2011-06-01

    Targeting at the development of an accurate and efficient dose calculation engine for online adaptive radiotherapy, we have implemented a finite-size pencil beam (FSPB) algorithm with a 3D-density correction method on graphics processing unit (GPU). This new GPU-based dose engine is built on our previously published ultrafast FSPB computational framework (Gu et al 2009 Phys. Med. Biol. 54 6287-97). Dosimetric evaluations against Monte Carlo dose calculations are conducted on ten IMRT treatment plans (five head-and-neck cases and five lung cases). For all cases, there is improvement with the 3D-density correction over the conventional FSPB algorithm and for most cases the improvement is significant. Regarding the efficiency, because of the appropriate arrangement of memory access and the usage of GPU intrinsic functions, the dose calculation for an IMRT plan can be accomplished well within 1 s (except for one case) with this new GPU-based FSPB algorithm. Compared to the previous GPU-based FSPB algorithm without 3D-density correction, this new algorithm, though slightly sacrificing the computational efficiency (~5-15% lower), has significantly improved the dose calculation accuracy, making it more suitable for online IMRT replanning.

  18. A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation.

    PubMed

    Gu, Xuejun; Jelen, Urszula; Li, Jinsheng; Jia, Xun; Jiang, Steve B

    2011-06-01

    Targeting at the development of an accurate and efficient dose calculation engine for online adaptive radiotherapy, we have implemented a finite-size pencil beam (FSPB) algorithm with a 3D-density correction method on graphics processing unit (GPU). This new GPU-based dose engine is built on our previously published ultrafast FSPB computational framework (Gu et al 2009 Phys. Med. Biol. 54 6287-97). Dosimetric evaluations against Monte Carlo dose calculations are conducted on ten IMRT treatment plans (five head-and-neck cases and five lung cases). For all cases, there is improvement with the 3D-density correction over the conventional FSPB algorithm and for most cases the improvement is significant. Regarding the efficiency, because of the appropriate arrangement of memory access and the usage of GPU intrinsic functions, the dose calculation for an IMRT plan can be accomplished well within 1 s (except for one case) with this new GPU-based FSPB algorithm. Compared to the previous GPU-based FSPB algorithm without 3D-density correction, this new algorithm, though slightly sacrificing the computational efficiency (∼5-15% lower), has significantly improved the dose calculation accuracy, making it more suitable for online IMRT replanning. PMID:21558589

  19. An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm

    PubMed Central

    2014-01-01

    Background The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds' number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed. Methods In this paper, a new separation model named parallel Independent Component Analysis constrained by Reference Curve (pICARC) was proposed to transform the separation problem to a multi-parameter optimization issue. It was not necessary to know the number of compounds in the optimization. In order to find all the solutions, an algorithm named multi-areas Genetic Algorithm (mGA) was proposed, where multiple areas of candidate solutions were constructed according to the fitness and distances among the chromosomes. Results Simulations and experiments on a real life HPLC-DAD data set were used to demonstrate our method and its effectiveness. Through simulations, it can be seen that our method can separate 3D chromatogram to chromatogram peaks and spectra successfully even when they severely overlapped. It is also shown by the experiments that our method is effective to solve real HPLC-DAD data set. Conclusions Our method can separate 3D chromatogram successfully without knowing the compounds' number in advance, which is fast and effective. PMID:25474487

  20. Combination therapy with BMP-2 and BMSCs enhances bone healing efficacy of PCL scaffold fabricated using the 3D plotting system in a large segmental defect model.

    PubMed

    Kang, Sun-Woong; Bae, Ji-Hoon; Park, Su-A; Kim, Wan-Doo; Park, Mi-Su; Ko, You-Jin; Jang, Hyon-Seok; Park, Jung-Ho

    2012-07-01

    The three-dimensional (3D) plotting system is a rapidly-developing scaffold fabrication method for bone tissue engineering. It yields a highly porous and inter-connective structure without the use of cytotoxic solvents. However, the therapeutic effects of a scaffold fabricated using the 3D plotting system in a large segmental defect model have not yet been demonstrated. We have tested two hypotheses: whether the bone healing efficacy of scaffold fabricated using the 3D plotting system would be enhanced by bone marrow-derived mesenchymal stem cell (BMSC) transplantation; and whether the combination of bone morphogenetic protein-2 (BMP-2) administration and BMSC transplantation onto the scaffold would act synergistically to enhance bone regeneration in a large segmental defect model. The use of the combined therapy did increase bone regeneration further as compared to that with monotherapy in large segmental bone defects. PMID:22447098

  1. A fast 3-D object recognition algorithm for the vision system of a special-purpose dexterous manipulator

    NASA Technical Reports Server (NTRS)

    Hung, Stephen H. Y.

    1989-01-01

    A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further.

  2. An implicit dispersive transport algorithm for the US Geological Survey MOC3D solute-transport model

    USGS Publications Warehouse

    Kipp, K.L., Jr.; Konikow, L.F.; Hornberger, G.Z.

    1998-01-01

    This report documents an extension to the U.S. Geological Survey MOC3D transport model that incorporates an implicit-in-time difference approximation for the dispersive transport equation, including source/sink terms. The original MOC3D transport model (Version 1) uses the method of characteristics to solve the transport equation on the basis of the velocity field. The original MOC3D solution algorithm incorporates particle tracking to represent advective processes and an explicit finite-difference formulation to calculate dispersive fluxes. The new implicit procedure eliminates several stability criteria required for the previous explicit formulation. This allows much larger transport time increments to be used in dispersion-dominated problems. The decoupling of advective and dispersive transport in MOC3D, however, is unchanged. With the implicit extension, the MOC3D model is upgraded to Version 2. A description of the numerical method of the implicit dispersion calculation, the data-input requirements and output options, and the results of simulator testing and evaluation are presented. Version 2 of MOC3D was evaluated for the same set of problems used for verification of Version 1. These test results indicate that the implicit calculation of Version 2 matches the accuracy of Version 1, yet is more efficient than the explicit calculation for transport problems that are characterized by a grid Peclet number less than about 1.0.

  3. Towards an automatic coronary artery segmentation algorithm.

    PubMed

    Fallavollita, Pascal; Cheriet, Farida

    2006-01-01

    A method is presented that aims at minimizing image processing time during X-ray fluoroscopy interventions. First, an automatic frame extraction algorithm is proposed in order to extract relevant image frames with respect to their cardiac phase (systole or diastole). Secondly, a 4-step filter is suggested in order to enhance vessel contours. The reciprocal of the enhanced image is used as an alternative speed function to initialize the fast marching method. The complete algorithm was tested on eight clinical angiographic data sets and comparisons with two other vessel enhancement filters (Lorenz and Frangi) are made for the centerline extraction procedure. In order to assess the suitability of our filter the extracted centerline coordinates are compared with the manually traced axis. PMID:17946540

  4. A fast vectorized multispin coding algorithm for 3D Monte Carlo simulations using Kawasaki spin-exchange dynamics

    NASA Astrophysics Data System (ADS)

    Zhang, M. Q.

    1989-09-01

    A new Monte Carlo algorithm for 3D Kawasaki spin-exchange simulations and its implementation on a CDC CYBER 205 is presented. This approach is applicable to lattices with sizes between 4×4×4 and 256×L2×L3 ((L2+2)(L3+4)/4⩽65535) and periodic boundary conditions. It is adjustable to various kinetic models in which the total magnetization is conserved. Maximum speed on 10 million steps per second can be reached for 3-D Ising model with Metropolis rate.

  5. [An automatic extraction algorithm for individual tree crown projection area and volume based on 3D point cloud data].

    PubMed

    Xu, Wei-Heng; Feng, Zhong-Ke; Su, Zhi-Fang; Xu, Hui; Jiao, You-Quan; Deng, Ou

    2014-02-01

    Tree crown projection area and crown volume are the important parameters for the estimation of biomass, tridimensional green biomass and other forestry science applications. Using conventional measurements of tree crown projection area and crown volume will produce a large area of errors in the view of practical situations referring to complicated tree crown structures or different morphological characteristics. However, it is difficult to measure and validate their accuracy through conventional measurement methods. In view of practical problems which include complicated tree crown structure, different morphological characteristics, so as to implement the objective that tree crown projection and crown volume can be extracted by computer program automatically. This paper proposes an automatic untouched measurement based on terrestrial three-dimensional laser scanner named FARO Photon120 using plane scattered data point convex hull algorithm and slice segmentation and accumulation algorithm to calculate the tree crown projection area. It is exploited on VC+6.0 and Matlab7.0. The experiments are exploited on 22 common tree species of Beijing, China. The results show that the correlation coefficient of the crown projection between Av calculated by new method and conventional method A4 reaches 0.964 (p<0.01); and the correlation coefficient of tree crown volume between V(VC) derived from new method and V(C) by the formula of a regular body is 0.960 (p<0.001). The results also show that the average of V(C) is smaller than that of V(VC) at the rate of 8.03%, and the average of A4 is larger than that of A(V) at the rate of 25.5%. Assumed Av and V(VC) as ture values, the deviations of the new method could be attributed to irregularity of the crowns' silhouettes. Different morphological characteristics of tree crown led to measurement error in forest simple plot survey. Based on the results, the paper proposes that: (1) the use of eight-point or sixteen-point projection with

  6. Automatic brain tumor segmentation with a fast Mumford-Shah algorithm

    NASA Astrophysics Data System (ADS)

    Müller, Sabine; Weickert, Joachim; Graf, Norbert

    2016-03-01

    We propose a fully-automatic method for brain tumor segmentation that does not require any training phase. Our approach is based on a sequence of segmentations using the Mumford-Shah cartoon model with varying parameters. In order to come up with a very fast implementation, we extend the recent primal-dual algorithm of Strekalovskiy et al. (2014) from the 2D to the medically relevant 3D setting. Moreover, we suggest a new confidence refinement and show that it can increase the precision of our segmentations substantially. Our method is evaluated on 188 data sets with high-grade gliomas and 25 with low-grade gliomas from the BraTS14 database. Within a computation time of only three minutes, we achieve Dice scores that are comparable to state-of-the-art methods.

  7. Increasing axial resolution of 3D data sets using deconvolution algorithms.

    PubMed

    Topor, P; Zimanyi, M; Mateasik, A

    2011-09-01

    Deconvolution algorithms are tools for the restoration of data degraded by blur and noise. An incorporation of regularization functions into the iterative form of reconstruction algorithms can improve the restoration performance and characteristics (e.g. noise and artefact handling). In this study, algorithms based on Richardson-Lucy deconvolution algorithm are tested. The ability of these algorithms to improve axial resolution of three-dimensional data sets is evaluated on model synthetic data. Finally, unregularized Richardson-Lucy algorithm is selected for the evaluation and reconstruction of three-dimensional chromosomal data sets of Drosophila melanogaster. Problems concerning the reconstruction process are discussed and further improvements are proposed. PMID:21599665

  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. A segmentation algorithm of intracranial hemorrhage CT image

    NASA Astrophysics Data System (ADS)

    Wang, Haibo; Chen, Zhiguo; Wang, Jianzhi

    2011-10-01

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

  10. Impact of Multiscale Retinex Computation on Performance of Segmentation Algorithms

    NASA Technical Reports Server (NTRS)

    Rahman, Zia-ur; Jobson, Daniel J.; Woodell, Glenn A.; Hines, Glenn D.

    2004-01-01

    Classical segmentation algorithms subdivide an image into its constituent components based upon some metric that defines commonality between pixels. Often, these metrics incorporate some measure of "activity" in the scene, e.g. the amount of detail that is in a region. The Multiscale Retinex with Color Restoration (MSRCR) is a general purpose, non-linear image enhancement algorithm that significantly affects the brightness, contrast and sharpness within an image. In this paper, we will analyze the impact the MSRCR has on segmentation results and performance.

  11. Accuracy of volume measurement using 3D ultrasound and development of CT-3D US image fusion algorithm for prostate cancer radiotherapy

    SciTech Connect

    Baek, Jihye; Huh, Jangyoung; Hyun An, So; Oh, Yoonjin; Kim, Myungsoo; Kim, DongYoung; Chung, Kwangzoo; Cho, Sungho; Lee, Rena

    2013-02-15

    Purpose: To evaluate the accuracy of measuring volumes using three-dimensional ultrasound (3D US), and to verify the feasibility of the replacement of CT-MR fusion images with CT-3D US in radiotherapy treatment planning. Methods: Phantoms, consisting of water, contrast agent, and agarose, were manufactured. The volume was measured using 3D US, CT, and MR devices. A CT-3D US and MR-3D US image fusion software was developed using the Insight Toolkit library in order to acquire three-dimensional fusion images. The quality of the image fusion was evaluated using metric value and fusion images. Results: Volume measurement, using 3D US, shows a 2.8 {+-} 1.5% error, 4.4 {+-} 3.0% error for CT, and 3.1 {+-} 2.0% error for MR. The results imply that volume measurement using the 3D US devices has a similar accuracy level to that of CT and MR. Three-dimensional image fusion of CT-3D US and MR-3D US was successfully performed using phantom images. Moreover, MR-3D US image fusion was performed using human bladder images. Conclusions: 3D US could be used in the volume measurement of human bladders and prostates. CT-3D US image fusion could be used in monitoring the target position in each fraction of external beam radiation therapy. Moreover, the feasibility of replacing the CT-MR image fusion to the CT-3D US in radiotherapy treatment planning was verified.

  12. An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem

    PubMed Central

    Shmygelska, Alena; Hoos, Holger H

    2005-01-01

    Background The protein folding problem is a fundamental problems in computational molecular biology and biochemical physics. Various optimisation methods have been applied to formulations of the ab-initio folding problem that are based on reduced models of protein structure, including Monte Carlo methods, Evolutionary Algorithms, Tabu Search and hybrid approaches. In our work, we have introduced an ant colony optimisation (ACO) algorithm to address the non-deterministic polynomial-time hard (NP-hard) combinatorial problem of predicting a protein's conformation from its amino acid sequence under a widely studied, conceptually simple model – the 2-dimensional (2D) and 3-dimensional (3D) hydrophobic-polar (HP) model. Results We present an improvement of our previous ACO algorithm for the 2D HP model and its extension to the 3D HP model. We show that this new algorithm, dubbed ACO-HPPFP-3, performs better than previous state-of-the-art algorithms on sequences whose native conformations do not contain structural nuclei (parts of the native fold that predominantly consist of local interactions) at the ends, but rather in the middle of the sequence, and that it generally finds a more diverse set of native conformations. Conclusions The application of ACO to this bioinformatics problem compares favourably with specialised, state-of-the-art methods for the 2D and 3D HP protein folding problem; our empirical results indicate that our rather simple ACO algorithm scales worse with sequence length but usually finds a more diverse ensemble of native states. Therefore the development of ACO algorithms for more complex and realistic models of protein structure holds significant promise. PMID:15710037

  13. Imaging bacterial 3D motion using digital in-line holographic microscopy and correlation-based de-noising algorithm.

    PubMed

    Molaei, Mehdi; Sheng, Jian

    2014-12-29

    Better understanding of bacteria environment interactions in the context of biofilm formation requires accurate 3-dimentional measurements of bacteria motility. Digital Holographic Microscopy (DHM) has demonstrated its capability in resolving 3D distribution and mobility of particulates in a dense suspension. Due to their low scattering efficiency, bacteria are substantially difficult to be imaged by DHM. In this paper, we introduce a novel correlation-based de-noising algorithm to remove the background noise and enhance the quality of the hologram. Implemented in conjunction with DHM, we demonstrate that the method allows DHM to resolve 3-D E. coli bacteria locations of a dense suspension (>107 cells/ml) with submicron resolutions (<0.5 µm) over substantial depth and to obtain thousands of 3D cell trajectories. PMID:25607177

  14. Imaging bacterial 3D motion using digital in-line holographic microscopy and correlation-based de-noising algorithm

    PubMed Central

    Molaei, Mehdi; Sheng, Jian

    2014-01-01

    Abstract: Better understanding of bacteria environment interactions in the context of biofilm formation requires accurate 3-dimentional measurements of bacteria motility. Digital Holographic Microscopy (DHM) has demonstrated its capability in resolving 3D distribution and mobility of particulates in a dense suspension. Due to their low scattering efficiency, bacteria are substantially difficult to be imaged by DHM. In this paper, we introduce a novel correlation-based de-noising algorithm to remove the background noise and enhance the quality of the hologram. Implemented in conjunction with DHM, we demonstrate that the method allows DHM to resolve 3-D E. coli bacteria locations of a dense suspension (>107 cells/ml) with submicron resolutions (<0.5 µm) over substantial depth and to obtain thousands of 3D cell trajectories. PMID:25607177

  15. 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.

  16. KmL3D: a non-parametric algorithm for clustering joint trajectories.

    PubMed

    Genolini, C; Pingault, J B; Driss, T; Côté, S; Tremblay, R E; Vitaro, F; Arnaud, C; Falissard, B

    2013-01-01

    In cohort studies, variables are measured repeatedly and can be considered as trajectories. A classic way to work with trajectories is to cluster them in order to detect the existence of homogeneous patterns of evolution. Since cohort studies usually measure a large number of variables, it might be interesting to study the joint evolution of several variables (also called joint-variable trajectories). To date, the only way to cluster joint-trajectories is to cluster each trajectory independently, then to cross the partitions obtained. This approach is unsatisfactory because it does not take into account a possible co-evolution of variable-trajectories. KmL3D is an R package that implements a version of k-means dedicated to clustering joint-trajectories. It provides facilities for the management of missing values, offers several quality criteria and its graphic interface helps the user to select the best partition. KmL3D can work with any number of joint-variable trajectories. In the restricted case of two joint trajectories, it proposes 3D tools to visualize the partitioning and then export 3D dynamic rotating-graphs to PDF format. PMID:23127283

  17. A computer-aided detection (CAD) system with a 3D algorithm for small acute intracranial hemorrhage

    NASA Astrophysics Data System (ADS)

    Wang, Ximing; Fernandez, James; Deshpande, Ruchi; Lee, Joon K.; Chan, Tao; Liu, Brent

    2012-02-01

    Acute Intracranial hemorrhage (AIH) requires urgent diagnosis in the emergency setting to mitigate eventual sequelae. However, experienced radiologists may not always be available to make a timely diagnosis. This is especially true for small AIH, defined as lesion smaller than 10 mm in size. A computer-aided detection (CAD) system for the detection of small AIH would facilitate timely diagnosis. A previously developed 2D algorithm shows high false positive rates in the evaluation based on LAC/USC cases, due to the limitation of setting up correct coordinate system for the knowledge-based classification system. To achieve a higher sensitivity and specificity, a new 3D algorithm is developed. The algorithm utilizes a top-hat transformation and dynamic threshold map to detect small AIH lesions. Several key structures of brain are detected and are used to set up a 3D anatomical coordinate system. A rule-based classification of the lesion detected is applied based on the anatomical coordinate system. For convenient evaluation in clinical environment, the CAD module is integrated with a stand-alone system. The CAD is evaluated by small AIH cases and matched normal collected in LAC/USC. The result of 3D CAD and the previous 2D CAD has been compared.

  18. A parallelized surface extraction algorithm for large binary image data sets based on an adaptive 3D delaunay subdivision strategy.

    PubMed

    Ma, Yingliang; Saetzler, Kurt

    2008-01-01

    In this paper we describe a novel 3D subdivision strategy to extract the surface of binary image data. This iterative approach generates a series of surface meshes that capture different levels of detail of the underlying structure. At the highest level of detail, the resulting surface mesh generated by our approach uses only about 10% of the triangles in comparison to the marching cube algorithm (MC) even in settings were almost no image noise is present. Our approach also eliminates the so-called "staircase effect" which voxel based algorithms like the MC are likely to show, particularly if non-uniformly sampled images are processed. Finally, we show how the presented algorithm can be parallelized by subdividing 3D image space into rectilinear blocks of subimages. As the algorithm scales very well with an increasing number of processors in a multi-threaded setting, this approach is suited to process large image data sets of several gigabytes. Although the presented work is still computationally more expensive than simple voxel-based algorithms, it produces fewer surface triangles while capturing the same level of detail, is more robust towards image noise and eliminates the above-mentioned "staircase" effect in anisotropic settings. These properties make it particularly useful for biomedical applications, where these conditions are often encountered. PMID:17993710

  19. Delaunay algorithm and principal component analysis for 3D visualization of mitochondrial DNA nucleoids by Biplane FPALM/dSTORM.

    PubMed

    Alán, Lukáš; Špaček, Tomáš; Ježek, Petr

    2016-07-01

    Data segmentation and object rendering is required for localization super-resolution microscopy, fluorescent photoactivation localization microscopy (FPALM), and direct stochastic optical reconstruction microscopy (dSTORM). We developed and validated methods for segmenting objects based on Delaunay triangulation in 3D space, followed by facet culling. We applied them to visualize mitochondrial nucleoids, which confine DNA in complexes with mitochondrial (mt) transcription factor A (TFAM) and gene expression machinery proteins, such as mt single-stranded-DNA-binding protein (mtSSB). Eos2-conjugated TFAM visualized nucleoids in HepG2 cells, which was compared with dSTORM 3D-immunocytochemistry of TFAM, mtSSB, or DNA. The localized fluorophores of FPALM/dSTORM data were segmented using Delaunay triangulation into polyhedron models and by principal component analysis (PCA) into general PCA ellipsoids. The PCA ellipsoids were normalized to the smoothed volume of polyhedrons or by the net unsmoothed Delaunay volume and remodeled into rotational ellipsoids to obtain models, termed DVRE. The most frequent size of ellipsoid nucleoid model imaged via TFAM was 35 × 45 × 95 nm; or 35 × 45 × 75 nm for mtDNA cores; and 25 × 45 × 100 nm for nucleoids imaged via mtSSB. Nucleoids encompassed different point density and wide size ranges, speculatively due to different activity stemming from different TFAM/mtDNA stoichiometry/density. Considering twofold lower axial vs. lateral resolution, only bulky DVRE models with an aspect ratio >3 and tilted toward the xy-plane were considered as two proximal nucleoids, suspicious occurring after division following mtDNA replication. The existence of proximal nucleoids in mtDNA-dSTORM 3D images of mtDNA "doubling"-supported possible direct observations of mt nucleoid division after mtDNA replication. PMID:26846371

  20. Evaluation of a segmentation algorithm designed for an FPGA implementation

    NASA Astrophysics Data System (ADS)

    Schwenk, Kurt; Schönermark, Maria; Huber, Felix

    2013-10-01

    The present work has to be seen in the context of real-time on-board image evaluation of optical satellite data. With on board image evaluation more useful data can be acquired, the time to get requested information can be decreased and new real-time applications are possible. Because of the relative high processing power in comparison to the low power consumption, Field Programmable Gate Array (FPGA) technology has been chosen as an adequate hardware platform for image processing tasks. One fundamental part for image evaluation is image segmentation. It is a basic tool to extract spatial image information which is very important for many applications such as object detection. Therefore a special segmentation algorithm using the advantages of FPGA technology has been developed. The aim of this work is the evaluation of this algorithm. Segmentation evaluation is a difficult task. The most common way for evaluating the performance of a segmentation method is still subjective evaluation, in which human experts determine the quality of a segmentation. This way is not in compliance with our needs. The evaluation process has to provide a reasonable quality assessment, should be objective, easy to interpret and simple to execute. To reach these requirements a so called Segmentation Accuracy Equality norm (SA EQ) was created, which compares the difference of two segmentation results. It can be shown that this norm is capable as a first quality measurement. Due to its objectivity and simplicity the algorithm has been tested on a specially chosen synthetic test model. In this work the most important results of the quality assessment will be presented.

  1. Volume learning algorithm artificial neural networks for 3D QSAR studies.

    PubMed

    Tetko, I V; Kovalishyn, V V; Livingstone, D J

    2001-07-19

    The current study introduces a new method, the volume learning algorithm (VLA), for the investigation of three-dimensional quantitative structure-activity relationships (QSAR) of chemical compounds. This method incorporates the advantages of comparative molecular field analysis (CoMFA) and artificial neural network approaches. VLA is a combination of supervised and unsupervised neural networks applied to solve the same problem. The supervised algorithm is a feed-forward neural network trained with a back-propagation algorithm while the unsupervised network is a self-organizing map of Kohonen. The use of both of these algorithms makes it possible to cluster the input CoMFA field variables and to use only a small number of the most relevant parameters to correlate spatial properties of the molecules with their activity. The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm. The results of the algorithm can be visualized and easily interpreted. Thus, VLA is a new convenient tool for three-dimensional QSAR studies. PMID:11448223

  2. Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization

    PubMed Central

    Chen, Guoliang; Meng, Xiaolin; Wang, Yunjia; Zhang, Yanzhe; Tian, Peng; Yang, Huachao

    2015-01-01

    Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals. PMID:26404314

  3. Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization.

    PubMed

    Chen, Guoliang; Meng, Xiaolin; Wang, Yunjia; Zhang, Yanzhe; Tian, Peng; Yang, Huachao

    2015-01-01

    Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone's acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals. PMID:26404314

  4. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study.

    PubMed

    Rudyanto, Rina D; Kerkstra, Sjoerd; van Rikxoort, Eva M; Fetita, Catalin; Brillet, Pierre-Yves; Lefevre, Christophe; Xue, Wenzhe; Zhu, Xiangjun; Liang, Jianming; Öksüz, Ilkay; Ünay, Devrim; Kadipaşaoğlu, Kamuran; Estépar, Raúl San José; Ross, James C; Washko, George R; Prieto, Juan-Carlos; Hoyos, Marcela Hernández; Orkisz, Maciej; Meine, Hans; Hüllebrand, Markus; Stöcker, Christina; Mir, Fernando Lopez; Naranjo, Valery; Villanueva, Eliseo; Staring, Marius; Xiao, Changyan; Stoel, Berend C; Fabijanska, Anna; Smistad, Erik; Elster, Anne C; Lindseth, Frank; Foruzan, Amir Hossein; Kiros, Ryan; Popuri, Karteek; Cobzas, Dana; Jimenez-Carretero, Daniel; Santos, Andres; Ledesma-Carbayo, Maria J; Helmberger, Michael; Urschler, Martin; Pienn, Michael; Bosboom, Dennis G H; Campo, Arantza; Prokop, Mathias; de Jong, Pim A; Ortiz-de-Solorzano, Carlos; Muñoz-Barrutia, Arrate; van Ginneken, Bram

    2014-10-01

    The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases. PMID:25113321

  5. 3D numerical modeling of subduction dynamics: plate stagnation and segmentation, and crustal advection in the mantle transition zone

    NASA Astrophysics Data System (ADS)

    Yoshida, M.; Tajima, F.

    2012-04-01

    Water content in the mantle transition zone (MTZ) has been broadly debated in the Earth science community as a key issue for plate dynamics [e.g., Bercovici and Karato, 2003]. In this study, a systematic series of three-dimensional (3D) numerical simulation are performed in an attempt to verify two hypotheses for plate subduction with effects of deep water transport: (1) the small-scale behavior of subducted oceanic plate in the MTZ; and (2) the role of subducted crust in the MTZ. These hypotheses are postulated based on the seismic observations characterized by large-scale flattened high velocity anomalies (i.e., stagnant slabs) in the MTZ and discontinuity depth variations. The proposed model states that under wet conditions the subducted plate main body of peridotite (olivine rich) is abutted by subducted crustal materials (majorite rich) at the base of the MTZ. The computational domain of mantle convection is confined to 3D regional spherical-shell geometry with a thickness of 1000 km and a lateral extent of 10° × 30° in the latitudinal and longitudinal directions. A semi-dynamic model of subduction zone [Morishige et al., 2010] is applied to let the highly viscous, cold oceanic plate subduct. Weak (low-viscosity) fault zones (WFZs), which presumably correspond to the fault boundaries of large subduction earthquakes, are imposed on the top part of subducting plates. The phase transitions of olivine to wadsleyite and ringwoodite to perovskite+magnesiowüstite with Clapeyron slopes under both "dry" and "wet" conditions are considered based on recent high pressure experiments [e.g., Ohtani and Litasov, 2006]. Another recent experiment provides new evidence for lower-viscosity (weaker strength) of garnet-rich zones than the olivine dominant mantle under wet conditions [Katayama and Karato, 2008]. According to this, the effect of viscosity reduction of oceanic crust is considered under wet condition in the MTZ. Results show that there is a substantial difference

  6. Efficient spectral and pseudospectral algorithms for 3D simulations of whistler-mode waves in a plasma

    NASA Astrophysics Data System (ADS)

    Gumerov, Nail A.; Karavaev, Alexey V.; Surjalal Sharma, A.; Shao, Xi; Papadopoulos, Konstantinos D.

    2011-04-01

    Efficient spectral and pseudospectral algorithms for simulation of linear and nonlinear 3D whistler waves in a cold electron plasma are developed. These algorithms are applied to the simulation of whistler waves generated by loop antennas and spheromak-like stationary waves of considerable amplitude. The algorithms are linearly stable and show good stability properties for computations of nonlinear waves over tens of thousands of time steps. Additional speedups by factors of 10-20 (comparing single core CPU and one GPU) are achieved by using graphics processors (GPUs), which enable efficient numerical simulation of the wave propagation on relatively high resolution meshes (tens of millions nodes) in personal computing environment. Comparisons of the numerical results with analytical solutions and experiments show good agreement. The limitations of the codes and the performance of the GPU computing are discussed.

  7. Comparison between upwind FEM and new algorithm based on the indirect BIEM for 3D moving conductor problems

    SciTech Connect

    Kim, D.H. . Living System Research Lab.); Jeon, D.Y.; Hahn, S.Y. . Dept. of Electrical Engineering)

    1999-05-01

    In general, an electromagnetic apparatus such as linear induction motors, MAGLEV vehicles or electromagnetic launchers, involves conducting parts in motion. This paper presents a new algorithm based on the indirect boundary integral equation method to analyze the electromagnetic system with a moving conductor. The proposed algorithm yields relatively stable and accurate solutions because a fundamental Green's function of diffusion type is used which is valid for any value of the Peclet number. In addition, computer memory and computing time for 3D computation can be saved considerably by using the boundary integral equations of minimum order and the singular property of the Green's function. In order to prove these, numerical results obtained by the proposed algorithm and the upwind finite element method are compared with their analytic solutions.

  8. PDB explorer -- a web based algorithm for protein annotation viewer and 3D visualization.

    PubMed

    Nayarisseri, Anuraj; Shardiwal, Rakesh Kumar; Yadav, Mukesh; Kanungo, Neha; Singh, Pooja; Shah, Pratik; Ahmed, Sheaza

    2014-12-01

    The PDB file format, is a text format characterizing the three dimensional structures of macro molecules available in the Protein Data Bank (PDB). Determined protein structure are found in coalition with other molecules or ions such as nucleic acids, water, ions, Drug molecules and so on, which therefore can be described in the PDB format and have been deposited in PDB database. PDB is a machine generated file, it's not human readable format, to read this file we need any computational tool to understand it. The objective of our present study is to develop a free online software for retrieval, visualization and reading of annotation of a protein 3D structure which is available in PDB database. Main aim is to create PDB file in human readable format, i.e., the information in PDB file is converted in readable sentences. It displays all possible information from a PDB file including 3D structure of that file. Programming languages and scripting languages like Perl, CSS, Javascript, Ajax, and HTML have been used for the development of PDB Explorer. The PDB Explorer directly parses the PDB file, calling methods for parsed element secondary structure element, atoms, coordinates etc. PDB Explorer is freely available at http://www.pdbexplorer.eminentbio.com/home with no requirement of log-in. PMID:25118648

  9. Magnetic resonance segmentation with the bubble wave algorithm

    NASA Astrophysics Data System (ADS)

    Cline, Harvey E.; Ludke, Siegwalt

    2003-05-01

    A new bubble wave algorithm provides automatic segmentation of three-dimensional magnetic resonance images of both the peripheral vasculature and the brain. Simple connectivity algorithms are not reliable in these medical applications because there are unwanted connections through background noise. The bubble wave algorithm restricts connectivity using curvature by testing spherical regions on a propagating active contour to eliminate noise bridges. After the user places seeds in both the selected regions and in the regions that are not desired, the method provides the critical threshold for segmentation using binary search. Today, peripheral vascular disease is diagnosed using magnetic resonance imaging with a timed contrast bolus. A new blood pool contrast agent MS-325 (Epix Medical) binds to albumen in the blood and provides high-resolution three-dimensional images of both arteries and veins. The bubble wave algorithm provides a means to automatically suppress the veins that obscure the arteries in magnetic resonance angiography. Monitoring brain atrophy is needed for trials of drugs that retard the progression of dementia. The brain volume is measured by placing seeds in both the brain and scalp to find the critical threshold that prevents connections between the brain volume and the scalp. Examples from both three-dimensional magnetic resonance brain and contrast enhanced vascular images were segmented with minimal user intervention.

  10. Joint graph cut and relative fuzzy connectedness image segmentation algorithm.

    PubMed

    Ciesielski, Krzysztof Chris; Miranda, Paulo A V; Falcão, Alexandre X; Udupa, Jayaram K

    2013-12-01

    We introduce an image segmentation algorithm, called GC(sum)(max), which combines, in novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding "shrinking problem" of GC), while keeping GC's stronger control over the problem of "leaking though poorly defined boundary segments." The analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC can be described within the framework of Generalized GC (GGC) segmentation algorithms. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Forest Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear. Experimental comparison of GC(sum)(max) to GC, an iterative version of RFC (IRFC), and power watershed (PW), based on a variety medical and non-medical images, indicates superior accuracy performance of GC(sum)(max) over these other methods, resulting in a rank ordering of GC(sum)(max)>PW∼IRFC>GC. PMID:23880374

  11. Hyperspectral images lossless compression using the 3D binary EZW algorithm

    NASA Astrophysics Data System (ADS)

    Cheng, Kai-jen; Dill, Jeffrey

    2013-02-01

    This paper presents a transform based lossless compression for hyperspectral images which is inspired by Shapiro (1993)'s EZW algorithm. The proposed compression method uses a hybrid transform which includes an integer Karhunrn-Loeve transform (KLT) and integer discrete wavelet transform (DWT). The integer KLT is employed to eliminate the presence of correlations among the bands of the hyperspectral image. The integer 2D discrete wavelet transform (DWT) is applied to eliminate the correlations in the spatial dimensions and produce wavelet coefficients. These coefficients are then coded by a proposed binary EZW algorithm. The binary EZW eliminates the subordinate pass of conventional EZW by coding residual values, and produces binary sequences. The binary EZW algorithm combines the merits of well-known EZW and SPIHT algorithms, and it is computationally simpler for lossless compression. The proposed method was applied to AVIRIS images and compared to other state-of-the-art image compression techniques. The results show that the proposed lossless image compression is more efficient and it also has higher compression ratio than other algorithms.

  12. Improved Algorithms and Methods for Solving Strongly Variable-Viscosity 3D Stokes flow and Strongly Variable Permeability 3D D’Arcy flow on a Parallel Computer

    NASA Astrophysics Data System (ADS)

    Morgan, J. P.; Hasenclever, J.; Shi, C.

    2009-12-01

    Computational studies of mantle convection face large challenges to obtain fast and accurate solutions for variable viscosity 3d flow. Recently we have been using parallel (MPI-based) MATLAB to more thoroughly explore possible pitfalls and algorithmic improvements to current ‘best-practice’ variable viscosity Stokes and D’Arcy flow solvers. Here we focus on study of finite-element solvers based on a decomposition of the equations for incompressible Stokes flow: Ku + Gp = f and G’u = 0 (K-velocity stiffness matrix, G-discretized gradient operator, G’=transpose(G)-discretized divergence operator) into a single equation for pressure Sp==G’K^-1Gp =G’K^-1f, in which the velocity is also updated as part of each pressure iteration. The outer pressure iteration is solved with preconditioned conjugate gradients (CG) (Maday and Patera, 1989), with a multigrid-preconditioned CG solver for the z=K^-1 (Gq) step of each pressure iteration. One fairly well-known pitfall (Fortin, 1985) is that constant-pressure elements can generate a spurious non-zero flow under a constant body force within non-rectangular geometries. We found a new pitfall when using an iterative method to solve the Kz=y operation in evaluating each G’K^-1Gq product -- even if the residual of the outer pressure equation converges to zero, the discrete divergence of this equation does not correspondingly converge; the error in the incompressibility depends on roughly the square of the tolerance used to solve each Kz=y velocity-like subproblem. Our current best recipe is: (1) Use flexible CG (cf. Notay, 2001) to solve the outer pressure problem. This is analogous to GMRES for a symmetric positive definite problem. It allows use of numerically unsymmetric and/or inexact preconditioners with CG. (2) In this outer-iteration, use an ‘alpha-bar’ technique to find the appropriate magnitude alpha to change the solution in each search direction. This improvement allows a similar iterative tolerance of

  13. 3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction.

    PubMed

    Khaji, Erfan; Karami, Masoumeh; Garkani-Nejad, Zahra

    2016-02-21

    Predicting the native structure of proteins based on half-sphere exposure and contact numbers has been studied deeply within recent years. Online predictors of these vectors and secondary structures of amino acids sequences have made it possible to design a function for the folding process. By choosing variant structures and directs for each secondary structure, a random conformation can be generated, and a potential function can then be assigned. Minimizing the potential function utilizing meta-heuristic algorithms is the final step of finding the native structure of a given amino acid sequence. In this work, Imperialist Competitive algorithm was used in order to accelerate the process of minimization. Moreover, we applied an adaptive procedure to apply revolutionary changes. Finally, we considered a more accurate tool for prediction of secondary structure. The results of the computational experiments on standard benchmark show the superiority of the new algorithm over the previous methods with similar potential function. PMID:26718864

  14. SVM algorithm based on wavelet kernel function for medical image segmentation

    NASA Astrophysics Data System (ADS)

    Yang, Jun; Tian, Jinwen; Liu, Jian; Wei, Fang

    2009-10-01

    Along with more demand for 3D reconstruction, quantitative analysis and visualization, the more precise segmentation of medical image is required, especially MR head image. But the segmentation of MRI will be much more complex and difficult because of indistinct boundaries between brain tissues due to their overlapping and penetrating with each other, intrinsic uncertainty of MR images induced by heterogeneity of magnetic field, partial volume effect and noise. After studying the kernel function conditions of support vector, we constructed wavelet SVM algorithm based on wavelet kernel function. Its convergence and commonality as well as generalization are analyzed. The comparative experiments are made using the different number of training samples and the different scans, and it .The wavelet SVM can be extended easily and experiment results show that the SVM classifier offers lower computational time and better classification precision and it has good function approximation ability.

  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. Test of 3D CT reconstructions by EM + TV algorithm from undersampled data

    SciTech Connect

    Evseev, Ivan; Ahmann, Francielle; Silva, Hamilton P. da

    2013-05-06

    Computerized tomography (CT) plays an important role in medical imaging for diagnosis and therapy. However, CT imaging is connected with ionization radiation exposure of patients. Therefore, the dose reduction is an essential issue in CT. In 2011, the Expectation Maximization and Total Variation Based Model for CT Reconstruction (EM+TV) was proposed. This method can reconstruct a better image using less CT projections in comparison with the usual filtered back projection (FBP) technique. Thus, it could significantly reduce the overall dose of radiation in CT. This work reports the results of an independent numerical simulation for cone beam CT geometry with alternative virtual phantoms. As in the original report, the 3D CT images of 128 Multiplication-Sign 128 Multiplication-Sign 128 virtual phantoms were reconstructed. It was not possible to implement phantoms with lager dimensions because of the slowness of code execution even by the CORE i7 CPU.

  17. Algorithm development with the integrated vision system to get the 3D location data

    NASA Astrophysics Data System (ADS)

    Lee, Ji-hyeon; Kim, Moo-hyun; Kim, Yeong-kyeong; Park, Mu-hun

    2011-10-01

    This paper introduces an Integrated Vision System that enables us to detect the image of slabs and coils and get the complete three dimensional location data without any other obstacles in the field of unmanned-crane automation system. Existing laser scanner research tends to be easily influenced by the environment of the work place and therefore cannot give the exact location information. Also, CCD cameras have some problems recognizing the pattern because of the illumination intensity caused in an industrial setting. To overcome these two weaknesses, this thesis suggests laser scanners should be combined with a CCD camera named Integrated Vision System. This system can draw clearer pictures and take advanced 3D location information. The suggested system is expected to help improve the unmanned-crane automation system.

  18. 3D vision sensor and its algorithm on clone seedlings plant system

    NASA Astrophysics Data System (ADS)

    Hayashi, Jun-ichiro; Hiroyasu, Takehisa; Hojo, Hirotaka; Hata, Seiji; Okada, Hiroshi

    2007-01-01

    Today, vision systems for robots had been widely applied to many important applications. But 3-D vision systems for industrial uses should face to many practical problems. Here, a vision system for bio-production has been introduced. Clone seedlings plants are one of the important applications of biotechnology. Most of the production processes of clone seedlings plants are highly automated, but the transplanting process of the small seedlings plants cannot be automated because the shape of small seedlings plants are not stable and in order to handle the seedlings plants it is required to observe the shapes of the small seedlings plants. In this research, a robot vision system has been introduced for the transplanting process in a plant factory.

  19. New robust 3-D phase unwrapping algorithms: application to magnetic field mapping and undistorting echoplanar images.

    PubMed

    Cusack, R; Papadakis, N

    2002-07-01

    The phase, as well as the magnitude, of MRI images can carry useful information. It may be used to encode flow or temperature, or to map the magnetic field for the undistorting of EPIs and automated shimming. In all cases, we measure the extra spin given to nuclei. Unfortunately, we can only measure the final phase of the spins: the rotation is wrapped into the range [-pi, +pi], and to obtain a measure of the parameter of interest the missing multiples of 2pi must be replaced--a process known as phase unwrapping. While simple in principle, standard phase unwrapping algorithms fail catastrophically in the presence of even small amounts of noise. Here we present a new algorithm for robust three-dimensional phase unwrapping, in which unwrapping is guided, so that it initially works on less noisy regions. We test the algorithm on simulated phase data, and on maps of magnetic field, which were then used to successfully undistort EPI images. The unwrapping algorithm could be directly applied to other kinds of phase data. PMID:12169259

  20. Multiprocessing and Correction Algorithm of 3D-models for Additive Manufacturing

    NASA Astrophysics Data System (ADS)

    Anamova, R. R.; Zelenov, S. V.; Kuprikov, M. U.; Ripetskiy, A. V.

    2016-07-01

    This article addresses matters related to additive manufacturing preparation. A layer-by-layer model presentation was developed on the basis of a routing method. Methods for correction of errors in the layer-by-layer model presentation were developed. A multiprocessing algorithm for forming an additive manufacturing batch file was realized.

  1. 4D BADA-based Trajectory Generator and 3D Guidance Algorithm

    NASA Technical Reports Server (NTRS)

    Palacios, Eduardo Sepulveda; Johnson, Marcus A.

    2013-01-01

    This paper presents a hybrid integration between aerodynamic, airline procedures and other BADA-based (Base of Aircraft Data) coefficients with a classical aircraft dynamic model. This paper also describes a three-dimensional guidance algorithm implemented in order to produce commands for the aircraft to follow a flight plan. The software chosen for this work is MATLAB.

  2. Automated real-time search and analysis algorithms for a non-contact 3D profiling system

    NASA Astrophysics Data System (ADS)

    Haynes, Mark; Wu, Chih-Hang John; Beck, B. Terry; Peterman, Robert J.

    2013-04-01

    The purpose of this research is to develop a new means of identifying and extracting geometrical feature statistics from a non-contact precision-measurement 3D profilometer. Autonomous algorithms have been developed to search through large-scale Cartesian point clouds to identify and extract geometrical features. These algorithms are developed with the intent of providing real-time production quality control of cold-rolled steel wires. The steel wires in question are prestressing steel reinforcement wires for concrete members. The geometry of the wire is critical in the performance of the overall concrete structure. For this research a custom 3D non-contact profilometry system has been developed that utilizes laser displacement sensors for submicron resolution surface profiling. Optimizations in the control and sensory system allow for data points to be collected at up to an approximate 400,000 points per second. In order to achieve geometrical feature extraction and tolerancing with this large volume of data, the algorithms employed are optimized for parsing large data quantities. The methods used provide a unique means of maintaining high resolution data of the surface profiles while keeping algorithm running times within practical bounds for industrial application. By a combination of regional sampling, iterative search, spatial filtering, frequency filtering, spatial clustering, and template matching a robust feature identification method has been developed. These algorithms provide an autonomous means of verifying tolerances in geometrical features. The key method of identifying the features is through a combination of downhill simplex and geometrical feature templates. By performing downhill simplex through several procedural programming layers of different search and filtering techniques, very specific geometrical features can be identified within the point cloud and analyzed for proper tolerancing. Being able to perform this quality control in real time

  3. Algorithm of pulmonary emphysema extraction using thoracic 3-D CT images

    NASA Astrophysics Data System (ADS)

    Saita, Shinsuke; Kubo, Mitsuru; Kawata, Yoshiki; Niki, Noboru; Nakano, Yasutaka; Ohmatsu, Hironobu; Tominaga, Keigo; Eguchi, Kenji; Moriyama, Noriyuki

    2008-03-01

    Emphysema patients have the tendency to increase due to aging and smoking. Emphysematous disease destroys alveolus and to repair is impossible, thus early detection is essential. CT value of lung tissue decreases due to the destruction of lung structure. This CT value becomes lower than the normal lung- low density absorption region or referred to as Low Attenuation Area (LAA). So far, the conventional way of extracting LAA by simple thresholding has been proposed. However, the CT value of CT image fluctuates due to the measurement conditions, with various bias components such as inspiration, expiration and congestion. It is therefore necessary to consider these bias components in the extraction of LAA. We removed these bias components and we proposed LAA extraction algorithm. This algorithm has been applied to the phantom image. Then, by using the low dose CT(normal: 30 cases, obstructive lung disease: 26 cases), we extracted early stage LAA and quantitatively analyzed lung lobes using lung structure.

  4. An efficient algorithm for mapping imaging data to 3D unstructured grids in computational biomechanics.

    PubMed

    Einstein, Daniel R; Kuprat, Andrew P; Jiao, Xiangmin; Carson, James P; Einstein, David M; Jacob, Richard E; Corley, Richard A

    2013-01-01

    Geometries for organ scale and multiscale simulations of organ function are now routinely derived from imaging data. However, medical images may also contain spatially heterogeneous information other than geometry that are relevant to such simulations either as initial conditions or in the form of model parameters. In this manuscript, we present an algorithm for the efficient and robust mapping of such data to imaging-based unstructured polyhedral grids in parallel. We then illustrate the application of our mapping algorithm to three different mapping problems: (i) the mapping of MRI diffusion tensor data to an unstructured ventricular grid; (ii) the mapping of serial cyrosection histology data to an unstructured mouse brain grid; and (iii) the mapping of computed tomography-derived volumetric strain data to an unstructured multiscale lung grid. Execution times and parallel performance are reported for each case. PMID:23293066

  5. An Efficient Algorithm for Mapping Imaging Data to 3D Unstructured Grids in Computational Biomechanics

    SciTech Connect

    Einstein, Daniel R.; Kuprat, Andrew P.; Jiao, Xiangmin; Carson, James P.; Einstein, David M.; Corley, Richard A.; Jacob, Rick E.

    2013-01-01

    Geometries for organ scale and multiscale simulations of organ function are now routinely derived from imaging data. However, medical images may also contain spatially heterogeneous information other than geometry that are relevant to such simulations either as initial conditions or in the form of model parameters. In this manuscript, we present an algorithm for the efficient and robust mapping of such data to imaging based unstructured polyhedral grids in parallel. We then illustrate the application of our mapping algorithm to three different mapping problems: 1) the mapping of MRI diffusion tensor data to an unstuctured ventricular grid; 2) the mapping of serial cyro-section histology data to an unstructured mouse brain grid; and 3) the mapping of CT-derived volumetric strain data to an unstructured multiscale lung grid. Execution times and parallel performance are reported for each case.

  6. A parallel dynamic load balancing algorithm for 3-D adaptive unstructured grids

    NASA Technical Reports Server (NTRS)

    Vidwans, A.; Kallinderis, Y.; Venkatakrishnan, V.

    1993-01-01

    Adaptive local grid refinement and coarsening results in unequal distribution of workload among the processors of a parallel system. A novel method for balancing the load in cases of dynamically changing tetrahedral grids is developed. The approach employs local exchange of cells among processors in order to redistribute the load equally. An important part of the load balancing algorithm is the method employed by a processor to determine which cells within its subdomain are to be exchanged. Two such methods are presented and compared. The strategy for load balancing is based on the Divide-and-Conquer approach which leads to an efficient parallel algorithm. This method is implemented on a distributed-memory MIMD system.

  7. 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.

  8. Feature extraction algorithm for 3D scene modeling and visualization using monostatic SAR

    NASA Astrophysics Data System (ADS)

    Jackson, Julie A.; Moses, Randolph L.

    2006-05-01

    We present a feature extraction algorithm to detect scattering centers in three dimensions using monostatic synthetic aperture radar imagery. We develop attributed scattering center models that describe the radar response of canonical shapes. We employ these models to characterize a complex target geometry as a superposition of simpler, low-dimensional structures. Such a characterization provides a means for target visualization. Fitting an attributed scattering model to sensed radar data is comprised of two problems: detection and estimation. The detection problem is to find canonical targets in clutter. The estimation problem then fits the detected canonical shape model with parameters, such as size and orientation, that correspond to the measured target response. We present an algorithm to detect canonical scattering structures amidst clutter and to estimate the corresponding model parameters. We employ full-polarimetric imagery to accurately classify canonical shapes. Interformetric processing allows us to estimate scattering center locations in three-dimensions. We apply the algorithm to scattering prediction data of a simple scene comprised of canonical scatterers and to scattering predictions of a backhoe.

  9. Real time planning, guidance and validation of surgical acts using 3D segmentations, augmented reality projections and surgical tools video tracking

    NASA Astrophysics Data System (ADS)

    Osorio, Angel; Galan, Juan-Antonio; Nauroy, Julien; Donars, Patricia

    2010-02-01

    When performing laparoscopies and punctures, the precise anatomic localizations are required. Current techniques very often rely on the mapping between the real situation and preoperative images. The PC based software we present realizes 3D segmentations of regions of interest from CT or MR slices. It allows the planning of punctures or trocars insertion trajectories, taking anatomical constraints into account. Geometrical transformations allow the projection over the patient's body of the organs and lesions shapes, realistically reconstructed, using a standard video projector in the operating room. We developed specific image processing software which automatically segments and registers images of a webcam used in the operating room to give feedback to the user.

  10. 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.

  11. The DANTE Boltzmann transport solver: An unstructured mesh, 3-D, spherical harmonics algorithm compatible with parallel computer architectures

    SciTech Connect

    McGhee, J.M.; Roberts, R.M.; Morel, J.E.

    1997-06-01

    A spherical harmonics research code (DANTE) has been developed which is compatible with parallel computer architectures. DANTE provides 3-D, multi-material, deterministic, transport capabilities using an arbitrary finite element mesh. The linearized Boltzmann transport equation is solved in a second order self-adjoint form utilizing a Galerkin finite element spatial differencing scheme. The core solver utilizes a preconditioned conjugate gradient algorithm. Other distinguishing features of the code include options for discrete-ordinates and simplified spherical harmonics angular differencing, an exact Marshak boundary treatment for arbitrarily oriented boundary faces, in-line matrix construction techniques to minimize memory consumption, and an effective diffusion based preconditioner for scattering dominated problems. Algorithm efficiency is demonstrated for a massively parallel SIMD architecture (CM-5), and compatibility with MPP multiprocessor platforms or workstation clusters is anticipated.

  12. Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine

    NASA Astrophysics Data System (ADS)

    Graham, James; Ternovskiy, Igor V.

    2013-06-01

    We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human - autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.

  13. Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm.

    PubMed

    Li, Bai; Lin, Mu; Liu, Qiao; Li, Ya; Zhou, Changjun

    2015-10-01

    Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques. PMID:26381910

  14. Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach

    PubMed Central

    Sun, Shanhui; Bauer, Christian; Beichel, Reinhard

    2013-01-01

    Segmentation of lungs with (large) lung cancer regions is a nontrivial problem. We present a new fully automated approach for segmentation of lungs with such high-density pathologies. Our method consists of two main processing steps. First, a novel robust active shape model (RASM) matching method is utilized to roughly segment the outline of the lungs. The initial position of the RASM is found by means of a rib cage detection method. Second, an optimal surface finding approach is utilized to further adapt the initial segmentation result to the lung. Left and right lungs are segmented individually. An evaluation on 30 data sets with 40 abnormal (lung cancer) and 20 normal left/right lungs resulted in an average Dice coefficient of 0.975 ± 0.006 and a mean absolute surface distance error of 0.84 ± 0.23 mm, respectively. Experiments on the same 30 data sets showed that our methods delivered statistically significant better segmentation results, compared to two commercially available lung segmentation approaches. In addition, our RASM approach is generally applicable and suitable for large shape models. PMID:21997248

  15. 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. PMID:25020067

  16. 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

  17. Rapidly convergent algorithms for 3-D tandem and stellarator equilibria in the paraxial approximation

    SciTech Connect

    McNamara, B.

    1984-04-01

    Tandem and stellarator equilibria at high ..beta.. have proved hard to compute and the relaxation methods of Bauer et al., Chodura and Schluter, Hirshman, Strauss, and Pearlstein et al. have been slow to converge. This paper reports an extension of the low-..beta.. analytic method of Pearlstein, Kaiser, and Newcomb to arbitrary ..beta.. for tandem mirrors which converges in 10 to 20 iterations. Extensions of the method to stellarator equilibria are proposed and are very close to the analytic method of Johnson and Greene - the stellarator expansion. Most of the results of all these calculations can be adequately described by low-..beta.. approximations since the MHD stability limits occur at low ..beta... The tandem mirror, having weak curvature and a long central cell, allows finite Larmor radius effects to eliminate most ballooning modes and offers the possibility of really high average ..beta... This is the interest in developing such three-dimensional numerical algorithms.

  18. 3D-2D registration in mobile radiographs: algorithm development and preliminary clinical evaluation

    NASA Astrophysics Data System (ADS)

    Otake, Yoshito; Wang, Adam S.; Uneri, Ali; Kleinszig, Gerhard; Vogt, Sebastian; Aygun, Nafi; Lo, Sheng-fu L.; Wolinsky, Jean-Paul; Gokaslan, Ziya L.; Siewerdsen, Jeffrey H.

    2015-03-01

    An image-based 3D-2D registration method is presented using radiographs acquired in the uncalibrated, unconstrained geometry of mobile radiography. The approach extends a previous method for six degree-of-freedom (DOF) registration in C-arm fluoroscopy (namely ‘LevelCheck’) to solve the 9-DOF estimate of geometry in which the position of the source and detector are unconstrained. The method was implemented using a gradient correlation similarity metric and stochastic derivative-free optimization on a GPU. Development and evaluation were conducted in three steps. First, simulation studies were performed that involved a CT scan of an anthropomorphic body phantom and 1000 randomly generated digitally reconstructed radiographs in posterior-anterior and lateral views. A median projection distance error (PDE) of 0.007 mm was achieved with 9-DOF registration compared to 0.767 mm for 6-DOF. Second, cadaver studies were conducted using mobile radiographs acquired in three anatomical regions (thorax, abdomen and pelvis) and three levels of source-detector distance (~800, ~1000 and ~1200 mm). The 9-DOF method achieved a median PDE of 0.49 mm (compared to 2.53 mm for the 6-DOF method) and demonstrated robustness in the unconstrained imaging geometry. Finally, a retrospective clinical study was conducted with intraoperative radiographs of the spine exhibiting real anatomical deformation and image content mismatch (e.g. interventional devices in the radiograph that were not in the CT), demonstrating a PDE = 1.1 mm for the 9-DOF approach. Average computation time was 48.5 s, involving 687 701 function evaluations on average, compared to 18.2 s for the 6-DOF method. Despite the greater computational load, the 9-DOF method may offer a valuable tool for target localization (e.g. decision support in level counting) as well as safety and quality assurance checks at the conclusion of a procedure (e.g. overlay of planning data on the radiograph for verification of

  19. Vegetation Height Estimation Near Power transmission poles Via satellite Stereo Images using 3D Depth Estimation Algorithms

    NASA Astrophysics Data System (ADS)

    Qayyum, A.; Malik, A. S.; Saad, M. N. M.; Iqbal, M.; Abdullah, F.; Rahseed, W.; Abdullah, T. A. R. B. T.; Ramli, A. Q.

    2015-04-01

    Monitoring vegetation encroachment under overhead high voltage power line is a challenging problem for electricity distribution companies. Absenc