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Sample records for 3d vessel segmentation

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

    PubMed

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

    2011-01-01

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

  2. 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.

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

    PubMed

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

    2009-01-01

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

  4. 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

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

    PubMed

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

    2015-12-01

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

  6. 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.

  7. 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.

  8. 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.

  9. 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).

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

    PubMed

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

    2010-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

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

    PubMed

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

    2013-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  15. Application of fuzzy connectedness in 3D blood vessel extraction.

    PubMed

    Lv, Xinrong; Zou, Hua

    2010-01-01

    Three-dimensional (3D) segmentation of blood vessels plays a very important role in solving some practical problems such as diagnosis of vessels diseases. Because of the effective segmentation for 2D images, the fuzzy connectedness segmentation method is introduced to extract vascular structures from 3D blood vessel volume dataset. In the experiments, three segmentation methods including thresholding method, region growing method and fuzzy connectedness method are all used to extract the vascular structures, and their results are compared. The results indicate that fuzzy connectedness method is better than thresholding method in connectivity of segmentation results, and better than region growing method in precision of segmentation results.

  16. Active segmentation of 3D axonal images.

    PubMed

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

    2012-01-01

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

  17. Probabilistic retinal vessel segmentation

    NASA Astrophysics Data System (ADS)

    Wu, Chang-Hua; Agam, Gady

    2007-03-01

    Optic fundus assessment is widely used for diagnosing vascular and non-vascular pathology. Inspection of the retinal vasculature may reveal hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. Due to various imaging conditions retinal images may be degraded. Consequently, the enhancement of such images and vessels in them is an important task with direct clinical applications. We propose a novel technique for vessel enhancement in retinal images that is capable of enhancing vessel junctions in addition to linear vessel segments. This is an extension of vessel filters we have previously developed for vessel enhancement in thoracic CT scans. The proposed approach is based on probabilistic models which can discern vessels and junctions. Evaluation shows the proposed filter is better than several known techniques and is comparable to the state of the art when evaluated on a standard dataset. A ridge-based vessel tracking process is applied on the enhanced image to demonstrate the effectiveness of the enhancement filter.

  18. Freehand 3D ultrasound breast tumor segmentation

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

  19. 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.

  20. 3D Gabor wavelet based vessel filtering of photoacoustic images.

    PubMed

    Haq, Israr Ul; Nagoaka, Ryo; Makino, Takahiro; Tabata, Takuya; Saijo, Yoshifumi

    2016-08-01

    Filtering and segmentation of vasculature is an important issue in medical imaging. The visualization of vasculature is crucial for the early diagnosis and therapy in numerous medical applications. This paper investigates the use of Gabor wavelet to enhance the effect of vasculature while eliminating the noise due to size, sensitivity and aperture of the detector in 3D Optical Resolution Photoacoustic Microscopy (OR-PAM). A detailed multi-scale analysis of wavelet filtering and Hessian based method is analyzed for extracting vessels of different sizes since the blood vessels usually vary with in a range of radii. The proposed algorithm first enhances the vasculature in the image and then tubular structures are classified by eigenvalue decomposition of the local Hessian matrix at each voxel in the image. The algorithm is tested on non-invasive experiments, which shows appreciable results to enhance vasculature in photo-acoustic images.

  1. Customizable engineered blood vessels using 3D printed inserts.

    PubMed

    Pinnock, Cameron B; Meier, Elizabeth M; Joshi, Neeraj N; Wu, Bin; Lam, Mai T

    2016-04-15

    Current techniques for tissue engineering blood vessels are not customizable for vascular size variation and vessel wall thickness. These critical parameters vary widely between the different arteries in the human body, and the ability to engineer vessels of varying sizes could increase capabilities for disease modeling and treatment options. We present an innovative method for producing customizable, tissue engineered, self-organizing vascular constructs by replicating a major structural component of blood vessels - the smooth muscle layer, or tunica media. We utilize a unique system combining 3D printed plate inserts to control construct size and shape, and cell sheets supported by a temporary fibrin hydrogel to encourage cellular self-organization into a tubular form resembling a natural artery. To form the vascular construct, 3D printed inserts are adhered to tissue culture plates, fibrin hydrogel is deposited around the inserts, and human aortic smooth muscle cells are then seeded atop the fibrin hydrogel. The gel, aided by the innate contractile properties of the smooth muscle cells, aggregates towards the center post insert, creating a tissue ring of smooth muscle cells. These rings are then stacked into the final tubular construct. Our methodology is robust, easily repeatable and allows for customization of cellular composition, vessel wall thickness, and length of the vessel construct merely by varying the size of the 3D printed inserts. This platform has potential for facilitating more accurate modeling of vascular pathology, serving as a drug discovery tool, or for vessel repair in disease treatment.

  2. Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images

    NASA Astrophysics Data System (ADS)

    Luu, Ha Manh; Klink, Camiel; Moelker, Adriaan; Niessen, Wiro; van Walsum, Theo

    2015-05-01

    Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p  <  0.05) Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.

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

    PubMed

    Chen, Ting; Metaxas, Dimitris

    2005-12-01

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

  4. CURVES: curve evolution for vessel segmentation.

    PubMed

    Lorigo, L M; Faugeras, O D; Grimson, W E; Keriven, R; Kikinis, R; Nabavi, A; Westin, C F

    2001-09-01

    The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning and performing neurosurgical procedures. In planning they provide information on where the lesion draws its blood supply and where it drains. During surgery the vessels serve as landmarks and guidelines to the lesion. The more minute the information is, the more precise the navigation and localization of computer guided procedures. Beyond neurosurgery and neurological study, vascular information is also crucial in cardiovascular surgery, diagnosis, and research. This paper addresses the problem of automatic segmentation of complicated curvilinear structures in three-dimensional imagery, with the primary application of segmenting vasculature in magnetic resonance angiography (MRA) images. The method presented is based on recent curve and surface evolution work in the computer vision community which models the object boundary as a manifold that evolves iteratively to minimize an energy criterion. This energy criterion is based both on intensity values in the image and on local smoothness properties of the object boundary, which is the vessel wall in this application. In particular, the method handles curves evolving in 3D, in contrast with previous work that has dealt with curves in 2D and surfaces in 3D. Results are presented on cerebral and aortic MRA data as well as lung computed tomography (CT) data.

  5. Vessel segmentation in screening mammograms

    NASA Astrophysics Data System (ADS)

    Mordang, J. J.; Karssemeijer, N.

    2015-03-01

    Blood vessels are a major cause of false positives in computer aided detection systems for the detection of breast cancer. Therefore, the purpose of this study is to construct a framework for the segmentation of blood vessels in screening mammograms. The proposed framework is based on supervised learning using a cascade classifier. This cascade classifier consists of several stages where in each stage a GentleBoost classifier is trained on Haar-like features. A total of 30 cases were included in this study. In each image, vessel pixels were annotated by selecting pixels on the centerline of the vessel, control samples were taken by annotating a region without any visible vascular structures. This resulted in a total of 31,000 pixels marked as vascular and over 4 million control pixels. After training, the classifier assigns a vesselness likelihood to the pixels. The proposed framework was compared to three other vessel enhancing methods, i) a vesselness filter, ii) a gaussian derivative filter, and iii) a tubeness filter. The methods were compared in terms of area under the receiver operating characteristics curves, the Az values. The Az value of the cascade approach is 0:85. This is superior to the vesselness, Gaussian, and tubeness methods, with Az values of 0:77, 0:81, and 0:78, respectively. From these results, it can be concluded that our proposed framework is a promising method for the detection of vessels in screening mammograms.

  6. Segmentation of 3D objects using live wire

    NASA Astrophysics Data System (ADS)

    Falcao, Alexandre X.; Udupa, Jayaram K.

    1997-04-01

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

  7. 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

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

    PubMed

    Lee, Sang-Hoon; Lee, Sanghoon

    2015-10-01

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

  9. 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

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

    NASA Astrophysics Data System (ADS)

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

    1993-07-01

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

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

    PubMed

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

    2011-01-01

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

  12. 3D model retrieval method based on mesh segmentation

    NASA Astrophysics Data System (ADS)

    Gan, Yuanchao; Tang, Yan; Zhang, Qingchen

    2012-04-01

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

  13. Microfabricated polymeric vessel mimetics for 3-D cancer cell culture

    PubMed Central

    Jaeger, Ashley A.; Das, Chandan K.; Morgan, Nicole Y.; Pursley, Randall H.; McQueen, Philip G.; Hall, Matthew D.; Pohida, Thomas J.; Gottesman, Michael M.

    2013-01-01

    Modeling tumor growth in vitro is essential for cost-effective testing of hypotheses in preclinical cancer research. 3-D cell culture offers an improvement over monolayer culture for studying cellular processes in cancer biology because of the preservation of cell-cell and cell-ECM interactions. Oxygen transport poses a major barrier to mimicking in vivo environments and is not replicated in conventional cell culture systems. We hypothesized that we can better mimic the tumor microenvironment using a bioreactor system for controlling gas exchange in cancer cell cultures with silicone hydrogel synthetic vessels. Soft-lithography techniques were used to fabricate oxygen-permeable silicone hydrogel membranes containing arrays of micropillars. These membranes were inserted into a bioreactor and surrounded by basement membrane extract (BME) within which fluorescent ovarian cancer (OVCAR8) cells were cultured. Cell clusters oxygenated by synthetic vessels showed a ∼100um drop-off to anoxia, consistent with in vivo studies of tumor nodules fed by the microvasculature. We showed oxygen tension gradients inside the clusters oxygenated by synthetic vessels had a ∼100 µm drop-off to anoxia, which is consistent with in vivo studies. Oxygen transport in the bioreactor system was characterized by experimental testing with a dissolved oxygen probe and finite element modeling of convective flow. Our study demonstrates differing growth patterns associated with controlling gas distributions to better mimic in vivo conditions. PMID:23911071

  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.

  15. Iterative Vessel Segmentation of Fundus Images.

    PubMed

    Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

    2015-07-01

    This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets.

  16. Accurate vessel segmentation with constrained B-snake.

    PubMed

    Yuanzhi Cheng; Xin Hu; Ji Wang; Yadong Wang; Tamura, Shinichi

    2015-08-01

    We describe an active contour framework with accurate shape and size constraints on the vessel cross-sectional planes to produce the vessel segmentation. It starts with a multiscale vessel axis tracing in a 3D computed tomography (CT) data, followed by vessel boundary delineation on the cross-sectional planes derived from the extracted axis. The vessel boundary surface is deformed under constrained movements on the cross sections and is voxelized to produce the final vascular segmentation. The novelty of this paper lies in the accurate contour point detection of thin vessels based on the CT scanning model, in the efficient implementation of missing contour points in the problematic regions and in the active contour model with accurate shape and size constraints. The main advantage of our framework is that it avoids disconnected and incomplete segmentation of the vessels in the problematic regions that contain touching vessels (vessels in close proximity to each other), diseased portions (pathologic structure attached to a vessel), and thin vessels. It is particularly suitable for accurate segmentation of thin and low contrast vessels. Our method is evaluated and demonstrated on CT data sets from our partner site, and its results are compared with three related methods. Our method is also tested on two publicly available databases and its results are compared with the recently published method. The applicability of the proposed method to some challenging clinical problems, the segmentation of the vessels in the problematic regions, is demonstrated with good results on both quantitative and qualitative experimentations; our segmentation algorithm can delineate vessel boundaries that have level of variability similar to those obtained manually.

  17. 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.

  18. 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.

  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. 3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed

    PubMed Central

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

    2017-01-01

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

  1. 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

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

    PubMed

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

    2016-01-01

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

  3. Recent Advancements in Retinal Vessel Segmentation.

    PubMed

    L Srinidhi, Chetan; Aparna, P; Rajan, Jeny

    2017-04-01

    Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.

  4. Brain blood vessel segmentation using line-shaped profiles.

    PubMed

    Babin, Danilo; Pižurica, Aleksandra; De Vylder, Jonas; Vansteenkiste, Ewout; Philips, Wilfried

    2013-11-21

    Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

  5. Brain blood vessel segmentation using line-shaped profiles

    NASA Astrophysics Data System (ADS)

    Babin, Danilo; Pižurica, Aleksandra; De Vylder, Jonas; Vansteenkiste, Ewout; Philips, Wilfried

    2013-11-01

    Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

  6. Image segmentation to inspect 3-D object sizes

    NASA Astrophysics Data System (ADS)

    Hsu, Jui-Pin; Fuh, Chiou-Shann

    1996-01-01

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

  7. 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. 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.

  9. Unsupervised Retinal Vessel Segmentation Using Combined Filters.

    PubMed

    Oliveira, Wendeson S; Teixeira, Joyce Vitor; Ren, Tsang Ing; Cavalcanti, George D C; Sijbers, Jan

    2016-01-01

    Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.

  10. 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

  11. Label-free 3D imaging of microstructure, blood, and lymphatic vessels within tissue beds in vivo.

    PubMed

    Zhi, Zhongwei; Jung, Yeongri; Wang, Ruikang K

    2012-03-01

    This Letter reports the use of an ultrahigh resolution optical microangiography (OMAG) system for simultaneous 3D imaging of microstructure and lymphatic and blood vessels without the use of an exogenous contrast agent. An automatic algorithm is developed to segment the lymphatic vessels from the microstructural images based on the fact that the lymph fluid is optically transparent. An OMAG system is developed that utilizes a broadband supercontinuum light source, providing an axial resolution of 2.3 μm and lateral resolution of 5.8 μm, capable of resolving the capillary vasculature and lymphatic vessels innervating microcirculatory tissue beds. Experimental demonstration is performed by showing detailed 3D lymphatic and blood vessel maps, coupled with morphology, within mouse ears in vivo.

  12. 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.

  13. 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.

  14. Robust RANSAC-based blood vessel segmentation

    NASA Astrophysics Data System (ADS)

    Yureidini, Ahmed; Kerrien, Erwan; Cotin, Stéphane

    2012-02-01

    Many vascular clinical applications require a vessel segmentation process that is able to extract both the centerline and the surface of the blood vessels. However, noise and topology issues (such as kissing vessels) prevent existing algorithm from being able to easily retrieve such a complex system as the brain vasculature. We propose here a new blood vessel tracking algorithm that 1) detects the vessel centerline; 2) provides a local radius estimate; and 3) extracts a dense set of points at the blood vessel surface. This algorithm is based on a RANSAC-based robust fitting of successive cylinders along the vessel. Our method was validated against the Multiple Hypothesis Tracking (MHT) algorithm on 10 3DRA patient data of the brain vasculature. Over 744 blood vessels of various sizes were considered for each patient. Our results demonstrated a greater ability of our algorithm to track small, tortuous and touching vessels (96% success rate), compared to MHT (65% success rate). The computed centerline precision was below 1 voxel when compared to MHT. Moreover, our results were obtained with the same set of parameters for all patients and all blood vessels, except for the seed point for each vessel, also necessary for MHT. The proposed algorithm is thereafter able to extract the full intracranial vasculature with little user interaction.

  15. 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).

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

    PubMed Central

    Rudyanto, Rina D.; Kerkstra, Sjoerd; van Rikxoort, Eva M.; Fetita, Catalin; Brillet, Pierre-Yves; Lefevre, Christophe; Xue, Wenzhe; Zhu, Xiangjun; Liang, Jianming; Öksüz, İlkay; Ünay, Devrim; Kadipaşaogandcaron;lu, 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

    2016-01-01

    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

  17. 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.

  18. Vascular active contour for vessel tree segmentation.

    PubMed

    Shang, Yanfeng; Deklerck, Rudi; Nyssen, Edgard; Markova, Aneta; de Mey, Johan; Yang, Xin; Sun, Kun

    2011-04-01

    In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels. The vector field is derived from the eigenanalysis of the Hessian matrix of the image intensity in a multiscale framework. Finally, a dual curvature strategy, which uses a vesselness measure-dependent function selecting between a minimal principal curvature and a mean curvature criterion, is added to smoothen the surface of the vessel without changing its shape. The developed model is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images. Comparisons are made with several classical active contour models and manual extraction. The experiments show that our model is more accurate and robust than these classical models and is, therefore, more suited for automatic vessel tree extraction.

  19. 3D Mesh Segmentation Based on Markov Random Fields and Graph Cuts

    NASA Astrophysics Data System (ADS)

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

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

  20. CoroEval: a multi-platform, multi-modality tool for the evaluation of 3D coronary vessel reconstructions

    NASA Astrophysics Data System (ADS)

    Schwemmer, C.; Forman, C.; Wetzl, J.; Maier, A.; Hornegger, J.

    2014-09-01

    We present a software, called CoroEval, for the evaluation of 3D coronary vessel reconstructions from clinical data. It runs on multiple operating systems and is designed to be independent of the imaging modality used. At this point, its purpose is the comparison of reconstruction algorithms or acquisition protocols, not the clinical diagnosis. Implemented metrics are vessel sharpness and diameter. All measurements are taken from the raw intensity data to be independent of display windowing functions. The user can either import a vessel centreline segmentation from other software, or perform a manual segmentation in CoroEval. An automated segmentation correction algorithm is provided to improve non-perfect centrelines. With default settings, measurements are taken at 1 mm intervals along the vessel centreline and from 10 different angles at each measurement point. This allows for outlier detection and noise-robust measurements without the burden and subjectivity a manual measurement process would incur. Graphical measurement results can be directly exported to vector or bitmap graphics for integration into scientific publications. Centreline and lumen segmentations can be exported as point clouds and in various mesh formats. We evaluated the diameter measurement process using three phantom datasets. An average deviation of 0.03 ± 0.03 mm was found. The software is available in binary and source code form at http://www5.cs.fau.de/CoroEval/.

  1. 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.

  2. Deformable registration of 3D vessel structures to a single projection image

    NASA Astrophysics Data System (ADS)

    Zikic, Darko; Groher, Martin; Khamene, Ali; Navab, Nassir

    2008-03-01

    Alignment of angiographic preoperative 3D scans to intraoperative 2D projections is an important issue for 3D depth perception and navigation during interventions. Currently, in a setting where only one 2D projection is available, methods employing a rigid transformation model present the state of the art for this problem. In this work, we introduce a method capable of deformably registering 3D vessel structures to a respective single projection of the scene. Our approach addresses the inherent ill-posedness of the problem by incorporating a priori knowledge about the vessel structures into the formulation. We minimize the distance between the 2D points and corresponding projected 3D points together with regularization terms encoding the properties of length preservation of vessel structures and smoothness of deformation. We demonstrate the performance and accuracy of the proposed method by quantitative tests on synthetic examples as well as real angiographic scenes.

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

    NASA Astrophysics Data System (ADS)

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

    1999-05-01

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

  4. Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications

    SciTech Connect

    Zhou Chuan; Chan, H.-P.; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chughtai, Aamer; Patel, Smita; Wei Jun; Ge Jun; Cascade, Philip N.; Kazerooni, Ella A.

    2007-12-15

    The authors are developing a computerized pulmonary vessel segmentation method for a computer-aided pulmonary embolism (PE) detection system on computed tomographic pulmonary angiography (CTPA) images. Because PE only occurs inside pulmonary arteries, an automatic and accurate segmentation of the pulmonary vessels in 3D CTPA images is an essential step for the PE CAD system. To segment the pulmonary vessels within the lung, the lung regions are first extracted using expectation-maximization (EM) analysis and morphological operations. The authors developed a 3D multiscale filtering technique to enhance the pulmonary vascular structures based on the analysis of eigenvalues of the Hessian matrix at multiple scales. A new response function of the filter was designed to enhance all vascular structures including the vessel bifurcations and suppress nonvessel structures such as the lymphoid tissues surrounding the vessels. An EM estimation is then used to segment the vascular structures by extracting the high response voxels at each scale. The vessel tree is finally reconstructed by integrating the segmented vessels at all scales based on a 'connected component' analysis. Two CTPA cases containing PEs were used to evaluate the performance of the system. One of these two cases also contained pleural effusion disease. Two experienced thoracic radiologists provided the gold standard of pulmonary vessels including both arteries and veins by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. The accuracy of vessel tree segmentation was evaluated by the percentage of the 'gold standard' vessel center points overlapping with the segmented vessels. The results show that 96.2% (2398/2494) and 96.3% (1910/1984) of the manually marked center points in the arteries overlapped with segmented vessels for the case without and with other lung diseases. For the manually marked center points in all vessels including arteries

  5. 3D Building Models Segmentation Based on K-Means++ Cluster Analysis

    NASA Astrophysics Data System (ADS)

    Zhang, C.; Mao, B.

    2016-10-01

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

  6. 3-D ultrafast Doppler imaging applied to the noninvasive mapping of blood vessels in vivo.

    PubMed

    Provost, Jean; Papadacci, Clement; Demene, Charlie; Gennisson, Jean-Luc; Tanter, Mickael; Pernot, Mathieu

    2015-08-01

    Ultrafast Doppler imaging was introduced as a technique to quantify blood flow in an entire 2-D field of view, expanding the field of application of ultrasound imaging to the highly sensitive anatomical and functional mapping of blood vessels. We have recently developed 3-D ultrafast ultrasound imaging, a technique that can produce thousands of ultrasound volumes per second, based on a 3-D plane and diverging wave emissions, and demonstrated its clinical feasibility in human subjects in vivo. In this study, we show that noninvasive 3-D ultrafast power Doppler, pulsed Doppler, and color Doppler imaging can be used to perform imaging of blood vessels in humans when using coherent compounding of 3-D tilted plane waves. A customized, programmable, 1024-channel ultrasound system was designed to perform 3-D ultrafast imaging. Using a 32 × 32, 3-MHz matrix phased array (Vermon, Tours, France), volumes were beamformed by coherently compounding successive tilted plane wave emissions. Doppler processing was then applied in a voxel-wise fashion. The proof of principle of 3-D ultrafast power Doppler imaging was first performed by imaging Tygon tubes of various diameters, and in vivo feasibility was demonstrated by imaging small vessels in the human thyroid. Simultaneous 3-D color and pulsed Doppler imaging using compounded emissions were also applied in the carotid artery and the jugular vein in one healthy volunteer.

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

    PubMed

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

    2007-04-01

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

  8. Construction of topological structure of 3D coronary vessels for analysis of catheter navigation in interventional cardiology simulation

    NASA Astrophysics Data System (ADS)

    Wang, Yaoping; Chui, Cheekong K.; Cai, Yiyu; Mak, KoonHou

    1998-06-01

    This study presents an approach to build a 3D vascular system of coronary for the development of a virtual cardiology simulator. The 3D model of the coronary arterial tree is reconstructed from the geometric information segmented from the Visible Human data set for physical analysis of catheterization. The process of segmentation is guided by a 3D topologic hierarchy structure of coronary vessels which is obtained from a mechanical model by using Coordinate Measuring Machine (CMM) probing. This mechanical professional model includes all major coronary arterials ranging from right coronary artery to atrioventricular branch and from left main trunk to left anterior descending branch. All those branches are considered as the main operating sites for cardiology catheterization. Along with the primary arterial vasculature and accompanying secondary and tertiary networks obtained from a previous work, a more complete vascular structure can then be built for the simulation of catheterization. A novel method has been developed for real time Finite Element Analysis of catheter navigation based on this featured vasculature of vessels.

  9. 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.

  10. Segmentation and interpretation of 3D protein images

    SciTech Connect

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

    1994-12-31

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

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

    PubMed

    Li, Gang; Guo, Lei

    2012-01-01

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

  12. Diffusive smoothing of 3D segmented medical data

    PubMed Central

    Patané, Giuseppe

    2014-01-01

    This paper proposes an accurate, computationally efficient, and spectrum-free formulation of the heat diffusion smoothing on 3D shapes, represented as triangle meshes. The idea behind our approach is to apply a (r,r)-degree Padé–Chebyshev rational approximation to the solution of the heat diffusion equation. The proposed formulation is equivalent to solve r sparse, symmetric linear systems, is free of user-defined parameters, and is robust to surface discretization. We also discuss a simple criterion to select the time parameter that provides the best compromise between approximation accuracy and smoothness of the solution. Finally, our experiments on anatomical data show that the spectrum-free approach greatly reduces the computational cost and guarantees a higher approximation accuracy than previous work. PMID:26257940

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

    PubMed

    Badura, P; Pietka, E

    2014-10-01

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

  14. Three-dimensional vessel segmentation using a novel combinatory filter framework

    NASA Astrophysics Data System (ADS)

    Ding, Y.; Ward, W. O. C.; Wästerlid, T.; Gowland, P. A.; Peters, A. M.; Yang, J.; Nakagawa, S.; Bai, L.

    2014-11-01

    Blood vessel segmentation is of great importance in medical diagnostic applications. Filter based methods that make use of Hessian matrices have been found to be very useful for blood vessel segmentation in both 2D and 3D medical images. However, these methods often fail on images that contain high density microvessels and background noise. The errors in the form of missing, undesired broken or incorrectly merged vessels eventually lead to poor segmentation results. In this paper, we present a novel method for 3D vessel segmentation that is also suitable for segmenting microvessels, incorporating the advantages of a line filter and a Hessian-based vessel filter to overcome the problems. The proposed method is shown to be reliable for noisy and inhomogeneous images. Vessels can also be separated based on their scale/thickness so that the method can be used for different medical applications. Furthermore, a quantitative vessel analysis method based on the multifractal analysis is performed on the segmented vasculature and fractal properties are found in all images.

  15. Three-dimensional vessel segmentation using a novel combinatory filter framework.

    PubMed

    Ding, Y; Ward, W O C; Wästerlid, T; Gowland, P A; Peters, A M; Yang, J; Bai, L

    2014-11-21

    Blood vessel segmentation is of great importance in medical diagnostic applications. Filter based methods that make use of Hessian matrices have been found to be very useful for blood vessel segmentation in both 2D and 3D medical images. However, these methods often fail on images that contain high density microvessels and background noise. The errors in the form of missing, undesired broken or incorrectly merged vessels eventually lead to poor segmentation results. In this paper, we present a novel method for 3D vessel segmentation that is also suitable for segmenting microvessels, incorporating the advantages of a line filter and a Hessian-based vessel filter to overcome the problems. The proposed method is shown to be reliable for noisy and inhomogeneous images. Vessels can also be separated based on their scale/thickness so that the method can be used for different medical applications. Furthermore, a quantitative vessel analysis method based on the multifractal analysis is performed on the segmented vasculature and fractal properties are found in all images.

  16. Method for accurate sizing of pulmonary vessels from 3D medical images

    NASA Astrophysics Data System (ADS)

    O'Dell, Walter G.

    2015-03-01

    Detailed characterization of vascular anatomy, in particular the quantification of changes in the distribution of vessel sizes and of vascular pruning, is essential for the diagnosis and management of a variety of pulmonary vascular diseases and for the care of cancer survivors who have received radiation to the thorax. Clinical estimates of vessel radii are typically based on setting a pixel intensity threshold and counting how many "On" pixels are present across the vessel cross-section. A more objective approach introduced recently involves fitting the image with a library of spherical Gaussian filters and utilizing the size of the best matching filter as the estimate of vessel diameter. However, both these approaches have significant accuracy limitations including mis-match between a Gaussian intensity distribution and that of real vessels. Here we introduce and demonstrate a novel approach for accurate vessel sizing using 3D appearance models of a tubular structure along a curvilinear trajectory in 3D space. The vessel branch trajectories are represented with cubic Hermite splines and the tubular branch surfaces represented as a finite element surface mesh. An iterative parameter adjustment scheme is employed to optimally match the appearance models to a patient's chest X-ray computed tomography (CT) scan to generate estimates for branch radii and trajectories with subpixel resolution. The method is demonstrated on pulmonary vasculature in an adult human CT scan, and on 2D simulated test cases.

  17. 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.

  18. 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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  20. Automated vessel shadow segmentation of fovea-centered spectral-domain images from multiple OCT devices

    NASA Astrophysics Data System (ADS)

    Wu, Jing; Gerendas, Bianca S.; Waldstein, Sebastian M.; Simader, Christian; Schmidt-Erfurth, Ursula

    2014-03-01

    Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high reso- lution, three-dimensional (3D) cross sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD) and glaucoma.1 Disease diagnosis, assessment, and treatment requires a patient to undergo multiple OCT scans, possibly using different scanning devices, to accurately and precisely gauge disease activity, progression and treatment success. However, the use of OCT imaging devices from different vendors, combined with patient movement may result in poor scan spatial correlation, potentially leading to incorrect patient diagnosis or treatment analysis. Image registration can be used to precisely compare disease states by registering differing 3D scans to one another. In order to align 3D scans from different time- points and vendors using registration, landmarks are required, the most obvious being the retinal vasculature. Presented here is a fully automated cross-vendor method to acquire retina vessel locations for OCT registration from fovea centred 3D SD-OCT scans based on vessel shadows. Noise filtered OCT scans are flattened based on vendor retinal layer segmentation, to extract the retinal pigment epithelium (RPE) layer of the retina. Voxel based layer profile analysis and k-means clustering is used to extract candidate vessel shadow regions from the RPE layer. In conjunction, the extracted RPE layers are combined to generate a projection image featuring all candidate vessel shadows. Image processing methods for vessel segmentation of the OCT constructed projection image are then applied to optimize the accuracy of OCT vessel shadow segmentation through the removal of false positive shadow regions such as those caused by exudates and cysts. Validation of segmented vessel shadows uses

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

    PubMed

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

    2016-02-27

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

  2. 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.

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

    PubMed Central

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

    2016-01-01

    This paper proposes a new semi-automatic segmentation method for the prostate on 3D transrectal ultrasound images (TRUS) by combining the region and classification information. We use a random walk algorithm to express the region information efficiently and flexibly because it can avoid segmentation leakage and shrinking bias. We further use the decision tree as the classifier to distinguish the prostate from the non-prostate tissue because of its fast speed and superior performance, especially for a binary classification problem. Our segmentation algorithm is initialized with the user roughly marking the prostate and non-prostate points on the mid-gland slice which are fitted into an ellipse for obtaining more points. Based on these fitted seed points, we run the random walk algorithm to segment the prostate on the mid-gland slice. The segmented contour and the information from the decision tree classification are combined to determine the initial seed points for the other slices. The random walk algorithm is then used to segment the prostate on the adjacent slice. We propagate the process until all slices are segmented. The segmentation method was tested in 32 3D transrectal ultrasound images. Manual segmentation by a radiologist serves as the gold standard for the validation. The experimental results show that the proposed method achieved a Dice similarity coefficient of 91.37±0.05%. The segmentation method can be applied to 3D ultrasound-guided prostate biopsy and other applications. PMID:27660383

  4. Robust vessel segmentation in fundus images.

    PubMed

    Budai, A; Bock, R; Maier, A; Hornegger, J; Michelson, G

    2013-01-01

    One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.

  5. Using flow information to support 3D vessel reconstruction from rotational angiography

    SciTech Connect

    Waechter, Irina; Bredno, Joerg; Weese, Juergen; Barratt, Dean C.; Hawkes, David J.

    2008-07-15

    For the assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) morphologic and hemodynamic information about the vessel system. Rotational angiography is routinely used to image the 3D vascular geometry and we have shown previously that rotational subtraction angiography has the potential to also give quantitative information about blood flow. Flow information can be determined when the angiographic sequence shows inflow and possibly outflow of contrast agent. However, a standard volume reconstruction assumes that the vessel tree is uniformly filled with contrast agent during the whole acquisition. If this is not the case, the reconstruction exhibits artifacts. Here, we show how flow information can be used to support the reconstruction of the 3D vessel centerline and radii in this case. Our method uses the fast marching algorithm to determine the order in which voxels are analyzed. For every voxel, the rotational time intensity curve (R-TIC) is determined from the image intensities at the projection points of the current voxel. Next, the bolus arrival time of the contrast agent at the voxel is estimated from the R-TIC. Then, a measure of the intensity and duration of the enhancement is determined, from which a speed value is calculated that steers the propagation of the fast marching algorithm. The results of the fast marching algorithm are used to determine the 3D centerline by backtracking. The 3D radius is reconstructed from 2D radius estimates on the projection images. The proposed method was tested on computer simulated rotational angiography sequences with systematically varied x-ray acquisition, blood flow, and contrast agent injection parameters and on datasets from an experimental setup using an anthropomorphic cerebrovascular phantom. For the computer simulation, the mean absolute error of the 3D centerline and 3D radius estimation was 0.42 and 0.25 mm, respectively. For the experimental datasets, the mean absolute

  6. Using flow information to support 3D vessel reconstruction from rotational angiography.

    PubMed

    Waechter, Irina; Bredno, Joerg; Weese, Juergen; Barratt, Dean C; Hawkes, David J

    2008-07-01

    For the assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) morphologic and hemodynamic information about the vessel system. Rotational angiography is routinely used to image the 3D vascular geometry and we have shown previously that rotational subtraction angiography has the potential to also give quantitative information about blood flow. Flow information can be determined when the angiographic sequence shows inflow and possibly outflow of contrast agent. However, a standard volume reconstruction assumes that the vessel tree is uniformly filled with contrast agent during the whole acquisition. If this is not the case, the reconstruction exhibits artifacts. Here, we show how flow information can be used to support the reconstruction of the 3D vessel centerline and radii in this case. Our method uses the fast marching algorithm to determine the order in which voxels are analyzed. For every voxel, the rotational time intensity curve (R-TIC) is determined from the image intensities at the projection points of the current voxel. Next, the bolus arrival time of the contrast agent at the voxel is estimated from the R-TIC. Then, a measure of the intensity and duration of the enhancement is determined, from which a speed value is calculated that steers the propagation of the fast marching algorithm. The results of the fast marching algorithm are used to determine the 3D centerline by backtracking. The 3D radius is reconstructed from 2D radius estimates on the projection images. The proposed method was tested on computer simulated rotational angiography sequences with systematically varied x-ray acquisition, blood flow, and contrast agent injection parameters and on datasets from an experimental setup using an anthropomorphic cerebrovascular phantom. For the computer simulation, the mean absolute error of the 3D centerline and 3D radius estimation was 0.42 and 0.25 mm, respectively. For the experimental datasets, the mean absolute

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

    NASA Astrophysics Data System (ADS)

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

    2001-07-01

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

  8. 3D modeling of geological anomalies based on segmentation of multiattribute fusion

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

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

    PubMed

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

    1997-05-01

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

  10. 3D-FIESTA Magnetic Resonance Angiography Fusion Imaging of Distal Segment of Occluded Middle Cerebral Artery.

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  12. 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.

  13. Hepatic vessel segmentation from computed tomography using three-dimensional hyper-complex edge detection operator

    NASA Astrophysics Data System (ADS)

    Ma, Yang; Li, Xingmin

    2014-01-01

    This paper proposes a three-dimensional(3D) segmentation algorithm using hyper-complex edge detection operator and applies the new algorithm to three-dimensional hepatic vessel segmentation from computed tomography (CT) volumetric data. A 3D hyper-complex edge detection operator is constructed by combining octonion and gradient operator. We replace every voxel of the volumetric data by one octonion which consist of its gray-level and its 6 neighborhoods' gray-level. Via this the original volumetric data is defined as octonion volumetric data. Similar to the Sobel operator, there are three principal directions (coordinate axes) in 3D hyper-complex edge detection operator, and each element in this operator is a octonion. The operator is circularly convoluted with octonion volumetric data to get the value of matching response. If matched, this voxel is the edge of vessel. Experimental results show that the algorithm can effectively segment small vascular tree branches.

  14. Liver vessel segmentation based on extreme learning machine.

    PubMed

    Zeng, Ye Zhan; Zhao, Yu Qian; Liao, Miao; Zou, Bei Ji; Wang, Xiao Fang; Wang, Wei

    2016-05-01

    Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  16. 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.

  17. 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.

  18. 3D Brain Segmentation Using Dual-Front Active Contours with Optional User Interaction

    PubMed Central

    Yezzi, Anthony; Cohen, Laurent D.

    2006-01-01

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

  19. 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.

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

    PubMed Central

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

    2015-01-01

    Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer. PMID:26547117

  1. 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.

  2. 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.

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

    PubMed

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

    2013-01-01

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

  4. 3D segmentations of neuronal nuclei from confocal microscope image stacks

    PubMed Central

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

    2013-01-01

    In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells). We have tested our algorithm in a real scenario—the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei. PMID:24409123

  5. Blood vessel staining in the myocardium for 3D visualization down to the smallest capillaries

    NASA Astrophysics Data System (ADS)

    Müller, Bert; Fischer, Jens; Dietz, Ulrich; Thurner, Philipp J.; Beckmann, Felix

    2006-05-01

    Blood vessels formed after medical interventions such as radiofrequency treatment have to be visualized down to the capillary level with diameters of about 5 μm to validate neo-vascularization. Synchrotron radiation-based micro-computed tomography (SRμCT) provides the necessary spatial resolution. Since both the vessels and the surrounding tissue mainly consist of water the difference in absorption is extremely weak. Therefore, it is necessary to search for appropriate contrast agents and to develop suitable staining protocols, which finally allow segmenting the vessel tree. Among the contrast agents used in medicine lyophilic salts with a mean particle diameter of 1.5 μm such as CaSO 4, SrSO 4 and BaSO 4 are most appropriate to stain the vessels. The combination of these salts with a commercially available embedding kit (JB-4, Polysciences Inc.) allows tissue fixation and long-term storage in solid state. Intensity-based segmentation algorithms enable the vessel tree extraction in selected parts of the stained myocardium using the SRμCT data.

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

    PubMed

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

    2016-07-01

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

  7. 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.

  8. Picture grammars in classification and semantic interpretation of 3D coronary vessels visualisations

    NASA Astrophysics Data System (ADS)

    Ogiela, M. R.; Tadeusiewicz, R.; Trzupek, M.

    2009-09-01

    The work presents the new opportunity for making semantic descriptions and analysis of medical structures, especially coronary vessels CT spatial reconstructions, with the use of AI graph-based linguistic formalisms. In the paper there will be discussed the manners of applying methods of computational intelligence to the development of a syntactic semantic description of spatial visualisations of the heart's coronary vessels. Such descriptions may be used for both smart ordering of images while archiving them and for their semantic searches in medical multimedia databases. Presented methodology of analysis can furthermore be used for attaining other goals related performance of computer-assisted semantic interpretation of selected elements and/or the entire 3D structure of the coronary vascular tree. These goals are achieved through the use of graph-based image formalisms based on IE graphs generating grammars that allow discovering and automatic semantic interpretation of irregularities visualised on the images obtained during diagnostic examinations of the heart muscle. The basis for the construction of 3D reconstructions of biological objects used in this work are visualisations obtained from helical CT scans, yet the method itself may be applied also for other methods of medical 3D images acquisition. The obtained semantic information makes it possible to make a description of the structure focused on the semantics of various morphological forms of the visualised vessels from the point of view of the operation of coronary circulation and the blood supply of the heart muscle. Thanks to these, the analysis conducted allows fast and — to a great degree — automated interpretation of the semantics of various morphological changes in the coronary vascular tree, and especially makes it possible to detect these stenoses in the lumen of the vessels that can cause critical decrease in blood supply to extensive or especially important fragments of the heart muscle.

  9. 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.

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

    PubMed

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

    2012-01-01

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

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

    PubMed

    Rathke, Fabian; Schmidt, Stefan; Schnörr, Christoph

    2014-07-01

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

  12. 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.

  13. 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.

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

    PubMed

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

    2015-05-01

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

  15. Skeletonization algorithm-based blood vessel quantification using in vivo 3D photoacoustic imaging

    NASA Astrophysics Data System (ADS)

    Meiburger, K. M.; Nam, S. Y.; Chung, E.; Suggs, L. J.; Emelianov, S. Y.; Molinari, F.

    2016-11-01

    Blood vessels are the only system to provide nutrients and oxygen to every part of the body. Many diseases can have significant effects on blood vessel formation, so that the vascular network can be a cue to assess malicious tumor and ischemic tissues. Various imaging techniques can visualize blood vessel structure, but their applications are often constrained by either expensive costs, contrast agents, ionizing radiations, or a combination of the above. Photoacoustic imaging combines the high-contrast and spectroscopic-based specificity of optical imaging with the high spatial resolution of ultrasound imaging, and image contrast depends on optical absorption. This enables the detection of light absorbing chromophores such as hemoglobin with a greater penetration depth compared to purely optical techniques. We present here a skeletonization algorithm for vessel architectural analysis using non-invasive photoacoustic 3D images acquired without the administration of any exogenous contrast agents. 3D photoacoustic images were acquired on rats (n  =  4) in two different time points: before and after a burn surgery. A skeletonization technique based on the application of a vesselness filter and medial axis extraction is proposed to extract the vessel structure from the image data and six vascular parameters (number of vascular trees (NT), vascular density (VD), number of branches (NB), 2D distance metric (DM), inflection count metric (ICM), and sum of angles metric (SOAM)) were calculated from the skeleton. The parameters were compared (1) in locations with and without the burn wound on the same day and (2) in the same anatomic location before (control) and after the burn surgery. Four out of the six descriptors were statistically different (VD, NB, DM, ICM, p  <  0.05) when comparing two anatomic locations on the same day and when considering the same anatomic location at two separate times (i.e. before and after burn surgery). The study demonstrates an

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

    PubMed Central

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

    2017-01-01

    In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM), which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed ‘occlusions of random textures model’ are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images. PMID:28125018

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

    PubMed

    Lu, Kongkuo; Higgins, William E

    2011-09-01

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

  19. 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.

  20. 3D Materials image segmentation by 2D propagation: a graph-cut approach considering homomorphism.

    PubMed

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

    2013-12-01

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

  1. 3-D segmentation of articular cartilages by graph cuts using knee MR images from osteoarthritis initiative

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

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

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

    PubMed

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

    2008-05-01

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

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

    PubMed

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

    2016-12-21

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

  4. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2003-05-01

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

  7. 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.

  8. A strain energy filter for 3D vessel enhancement with application to pulmonary CT images.

    PubMed

    Xiao, Changyan; Staring, Marius; Shamonin, Denis; Reiber, Johan H C; Stolk, Jan; Stoel, Berend C

    2011-02-01

    The traditional Hessian-related vessel filters often suffer from detecting complex structures like bifurcations due to an over-simplified cylindrical model. To solve this problem, we present a shape-tuned strain energy density function to measure vessel likelihood in 3D medical images. This method is initially inspired by established stress-strain principles in mechanics. By considering the Hessian matrix as a stress tensor, the three invariants from orthogonal tensor decomposition are used independently or combined to formulate distinctive functions for vascular shape discrimination, brightness contrast and structure strength measuring. Moreover, a mathematical description of Hessian eigenvalues for general vessel shapes is obtained, based on an intensity continuity assumption, and a relative Hessian strength term is presented to ensure the dominance of second-order derivatives as well as suppress undesired step-edges. Finally, we adopt the multi-scale scheme to find an optimal solution through scale space. The proposed method is validated in experiments with a digital phantom and non-contrast-enhanced pulmonary CT data. It is shown that our model performed more effectively in enhancing vessel bifurcations and preserving details, compared to three existing filters.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-07-01

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

  11. Segmentation of Opacified Thorax Vessels using Model-driven Active Contour.

    PubMed

    Sebbe, Raphael; Gosselin, Bernard; Coche, Emmanuel; Macq, Benoit

    2005-01-01

    We propose a novel method, guided slice marching to segment opacified vessels tree in 3D image sets (CT scans). It combines a front propagation technique, slice marching, and an anatomical model to guide the propagation for solving the particular case of touching vessels. The formulation of this method, which is based on interface evolution theory, enables easy integration of an a priori model of knowledge of vessels topology to handle the case of touching vessels, where image-based method systematically fails. The a priori knowledge is expressed as parametric curves that model vessels centerline. That information is injected in the fast marching method through the speed of propagation, setting it to zero at missing vessels boundaries. The model is intended to be reused across patients, and must therefore be registered with the image.

  12. 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

  13. 3D geometric split-merge segmentation of brain MRI datasets.

    PubMed

    Marras, Ioannis; Nikolaidis, Nikolaos; Pitas, Ioannis

    2014-05-01

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

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

    PubMed

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

    2017-03-08

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

  16. 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.

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

    NASA Astrophysics Data System (ADS)

    Lee, Cheon; Ho, Yo-Sung

    2013-06-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

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

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

    PubMed

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

    2014-12-07

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

  1. A region growing vessel segmentation algorithm based on spectrum information.

    PubMed

    Jiang, Huiyan; He, Baochun; Fang, Di; Ma, Zhiyuan; Yang, Benqiang; Zhang, Libo

    2013-01-01

    We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure's center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention.

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2012-12-12

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

  4. 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.

  5. 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

  6. Generalization of geometrical flux maximizing flow on Riemannian manifolds for improved volumetric blood vessel segmentation.

    PubMed

    Gooya, Ali; Liao, Hongen; Sakuma, Ichiro

    2012-09-01

    Geometric flux maximizing flow (FLUX) is an active contour based method which evolves an initial surface to maximize the flux of a vector field on the surface. For blood vessel segmentation, the vector field is defined as the vectors specified by vascular edge strengths and orientations. Hence, the segmentation performance depends on the quality of the detected edge vector field. In this paper, we propose a new method for level set based segmentation of blood vessels by generalizing the FLUX on a Riemannian manifold (R-FLUX). We consider a 3D scalar image I(x) as a manifold embedded in the 4D space (x, I(x)) and compute the image metric by pullback from the 4D space, whose metric tensor depends on the vessel enhancing diffusion (VED) tensor. This allows us to devise a non-linear filter which both projects and normalizes the original image gradient vectors under the inverse of local VED tensors. The filtered gradient vectors pertaining to the vessels are less sensitive to the local image contrast and more coherent with the local vessel orientation. The method has been applied to both synthetic and real TOF MRA data sets. Comparisons are made with the FLUX and vesselsness response based segmentations, indicating that the R-FLUX outperforms both methods in terms of leakage minimization and thiner vessel delineation.

  7. Segmentation of Retinal Blood Vessels Based on Cake Filter

    PubMed Central

    Bao, Xi-Rong; Ge, Xin; She, Li-Huang; Zhang, Shi

    2015-01-01

    Segmentation of retinal blood vessels is significant to diagnosis and evaluation of ocular diseases like glaucoma and systemic diseases such as diabetes and hypertension. The retinal blood vessel segmentation for small and low contrast vessels is still a challenging problem. To solve this problem, a new method based on cake filter is proposed. Firstly, a quadrature filter band called cake filter band is made up in Fourier field. Then the real component fusion is used to separate the blood vessel from the background. Finally, the blood vessel network is got by a self-adaption threshold. The experiments implemented on the STARE database indicate that the new method has a better performance than the traditional ones on the small vessels extraction, average accuracy rate, and true and false positive rate. PMID:26636095

  8. Segmentation of Retinal Blood Vessels Based on Cake Filter.

    PubMed

    Bao, Xi-Rong; Ge, Xin; She, Li-Huang; Zhang, Shi

    2015-01-01

    Segmentation of retinal blood vessels is significant to diagnosis and evaluation of ocular diseases like glaucoma and systemic diseases such as diabetes and hypertension. The retinal blood vessel segmentation for small and low contrast vessels is still a challenging problem. To solve this problem, a new method based on cake filter is proposed. Firstly, a quadrature filter band called cake filter band is made up in Fourier field. Then the real component fusion is used to separate the blood vessel from the background. Finally, the blood vessel network is got by a self-adaption threshold. The experiments implemented on the STARE database indicate that the new method has a better performance than the traditional ones on the small vessels extraction, average accuracy rate, and true and false positive rate.

  9. Robust Retinal Blood Vessel Segmentation Based on Reinforcement Local Descriptions

    PubMed Central

    Li, Meng; Ma, Zhenshen; Liu, Chao; Han, Zhe

    2017-01-01

    Retinal blood vessels segmentation plays an important role for retinal image analysis. In this paper, we propose robust retinal blood vessel segmentation method based on reinforcement local descriptions. A novel line set based feature is firstly developed to capture local shape information of vessels by employing the length prior of vessels, which is robust to intensity variety. After that, local intensity feature is calculated for each pixel, and then morphological gradient feature is extracted for enhancing the local edge of smaller vessel. At last, line set based feature, local intensity feature, and morphological gradient feature are combined to obtain the reinforcement local descriptions. Compared with existing local descriptions, proposed reinforcement local description contains more local information of local shape, intensity, and edge of vessels, which is more robust. After feature extraction, SVM is trained for blood vessel segmentation. In addition, we also develop a postprocessing method based on morphological reconstruction to connect some discontinuous vessels and further obtain more accurate segmentation result. Experimental results on two public databases (DRIVE and STARE) demonstrate that proposed reinforcement local descriptions outperform the state-of-the-art method. PMID:28194407

  10. Robust Retinal Blood Vessel Segmentation Based on Reinforcement Local Descriptions.

    PubMed

    Li, Meng; Ma, Zhenshen; Liu, Chao; Zhang, Guang; Han, Zhe

    2017-01-01

    Retinal blood vessels segmentation plays an important role for retinal image analysis. In this paper, we propose robust retinal blood vessel segmentation method based on reinforcement local descriptions. A novel line set based feature is firstly developed to capture local shape information of vessels by employing the length prior of vessels, which is robust to intensity variety. After that, local intensity feature is calculated for each pixel, and then morphological gradient feature is extracted for enhancing the local edge of smaller vessel. At last, line set based feature, local intensity feature, and morphological gradient feature are combined to obtain the reinforcement local descriptions. Compared with existing local descriptions, proposed reinforcement local description contains more local information of local shape, intensity, and edge of vessels, which is more robust. After feature extraction, SVM is trained for blood vessel segmentation. In addition, we also develop a postprocessing method based on morphological reconstruction to connect some discontinuous vessels and further obtain more accurate segmentation result. Experimental results on two public databases (DRIVE and STARE) demonstrate that proposed reinforcement local descriptions outperform the state-of-the-art method.

  11. 3D neutronic calculations: CAD-MCNP methodology applied to vessel activation in KOYO-F

    NASA Astrophysics Data System (ADS)

    Herreras, Y.; Lafuente, A.; Sordo, F.; Cabellos, O.; Perlado, J. M.

    2008-05-01

    This paper presents a methodology for 3D neutronic calculations suitable for complex and extensive geometries. The geometry of the system design is first fully modelled with a CAD program, and subsequently processed through a MCNP-CAD interface in order to generate an MCNP geometry file. Neutronic irradiation results are finally achieved running the MCNPX program, where the geometry input card used is directly the MCNP-CAD interface output. This methodology enables accurate neutronic calculations for complex geometries characterised by high detail levels. This procedure will be applied to the Fast Ignition Fusion Reactor KOYO-F to determine first neutron fluxes calculations along the blanket as well as the material activation in the reduced martensitic 9Cr-1Mo steel vessel.

  12. 3D/2D registration and segmentation of scoliotic vertebrae using statistical models.

    PubMed

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

    2003-01-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed

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

    2017-01-01

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

  15. Lung vessel segmentation in CT images using graph-cuts

    NASA Astrophysics Data System (ADS)

    Zhai, Zhiwei; Staring, Marius; Stoel, Berend C.

    2016-03-01

    Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.

  16. 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

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

    PubMed

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

    2010-04-01

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

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

    PubMed

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

    2016-12-01

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

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

    PubMed

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

    2017-03-01

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

  20. Fuzzy pulmonary vessel segmentation in contrast enhanced CT data

    NASA Astrophysics Data System (ADS)

    Kaftan, Jens N.; Kiraly, Atilla P.; Bakai, Annemarie; Das, Marco; Novak, Carol L.; Aach, Til

    2008-03-01

    Pulmonary vascular tree segmentation has numerous applications in medical imaging and computer-aided diagnosis (CAD), including detection and visualization of pulmonary emboli (PE), improved lung nodule detection, and quantitative vessel analysis. We present a novel approach to pulmonary vessel segmentation based on a fuzzy segmentation concept, combining the strengths of both threshold and seed point based methods. The lungs of the original image are first segmented and a threshold-based approach identifies core vessel components with a high specificity. These components are then used to automatically identify reliable seed points for a fuzzy seed point based segmentation method, namely fuzzy connectedness. The output of the method consists of the probability of each voxel belonging to the vascular tree. Hence, our method provides the possibility to adjust the sensitivity/specificity of the segmentation result a posteriori according to application-specific requirements, through definition of a minimum vessel-probability required to classify a voxel as belonging to the vascular tree. The method has been evaluated on contrast-enhanced thoracic CT scans from clinical PE cases and demonstrates overall promising results. For quantitative validation we compare the segmentation results to randomly selected, semi-automatically segmented sub-volumes and present the resulting receiver operating characteristic (ROC) curves. Although we focus on contrast enhanced chest CT data, the method can be generalized to other regions of the body as well as to different imaging modalities.

  1. 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.

  2. Blood vessel segmentation methodologies in retinal images--a survey.

    PubMed

    Fraz, M M; Remagnino, P; Hoppe, A; Uyyanonvara, B; Rudnicka, A R; Owen, C G; Barman, S A

    2012-10-01

    Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal vessel segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve.

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

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

  6. 3D segmentation and image annotation for quantitative diagnosis in lung CT images with pulmonary lesions

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

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

    PubMed

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

    2014-04-01

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

  8. 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

  9. 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.

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

    PubMed

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

    2015-07-10

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

  11. 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.

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

    NASA Astrophysics Data System (ADS)

    Rouhani, Mohammad; Lafarge, Florent; Alliez, Pierre

    2017-01-01

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

  13. 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.

  14. 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.

  15. 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

  16. Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification.

    PubMed

    Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

    2015-05-01

    This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions. Next, the regions common to both the binary images are extracted as the major vessels. In the second stage, all remaining pixels in the two binary images are classified using a Gaussian mixture model (GMM) classifier using a set of eight features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods. The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15%, and 95.3% in an average of 3.1, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE_DB1, respectively.

  17. 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

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

    PubMed

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

    2010-01-01

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

  19. 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.

  20. 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.

  1. 3D variational brain tumor segmentation on a clustered feature set

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2001-05-01

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

  3. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

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

  5. Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding.

    PubMed

    Yousefi, Siavash; Liu, Ting; Wang, Ruikang K

    2015-01-01

    Optical coherence tomography (OCT) based microangiography is capable of visualizing 3D functional blood vessel networks within microcirculatory tissue beds in vivo. To provide the quantitative information of vasculature from the microangiograms such as vessel diameter and morphology, it is necessary to develop efficient vessel segmentation algorithms. In this paper, we propose to develop a hybrid Hessian/intensity based method to segment and quantify shape and diameter of the blood vessels innervating capillary beds that are imaged by functional OCT in vivo. The proposed method utilizes multi-scale Hessian filters to segment tubular structures such as blood vessels, but compounded by the intensity-based segmentation method to mitigate the limitations of Hessian filters' sensitivity to the selection of scale parameters. Such compounding segmentation scheme takes advantage of the morphological nature of Hessian filters while correcting for the scale parameter selection by intensity-based segmentation. The proposed algorithm is tested on a wound healing model and its performance of segmentation vessels is quantified by a publicly available manual segmentation dataset. We believe that this method will play an important role in the quantification of micro-angiograms for microcirculation research in ophthalmology and diagnosing retinal eye diseases involved with microcirculation.

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

    PubMed

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

    2017-01-01

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

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

    PubMed

    Kelemen, A; Székely, G; Gerig, G

    1999-10-01

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

  8. 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.

  9. 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.

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

    PubMed

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

    2013-10-01

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

  11. 3-D Ultrasound Segmentation of the Placenta Using the Random Walker Algorithm: Reliability and Agreement.

    PubMed

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

    2015-12-01

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

  12. 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

  13. Automatic segmentation of blood vessels from dynamic MRI datasets.

    PubMed

    Kubassova, Olga

    2007-01-01

    In this paper we present an approach for blood vessel segmentation from dynamic contrast-enhanced MRI datasets of the hand joints acquired from patients with active rheumatoid arthritis. Exclusion of the blood vessels is needed for accurate visualisation of the activation events and objective evaluation of the degree of inflammation. The segmentation technique is based on statistical modelling motivated by the physiological properties of the individual tissues, such as speed of uptake and concentration of the contrast agent; it incorporates Markov random field probabilistic framework and principal component analysis. The algorithm was tested on 60 temporal slices and has shown promising results.

  14. Improvement of a retinal blood vessel segmentation method using the Insight Segmentation and Registration Toolkit (ITK).

    PubMed

    Martinez-Perez, M; Hughes, Alun D; Thom, Simon A; Parker, Kim H

    2007-01-01

    We describe an improved implementation of a segmentation method for retinal blood vessels based on a multi-scale approach and region growing employing modules from the Insight Segmentation and Registration Toolkit (ITK). We present the results of segmentation of retinal blood vessels using this improved method and compare these with results obtained using the original implementation in Matlab, as well as with expert manual segmentations obtained from a public database. We show that the ITK implementation achieves high quality segmentations with markedly improved computational efficiency. The ITK version has greater segmentation accuracy, from 0.94 to 0.96, than the Matlab version due to a decrease in FPR values and it is between 8 and 12 times faster than the original version. Furthermore, the ITK implementation is able to segment high-resolution images in an acceptable timescale.

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

    PubMed Central

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

    2012-01-01

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

  16. 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.

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

    PubMed

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

    2013-08-09

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  19. 3D Near Infrared and Ultrasound Imaging of Peripheral Blood Vessels for Real-Time Localization and Needle Guidance

    PubMed Central

    Chen, Alvin I.; Balter, Max L.; Maguire, Timothy J.; Yarmush, Martin L.

    2016-01-01

    This paper presents a portable imaging device designed to detect peripheral blood vessels for cannula insertion that are otherwise difficult to visualize beneath the skin. The device combines near infrared stereo vision, ultrasound, and real-time image analysis to map the 3D structure of subcutaneous vessels. We show that the device can identify adult forearm vessels and be used to guide manual insertions in tissue phantoms with increased first-stick accuracy compared to unassisted cannulation. We also demonstrate that the system may be coupled with a robotic manipulator to perform automated, image-guided venipuncture. PMID:27981261

  20. [Segmentation of retinal blood vessels based on centerline extraction].

    PubMed

    Zhou, Lin; Shen, Jianxin; Liao, Wenhe; Wang, Yuliang

    2012-02-01

    The precise estimation of blood vessel centerline and width is a prerequisite condition for the quantitative and visualized diagnosis of blood vessel disease in fundus images. In this paper, a retinal blood vessel segmentation algorithm based on centerline extraction is proposed. According to the characteristics of the fundus image and retinal blood vessels, the image is convoluted with the masks of discrete Gaussian partial derivative kernels. The centerline is determined by differential geometric properties of the blood vessels and the width is also calculated. The precision of our method can reach sub-pixel level with a fast computation speed. The experiments on several kinds of fundus images showed that the method worked quickly and accurately.

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  2. 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.

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

    PubMed

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

    2011-06-01

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

  4. 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.

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

    PubMed

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

    2016-12-01

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

  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. 3-D Ultrafast Doppler Imaging Applied to the Noninvasive and Quantitative Imaging of Blood Vessels in Vivo

    PubMed Central

    Provost, J.; Papadacci, C.; Demene, C.; Gennisson, J-L.; Tanter, M.; Pernot, M.

    2016-01-01

    Ultrafast Doppler Imaging was introduced as a technique to quantify blood flow in an entire 2-D field of view, expanding the field of application of ultrasound imaging to the highly sensitive anatomical and functional mapping of blood vessels. We have recently developed 3-D Ultrafast Ultrasound Imaging, a technique that can produce thousands of ultrasound volumes per second, based on three-dimensional plane and diverging wave emissions, and demonstrated its clinical feasibility in human subjects in vivo. In this study, we show that non-invasive 3-D Ultrafast Power Doppler, Pulsed Doppler, and Color Doppler Imaging can be used to perform quantitative imaging of blood vessels in humans when using coherent compounding of three-dimensional tilted plane waves. A customized, programmable, 1024-channel ultrasound system was designed to perform 3-D Ultrafast Imaging. Using a 32X32, 3-MHz matrix phased array (Vermon, France), volumes were beamformed by coherently compounding successive tilted plane wave emissions. Doppler processing was then applied in a voxel-wise fashion. 3-D Ultrafast Power Doppler Imaging was first validated by imaging Tygon tubes of varying diameter and its in vivo feasibility was demonstrated by imaging small vessels in the human thyroid. Simultaneous 3-D Color and Pulsed Doppler Imaging using compounded emissions were also applied in the carotid artery and the jugular vein in one healthy volunteer. PMID:26276956

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  9. 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.

  10. Performance comparison of publicly available retinal blood vessel segmentation methods.

    PubMed

    Vostatek, Pavel; Claridge, Ela; Uusitalo, Hannu; Hauta-Kasari, Markku; Fält, Pauli; Lensu, Lasse

    2017-01-01

    Retinal blood vessel structure is an important indicator of many retinal and systemic diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and three unsupervised segmentation methods with a publicly available implementation are reviewed and quantitatively compared with each other on five public databases with ground truth segmentation of the vessels. Each method is tested under consistent conditions with two types of preprocessing, and the parameters of the methods are optimized for each database. Additionally, possibility to predict the parameters of the methods by the linear regression model is tested for each database. Resolution of the input images and amount of the vessel pixels in the ground truth are used as predictors. The results show the positive influence of preprocessing on the performance of the unsupervised methods. The methods show similar performance for segmentation accuracy, with the best performance achieved by the method by Azzopardi et al. (Acc 94.0) on ARIADB, the method by Soares et al. (Acc 94.6, 94.7) on CHASEDB1 and DRIVE, and the method by Nguyen et al. (Acc 95.8, 95.5) on HRF and STARE. The method by Soares et al. performed better with regard to the area under the ROC curve. Qualitative differences between the methods are discussed. Finally, it was possible to predict the parameter settings that give performance close to the optimized performance of each method.

  11. 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

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

    NASA Astrophysics Data System (ADS)

    Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

    2011-10-01

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

  13. Improved Visualization of Intracranial Vessels with Intraoperative Coregistration of Rotational Digital Subtraction Angiography and Intraoperative 3D Ultrasound

    PubMed Central

    Podlesek, Dino; Meyer, Tobias; Morgenstern, Ute; Schackert, Gabriele; Kirsch, Matthias

    2015-01-01

    Introduction Ultrasound can visualize and update the vessel status in real time during cerebral vascular surgery. We studied the depiction of parent vessels and aneurysms with a high-resolution 3D intraoperative ultrasound imaging system during aneurysm clipping using rotational digital subtraction angiography as a reference. Methods We analyzed 3D intraoperative ultrasound in 39 patients with cerebral aneurysms to visualize the aneurysm intraoperatively and the nearby vascular tree before and after clipping. Simultaneous coregistration of preoperative subtraction angiography data with 3D intraoperative ultrasound was performed to verify the anatomical assignment. Results Intraoperative ultrasound detected 35 of 43 aneurysms (81%) in 39 patients. Thirty-nine intraoperative ultrasound measurements were matched with rotational digital subtraction angiography and were successfully reconstructed during the procedure. In 7 patients, the aneurysm was partially visualized by 3D-ioUS or was not in field of view. Post-clipping intraoperative ultrasound was obtained in 26 and successfully reconstructed in 18 patients (69%) despite clip related artefacts. The overlap between 3D-ioUS aneurysm volume and preoperative rDSA aneurysm volume resulted in a mean accuracy of 0.71 (Dice coefficient). Conclusions Intraoperative coregistration of 3D intraoperative ultrasound data with preoperative rotational digital subtraction angiography is possible with high accuracy. It allows the immediate visualization of vessels beyond the microscopic field, as well as parallel assessment of blood velocity, aneurysm and vascular tree configuration. Although spatial resolution is lower than for standard angiography, the method provides an excellent vascular overview, advantageous interpretation of 3D-ioUS and immediate intraoperative feedback of the vascular status. A prerequisite for understanding vascular intraoperative ultrasound is image quality and a successful match with preoperative

  14. 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.

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

    PubMed

    Azimifar, Zohreh; Mohaddesi, Mahsa

    2015-04-01

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

  16. 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.

  17. Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes

    NASA Astrophysics Data System (ADS)

    Zheng, Yefeng; Loziczonek, Maciej; Georgescu, Bogdan; Zhou, S. Kevin; Vega-Higuera, Fernando; Comaniciu, Dorin

    2011-03-01

    Automatic coronary centerline extraction and lumen segmentation facilitate the diagnosis of coronary artery disease (CAD), which is a leading cause of death in developed countries. Various coronary centerline extraction methods have been proposed and most of them are based on shortest path computation given one or two end points on the artery. The major variation of the shortest path based approaches is in the different vesselness measurements used for the path cost. An empirically designed measurement (e.g., the widely used Hessian vesselness) is by no means optimal in the use of image context information. In this paper, a machine learning based vesselness is proposed by exploiting the rich domain specific knowledge embedded in an expert-annotated dataset. For each voxel, we extract a set of geometric and image features. The probabilistic boosting tree (PBT) is then used to train a classifier, which assigns a high score to voxels inside the artery and a low score to those outside. The detection score can be treated as a vesselness measurement in the computation of the shortest path. Since the detection score measures the probability of a voxel to be inside the vessel lumen, it can also be used for the coronary lumen segmentation. To speed up the computation, we perform classification only for voxels around the heart surface, which is achieved by automatically segmenting the whole heart from the 3D volume in a preprocessing step. An efficient voxel-wise classification strategy is used to further improve the speed. Experiments demonstrate that the proposed learning based vesselness outperforms the conventional Hessian vesselness in both speed and accuracy. On average, it only takes approximately 2.3 seconds to process a large volume with a typical size of 512x512x200 voxels.

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

    PubMed

    Oh, Sejong; Choe, Yoonsuck

    2007-08-01

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

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

    PubMed

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

    2011-09-01

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

  20. Optic Disc Boundary and Vessel Origin Segmentation of Fundus Images.

    PubMed

    Roychowdhury, Sohini; Koozekanani, Dara D; Kuchinka, Sam N; Parhi, Keshab K

    2016-11-01

    This paper presents a novel classification-based optic disc (OD) segmentation algorithm that detects the OD boundary and the location of vessel origin (VO) pixel. First, the green plane of each fundus image is resized and morphologically reconstructed using a circular structuring element. Bright regions are then extracted from the morphologically reconstructed image that lie in close vicinity of the major blood vessels. Next, the bright regions are classified as bright probable OD regions and non-OD regions using six region-based features and a Gaussian mixture model classifier. The classified bright probable OD region with maximum Vessel-Sum and Solidity is detected as the best candidate region for the OD. Other bright probable OD regions within 1-disc diameter from the centroid of the best candidate OD region are then detected as remaining candidate regions for the OD. A convex hull containing all the candidate OD regions is then estimated, and a best-fit ellipse across the convex hull becomes the segmented OD boundary. Finally, the centroid of major blood vessels within the segmented OD boundary is detected as the VO pixel location. The proposed algorithm has low computation time complexity and it is robust to variations in image illumination, imaging angles, and retinal abnormalities. This algorithm achieves 98.8%-100% OD segmentation success and OD segmentation overlap score in the range of 72%-84% on images from the six public datasets of DRIVE, DIARETDB1, DIARETDB0, CHASE_DB1, MESSIDOR, and STARE in less than 2.14 s per image. Thus, the proposed algorithm can be used for automated detection of retinal pathologies, such as glaucoma, diabetic retinopathy, and maculopathy.

  1. 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.

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

    PubMed

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

    2013-12-01

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

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

    PubMed

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

    2016-12-01

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

  5. 3D-2D registration of cerebral angiograms based on vessel directions and intensity gradients

    NASA Astrophysics Data System (ADS)

    Mitrovic, Uroš; Špiclin, Žiga; Štern, Darko; Markelj, Primož; Likar, Boštjan; Miloševic, Zoran; Pernuš, Franjo

    2012-02-01

    Endovascular treatment of cerebral aneurysms and arteriovenous malformations (AVM) involves navigation of a catheter through the femoral artery and vascular system to the site of pathology. Intra-interventional navigation is done under the guidance of one or at most two two-dimensional (2D) X-ray fluoroscopic images or 2D digital subtracted angiograms (DSA). Due to the projective nature of 2D images, the interventionist needs to mentally reconstruct the position of the catheter in respect to the three-dimensional (3D) patient vasculature, which is not a trivial task. By 3D-2D registration of pre-interventional 3D images like CTA, MRA or 3D-DSA and intra-interventional 2D images, intra-interventional tools such as catheters can be visualized on the 3D model of patient vasculature, allowing easier and faster navigation. Such a navigation may consequently lead to the reduction of total ionizing dose and delivered contrast medium. In the past, development and evaluation of 3D-2D registration methods for endovascular treatments received considerable attention. The main drawback of these methods is that they have to be initialized rather close to the correct position as they mostly have a rather small capture range. In this paper, a novel registration method that has a higher capture range and success rate is proposed. The proposed method and a state-of-the-art method were tested and evaluated on synthetic and clinical 3D-2D image-pairs. The results on both databases indicate that although the proposed method was slightly less accurate, it significantly outperformed the state-of-the-art 3D-2D registration method in terms of robustness measured by capture range and success rate.

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

    PubMed

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

    2006-07-01

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

  7. 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

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

    PubMed

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

    2015-04-01

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

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

    PubMed

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

    2009-01-01

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

  10. 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

  11. 3D Segmentation with an application of level set-method using MRI volumes for image guided surgery.

    PubMed

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

    2007-01-01

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

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

    PubMed

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

    2012-01-01

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

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

    PubMed

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

    2017-02-07

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

  14. Novel algorithm by low complexity filter on retinal vessel segmentation

    NASA Astrophysics Data System (ADS)

    Rostampour, Samad

    2011-10-01

    This article shows a new method to detect blood vessels in the retina by digital images. Retinal vessel segmentation is important for detection of side effect of diabetic disease, because diabetes can form new capillaries which are very brittle. The research has been done in two phases: preprocessing and processing. Preprocessing phase consists to apply a new filter that produces a suitable output. It shows vessels in dark color on white background and make a good difference between vessels and background. The complexity is very low and extra images are eliminated. The second phase is processing and used the method is called Bayesian. It is a built-in in supervision classification method. This method uses of mean and variance of intensity of pixels for calculate of probability. Finally Pixels of image are divided into two classes: vessels and background. Used images are related to the DRIVE database. After performing this operation, the calculation gives 95 percent of efficiency average. The method also was performed from an external sample DRIVE database which has retinopathy, and perfect result was obtained

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

    PubMed

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

    2016-02-01

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

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

    PubMed

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

    2005-02-01

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

  17. Automatic segmentation of abdominal vessels for improved pancreas localization

    NASA Astrophysics Data System (ADS)

    Farag, Amal; Liu, Jiamin; Summers, Ronald M.

    2014-03-01

    Accurate automatic detection and segmentation of abdominal organs from CT images is important for quantitative and qualitative organ tissue analysis as well as computer-aided diagnosis. The large variability of organ locations, the spatial interaction between organs that appear similar in medical scans and orientation and size variations are among the major challenges making the task very difficult. The pancreas poses these challenges in addition to its flexibility which allows for the shape of the tissue to vastly change. Due to the close proximity of the pancreas to numerous surrounding organs within the abdominal cavity the organ shifts according to the conditions of the organs within the abdomen, as such the pancreas is constantly changing. Combining these challenges with typically found patient-to-patient variations and scanning conditions the pancreas becomes harder to localize. In this paper we focus on three abdominal vessels that almost always abut the pancreas tissue and as such useful landmarks to identify the relative location of the pancreas. The splenic and portal veins extend from the hila of the spleen and liver, respectively, travel through the abdominal cavity and join at a position close to the head of the pancreas known as the portal confluence. A third vein, the superior mesenteric vein, anastomoses with the other two veins at the portal confluence. An automatic segmentation framework for obtaining the splenic vein, portal confluence and superior mesenteric vein is proposed using 17 contrast enhanced computed-tomography datasets. The proposed method uses outputs from the multi-organ multi-atlas label fusion and Frangi vesselness filter to obtain automatic seed points for vessel tracking and generation of statistical models of the desired vessels. The approach shows ability to identify the vessels and improve localization of the pancreas within the abdomen.

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

    PubMed

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

    2007-12-01

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

  19. Selective Search and Intensity Context Based Retina Vessel Image Segmentation.

    PubMed

    Tang, Zhaohui; Zhang, Jin; Gui, Weihua

    2017-03-01

    In the framework of computer-aided diagnosis of eye disease, a new contextual image feature named influence degree of average intensity is proposed for retinal vessel image segmentation. This new feature evaluates the influence degree of current detected pixel decreasing the average intensity of the local row where that pixel located. Firstly, Hessian matrix is introduced to detect candidate regions, for the reason of accelerating segmentation. Then, the influence degree of average intensity of each pixel is extracted. Next, contextual feature vector for each pixel is constructed by concatenating the 8 feature neighbors. Finally, a classifier is built to classify each pixel into vessel or non-vessel based on its contextual feature. The effectiveness of the proposed method is demonstrated through receiver operating characteristic analysis on the benchmarked databases of DRIVE and STARE. Experiment results show that our method is comparable with the state-of-the-art methods. For example, the average accuracy, sensitivity, specificity achieved on the database DRIVE and STARE are 0.9611, 0.8174, 0.9747 and 0.9547, 0.7768, 0.9751, respectively.

  20. Bayesian method with spatial constraint for retinal vessel segmentation.

    PubMed

    Xiao, Zhiyong; Adel, Mouloud; Bourennane, Salah

    2013-01-01

    A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can identify blood vessels in the retinal image. Evaluation of the developed method is done on real retinal images which are from the DRIVE database and the STARE database. The performance is analyzed and compared to other published methods using a number of measures which include accuracy, sensitivity, and specificity. The proposed approach is proved to be effective on these two databases. The average accuracy, sensitivity, and specificity on the DRIVE database are 0.9529, 0.7513, and 0.9792, respectively, and for the STARE database 0.9476, 0.7147, and 0.9735, respectively. The performance is better than that of other vessel segmentation methods.

  1. Segmenting Retinal Blood Vessels With Deep Neural Networks.

    PubMed

    Liskowski, Pawel; Krawiec, Krzysztof

    2016-11-01

    The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400[Formula: see text]000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to > 0.99) and accuracy of classification (up to > 0.97 ). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( sensitivity > 0.87 ), and fares well on pathological cases.

  2. CAD scheme for detection of intracranial aneurysms in MRA based on 3D analysis of vessel skeletons and enhanced aneurysms

    NASA Astrophysics Data System (ADS)

    Arimura, Hidetaka; Li, Qiang; Korogi, Yukunori; Hirai, Toshinori; Yamashita, Yasuyuki; Katsuragawa, Shigehiko; Ikeda, Ryuji; Doi, Kunio

    2005-04-01

    We have developed a computer-aided diagnostic (CAD) scheme for detection of unruptured intracranial aneurysms in magnetic resonance angiography (MRA) based on findings of short branches in vessel skeletons, and a three-dimensional (3D) selective enhancement filter for dots (aneurysms). Fifty-three cases with 61 unruptured aneurysms and 62 non-aneurysm cases were tested in this study. The isotropic 3D MRA images with 400 x 400 x 128 voxels (a voxel size of 0.5 mm) were processed by use of the dot enhancement filter. The initial candidates were identified not only on the dot-enhanced images by use of a multiple gray-level thresholding technique, but also on the vessel skeletons by finding short branches on parent skeletons, which can indicate a high likelihood of small aneurysms. All candidates were classified into four categories of candidates according to effective diameter and local structure of the vessel skeleton. In each category, a number of false positives were removed by use of two rule-based schemes and by linear discriminant analysis on localized image features related to gray level and morphology. Our CAD scheme achieved a sensitivity of 97% with 5.0 false positives per patient by use of a leave-one-out-by-patient test method. This CAD system may be useful in assisting radiologists in the detection of small intracranial aneurysms as well as medium-size aneurysms in MRA.

  3. 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

  4. Reactor Vessel and Reactor Vessel Internals Segmentation at Zion Nuclear Power Station - 13230

    SciTech Connect

    Cooke, Conrad; Spann, Holger

    2013-07-01

    Zion Nuclear Power Station (ZNPS) is a dual-unit Pressurized Water Reactor (PWR) nuclear power plant located on the Lake Michigan shoreline, in the city of Zion, Illinois approximately 64 km (40 miles) north of Chicago, Illinois and 67 km (42 miles) south of Milwaukee, Wisconsin. Each PWR is of the Westinghouse design and had a generation capacity of 1040 MW. Exelon Corporation operated both reactors with the first unit starting production of power in 1973 and the second unit coming on line in 1974. The operation of both reactors ceased in 1996/1997. In 2010 the Nuclear Regulatory Commission approved the transfer of Exelon Corporation's license to ZionSolutions, the Long Term Stewardship subsidiary of EnergySolutions responsible for the decommissioning of ZNPS. In October 2010, ZionSolutions awarded Siempelkamp Nuclear Services, Inc. (SNS) the contract to plan, segment, remove, and package both reactor vessels and their respective internals. This presentation discusses the tools employed by SNS to remove and segment the Reactor Vessel Internals (RVI) and Reactor Vessels (RV) and conveys the recent progress. SNS's mechanical segmentation tooling includes the C-HORCE (Circumferential Hydraulically Operated Cutting Equipment), BMT (Bolt Milling Tool), FaST (Former Attachment Severing Tool) and the VRS (Volume Reduction Station). Thermal segmentation of the reactor vessels will be accomplished using an Oxygen- Propane cutting system. The tools for internals segmentation were designed by SNS using their experience from other successful reactor and large component decommissioning and demolition (D and D) projects in the US. All of the designs allow for the mechanical segmentation of the internals remotely in the water-filled reactor cavities. The C-HORCE is designed to saw seven circumferential cuts through the Core Barrel and Thermal Shield walls with individual thicknesses up to 100 mm (4 inches). The BMT is designed to remove the bolts that fasten the Baffle Plates to

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

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

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

    NASA Astrophysics Data System (ADS)

    Poon, Kelvin; Hamarneh, Ghassan; Abugharbieh, Rafeef

    2007-03-01

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

  8. Improvements to the Pool Critical Assembly Pressure Vessel Benchmark with 3-D Parallel SN PENTRAN

    NASA Astrophysics Data System (ADS)

    Edgar, Christopher A.; Sjoden, Glenn E.; Yi, Ce

    2014-06-01

    The internationally circulated Pool Critical Assembly (PCA) Pressure Vessel Benchmark was analyzed using the PENTRAN Parallel SN code system for the geometry, material, and source specifications as described in the PCA Benchmark documentation. Improvements to the benchmark are proposed here through the application of more representative flux and volume weighted homogenized cross sections for the PCA reactor core, which were obtained from a rigorous heterogeneous modeling of all fuel assembly types in the core. A new source term definition is also proposed based on calculated relative power in each core fuel assembly with a spectrum based on the Uranium-235 fission spectra. This research focused on utilizing the BUGLE-96 cross section library and accompanying reaction rates, while also examining PENTRAN's adaptive differencing implemented on a coarse mesh basis, as well as fixed use of Directional Theta-Weighted (DTW) SN differencing scheme in order to compare the calculated PENTRAN results to measured data. The results show good comparison with the measured benchmark data, which suggests PENTRAN is a viable, reliable code system for calculation of light water reactor neutron shielding and pressure vessel dosimetry calculations. Furthermore, the improvements to the benchmark methodology resulting from this work provide a 6 percent increase in accuracy of the calculation (based on the average of all calculation points), when compared with experimentally measured results at the same spatial locations in the PCA pressure vessel simulator.

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

    PubMed

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

    2015-02-01

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

  10. Vessel Enhancement and Segmentation of 4D CT Lung Image Using Stick Tensor Voting

    NASA Astrophysics Data System (ADS)

    Cong, Tan; Hao, Yang; Jingli, Shi; Xuan, Yang

    2016-12-01

    Vessel enhancement and segmentation plays a significant role in medical image analysis. This paper proposes a novel vessel enhancement and segmentation method for 4D CT lung image using stick tensor voting algorithm, which focuses on addressing the vessel distortion issue of vessel enhancement diffusion (VED) method. Furthermore, the enhanced results are easily segmented using level-set segmentation. In our method, firstly, vessels are filtered using Frangi's filter to reduce intrapulmonary noises and extract rough blood vessels. Secondly, stick tensor voting algorithm is employed to estimate the correct direction along the vessel. Then the estimated direction along the vessel is used as the anisotropic diffusion direction of vessel in VED algorithm, which makes the intensity diffusion of points locating at the vessel wall be consistent with the directions of vessels and enhance the tubular features of vessels. Finally, vessels can be extracted from the enhanced image by applying level-set segmentation method. A number of experiments results show that our method outperforms traditional VED method in vessel enhancement and results in satisfied segmented vessels.

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

    PubMed

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

    2006-08-01

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

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

    PubMed Central

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

    2016-01-01

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

  13. 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).

  14. A boosted optimal linear learner for retinal vessel segmentation

    NASA Astrophysics Data System (ADS)

    Poletti, E.; Grisan, E.

    2014-03-01

    Ocular fundus images provide important information about retinal degeneration, which may be related to acute pathologies or to early signs of systemic diseases. An automatic and quantitative assessment of vessel morphological features, such as diameters and tortuosity, can improve clinical diagnosis and evaluation of retinopathy. At variance with available methods, we propose a data-driven approach, in which the system learns a set of optimal discriminative convolution kernels (linear learner). The set is progressively built based on an ADA-boost sample weighting scheme, providing seamless integration between linear learner estimation and classification. In order to capture the vessel appearance changes at different scales, the kernels are estimated on a pyramidal decomposition of the training samples. The set is employed as a rotating bank of matched filters, whose response is used by the boosted linear classifier to provide a classification of each image pixel into the two classes of interest (vessel/background). We tested the approach fundus images available from the DRIVE dataset. We show that the segmentation performance yields an accuracy of 0.94.

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

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

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

    PubMed

    Eslami, Abouzar; Yigitsoy, Mehmet; Navab, Nassir

    2011-01-01

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

  18. 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.

  19. 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.

  20. 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

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

    PubMed Central

    Osman, Onur; Ucan, Osman N.

    2008-01-01

    Objective The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. Results The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Conclusion Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules. PMID:18253070

  2. 3-D trajectory model for MDT using micro-spheres implanted within large blood vessels

    NASA Astrophysics Data System (ADS)

    Choomphon-anomakhun, Natthaphon; Natenapit, Mayuree

    2016-09-01

    Implant assisted magnetic drug targeting (IA-MDT) using ferromagnetic spherical targets implanted within large blood vessels and subjected to a uniform externally applied magnetic field (H0) has been investigated and reported for the first time. The capture areas (As) of magnetic drug carrier particles (MDCPs) were determined from the analysis of particle trajectories simulated from equations of motion. Then, the effects of various parameters, such as types of ferromagnetic materials in the targets and MDCPs, blood flow rates, mass fraction of the ferromagnetic material in the MDCPs, average radii of MDCPs (Rp) and the strength of H0 on the As were obtained. Furthermore, the effects of saturation magnetization of the ferromagnetic materials in the MDCPs and within the targets on the As were analyzed. After this, the suitable strengths of H0 and Rp for IA-MDT designs were reported. Dimensionless As, ranging from 2 to 7, was obtained with Rp ranging from 500 to 2500 nm, μ0H0 less than 0.8 T and a blood flow rate of 0.1 m s-1. The target-MDCP materials considered are iron-iron, iron-magnetite and SS409-magnetite, respectively.

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

    SciTech Connect

    Barbiere, J; Beninati, G; Ndlovu, A

    2015-06-15

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

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

    PubMed

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

    2016-02-27

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

  6. 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.

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

    PubMed Central

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

    2016-01-01

    Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy. PMID:27660382

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

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

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

    PubMed

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  12. 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.

  13. 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.

  14. 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.

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

    PubMed

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

    2017-01-01

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

  16. Automated segmentation of retinal blood vessels in spectral domain optical coherence tomography scans.

    PubMed

    Pilch, Matthäus; Wenner, Yaroslava; Strohmayr, Elisabeth; Preising, Markus; Friedburg, Christoph; Meyer Zu Bexten, Erdmuthe; Lorenz, Birgit; Stieger, Knut

    2012-07-01

    The correct segmentation of blood vessels in optical coherence tomography (OCT) images may be an important requirement for the analysis of intra-retinal layer thickness in human retinal diseases. We developed a shape model based procedure for the automatic segmentation of retinal blood vessels in spectral domain (SD)-OCT scans acquired with the Spectralis OCT system. The segmentation procedure is based on a statistical shape model that has been created through manual segmentation of vessels in a training phase. The actual segmentation procedure is performed after the approximate vessel position has been defined by a shadowgraph that assigns the lateral vessel positions. The active shape model method is subsequently used to segment blood vessel contours in axial direction. The automated segmentation results were validated against the manual segmentation of the same vessels by three expert readers. Manual and automated segmentations of 168 blood vessels from 34 B-scans were analyzed with respect to the deviations in the mean Euclidean distance and surface area. The mean Euclidean distance between the automatically and manually segmented contours (on average 4.0 pixels respectively 20 µm against all three experts) was within the range of the manually marked contours among the three readers (approximately 3.8 pixels respectively 18 µm for all experts). The area deviations between the automated and manual segmentation also lie within the range of the area deviations among the 3 clinical experts. Intra reader variability for the experts was between 0.9 and 0.94. We conclude that the automated segmentation approach is able to segment blood vessels with comparable accuracy as expert readers and will provide a useful tool in vessel analysis of whole C-scans, and in particular in multicenter trials.

  17. An improved retinal vessel segmentation method based on high level features for pathological images.

    PubMed

    Ganjee, Razieh; Azmi, Reza; Gholizadeh, Behrouz

    2014-09-01

    Most of the retinal blood vessel segmentation approaches use low level features, resulting in segmenting non-vessel structures together with vessel structures in pathological retinal images. In this paper, a new segmentation method based on high level features is proposed which can process the structure of vessel and non-vessel independently. In this method, segmentation is done in two steps. First, using low level features segmentation is accomplished. Second, using high level features, the non-vessel components are removed. For evaluation, STARE database is used which is publicly available in this field. The results show that the proposed method has 0.9536 accuracy and 0.0191 false positive average on all images of the database and 0.9542 accuracy and 0.0236 false positive average on pathological images. Therefore, the proposed approach shows acceptable accuracy on all images compared to other state of the art methods, and the least false positive average on pathological images.

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

    PubMed

    Ciofolo, Cybèle; Barillot, Christian

    2009-06-01

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

  19. 3D cerebral MR image segmentation using multiple-classifier system.

    PubMed

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

    2017-03-01

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

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

    NASA Astrophysics Data System (ADS)

    Harizanov, S.; Georgiev, I.

    2016-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2005-04-01

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

  2. 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.

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

    PubMed

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

    2012-06-01

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

  4. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images.

    PubMed

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

    2014-12-21

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

  5. 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.

  6. Validation of 3D Code KATRIN For Fast Neutron Fluence Calculation of VVER-1000 Reactor Pressure Vessel by Ex-Vessel Measurements and Surveillance Specimens Results

    NASA Astrophysics Data System (ADS)

    Dzhalandinov, A.; Tsofin, V.; Kochkin, V.; Panferov, P.; Timofeev, A.; Reshetnikov, A.; Makhotin, D.; Erak, D.; Voloschenko, A.

    2016-02-01

    Usually the synthesis of two-dimensional and one-dimensional discrete ordinate calculations is used to evaluate neutron fluence on VVER-1000 reactor pressure vessel (RPV) for prognosis of radiation embrittlement. But there are some cases when this approach is not applicable. For example the latest projects of VVER-1000 have upgraded surveillance program. Containers with surveillance specimens are located on the inner surface of RPV with fast neutron flux maximum. Therefore, the synthesis approach is not suitable enough for calculation of local disturbance of neutron field in RPV inner surface behind the surveillance specimens because of their complicated and heterogeneous structure. In some cases the VVER-1000 core loading consists of fuel assemblies with different fuel height and the applicability of synthesis approach is also ambiguous for these fuel cycles. Also, the synthesis approach is not enough correct for the neutron fluence estimation at the RPV area above core top. Because of these reasons only the 3D neutron transport codes seem to be satisfactory for calculation of neutron fluence on the VVER-1000 RPV. The direct 3D calculations are also recommended by modern regulations.

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

    DTIC Science & Technology

    2014-04-26

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

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

    NASA Astrophysics Data System (ADS)

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

    2006-03-01

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

  9. 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.

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

    NASA Astrophysics Data System (ADS)

    Voorn, Maarten; Exner, Ulrike; Rath, Alexander

    2013-08-01

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

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

    NASA Astrophysics Data System (ADS)

    Orme, Haydn; Bell, Rebecca; Jackson, Christopher

    2016-04-01

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

  12. Multimodal retinal vessel segmentation from spectral-domain optical coherence tomography and fundus photography.

    PubMed

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

    2012-10-01

    Segmenting retinal vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes is particularly challenging due to the projected neural canal opening (NCO) and relatively low visibility in the ONH center. Color fundus photographs provide a relatively high vessel contrast in the region inside the NCO, but have not been previously used to aid the SD-OCT vessel segmentation process. Thus, in this paper, we present two approaches for the segmentation of retinal vessels in SD-OCT volumes that each take advantage of complimentary information from fundus photographs. In the first approach (referred to as the registered-fundus vessel segmentation approach), vessels are first segmented on the fundus photograph directly (using a k-NN pixel classifier) and this vessel segmentation result is mapped to the SD-OCT volume through the registration of the fundus photograph to the SD-OCT volume. In the second approach (referred to as the multimodal vessel segmentation approach), after fundus-to-SD-OCT registration, vessels are simultaneously segmented with a k -NN classifier using features from both modalities. Three-dimensional structural information from the intraretinal layers and neural canal opening obtained through graph-theoretic segmentation approaches of the SD-OCT volume are used in combination with Gaussian filter banks and Gabor wavelets to generate the features. The approach is trained on 15 and tested on 19 randomly chosen independent image pairs of SD-OCT volumes and fundus images from 34 subjects with glaucoma. Based on a receiver operating characteristic (ROC) curve analysis, the present registered-fundus and multimodal vessel segmentation approaches [area under the curve (AUC) of 0.85 and 0.89, respectively] both perform significantly better than the two previous OCT-based approaches (AUC of 0.78 and 0.83, p < 0.05). The multimodal approach overall performs significantly better than the other three approaches (p < 0.05).

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

    PubMed

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

    2015-01-01

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

  14. 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.

  15. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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

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

    PubMed

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1989-03-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2014-03-31

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

  6. 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.

  7. Fpga based hardware synthesis for automatic segmentation of retinal blood vessels in diabetic retinopathy images.

    PubMed

    Sivakamasundari, J; Kavitha, G; Sujatha, C M; Ramakrishnan, S

    2014-01-01

    Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Real-Time mass screening system for DR is vital for timely diagnosis and periodic screening to prevent the patient from severe visual loss. Human retinal fundus images are widely used for an automated segmentation of blood vessel and diagnosis of various blood vessel disorders. In this work, an attempt has been made to perform hardware synthesis of Kirsch template based edge detection for segmentation of blood vessels. This method is implemented using LabVIEW software and is synthesized in field programmable gate array board to yield results in real-time application. The segmentation of blood vessels using Kirsch based edge detection is compared with other edge detection methods such as Sobel, Prewitt and Canny. The texture features such as energy, entropy, contrast, mean, homogeneity and structural feature namely ratio of vessel to vessel free area are obtained from the segmented images. The performance of segmentation is analysed in terms of sensitivity, specificity and accuracy. It is observed from the results that the Kirsch based edge detection technique segmented the edges of blood vessels better than other edge detection techniques. The ratio of vessel to vessel free area classified the normal and DR affected retinal images more significantly than other texture based features. FPGA based hardware synthesis of Kirsch edge detection method is able to differentiate normal and diseased images with high specificity (93%). This automated segmentation of retinal blood vessels system could be used in computer-assisted diagnosis for diabetic retinopathy screening in real-time application.

  8. A new strategy to obtain robust markers for blood vessels segmentation by using the watersheds method.

    PubMed

    Rodríguez, Roberto; Alarcón, Teresa E; Pacheco, Oriana

    2005-10-01

    The watersheds method is a powerful segmentation tool developed in mathematical morphology. In order to prevent its over-segmentation, in this paper, we present a new strategy to obtain robust markers for segmentation of blood vessels from malignant tumors. For this purpose, we introduced a new algorithm. We propose a two-stage segmentation strategy which involves: (1) extracting an approximate region containing the blood vessel and part of the background near the blood vessel, and (2) segmenting the blood vessel from the background within this region. The approach effectively reduces the influence of peripheral background intensities on the extraction of a blood vessel region. In this application the important information to be extracted from images is only the number of blood vessels present in the images. The proposed strategy was tested on manual segmentation, where segmentation errors less than 10% for false positives and 0% for false negatives are observed. It is demonstrated by extensive experimentation, by using real images, that the proposed strategy was suitable for our application in the environment of a personal computer.

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

    PubMed

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

    2014-01-01

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

  10. 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.

  11. A Vessel Active Contour Model for Vascular Segmentation

    PubMed Central

    Chen, Qingli; Wang, Wei; Peng, Yu; Wang, Qingjun; Wu, Zhongke; Zhou, Mingquan

    2014-01-01

    This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images. PMID:25101262

  12. 3D rotating wall vessel and 2D cell culture of four veterinary virus pathogens: A comparison of virus yields, portions of infectious particles and virus growth curves.

    PubMed

    Malenovská, Hana

    2016-02-01

    Only very few comparative studies have been performed that evaluate general trends of virus growth under 3D in comparison with 2D cell culture conditions. The aim of this study was to investigate differences when four animal viruses are cultured in 2D and 3D. Suid herpesvirus 1 (SuHV-1), Vesicular stomatitis virus (VSIV), Bovine adenovirus (BAdV) and Bovine parainfluenza 3 virus (BPIV-3) were cultivated in 3D rotating wall vessels (RWVs) and conventional 2D cultures. The production of virus particles, the portion of infectious particles, and the infectious growth curves were compared. For all viruses, the production of virus particles (related to cell density), including the non-infectious ones, was lower in 3D than in 2D culture. The production of only infectious particles was significantly lower in BAdV and BPIV-3 in 3D cultures in relation to cell density. The two cultivation approaches resulted in significantly different virus particle-to-TCID50 ratios in three of the four viruses: lower in SuHV-1 and BPIV-3 and higher in BAdV in 3D culture. The infectious virus growth rates were not significantly different in all viruses. Although 3D RWV culture resulted in lower production of virus particles compared to 2D systems, the portion of infectious particles was higher for some viruses.

  13. Segmentation of vessels cluttered with cells using a physics based model.

    PubMed

    Schmugge, Stephen J; Keller, Steve; Nguyen, Nhat; Souvenir, Richard; Huynh, Toan; Clemens, Mark; Shin, Min C

    2008-01-01

    Segmentation of vessels in biomedical images is important as it can provide insight into analysis of vascular morphology, topology and is required for kinetic analysis of flow velocity and vessel permeability. Intravital microscopy is a powerful tool as it enables in vivo imaging of both vasculature and circulating cells. However, the analysis of vasculature in those images is difficult due to the presence of cells and their image gradient. In this paper, we provide a novel method of segmenting vessels with a high level of cell related clutter. A set of virtual point pairs ("vessel probes") are moved reacting to forces including Vessel Vector Flow (VVF) and Vessel Boundary Vector Flow (VBVF) forces. Incorporating the cell detection, the VVF force attracts the probes toward the vessel, while the VBVF force attracts the virtual points of the probes to localize the vessel boundary without being distracted by the image features of the cells. The vessel probes are moved according to Newtonian Physics reacting to the net of forces applied on them. We demonstrate the results on a set of five real in vivo images of liver vasculature cluttered by white blood cells. When compared against the ground truth prepared by the technician, the Root Mean Squared Error (RMSE) of segmentation with VVF and VBVF was 55% lower than the method without VVF and VBVF.

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

    PubMed

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

    2014-03-11

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

  15. Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation.

    PubMed

    Javidi, Malihe; Pourreza, Hamid-Reza; Harati, Ahad

    2017-02-01

    Diabetic retinopathy (DR) is a major cause of visual impairment, and the analysis of retinal image can assist patients to take action earlier when it is more likely to be effective. The accurate segmentation of blood vessels in the retinal image can diagnose DR directly. In this paper, a novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed. The proposed system yields a strong representation which contains the semantic concept of the image. To extract blood vessel, two separate dictionaries, for vessel and non-vessel, capable of providing reconstructive and discriminative information of the retinal image are learned. In the test step, an unseen retinal image is divided into overlapping patches and classified to vessel and non-vessel patches. Then, a voting scheme is applied to generate the binary vessel map. The proposed vessel segmentation method can achieve the accuracy of 95% and a sensitivity of 75% in the same range of specificity 97% on two public datasets. The results show that the proposed method can achieve comparable results to existing methods and decrease false positive vessels in abnormal retinal images with pathological regions. Microaneurysm (MA) is the earliest sign of DR that appears as a small red dot on the surface of the retina. Despite several attempts to develop automated MA detection systems, it is still a challenging problem. In this paper, a method for MA detection, which is similar to our vessel segmentation approach, is proposed. In our method, a candidate detection algorithm based on the Morlet wavelet is applied to identify all possible MA candidates. In the next step, two discriminative dictionaries with the ability to distinguish MA from non-MA object are learned. These dictionaries are then used to classify the detected candidate objects. The evaluations indicate that the proposed MA detection method achieves higher average sensitivity about 2

  16. Sector-based optic cup segmentation with intensity and blood vessel priors.

    PubMed

    Yin, Fengshou; Liu, Jiang; Wong, Damon W K; Tan, Ngan Meng; Cheng, Jun; Cheng, Ching-Yu; Tham, Yih Chung; Wong, Tien Yin

    2012-01-01

    The optic cup segmentation is critical for automated cup-to-disk ratio measurement, and hence computer-aided diagnosis of glaucoma. In this paper, we propose a novel sector-based method for optic cup segmentation. The method comprises two parts: intensity-based cup segmentation with shape constraints and blood vessel-based refinement. The initial estimation of the cup is obtained by applying a statistical deformable model on the vessel free image. At the same time, blood vessels within the optic disk are extracted, after which vessel bendings and vessel boundaries in the nasal side are located. Subsequently, these key points in the blood vessels are used to fine tune the cup. The algorithm is evaluated on 650 fundus images from the ORIGA(-light) database. Experimental results show that the Dice coefficient for the optic cup segmentation can be as high as 0.83, which outperforms other existing methods. The results demonstrate good potential for the proposed method to be used in automated optic cup segmentation and glaucoma diagnosis.

  17. Segmentation of the blood vessels and optic disk in retinal images.

    PubMed

    Salazar-Gonzalez, Ana; Kaba, Djibril; Li, Yongmin; Liu, Xiaohui

    2014-11-01

    Retinal image analysis is increasingly prominent as a nonintrusive diagnosis method in modern ophthalmology. In this paper, we present a novel method to segment blood vessels and optic disk in the fundus retinal images. The method could be used to support nonintrusive diagnosis in modern ophthalmology since the morphology of the blood vessel and the optic disk is an important indicator for diseases like diabetic retinopathy, glaucoma, and hypertension. Our method takes as first step the extraction of the retina vascular tree using the graph cut technique. The blood vessel information is then used to estimate the location of the optic disk. The optic disk segmentation is performed using two alternative methods. The Markov random field (MRF) image reconstruction method segments the optic disk by removing vessels from the optic disk region, and the compensation factor method segments the optic disk using the prior local intensity knowledge of the vessels. The proposed method is tested on three public datasets, DIARETDB1, DRIVE, and STARE. The results and comparison with alternative methods show that our method achieved exceptional performance in segmenting the blood vessel and optic disk.

  18. Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography

    PubMed Central

    Shim, Hackjoon; Jeon, Byunghwan; Jang, Yeonggul; Hong, Youngtaek; Jung, Sunghee; Ha, Seongmin; Chang, Hyuk-Jae

    2016-01-01

    We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown. PMID:27536939

  19. 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.

  20. 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

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

    PubMed Central

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

    2014-01-01

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

  2. 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.

  3. Blood vessel segmentation using line-direction vector based on Hessian analysis

    NASA Astrophysics Data System (ADS)

    Nimura, Yukitaka; Kitasaka, Takayuki; Mori, Kensaku

    2010-03-01

    For decision of the treatment strategy, grading of stenoses is important in diagnosis of vascular disease such as arterial occlusive disease or thromboembolism. It is also important to understand the vasculature in minimally invasive surgery such as laparoscopic surgery or natural orifice translumenal endoscopic surgery. Precise segmentation and recognition of blood vessel regions are indispensable tasks in medical image processing systems. Previous methods utilize only ``lineness'' measure, which is computed by Hessian analysis. However, difference of the intensity values between a voxel of thin blood vessel and a voxel of surrounding tissue is generally decreased by the partial volume effect. Therefore, previous methods cannot extract thin blood vessel regions precisely. This paper describes a novel blood vessel segmentation method that can extract thin blood vessels with suppressing false positives. The proposed method utilizes not only lineness measure but also line-direction vector corresponding to the largest eigenvalue in Hessian analysis. By introducing line-direction information, it is possible to distinguish between a blood vessel voxel and a voxel having a low lineness measure caused by noise. In addition, we consider the scale of blood vessel. The proposed method can reduce false positives in some line-like tissues close to blood vessel regions by utilization of iterative region growing with scale information. The experimental result shows thin blood vessel (0.5 mm in diameter, almost same as voxel spacing) can be extracted finely by the proposed method.

  4. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.

    PubMed

    Karasawa, Ken'ichi; Oda, Masahiro; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Chu, Chengwen; Zheng, Guoyan; Rueckert, Daniel; Mori, Kensaku

    2017-03-31

    Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%.

  5. 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.

  6. Fabrication and assessment of 3D printed anatomical models of the lower limb for anatomical teaching and femoral vessel access training in medicine.

    PubMed

    O'Reilly, Michael K; Reese, Sven; Herlihy, Therese; Geoghegan, Tony; Cantwell, Colin P; Feeney, Robin N M; Jones, James F X

    2016-01-01

    For centuries, cadaveric dissection has been the touchstone of anatomy education. It offers a medical student intimate access to his or her first patient. In contrast to idealized artisan anatomical models, it presents the natural variation of anatomy in fine detail. However, a new teaching construct has appeared recently in which artificial cadavers are manufactured through three-dimensional (3D) printing of patient specific radiological data sets. In this article, a simple powder based printer is made more versatile to manufacture hard bones, silicone muscles and perfusable blood vessels. The approach involves blending modern approaches (3D printing) with more ancient ones (casting and lost-wax techniques). These anatomically accurate models can augment the approach to anatomy teaching from dissection to synthesis of 3D-printed parts held together with embedded rare earth magnets. Vascular simulation is possible through application of pumps and artificial blood. The resulting arteries and veins can be cannulated and imaged with Doppler ultrasound. In some respects, 3D-printed anatomy is superior to older teaching methods because the parts are cheap, scalable, they can cover the entire age span, they can be both dissected and reassembled and the data files can be printed anywhere in the world and mass produced. Anatomical diversity can be collated as a digital repository and reprinted rather than waiting for the rare variant to appear in the dissection room. It is predicted that 3D printing will revolutionize anatomy when poly-material printing is perfected in the early 21st century.

  7. Active Contours Driven by Multi-Feature Gaussian Distribution Fitting Energy with Application to Vessel Segmentation.

    PubMed

    Wang, Lei; Zhang, Huimao; He, Kan; Chang, Yan; Yang, Xiaodong

    2015-01-01

    Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and 'vesselness values' from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width.

  8. Pre-Operative Image-based Segmentation of the Cranial Nerves and Blood Vessels in Microvascular Decompression: Can we Prevent Unnecessary Explorations?

    PubMed Central

    Dolati, P; Golby, A; Eichberg, D; Abolfotoh, M; Dunn, IF; Mukundan, S; Hulou, MM; Al-Mefty, O

    2016-01-01

    Objectives This study was conducted to validate the accuracy of image-based pre-operative segmentation using the gold standard endoscopic and microscopic findings for localization and pre-operative diagnosis of the offensive vessel. Patients and Methods Fourteen TN and 6 HS cases were randomly selected. All patients had 3T MRI, which included thin-sectioned 3D space T2, 3D Time of Flight and MPRAGE Sequences. Imaging sequences were loaded in BrainLab iPlanNet and fused. Individual segmentation of the affected cranial nerves and the compressing vascular structure was performed by a neurosurgeon, and the results were compared with the microscopic and endoscopic findings by two blinded neurosurgeons. For each case, at least three neurovascular landmarks were targeted. Each segmented neurovascular element was validated by manual placement of the navigation probe over each target, and errors of localization were measured in mm. Results All patients underwent retro-sigmoid craniotomy and MVD using both microscope and endoscope. Based on image segmentation, the compressing vessel was identified in all cases except one, which was also negative intraoperatively. Perfect correspondence was found between image-based segmentation and endoscopic and microscopic images and videos (Dice coefficient of 1). Measurement accuracy was 0.45+/-0.21 mm (mean +/-SD). Conclusion Image-based segmentation is a promising method for pre-operative identification and localization of offending blood vessels causing HFS and TN. Using this method may prevent some unnecessary explorations on especially atypical cases with no vascular contacts. However, negative pre-operative image segmentation may not preclude one from exploration in classic cases of TN or HFS. A multicenter study with larger number of cases is recommended. PMID:26476700

  9. A novel lung nodules detection scheme based on vessel segmentation on CT images.

    PubMed

    Jia, Tong; Zhang, Hao; Meng, Haixiu

    2014-01-01

    Lung vessels often interfere with the detection of lung nodules. In this paper, a novel computer-aided lung nodule detection scheme on vessel segmentation is proposed. This paper describes an active contour model which can combine image region mean gray value and image edge energy. It is used to segment and remove lung vessels. A selective shape filter based on Hessian Matrix is used to detect suspicious nodules and remove omitted lung vessels. This paper extracts density, shape and position features of suspicious nodules, and uses a Rule-Based Classification (RBC) method to identify true positive nodules. In the experiment results, the detection sensitivity is about 90% and FP is 1/scan.

  10. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction.

    PubMed

    Mendonça, Ana Maria; Campilho, Aurélio

    2006-09-01

    This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width dependent morphological filters. Our approach was tested on two publicly available databases and its results are compared with recently published methods. The results demonstrate that our algorithm outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity.

  11. Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering.

    PubMed

    Saffarzadeh, Vahid Mohammadi; Osareh, Alireza; Shadgar, Bita

    2014-04-01

    Detecting blood vessels is a vital task in retinal image analysis. The task is more challenging with the presence of bright and dark lesions in retinal images. Here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. First, the negative impact of bright lesions is reduced by using K-means segmentation in a perceptive space. Then, a multi-scale line operator is utilized to detect vessels while ignoring some of the dark lesions, which have intensity structures different from the line-shaped vessels in the retina. The proposed algorithm is tested on two publicly available STARE and DRIVE databases. The performance of the method is measured by calculating the area under the receiver operating characteristic curve and the segmentation accuracy. The proposed method achieves 0.9483 and 0.9387 localization accuracy against STARE and DRIVE respectively.

  12. Accurate 3d Textured Models of Vessels for the Improvement of the Educational Tools of a Museum

    NASA Astrophysics Data System (ADS)

    Soile, S.; Adam, K.; Ioannidis, C.; Georgopoulos, A.

    2013-02-01

    Besides the demonstration of the findings, modern museums organize educational programs which aim to experience and knowledge sharing combined with entertainment rather than to pure learning. Toward that effort, 2D and 3D digital representations are gradually replacing the traditional recording of the findings through photos or drawings. The present paper refers to a project that aims to create 3D textured models of two lekythoi that are exhibited in the National Archaeological Museum of Athens in Greece; on the surfaces of these lekythoi scenes of the adventures of Odysseus are depicted. The project is expected to support the production of an educational movie and some other relevant interactive educational programs for the museum. The creation of accurate developments of the paintings and of accurate 3D models is the basis for the visualization of the adventures of the mythical hero. The data collection was made by using a structured light scanner consisting of two machine vision cameras that are used for the determination of geometry of the object, a high resolution camera for the recording of the texture, and a DLP projector. The creation of the final accurate 3D textured model is a complicated and tiring procedure which includes the collection of geometric data, the creation of the surface, the noise filtering, the merging of individual surfaces, the creation of a c-mesh, the creation of the UV map, the provision of the texture and, finally, the general processing of the 3D textured object. For a better result a combination of commercial and in-house software made for the automation of various steps of the procedure was used. The results derived from the above procedure were especially satisfactory in terms of accuracy and quality of the model. However, the procedure was proved to be time consuming while the use of various software packages presumes the services of a specialist.

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

    PubMed

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

    2013-05-01

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

  14. An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding.

    PubMed

    Saleh, Marwan D; Eswaran, C

    2012-01-01

    Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.

  15. Retinal vessel segmentation: an efficient graph cut approach with retinex and local phase.

    PubMed

    Zhao, Yitian; Liu, Yonghuai; Wu, Xiangqian; Harding, Simon P; Zheng, Yalin

    2015-01-01

    Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer.

  16. Dual-wavelength retinal image registration based on vessel segmentation and optic disc detection

    NASA Astrophysics Data System (ADS)

    Xian, Yong-li; Dai, Yun; Gao, Chun-ming; Du, Rui

    2016-09-01

    The dual-wavelength retinal image registration is one of the critical steps in the spectrophotometric measurements of oxygen saturation in the retinal vasculature. The dual-wavelength images (570 nm and 600 nm) are simultaneously captured by dual-wavelength retinal oximeter based on commercial fundus camera. The retinal oxygen saturation is finally measured after vessel segmentation, image registration and calculation of optical density ratio of the two images. Because the dual-wavelength images are acquired from different optical path, it is necessary to go through image registration before they are used to analyze the oxygen saturation. This paper presents a new approach to dual-wavelength retinal image registration based on vessel segmentation and optic disc detection. Firstly, the multi-scale segmentation algorithm based on the Hessian matrix is used to realize vessel segmentation. Secondly, after optic disc is detected by convergence index filter and the center of the optic disc is obtained by centriod algorithm, the translational difference between the images can be determined. The center of the optic disc is used as the center of rotation, and the registration based on mutual information can be achieved using contour and gray information of vessels through segmented image. So the rotational difference between the images can be determined too. The result shows that the algorithm can provide an accurate registration for the dual-wavelength retinal image.

  17. Margin for In-Vessel Retention in the APR1400 - VESTA and SCDAP/RELAP5-3D Analyses

    SciTech Connect

    Joy Rempe; D. Knudson

    2004-12-01

    If cooling is inadequate during a reactor accident, a significant amount of core material could become molten and relocate to the lower head of the reactor vessel, as happened in the Three Mile Island Unit 2 (TMI-2) accident. If it is possible to ensure that the lower head remains intact so that relocated core materials are retained within the vessel, the enhanced safety associated with such plants can reduce concerns about containment failure and associated risk. For example, the enhanced safety of the Westinghouse Advanced 600 MWe pressurized water reactor (PWR) (AP600), which relied upon external reactor vessel cooling (ERVC) for in-vessel retention (IVR), resulted in the U.S. Nuclear Regulatory Commission (USNRC) approving the design without requiring certain conventional features common to existing light water reactors (LWRs). IVR of core melt is therefore a key severe accident management strategy adopted by some operating nuclear power plants and proposed for some advanced LWRs. However, it is not clear that currently proposed ERVC without additional enhancements could provide sufficient heat removal for higher-power reactors (up to 1500 MWe). Hence, a three-year, United States (U.S.) -Korean International Nuclear Energy Research Initiative (INERI) project was initiated in which the Idaho National Engineering and Environmental Laboratory (INEEL), Seoul National University (SNU), Pennsylvania State University (PSU), and the Korean Atomic Energy Research Institute (KAERI) explored options, such as enhanced ERVC performance and an enhanced in-vessel core catcher (IVCC), that have the potential to ensure that IVR is feasible for higher power reactors.

  18. Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.

    PubMed

    Du, Jiang; Karimi, Afshin; Wu, Yijing; Korosec, Frank R; Grist, Thomas M; Mistretta, Charles A

    2011-04-01

    Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique.

  19. Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels.

    PubMed

    Waheed, Amna; Akram, M Usman; Khalid, Shehzad; Waheed, Zahra; Khan, Muazzam A; Shaukat, Arslan

    2015-10-01

    Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic system for retinal diseases. Most popular methods for vessel segmentation are based on matched filters and Gabor wavelets which give good response against blood vessels. One major drawback in these techniques is that they also give strong response for lesion (exudates, hemorrhages) boundaries which give rise to false vessels. These false vessels may lead to incorrect detection of vascular changes. In this paper, we propose a new hybrid feature set along with new classification technique for accurate detection of blood vessels. The main motivation is to lower the false positives especially from retinal images with severe disease level. A novel region based hybrid feature set is presented for proper discrimination between true and false vessels. A new modified m-mediods based classification is also presented which uses most discriminating features to categorize vessel regions into true and false vessels. The evaluation of proposed system is done thoroughly on publicly available databases along with a locally gathered database with images of advanced level of retinal diseases. The results demonstrate the validity of the proposed system as compared to existing state of the art techniques.

  20. Segmentation of blood vessels from red-free and fluorescein retinal images.

    PubMed

    Martinez-Perez, M Elena; Hughes, Alun D; Thom, Simon A; Bharath, Anil A; Parker, Kim H

    2007-02-01

    The morphology of the retinal blood vessels can be an important indicator for diseases like diabetes, hypertension and retinopathy of prematurity (ROP). Thus, the measurement of changes in morphology of arterioles and venules can be of diagnostic value. Here we present a method to automatically segment retinal blood vessels based upon multiscale feature extraction. This method overcomes the problem of variations in contrast inherent in these images by using the first and second spatial derivatives of the intensity image that gives information about vessel topology. This approach also enables the detection of blood vessels of different widths, lengths and orientations. The local maxima over scales of the magnitude of the gradient and the maximum principal curvature of the Hessian tensor are used in a multiple pass region growing procedure. The growth progressively segments the blood vessels using feature information together with spatial information. The algorithm is tested on red-free and fluorescein retinal images, taken from two local and two public databases. Comparison with first public database yields values of 75.05% true positive rate (TPR) and 4.38% false positive rate (FPR). Second database values are of 72.46% TPR and 3.45% FPR. Our results on both public databases were comparable in performance with other authors. However, we conclude that these values are not sensitive enough so as to evaluate the performance of vessel geometry detection. Therefore we propose a new approach that uses measurements of vessel diameters and branching angles as a validation criterion to compare our segmented images with those hand segmented from public databases. Comparisons made between both hand segmented images from public databases showed a large inter-subject variability on geometric values. A last evaluation was made comparing vessel geometric values obtained from our segmented images between red-free and fluorescein paired images with the latter as the "ground truth

  1. Effect of blood vessel segmentation on the outcome of electroporation-based treatments of liver tumors.

    PubMed

    Marčan, Marija; Kos, Bor; Miklavčič, Damijan

    2015-01-01

    Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses applied to tissue via electrodes. To ensure that the whole tumor is covered with sufficiently high electric field, accurate numerical models are built based on individual patient anatomy. Extraction of patient's anatomy through segmentation of medical images inevitably produces some errors. In order to ensure the robustness of treatment planning, it is necessary to evaluate the potential effect of such errors on the electric field distribution. In this work we focus on determining the effect of errors in automatic segmentation of hepatic vessels on the electric field distribution in electroporation-based treatments in the liver. First, a numerical analysis was performed on a simple 'sphere and cylinder' model for tumors and vessels of different sizes and relative positions. Second, an analysis of two models extracted from medical images of real patients in which we introduced variations of an error of the automatic vessel segmentation method was performed. The results obtained from a simple model indicate that ignoring the vessels when calculating the electric field distribution can cause insufficient coverage of the tumor with electric fields. Results of this study indicate that this effect happens for small (10 mm) and medium-sized (30 mm) tumors, especially in the absence of a central electrode inserted in the tumor. The results obtained from the real-case models also show higher negative impact of automatic vessel segmentation errors on the electric field distribution when the central electrode is absent. However, the average error of the automatic vessel segmentation did not have an impact on the electric field distribution if the central electrode was present. This suggests the algorithm is robust enough to be used in creating a model for treatment parameter optimization, but with a central electrode.

  2. Computed tomography quantification of pulmonary vessels in chronic obstructive pulmonary disease as identified by 3D automated approach

    PubMed Central

    Yu, Nan; Wei, Xia; Li, Yan; Deng, Lei; Jin, Chen-wang; Guo, Youmin

    2016-01-01

    Abstract The aim of this study was to investigate the vascular alteration of the whole lung and individual lobes in patients with COPD, and assess the association between pulmonary vessels and the extent and distribution of emphysema as well as pulmonary function by a 3-dimensional automated approach. A total of 83 computed tomography images from COPD patients were analyzed. Automated computerized approach was used to measure the total number of vessels at the fifth generation. The extent of emphysema (%LAA-950) in the whole lung and individual lobes were also calculated automatically. The association between the vascular number and the extent and distribution of emphysema, as well as the pulmonary function were assessed. Both the vascular number of fifth generation in the upper lobe and in the lower lobe were significantly negatively correlated with %LAA-950 (P < 0.05). Furthermore, there were significant, yet weak correlations between the vascular number and FEV1% predicted (R = 0.556, P = 0.039) and FEV1/FVC (R = 0.538, P = 0.047). In contrast, the vascular numbers were strongly correlated with DLco (R = 0.770, P = 0.003). Finally, the vascular number correlated closer with %LAA-950 of upper lobes than with %LAA-950 of lower lobes. Pulmonary vessel alteration can be measured; it is related to the extent of emphysema rather than the distribution of emphysema. PMID:27749587

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

    NASA Astrophysics Data System (ADS)

    Wang, Jiahui; Engelmann, Roger; Li, Qiang

    2008-03-01

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

  4. Complementary X-ray tomography techniques for histology-validated 3D imaging of soft and hard tissues using plaque-containing blood vessels as examples.

    PubMed

    Holme, Margaret N; Schulz, Georg; Deyhle, Hans; Weitkamp, Timm; Beckmann, Felix; Lobrinus, Johannes A; Rikhtegar, Farhad; Kurtcuoglu, Vartan; Zanette, Irene; Saxer, Till; Müller, Bert

    2014-01-01

    A key problem in X-ray computed tomography is choosing photon energies for postmortem specimens containing both soft and hard tissues. Increasing X-ray energy reduces image artifacts from highly absorbing hard tissues including plaque, but it simultaneously decreases contrast in soft tissues including the endothelium. Therefore, identifying the lumen within plaque-containing vessels is challenging. Destructive histology, the gold standard for tissue evaluation, reaches submicron resolution in two dimensions, whereas slice thickness limits spatial resolution in the third. We present a protocol to systematically analyze heterogeneous tissues containing weakly and highly absorbing components in the original wet state, postmortem. Taking the example of atherosclerotic human coronary arteries, the successively acquired 3D data of benchtop and synchrotron radiation-based tomography are validated by histology. The entire protocol requires ∼20 working days, enables differentiation between plaque, muscle and fat tissues without using contrast agents and permits blood flow simulations in vessels with plaque-induced constrictions.

  5. Learning fully-connected CRFs for blood vessel segmentation in retinal images.

    PubMed

    Orlando, José Ignacio; Blaschko, Matthew

    2014-01-01

    In this work, we present a novel method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Retinal image analysis is greatly aided by blood vessel segmentation as the vessel structure may be considered both a key source of signal, e.g. in the diagnosis of diabetic retinopathy, or a nuisance, e.g. in the analysis of pigment epithelium or choroid related abnormalities. Blood vessel segmentation in fundus images has been considered extensively in the literature, but remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors such as a Potts model or total variation. In this work, we overcome this difficulty using a discriminatively trained conditional random field model with more expressive potentials. In particular, we employ recent results enabling extremely fast inference in a fully connected model. We find that this rich but computationally efficient model family, combined with principled discriminative training based on a structured output support vector machine yields a fully automated system that achieves results statistically indistinguishable from an expert human annotator. Implementation details are available at http://pages.saclay.inria.fr/ matthew.blaschko/projects/retina/.

  6. 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

  7. A comparison between two robust techniques for segmentation of blood vessels.

    PubMed

    Rodríguez, Roberto; Castillo, Patricio J; Guerra, Valia; Azuela, Juan Humberto Sossa; Suáreza, Ana G; Izquierdo, Ebroul

    2008-08-01

    Image segmentation plays an important role in image analysis. According to several authors, segmentation terminates when the observer's goal is satisfied. For this reason, a unique method that can be applied to all possible cases does not yet exist. In this paper, we have carried out a comparison between two current segmentation techniques, namely the mean shift method, for which we propose a new algorithm, and the so-called spectral method. In this investigation the important information to be extracted from an image is the number of blood vessels (BV) present in the image. The results obtained by both strategies were compared with the results provided by manual segmentation. We have found that using the mean shift segmentation an error less than 20% for false positives (FP) and 0% for false negatives (FN) was observed, while for the spectral method more than 45% for FP and 0% for FN were obtained. We discuss the advantages and disadvantages of both methods.

  8. Segmentation of vessels in retinal images based on directional height statistics.

    PubMed

    Lazar, Istvan; Hajdu, Andras

    2012-01-01

    In this paper we present a fast and simple, yet accurate method for the segmentation of retinal blood vessels. Many diseases of the eye result in the distortions of the vessels. The precise location of the major optic veins may be used for the localization of other anatomical parts, such as the macula and the optic disc. Also, many microaneurysm detection methods consider an additional vessel segmentation step. The proposed method realizes the recognition of vessels through considering cross-sections of the image at different orientations. Peaks on the profiles are localized and their heights are measured. This way, a set of height values are assigned to every pixel of the image. Simple statistics are calculated for every pixel, and combined to construct a vessel score map. We apply a simple thresholding procedure and postprocessing step to obtain a binary vessel mask. The method has been tested on the publicly available DRIVE database, and it proved to be competitive with the state-of-the-art.

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

    PubMed

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

    2015-01-01

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

  10. Hepatic vessel segmentation using variational level set combined with non-local robust statistics.

    PubMed

    Lu, Siyu; Huang, Hui; Liang, Ping; Chen, Gang; Xiao, Liang

    2017-02-01

    Hepatic vessel segmentation is a challenging step in therapy guided by magnetic resonance imaging (MRI). This paper presents an improved variational level set method, which uses non-local robust statistics to suppress the influence of noise in MR images. The non-local robust statistics, which represent vascular features, are learned adaptively from seeds provided by users. K-means clustering in neighborhoods of seeds is utilized to exclude inappropriate seeds, which are obviously corrupted by noise. The neighborhoods of appropriate seeds are placed in an array to calculate the non-local robust statistics, and the variational level set formulation can be constructed. Bias correction is utilized in the level set formulation to reduce the influence of intensity inhomogeneity of MRI. Experiments were conducted over real MR images, and showed that the proposed method performed better on small hepatic vessel segmentation compared with other segmentation methods.

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

    PubMed

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

    2014-03-01

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

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

    PubMed

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

    2016-10-05

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

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

    PubMed

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

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  15. 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.

  16. Segmentation of retinal vessels by means of directional response vector similarity and region growing.

    PubMed

    Lázár, István; Hajdu, András

    2015-11-01

    This paper presents a novel retinal vessel segmentation method. Opposed to the general approach in similar directional methods, where only the maximal or summed responses of a pixel are used, here, the directional responses of a pixel are considered as a vector. The segmentation method is a unique region growing procedure which combines a hysteresis thresholding scheme with the response vector similarity of adjacent pixels. A vessel score map is constructed as the combination of the statistical measures of the response vectors and its local maxima to provide the seeds for the region growing procedure. A nearest neighbor classifier based on a rotation invariant response vector similarity measure is used to filter the seed points. Many techniques in the literature that capture the Gaussian-like cross-section of vessels suffer from the drawback of giving false high responses to the steep intensity transitions at the boundary of the optic disc and bright lesions. To overcome this issue, we also propose a symmetry constrained multiscale matched filtering technique. The proposed vessel segmentation method has been tested on three publicly available image sets, where its performance proved to be competitive with the state-of-the-art and comparable to the accuracy of a human observer, as well.

  17. Retinal vessel segmentation in colour fundus images using Extreme Learning Machine.

    PubMed

    Zhu, Chengzhang; Zou, Beiji; Zhao, Rongchang; Cui, Jinkai; Duan, Xuanchu; Chen, Zailiang; Liang, Yixiong

    2017-01-01

    Attributes of the retinal vessel play important role in systemic conditions and ophthalmic diagnosis. In this paper, a supervised method based on Extreme Learning Machine (ELM) is proposed to segment retinal vessel. Firstly, a set of 39-D discriminative feature vectors, consisting of local features, morphological features, phase congruency, Hessian and divergence of vector fields, is extracted for each pixel of the fundus image. Then a matrix is constructed for pixel of the training set based on the feature vector and the manual labels, and acts as the input of the ELM classifier. The output of classifier is the binary retinal vascular segmentation. Finally, an optimization processing is implemented to remove the region less than 30 pixels which is isolated from the retinal vascilar. The experimental results testing on the public Digital Retinal Images for Vessel Extraction (DRIVE) database demonstrate that the proposed method is much faster than the other methods in segmenting the retinal vessels. Meanwhile the average accuracy, sensitivity, and specificity are 0.9607, 0.7140 and 0.9868, respectively. Moreover the proposed method exhibits high speed and robustness on a new Retinal Images for Screening (RIS) database. Therefore it has potential applications for real-time computer-aided diagnosis and disease screening.

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

    PubMed

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

    2014-01-01

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

  19. Detecting and Analyzing Corrosion Spots on the Hull of Large Marine Vessels Using Colored 3d LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Aijazi, A. K.; Malaterre, L.; Tazir, M. L.; Trassoudaine, L.; Checchin, P.

    2016-06-01

    This work presents a new method that automatically detects and analyzes surface defects such as corrosion spots of different shapes and sizes, on large ship hulls. In the proposed method several scans from different positions and viewing angles around the ship are registered together to form a complete 3D point cloud. The R, G, B values associated with each scan, obtained with the help of an integrated camera are converted into HSV space to separate out the illumination invariant color component from the intensity. Using this color component, different surface defects such as corrosion spots of different shapes and sizes are automatically detected, within a selected zone, using two different methods depending upon the level of corrosion/defects. The first method relies on a histogram based distribution whereas the second on adaptive thresholds. The detected corrosion spots are then analyzed and quantified to help better plan and estimate the cost of repair and maintenance. Results are evaluated on real data using different standard evaluation metrics to demonstrate the efficacy as well as the technical strength of the proposed method.

  20. 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.

  1. 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

  2. Device for Investigation of Mechanical Tension of Isolated Smooth Muscle Vessels and Airway Segments of Animals

    NASA Astrophysics Data System (ADS)

    Aleinik, A.; Karpovich, N.; Turgunova, N.; Nosarev, A.

    2016-11-01

    For the purpose of testing and the search for new drug compounds, designed to heal many human diseases, it is necessary to investigate the deformation of experimental tissue samples under influence of these drugs. For this task a precision force sensor for measuring the mechanical tension, produced by isolated ring segments of blood vessels and airways was created. The hardware and software systems for the study of changes in contractile responses of the airway smooth muscles and blood vessels of experimental animals was developed.

  3. An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection.

    PubMed

    Saleh, Marwan D; Eswaran, C; Mueen, Ahmed

    2011-08-01

    This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

  4. 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.

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

    SciTech Connect

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

    2015-06-15

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  7. 3D morphological measurement of whole slide histological vasculature reconstructions

    NASA Astrophysics Data System (ADS)

    Xu, Yiwen; Pickering, J. G.; Nong, Zengxuan; Ward, Aaron D.

    2016-03-01

    Properties of the microvasculature that contribute to tissue perfusion can be assessed using immunohistochemistry on 2D histology sections. However, the vasculature is inherently 3D and the ability to measure and visualize the vessel wall components in 3D will aid in detecting focal pathologies. Our objectives were (1) to develop a method for 3D measurement and visualization of microvasculature in 3D, (2) to compare the normal and regenerated post-ischemia mouse hind limb microvasculature, and (3) to compare the 2D and 3D vessel morphology measures. Vessels were stained for smooth muscle using 3,3'-Diaminobenzidine (DAB) immunostain for both normal (n = 6 mice) and regenerated vasculature (n = 5 mice). 2D vessel segmentations were reconstructed into 3D using landmark based registration. No substantial bias was found in the 2D measurements relative to 3D, but larger differences were observed for individual vessels oriented non-orthogonally to the plane of sectioning. A larger value of area, perimeter, and vessel wall thickness was found in the normal vasculature as compared to the regenerated vasculature, for both the 2D and 3D measurements (p < 0.01). Aggregated 2D measurements are sufficient for identifying morphological differences between groups of mice; however, one must interpret individual 2D measurements with caution if the vessel centerline direction is unknown. Visualization of 3D measurements permits the detection of localized vessel morphology aberrations that are not revealed by 2D measurements. With vascular measure visualization methodologies in 3D, we are now capable of locating focal pathologies on a whole slide level.

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

    In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively. PMID:26599505

  10. Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy.

    PubMed

    Jelinek, Herbert F; Cree, Michael J; Leandro, Jorge J G; Soares, João V B; Cesar, Roberto M; Luckie, A

    2007-05-01

    Proliferative diabetic retinopathy can lead to blindness. However, early recognition allows appropriate, timely intervention. Fluorescein-labeled retinal blood vessels of 27 digital images were automatically segmented using the Gabor wavelet transform and classified using traditional features such as area, perimeter, and an additional five morphological features based on the derivatives-of-Gaussian wavelet-derived data. Discriminant analysis indicated that traditional features do not detect early proliferative retinopathy. The best single feature for discrimination was the wavelet curvature with an area under the curve (AUC) of 0.76. Linear discriminant analysis with a selection of six features achieved an AUC of 0.90 (0.73-0.97, 95% confidence interval). The wavelet method was able to segment retinal blood vessels and classify the images according to the presence or absence of proliferative retinopathy.

  11. 3D multi-object segmentation of cardiac MSCT imaging by using a multi-agent approach.

    PubMed

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

    2007-01-01

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

  12. 3D Multi-Object Segmentation of Cardiac MSCT Imaging by using a Multi-Agent Approach

    PubMed Central

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

    2007-01-01

    We propose a new technique for general purpose, semi-interactive and multi-object segmentation in N-dimensional images, applied to the extraction of cardiac structures in MultiSlice Computed Tomography (MSCT) imaging. The proposed approach makes use of a multi-agent scheme combined with a supervised classification methodology allowing the introduction of a priori information and presenting fast computing times. The multi-agent system is organised around a communicating agent which manages a population of situated agents which segment the image through cooperative and competitive interactions. The proposed technique has been tested on several patient data sets. Some typical results are finally presented and discussed. PMID:18003382

  13. Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy.

    PubMed

    Salem, Sameh A; Salem, Nancy M; Nandi, Asoke K

    2007-03-01

    In this paper, segmentation of blood vessels from colour retinal images using a novel clustering algorithm with a partial supervision strategy is proposed. The proposed clustering algorithm, which is a RAdius based Clustering ALgorithm (RACAL), uses a distance based principle to map the distributions of the data by utilising the premise that clusters are determined by a distance parameter, without having to specify the number of clusters. Additionally, the proposed clustering algorithm is enhanced with a partial supervision strategy and it is demonstrated that it is able to segment blood vessels of small diameters and low contrasts. Results are compared with those from the KNN classifier and show that the proposed RACAL performs better than the KNN in case of abnormal images as it succeeds in segmenting small and low contrast blood vessels, while it achieves comparable results for normal images. For automation process, RACAL can be used as a classifier and results show that it performs better than the KNN classifier in both normal and abnormal images.

  14. Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography.

    PubMed

    Lahiri, A; Roy, Abhijit Guha; Sheet, Debdoot; Biswas, Prabir Kumar

    2016-08-01

    Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member autoencoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708. Comparison with other major algorithms substantiates the high efficacy of our model.

  15. Segmentation of vessel structures in serial whole slide sections using region-based context features

    NASA Astrophysics Data System (ADS)

    Schwier, Michael; Hahn, Horst K.; Dahmen, Uta; Dirsch, Olaf

    2016-03-01

    We present a method for the automatic segmentation of vascular structures in stacks of serial sections. It was initially motivated within the Virtual Liver Network research project that aims at creating a multi-scale virtual model of the liver. For this the vascular systems of several murine livers under different conditions need to be analyzed. To get highly detailed datasets, stacks of serial sections of the whole organs are prepared. Due to the huge amount of image data an automatic approach for segmenting the vessels is required. After registering the slides with an established method we use a set of Random Forest classifiers to distinguish vessels from tissue. Instead of a pixel-wise approach we perform the classification on small regions. This allows us to use more meaningful features. Besides basic intensity and texture features we introduce the concept of context features, which allow the classifiers to also consider the neighborhood of a region. Classification is performed in two stages. In the second stage the previous classification result of a region and its neighbors is used to refine the decision for a particular region. The context features and two stage classification process make our method very successful. It can handle different stainings and also detect vessels in which residue like blood cells remained. The specificity reaches 95%-99% for pure tissue, depending on staining and zoom level. Only in the direct vicinity of vessels the specificity declines to 88%-96%. The sensitivity rates reach between 89% and 98%.

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

    PubMed

    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.

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

    PubMed

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

    2008-08-01

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

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

    PubMed

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

    2008-11-01

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

  19. 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

  20. Phantom-based ground-truth generation for cerebral vessel segmentation and pulsatile deformation analysis

    NASA Astrophysics Data System (ADS)

    Schetelig, Daniel; Säring, Dennis; Illies, Till; Sedlacik, Jan; Kording, Fabian; Werner, René

    2016-03-01

    Hemodynamic and mechanical factors of the vascular system are assumed to play a major role in understanding, e.g., initiation, growth and rupture of cerebral aneurysms. Among those factors, cardiac cycle-related pulsatile motion and deformation of cerebral vessels currently attract much interest. However, imaging of those effects requires high spatial and temporal resolution and remains challenging { and similarly does the analysis of the acquired images: Flow velocity changes and contrast media inflow cause vessel intensity variations in related temporally resolved computed tomography and magnetic resonance angiography data over the cardiac cycle and impede application of intensity threshold-based segmentation and subsequent motion analysis. In this work, a flow phantom for generation of ground-truth images for evaluation of appropriate segmentation and motion analysis algorithms is developed. The acquired ground-truth data is used to illustrate the interplay between intensity fluctuations and (erroneous) motion quantification by standard threshold-based segmentation, and an adaptive threshold-based segmentation approach is proposed that alleviates respective issues. The results of the phantom study are further demonstrated to be transferable to patient data.

  1. Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis.

    PubMed

    Kumar, Rahul Prasanna; Albregtsen, Fritz; Reimers, Martin; Edwin, Bjørn; Langø, Thomas; Elle, Ole Jakob

    2015-05-01

    The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. In this paper we present a novel, semi-automatic method for blood vessel segmentation and centerline extraction, by tracking the blood vessel tree from a user-initiated seed point to the ends of the blood vessel tree. The novelty of our method is in performing only two-dimensional cross-section analysis for segmentation of the connected blood vessels. The cross-section analysis is done by our novel single-scale or multi-scale circle enhancement filter, used at the blood vessel trunk or bifurcation, respectively. The method was validated for both synthetic and medical images. Our validation has shown that the cross-sectional centerline error for our method is below 0.8 pixels and the Dice coefficient for our segmentation is 80% ± 2.7%. On combining our method with an optional active contour post-processing, the Dice coefficient for the resulting segmentation is found to be 94% ± 2.4%. Furthermore, by restricting the image analysis to the regions of interest and converting most of the three-dimensional calculations to two-dimensional calculations, the processing was found to be more than 18 times faster than Frangi vesselness with thinning, 8 times faster than user-initiated active contour segmentation with thinning and 7 times faster than our previous method.

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

    PubMed

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

    2008-04-01

    An automatic method for delineating the prostate (including the seminal vesicles) in three-dimensional magnetic resonance scans is presented. The method is based on nonrigid registration of a set of prelabeled atlas images. Each atlas image is nonrigidly registered with the target patient image. Subsequently, the deformed atlas label images are fused to yield a single segmentation of the patient image. The proposed method is evaluated on 50 clinical scans, which were manually segmented by three experts. The Dice similarity coefficient (DSC) is used to quantify the overlap between the automatic and manual segmentations. We investigate the impact of several factors on the performance of the segmentation method. For the registration, two similarity measures are compared: Mutual information and a localized version of mutual information. The latter turns out to be superior (median DeltaDSC approximately equal 0.02, p < 0.01 with a paired two-sided Wilcoxon test) and comes at no added computational cost, thanks to the use of a novel stochastic optimization scheme. For the atlas fusion step we consider a majority voting rule and the "simultaneous truth and performance level estimation" algorithm, both with and without a preceding atlas selection stage. The differences between the various fusion methods appear to be small and mostly not statistically significant (p > 0.05). To assess the influence of the atlas composition, two atlas sets are compared. The first set consists of 38 scans of healthy volunteers. The second set is constructed by a leave-one-out approach using the 50 clinical scans that are used for evaluation. The second atlas set gives substantially better performance (DeltaDSC=0.04, p < 0.01), stressing the importance of a careful atlas definition. With the best settings, a median DSC of around 0.85 is achieved, which is close to the median interobserver DSC of 0.87. The segmentation quality is especially good at the prostate-rectum interface, where the

  3. An enhanced segmentation of blood vessels in retinal images using contourlet.

    PubMed

    Rezatofighi, S H; Roodaki, A; Ahmadi Noubari, H

    2008-01-01

    Retinal images acquired using a fundus camera often contain low grey, low level contrast and are of low dynamic range. This may seriously affect the automatic segmentation stage and subsequent results; hence, it is necessary to carry-out preprocessing to improve image contrast results before segmentation. Here we present a new multi-scale method for retinal image contrast enhancement using Contourlet transform. In this paper, a combination of feature extraction approach which utilizes Local Binary Pattern (LBP), morphological method and spatial image processing is proposed for segmenting the retinal blood vessels in optic fundus images. Furthermore, performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) is investigated in the classification section. The performance of the proposed algorithm is tested on the publicly available DRIVE database. The results are numerically assessed for different proposed algorithms.

  4. Fast vessel segmentation in retinal images using multi-scale enhancement and second-order local entropy

    NASA Astrophysics Data System (ADS)

    Yu, H.; Barriga, S.; Agurto, C.; Zamora, G.; Bauman, W.; Soliz, P.

    2012-03-01

    Retinal vasculature is one of the most important anatomical structures in digital retinal photographs. Accurate segmentation of retinal blood vessels is an essential task in automated analysis of retinopathy. This paper presents a new and effective vessel segmentation algorithm that features computational simplicity and fast implementation. This method uses morphological pre-processing to decrease the disturbance of bright structures and lesions before vessel extraction. Next, a vessel probability map is generated by computing the eigenvalues of the second derivatives of Gaussian filtered image at multiple scales. Then, the second order local entropy thresholding is applied to segment the vessel map. Lastly, a rule-based decision step, which measures the geometric shape difference between vessels and lesions is applied to reduce false positives. The algorithm is evaluated on the low-resolution DRIVE and STARE databases and the publicly available high-resolution image database from Friedrich-Alexander University Erlangen-Nuremberg, Germany). The proposed method achieved comparable performance to state of the art unsupervised vessel segmentation methods with a competitive faster speed on the DRIVE and STARE databases. For the high resolution fundus image database, the proposed algorithm outperforms an existing approach both on performance and speed. The efficiency and robustness make the blood vessel segmentation method described here suitable for broad application in automated analysis of retinal images.

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

    NASA Astrophysics Data System (ADS)

    Kim, Hyeonjin; Kim, Hannah; Hong, Helen

    2015-03-01

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

  6. 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.

  7. 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.

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

    DTIC Science & Technology

    2015-01-05

    Cao, Q. Li, Q. Mao, and S. Wang. Cracktree: Automatic crack detection from pavement images . Pattern Recognition Letters, 33:227–238, 2012. 17 Personnel...Y. Cao, Q. Li, Q. Mao, S. Wang. CrackTree: Automatic Crack Detection from Pavement Images , Pattern Recognition Letters, 33(3):227-238, 2012 20 34. F...Superalloy Image Segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 17-24, Colorado Springs, CO, 2011 Dissertations: Four

  9. Retinal hyperaemia-related blood vessel artifacts are relevant to automated OCT layer segmentation.

    PubMed

    Balk, L J; Mayer, M; Uitdehaag, B M J; Petzold, A

    2014-03-01

    A frequently observed local measurement artifact with spectral domain OCT is caused by the void signal of the retinal vasculature. This study investigated the effect of suppression of blood vessel artifacts with and without retinal hyperaemia. Spectral domain OCT scans, centred on the optic nerve head, were performed in 46 healthy subjects (92 eyes). Baseline scans were made during rest, while for the follow-up scan, 23 subjects (50 %) performed strenuous physical exercise. Systemic and retinal hyperaemia were quantified. Quantification of retinal nerve fibre layer (RNFL) thickness was performed with and without suppression of retinal blood vessel artifacts. The potential systematic effect on RNFL thickness measurements was analysed using Bland-Altman plots. At baseline (no retinal hyperaemia), there was a systematic difference in RNFL thickness (3.4 μm, limits of agreement -0.9 to 7.7) with higher values if blood vessel artifacts were not suppressed. There was significant retinal hyperaemia in the exercise group (p < 0.0001). Baseline thickness increased from 93.18 to 93.83 μm (p < 0.05) in the exercise group using the algorithm with blood vessel artifact suppression, but no significant changes were observed using the algorithm without blood vessel artifact suppression. Retinal hyperaemia leads to blood vessel artifacts which are relevant to the precision of OCT layer segmentation algorithms. The two algorithms investigated in this study can not be used interchangeably. The algorithm with blood vessel artifact suppression was more sensitive in detecting small changes in RNFL thickness. This may be relevant for the use of OCT in a range of neurodegenerative diseases were only a small degree of retinal layer atrophy have been found so far.

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

    DOE Data Explorer

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

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

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

    SciTech Connect

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

    2015-07-15

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

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

    PubMed

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

    2013-01-01

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

  13. Automated detection of pulmonary embolism (PE) in computed tomographic pulmonary angiographic (CTPA) images: multiscale hierachical expectation-maximization segmentation of vessels and PEs

    NASA Astrophysics Data System (ADS)

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

    2007-03-01

    CT pulmonary angiography (CTPA) has been reported to be an effective means for clinical diagnosis of pulmonary embolism (PE). We are developing a computer-aided detection (CAD) system to assist radiologist in PE detection in CTPA images. 3D multiscale filters in combination with a newly designed response function derived from the eigenvalues of Hessian matrices is used to enhance vascular structures including the vessel bifurcations and suppress non-vessel structures such as the lymphoid tissues surrounding the vessels. A hierarchical EM estimation is then used to segment the vessels by extracting the high response voxels at each scale. The segmented vessels are pre-screened for suspicious PE areas using a second adaptive multiscale EM estimation. A rule-based false positive (FP) reduction method was designed to identify the true PEs based on the features of PE and vessels. 43 CTPA scans were used as an independent test set to evaluate the performance of PE detection. Experienced chest radiologists identified the PE locations which were used as "gold standard". 435 PEs were identified in the artery branches, of which 172 and 263 were subsegmental and proximal to the subsegmental, respectively. The computer-detected volume was considered true positive (TP) when it overlapped with 10% or more of the gold standard PE volume. Our preliminary test results show that, at an average of 33 and 24 FPs/case, the sensitivities of our PE detection method were 81% and 78%, respectively, for proximal PEs, and 79% and 73%, respectively, for subsegmental PEs. The study demonstrates the feasibility that the automated method can identify PE accurately on CTPA images. Further study is underway to improve the sensitivity and reduce the FPs.

  14. 3D dento-maxillary osteolytic lesion and active contour segmentation pilot study in CBCT: semi-automatic vs manual methods

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2011-04-01

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

  16. Protocol for volumetric segmentation of medial temporal structures using high-resolution 3-D magnetic resonance imaging.

    PubMed

    Bonilha, Leonardo; Kobayashi, Eliane; Cendes, Fernando; Min Li, Li

    2004-06-01

    Quantitative analysis of brain structures in normal subjects and in different neurological conditions can be carried out in vivo through magnetic resonance imaging (MRI) volumetric studies. The use of high-resolution MRI combined with image post-processing that allows simultaneous multiplanar view may facilitate volumetric segmentation of temporal lobe structures. We define a protocol for volumetric studies of medial temporal lobe structures using high-resolution MR images and we studied 30 healthy subjects (19 women; mean age, 33 years; age range, 21-55 years). Images underwent field non-homogeneity correction and linear stereotaxic transformation into a standard space. Structures of interest comprised temporopolar, entorhinal, perirhinal, parahippocampal cortices, hippocampus, and the amygdala. Segmentation was carried out with multiplanar assessment. There was no statistically significant left/right-sided asymmetry concerning any structure analyzed. Neither gender nor age influenced the volumes obtained. The coefficient of repeatability showed no significant difference of intra- and interobserver measurements. Imaging post-processing and simultaneous multiplanar view of high-resolution MRI facilitates volumetric assessment of the medial portion of the temporal lobe with strict adherence to anatomic landmarks. This protocol shows no significant inter- and intraobserver variations and thus is reliable for longitudinal studies.

  17. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2001-07-01

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

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

    PubMed

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

    2016-01-01

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

  20. An ensemble classification-based approach applied to retinal blood vessel segmentation.

    PubMed

    Fraz, Muhammad Moazam; Remagnino, Paolo; Hoppe, Andreas; Uyyanonvara, Bunyarit; Rudnicka, Alicja R; Owen, Christopher G; Barman, Sarah A

    2012-09-01

    This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.

  1. Computational study of pulsatile blood flow in prototype vessel geometries of coronary segments

    PubMed Central

    Chaniotis, A.K.; Kaiktsis, L.; Katritsis, D.; Efstathopoulos, E.; Pantos, I.; Marmarellis, V.

    2010-01-01

    The spatial and temporal distributions of wall shear stress (WSS) in prototype vessel geometries of coronary segments are investigated via numerical simulation, and the potential association with vascular disease and specifically atherosclerosis and plaque rupture is discussed. In particular, simulation results of WSS spatio-temporal distributions are presented for pulsatile, non-Newtonian blood flow conditions for: (a) curved pipes with different curvatures, and (b) bifurcating pipes with different branching angles and flow division. The effects of non-Newtonian flow on WSS (compared to Newtonian flow) are found to be small at Reynolds numbers representative of blood flow in coronary arteries. Specific preferential sites of average low WSS (and likely atherogenesis) were found at the outer regions of the bifurcating branches just after the bifurcation, and at the outer-entry and inner-exit flow regions of the curved vessel segment. The drop in WSS was more dramatic at the bifurcating vessel sites (less than 5% of the pre-bifurcation value). These sites were also near rapid gradients of WSS changes in space and time – a fact that increases the risk of rupture of plaque likely to develop at these sites. The time variation of the WSS spatial distributions was very rapid around the start and end of the systolic phase of the cardiac cycle, when strong fluctuations of intravascular pressure were also observed. These rapid and strong changes of WSS and pressure coincide temporally with the greatest flexion and mechanical stresses induced in the vessel wall by myocardial motion (ventricular contraction). The combination of these factors may increase the risk of plaque rupture and thrombus formation at these sites. PMID:20400349

  2. A quantum mechanics-based algorithm for vessel segmentation in retinal images

    NASA Astrophysics Data System (ADS)

    Youssry, Akram; El-Rafei, Ahmed; Elramly, Salwa

    2016-06-01

    Blood vessel segmentation is an important step in retinal image analysis. It is one of the steps required for computer-aided detection of ophthalmic diseases. In this paper, a novel quantum mechanics-based algorithm for retinal vessel segmentation is presented. The algorithm consists of three major steps. The first step is the preprocessing of the images to prepare the images for further processing. The second step is feature extraction where a set of four features is generated at each image pixel. These features are then combined using a nonlinear transformation for dimensionality reduction. The final step is applying a recently proposed quantum mechanics-based framework for image processing. In this step, pixels are mapped to quantum systems that are allowed to evolve from an initial state to a final state governed by Schrödinger's equation. The evolution is controlled by the Hamiltonian operator which is a function of the extracted features at each pixel. A measurement step is consequently performed to determine whether the pixel belongs to vessel or non-vessel classes. Many functional forms of the Hamiltonian are proposed, and the best performing form was selected. The algorithm is tested on the publicly available DRIVE database. The average results for sensitivity, specificity, and accuracy are 80.29, 97.34, and 95.83 %, respectively. These results are compared to some recently published techniques showing the superior performance of the proposed method. Finally, the implementation of the algorithm on a quantum computer and the challenges facing this implementation are introduced.

  3. Segmentation of arterial vessel wall motion to sub-pixel resolution using M-mode ultrasound.

    PubMed

    Fancourt, Craig; Azer, Karim; Ramcharan, Sharmilee L; Bunzel, Michelle; Cambell, Barry R; Sachs, Jeffrey R; Walker, Matthew

    2008-01-01

    We describe a method for segmenting arterial vessel wall motion to sub-pixel resolution, using the returns from M-mode ultrasound. The technique involves measuring the spatial offset between all pairs of scans from their cross-correlation, converting the spatial offsets to relative wall motion through a global optimization, and finally translating from relative to absolute wall motion by interpolation over the M-mode image. The resulting detailed wall distension waveform has the potential to enhance existing vascular biomarkers, such as strain and compliance, as well as enable new ones.

  4. 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

  5. Blood vessel-based liver segmentation through the portal phase of a CT dataset

    NASA Astrophysics Data System (ADS)

    Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Moriyama, Noriyuki; Utsunomiya, Toru; Shimada, Mitsuo

    2013-02-01

    Blood vessels are dispersed throughout the human body organs and carry unique information for each person. This information can be used to delineate organ boundaries. The proposed method relies on abdominal blood vessels (ABV) to segment the liver considering the potential presence of tumors through the portal phase of a CT dataset. ABV are extracted and classified into hepatic (HBV) and nonhepatic (non-HBV) with a small number of interactions. HBV and non-HBV are used to guide an automatic segmentation of the liver. HBV are used to individually segment the core region of the liver. This region and non-HBV are used to construct a boundary surface between the liver and other organs to separate them. The core region is classified based on extracted posterior distributions of its histogram into low intensity tumor (LIT) and non-LIT core regions. Non-LIT case includes normal part of liver, HBV, and high intensity tumors if exist. Each core region is extended based on its corresponding posterior distribution. Extension is completed when it reaches either a variation in intensity or the constructed boundary surface. The method was applied to 80 datasets (30 Medical Image Computing and Computer Assisted Intervention (MICCAI) and 50 non-MICCAI data) including 60 datasets with tumors. Our results for the MICCAI-test data were evaluated by sliver07 [1] with an overall score of 79.7, which ranks seventh best on the site (December 2013). This approach seems a promising method for extraction of liver volumetry of various shapes and sizes and low intensity hepatic tumors.

  6. A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images.

    PubMed

    Christodoulidis, Argyrios; Hurtut, Thomas; Tahar, Houssem Ben; Cheriet, Farida

    2016-09-01

    Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05).

  7. New High-Resolution 3D Imagery of Fault Deformation and Segmentation of the San Onofre and San Mateo Trends in the Inner California Borderlands

    NASA Astrophysics Data System (ADS)

    Holmes, J. J.; Driscoll, N. W.; Kent, G. M.; Bormann, J. M.; Harding, A. J.

    2015-12-01

    The Inner California Borderlands (ICB) is situated off the coast of southern California and northern Baja. The structural and geomorphic characteristics of the area record a middle Oligocene transition from subduction to microplate capture along the California coast. Marine stratigraphic evidence shows large-scale extension and rotation overprinted by modern strike-slip deformation. Geodetic and geologic observations indicate that approximately 6-8 mm/yr of Pacific-North American relative plate motion is accommodated by offshore strike-slip faulting in the ICB. The farthest inshore fault system, the Newport-Inglewood Rose Canyon (NIRC) fault complex is a dextral strike-slip system that extends primarily offshore approximately 120 km from San Diego to the San Joaquin Hills near Newport Beach, California. Based on trenching and well data, the NIRC fault system Holocene slip rate is 1.5-2.0 mm/yr to the south and 0.5-1.0 mm/yr along its northern extent. An earthquake rupturing the entire length of the system could produce an Mw 7.0 earthquake or larger. West of the main segments of the NIRC fault complex are the San Mateo and San Onofre fault trends along the continental slope. Previous work concluded that these were part of a strike-slip system that eventually merged with the NIRC complex. Others have interpreted these trends as deformation associated with the Oceanside Blind Thrust fault purported to underlie most of the region. In late 2013, we acquired the first high-resolution 3D P-Cable seismic surveys (3.125 m bin resolution) of the San Mateo and San Onofre trends as part of the Southern California Regional Fault Mapping project aboard the R/V New Horizon. Analysis of these volumes provides important new insights and constraints on the fault segmentation and transfer of deformation. Based on the new 3D sparker seismic data, our preferred interpretation for the San Mateo and San Onofre fault trends is they are transpressional features associated with westward

  8. A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

    PubMed Central

    BahadarKhan, Khan; A Khaliq, Amir; Shahid, Muhammad

    2016-01-01

    Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts. PMID:27441646

  9. A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding.

    PubMed

    BahadarKhan, Khan; A Khaliq, Amir; Shahid, Muhammad

    2016-01-01

    Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts.

  10. 3D-segmentation of the 18F-choline PET signal for target volume definition in radiation therapy of the prostate.

    PubMed

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

    2007-02-01

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

  11. Retinal image analysis aimed at blood vessel tree segmentation and early detection of neural-layer deterioration.

    PubMed

    Jan, J; Odstrcilik, J; Gazarek, J; Kolar, R

    2012-09-01

    An automatic method of segmenting the retinal vessel tree and estimating status of retinal neural fibre layer (NFL) from high resolution fundus camera images is presented. First, reliable blood vessel segmentation, using 2D directional matched filtering, enables to remove areas occluded by blood vessels thus leaving remaining retinal area available to the following NFL detection. The local existence of rather faint and hardly visible NFL is detected by combining several newly designed local textural features, sensitive to subtle NFL characteristics, into feature vectors submitted to a trained neural-network classifier. Obtained binary retinal maps of NFL distribution show a good agreement with both medical expert evaluations and quantitative results obtained by optical coherence tomography.

  12. Segmentation of the heart and great vessels in CT images using a model-based adaptation framework.

    PubMed

    Ecabert, Olivier; Peters, Jochen; Walker, Matthew J; Ivanc, Thomas; Lorenz, Cristian; von Berg, Jens; Lessick, Jonathan; Vembar, Mani; Weese, Jürgen

    2011-12-01

    Recently, model-based methods for the automatic segmentation of the heart chambers have been proposed. An important application of these methods is the characterization of the heart function. Heart models are, however, increasingly used for interventional guidance making it necessary to also extract the attached great vessels. It is, for instance, important to extract the left atrium and the proximal part of the pulmonary veins to support guidance of ablation procedures for atrial fibrillation treatment. For cardiac resynchronization therapy, a heart model including the coronary sinus is needed. We present a heart model comprising the four heart chambers and the attached great vessels. By assigning individual linear transformations to the heart chambers and to short tubular segments building the great vessels, variable sizes of the heart chambers and bending of the vessels can be described in a consistent way. A configurable algorithmic framework that we call adaptation engine matches the heart model automatically to cardiac CT angiography images in a multi-stage process. First, the heart is detected using a Generalized Hough Transformation. Subsequently, the heart chambers are adapted. This stage uses parametric as well as deformable mesh adaptation techniques. In the final stage, segments of the large vascular structures are successively activated and adapted. To optimize the computational performance, the adaptation engine can vary the mesh resolution and freeze already adapted mesh parts. The data used for validation were independent from the data used for model-building. Ground truth segmentations were generated for 37 CT data sets reconstructed at several cardiac phases from 17 patients. Segmentation errors were assessed for anatomical sub-structures resulting in a mean surface-to-surface error ranging 0.50-0.82mm for the heart chambers and 0.60-1.32mm for the parts of the great vessels visible in the images.

  13. Assessment of diffuse coronary artery disease by quantitative analysis of coronary morphology based upon 3-D reconstruction from biplane angiograms

    SciTech Connect

    Wahel, A.; Wellnhofer, E.; Mugaragu, I.; Sauer, H.U.; Oswald, H.; Fleck, E. |

    1995-06-01

    Quantitative evaluations on coronary vessel systems are of increasing importance in cardiovascular diagnosis, therapy planning, and surgical verification. Whereas local evaluations, such as stenosis analysis, are already available with sufficient accuracy, global evaluations of vessel segments or vessel subsystems are not yet common. Especially for the diagnosis of diffuse coronary artery diseases, the authors combined a 3-D reconstruction system operating on biplane angiograms with a length/volume calculation. The 3-D reconstruction results in a 3-D model of the coronary vessel system, consisting of the vessel skeleton and a discrete number of contours. To obtain an utmost accurate model, the authors focused on exact geometry determination. Several algorithms for calculating missing geometric parameters and correcting remaining geometry errors were implemented and verified. The length/volume evaluation can be performed either on single vessel segments, on a set of segments, or on subtrees. A volume model based on generalized elliptical conic sections is created for the selected segments. Volumes and lengths (measured along the vessel course) of those elements are summed up. In this way, the morphological parameters of a vessel subsystem can be set in relation to the parameters of the proximal segment supplying it. These relations allow objective assessments of diffuse coronary artery diseases.

  14. Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver

    PubMed Central

    Marcan, Marija; Pavliha, Denis; Music, Maja Marolt; Fuckan, Igor; Magjarevic, Ratko; Miklavcic, Damijan

    2014-01-01

    Introduction. Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input. Materials and methods. We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data. Results Results show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm. Conclusions The proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation-based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically. Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field. PMID:25177241

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  16. A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry.

    PubMed

    Tsou, Chi-Hsuan; Lu, Yi-Chien; Yuan, Ang; Chang, Yeun-Chung; Chen, Chung-Ming

    2015-01-01

    The blood vessel density in a cancerous tissue sample may represent increased levels of tumor growth. However, identifying blood vessels in the histological (tissue) image is difficult and time-consuming and depends heavily on the observer's experience. To overcome this drawback, computer-aided image analysis frameworks have been investigated in order to boost object identification in histological images. We present a novel algorithm to automatically abstract the salient regions in blood vessel images. Experimental results show that the proposed framework is capable of deriving vessel boundaries that are comparable to those demarcated manually, even for vessel regions with weak contrast between the object boundaries and background clutter.

  17. Cardiac 3D Printing and its Future Directions.

    PubMed

    Vukicevic, Marija; Mosadegh, Bobak; Min, James K; Little, Stephen H

    2017-02-01

    Three-dimensional (3D) printing is at the crossroads of printer and materials engineering, noninvasive diagnostic imaging, computer-aided design, and structural heart intervention. Cardiovascular applications of this technology development include the use of patient-specific 3D models for medical teaching, exploration of valve and vessel function, surgical and catheter-based procedural planning, and early work in designing and refining the latest innovations in percutaneous structural devices. In this review, we discuss the methods and materials being used for 3D printing today. We discuss the basic principles of clinical image segmentation, including coregistration of multiple imaging datasets to create an anatomic model of interest. With applications in congenital heart disease, coronary artery disease, and surgical and catheter-based structural disease, 3D printing is a new tool that is challenging how we image, plan, and carry out cardiovascular interventions.

  18. Evaluation of an improved technique for lumen path definition and lumen segmentation of atherosclerotic vessels in CT angiography.

    PubMed

    van Velsen, Evert F S; Niessen, Wiro J; de Weert, Thomas T; de Monyé, Cécile; van der Lugt, Aad; Meijering, Erik; Stokking, Rik

    2007-07-01

    Vessel image analysis is crucial when considering therapeutical options for (cardio-) vascular diseases. Our method, VAMPIRE (Vascular Analysis using Multiscale Paths Inferred from Ridges and Edges), involves two parts: a user defines a start- and endpoint upon which a lumen path is automatically defined, and which is used for initialization; the automatic segmentation of the vessel lumen on computed tomographic angiography (CTA) images. Both parts are based on the detection of vessel-like structures by analyzing intensity, edge, and ridge information. A multi-observer evaluation study was performed to compare VAMPIRE with a conventional method on the CTA data of 15 patients with carotid artery stenosis. In addition to the start- and endpoint, the two radiologists required on average 2.5 (SD: 1.9) additional points to define a lumen path when using the conventional method, and 0.1 (SD: 0.3) when using VAMPIRE. The segmentation results were quantitatively evaluated using Similarity Indices, which were slightly lower between VAMPIRE and the two radiologists (respectively 0.90 and 0.88) compared with the Similarity Index between the radiologists (0.92). The evaluation shows that the improved definition of a lumen path requires minimal user interaction, and that using this path as initialization leads to good automatic lumen segmentation results.

  19. A fast 3D region growing approach for CT angiography applications

    NASA Astrophysics Data System (ADS)

    Ye, Zhen; Lin, Zhongmin; Lu, Cheng-chang

    2004-05-01

    Region growing is one of the most popular methods for low-level image segmentation. Many researches on region growing have focused on the definition of the homogeneity criterion or growing and merging criterion. However, one disadvantage of conventional region growing is redundancy. It requires a large memory usage, and the computation-efficiency is very low especially for 3D images. To overcome this problem, a non-recursive single-pass 3D region growing algorithm named SymRG is implemented and successfully applied to 3D CT angiography (CTA) applications for vessel segmentation and bone removal. The method consists of three steps: segmenting one-dimensional regions of each row; doing region merging to adjacent rows to obtain the region segmentation of each slice; and doing region merging to adjacent slices to obtain the final region segmentation of 3D images. To improve the segmentation speed for very large volume 3D CTA images, this algorithm is applied repeatedly to newly updated local cubes. The next new cube can be estimated by checking isolated segmented regions on all 6 faces of the current local cube. This local non-recursive 3D region-growing algorithm is memory-efficient and computation-efficient. Clinical testings of this algorithm on Brain CTA show this technique could effectively remove whole skull, most of the bones on the skull base, and reveal the cerebral vascular structures clearly.

  20. The impact of including spatially longitudinal heterogeneities of vessel oxygen content and vascular fraction in 3D tumor oxygenation models on predicted radiation sensitivity

    SciTech Connect

    Lagerlöf, Jakob H.; Kindblom, Jon; Bernhardt, Peter

    2014-04-15

    Purpose: Oxygen distribution models have been used to analyze the influences of oxygen tensions on tissue response after radiotherapy. These distributions are often generated assuming constant oxygen tension in the blood vessels. However, as red blood cells progress through the vessels, oxygen is continuously released into the plasma and the surrounding tissue, resulting in longitudinally varying oxygen levels in the blood vessels. In the present study, the authors investigated whether a tumor oxygenation model that incorporated longitudinally varying oxygen levels would provide different predictions of necrotic fractions and radiosensitivity compared to commonly used models with a constant oxygen pressure. Methods: Our models simulated oxygen diffusion based on a Green's function approach and oxygen consumption according to the Michaelis-Menten equation. The authors constructed tumor models with different vascular fractions (VFs), from which they generated depth oxygenation curves and a look-up table of oxygen pressure gradients. The authors evaluated models of spherical tumors of various sizes, from 1 to 10{sup 4} mg. The authors compared the results from a model with constant vessel oxygen (CVO) pressure to those from models with longitudinal variations in oxygen saturation and either a constant VF (CVF) or variable VF (VVF) within the tumor tissue. The authors monitored the necrotic fractions, defined as tumor regions with an oxygen pressure below 1 mmHg. Tumor radiation sensitivity was expressed as D{sub 99,} the homogeneous radiation dose required for a tumor control probability of 0.99. Results: In the CVO saturation model, no necrosis was observed, and decreasing the VF could only decrease the D{sub 99} by up to 10%. Furthermore, the D{sub 99} vs VF dependence was similar for different tumor masses. Compared to the CVO model, the extended CVF and VVF models provided clearly different results, including pronounced effects of VF and tumor size on the necrotic

  1. 3D Reconstruction of Coronary Artery Vascular Smooth Muscle Cells

    PubMed Central

    Luo, Tong; Chen, Huan; Kassab, Ghassan S.

    2016-01-01

    Aims The 3D geometry of individual vascular smooth muscle cells (VSMCs), which are essential for understanding the mechanical function of blood vessels, are currently not available. This paper introduces a new 3D segmentation algorithm to determine VSMC morphology and orientation. Methods and Results A total of 112 VSMCs from six porcine coronary arteries were used in the analysis. A 3D semi-automatic segmentation method was developed to reconstruct individual VSMCs from cell clumps as well as to extract the 3D geometry of VSMCs. A new edge blocking model was introduced to recognize cell boundary while an edge growing was developed for optimal interpolation and edge verification. The proposed methods were designed based on Region of Interest (ROI) selected by user and interactive responses of limited key edges. Enhanced cell boundary features were used to construct the cell’s initial boundary for further edge growing. A unified framework of morphological parameters (dimensions and orientations) was proposed for the 3D volume data. Virtual phantom was designed to validate the tilt angle measurements, while other parameters extracted from 3D segmentations were compared with manual measurements to assess the accuracy of the algorithm. The length, width and thickness of VSMCs were 62.9±14.9μm, 4.6±0.6μm and 6.2±1.8μm (mean±SD). In longitudinal-circumferential plane of blood vessel, VSMCs align off the circumferential direction with two mean angles of -19.4±9.3° and 10.9±4.7°, while an out-of-plane angle (i.e., radial tilt angle) was found to be 8±7.6° with median as 5.7°. Conclusions A 3D segmentation algorithm was developed to reconstruct individual VSMCs of blood vess