MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

International Nuclear Information System (INIS)

Chen, G; Pan, X; Stayman, J; Samei, E

2014-01-01

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within the reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical

[A Method to Reconstruct Surface Reflectance Spectrum from Multispectral Image Based on Canopy Radiation Transfer Model].

Science.gov (United States)

Zhao, Yong-guang; Ma, Ling-ling; Li, Chuan-rong; Zhu, Xiao-hua; Tang, Ling-li

2015-07-01

Due to the lack of enough spectral bands for multi-spectral sensor, it is difficult to reconstruct surface retlectance spectrum from finite spectral information acquired by multi-spectral instrument. Here, taking into full account of the heterogeneity of pixel from remote sensing image, a method is proposed to simulate hyperspectral data from multispectral data based on canopy radiation transfer model. This method first assumes the mixed pixels contain two types of land cover, i.e., vegetation and soil. The sensitive parameters of Soil-Leaf-Canopy (SLC) model and a soil ratio factor were retrieved from multi-spectral data based on Look-Up Table (LUT) technology. Then, by combined with a soil ratio factor, all the parameters were input into the SLC model to simulate the surface reflectance spectrum from 400 to 2 400 nm. Taking Landsat Enhanced Thematic Mapper Plus (ETM+) image as reference image, the surface reflectance spectrum was simulated. The simulated reflectance spectrum revealed different feature information of different surface types. To test the performance of this method, the simulated reflectance spectrum was convolved with the Landsat ETM + spectral response curves and Moderate Resolution Imaging Spectrometer (MODIS) spectral response curves to obtain the simulated Landsat ETM+ and MODIS image. Finally, the simulated Landsat ETM+ and MODIS images were compared with the observed Landsat ETM+ and MODIS images. The results generally showed high correction coefficients (Landsat: 0.90-0.99, MODIS: 0.74-0.85) between most simulated bands and observed bands and indicated that the simulated reflectance spectrum was well simulated and reliable.

Remote Sensing Image Enhancement Based on Non-subsampled Shearlet Transform and Parameterized Logarithmic Image Processing Model

Directory of Open Access Journals (Sweden)

TAO Feixiang

2015-08-01

Full Text Available Aiming at parts of remote sensing images with dark brightness and low contrast, a remote sensing image enhancement method based on non-subsampled Shearlet transform and parameterized logarithmic image processing model is proposed in this paper to improve the visual effects and interpretability of remote sensing images. Firstly, a remote sensing image is decomposed into a low-frequency component and high frequency components by non-subsampled Shearlet transform.Then the low frequency component is enhanced according to PLIP (parameterized logarithmic image processing model, which can improve the contrast of image, while the improved fuzzy enhancement method is used to enhance the high frequency components in order to highlight the information of edges and details. A large number of experimental results show that, compared with five kinds of image enhancement methods such as bidirectional histogram equalization method, the method based on stationary wavelet transform and the method based on non-subsampled contourlet transform, the proposed method has advantages in both subjective visual effects and objective quantitative evaluation indexes such as contrast and definition, which can more effectively improve the contrast of remote sensing image and enhance edges and texture details with better visual effects.

Dynamic Chest Image Analysis: Model-Based Perfusion Analysis in Dynamic Pulmonary Imaging

Directory of Open Access Journals (Sweden)

Kiuru Aaro

2003-01-01

Full Text Available The "Dynamic Chest Image Analysis" project aims to develop model-based computer analysis and visualization methods for showing focal and general abnormalities of lung ventilation and perfusion based on a sequence of digital chest fluoroscopy frames collected with the dynamic pulmonary imaging technique. We have proposed and evaluated a multiresolutional method with an explicit ventilation model for ventilation analysis. This paper presents a new model-based method for pulmonary perfusion analysis. According to perfusion properties, we first devise a novel mathematical function to form a perfusion model. A simple yet accurate approach is further introduced to extract cardiac systolic and diastolic phases from the heart, so that this cardiac information may be utilized to accelerate the perfusion analysis and improve its sensitivity in detecting pulmonary perfusion abnormalities. This makes perfusion analysis not only fast but also robust in computation; consequently, perfusion analysis becomes computationally feasible without using contrast media. Our clinical case studies with 52 patients show that this technique is effective for pulmonary embolism even without using contrast media, demonstrating consistent correlations with computed tomography (CT and nuclear medicine (NM studies. This fluoroscopical examination takes only about 2 seconds for perfusion study with only low radiation dose to patient, involving no preparation, no radioactive isotopes, and no contrast media.

Development and practice for a PACS-based interactive teaching model for CT image

International Nuclear Information System (INIS)

Tian Junzhang; Jiang Guihua; Zheng Liyin; Wang Ling; Wenhua; Liang Lianbao

2002-01-01

Objective: To explore the interactive teaching model for CT imaging based on PACS, and provide the clinician and young radiologist with continued medical education. Methods: 100 M trunk net was adopted in PACS and 10 M was exchanged on desktop. Teaching model was installed in browse and diagnosis workstation. Teaching contents were classified according to region and managed according to branch model. Text data derived from authoritative textbooks, monograph, and periodicals. Imaging data derived from cases proved by pathology and clinic. The data were obtained through digital camera and scanner or from PACS. After edited and transformed into standard digital image through DICOM server, they were saved in HD of PACS image server with file form. Results: Teaching model for CT imaging provided kinds of cases of CT sign, clinic characteristics, pathology and distinguishing diagnosis. Normal section anatomy, typical image, and its notation could be browsed real time. Teaching model for CT imaging could provide reference to teaching, diagnosis and report. Conclusion: PACS-based teaching model for CT imaging could provide interactive teaching and scientific research tool and improve work quality and efficiency

Models for Patch-Based Image Restoration

Directory of Open Access Journals (Sweden)

Petrovic Nemanja

2009-01-01

Full Text Available Abstract We present a supervised learning approach for object-category specific restoration, recognition, and segmentation of images which are blurred using an unknown kernel. The novelty of this work is a multilayer graphical model which unifies the low-level vision task of restoration and the high-level vision task of recognition in a cooperative framework. The graphical model is an interconnected two-layer Markov random field. The restoration layer accounts for the compatibility between sharp and blurred images and models the association between adjacent patches in the sharp image. The recognition layer encodes the entity class and its location in the underlying scene. The potentials are represented using nonparametric kernel densities and are learnt from training data. Inference is performed using nonparametric belief propagation. Experiments demonstrate the effectiveness of our model for the restoration and recognition of blurred license plates as well as face images.

Models for Patch-Based Image Restoration

Directory of Open Access Journals (Sweden)

Mithun Das Gupta

2009-01-01

Full Text Available We present a supervised learning approach for object-category specific restoration, recognition, and segmentation of images which are blurred using an unknown kernel. The novelty of this work is a multilayer graphical model which unifies the low-level vision task of restoration and the high-level vision task of recognition in a cooperative framework. The graphical model is an interconnected two-layer Markov random field. The restoration layer accounts for the compatibility between sharp and blurred images and models the association between adjacent patches in the sharp image. The recognition layer encodes the entity class and its location in the underlying scene. The potentials are represented using nonparametric kernel densities and are learnt from training data. Inference is performed using nonparametric belief propagation. Experiments demonstrate the effectiveness of our model for the restoration and recognition of blurred license plates as well as face images.

Model-based T{sub 2} relaxometry using undersampled magnetic resonance imaging

Energy Technology Data Exchange (ETDEWEB)

Sumpf, Tilman

2013-11-01

T{sub 2} relaxometry refers to the quantitative determination of spin-spin relaxation times in magnetic resonance imaging (MRI). Particularly in clinical diagnostics, the method provides important information about tissue structures and respective pathologic alterations. Unfortunately, it also requires comparatively long measurement times which preclude widespread practical applications. To overcome such limitations, a so-called model-based reconstruction concept has recently been proposed. The method allows for the estimation of spin-density and T{sub 2} parameter maps from only a fraction of the usually required data. So far, promising results have been reported for a radial data acquisition scheme. However, due to technical reasons, radial imaging is only available on a very limited number of MRI systems. The present work deals with the realization and evaluation of different model-based T{sub 2} reconstruction methods that are applicable for the most widely available Cartesian (rectilinear) acquisition scheme. The initial implementation is based on the conventional assumption of a mono-exponential T{sub 2} signal decay. A suitable sampling scheme as well as an automatic scaling procedure are developed, which remove the necessity of manual parameter tuning. As demonstrated for human brain MRI data, the technique allows for a more than 5-fold acceleration of the underlying data acquisition. Furthermore, general limitations and specific error sources are identified and suitable simulation programs are developed for their detailed analysis. In addition to phase variations in image space, the simulations reveal truncation effects as a relevant cause of reconstruction artifacts. To reduce the latter, an alternative model formulation is developed and tested. For noise-free simulated data, the method yields an almost complete suppression of associated artifacts. Residual problems in the reconstruction of experimental MRI data point to the predominant influence of other

Image Restoration Based on the Hybrid Total-Variation-Type Model

OpenAIRE

Shi, Baoli; Pang, Zhi-Feng; Yang, Yu-Fei

2012-01-01

We propose a hybrid total-variation-type model for the image restoration problem based on combining advantages of the ROF model with the LLT model. Since two ${L}^{1}$ -norm terms in the proposed model make it difficultly solved by using some classically numerical methods directly, we first employ the alternating direction method of multipliers (ADMM) to solve a general form of the proposed model. Then, based on the ADMM and the Moreau-Yosida decomposition theory, a more efficient method call...

Improved Denoising via Poisson Mixture Modeling of Image Sensor Noise.

Science.gov (United States)

Zhang, Jiachao; Hirakawa, Keigo

2017-04-01

This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. A quantile analysis in pixel, wavelet transform, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. A new Poisson mixture noise model is proposed to correct the mismatch of tail behavior. Based on the fact that noise model mismatch results in image denoising that undersmoothes real sensor data, we propose a mixture of Poisson denoising method to remove the denoising artifacts without affecting image details, such as edge and textures. Experiments with real sensor data verify that denoising for real image sensor data is indeed improved by this new technique.

Novel Polyurethane Matrix Systems Reveal a Particular Sustained Release Behavior Studied by Imaging and Computational Modeling.

Science.gov (United States)

Campiñez, María Dolores; Caraballo, Isidoro; Puchkov, Maxim; Kuentz, Martin

2017-07-01

The aim of the present work was to better understand the drug-release mechanism from sustained release matrices prepared with two new polyurethanes, using a novel in silico formulation tool based on 3-dimensional cellular automata. For this purpose, two polymers and theophylline as model drug were used to prepare binary matrix tablets. Each formulation was simulated in silico, and its release behavior was compared to the experimental drug release profiles. Furthermore, the polymer distributions in the tablets were imaged by scanning electron microscopy (SEM) and the changes produced by the tortuosity were quantified and verified using experimental data. The obtained results showed that the polymers exhibited a surprisingly high ability for controlling drug release at low excipient concentrations (only 10% w/w of excipient controlled the release of drug during almost 8 h). The mesoscopic in silico model helped to reveal how the novel biopolymers were controlling drug release. The mechanism was found to be a special geometrical arrangement of the excipient particles, creating an almost continuous barrier surrounding the drug in a very effective way, comparable to lipid or waxy excipients but with the advantages of a much higher compactability, stability, and absence of excipient polymorphism.

Metal artifact reduction algorithm based on model images and spatial information

Energy Technology Data Exchange (ETDEWEB)

Wu, Jay [Institute of Radiological Science, Central Taiwan University of Science and Technology, Taichung, Taiwan (China); Shih, Cheng-Ting [Department of Biomedical Engineering and Environmental Sciences, National Tsing-Hua University, Hsinchu, Taiwan (China); Chang, Shu-Jun [Health Physics Division, Institute of Nuclear Energy Research, Taoyuan, Taiwan (China); Huang, Tzung-Chi [Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan (China); Sun, Jing-Yi [Institute of Radiological Science, Central Taiwan University of Science and Technology, Taichung, Taiwan (China); Wu, Tung-Hsin, E-mail: tung@ym.edu.tw [Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, No.155, Sec. 2, Linong Street, Taipei 112, Taiwan (China)

2011-10-01

Computed tomography (CT) has become one of the most favorable choices for diagnosis of trauma. However, high-density metal implants can induce metal artifacts in CT images, compromising image quality. In this study, we proposed a model-based metal artifact reduction (MAR) algorithm. First, we built a model image using the k-means clustering technique with spatial information and calculated the difference between the original image and the model image. Then, the projection data of these two images were combined using an exponential weighting function. At last, the corrected image was reconstructed using the filter back-projection algorithm. Two metal-artifact contaminated images were studied. For the cylindrical water phantom image, the metal artifact was effectively removed. The mean CT number of water was improved from -28.95{+-}97.97 to -4.76{+-}4.28. For the clinical pelvic CT image, the dark band and the metal line were removed, and the continuity and uniformity of the soft tissue were recovered as well. These results indicate that the proposed MAR algorithm is useful for reducing metal artifact and could improve the diagnostic value of metal-artifact contaminated CT images.

Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description.

Science.gov (United States)

Gu, Ke; Jakhetiya, Vinit; Qiao, Jun-Fei; Li, Xiaoli; Lin, Weisi; Thalmann, Daniel

2017-07-28

New challenges have been brought out along with the emerging of 3D-related technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). Free viewpoint video (FVV), due to its applications in remote surveillance, remote education, etc, based on the flexible selection of direction and viewpoint, has been perceived as the development direction of next-generation video technologies and has drawn a wide range of researchers' attention. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a reliable real-time blind quality evaluation and monitoring system is urgently required. But existing assessment metrics do not render human judgments faithfully mainly because geometric distortions are generated by DIBR. To this end, this paper proposes a novel referenceless quality metric of DIBR-synthesized images using the autoregression (AR)-based local image description. It was found that, after the AR prediction, the reconstructed error between a DIBR-synthesized image and its AR-predicted image can accurately capture the geometry distortion. The visual saliency is then leveraged to modify the proposed blind quality metric to a sizable margin. Experiments validate the superiority of our no-reference quality method as compared with prevailing full-, reduced- and no-reference models.

Cardiac magnetic source imaging based on current multipole model

International Nuclear Information System (INIS)

Tang Fa-Kuan; Wang Qian; Hua Ning; Lu Hong; Tang Xue-Zheng; Ma Ping

2011-01-01

It is widely accepted that the heart current source can be reduced into a current multipole. By adopting three linear inverse methods, the cardiac magnetic imaging is achieved in this article based on the current multipole model expanded to the first order terms. This magnetic imaging is realized in a reconstruction plane in the centre of human heart, where the current dipole array is employed to represent realistic cardiac current distribution. The current multipole as testing source generates magnetic fields in the measuring plane, serving as inputs of cardiac magnetic inverse problem. In the heart-torso model constructed by boundary element method, the current multipole magnetic field distribution is compared with that in the homogeneous infinite space, and also with the single current dipole magnetic field distribution. Then the minimum-norm least-squares (MNLS) method, the optimal weighted pseudoinverse method (OWPIM), and the optimal constrained linear inverse method (OCLIM) are selected as the algorithms for inverse computation based on current multipole model innovatively, and the imaging effects of these three inverse methods are compared. Besides, two reconstructing parameters, residual and mean residual, are also discussed, and their trends under MNLS, OWPIM and OCLIM each as a function of SNR are obtained and compared. (general)

Model-based satellite image fusion

DEFF Research Database (Denmark)

Aanæs, Henrik; Sveinsson, J. R.; Nielsen, Allan Aasbjerg

2008-01-01

A method is proposed for pixel-level satellite image fusion derived directly from a model of the imaging sensor. By design, the proposed method is spectrally consistent. It is argued that the proposed method needs regularization, as is the case for any method for this problem. A framework for pixel...... neighborhood regularization is presented. This framework enables the formulation of the regularization in a way that corresponds well with our prior assumptions of the image data. The proposed method is validated and compared with other approaches on several data sets. Lastly, the intensity......-hue-saturation method is revisited in order to gain additional insight of what implications the spectral consistency has for an image fusion method....

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

Science.gov (United States)

Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

Polarimetric SAR image classification based on discriminative dictionary learning model

Science.gov (United States)

Sang, Cheng Wei; Sun, Hong

2018-03-01

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

Hubble Images Reveal Jupiter's Auroras

Science.gov (United States)

1996-01-01

These images, taken by the Hubble Space Telescope, reveal changes in Jupiter's auroral emissions and how small auroral spots just outside the emission rings are linked to the planet's volcanic moon, Io. The images represent the most sensitive and sharply-detailed views ever taken of Jovian auroras.The top panel pinpoints the effects of emissions from Io, which is about the size of Earth's moon. The black-and-white image on the left, taken in visible light, shows how Io and Jupiter are linked by an invisible electrical current of charged particles called a 'flux tube.' The particles - ejected from Io (the bright spot on Jupiter's right) by volcanic eruptions - flow along Jupiter's magnetic field lines, which thread through Io, to the planet's north and south magnetic poles. This image also shows the belts of clouds surrounding Jupiter as well as the Great Red Spot.The black-and-white image on the right, taken in ultraviolet light about 15 minutes later, shows Jupiter's auroral emissions at the north and south poles. Just outside these emissions are the auroral spots. Called 'footprints,' the spots are created when the particles in Io's 'flux tube' reach Jupiter's upper atmosphere and interact with hydrogen gas, making it fluoresce. In this image, Io is not observable because it is faint in the ultraviolet.The two ultraviolet images at the bottom of the picture show how the auroral emissions change in brightness and structure as Jupiter rotates. These false-color images also reveal how the magnetic field is offset from Jupiter's spin axis by 10 to 15 degrees. In the right image, the north auroral emission is rising over the left limb; the south auroral oval is beginning to set. The image on the left, obtained on a different date, shows a full view of the north aurora, with a strong emission inside the main auroral oval.The images were taken by the telescope's Wide Field and Planetary Camera 2 between May 1994 and September 1995.This image and other images and data

Quantum dot-based local field imaging reveals plasmon-based interferometric logic in silver nanowire networks.

Science.gov (United States)

Wei, Hong; Li, Zhipeng; Tian, Xiaorui; Wang, Zhuoxian; Cong, Fengzi; Liu, Ning; Zhang, Shunping; Nordlander, Peter; Halas, Naomi J; Xu, Hongxing

2011-02-09

We show that the local electric field distribution of propagating plasmons along silver nanowires can be imaged by coating the nanowires with a layer of quantum dots, held off the surface of the nanowire by a nanoscale dielectric spacer layer. In simple networks of silver nanowires with two optical inputs, control of the optical polarization and phase of the input fields directs the guided waves to a specific nanowire output. The QD-luminescent images of these structures reveal that a complete family of phase-dependent, interferometric logic functions can be performed on these simple networks. These results show the potential for plasmonic waveguides to support compact interferometric logic operations.

Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing

Directory of Open Access Journals (Sweden)

Jaewook Jung

2016-06-01

Full Text Available A city is a dynamic entity, which environment is continuously changing over time. Accordingly, its virtual city models also need to be regularly updated to support accurate model-based decisions for various applications, including urban planning, emergency response and autonomous navigation. A concept of continuous city modeling is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. A first critical step for continuous city modeling is to coherently register remotely sensed data taken at different epochs with existing building models. This paper presents a new model-to-image registration method using a context-based geometric hashing (CGH method to align a single image with existing 3D building models. This model-to-image registration process consists of three steps: (1 feature extraction; (2 similarity measure; and matching, and (3 estimating exterior orientation parameters (EOPs of a single image. For feature extraction, we propose two types of matching cues: edged corner features representing the saliency of building corner points with associated edges, and contextual relations among the edged corner features within an individual roof. A set of matched corners are found with given proximity measure through geometric hashing, and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on collinearity equations. The result shows that acceptable accuracy of EOPs of a single image can be achievable using the proposed registration approach as an alternative to a labor-intensive manual registration process.

Image-based modeling of flow and reactive transport in porous media

Science.gov (United States)

Qin, Chao-Zhong; Hoang, Tuong; Verhoosel, Clemens V.; Harald van Brummelen, E.; Wijshoff, Herman M. A.

2017-04-01

Due to the availability of powerful computational resources and high-resolution acquisition of material structures, image-based modeling has become an important tool in studying pore-scale flow and transport processes in porous media [Scheibe et al., 2015]. It is also playing an important role in the upscaling study for developing macroscale porous media models. Usually, the pore structure of a porous medium is directly discretized by the voxels obtained from visualization techniques (e.g. micro CT scanning), which can avoid the complex generation of computational mesh. However, this discretization may considerably overestimate the interfacial areas between solid walls and pore spaces. As a result, it could impact the numerical predictions of reactive transport and immiscible two-phase flow. In this work, two types of image-based models are used to study single-phase flow and reactive transport in a porous medium of sintered glass beads. One model is from a well-established voxel-based simulation tool. The other is based on the mixed isogeometric finite cell method [Hoang et al., 2016], which has been implemented in the open source Nutils (http://www.nutils.org). The finite cell method can be used in combination with isogeometric analysis to enable the higher-order discretization of problems on complex volumetric domains. A particularly interesting application of this immersed simulation technique is image-based analysis, where the geometry is smoothly approximated by segmentation of a B-spline level set approximation of scan data [Verhoosel et al., 2015]. Through a number of case studies by the two models, we will show the advantages and disadvantages of each model in modeling single-phase flow and reactive transport in porous media. Particularly, we will highlight the importance of preserving high-resolution interfaces between solid walls and pore spaces in image-based modeling of porous media. References Hoang, T., C. V. Verhoosel, F. Auricchio, E. H. van

Image analysis and modeling in medical image computing. Recent developments and advances.

Science.gov (United States)

Handels, H; Deserno, T M; Meinzer, H-P; Tolxdorff, T

2012-01-01

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body

Beam-hardening correction in CT based on basis image and TV model

International Nuclear Information System (INIS)

Li Qingliang; Yan Bin; Li Lei; Sun Hongsheng; Zhang Feng

2012-01-01

In X-ray computed tomography, the beam hardening leads to artifacts and reduces the image quality. It analyzes how beam hardening influences on original projection. According, it puts forward a kind of new beam-hardening correction method based on the basis images and TV model. Firstly, according to physical characteristics of the beam hardening an preliminary correction model with adjustable parameters is set up. Secondly, using different parameters, original projections are operated by the correction model. Thirdly, the projections are reconstructed to obtain a series of basis images. Finally, the linear combination of basis images is the final reconstruction image. Here, with total variation for the final reconstruction image as the cost function, the linear combination coefficients for the basis images are determined according to iterative method. To verify the effectiveness of the proposed method, the experiments are carried out on real phantom and industrial part. The results show that the algorithm significantly inhibits cup and strip artifacts in CT image. (authors)

Image-based modeling of tumor shrinkage in head and neck radiation therapy

International Nuclear Information System (INIS)

Chao Ming; Xie Yaoqin; Moros, Eduardo G.; Le, Quynh-Thu; Xing Lei

2010-01-01

Purpose: Understanding the kinetics of tumor growth/shrinkage represents a critical step in quantitative assessment of therapeutics and realization of adaptive radiation therapy. This article presents a novel framework for image-based modeling of tumor change and demonstrates its performance with synthetic images and clinical cases. Methods: Due to significant tumor tissue content changes, similarity-based models are not suitable for describing the process of tumor volume changes. Under the hypothesis that tissue features in a tumor volume or at the boundary region are partially preserved, the kinetic change was modeled in two steps: (1) Autodetection of homologous tissue features shared by two input images using the scale invariance feature transformation (SIFT) method; and (2) establishment of a voxel-to-voxel correspondence between the images for the remaining spatial points by interpolation. The correctness of the tissue feature correspondence was assured by a bidirectional association procedure, where SIFT features were mapped from template to target images and reversely. A series of digital phantom experiments and five head and neck clinical cases were used to assess the performance of the proposed technique. Results: The proposed technique can faithfully identify the known changes introduced when constructing the digital phantoms. The subsequent feature-guided thin plate spline calculation reproduced the ''ground truth'' with accuracy better than 1.5 mm. For the clinical cases, the new algorithm worked reliably for a volume change as large as 30%. Conclusions: An image-based tumor kinetic algorithm was developed to model the tumor response to radiation therapy. The technique provides a practical framework for future application in adaptive radiation therapy.

Modeling susceptibility difference artifacts produced by metallic implants in magnetic resonance imaging with point-based thin-plate spline image registration.

Science.gov (United States)

Pauchard, Y; Smith, M; Mintchev, M

2004-01-01

Magnetic resonance imaging (MRI) suffers from geometric distortions arising from various sources. One such source are the non-linearities associated with the presence of metallic implants, which can profoundly distort the obtained images. These non-linearities result in pixel shifts and intensity changes in the vicinity of the implant, often precluding any meaningful assessment of the entire image. This paper presents a method for correcting these distortions based on non-rigid image registration techniques. Two images from a modelled three-dimensional (3D) grid phantom were subjected to point-based thin-plate spline registration. The reference image (without distortions) was obtained from a grid model including a spherical implant, and the corresponding test image containing the distortions was obtained using previously reported technique for spatial modelling of magnetic susceptibility artifacts. After identifying the nonrecoverable area in the distorted image, the calculated spline model was able to quantitatively account for the distortions, thus facilitating their compensation. Upon the completion of the compensation procedure, the non-recoverable area was removed from the reference image and the latter was compared to the compensated image. Quantitative assessment of the goodness of the proposed compensation technique is presented.

Projection model for flame chemiluminescence tomography based on lens imaging

Science.gov (United States)

Wan, Minggang; Zhuang, Jihui

2018-04-01

For flame chemiluminescence tomography (FCT) based on lens imaging, the projection model is essential because it formulates the mathematical relation between the flame projections captured by cameras and the chemiluminescence field, and, through this relation, the field is reconstructed. This work proposed the blurry-spot (BS) model, which takes more universal assumptions and has higher accuracy than the widely applied line-of-sight model. By combining the geometrical camera model and the thin-lens equation, the BS model takes into account perspective effect of the camera lens; by combining ray-tracing technique and Monte Carlo simulation, it also considers inhomogeneous distribution of captured radiance on the image plane. Performance of these two models in FCT was numerically compared, and results showed that using the BS model could lead to better reconstruction quality in wider application ranges.

Residual stress distribution analysis of heat treated APS TBC using image based modelling.

Science.gov (United States)

Li, Chun; Zhang, Xun; Chen, Ying; Carr, James; Jacques, Simon; Behnsen, Julia; di Michiel, Marco; Xiao, Ping; Cernik, Robert

2017-08-01

We carried out a residual stress distribution analysis in a APS TBC throughout the depth of the coatings. The samples were heat treated at 1150 °C for 190 h and the data analysis used image based modelling based on the real 3D images measured by Computed Tomography (CT). The stress distribution in several 2D slices from the 3D model is included in this paper as well as the stress distribution along several paths shown on the slices. Our analysis can explain the occurrence of the "jump" features near the interface between the top coat and the bond coat. These features in the residual stress distribution trend were measured (as a function of depth) by high-energy synchrotron XRD (as shown in our related research article entitled 'Understanding the Residual Stress Distribution through the Thickness of Atmosphere Plasma Sprayed (APS) Thermal Barrier Coatings (TBCs) by high energy Synchrotron XRD; Digital Image Correlation (DIC) and Image Based Modelling') (Li et al., 2017) [1].

Structural brain abnormalities in women with subclinical depression, as revealed by voxel-based morphometry and diffusion tensor imaging.

Science.gov (United States)

Hayakawa, Yayoi K; Sasaki, Hiroki; Takao, Hidemasa; Mori, Harushi; Hayashi, Naoto; Kunimatsu, Akira; Aoki, Shigeki; Ohtomo, Kuni

2013-01-25

Brain structural changes accompany major depressive disorder, but whether subclinical depression is accompanied by similar changes in brain volume and white matter integrity is unknown. By using voxel-based morphometry (VBM) of the gray matter and tract-specific analysis based on diffusion tensor imaging (DTI) of the white matter, we explored the extent to which abnormalities could be identified in specific brain structures of healthy adults with subclinical depression. The subjects were 21 community-dwelling adults with subclinical depression, as measured by their Center for Epidemiologic Studies Depression Scale (CES-D) scores. They were not demented and had no neurological or psychiatric history. We collected brain magnetic resonance images of the patients and of 21 matched control subjects, and we used VBM to analyze the differences in regional gray matter volume between the two groups. Moreover, we examined the white matter integrity by using tract-specific analysis based on the gray matter volume changes revealed by VBM. VBM revealed that the volumes of both anterior cingulate gyri and the right rectal gyrus were smaller in subclinically depressed women than in control women. Calculation of DTI measures in the anterior cingulum bundle revealed a positive correlation between CES-D scale score and radial diffusivity in the right anterior cingulum in subclinically depressed women. The small sample size limits the stability of the reported findings. Gray matter volume reduction and white matter integrity change in specific frontal brain regions may be associated with depressive symptoms in women, even at a subclinical level. Copyright © 2012 Elsevier B.V. All rights reserved.

Generation of synthetic Kinect depth images based on empirical noise model

DEFF Research Database (Denmark)

Iversen, Thorbjørn Mosekjær; Kraft, Dirk

2017-01-01

The development, training and evaluation of computer vision algorithms rely on the availability of a large number of images. The acquisition of these images can be time-consuming if they are recorded using real sensors. An alternative is to rely on synthetic images which can be rapidly generated....... This Letter describes a novel method for the simulation of Kinect v1 depth images. The method is based on an existing empirical noise model from the literature. The authors show that their relatively simple method is able to provide depth images which have a high similarity with real depth images....

A multiscale MDCT image-based breathing lung model with time-varying regional ventilation

Science.gov (United States)

Yin, Youbing; Choi, Jiwoong; Hoffman, Eric A.; Tawhai, Merryn H.; Lin, Ching-Long

2012-01-01

A novel algorithm is presented that links local structural variables (regional ventilation and deforming central airways) to global function (total lung volume) in the lung over three imaged lung volumes, to derive a breathing lung model for computational fluid dynamics simulation. The algorithm constitutes the core of an integrative, image-based computational framework for subject-specific simulation of the breathing lung. For the first time, the algorithm is applied to three multi-detector row computed tomography (MDCT) volumetric lung images of the same individual. A key technique in linking global and local variables over multiple images is an in-house mass-preserving image registration method. Throughout breathing cycles, cubic interpolation is employed to ensure C1 continuity in constructing time-varying regional ventilation at the whole lung level, flow rate fractions exiting the terminal airways, and airway deformation. The imaged exit airway flow rate fractions are derived from regional ventilation with the aid of a three-dimensional (3D) and one-dimensional (1D) coupled airway tree that connects the airways to the alveolar tissue. An in-house parallel large-eddy simulation (LES) technique is adopted to capture turbulent-transitional-laminar flows in both normal and deep breathing conditions. The results obtained by the proposed algorithm when using three lung volume images are compared with those using only one or two volume images. The three-volume-based lung model produces physiologically-consistent time-varying pressure and ventilation distribution. The one-volume-based lung model under-predicts pressure drop and yields un-physiological lobar ventilation. The two-volume-based model can account for airway deformation and non-uniform regional ventilation to some extent, but does not capture the non-linear features of the lung. PMID:23794749

A multiscale MDCT image-based breathing lung model with time-varying regional ventilation

Energy Technology Data Exchange (ETDEWEB)

Yin, Youbing, E-mail: youbing-yin@uiowa.edu [Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States); IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Department of Radiology, The University of Iowa, Iowa City, IA 52242 (United States); Choi, Jiwoong, E-mail: jiwoong-choi@uiowa.edu [Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States); IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Hoffman, Eric A., E-mail: eric-hoffman@uiowa.edu [Department of Radiology, The University of Iowa, Iowa City, IA 52242 (United States); Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Department of Internal Medicine, The University of Iowa, Iowa City, IA 52242 (United States); Tawhai, Merryn H., E-mail: m.tawhai@auckland.ac.nz [Auckland Bioengineering Institute, The University of Auckland, Auckland (New Zealand); Lin, Ching-Long, E-mail: ching-long-lin@uiowa.edu [Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States); IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 (United States)

2013-07-01

A novel algorithm is presented that links local structural variables (regional ventilation and deforming central airways) to global function (total lung volume) in the lung over three imaged lung volumes, to derive a breathing lung model for computational fluid dynamics simulation. The algorithm constitutes the core of an integrative, image-based computational framework for subject-specific simulation of the breathing lung. For the first time, the algorithm is applied to three multi-detector row computed tomography (MDCT) volumetric lung images of the same individual. A key technique in linking global and local variables over multiple images is an in-house mass-preserving image registration method. Throughout breathing cycles, cubic interpolation is employed to ensure C{sub 1} continuity in constructing time-varying regional ventilation at the whole lung level, flow rate fractions exiting the terminal airways, and airway deformation. The imaged exit airway flow rate fractions are derived from regional ventilation with the aid of a three-dimensional (3D) and one-dimensional (1D) coupled airway tree that connects the airways to the alveolar tissue. An in-house parallel large-eddy simulation (LES) technique is adopted to capture turbulent-transitional-laminar flows in both normal and deep breathing conditions. The results obtained by the proposed algorithm when using three lung volume images are compared with those using only one or two volume images. The three-volume-based lung model produces physiologically-consistent time-varying pressure and ventilation distribution. The one-volume-based lung model under-predicts pressure drop and yields un-physiological lobar ventilation. The two-volume-based model can account for airway deformation and non-uniform regional ventilation to some extent, but does not capture the non-linear features of the lung.

MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING

Directory of Open Access Journals (Sweden)

J. Jung

2016-06-01

Full Text Available In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs of a single image. This model-to-image matching process consists of three steps: 1 feature extraction, 2 similarity measure and matching, and 3 adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labour-intensive manual registration process.

Image-based Modeling of PSF Deformation with Application to Limited Angle PET Data

Science.gov (United States)

Matej, Samuel; Li, Yusheng; Panetta, Joseph; Karp, Joel S.; Surti, Suleman

2016-01-01

The point-spread-functions (PSFs) of reconstructed images can be deformed due to detector effects such as resolution blurring and parallax error, data acquisition geometry such as insufficient sampling or limited angular coverage in dual-panel PET systems, or reconstruction imperfections/simplifications. PSF deformation decreases quantitative accuracy and its spatial variation lowers consistency of lesion uptake measurement across the imaging field-of-view (FOV). This can be a significant problem with dual panel PET systems even when using TOF data and image reconstruction models of the detector and data acquisition process. To correct for the spatially variant reconstructed PSF distortions we propose to use an image-based resolution model (IRM) that includes such image PSF deformation effects. Originally the IRM was mostly used for approximating data resolution effects of standard PET systems with full angular coverage in a computationally efficient way, but recently it was also used to mitigate effects of simplified geometric projectors. Our work goes beyond this by including into the IRM reconstruction imperfections caused by combination of the limited angle, parallax errors, and any other (residual) deformation effects and testing it for challenging dual panel data with strongly asymmetric and variable PSF deformations. We applied and tested these concepts using simulated data based on our design for a dedicated breast imaging geometry (B-PET) consisting of dual-panel, time-of-flight (TOF) detectors. We compared two image-based resolution models; i) a simple spatially invariant approximation to PSF deformation, which captures only the general PSF shape through an elongated 3D Gaussian function, and ii) a spatially variant model using a Gaussian mixture model (GMM) to more accurately capture the asymmetric PSF shape in images reconstructed from data acquired with the B-PET scanner geometry. Results demonstrate that while both IRMs decrease the overall uptake

Image-based modeling of tumor shrinkage in head and neck radiation therapy

Energy Technology Data Exchange (ETDEWEB)

Chao Ming; Xie Yaoqin; Moros, Eduardo G.; Le, Quynh-Thu; Xing Lei [Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305-5847 and Department of Radiation Oncology, University of Arkansas for Medical Sciences, 4301 W. Markham Street, Little Rock, Arkansas 72205-1799 (United States); Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305-5847 (United States); Department of Radiation Oncology, University of Arkansas for Medical Sciences, 4301 W. Markham Street, Little Rock, Arkansas 72205-1799 (United States); Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305-5847 (United States)

2010-05-15

Purpose: Understanding the kinetics of tumor growth/shrinkage represents a critical step in quantitative assessment of therapeutics and realization of adaptive radiation therapy. This article presents a novel framework for image-based modeling of tumor change and demonstrates its performance with synthetic images and clinical cases. Methods: Due to significant tumor tissue content changes, similarity-based models are not suitable for describing the process of tumor volume changes. Under the hypothesis that tissue features in a tumor volume or at the boundary region are partially preserved, the kinetic change was modeled in two steps: (1) Autodetection of homologous tissue features shared by two input images using the scale invariance feature transformation (SIFT) method; and (2) establishment of a voxel-to-voxel correspondence between the images for the remaining spatial points by interpolation. The correctness of the tissue feature correspondence was assured by a bidirectional association procedure, where SIFT features were mapped from template to target images and reversely. A series of digital phantom experiments and five head and neck clinical cases were used to assess the performance of the proposed technique. Results: The proposed technique can faithfully identify the known changes introduced when constructing the digital phantoms. The subsequent feature-guided thin plate spline calculation reproduced the ''ground truth'' with accuracy better than 1.5 mm. For the clinical cases, the new algorithm worked reliably for a volume change as large as 30%. Conclusions: An image-based tumor kinetic algorithm was developed to model the tumor response to radiation therapy. The technique provides a practical framework for future application in adaptive radiation therapy.

Efficient fully 3D list-mode TOF PET image reconstruction using a factorized system matrix with an image domain resolution model

International Nuclear Information System (INIS)

Zhou, Jian; Qi, Jinyi

2014-01-01

A factorized system matrix utilizing an image domain resolution model is attractive in fully 3D time-of-flight PET image reconstruction using list-mode data. In this paper, we study a factored model based on sparse matrix factorization that is comprised primarily of a simplified geometrical projection matrix and an image blurring matrix. Beside the commonly-used Siddon’s ray-tracer, we propose another more simplified geometrical projector based on the Bresenham’s ray-tracer which further reduces the computational cost. We discuss in general how to obtain an image blurring matrix associated with a geometrical projector, and provide theoretical analysis that can be used to inspect the efficiency in model factorization. In simulation studies, we investigate the performance of the proposed sparse factorization model in terms of spatial resolution, noise properties and computational cost. The quantitative results reveal that the factorization model can be as efficient as a non-factored model, while its computational cost can be much lower. In addition we conduct Monte Carlo simulations to identify the conditions under which the image resolution model can become more efficient in terms of image contrast recovery. We verify our observations using the provided theoretical analysis. The result offers a general guide to achieve the optimal reconstruction performance based on a sparse factorization model with an image domain resolution model. (paper)

Fog Density Estimation and Image Defogging Based on Surrogate Modeling for Optical Depth.

Science.gov (United States)

Jiang, Yutong; Sun, Changming; Zhao, Yu; Yang, Li

2017-05-03

In order to estimate fog density correctly and to remove fog from foggy images appropriately, a surrogate model for optical depth is presented in this paper. We comprehensively investigate various fog-relevant features and propose a novel feature based on the hue, saturation, and value color space which correlate well with the perception of fog density. We use a surrogate-based method to learn a refined polynomial regression model for optical depth with informative fog-relevant features such as dark-channel, saturation-value, and chroma which are selected on the basis of sensitivity analysis. Based on the obtained accurate surrogate model for optical depth, an effective method for fog density estimation and image defogging is proposed. The effectiveness of our proposed method is verified quantitatively and qualitatively by the experimental results on both synthetic and real-world foggy images.

Edge detection of solid motor' CT image based on gravitation model

International Nuclear Information System (INIS)

Yu Guanghui; Lu Hongyi; Zhu Min; Liu Xudong; Hou Zhiqiang

2012-01-01

In order to detect the edge of solid motor' CT image much better, a new edge detection operator base on gravitation model was put forward. The edge of CT image is got by the new operator. The superiority turned out by comparing the edge got by ordinary operator. The comparison among operators with different size shows that higher quality CT images need smaller size operator while the lower need the larger. (authors)

Image Restoration Based on the Hybrid Total-Variation-Type Model

Directory of Open Access Journals (Sweden)

Baoli Shi

2012-01-01

Full Text Available We propose a hybrid total-variation-type model for the image restoration problem based on combining advantages of the ROF model with the LLT model. Since two L1-norm terms in the proposed model make it difficultly solved by using some classically numerical methods directly, we first employ the alternating direction method of multipliers (ADMM to solve a general form of the proposed model. Then, based on the ADMM and the Moreau-Yosida decomposition theory, a more efficient method called the proximal point method (PPM is proposed and the convergence of the proposed method is proved. Some numerical results demonstrate the viability and efficiency of the proposed model and methods.

Model-based VQ for image data archival, retrieval and distribution

Science.gov (United States)

Manohar, Mareboyana; Tilton, James C.

1995-01-01

An ideal image compression technique for image data archival, retrieval and distribution would be one with the asymmetrical computational requirements of Vector Quantization (VQ), but without the complications arising from VQ codebooks. Codebook generation and maintenance are stumbling blocks which have limited the use of VQ as a practical image compression algorithm. Model-based VQ (MVQ), a variant of VQ described here, has the computational properties of VQ but does not require explicit codebooks. The codebooks are internally generated using mean removed error and Human Visual System (HVS) models. The error model assumed is the Laplacian distribution with mean, lambda-computed from a sample of the input image. A Laplacian distribution with mean, lambda, is generated with uniform random number generator. These random numbers are grouped into vectors. These vectors are further conditioned to make them perceptually meaningful by filtering the DCT coefficients from each vector. The DCT coefficients are filtered by multiplying by a weight matrix that is found to be optimal for human perception. The inverse DCT is performed to produce the conditioned vectors for the codebook. The only image dependent parameter used in the generation of codebook is the mean, lambda, that is included in the coded file to repeat the codebook generation process for decoding.

A generalized logarithmic image processing model based on the gigavision sensor model.

Science.gov (United States)

Deng, Guang

2012-03-01

The logarithmic image processing (LIP) model is a mathematical theory providing generalized linear operations for image processing. The gigavision sensor (GVS) is a new imaging device that can be described by a statistical model. In this paper, by studying these two seemingly unrelated models, we develop a generalized LIP (GLIP) model. With the LIP model being its special case, the GLIP model not only provides new insights into the LIP model but also defines new image representations and operations for solving general image processing problems that are not necessarily related to the GVS. A new parametric LIP model is also developed. To illustrate the application of the new scalar multiplication operation, we propose an energy-preserving algorithm for tone mapping, which is a necessary step in image dehazing. By comparing with results using two state-of-the-art algorithms, we show that the new scalar multiplication operation is an effective tool for tone mapping.

Moving object detection using dynamic motion modelling from UAV aerial images.

Science.gov (United States)

Saif, A F M Saifuddin; Prabuwono, Anton Satria; Mahayuddin, Zainal Rasyid

2014-01-01

Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

Generalized image contrast enhancement technique based on the Heinemann contrast discrimination model

Science.gov (United States)

Liu, Hong; Nodine, Calvin F.

1996-07-01

This paper presents a generalized image contrast enhancement technique, which equalizes the perceived brightness distribution based on the Heinemann contrast discrimination model. It is based on the mathematically proven existence of a unique solution to a nonlinear equation, and is formulated with easily tunable parameters. The model uses a two-step log-log representation of luminance contrast between targets and surround in a luminous background setting. The algorithm consists of two nonlinear gray scale mapping functions that have seven parameters, two of which are adjustable Heinemann constants. Another parameter is the background gray level. The remaining four parameters are nonlinear functions of the gray-level distribution of the given image, and can be uniquely determined once the previous three are set. Tests have been carried out to demonstrate the effectiveness of the algorithm for increasing the overall contrast of radiology images. The traditional histogram equalization can be reinterpreted as an image enhancement technique based on the knowledge of human contrast perception. In fact, it is a special case of the proposed algorithm.

Image-Based 3D Face Modeling System

Directory of Open Access Journals (Sweden)

Vladimir Vezhnevets

2005-08-01

Full Text Available This paper describes an automatic system for 3D face modeling using frontal and profile images taken by an ordinary digital camera. The system consists of four subsystems including frontal feature detection, profile feature detection, shape deformation, and texture generation modules. The frontal and profile feature detection modules automatically extract the facial parts such as the eye, nose, mouth, and ear. The shape deformation module utilizes the detected features to deform the generic head mesh model such that the deformed model coincides with the detected features. A texture is created by combining the facial textures augmented from the input images and the synthesized texture and mapped onto the deformed generic head model. This paper provides a practical system for 3D face modeling, which is highly automated by aggregating, customizing, and optimizing a bunch of individual computer vision algorithms. The experimental results show a highly automated process of modeling, which is sufficiently robust to various imaging conditions. The whole model creation including all the optional manual corrections takes only 2∼3 minutes.

Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

Science.gov (United States)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

Image-based quantification and mathematical modeling of spatial heterogeneity in ESC colonies.

Science.gov (United States)

Herberg, Maria; Zerjatke, Thomas; de Back, Walter; Glauche, Ingmar; Roeder, Ingo

2015-06-01

Pluripotent embryonic stem cells (ESCs) have the potential to differentiate into cells of all three germ layers. This unique property has been extensively studied on the intracellular, transcriptional level. However, ESCs typically form clusters of cells with distinct size and shape, and establish spatial structures that are vital for the maintenance of pluripotency. Even though it is recognized that the cells' arrangement and local interactions play a role in fate decision processes, the relations between transcriptional and spatial patterns have not yet been studied. We present a systems biology approach which combines live-cell imaging, quantitative image analysis, and multiscale, mathematical modeling of ESC growth. In particular, we develop quantitative measures of the morphology and of the spatial clustering of ESCs with different expression levels and apply them to images of both in vitro and in silico cultures. Using the same measures, we are able to compare model scenarios with different assumptions on cell-cell adhesions and intercellular feedback mechanisms directly with experimental data. Applying our methodology to microscopy images of cultured ESCs, we demonstrate that the emerging colonies are highly variable regarding both morphological and spatial fluorescence patterns. Moreover, we can show that most ESC colonies contain only one cluster of cells with high self-renewing capacity. These cells are preferentially located in the interior of a colony structure. The integrated approach combining image analysis with mathematical modeling allows us to reveal potential transcription factor related cellular and intercellular mechanisms behind the emergence of observed patterns that cannot be derived from images directly. © 2015 International Society for Advancement of Cytometry.

Photometric Modeling of Simulated Surace-Resolved Bennu Images

Science.gov (United States)

Golish, D.; DellaGiustina, D. N.; Clark, B.; Li, J. Y.; Zou, X. D.; Bennett, C. A.; Lauretta, D. S.

2017-12-01

The Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer (OSIRIS-REx) is a NASA mission to study and return a sample of asteroid (101955) Bennu. Imaging data from the mission will be used to develop empirical surface-resolved photometric models of Bennu at a series of wavelengths. These models will be used to photometrically correct panchromatic and color base maps of Bennu, compensating for variations due to shadows and photometric angle differences, thereby minimizing seams in mosaicked images. Well-corrected mosaics are critical to the generation of a global hazard map and a global 1064-nm reflectance map which predicts LIDAR response. These data products directly feed into the selection of a site from which to safely acquire a sample. We also require photometric correction for the creation of color ratio maps of Bennu. Color ratios maps provide insight into the composition and geological history of the surface and allow for comparison to other Solar System small bodies. In advance of OSIRIS-REx's arrival at Bennu, we use simulated images to judge the efficacy of both the photometric modeling software and the mission observation plan. Our simulation software is based on USGS's Integrated Software for Imagers and Spectrometers (ISIS) and uses a synthetic shape model, a camera model, and an empirical photometric model to generate simulated images. This approach gives us the flexibility to create simulated images of Bennu based on analog surfaces from other small Solar System bodies and to test our modeling software under those conditions. Our photometric modeling software fits image data to several conventional empirical photometric models and produces the best fit model parameters. The process is largely automated, which is crucial to the efficient production of data products during proximity operations. The software also produces several metrics on the quality of the observations themselves, such as surface coverage and the

On Signal Modeling of Moon-Based Synthetic Aperture Radar (SAR Imaging of Earth

Directory of Open Access Journals (Sweden)

Zhen Xu

2018-03-01

Full Text Available The Moon-based Synthetic Aperture Radar (Moon-Based SAR, using the Moon as a platform, has a great potential to offer global-scale coverage of the earth’s surface with a high revisit cycle and is able to meet the scientific requirements for climate change study. However, operating in the lunar orbit, Moon-Based SAR imaging is confined within a complex geometry of the Moon-Based SAR, Moon, and Earth, where both rotation and revolution have effects. The extremely long exposure time of Moon-Based SAR presents a curved moving trajectory and the protracted time-delay in propagation makes the “stop-and-go” assumption no longer valid. Consequently, the conventional SAR imaging technique is no longer valid for Moon-Based SAR. This paper develops a Moon-Based SAR theory in which a signal model is derived. The Doppler parameters in the context of lunar revolution with the removal of ‘stop-and-go’ assumption are first estimated, and then characteristics of Moon-Based SAR imaging’s azimuthal resolution are analyzed. In addition, a signal model of Moon-Based SAR and its two-dimensional (2-D spectrum are further derived. Numerical simulation using point targets validates the signal model and enables Doppler parameter estimation for image focusing.

Live cell CRISPR-imaging in plants reveals dynamic telomere movements

KAUST Repository

Dreissig, Steven

2017-05-16

Elucidating the spatio-temporal organization of the genome inside the nucleus is imperative to understand the regulation of genes and non-coding sequences during development and environmental changes. Emerging techniques of chromatin imaging promise to bridge the long-standing gap between sequencing studies which reveal genomic information and imaging studies that provide spatial and temporal information of defined genomic regions. Here, we demonstrate such an imaging technique based on two orthologues of the bacterial CRISPR-Cas9 system. By fusing eGFP/mRuby2 to the catalytically inactive version of Streptococcus pyogenes and Staphylococcus aureus Cas9, we show robust visualization of telomere repeats in live leaf cells of Nicotiana benthamiana. By tracking the dynamics of telomeres visualized by CRISPR-dCas9, we reveal dynamic telomere movements of up to 2 μm within 30 minutes during interphase. Furthermore, we show that CRISPR-dCas9 can be combined with fluorescence-labelled proteins to visualize DNA-protein interactions in vivo. By simultaneously using two dCas9 orthologues, we pave the way for imaging of multiple genomic loci in live plants cells. CRISPR-imaging bears the potential to significantly improve our understanding of the dynamics of chromosomes in live plant cells.

Model-Based Photoacoustic Image Reconstruction using Compressed Sensing and Smoothed L0 Norm

OpenAIRE

Mozaffarzadeh, Moein; Mahloojifar, Ali; Nasiriavanaki, Mohammadreza; Orooji, Mahdi

2018-01-01

Photoacoustic imaging (PAI) is a novel medical imaging modality that uses the advantages of the spatial resolution of ultrasound imaging and the high contrast of pure optical imaging. Analytical algorithms are usually employed to reconstruct the photoacoustic (PA) images as a result of their simple implementation. However, they provide a low accurate image. Model-based (MB) algorithms are used to improve the image quality and accuracy while a large number of transducers and data acquisition a...

Model-based estimation of breast percent density in raw and processed full-field digital mammography images from image-acquisition physics and patient-image characteristics

Science.gov (United States)

Keller, Brad M.; Nathan, Diane L.; Conant, Emily F.; Kontos, Despina

2012-03-01

Breast percent density (PD%), as measured mammographically, is one of the strongest known risk factors for breast cancer. While the majority of studies to date have focused on PD% assessment from digitized film mammograms, digital mammography (DM) is becoming increasingly common, and allows for direct PD% assessment at the time of imaging. This work investigates the accuracy of a generalized linear model-based (GLM) estimation of PD% from raw and postprocessed digital mammograms, utilizing image acquisition physics, patient characteristics and gray-level intensity features of the specific image. The model is trained in a leave-one-woman-out fashion on a series of 81 cases for which bilateral, mediolateral-oblique DM images were available in both raw and post-processed format. Baseline continuous and categorical density estimates were provided by a trained breast-imaging radiologist. Regression analysis is performed and Pearson's correlation, r, and Cohen's kappa, κ, are computed. The GLM PD% estimation model performed well on both processed (r=0.89, p<0.001) and raw (r=0.75, p<0.001) images. Model agreement with radiologist assigned density categories was also high for processed (κ=0.79, p<0.001) and raw (κ=0.76, p<0.001) images. Model-based prediction of breast PD% could allow for a reproducible estimation of breast density, providing a rapid risk assessment tool for clinical practice.

An Improved Physics-Based Model for Topographic Correction of Landsat TM Images

Directory of Open Access Journals (Sweden)

Ainong Li

2015-05-01

Full Text Available Optical remotely sensed images in mountainous areas are subject to radiometric distortions induced by topographic effects, which need to be corrected before quantitative applications. Based on Li model and Sandmeier model, this paper proposed an improved physics-based model for the topographic correction of Landsat Thematic Mapper (TM images. The model employed Normalized Difference Vegetation Index (NDVI thresholds to approximately divide land targets into eleven groups, due to NDVI’s lower sensitivity to topography and its significant role in indicating land cover type. Within each group of terrestrial targets, corresponding MODIS BRDF (Bidirectional Reflectance Distribution Function products were used to account for land surface’s BRDF effect, and topographic effects are corrected without Lambertian assumption. The methodology was tested with two TM scenes of severely rugged mountain areas acquired under different sun elevation angles. Results demonstrated that reflectance of sun-averted slopes was evidently enhanced, and the overall quality of images was improved with topographic effect being effectively suppressed. Correlation coefficients between Near Infra-Red band reflectance and illumination condition reduced almost to zero, and coefficients of variance also showed some reduction. By comparison with the other two physics-based models (Sandmeier model and Li model, the proposed model showed favorable results on two tested Landsat scenes. With the almost half-century accumulation of Landsat data and the successive launch and operation of Landsat 8, the improved model in this paper can be potentially helpful for the topographic correction of Landsat and Landsat-like data.

Label fusion based brain MR image segmentation via a latent selective model

Science.gov (United States)

Liu, Gang; Guo, Xiantang; Zhu, Kai; Liao, Hengxu

2018-04-01

Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.

Image contrast enhancement based on a local standard deviation model

International Nuclear Information System (INIS)

Chang, Dah-Chung; Wu, Wen-Rong

1996-01-01

The adaptive contrast enhancement (ACE) algorithm is a widely used image enhancement method, which needs a contrast gain to adjust high frequency components of an image. In the literature, the gain is usually inversely proportional to the local standard deviation (LSD) or is a constant. But these cause two problems in practical applications, i.e., noise overenhancement and ringing artifact. In this paper a new gain is developed based on Hunt's Gaussian image model to prevent the two defects. The new gain is a nonlinear function of LSD and has the desired characteristic emphasizing the LSD regions in which details are concentrated. We have applied the new ACE algorithm to chest x-ray images and the simulations show the effectiveness of the proposed algorithm

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

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Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

Model-based imaging of cardiac electrical function in human atria

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Modre, Robert; Tilg, Bernhard; Fischer, Gerald; Hanser, Friedrich; Messnarz, Bernd; Schocke, Michael F. H.; Kremser, Christian; Hintringer, Florian; Roithinger, Franz

2003-05-01

Noninvasive imaging of electrical function in the human atria is attained by the combination of data from electrocardiographic (ECG) mapping and magnetic resonance imaging (MRI). An anatomical computer model of the individual patient is the basis for our computer-aided diagnosis of cardiac arrhythmias. Three patients suffering from Wolff-Parkinson-White syndrome, from paroxymal atrial fibrillation, and from atrial flutter underwent an electrophysiological study. After successful treatment of the cardiac arrhythmia with invasive catheter technique, pacing protocols with stimuli at several anatomical sites (coronary sinus, left and right pulmonary vein, posterior site of the right atrium, right atrial appendage) were performed. Reconstructed activation time (AT) maps were validated with catheter-based electroanatomical data, with invasively determined pacing sites, and with pacing at anatomical markers. The individual complex anatomical model of the atria of each patient in combination with a high-quality mesh optimization enables accurate AT imaging, resulting in a localization error for the estimated pacing sites within 1 cm. Our findings may have implications for imaging of atrial activity in patients with focal arrhythmias.

SU-E-J-01: 3D Fluoroscopic Image Estimation From Patient-Specific 4DCBCT-Based Motion Models

International Nuclear Information System (INIS)

Dhou, S; Hurwitz, M; Lewis, J; Mishra, P

2014-01-01

Purpose: 3D motion modeling derived from 4DCT images, taken days or weeks before treatment, cannot reliably represent patient anatomy on the day of treatment. We develop a method to generate motion models based on 4DCBCT acquired at the time of treatment, and apply the model to estimate 3D time-varying images (referred to as 3D fluoroscopic images). Methods: Motion models are derived through deformable registration between each 4DCBCT phase, and principal component analysis (PCA) on the resulting displacement vector fields. 3D fluoroscopic images are estimated based on cone-beam projections simulating kV treatment imaging. PCA coefficients are optimized iteratively through comparison of these cone-beam projections and projections estimated based on the motion model. Digital phantoms reproducing ten patient motion trajectories, and a physical phantom with regular and irregular motion derived from measured patient trajectories, are used to evaluate the method in terms of tumor localization, and the global voxel intensity difference compared to ground truth. Results: Experiments included: 1) assuming no anatomic or positioning changes between 4DCT and treatment time; and 2) simulating positioning and tumor baseline shifts at the time of treatment compared to 4DCT acquisition. 4DCBCT were reconstructed from the anatomy as seen at treatment time. In case 1) the tumor localization error and the intensity differences in ten patient were smaller using 4DCT-based motion model, possible due to superior image quality. In case 2) the tumor localization error and intensity differences were 2.85 and 0.15 respectively, using 4DCT-based motion models, and 1.17 and 0.10 using 4DCBCT-based models. 4DCBCT performed better due to its ability to reproduce daily anatomical changes. Conclusion: The study showed an advantage of 4DCBCT-based motion models in the context of 3D fluoroscopic images estimation. Positioning and tumor baseline shift uncertainties were mitigated by the 4DCBCT-based

Mathematical Foundation Based Inter-Connectivity modelling of Thermal Image processing technique for Fire Protection

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Sayantan Nath

2015-09-01

Full Text Available In this paper, integration between multiple functions of image processing and its statistical parameters for intelligent alarming series based fire detection system is presented. The proper inter-connectivity mapping between processing elements of imagery based on classification factor for temperature monitoring and multilevel intelligent alarm sequence is introduced by abstractive canonical approach. The flow of image processing components between core implementation of intelligent alarming system with temperature wise area segmentation as well as boundary detection technique is not yet fully explored in the present era of thermal imaging. In the light of analytical perspective of convolutive functionalism in thermal imaging, the abstract algebra based inter-mapping model between event-calculus supported DAGSVM classification for step-by-step generation of alarm series with gradual monitoring technique and segmentation of regions with its affected boundaries in thermographic image of coal with respect to temperature distinctions is discussed. The connectedness of the multifunctional operations of image processing based compatible fire protection system with proper monitoring sequence is presently investigated here. The mathematical models representing the relation between the temperature affected areas and its boundary in the obtained thermal image defined in partial derivative fashion is the core contribution of this study. The thermal image of coal sample is obtained in real-life scenario by self-assembled thermographic camera in this study. The amalgamation between area segmentation, boundary detection and alarm series are described in abstract algebra. The principal objective of this paper is to understand the dependency pattern and the principles of working of image processing components and structure an inter-connected modelling technique also for those components with the help of mathematical foundation.

Registration-based segmentation with articulated model from multipostural magnetic resonance images for hand bone motion animation.

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Chen, Hsin-Chen; Jou, I-Ming; Wang, Chien-Kuo; Su, Fong-Chin; Sun, Yung-Nien

2010-06-01

The quantitative measurements of hand bones, including volume, surface, orientation, and position are essential in investigating hand kinematics. Moreover, within the measurement stage, bone segmentation is the most important step due to its certain influences on measuring accuracy. Since hand bones are small and tubular in shape, magnetic resonance (MR) imaging is prone to artifacts such as nonuniform intensity and fuzzy boundaries. Thus, greater detail is required for improving segmentation accuracy. The authors then propose using a novel registration-based method on an articulated hand model to segment hand bones from multipostural MR images. The proposed method consists of the model construction and registration-based segmentation stages. Given a reference postural image, the first stage requires construction of a drivable reference model characterized by hand bone shapes, intensity patterns, and articulated joint mechanism. By applying the reference model to the second stage, the authors initially design a model-based registration pursuant to intensity distribution similarity, MR bone intensity properties, and constraints of model geometry to align the reference model to target bone regions of the given postural image. The authors then refine the resulting surface to improve the superimposition between the registered reference model and target bone boundaries. For each subject, given a reference postural image, the proposed method can automatically segment all hand bones from all other postural images. Compared to the ground truth from two experts, the resulting surface image had an average margin of error within 1 mm (mm) only. In addition, the proposed method showed good agreement on the overlap of bone segmentations by dice similarity coefficient and also demonstrated better segmentation results than conventional methods. The proposed registration-based segmentation method can successfully overcome drawbacks caused by inherent artifacts in MR images and

Quantification of root water uptake in soil using X-ray computed tomography and image-based modelling.

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Daly, Keith R; Tracy, Saoirse R; Crout, Neil M J; Mairhofer, Stefan; Pridmore, Tony P; Mooney, Sacha J; Roose, Tiina

2018-01-01

Spatially averaged models of root-soil interactions are often used to calculate plant water uptake. Using a combination of X-ray computed tomography (CT) and image-based modelling, we tested the accuracy of this spatial averaging by directly calculating plant water uptake for young wheat plants in two soil types. The root system was imaged using X-ray CT at 2, 4, 6, 8 and 12 d after transplanting. The roots were segmented using semi-automated root tracking for speed and reproducibility. The segmented geometries were converted to a mesh suitable for the numerical solution of Richards' equation. Richards' equation was parameterized using existing pore scale studies of soil hydraulic properties in the rhizosphere of wheat plants. Image-based modelling allows the spatial distribution of water around the root to be visualized and the fluxes into the root to be calculated. By comparing the results obtained through image-based modelling to spatially averaged models, the impact of root architecture and geometry in water uptake was quantified. We observed that the spatially averaged models performed well in comparison to the image-based models with <2% difference in uptake. However, the spatial averaging loses important information regarding the spatial distribution of water near the root system. © 2017 John Wiley & Sons Ltd.

Model-based measurement of food portion size for image-based dietary assessment using 3D/2D registration

International Nuclear Information System (INIS)

Chen, Hsin-Chen; Yue, Yaofeng; Sun, Mingui; Jia, Wenyan; Li, Zhaoxin; Sun, Yung-Nien; Fernstrom, John D

2013-01-01

Dietary assessment is important in health maintenance and intervention in many chronic conditions, such as obesity, diabetes and cardiovascular disease. However, there is currently a lack of convenient methods for measuring the volume of food (portion size) in real-life settings. We present a computational method to estimate food volume from a single photographic image of food contained on a typical dining plate. First, we calculate the food location with respect to a 3D camera coordinate system using the plate as a scale reference. Then, the food is segmented automatically from the background in the image. Adaptive thresholding and snake modeling are implemented based on several image features, such as color contrast, regional color homogeneity and curve bending degree. Next, a 3D model representing the general shape of the food (e.g., a cylinder, a sphere, etc) is selected from a pre-constructed shape model library. The position, orientation and scale of the selected shape model are determined by registering the projected 3D model and the food contour in the image, where the properties of the reference are used as constraints. Experimental results using various realistically shaped foods with known volumes demonstrated satisfactory performance of our image-based food volume measurement method even if the 3D geometric surface of the food is not completely represented in the input image. (paper)

Analyzer-based imaging of spinal fusion in an animal model

International Nuclear Information System (INIS)

Kelly, M E; Beavis, R C; Allen, L A; Fiorella, David; Schueltke, E; Juurlink, B H; Chapman, L D; Zhong, Z

2008-01-01

Analyzer-based imaging (ABI) utilizes synchrotron radiation sources to create collimated monochromatic x-rays. In addition to x-ray absorption, this technique uses refraction and scatter rejection to create images. ABI provides dramatically improved contrast over standard imaging techniques. Twenty-one adult male Wistar rats were divided into four experimental groups to undergo the following interventions: (1) non-injured control, (2) decortication alone, (3) decortication with iliac crest bone grafting and (4) decortication with iliac crest bone grafting and interspinous wiring. Surgical procedures were performed at the L5-6 level. Animals were killed at 2, 4 and 6 weeks after the intervention and the spine muscle blocks were excised. Specimens were assessed for the presence of fusion by (1) manual testing, (2) conventional absorption radiography and (3) ABI. ABI showed no evidence of bone fusion in groups 1 and 2 and showed solid or possibly solid fusion in subjects from groups 3 and 4 at 6 weeks. Metal artifacts were not present in any of the ABI images. Conventional absorption radiographs did not provide diagnostic quality imaging of either the graft material or fusion masses in any of the specimens in any of the groups. Synchrotron-based ABI represents a novel imaging technique which can be used to assess spinal fusion in a small animal model. ABI produces superior image quality when compared to conventional radiographs

Analyzer-based imaging of spinal fusion in an animal model

Science.gov (United States)

Kelly, M. E.; Beavis, R. C.; Fiorella, David; Schültke, E.; Allen, L. A.; Juurlink, B. H.; Zhong, Z.; Chapman, L. D.

2008-05-01

Analyzer-based imaging (ABI) utilizes synchrotron radiation sources to create collimated monochromatic x-rays. In addition to x-ray absorption, this technique uses refraction and scatter rejection to create images. ABI provides dramatically improved contrast over standard imaging techniques. Twenty-one adult male Wistar rats were divided into four experimental groups to undergo the following interventions: (1) non-injured control, (2) decortication alone, (3) decortication with iliac crest bone grafting and (4) decortication with iliac crest bone grafting and interspinous wiring. Surgical procedures were performed at the L5-6 level. Animals were killed at 2, 4 and 6 weeks after the intervention and the spine muscle blocks were excised. Specimens were assessed for the presence of fusion by (1) manual testing, (2) conventional absorption radiography and (3) ABI. ABI showed no evidence of bone fusion in groups 1 and 2 and showed solid or possibly solid fusion in subjects from groups 3 and 4 at 6 weeks. Metal artifacts were not present in any of the ABI images. Conventional absorption radiographs did not provide diagnostic quality imaging of either the graft material or fusion masses in any of the specimens in any of the groups. Synchrotron-based ABI represents a novel imaging technique which can be used to assess spinal fusion in a small animal model. ABI produces superior image quality when compared to conventional radiographs.

A new approach towards image based virtual 3D city modeling by using close range photogrammetry

Science.gov (United States)

Singh, S. P.; Jain, K.; Mandla, V. R.

2014-05-01

3D city model is a digital representation of the Earth's surface and it's related objects such as building, tree, vegetation, and some manmade feature belonging to urban area. The demand of 3D city modeling is increasing day to day for various engineering and non-engineering applications. Generally three main image based approaches are using for virtual 3D city models generation. In first approach, researchers used Sketch based modeling, second method is Procedural grammar based modeling and third approach is Close range photogrammetry based modeling. Literature study shows that till date, there is no complete solution available to create complete 3D city model by using images. These image based methods also have limitations This paper gives a new approach towards image based virtual 3D city modeling by using close range photogrammetry. This approach is divided into three sections. First, data acquisition process, second is 3D data processing, and third is data combination process. In data acquisition process, a multi-camera setup developed and used for video recording of an area. Image frames created from video data. Minimum required and suitable video image frame selected for 3D processing. In second section, based on close range photogrammetric principles and computer vision techniques, 3D model of area created. In third section, this 3D model exported to adding and merging of other pieces of large area. Scaling and alignment of 3D model was done. After applying the texturing and rendering on this model, a final photo-realistic textured 3D model created. This 3D model transferred into walk-through model or in movie form. Most of the processing steps are automatic. So this method is cost effective and less laborious. Accuracy of this model is good. For this research work, study area is the campus of department of civil engineering, Indian Institute of Technology, Roorkee. This campus acts as a prototype for city. Aerial photography is restricted in many country

Image-based modeling of tumor shrinkage in head and neck radiation therapy1

Science.gov (United States)

Chao, Ming; Xie, Yaoqin; Moros, Eduardo G.; Le, Quynh-Thu; Xing, Lei

2010-01-01

Purpose: Understanding the kinetics of tumor growth∕shrinkage represents a critical step in quantitative assessment of therapeutics and realization of adaptive radiation therapy. This article presents a novel framework for image-based modeling of tumor change and demonstrates its performance with synthetic images and clinical cases. Methods: Due to significant tumor tissue content changes, similarity-based models are not suitable for describing the process of tumor volume changes. Under the hypothesis that tissue features in a tumor volume or at the boundary region are partially preserved, the kinetic change was modeled in two steps: (1) Autodetection of homologous tissue features shared by two input images using the scale invariance feature transformation (SIFT) method; and (2) establishment of a voxel-to-voxel correspondence between the images for the remaining spatial points by interpolation. The correctness of the tissue feature correspondence was assured by a bidirectional association procedure, where SIFT features were mapped from template to target images and reversely. A series of digital phantom experiments and five head and neck clinical cases were used to assess the performance of the proposed technique. Results: The proposed technique can faithfully identify the known changes introduced when constructing the digital phantoms. The subsequent feature-guided thin plate spline calculation reproduced the “ground truth” with accuracy better than 1.5 mm. For the clinical cases, the new algorithm worked reliably for a volume change as large as 30%. Conclusions: An image-based tumor kinetic algorithm was developed to model the tumor response to radiation therapy. The technique provides a practical framework for future application in adaptive radiation therapy. PMID:20527569

Defogging of road images using gain coefficient-based trilateral filter

Science.gov (United States)

Singh, Dilbag; Kumar, Vijay

2018-01-01

Poor weather conditions are responsible for most of the road accidents year in and year out. Poor weather conditions, such as fog, degrade the visibility of objects. Thus, it becomes difficult for drivers to identify the vehicles in a foggy environment. The dark channel prior (DCP)-based defogging techniques have been found to be an efficient way to remove fog from road images. However, it produces poor results when image objects are inherently similar to airlight and no shadow is cast on them. To eliminate this problem, a modified restoration model-based DCP is developed to remove the fog from road images. The transmission map is also refined by developing a gain coefficient-based trilateral filter. Thus, the proposed technique has an ability to remove fog from road images in an effective manner. The proposed technique is compared with seven well-known defogging techniques on two benchmark foggy images datasets and five real-time foggy images. The experimental results demonstrate that the proposed approach is able to remove the different types of fog from roadside images as well as significantly improve the image's visibility. It also reveals that the restored image has little or no artifacts.

MIDA: A Multimodal Imaging-Based Detailed Anatomical Model of the Human Head and Neck.

Directory of Open Access Journals (Sweden)

Maria Ida Iacono

Full Text Available Computational modeling and simulations are increasingly being used to complement experimental testing for analysis of safety and efficacy of medical devices. Multiple voxel- and surface-based whole- and partial-body models have been proposed in the literature, typically with spatial resolution in the range of 1-2 mm and with 10-50 different tissue types resolved. We have developed a multimodal imaging-based detailed anatomical model of the human head and neck, named "MIDA". The model was obtained by integrating three different magnetic resonance imaging (MRI modalities, the parameters of which were tailored to enhance the signals of specific tissues: i structural T1- and T2-weighted MRIs; a specific heavily T2-weighted MRI slab with high nerve contrast optimized to enhance the structures of the ear and eye; ii magnetic resonance angiography (MRA data to image the vasculature, and iii diffusion tensor imaging (DTI to obtain information on anisotropy and fiber orientation. The unique multimodal high-resolution approach allowed resolving 153 structures, including several distinct muscles, bones and skull layers, arteries and veins, nerves, as well as salivary glands. The model offers also a detailed characterization of eyes, ears, and deep brain structures. A special automatic atlas-based segmentation procedure was adopted to include a detailed map of the nuclei of the thalamus and midbrain into the head model. The suitability of the model to simulations involving different numerical methods, discretization approaches, as well as DTI-based tensorial electrical conductivity, was examined in a case-study, in which the electric field was generated by transcranial alternating current stimulation. The voxel- and the surface-based versions of the models are freely available to the scientific community.

A model-based radiography restoration method based on simple scatter-degradation scheme for improving image visibility

Science.gov (United States)

Kim, K.; Kang, S.; Cho, H.; Kang, W.; Seo, C.; Park, C.; Lee, D.; Lim, H.; Lee, H.; Kim, G.; Park, S.; Park, J.; Kim, W.; Jeon, D.; Woo, T.; Oh, J.

2018-02-01

In conventional planar radiography, image visibility is often limited mainly due to the superimposition of the object structure under investigation and the artifacts caused by scattered x-rays and noise. Several methods, including computed tomography (CT) as a multiplanar imaging modality, air-gap and grid techniques for the reduction of scatters, phase-contrast imaging as another image-contrast modality, etc., have extensively been investigated in attempt to overcome these difficulties. However, those methods typically require higher x-ray doses or special equipment. In this work, as another approach, we propose a new model-based radiography restoration method based on simple scatter-degradation scheme where the intensity of scattered x-rays and the transmission function of a given object are estimated from a single x-ray image to restore the original degraded image. We implemented the proposed algorithm and performed an experiment to demonstrate its viability. Our results indicate that the degradation of image characteristics by scattered x-rays and noise was effectively recovered by using the proposed method, which improves the image visibility in radiography considerably.

A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging.

Science.gov (United States)

Zhou, Ning; Cheung, William K; Qiu, Guoping; Xue, Xiangyang

2011-07-01

The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

Normal Inverse Gaussian Model-Based Image Denoising in the NSCT Domain

Directory of Open Access Journals (Sweden)

Jian Jia

2015-01-01

Full Text Available The objective of image denoising is to retain useful details while removing as much noise as possible to recover an original image from its noisy version. This paper proposes a novel normal inverse Gaussian (NIG model-based method that uses a Bayesian estimator to carry out image denoising in the nonsubsampled contourlet transform (NSCT domain. In the proposed method, the NIG model is first used to describe the distributions of the image transform coefficients of each subband in the NSCT domain. Then, the corresponding threshold function is derived from the model using Bayesian maximum a posteriori probability estimation theory. Finally, optimal linear interpolation thresholding algorithm (OLI-Shrink is employed to guarantee a gentler thresholding effect. The results of comparative experiments conducted indicate that the denoising performance of our proposed method in terms of peak signal-to-noise ratio is superior to that of several state-of-the-art methods, including BLS-GSM, K-SVD, BivShrink, and BM3D. Further, the proposed method achieves structural similarity (SSIM index values that are comparable to those of the block-matching 3D transformation (BM3D method.

Fiducial-based fusion of 3D dental models with magnetic resonance imaging.

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Abdi, Amir H; Hannam, Alan G; Fels, Sidney

2018-04-16

Magnetic resonance imaging (MRI) is widely used in study of maxillofacial structures. While MRI is the modality of choice for soft tissues, it fails to capture hard tissues such as bone and teeth. Virtual dental models, acquired by optical 3D scanners, are becoming more accessible for dental practice and are starting to replace the conventional dental impressions. The goal of this research is to fuse the high-resolution 3D dental models with MRI to enhance the value of imaging for applications where detailed analysis of maxillofacial structures are needed such as patient examination, surgical planning, and modeling. A subject-specific dental attachment was digitally designed and 3D printed based on the subject's face width and dental anatomy. The attachment contained 19 semi-ellipsoidal concavities in predetermined positions where oil-based ellipsoidal fiducial markers were later placed. The MRI was acquired while the subject bit on the dental attachment. The spatial position of the center of mass of each fiducial in the resultant MR Image was calculated by averaging its voxels' spatial coordinates. The rigid transformation to fuse dental models to MRI was calculated based on the least squares mapping of corresponding fiducials and solved via singular-value decomposition. The target registration error (TRE) of the proposed fusion process, calculated in a leave-one-fiducial-out fashion, was estimated at 0.49 mm. The results suggest that 6-9 fiducials suffice to achieve a TRE of equal to half the MRI voxel size. Ellipsoidal oil-based fiducials produce distinguishable intensities in MRI and can be used as registration fiducials. The achieved accuracy of the proposed approach is sufficient to leverage the merged 3D dental models with the MRI data for a finer analysis of the maxillofacial structures where complete geometry models are needed.

Model-based magnetization retrieval from holographic phase images

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Röder, Falk, E-mail: f.roeder@hzdr.de [Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Materialforschung, Bautzner Landstr. 400, D-01328 Dresden (Germany); Triebenberg Labor, Institut für Strukturphysik, Technische Universität Dresden, D-01062 Dresden (Germany); Vogel, Karin [Triebenberg Labor, Institut für Strukturphysik, Technische Universität Dresden, D-01062 Dresden (Germany); Wolf, Daniel [Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Materialforschung, Bautzner Landstr. 400, D-01328 Dresden (Germany); Triebenberg Labor, Institut für Strukturphysik, Technische Universität Dresden, D-01062 Dresden (Germany); Hellwig, Olav [Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Materialforschung, Bautzner Landstr. 400, D-01328 Dresden (Germany); AG Magnetische Funktionsmaterialien, Institut für Physik, Technische Universität Chemnitz, D-09126 Chemnitz (Germany); HGST, A Western Digital Company, 3403 Yerba Buena Rd., San Jose, CA 95135 (United States); Wee, Sung Hun [HGST, A Western Digital Company, 3403 Yerba Buena Rd., San Jose, CA 95135 (United States); Wicht, Sebastian; Rellinghaus, Bernd [IFW Dresden, Institute for Metallic Materials, P.O. Box 270116, D-01171 Dresden (Germany)

2017-05-15

The phase shift of the electron wave is a useful measure for the projected magnetic flux density of magnetic objects at the nanometer scale. More important for materials science, however, is the knowledge about the magnetization in a magnetic nano-structure. As demonstrated here, a dominating presence of stray fields prohibits a direct interpretation of the phase in terms of magnetization modulus and direction. We therefore present a model-based approach for retrieving the magnetization by considering the projected shape of the nano-structure and assuming a homogeneous magnetization therein. We apply this method to FePt nano-islands epitaxially grown on a SrTiO{sub 3} substrate, which indicates an inclination of their magnetization direction relative to the structural easy magnetic [001] axis. By means of this real-world example, we discuss prospects and limits of this approach. - Highlights: • Retrieval of the magnetization from holographic phase images. • Magnetostatic model constructed for a magnetic nano-structure. • Decomposition into homogeneously magnetized components. • Discretization of a each component by elementary cuboids. • Analytic solution for the phase of a magnetized cuboid considered. • Fitting a set of magnetization vectors to experimental phase images.

On Feature Relevance in Image-Based Prediction Models: An Empirical Study

DEFF Research Database (Denmark)

Konukoglu, E.; Ganz, Melanie; Van Leemput, Koen

2013-01-01

Determining disease-related variations of the anatomy and function is an important step in better understanding diseases and developing early diagnostic systems. In particular, image-based multivariate prediction models and the “relevant features” they produce are attracting attention from the co...

Aircraft Segmentation in SAR Images Based on Improved Active Shape Model

Science.gov (United States)

Zhang, X.; Xiong, B.; Kuang, G.

2018-04-01

In SAR image interpretation, aircrafts are the important targets arousing much attention. However, it is far from easy to segment an aircraft from the background completely and precisely in SAR images. Because of the complex structure, different kinds of electromagnetic scattering take place on the aircraft surfaces. As a result, aircraft targets usually appear to be inhomogeneous and disconnected. It is a good idea to extract an aircraft target by the active shape model (ASM), since combination of the geometric information controls variations of the shape during the contour evolution. However, linear dimensionality reduction, used in classic ACM, makes the model rigid. It brings much trouble to segment different types of aircrafts. Aiming at this problem, an improved ACM based on ISOMAP is proposed in this paper. ISOMAP algorithm is used to extract the shape information of the training set and make the model flexible enough to deal with different aircrafts. The experiments based on real SAR data shows that the proposed method achieves obvious improvement in accuracy.

TU-CD-BRA-05: Atlas Selection for Multi-Atlas-Based Image Segmentation Using Surrogate Modeling

International Nuclear Information System (INIS)

Zhao, T; Ruan, D

2015-01-01

Purpose: The growing size and heterogeneity in training atlas necessitates sophisticated schemes to identify only the most relevant atlases for the specific multi-atlas-based image segmentation problem. This study aims to develop a model to infer the inaccessible oracle geometric relevance metric from surrogate image similarity metrics, and based on such model, provide guidance to atlas selection in multi-atlas-based image segmentation. Methods: We relate the oracle geometric relevance metric in label space to the surrogate metric in image space, by a monotonically non-decreasing function with additive random perturbations. Subsequently, a surrogate’s ability to prognosticate the oracle order for atlas subset selection is quantified probabilistically. Finally, important insights and guidance are provided for the design of fusion set size, balancing the competing demands to include the most relevant atlases and to exclude the most irrelevant ones. A systematic solution is derived based on an optimization framework. Model verification and performance assessment is performed based on clinical prostate MR images. Results: The proposed surrogate model was exemplified by a linear map with normally distributed perturbation, and verified with several commonly-used surrogates, including MSD, NCC and (N)MI. The derived behaviors of different surrogates in atlas selection and their corresponding performance in ultimate label estimate were validated. The performance of NCC and (N)MI was similarly superior to MSD, with a 10% higher atlas selection probability and a segmentation performance increase in DSC by 0.10 with the first and third quartiles of (0.83, 0.89), compared to (0.81, 0.89). The derived optimal fusion set size, valued at 7/8/8/7 for MSD/NCC/MI/NMI, agreed well with the appropriate range [4, 9] from empirical observation. Conclusion: This work has developed an efficacious probabilistic model to characterize the image-based surrogate metric on atlas selection

Remote sensing image ship target detection method based on visual attention model

Science.gov (United States)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

Live cell imaging reveals at novel view of DNA

International Nuclear Information System (INIS)

Moritomi-Yano, Keiko; Yano, Ken-ichi

2010-01-01

Non-homologous end-joining (NHEJ) is the major repair pathway for DNA double-strand breaks (DSBs) that are the most severe form of DNA damages. Recently, live cell imaging techniques coupled with laser micro-irradiation were used to analyze the spatio-temporal behavior of the NHEJ core factors upon DSB induction in living cells. Based on the live cell imaging studies, we proposed a novel two-phase model for DSB sensing and protein assembly in the NHEJ pathway. This new model provides a novel view of the dynamic protein behavior on DSBs and broad implications for the molecular mechanism of NHEJ. (author)

Using optical remote sensing model to estimate oil slick thickness based on satellite image

International Nuclear Information System (INIS)

Lu, Y C; Tian, Q J; Lyu, C G; Fu, W X; Han, W C

2014-01-01

An optical remote sensing model has been established based on two-beam interference theory to estimate marine oil slick thickness. Extinction coefficient and normalized reflectance of oil are two important parts in this model. Extinction coefficient is an important inherent optical property and will not vary with the background reflectance changed. Normalized reflectance can be used to eliminate the background differences between in situ measured spectra and remotely sensing image. Therefore, marine oil slick thickness and area can be estimated and mapped based on optical remotely sensing image and extinction coefficient

A Learning State-Space Model for Image Retrieval

Directory of Open Access Journals (Sweden)

Lee Greg C

2007-01-01

Full Text Available This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.

NMDA receptor antagonism by repetitive MK801 administration induces schizophrenia-like structural changes in the rat brain as revealed by voxel-based morphometry and diffusion tensor imaging.

Science.gov (United States)

Wu, H; Wang, X; Gao, Y; Lin, F; Song, T; Zou, Y; Xu, L; Lei, H

2016-05-13

Animal models of N-methyl-d-aspartate receptor (NMDAR) antagonism have been widely used for schizophrenia research. Less is known whether these models are associated with macroscopic brain structural changes that resemble those in clinical schizophrenia. Magnetic resonance imaging (MRI) was used to measure brain structural changes in rats subjected to repeated administration of MK801 in a regimen (daily dose of 0.2mg/kg for 14 consecutive days) known to be able to induce schizophrenia-like cognitive impairments. Voxel-based morphometry (VBM) revealed significant gray matter (GM) atrophy in the hippocampus, ventral striatum (vStr) and cortex. Diffusion tensor imaging (DTI) demonstrated microstructural impairments in the corpus callosum (cc). Histopathological results corroborated the MRI findings. Treatment-induced behavioral abnormalities were not measured such that correlation between the brain structural changes observed and schizophrenia-like behaviors could not be established. Chronic MK801 administration induces MRI-observable brain structural changes that are comparable to those observed in schizophrenia patients, supporting the notion that NMDAR hypofunction contributes to the pathology of schizophrenia. Imaging-derived brain structural changes in animal models of NMDAR antagonism may be useful measurements for studying the effects of treatments and interventions targeting schizophrenia. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

An automatic image-based modelling method applied to forensic infography.

Directory of Open Access Journals (Sweden)

Sandra Zancajo-Blazquez

Full Text Available This paper presents a new method based on 3D reconstruction from images that demonstrates the utility and integration of close-range photogrammetry and computer vision as an efficient alternative to modelling complex objects and scenarios of forensic infography. The results obtained confirm the validity of the method compared to other existing alternatives as it guarantees the following: (i flexibility, permitting work with any type of camera (calibrated and non-calibrated, smartphone or tablet and image (visible, infrared, thermal, etc.; (ii automation, allowing the reconstruction of three-dimensional scenarios in the absence of manual intervention, and (iii high quality results, sometimes providing higher resolution than modern laser scanning systems. As a result, each ocular inspection of a crime scene with any camera performed by the scientific police can be transformed into a scaled 3d model.

SALIENCY BASED SEGMENTATION OF SATELLITE IMAGES

Directory of Open Access Journals (Sweden)

A. Sharma

2015-03-01

Full Text Available Saliency gives the way as humans see any image and saliency based segmentation can be eventually helpful in Psychovisual image interpretation. Keeping this in view few saliency models are used along with segmentation algorithm and only the salient segments from image have been extracted. The work is carried out for terrestrial images as well as for satellite images. The methodology used in this work extracts those segments from segmented image which are having higher or equal saliency value than a threshold value. Salient and non salient regions of image become foreground and background respectively and thus image gets separated. For carrying out this work a dataset of terrestrial images and Worldview 2 satellite images (sample data are used. Results show that those saliency models which works better for terrestrial images are not good enough for satellite image in terms of foreground and background separation. Foreground and background separation in terrestrial images is based on salient objects visible on the images whereas in satellite images this separation is based on salient area rather than salient objects.

Measurable realistic image-based 3D mapping

Science.gov (United States)

Liu, W.; Wang, J.; Wang, J. J.; Ding, W.; Almagbile, A.

2011-12-01

Maps with 3D visual models are becoming a remarkable feature of 3D map services. High-resolution image data is obtained for the construction of 3D visualized models.The3D map not only provides the capabilities of 3D measurements and knowledge mining, but also provides the virtual experienceof places of interest, such as demonstrated in the Google Earth. Applications of 3D maps are expanding into the areas of architecture, property management, and urban environment monitoring. However, the reconstruction of high quality 3D models is time consuming, and requires robust hardware and powerful software to handle the enormous amount of data. This is especially for automatic implementation of 3D models and the representation of complicated surfacesthat still need improvements with in the visualisation techniques. The shortcoming of 3D model-based maps is the limitation of detailed coverage since a user can only view and measure objects that are already modelled in the virtual environment. This paper proposes and demonstrates a 3D map concept that is realistic and image-based, that enables geometric measurements and geo-location services. Additionally, image-based 3D maps provide more detailed information of the real world than 3D model-based maps. The image-based 3D maps use geo-referenced stereo images or panoramic images. The geometric relationships between objects in the images can be resolved from the geometric model of stereo images. The panoramic function makes 3D maps more interactive with users but also creates an interesting immersive circumstance. Actually, unmeasurable image-based 3D maps already exist, such as Google street view, but only provide virtual experiences in terms of photos. The topographic and terrain attributes, such as shapes and heights though are omitted. This paper also discusses the potential for using a low cost land Mobile Mapping System (MMS) to implement realistic image 3D mapping, and evaluates the positioning accuracy that a measureable

USE OF IMAGE BASED MODELLING FOR DOCUMENTATION OF INTRICATELY SHAPED OBJECTS

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M. Marčiš

2016-06-01

Full Text Available In the documentation of cultural heritage, we can encounter three dimensional shapes and structures which are complicated to measure. Such objects are for example spiral staircases, timber roof trusses, historical furniture or folk costume where it is nearly impossible to effectively use the traditional surveying or the terrestrial laser scanning due to the shape of the object, its dimensions and the crowded environment. The actual methods of digital photogrammetry can be very helpful in such cases with the emphasis on the automated processing of the extensive image data. The created high resolution 3D models and 2D orthophotos are very important for the documentation of architectural elements and they can serve as an ideal base for the vectorization and 2D drawing documentation. This contribution wants to describe the various usage of image based modelling in specific interior spaces and specific objects. The advantages and disadvantages of the photogrammetric measurement of such objects in comparison to other surveying methods are reviewed.

Use of Image Based Modelling for Documentation of Intricately Shaped Objects

Science.gov (United States)

Marčiš, M.; Barták, P.; Valaška, D.; Fraštia, M.; Trhan, O.

2016-06-01

In the documentation of cultural heritage, we can encounter three dimensional shapes and structures which are complicated to measure. Such objects are for example spiral staircases, timber roof trusses, historical furniture or folk costume where it is nearly impossible to effectively use the traditional surveying or the terrestrial laser scanning due to the shape of the object, its dimensions and the crowded environment. The actual methods of digital photogrammetry can be very helpful in such cases with the emphasis on the automated processing of the extensive image data. The created high resolution 3D models and 2D orthophotos are very important for the documentation of architectural elements and they can serve as an ideal base for the vectorization and 2D drawing documentation. This contribution wants to describe the various usage of image based modelling in specific interior spaces and specific objects. The advantages and disadvantages of the photogrammetric measurement of such objects in comparison to other surveying methods are reviewed.

NSCT BASED LOCAL ENHANCEMENT FOR ACTIVE CONTOUR BASED IMAGE SEGMENTATION APPLICATION

Directory of Open Access Journals (Sweden)

Hiren Mewada

2010-08-01

Full Text Available Because of cross-disciplinary nature, Active Contour modeling techniques have been utilized extensively for the image segmentation. In traditional active contour based segmentation techniques based on level set methods, the energy functions are defined based on the intensity gradient. This makes them highly sensitive to the situation where the underlying image content is characterized by image nonhomogeneities due to illumination and contrast condition. This is the most difficult problem to make them as fully automatic image segmentation techniques. This paper introduces one of the approaches based on image enhancement to this problem. The enhanced image is obtained using NonSubsampled Contourlet Transform, which improves the edges strengths in the direction where the illumination is not proper and then active contour model based on level set technique is utilized to segment the object. Experiment results demonstrate that proposed method can be utilized along with existing active contour model based segmentation method under situation characterized by intensity non-homogeneity to make them fully automatic.

Historical Single Image-Based Modeling: The Case of Gobierna Tower, Zamora (Spain

Directory of Open Access Journals (Sweden)

Jesús Garcia-Gago

2014-01-01

Full Text Available Historical perspective images have been proved to be very useful to properly provide a dimensional analysis of buildings façades or even to generate a pseudo-3D reconstruction based on rectified images of the whole structure. In this paper, the case of Gobierna Tower (Zamora, Spain is analyzed from a historical single image-based modeling approach. In particular, a bottom-up approach, which takes advantage from the perspective of the image, the existence of the three vanishing points and the usual geometric constraints (i.e., planarity, orthogonality, and parallelism is applied for the dimensional analysis of a destroyed historical building. Results were compared with ground truth measurements existing in a historical topographical surveying obtaining deviations of about 1%.

Deformation Measurements of Gabion Walls Using Image Based Modeling

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Marek Fraštia

2014-06-01

Full Text Available The image based modeling finds use in applications where it is necessary to reconstructthe 3D surface of the observed object with a high level of detail. Previous experiments showrelatively high variability of the results depending on the camera type used, the processingsoftware, or the process evaluation. The authors tested the method of SFM (Structure fromMotion to determine the stability of gabion walls. The results of photogrammetricmeasurements were compared to precise geodetic point measurements.

Constructing a Computer Model of the Human Eye Based on Tissue Slice Images

OpenAIRE

Dai, Peishan; Wang, Boliang; Bao, Chunbo; Ju, Ying

2010-01-01

Computer simulation of the biomechanical and biological heat transfer in ophthalmology greatly relies on having a reliable computer model of the human eye. This paper proposes a novel method on the construction of a geometric model of the human eye based on tissue slice images. Slice images were obtained from an in vitro Chinese human eye through an embryo specimen processing methods. A level set algorithm was used to extract contour points of eye tissues while a principle component analysi...

Modeling multiple visual words assignment for bag-of-features based medical image retrieval

KAUST Repository

Wang, Jim Jing-Yan

2012-01-01

In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments\\' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

Modeling multiple visual words assignment for bag-of-features based medical image retrieval

KAUST Repository

Wang, Jim Jing-Yan; Almasri, Islam

2012-01-01

In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

Facial motion parameter estimation and error criteria in model-based image coding

Science.gov (United States)

Liu, Yunhai; Yu, Lu; Yao, Qingdong

2000-04-01

Model-based image coding has been given extensive attention due to its high subject image quality and low bit-rates. But the estimation of object motion parameter is still a difficult problem, and there is not a proper error criteria for the quality assessment that are consistent with visual properties. This paper presents an algorithm of the facial motion parameter estimation based on feature point correspondence and gives the motion parameter error criteria. The facial motion model comprises of three parts. The first part is the global 3-D rigid motion of the head, the second part is non-rigid translation motion in jaw area, and the third part consists of local non-rigid expression motion in eyes and mouth areas. The feature points are automatically selected by a function of edges, brightness and end-node outside the blocks of eyes and mouth. The numbers of feature point are adjusted adaptively. The jaw translation motion is tracked by the changes of the feature point position of jaw. The areas of non-rigid expression motion can be rebuilt by using block-pasting method. The estimation approach of motion parameter error based on the quality of reconstructed image is suggested, and area error function and the error function of contour transition-turn rate are used to be quality criteria. The criteria reflect the image geometric distortion caused by the error of estimated motion parameters properly.

Sketch-based 3D modeling by aligning outlines of an image

Directory of Open Access Journals (Sweden)

Chunxiao Li

2016-07-01

Full Text Available In this paper we present an efficient technique for sketch-based 3D modeling using automatically extracted image features. Creating a 3D model often requires a drawing of irregular shapes composed of curved lines as a starting point but it is difficult to hand-draw such lines without introducing awkward bumps and edges along the lines. We propose an automatic alignment of a user׳s hand-drawn sketch lines to the contour lines of an image, facilitating a considerable level of ease with which the user can carelessly continue sketching while the system intelligently snaps the sketch lines to a background image contour, no longer requiring the strenuous effort and stress of trying to make a perfect line during the modeling task. This interactive technique seamlessly combines the efficiency and perception of the human user with the accuracy of computational power, applied to the domain of 3D modeling where the utmost precision of on-screen drawing has been one of the hurdles of the task hitherto considered a job requiring a highly skilled and careful manipulation by the user. We provide several examples to demonstrate the accuracy and efficiency of the method with which complex shapes were achieved easily and quickly in the interactive outline drawing task.

Image-Based Models for Specularity Propagation in Diminished Reality.

Science.gov (United States)

Said, Souheil Hadj; Tamaazousti, Mohamed; Bartoli, Adrien

2018-07-01

The aim of Diminished Reality (DR) is to remove a target object in a live video stream seamlessly. In our approach, the area of the target object is replaced with new texture that blends with the rest of the image. The result is then propagated to the next frames of the video. One of the important stages of this technique is to update the target region with respect to the illumination change. This is a complex and recurrent problem when the viewpoint changes. We show that the state-of-the-art in DR fails in solving this problem, even under simple scenarios. We then use local illumination models to address this problem. According to these models, the variation in illumination only affects the specular component of the image. In the context of DR, the problem is therefore solved by propagating the specularities in the target area. We list a set of structural properties of specularities which we incorporate in two new models for specularity propagation. Our first model includes the same property as the previous approaches, which is the smoothness of illumination variation, but has a different estimation method based on the Thin-Plate Spline. Our second model incorporates more properties of the specularity's shape on planar surfaces. Experimental results on synthetic and real data show that our strategy substantially improves the rendering quality compared to the state-of-the-art in DR.

A Spherical Model Based Keypoint Descriptor and Matching Algorithm for Omnidirectional Images

Directory of Open Access Journals (Sweden)

Guofeng Tong

2014-04-01

Full Text Available Omnidirectional images generally have nonlinear distortion in radial direction. Unfortunately, traditional algorithms such as scale-invariant feature transform (SIFT and Descriptor-Nets (D-Nets do not work well in matching omnidirectional images just because they are incapable of dealing with the distortion. In order to solve this problem, a new voting algorithm is proposed based on the spherical model and the D-Nets algorithm. Because the spherical-based keypoint descriptor contains the distortion information of omnidirectional images, the proposed matching algorithm is invariant to distortion. Keypoint matching experiments are performed on three pairs of omnidirectional images, and comparison is made among the proposed algorithm, the SIFT and the D-Nets. The result shows that the proposed algorithm is more robust and more precise than the SIFT, and the D-Nets in matching omnidirectional images. Comparing with the SIFT and the D-Nets, the proposed algorithm has two main advantages: (a there are more real matching keypoints; (b the coverage range of the matching keypoints is wider, including the seriously distorted areas.

Image-Based Modeling Reveals Dynamic Redistribution of DNA Damageinto Nuclear Sub-Domains

Energy Technology Data Exchange (ETDEWEB)

Costes Sylvain V., Ponomarev Artem, Chen James L.; Nguyen, David; Cucinotta, Francis A.; Barcellos-Hoff, Mary Helen

2007-08-03

Several proteins involved in the response to DNA doublestrand breaks (DSB) f orm microscopically visible nuclear domains, orfoci, after exposure to ionizing radiation. Radiation-induced foci (RIF)are believed to be located where DNA damage occurs. To test thisassumption, we analyzed the spatial distribution of 53BP1, phosphorylatedATM, and gammaH2AX RIF in cells irradiated with high linear energytransfer (LET) radiation and low LET. Since energy is randomly depositedalong high-LET particle paths, RIF along these paths should also berandomly distributed. The probability to induce DSB can be derived fromDNA fragment data measured experimentally by pulsed-field gelelectrophoresis. We used this probability in Monte Carlo simulations topredict DSB locations in synthetic nuclei geometrically described by acomplete set of human chromosomes, taking into account microscope opticsfrom real experiments. As expected, simulations produced DNA-weightedrandom (Poisson) distributions. In contrast, the distributions of RIFobtained as early as 5 min after exposure to high LET (1 GeV/amu Fe) werenon-random. This deviation from the expected DNA-weighted random patterncan be further characterized by "relative DNA image measurements." Thisnovel imaging approach shows that RIF were located preferentially at theinterface between high and low DNA density regions, and were morefrequent than predicted in regions with lower DNA density. The samepreferential nuclear location was also measured for RIF induced by 1 Gyof low-LET radiation. This deviation from random behavior was evidentonly 5 min after irradiation for phosphorylated ATM RIF, while gammaH2AXand 53BP1 RIF showed pronounced deviations up to 30 min after exposure.These data suggest that DNA damage induced foci are restricted to certainregions of the nucleus of human epithelial cells. It is possible that DNAlesions are collected in these nuclear sub-domains for more efficientrepair.

A robust sub-pixel edge detection method of infrared image based on tremor-based retinal receptive field model

Science.gov (United States)

Gao, Kun; Yang, Hu; Chen, Xiaomei; Ni, Guoqiang

2008-03-01

Because of complex thermal objects in an infrared image, the prevalent image edge detection operators are often suitable for a certain scene and extract too wide edges sometimes. From a biological point of view, the image edge detection operators work reliably when assuming a convolution-based receptive field architecture. A DoG (Difference-of- Gaussians) model filter based on ON-center retinal ganglion cell receptive field architecture with artificial eye tremors introduced is proposed for the image contour detection. Aiming at the blurred edges of an infrared image, the subsequent orthogonal polynomial interpolation and sub-pixel level edge detection in rough edge pixel neighborhood is adopted to locate the foregoing rough edges in sub-pixel level. Numerical simulations show that this method can locate the target edge accurately and robustly.

Image reconstruction method for electrical capacitance tomography based on the combined series and parallel normalization model

International Nuclear Information System (INIS)

Dong, Xiangyuan; Guo, Shuqing

2008-01-01

In this paper, a novel image reconstruction method for electrical capacitance tomography (ECT) based on the combined series and parallel model is presented. A regularization technique is used to obtain a stabilized solution of the inverse problem. Also, the adaptive coefficient of the combined model is deduced by numerical optimization. Simulation results indicate that it can produce higher quality images when compared to the algorithm based on the parallel or series models for the cases tested in this paper. It provides a new algorithm for ECT application

Relationship model among sport event image, destination image, and tourist satisfaction of Tour de Singkarak in West Sumatera

Directory of Open Access Journals (Sweden)

Ratni Prima Lita

2015-06-01

Full Text Available Sport events Tour de Singkarak (TDS can increase tourist arrivals to West Sumatera. At least at the time of execution, the majority of participants and team supporters (sports tourist brings the families. Although there are claims about the arrival of tourists, it requires to see the impact of sports events TDS and comprehensive long-term basis to the West Sumatera image as a tourist destination (destination image and its impact on tourist satisfaction. This study re-conceptualizes the interconnec-tedness among sport event image, tourist destination image, perception and the effect on tourists satisfaction. The investigation on this interconnection is expected to reveal empirically tested model. As an explanatory in nature, this study uses explanatory survey and cross sectional data. In total of 100 spectators of Tour de Singkarak in West Sumatera, they got involved in survey and they were taken by convenience sam-pling technique. Analysis of this data was done by using variance based structural equation modeling. It was found that sport event image and destination image signifi-cantly affect the satisfaction of spectators of Tour de Singkarak.

Model-based normalization for iterative 3D PET image

International Nuclear Information System (INIS)

Bai, B.; Li, Q.; Asma, E.; Leahy, R.M.; Holdsworth, C.H.; Chatziioannou, A.; Tai, Y.C.

2002-01-01

We describe a method for normalization in 3D PET for use with maximum a posteriori (MAP) or other iterative model-based image reconstruction methods. This approach is an extension of previous factored normalization methods in which we include separate factors for detector sensitivity, geometric response, block effects and deadtime. Since our MAP reconstruction approach already models some of the geometric factors in the forward projection, the normalization factors must be modified to account only for effects not already included in the model. We describe a maximum likelihood approach to joint estimation of the count-rate independent normalization factors, which we apply to data from a uniform cylindrical source. We then compute block-wise and block-profile deadtime correction factors using singles and coincidence data, respectively, from a multiframe cylindrical source. We have applied this method for reconstruction of data from the Concorde microPET P4 scanner. Quantitative evaluation of this method using well-counter measurements of activity in a multicompartment phantom compares favourably with normalization based directly on cylindrical source measurements. (author)

A semi-automatic image-based close range 3D modeling pipeline using a multi-camera configuration.

Science.gov (United States)

Rau, Jiann-Yeou; Yeh, Po-Chia

2012-01-01

The generation of photo-realistic 3D models is an important task for digital recording of cultural heritage objects. This study proposes an image-based 3D modeling pipeline which takes advantage of a multi-camera configuration and multi-image matching technique that does not require any markers on or around the object. Multiple digital single lens reflex (DSLR) cameras are adopted and fixed with invariant relative orientations. Instead of photo-triangulation after image acquisition, calibration is performed to estimate the exterior orientation parameters of the multi-camera configuration which can be processed fully automatically using coded targets. The calibrated orientation parameters of all cameras are applied to images taken using the same camera configuration. This means that when performing multi-image matching for surface point cloud generation, the orientation parameters will remain the same as the calibrated results, even when the target has changed. Base on this invariant character, the whole 3D modeling pipeline can be performed completely automatically, once the whole system has been calibrated and the software was seamlessly integrated. Several experiments were conducted to prove the feasibility of the proposed system. Images observed include that of a human being, eight Buddhist statues, and a stone sculpture. The results for the stone sculpture, obtained with several multi-camera configurations were compared with a reference model acquired by an ATOS-I 2M active scanner. The best result has an absolute accuracy of 0.26 mm and a relative accuracy of 1:17,333. It demonstrates the feasibility of the proposed low-cost image-based 3D modeling pipeline and its applicability to a large quantity of antiques stored in a museum.

Image processor of model-based vision system for assembly robots

International Nuclear Information System (INIS)

Moribe, H.; Nakano, M.; Kuno, T.; Hasegawa, J.

1987-01-01

A special purpose image preprocessor for the visual system of assembly robots has been developed. The main function unit is composed of lookup tables to utilize the advantage of semiconductor memory for large scale integration, high speed and low price. More than one unit may be operated in parallel since it is designed on the standard IEEE 796 bus. The operation time of the preprocessor in line segment extraction is usually 200 ms per 500 segments, though it differs according to the complexity of scene image. The gray-scale visual system supported by the model-based analysis program using the extracted line segments recognizes partially visible or overlapping industrial workpieces, and detects these locations and orientations

Model-based crosstalk compensation for simultaneous 99mTc/123I dual-isotope brain SPECT imaging.

Science.gov (United States)

Du, Yong; Tsui, Benjamin M W; Frey, Eric C

2007-09-01

In this work, we developed a model-based method to estimate and compensate for the crosstalk contamination in simultaneous 123I and 99mTc dual isotope brain single photo emission computed tomography imaging. The model-based crosstalk compensation (MBCC) includes detailed modeling of photon interactions inside both the object and the detector system. In the method, scatter in the object is modeled using the effective source scatter estimation technique, including contributions from all the photon emissions. The effects of the collimator-detector response, including the penetration and scatter components due to high-energy 123I photons, are modeled using precalculated tables of Monte Carlo simulated point-source response functions obtained from sources in air at various distances from the face of the collimator. The model-based crosstalk estimation method was combined with iterative reconstruction based compensation to reduce contamination due to crosstalk. The MBCC method was evaluated using Monte Carlo simulated and physical phantom experimentally acquired simultaneous dual-isotope data. Results showed that, for both experimental and simulation studies, the model-based method provided crosstalk estimates that were in good agreement with the true crosstalk. Compensation using MBCC improved image contrast and removed the artifacts for both Monte Carlo simulated and experimentally acquired data. The results were in good agreement with images acquired without any crosstalk contamination.

Model-based crosstalk compensation for simultaneous Tc99m∕I123 dual-isotope brain SPECT imaging.

Science.gov (United States)

Du, Yong; Tsui, Benjamin M W; Frey, Eric C

2007-09-01

In this work, we developed a model-based method to estimate and compensate for the crosstalk contamination in simultaneous I123 and Tc99m dual isotope brain single photo emission computed tomography imaging. The model-based crosstalk compensation (MBCC) includes detailed modeling of photon interactions inside both the object and the detector system. In the method, scatter in the object is modeled using the effective source scatter estimation technique, including contributions from all the photon emissions. The effects of the collimator-detector response, including the penetration and scatter components due to high-energy I123 photons, are modeled using pre-calculated tables of Monte Carlo simulated point-source response functions obtained from sources in air at various distances from the face of the collimator. The model-based crosstalk estimation method was combined with iterative reconstruction based compensation to reduce contamination due to crosstalk. The MBCC method was evaluated using Monte Carlo simulated and physical phantom experimentally acquired simultaneous dual-isotope data. Results showed that, for both experimental and simulation studies, the model-based method provided crosstalk estimates that were in good agreement with the true crosstalk. Compensation using MBCC improved image contrast and removed the artifacts for both Monte Carlo simulated and experimentally acquired data. The results were in good agreement with images acquired without any crosstalk contamination. © 2007 American Association of Physicists in Medicine.

Reconstructed image of human heart for total artificial heart implantation, based on MR image and cast silicone model of heart

International Nuclear Information System (INIS)

Komoda, Takashi; Maeta, Hajime; Uyama, Chikao.

1991-01-01

Based on transverse (TRN) and LV long axis (LAX) MR images of two cadaver hearts, three-dimensional (3-D) computer models of the connecting interface between remaining heart and total artificial heart, i.e., mitral and tricuspid valvular annuli (MVA and TVA), ascending aorta (Ao) and pulmonary artery (PA), were reconstructed to compare the shape and the size of MVA and those of TVA, the distance between the center of MVA and TVA (D G ), the angle between the plane of MVA and that of TVA (R T ), and the angles of Ao and PA, respectively, to the plane of MVA (R A , R P ), with those obtained in cast silicone models. It was found that based on LAX rather than TRN MR image, MVA and TVA might be more precisely reconstructed. The data obtained in 3-D images of MVA, TVA, Ao and PA based on silicone models of 32 hearts were as follows: D G (cm): 4.17±0.43, R T (degrees): 22.1±11.3, R A (degrees): 54.9±15.3, R P (degrees): 30.8±17.1. (author)

Multi-modal imaging, model-based tracking, and mixed reality visualisation for orthopaedic surgery

Science.gov (United States)

Fuerst, Bernhard; Tateno, Keisuke; Johnson, Alex; Fotouhi, Javad; Osgood, Greg; Tombari, Federico; Navab, Nassir

2017-01-01

Orthopaedic surgeons are still following the decades old workflow of using dozens of two-dimensional fluoroscopic images to drill through complex 3D structures, e.g. pelvis. This Letter presents a mixed reality support system, which incorporates multi-modal data fusion and model-based surgical tool tracking for creating a mixed reality environment supporting screw placement in orthopaedic surgery. A red–green–blue–depth camera is rigidly attached to a mobile C-arm and is calibrated to the cone-beam computed tomography (CBCT) imaging space via iterative closest point algorithm. This allows real-time automatic fusion of reconstructed surface and/or 3D point clouds and synthetic fluoroscopic images obtained through CBCT imaging. An adapted 3D model-based tracking algorithm with automatic tool segmentation allows for tracking of the surgical tools occluded by hand. This proposed interactive 3D mixed reality environment provides an intuitive understanding of the surgical site and supports surgeons in quickly localising the entry point and orienting the surgical tool during screw placement. The authors validate the augmentation by measuring target registration error and also evaluate the tracking accuracy in the presence of partial occlusion. PMID:29184659

Low contrast detectability and spatial resolution with model-based iterative reconstructions of MDCT images: a phantom and cadaveric study

Energy Technology Data Exchange (ETDEWEB)

Millon, Domitille; Coche, Emmanuel E. [Universite Catholique de Louvain, Department of Radiology and Medical Imaging, Cliniques Universitaires Saint Luc, Brussels (Belgium); Vlassenbroek, Alain [Philips Healthcare, Brussels (Belgium); Maanen, Aline G. van; Cambier, Samantha E. [Universite Catholique de Louvain, Statistics Unit, King Albert II Cancer Institute, Brussels (Belgium)

2017-03-15

To compare image quality [low contrast (LC) detectability, noise, contrast-to-noise (CNR) and spatial resolution (SR)] of MDCT images reconstructed with an iterative reconstruction (IR) algorithm and a filtered back projection (FBP) algorithm. The experimental study was performed on a 256-slice MDCT. LC detectability, noise, CNR and SR were measured on a Catphan phantom scanned with decreasing doses (48.8 down to 0.7 mGy) and parameters typical of a chest CT examination. Images were reconstructed with FBP and a model-based IR algorithm. Additionally, human chest cadavers were scanned and reconstructed using the same technical parameters. Images were analyzed to illustrate the phantom results. LC detectability and noise were statistically significantly different between the techniques, supporting model-based IR algorithm (p < 0.0001). At low doses, the noise in FBP images only enabled SR measurements of high contrast objects. The superior CNR of model-based IR algorithm enabled lower dose measurements, which showed that SR was dose and contrast dependent. Cadaver images reconstructed with model-based IR illustrated that visibility and delineation of anatomical structure edges could be deteriorated at low doses. Model-based IR improved LC detectability and enabled dose reduction. At low dose, SR became dose and contrast dependent. (orig.)

Feature-based Alignment of Volumetric Multi-modal Images

Science.gov (United States)

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

Infrared image background modeling based on improved Susan filtering

Science.gov (United States)

Yuehua, Xia

2018-02-01

When SUSAN filter is used to model the infrared image, the Gaussian filter lacks the ability of direction filtering. After filtering, the edge information of the image cannot be preserved well, so that there are a lot of edge singular points in the difference graph, increase the difficulties of target detection. To solve the above problems, the anisotropy algorithm is introduced in this paper, and the anisotropic Gauss filter is used instead of the Gauss filter in the SUSAN filter operator. Firstly, using anisotropic gradient operator to calculate a point of image's horizontal and vertical gradient, to determine the long axis direction of the filter; Secondly, use the local area of the point and the neighborhood smoothness to calculate the filter length and short axis variance; And then calculate the first-order norm of the difference between the local area of the point's gray-scale and mean, to determine the threshold of the SUSAN filter; Finally, the built SUSAN filter is used to convolution the image to obtain the background image, at the same time, the difference between the background image and the original image is obtained. The experimental results show that the background modeling effect of infrared image is evaluated by Mean Squared Error (MSE), Structural Similarity (SSIM) and local Signal-to-noise Ratio Gain (GSNR). Compared with the traditional filtering algorithm, the improved SUSAN filter has achieved better background modeling effect, which can effectively preserve the edge information in the image, and the dim small target is effectively enhanced in the difference graph, which greatly reduces the false alarm rate of the image.

New deconvolution method for microscopic images based on the continuous Gaussian radial basis function interpolation model.

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Chen, Zhaoxue; Chen, Hao

2014-01-01

A deconvolution method based on the Gaussian radial basis function (GRBF) interpolation is proposed. Both the original image and Gaussian point spread function are expressed as the same continuous GRBF model, thus image degradation is simplified as convolution of two continuous Gaussian functions, and image deconvolution is converted to calculate the weighted coefficients of two-dimensional control points. Compared with Wiener filter and Lucy-Richardson algorithm, the GRBF method has an obvious advantage in the quality of restored images. In order to overcome such a defect of long-time computing, the method of graphic processing unit multithreading or increasing space interval of control points is adopted, respectively, to speed up the implementation of GRBF method. The experiments show that based on the continuous GRBF model, the image deconvolution can be efficiently implemented by the method, which also has a considerable reference value for the study of three-dimensional microscopic image deconvolution.

AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO PROTEOMICS DATA.

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Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Herrick, Richard C; Sanna, Pietro; Gutstein, Howard

2011-01-01

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper, we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate. Although the method we present is general and can be applied to quantitative image data from any application, in this paper we focus on image-based proteomic data. We apply our method to an animal study investigating the effects of opiate addiction on the brain proteome. Our image-based functional mixed model approach finds results that are missed with conventional spot-based analysis approaches. In particular, we find that the significant regions of the image identified by the proposed method

Active vision and image/video understanding with decision structures based on the network-symbolic models

Science.gov (United States)

Kuvich, Gary

2003-08-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. The ability of human brain to emulate knowledge structures in the form of networks-symbolic models is found. And that means an important shift of paradigm in our knowledge about brain from neural networks to "cortical software". Symbols, predicates and grammars naturally emerge in such active multilevel hierarchical networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type decision structure created via multilevel hierarchical compression of visual information. Mid-level vision processes like clustering, perceptual grouping, separation of figure from ground, are special kinds of graph/network transformations. They convert low-level image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models works similar to frames and agents, combines learning, classification, analogy together with higher-level model-based reasoning into a single framework. Such models do not require supercomputers. Based on such principles, and using methods of Computational intelligence, an Image Understanding system can convert images into the network-symbolic knowledge models, and effectively resolve uncertainty and ambiguity, providing unifying representation for perception and cognition. That allows creating new intelligent computer vision systems for robotic and defense industries.

Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

DEFF Research Database (Denmark)

Huang, Sijia; Chong, Nicole; Lewis, Nathan

2016-01-01

diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. Results: The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under.......993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. Conclusions: We have successfully developed a new type of pathway-based model to study...... metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease...

Application of Finite Element Modeling Methods in Magnetic Resonance Imaging-Based Research and Clinical Management

Science.gov (United States)

Fwu, Peter Tramyeon

The medical image is very complex by its nature. Modeling built upon the medical image is challenging due to the lack of analytical solution. Finite element method (FEM) is a numerical technique which can be used to solve the partial differential equations. It utilized the transformation from a continuous domain into solvable discrete sub-domains. In three-dimensional space, FEM has the capability dealing with complicated structure and heterogeneous interior. That makes FEM an ideal tool to approach the medical-image based modeling problems. In this study, I will address the three modeling in (1) photon transport inside the human breast by implanting the radiative transfer equation to simulate the diffuse optical spectroscopy imaging (DOSI) in order to measurement the percent density (PD), which has been proven as a cancer risk factor in mammography. Our goal is to use MRI as the ground truth to optimize the DOSI scanning protocol to get a consistent measurement of PD. Our result shows DOSI measurement is position and depth dependent and proper scanning scheme and body configuration are needed; (2) heat flow in the prostate by implementing the Penne's bioheat equation to evaluate the cooling performance of regional hypothermia during the robot assisted radical prostatectomy for the individual patient in order to achieve the optimal cooling setting. Four factors are taken into account during the simulation: blood abundance, artery perfusion, cooling balloon temperature, and the anatomical distance. The result shows that blood abundance, prostate size, and anatomical distance are significant factors to the equilibrium temperature of neurovascular bundle; (3) shape analysis in hippocampus by using the radial distance mapping, and two registration methods to find the correlation between sub-regional change to the age and cognition performance, which might not reveal in the volumetric analysis. The result gives a fundamental knowledge of normal distribution in young

Generalized image contrast enhancement technique based on Heinemann contrast discrimination model

Science.gov (United States)

Liu, Hong; Nodine, Calvin F.

1994-03-01

This paper presents a generalized image contrast enhancement technique which equalizes perceived brightness based on the Heinemann contrast discrimination model. This is a modified algorithm which presents an improvement over the previous study by Mokrane in its mathematically proven existence of a unique solution and in its easily tunable parameterization. The model uses a log-log representation of contrast luminosity between targets and the surround in a fixed luminosity background setting. The algorithm consists of two nonlinear gray-scale mapping functions which have seven parameters, two of which are adjustable Heinemann constants. Another parameter is the background gray level. The remaining four parameters are nonlinear functions of gray scale distribution of the image, and can be uniquely determined once the previous three are given. Tests have been carried out to examine the effectiveness of the algorithm for increasing the overall contrast of images. It can be demonstrated that the generalized algorithm provides better contrast enhancement than histogram equalization. In fact, the histogram equalization technique is a special case of the proposed mapping.

Image Structure-Preserving Denoising Based on Difference Curvature Driven Fractional Nonlinear Diffusion

Directory of Open Access Journals (Sweden)

Xuehui Yin

2015-01-01

Full Text Available The traditional integer-order partial differential equations and gradient regularization based image denoising techniques often suffer from staircase effect, speckle artifacts, and the loss of image contrast and texture details. To address these issues, in this paper, a difference curvature driven fractional anisotropic diffusion for image noise removal is presented, which uses two new techniques, fractional calculus and difference curvature, to describe the intensity variations in images. The fractional-order derivatives information of an image can deal well with the textures of the image and achieve a good tradeoff between eliminating speckle artifacts and restraining staircase effect. The difference curvature constructed by the second order derivatives along the direction of gradient of an image and perpendicular to the gradient can effectively distinguish between ramps and edges. Fourier transform technique is also proposed to compute the fractional-order derivative. Experimental results demonstrate that the proposed denoising model can avoid speckle artifacts and staircase effect and preserve important features such as curvy edges, straight edges, ramps, corners, and textures. They are obviously superior to those of traditional integral based methods. The experimental results also reveal that our proposed model yields a good visual effect and better values of MSSIM and PSNR.

The model of illumination-transillumination for image enhancement of X-ray images

Energy Technology Data Exchange (ETDEWEB)

Lyu, Kwang Yeul [Shingu College, Sungnam (Korea, Republic of); Rhee, Sang Min [Kangwon National Univ., Chuncheon (Korea, Republic of)

2001-06-01

In digital image processing, the homomorphic filtering approach is derived from an illumination - reflectance model of the image. It can also be used with an illumination-transillumination model X-ray film. Several X-ray images were applied to enhancement with histogram equalization and homomorphic filter based on an illumination-transillumination model. The homomorphic filter has proven theoretical claim of image density range compression and balanced contrast enhancement, and also was found a valuable tool to process analog X-ray images to digital images.

Empirical study of travel mode forecasting improvement for the combined revealed preference/stated preference data–based discrete choice model

Directory of Open Access Journals (Sweden)

Yanfu Qiao

2016-01-01

Full Text Available The combined revealed preference/stated preference data–based discrete choice model has provided the actual choice-making restraints as well as reduced the prediction errors. But the random error variance of alternatives belonging to different data would impact its universality. In this article, we studied the traffic corridor between Chengdu and Longquan with the revealed preference/stated preference joint model, and the single stated preference data model separately predicted the choice probability of each mode. We found the revealed preference/stated preference joint model is universal only when there is a significant difference between the random error terms in different data. The single stated preference data would amplify the travelers’ preference and cause prediction error. We proposed a universal way that uses revealed preference data to modify the single stated preference data parameter estimation results to achieve the composite utility and reduce the prediction error. And the result suggests that prediction results are more reasonable based on the composite utility than the results based on the single stated preference data, especially forecasting the mode share of bus. The future metro line will be the main travel mode in this corridor, and 45% of passenger flow will transfer to the metro.

Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids.

Science.gov (United States)

Schmitz, Alexander; Fischer, Sabine C; Mattheyer, Christian; Pampaloni, Francesco; Stelzer, Ernst H K

2017-03-03

Three-dimensional multicellular aggregates such as spheroids provide reliable in vitro substitutes for tissues. Quantitative characterization of spheroids at the cellular level is fundamental. We present the first pipeline that provides three-dimensional, high-quality images of intact spheroids at cellular resolution and a comprehensive image analysis that completes traditional image segmentation by algorithms from other fields. The pipeline combines light sheet-based fluorescence microscopy of optically cleared spheroids with automated nuclei segmentation (F score: 0.88) and concepts from graph analysis and computational topology. Incorporating cell graphs and alpha shapes provided more than 30 features of individual nuclei, the cellular neighborhood and the spheroid morphology. The application of our pipeline to a set of breast carcinoma spheroids revealed two concentric layers of different cell density for more than 30,000 cells. The thickness of the outer cell layer depends on a spheroid's size and varies between 50% and 75% of its radius. In differently-sized spheroids, we detected patches of different cell densities ranging from 5 × 10 5 to 1 × 10 6  cells/mm 3 . Since cell density affects cell behavior in tissues, structural heterogeneities need to be incorporated into existing models. Our image analysis pipeline provides a multiscale approach to obtain the relevant data for a system-level understanding of tissue architecture.

A Waterline Extraction Method from Remote Sensing Image Based on Quad-tree and Multiple Active Contour Model

Directory of Open Access Journals (Sweden)

YU Jintao

2016-09-01

Full Text Available After the characteristics of geodesic active contour model (GAC, Chan-Vese model(CV and local binary fitting model(LBF are analyzed, and the active contour model based on regions and edges is combined with image segmentation method based on quad-tree, a waterline extraction method based on quad-tree and multiple active contour model is proposed in this paper. Firstly, the method provides an initial contour according to quad-tree segmentation. Secondly, a new signed pressure force(SPF function based on global image statistics information of CV model and local image statistics information of LBF model has been defined, and then ,the edge stopping function(ESF is replaced by the proposed SPF function, which solves the problem such as evolution stopped in advance and excessive evolution. Finally, the selective binary and Gaussian filtering level set method is used to avoid reinitializing and regularization to improve the evolution efficiency. The experimental results show that this method can effectively extract the weak edges and serious concave edges, and owns some properties such as sub-pixel accuracy, high efficiency and reliability for waterline extraction.

Model-based respiratory motion compensation for emission tomography image reconstruction

International Nuclear Information System (INIS)

Reyes, M; Malandain, G; Koulibaly, P M; Gonzalez-Ballester, M A; Darcourt, J

2007-01-01

In emission tomography imaging, respiratory motion causes artifacts in lungs and cardiac reconstructed images, which lead to misinterpretations, imprecise diagnosis, impairing of fusion with other modalities, etc. Solutions like respiratory gating, correlated dynamic PET techniques, list-mode data based techniques and others have been tested, which lead to improvements over the spatial activity distribution in lungs lesions, but which have the disadvantages of requiring additional instrumentation or the need of discarding part of the projection data used for reconstruction. The objective of this study is to incorporate respiratory motion compensation directly into the image reconstruction process, without any additional acquisition protocol consideration. To this end, we propose an extension to the maximum likelihood expectation maximization (MLEM) algorithm that includes a respiratory motion model, which takes into account the displacements and volume deformations produced by the respiratory motion during the data acquisition process. We present results from synthetic simulations incorporating real respiratory motion as well as from phantom and patient data

Reconstruction and simplification of urban scene models based on oblique images

Science.gov (United States)

Liu, J.; Guo, B.

2014-08-01

We describe a multi-view stereo reconstruction and simplification algorithms for urban scene models based on oblique images. The complexity, diversity, and density within the urban scene, it increases the difficulty to build the city models using the oblique images. But there are a lot of flat surfaces existing in the urban scene. One of our key contributions is that a dense matching algorithm based on Self-Adaptive Patch in view of the urban scene is proposed. The basic idea of matching propagating based on Self-Adaptive Patch is to build patches centred by seed points which are already matched. The extent and shape of the patches can adapt to the objects of urban scene automatically: when the surface is flat, the extent of the patch would become bigger; while the surface is very rough, the extent of the patch would become smaller. The other contribution is that the mesh generated by Graph Cuts is 2-manifold surface satisfied the half edge data structure. It is solved by clustering and re-marking tetrahedrons in s-t graph. The purpose of getting 2- manifold surface is to simply the mesh by edge collapse algorithm which can preserve and stand out the features of buildings.

Image fusion in craniofacial virtual reality modeling based on CT and 3dMD photogrammetry.

Science.gov (United States)

Xin, Pengfei; Yu, Hongbo; Cheng, Huanchong; Shen, Shunyao; Shen, Steve G F

2013-09-01

The aim of this study was to demonstrate the feasibility of building a craniofacial virtual reality model by image fusion of 3-dimensional (3D) CT models and 3 dMD stereophotogrammetric facial surface. A CT scan and stereophotography were performed. The 3D CT models were reconstructed by Materialise Mimics software, and the stereophotogrammetric facial surface was reconstructed by 3 dMD patient software. All 3D CT models were exported as Stereo Lithography file format, and the 3 dMD model was exported as Virtual Reality Modeling Language file format. Image registration and fusion were performed in Mimics software. Genetic algorithm was used for precise image fusion alignment with minimum error. The 3D CT models and the 3 dMD stereophotogrammetric facial surface were finally merged into a single file and displayed using Deep Exploration software. Errors between the CT soft tissue model and 3 dMD facial surface were also analyzed. Virtual model based on CT-3 dMD image fusion clearly showed the photorealistic face and bone structures. Image registration errors in virtual face are mainly located in bilateral cheeks and eyeballs, and the errors are more than 1.5 mm. However, the image fusion of whole point cloud sets of CT and 3 dMD is acceptable with a minimum error that is less than 1 mm. The ease of use and high reliability of CT-3 dMD image fusion allows the 3D virtual head to be an accurate, realistic, and widespread tool, and has a great benefit to virtual face model.

Image based Monument Recognition using Graph based Visual Saliency

DEFF Research Database (Denmark)

Kalliatakis, Grigorios; Triantafyllidis, Georgios

2013-01-01

This article presents an image-based application aiming at simple image classification of well-known monuments in the area of Heraklion, Crete, Greece. This classification takes place by utilizing Graph Based Visual Saliency (GBVS) and employing Scale Invariant Feature Transform (SIFT) or Speeded......, the images have been previously processed according to the Graph Based Visual Saliency model in order to keep either SIFT or SURF features corresponding to the actual monuments while the background “noise” is minimized. The application is then able to classify these images, helping the user to better...

Image-optimized Coronal Magnetic Field Models

Energy Technology Data Exchange (ETDEWEB)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M., E-mail: shaela.i.jones-mecholsky@nasa.gov, E-mail: shaela.i.jonesmecholsky@nasa.gov [NASA Goddard Space Flight Center, Code 670, Greenbelt, MD 20771 (United States)

2017-08-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work, we presented early tests of the method, which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper, we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane and the effect on the outcome of the optimization of errors in the localization of constraints. We find that substantial improvement in the model field can be achieved with these types of constraints, even when magnetic features in the images are located outside of the image plane.

Image-Optimized Coronal Magnetic Field Models

Science.gov (United States)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M.

2017-01-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work we presented early tests of the method which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane, and the effect on the outcome of the optimization of errors in localization of constraints. We find that substantial improvement in the model field can be achieved with this type of constraints, even when magnetic features in the images are located outside of the image plane.

Image-based compound profiling reveals a dual inhibitor of tyrosine kinase and microtubule polymerization.

Science.gov (United States)

Tanabe, Kenji

2016-04-27

Small-molecule compounds are widely used as biological research tools and therapeutic drugs. Therefore, uncovering novel targets of these compounds should provide insights that are valuable in both basic and clinical studies. I developed a method for image-based compound profiling by quantitating the effects of compounds on signal transduction and vesicle trafficking of epidermal growth factor receptor (EGFR). Using six signal transduction molecules and two markers of vesicle trafficking, 570 image features were obtained and subjected to multivariate analysis. Fourteen compounds that affected EGFR or its pathways were classified into four clusters, based on their phenotypic features. Surprisingly, one EGFR inhibitor (CAS 879127-07-8) was classified into the same cluster as nocodazole, a microtubule depolymerizer. In fact, this compound directly depolymerized microtubules. These results indicate that CAS 879127-07-8 could be used as a chemical probe to investigate both the EGFR pathway and microtubule dynamics. The image-based multivariate analysis developed herein has potential as a powerful tool for discovering unexpected drug properties.

Nephrus: expert system model in intelligent multilayers for evaluation of urinary system based on scintigraphic image analysis

International Nuclear Information System (INIS)

Silva, Jorge Wagner Esteves da; Schirru, Roberto; Boasquevisque, Edson Mendes

1999-01-01

Renal function can be measured noninvasively with radionuclides in a extremely safe way compared to other diagnosis techniques. Nevertheless, due to the fact that radioactive materials are used in this procedure, it is necessary to maximize its benefits, therefore all efforts are justifiable in the development of data analysis support tools for this diagnosis modality. The objective of this work is to develop a prototype for a system model based on Artificial Intelligence devices able to perform functions related to cintilographic image analysis of the urinary system. Rules used by medical experts in the analysis of images obtained with 99m Tc+DTPA and /or 99m Tc+DMSA were modeled and a Neural Network diagnosis technique was implemented. Special attention was given for designing programs user-interface. Human Factor Engineering techniques were taking in account allowing friendliness and robustness. The image segmentation adopts a model based on Ideal ROIs, which represent the normal anatomic concept for urinary system organs. Results obtained using Artificial Neural Networks for qualitative image analysis and knowledge model constructed show the feasibility of Artificial Neural Networks for qualitative image analysis and knowledge model constructed show feasibility of Artificial Intelligence implementation that uses inherent abilities of each technique in the medical diagnosis image analysis. (author)

Image-Based Modeling Techniques for Architectural Heritage 3d Digitalization: Limits and Potentialities

Science.gov (United States)

Santagati, C.; Inzerillo, L.; Di Paola, F.

2013-07-01

3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS), the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases) to large scale buildings for practitioner purpose.

WE-G-207-06: 3D Fluoroscopic Image Generation From Patient-Specific 4DCBCT-Based Motion Models Derived From Physical Phantom and Clinical Patient Images

International Nuclear Information System (INIS)

Dhou, S; Cai, W; Hurwitz, M; Rottmann, J; Myronakis, M; Cifter, F; Berbeco, R; Lewis, J; Williams, C; Mishra, P; Ionascu, D

2015-01-01

Purpose: Respiratory-correlated cone-beam CT (4DCBCT) images acquired immediately prior to treatment have the potential to represent patient motion patterns and anatomy during treatment, including both intra- and inter-fractional changes. We develop a method to generate patient-specific motion models based on 4DCBCT images acquired with existing clinical equipment and used to generate time varying volumetric images (3D fluoroscopic images) representing motion during treatment delivery. Methods: Motion models are derived by deformably registering each 4DCBCT phase to a reference phase, and performing principal component analysis (PCA) on the resulting displacement vector fields. 3D fluoroscopic images are estimated by optimizing the resulting PCA coefficients iteratively through comparison of the cone-beam projections simulating kV treatment imaging and digitally reconstructed radiographs generated from the motion model. Patient and physical phantom datasets are used to evaluate the method in terms of tumor localization error compared to manually defined ground truth positions. Results: 4DCBCT-based motion models were derived and used to generate 3D fluoroscopic images at treatment time. For the patient datasets, the average tumor localization error and the 95th percentile were 1.57 and 3.13 respectively in subsets of four patient datasets. For the physical phantom datasets, the average tumor localization error and the 95th percentile were 1.14 and 2.78 respectively in two datasets. 4DCBCT motion models are shown to perform well in the context of generating 3D fluoroscopic images due to their ability to reproduce anatomical changes at treatment time. Conclusion: This study showed the feasibility of deriving 4DCBCT-based motion models and using them to generate 3D fluoroscopic images at treatment time in real clinical settings. 4DCBCT-based motion models were found to account for the 3D non-rigid motion of the patient anatomy during treatment and have the potential

Fourier-Mellin moment-based intertwining map for image encryption

Science.gov (United States)

Kaur, Manjit; Kumar, Vijay

2018-03-01

In this paper, a robust image encryption technique that utilizes Fourier-Mellin moments and intertwining logistic map is proposed. Fourier-Mellin moment-based intertwining logistic map has been designed to overcome the issue of low sensitivity of an input image. Multi-objective Non-Dominated Sorting Genetic Algorithm (NSGA-II) based on Reinforcement Learning (MNSGA-RL) has been used to optimize the required parameters of intertwining logistic map. Fourier-Mellin moments are used to make the secret keys more secure. Thereafter, permutation and diffusion operations are carried out on input image using secret keys. The performance of proposed image encryption technique has been evaluated on five well-known benchmark images and also compared with seven well-known existing encryption techniques. The experimental results reveal that the proposed technique outperforms others in terms of entropy, correlation analysis, a unified average changing intensity and the number of changing pixel rate. The simulation results reveal that the proposed technique provides high level of security and robustness against various types of attacks.

Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images

Science.gov (United States)

Yao, Shoukui; Qin, Xiaojuan

2018-02-01

Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.

Application of REVEAL-W to risk-based configuration control

International Nuclear Information System (INIS)

Dezfuli, H.; Meyer, J.; Modarres, M.

1994-01-01

Over the past two years, the concept of risk-based configuration control has been introduced to the US Nuclear Regulatory Commission and the nuclear industry. Converting much of the current, deterministically based regulation of nuclear power plants to risk-based regulation can result in lower levels of risk while relieving unnecessary burdens on power plant operators and regulatory staff. To achieve the potential benefits of risk-based configuration control, the risk models developed for nuclear power plants should be (1) flexible enough to effectively support necessary risk calculations, and (2) transparent enough to encourage their use by all parties. To address these needs, SCIENTECH, Inc., has developed the PC-based REVEAL W (formerly known as SMART). This graphic-oriented and user-friendly application software allows the user to develop transparent complex logic models based on the concept of the master plant logic diagram. The logic model is success-oriented and compact. The analytical capability built into REVEAL W is generic, so the software can support different types of risk-based evaluations, such as probabilistic safety assessment, accident sequence precursor analysis, design evaluation and configuration management. In this paper, we focus on the application of REVEAL W to support risk-based configuration control of nuclear power plants. (author)

Spiking cortical model-based nonlocal means method for speckle reduction in optical coherence tomography images

Science.gov (United States)

Zhang, Xuming; Li, Liu; Zhu, Fei; Hou, Wenguang; Chen, Xinjian

2014-06-01

Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details very well due to its effectiveness in finding reliable similar patches under high speckle noise contamination. When applied to the retinal OCT image, this method provides signal-to-noise ratio improvements of >16 dB with a small 5.4% loss of similarity.

Unified modeling language and design of a case-based retrieval system in medical imaging.

Science.gov (United States)

LeBozec, C; Jaulent, M C; Zapletal, E; Degoulet, P

1998-01-01

One goal of artificial intelligence research into case-based reasoning (CBR) systems is to develop approaches for designing useful and practical interactive case-based environments. Explaining each step of the design of the case-base and of the retrieval process is critical for the application of case-based systems to the real world. We describe herein our approach to the design of IDEM--Images and Diagnosis from Examples in Medicine--a medical image case-based retrieval system for pathologists. Our approach is based on the expressiveness of an object-oriented modeling language standard: the Unified Modeling Language (UML). We created a set of diagrams in UML notation illustrating the steps of the CBR methodology we used. The key aspect of this approach was selecting the relevant objects of the system according to user requirements and making visualization of cases and of the components of the case retrieval process. Further evaluation of the expressiveness of the design document is required but UML seems to be a promising formalism, improving the communication between the developers and users.

Image degradation characteristics and restoration based on regularization for diffractive imaging

Science.gov (United States)

Zhi, Xiyang; Jiang, Shikai; Zhang, Wei; Wang, Dawei; Li, Yun

2017-11-01

The diffractive membrane optical imaging system is an important development trend of ultra large aperture and lightweight space camera. However, related investigations on physics-based diffractive imaging degradation characteristics and corresponding image restoration methods are less studied. In this paper, the model of image quality degradation for the diffraction imaging system is first deduced mathematically based on diffraction theory and then the degradation characteristics are analyzed. On this basis, a novel regularization model of image restoration that contains multiple prior constraints is established. After that, the solving approach of the equation with the multi-norm coexistence and multi-regularization parameters (prior's parameters) is presented. Subsequently, the space-variant PSF image restoration method for large aperture diffractive imaging system is proposed combined with block idea of isoplanatic region. Experimentally, the proposed algorithm demonstrates its capacity to achieve multi-objective improvement including MTF enhancing, dispersion correcting, noise and artifact suppressing as well as image's detail preserving, and produce satisfactory visual quality. This can provide scientific basis for applications and possesses potential application prospects on future space applications of diffractive membrane imaging technology.

SU-F-J-178: A Computer Simulation Model Observer for Task-Based Image Quality Assessment in Radiation Therapy

International Nuclear Information System (INIS)

Dolly, S; Mutic, S; Anastasio, M; Li, H; Yu, L

2016-01-01

Purpose: Traditionally, image quality in radiation therapy is assessed subjectively or by utilizing physically-based metrics. Some model observers exist for task-based medical image quality assessment, but almost exclusively for diagnostic imaging tasks. As opposed to disease diagnosis, the task for image observers in radiation therapy is to utilize the available images to design and deliver a radiation dose which maximizes patient disease control while minimizing normal tissue damage. The purpose of this study was to design and implement a new computer simulation model observer to enable task-based image quality assessment in radiation therapy. Methods: A modular computer simulation framework was developed to resemble the radiotherapy observer by simulating an end-to-end radiation therapy treatment. Given images and the ground-truth organ boundaries from a numerical phantom as inputs, the framework simulates an external beam radiation therapy treatment and quantifies patient treatment outcomes using the previously defined therapeutic operating characteristic (TOC) curve. As a preliminary demonstration, TOC curves were calculated for various CT acquisition and reconstruction parameters, with the goal of assessing and optimizing simulation CT image quality for radiation therapy. Sources of randomness and bias within the system were analyzed. Results: The relationship between CT imaging dose and patient treatment outcome was objectively quantified in terms of a singular value, the area under the TOC (AUTOC) curve. The AUTOC decreases more rapidly for low-dose imaging protocols. AUTOC variation introduced by the dose optimization algorithm was approximately 0.02%, at the 95% confidence interval. Conclusion: A model observer has been developed and implemented to assess image quality based on radiation therapy treatment efficacy. It enables objective determination of appropriate imaging parameter values (e.g. imaging dose). Framework flexibility allows for incorporation

GPU based Monte Carlo for PET image reconstruction: detector modeling

International Nuclear Information System (INIS)

Légrády; Cserkaszky, Á; Lantos, J.; Patay, G.; Bükki, T.

2011-01-01

Monte Carlo (MC) calculations and Graphical Processing Units (GPUs) are almost like the dedicated hardware designed for the specific task given the similarities between visible light transport and neutral particle trajectories. A GPU based MC gamma transport code has been developed for Positron Emission Tomography iterative image reconstruction calculating the projection from unknowns to data at each iteration step taking into account the full physics of the system. This paper describes the simplified scintillation detector modeling and its effect on convergence. (author)

Task-Based Modeling of a 5k Ultra-High-Resolution Medical Imaging System for Digital Breast Tomosynthesis.

Science.gov (United States)

Zhao, Chumin; Kanicki, Jerzy

2017-09-01

High-resolution, low-noise X-ray detectors based on CMOS active pixel sensor (APS) technology have demonstrated superior imaging performance for digital breast tomosynthesis (DBT). This paper presents a task-based model for a high-resolution medical imaging system to evaluate its ability to detect simulated microcalcifications and masses as lesions for breast cancer. A 3-D cascaded system analysis for a 50- [Formula: see text] pixel pitch CMOS APS X-ray detector was integrated with an object task function, a medical imaging display model, and the human eye contrast sensitivity function to calculate the detectability index and area under the ROC curve (AUC). It was demonstrated that the display pixel pitch and zoom factor should be optimized to improve the AUC for detecting small microcalcifications. In addition, detector electronic noise of smaller than 300 e - and a high display maximum luminance (>1000 cd/cm 2 ) are desirable to distinguish microcalcifications of [Formula: see text] in size. For low contrast mass detection, a medical imaging display with a minimum of 12-bit gray levels is recommended to realize accurate luminance levels. A wide projection angle range of greater than ±30° in combination with the image gray level magnification could improve the mass detectability especially when the anatomical background noise is high. On the other hand, a narrower projection angle range below ±20° can improve the small, high contrast object detection. Due to the low mass contrast and luminance, the ambient luminance should be controlled below 5 cd/ [Formula: see text]. Task-based modeling provides important firsthand imaging performance of the high-resolution CMOS-based medical imaging system that is still at early stage development for DBT. The modeling results could guide the prototype design and clinical studies in the future.

Pc-Based Floating Point Imaging Workstation

Science.gov (United States)

Guzak, Chris J.; Pier, Richard M.; Chinn, Patty; Kim, Yongmin

1989-07-01

The medical, military, scientific and industrial communities have come to rely on imaging and computer graphics for solutions to many types of problems. Systems based on imaging technology are used to acquire and process images, and analyze and extract data from images that would otherwise be of little use. Images can be transformed and enhanced to reveal detail and meaning that would go undetected without imaging techniques. The success of imaging has increased the demand for faster and less expensive imaging systems and as these systems become available, more and more applications are discovered and more demands are made. From the designer's perspective the challenge to meet these demands forces him to attack the problem of imaging from a different perspective. The computing demands of imaging algorithms must be balanced against the desire for affordability and flexibility. Systems must be flexible and easy to use, ready for current applications but at the same time anticipating new, unthought of uses. Here at the University of Washington Image Processing Systems Lab (IPSL) we are focusing our attention on imaging and graphics systems that implement imaging algorithms for use in an interactive environment. We have developed a PC-based imaging workstation with the goal to provide powerful and flexible, floating point processing capabilities, along with graphics functions in an affordable package suitable for diverse environments and many applications.

Featured Image: Revealing Hidden Objects with Color

Science.gov (United States)

Kohler, Susanna

2018-02-01

Stunning color astronomical images can often be the motivation for astronomers to continue slogging through countless data files, calculations, and simulations as we seek to understand the mysteries of the universe. But sometimes the stunning images can, themselves, be the source of scientific discovery. This is the case with the below image of Lynds Dark Nebula 673, located in the Aquila constellation, that was captured with the Mayall 4-meter telescope at Kitt Peak National Observatory by a team of scientists led by Travis Rector (University of Alaska Anchorage). After creating the image with a novel color-composite imaging method that reveals faint H emission (visible in red in both images here), Rector and collaborators identified the presence of a dozen new Herbig-Haro objects small cloud patches that are caused when material is energetically flung out from newly born stars. The image adapted above shows three of the new objects, HH 118789, aligned with two previously known objects, HH 32 and 332 suggesting they are driven by the same source. For more beautiful images and insight into the authors discoveries, check out the article linked below!Full view of Lynds Dark Nebula 673. Click for the larger view this beautiful composite image deserves! [T.A. Rector (University of Alaska Anchorage) and H. Schweiker (WIYN and NOAO/AURA/NSF)]CitationT. A. Rector et al 2018 ApJ 852 13. doi:10.3847/1538-4357/aa9ce1

Quantitative imaging reveals heterogeneous growth dynamics and treatment-dependent residual tumor distributions in a three-dimensional ovarian cancer model

Science.gov (United States)

Celli, Jonathan P.; Rizvi, Imran; Evans, Conor L.; Abu-Yousif, Adnan O.; Hasan, Tayyaba

2010-09-01

Three-dimensional tumor models have emerged as valuable in vitro research tools, though the power of such systems as quantitative reporters of tumor growth and treatment response has not been adequately explored. We introduce an approach combining a 3-D model of disseminated ovarian cancer with high-throughput processing of image data for quantification of growth characteristics and cytotoxic response. We developed custom MATLAB routines to analyze longitudinally acquired dark-field microscopy images containing thousands of 3-D nodules. These data reveal a reproducible bimodal log-normal size distribution. Growth behavior is driven by migration and assembly, causing an exponential decay in spatial density concomitant with increasing mean size. At day 10, cultures are treated with either carboplatin or photodynamic therapy (PDT). We quantify size-dependent cytotoxic response for each treatment on a nodule by nodule basis using automated segmentation combined with ratiometric batch-processing of calcein and ethidium bromide fluorescence intensity data (indicating live and dead cells, respectively). Both treatments reduce viability, though carboplatin leaves micronodules largely structurally intact with a size distribution similar to untreated cultures. In contrast, PDT treatment disrupts micronodular structure, causing punctate regions of toxicity, shifting the distribution toward smaller sizes, and potentially increasing vulnerability to subsequent chemotherapeutic treatment.

Supervised Gaussian mixture model based remote sensing image ...

African Journals Online (AJOL)

Using the supervised classification technique, both simulated and empirical satellite remote sensing data are used to train and test the Gaussian mixture model algorithm. For the purpose of validating the experiment, the resulting classified satellite image is compared with the ground truth data. For the simulated modelling, ...

A classification model of Hyperion image base on SAM combined decision tree

Science.gov (United States)

Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin

2009-10-01

Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model

A 4D global respiratory motion model of the thorax based on CT images: A proof of concept.

Science.gov (United States)

Fayad, Hadi; Gilles, Marlene; Pan, Tinsu; Visvikis, Dimitris

2018-05-17

Respiratory motion reduces the sensitivity and specificity of medical images especially in the thoracic and abdominal areas. It may affect applications such as cancer diagnostic imaging and/or radiation therapy (RT). Solutions to this issue include modeling of the respiratory motion in order to optimize both diagnostic and therapeutic protocols. Personalized motion modeling required patient-specific four-dimensional (4D) imaging which in the case of 4D computed tomography (4D CT) acquisition is associated with an increased dose. The goal of this work was to develop a global respiratory motion model capable of relating external patient surface motion to internal structure motion without the need for a patient-specific 4D CT acquisition. The proposed global model is based on principal component analysis and can be adjusted to a given patient anatomy using only one or two static CT images in conjunction with a respiratory synchronized patient external surface motion. It is based on the relation between the internal motion described using deformation fields obtained by registering 4D CT images and patient surface maps obtained either from optical imaging devices or extracted from CT image-based patient skin segmentation. 4D CT images of six patients were used to generate the global motion model which was validated by adapting it on four different patients having skin segmented surfaces and two other patients having time of flight camera acquired surfaces. The reproducibility of the proposed model was also assessed on two patients with two 4D CT series acquired within 2 weeks of each other. Profile comparison shows the efficacy of the global respiratory motion model and an improvement while using two CT images in order to adapt the model. This was confirmed by the correlation coefficient with a mean correlation of 0.9 and 0.95 while using one or two CT images respectively and when comparing acquired to model generated 4D CT images. For the four patients with segmented

Superpixel-Based Feature for Aerial Image Scene Recognition

Directory of Open Access Journals (Sweden)

Hongguang Li

2018-01-01

Full Text Available Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV. The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features.

Image segmentation of overlapping leaves based on Chan–Vese model and Sobel operator

Directory of Open Access Journals (Sweden)

Zhibin Wang

2018-03-01

Full Text Available To improve the segmentation precision of overlapping crop leaves, this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator. The approach consists of three stages. First, a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background. Second, the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges, respectively. Third, a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator. To verify the effectiveness of the proposed algorithm, a segmentation experiment was performed on 30 images of cucumber leaf. The mean error rate of the proposed method is 0.0428, which is a decrease of 6.54% compared with the mean error rate of the level set method. Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.

IMAGE-BASED MODELING TECHNIQUES FOR ARCHITECTURAL HERITAGE 3D DIGITALIZATION: LIMITS AND POTENTIALITIES

Directory of Open Access Journals (Sweden)

C. Santagati

2013-07-01

Full Text Available 3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS, the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases to large scale buildings for practitioner purpose.

1H NMR-based metabolic profiling reveals inherent biological variation in yeast and nematode model systems

International Nuclear Information System (INIS)

Szeto, Samuel S. W.; Reinke, Stacey N.; Lemire, Bernard D.

2011-01-01

The application of metabolomics to human and animal model systems is poised to provide great insight into our understanding of disease etiology and the metabolic changes that are associated with these conditions. However, metabolomic studies have also revealed that there is significant, inherent biological variation in human samples and even in samples from animal model systems where the animals are housed under carefully controlled conditions. This inherent biological variability is an important consideration for all metabolomics analyses. In this study, we examined the biological variation in 1 H NMR-based metabolic profiling of two model systems, the yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans. Using relative standard deviations (RSD) as a measure of variability, our results reveal that both model systems have significant amounts of biological variation. The C. elegans metabolome possesses greater metabolic variance with average RSD values of 29 and 39%, depending on the food source that was used. The S. cerevisiae exometabolome RSD values ranged from 8% to 12% for the four strains examined. We also determined whether biological variation occurs between pairs of phenotypically identical yeast strains. Multivariate statistical analysis allowed us to discriminate between pair members based on their metabolic phenotypes. Our results highlight the variability of the metabolome that exists even for less complex model systems cultured under defined conditions. We also highlight the efficacy of metabolic profiling for defining these subtle metabolic alterations.

Snake Model Based on Improved Genetic Algorithm in Fingerprint Image Segmentation

Directory of Open Access Journals (Sweden)

Mingying Zhang

2016-12-01

Full Text Available Automatic fingerprint identification technology is a quite mature research field in biometric identification technology. As the preprocessing step in fingerprint identification, fingerprint segmentation can improve the accuracy of fingerprint feature extraction, and also reduce the time of fingerprint preprocessing, which has a great significance in improving the performance of the whole system. Based on the analysis of the commonly used methods of fingerprint segmentation, the existing segmentation algorithm is improved in this paper. The snake model is used to segment the fingerprint image. Additionally, it is improved by using the global optimization of the improved genetic algorithm. Experimental results show that the algorithm has obvious advantages both in the speed of image segmentation and in the segmentation effect.

Fast single image dehazing based on image fusion

Science.gov (United States)

Liu, Haibo; Yang, Jie; Wu, Zhengping; Zhang, Qingnian

2015-01-01

Images captured in foggy weather conditions often fade the colors and reduce the contrast of the observed objects. An efficient image fusion method is proposed to remove haze from a single input image. First, the initial medium transmission is estimated based on the dark channel prior. Second, the method adopts an assumption that the degradation level affected by haze of each region is the same, which is similar to the Retinex theory, and uses a simple Gaussian filter to get the coarse medium transmission. Then, pixel-level fusion is achieved between the initial medium transmission and coarse medium transmission. The proposed method can recover a high-quality haze-free image based on the physical model, and the complexity of the proposed method is only a linear function of the number of input image pixels. Experimental results demonstrate that the proposed method can allow a very fast implementation and achieve better restoration for visibility and color fidelity compared to some state-of-the-art methods.

Normal and Pathological NCAT Image and Phantom Data Based on Physiologically Realistic Left Ventricle Finite-Element Models

International Nuclear Information System (INIS)

Veress, Alexander I.; Segars, W. Paul; Weiss, Jeffrey A.; Tsui, Benjamin M.W.; Gullberg, Grant T.

2006-01-01

The 4D NURBS-based Cardiac-Torso (NCAT) phantom, which provides a realistic model of the normal human anatomy and cardiac and respiratory motions, is used in medical imaging research to evaluate and improve imaging devices and techniques, especially dynamic cardiac applications. One limitation of the phantom is that it lacks the ability to accurately simulate altered functions of the heart that result from cardiac pathologies such as coronary artery disease (CAD). The goal of this work was to enhance the 4D NCAT phantom by incorporating a physiologically based, finite-element (FE) mechanical model of the left ventricle (LV) to simulate both normal and abnormal cardiac motions. The geometry of the FE mechanical model was based on gated high-resolution x-ray multi-slice computed tomography (MSCT) data of a healthy male subject. The myocardial wall was represented as transversely isotropichyperelastic material, with the fiber angle varying from -90 degrees at the epicardial surface, through 0 degrees at the mid-wall, to 90 degrees at the endocardial surface. A time varying elastance model was used to simulate fiber contraction, and physiological intraventricular systolic pressure-time curves were applied to simulate the cardiac motion over the entire cardiac cycle. To demonstrate the ability of the FE mechanical model to accurately simulate the normal cardiac motion as well abnormal motions indicative of CAD, a normal case and two pathologic cases were simulated and analyzed. In the first pathologic model, a subendocardial anterior ischemic region was defined. A second model was created with a transmural ischemic region defined in the same location. The FE based deformations were incorporated into the 4D NCAT cardiac model through the control points that define the cardiac structures in the phantom which were set to move according to the predictions of the mechanical model. A simulation study was performed using the FE-NCAT combination to investigate how the

An image-based skeletal tissue model for the ICRP reference newborn

Energy Technology Data Exchange (ETDEWEB)

Pafundi, Deanna; Lee, Choonsik; Bolch, Wesley [Department of Nuclear and Radiological Engineering, University of Florida, Gainesville, FL (United States); Watchman, Christopher; Bourke, Vincent [Department of Radiation Oncology, University of Arizona, Tucson, AZ (United States); Aris, John [Department of Anatomy and Cell Biology, University of Florida, Gainesville, FL (United States); Shagina, Natalia [Urals Research Center for Radiation Medicine, Chelyabinsk (Russian Federation); Harrison, John; Fell, Tim [Radiation Protection Division, Health Protection Agency, Chilton (United Kingdom)], E-mail: wbolch@ufl.edu

2009-07-21

Hybrid phantoms represent a third generation of computational models of human anatomy needed for dose assessment in both external and internal radiation exposures. Recently, we presented the first whole-body hybrid phantom of the ICRP reference newborn with a skeleton constructed from both non-uniform rational B-spline and polygon-mesh surfaces (Lee et al 2007 Phys. Med. Biol. 52 3309-33). The skeleton in that model included regions of cartilage and fibrous connective tissue, with the remainder given as a homogenous mixture of cortical and trabecular bone, active marrow and miscellaneous skeletal tissues. In the present study, we present a comprehensive skeletal tissue model of the ICRP reference newborn to permit a heterogeneous representation of the skeleton in that hybrid phantom set-both male and female-that explicitly includes a delineation of cortical bone so that marrow shielding effects are correctly modeled for low-energy photons incident upon the newborn skeleton. Data sources for the tissue model were threefold. First, skeletal site-dependent volumes of homogeneous bone were obtained from whole-cadaver CT image analyses. Second, selected newborn bone specimens were acquired at autopsy and subjected to micro-CT image analysis to derive model parameters of the marrow cavity and bone trabecular 3D microarchitecture. Third, data given in ICRP Publications 70 and 89 were selected to match reference values on total skeletal tissue mass. Active marrow distributions were found to be in reasonable agreement with those given previously by the ICRP. However, significant differences were seen in total skeletal and site-specific masses of trabecular and cortical bone between the current and ICRP newborn skeletal tissue models. The latter utilizes an age-independent ratio of 80%/20% cortical and trabecular bone for the reference newborn. In the current study, a ratio closer to 40%/60% is used based upon newborn CT and micro-CT skeletal image analyses. These changes in

Neutron Imaging Reveals Internal Plant Hydraulic Dynamics

Energy Technology Data Exchange (ETDEWEB)

Warren, Jeffrey [ORNL; Bilheux, Hassina Z [ORNL; Kang, Misun [ORNL; Voisin, Sophie [ORNL; Cheng, Chu-Lin [ORNL; Horita, Jusuke [ORNL; Perfect, Edmund [ORNL

2013-01-01

Many terrestrial ecosystem processes are constrained by water availability and transport within the soil. Knowledge of plant water fluxes is thus critical for assessing mechanistic processes linked to biogeochemical cycles, yet resolution of root structure and xylem water transport dynamics has been a particularly daunting task for the ecologist. Through neutron imaging, we demonstrate the ability to non-invasively monitor individual root functionality and water fluxes within Zea mays L. (maize) and Panicum virgatum L. (switchgrass) seedlings growing in a sandy medium. Root structure and growth were readily imaged by neutron radiography and neutron computed tomography. Seedlings were irrigated with water or deuterium oxide and imaged through time as a growth lamp was cycled on to alter leaf demand for water. Sub-millimeter scale resolution reveals timing and magnitudes of root water uptake, redistribution within the roots, and root-shoot hydraulic linkages, relationships not well characterized by other techniques.

Normal and Pathological NCAT Image and PhantomData Based onPhysiologically Realistic Left Ventricle Finite-Element Models

Energy Technology Data Exchange (ETDEWEB)

Veress, Alexander I.; Segars, W. Paul; Weiss, Jeffrey A.; Tsui,Benjamin M.W.; Gullberg, Grant T.

2006-08-02

The 4D NURBS-based Cardiac-Torso (NCAT) phantom, whichprovides a realistic model of the normal human anatomy and cardiac andrespiratory motions, is used in medical imaging research to evaluate andimprove imaging devices and techniques, especially dynamic cardiacapplications. One limitation of the phantom is that it lacks the abilityto accurately simulate altered functions of the heart that result fromcardiac pathologies such as coronary artery disease (CAD). The goal ofthis work was to enhance the 4D NCAT phantom by incorporating aphysiologically based, finite-element (FE) mechanical model of the leftventricle (LV) to simulate both normal and abnormal cardiac motions. Thegeometry of the FE mechanical model was based on gated high-resolutionx-ray multi-slice computed tomography (MSCT) data of a healthy malesubject. The myocardial wall was represented as transversely isotropichyperelastic material, with the fiber angle varying from -90 degrees atthe epicardial surface, through 0 degreesat the mid-wall, to 90 degreesat the endocardial surface. A time varying elastance model was used tosimulate fiber contraction, and physiological intraventricular systolicpressure-time curves were applied to simulate the cardiac motion over theentire cardiac cycle. To demonstrate the ability of the FE mechanicalmodel to accurately simulate the normal cardiac motion as well abnormalmotions indicative of CAD, a normal case and two pathologic cases weresimulated and analyzed. In the first pathologic model, a subendocardialanterior ischemic region was defined. A second model was created with atransmural ischemic region defined in the same location. The FE baseddeformations were incorporated into the 4D NCAT cardiac model through thecontrol points that define the cardiac structures in the phantom whichwere set to move according to the predictions of the mechanical model. Asimulation study was performed using the FE-NCAT combination toinvestigate how the differences in contractile function

Model-based image reconstruction for four-dimensional PET

International Nuclear Information System (INIS)

Li Tianfang; Thorndyke, Brian; Schreibmann, Eduard; Yang Yong; Xing Lei

2006-01-01

Positron emission tonography (PET) is useful in diagnosis and radiation treatment planning for a variety of cancers. For patients with cancers in thoracic or upper abdominal region, the respiratory motion produces large distortions in the tumor shape and size, affecting the accuracy in both diagnosis and treatment. Four-dimensional (4D) (gated) PET aims to reduce the motion artifacts and to provide accurate measurement of the tumor volume and the tracer concentration. A major issue in 4D PET is the lack of statistics. Since the collected photons are divided into several frames in the 4D PET scan, the quality of each reconstructed frame degrades as the number of frames increases. The increased noise in each frame heavily degrades the quantitative accuracy of the PET imaging. In this work, we propose a method to enhance the performance of 4D PET by developing a new technique of 4D PET reconstruction with incorporation of an organ motion model derived from 4D-CT images. The method is based on the well-known maximum-likelihood expectation-maximization (ML-EM) algorithm. During the processes of forward- and backward-projection in the ML-EM iterations, all projection data acquired at different phases are combined together to update the emission map with the aid of deformable model, the statistics is therefore greatly improved. The proposed algorithm was first evaluated with computer simulations using a mathematical dynamic phantom. Experiment with a moving physical phantom was then carried out to demonstrate the accuracy of the proposed method and the increase of signal-to-noise ratio over three-dimensional PET. Finally, the 4D PET reconstruction was applied to a patient case

Diffraction enhanced imaging of a rat model of gastric acid aspiration pneumonitis.

Science.gov (United States)

Connor, Dean M; Zhong, Zhong; Foda, Hussein D; Wiebe, Sheldon; Parham, Christopher A; Dilmanian, F Avraham; Cole, Elodia B; Pisano, Etta D

2011-12-01

Diffraction-enhanced imaging (DEI) is a type of phase contrast x-ray imaging that has improved image contrast at a lower dose than conventional radiography for many imaging applications, but no studies have been done to determine if DEI might be useful for diagnosing lung injury. The goals of this study were to determine if DEI could differentiate between healthy and injured lungs for a rat model of gastric aspiration and to compare diffraction-enhanced images with chest radiographs. Radiographs and diffraction-enhanced chest images of adult Sprague Dawley rats were obtained before and 4 hours after the aspiration of 0.4 mL/kg of 0.1 mol/L hydrochloric acid. Lung damage was confirmed with histopathology. The radiographs and diffraction-enhanced peak images revealed regions of atelectasis in the injured rat lung. The diffraction-enhanced peak images revealed the full extent of the lung with improved clarity relative to the chest radiographs, especially in the portion of the lower lobe that extended behind the diaphragm on the anteroposterior projection. For a rat model of gastric acid aspiration, DEI is capable of distinguishing between a healthy and an injured lung and more clearly than radiography reveals the full extent of the lung and the lung damage. Copyright © 2011 AUR. All rights reserved.

Software for medical image based phantom modelling

International Nuclear Information System (INIS)

Possani, R.G.; Massicano, F.; Coelho, T.S.; Yoriyaz, H.

2011-01-01

Latest treatment planning systems depends strongly on CT images, so the tendency is that the dosimetry procedures in nuclear medicine therapy be also based on images, such as magnetic resonance imaging (MRI) or computed tomography (CT), to extract anatomical and histological information, as well as, functional imaging or activities map as PET or SPECT. This information associated with the simulation of radiation transport software is used to estimate internal dose in patients undergoing treatment in nuclear medicine. This work aims to re-engineer the software SCMS, which is an interface software between the Monte Carlo code MCNP, and the medical images, that carry information from the patient in treatment. In other words, the necessary information contained in the images are interpreted and presented in a specific format to the Monte Carlo MCNP code to perform the simulation of radiation transport. Therefore, the user does not need to understand complex process of inputting data on MCNP, as the SCMS is responsible for automatically constructing anatomical data from the patient, as well as the radioactive source data. The SCMS was originally developed in Fortran- 77. In this work it was rewritten in an object-oriented language (JAVA). New features and data options have also been incorporated into the software. Thus, the new software has a number of improvements, such as intuitive GUI and a menu for the selection of the energy spectra correspondent to a specific radioisotope stored in a XML data bank. The new version also supports new materials and the user can specify an image region of interest for the calculation of absorbed dose. (author)

Color correction with blind image restoration based on multiple images using a low-rank model

Science.gov (United States)

Li, Dong; Xie, Xudong; Lam, Kin-Man

2014-03-01

We present a method that can handle the color correction of multiple photographs with blind image restoration simultaneously and automatically. We prove that the local colors of a set of images of the same scene exhibit the low-rank property locally both before and after a color-correction operation. This property allows us to correct all kinds of errors in an image under a low-rank matrix model without particular priors or assumptions. The possible errors may be caused by changes of viewpoint, large illumination variations, gross pixel corruptions, partial occlusions, etc. Furthermore, a new iterative soft-segmentation method is proposed for local color transfer using color influence maps. Due to the fact that the correct color information and the spatial information of images can be recovered using the low-rank model, more precise color correction and many other image-restoration tasks-including image denoising, image deblurring, and gray-scale image colorizing-can be performed simultaneously. Experiments have verified that our method can achieve consistent and promising results on uncontrolled real photographs acquired from the Internet and that it outperforms current state-of-the-art methods.

Biomedical Imaging and Computational Modeling in Biomechanics

CERN Document Server

Iacoviello, Daniela

2013-01-01

This book collects the state-of-art and new trends in image analysis and biomechanics. It covers a wide field of scientific and cultural topics, ranging from remodeling of bone tissue under the mechanical stimulus up to optimizing the performance of sports equipment, through the patient-specific modeling in orthopedics, microtomography and its application in oral and implant research, computational modeling in the field of hip prostheses, image based model development and analysis of the human knee joint, kinematics of the hip joint, micro-scale analysis of compositional and mechanical properties of dentin, automated techniques for cervical cell image analysis, and iomedical imaging and computational modeling in cardiovascular disease.   The book will be of interest to researchers, Ph.D students, and graduate students with multidisciplinary interests related to image analysis and understanding, medical imaging, biomechanics, simulation and modeling, experimental analysis.

Image-based reflectance conversion of ASTER and IKONOS ...

African Journals Online (AJOL)

Spectral signatures derived from different image-based models for ASTER and IKONOS were inspected visually as first departure. This was followed by comparison of the total accuracy and Kappa index computed from supervised classification of images that were derived from different image-based atmospheric correction ...

AUGUSTO'S Sundial: Image-Based Modeling for Reverse Engeneering Purposes

Science.gov (United States)

Baiocchi, V.; Barbarella, M.; Del Pizzo, S.; Giannone, F.; Troisi, S.; Piccaro, C.; Marcantonio, D.

2017-02-01

A photogrammetric survey of a unique archaeological site is reported in this paper. The survey was performed using both a panoramic image-based solution and by classical procedure. The panoramic image-based solution was carried out employing a commercial solution: the Trimble V10 Imaging Rover (IR). Such instrument is an integrated cameras system that captures 360 degrees digital panoramas, composed of 12 images, with a single push. The direct comparison of the point clouds obtained with traditional photogrammetric procedure and V10 stations, using the same GCP coordinates has been carried out in Cloud Compare, open source software that can provide the comparison between two point clouds supplied by all the main statistical data. The site is a portion of the dial plate of the "Horologium Augusti" inaugurated in 9 B.C.E. in the area of Campo Marzio and still present intact in the same position, in a cellar of a building in Rome, around 7 meter below the present ground level.

Imaging mass spectrometry reveals elevated nigral levels of dynorphin neuropeptides in L-DOPA-induced dyskinesia in rat model of Parkinson's disease.

Directory of Open Access Journals (Sweden)

Anna Ljungdahl

Full Text Available L-DOPA-induced dyskinesia is a troublesome complication of L-DOPA pharmacotherapy of Parkinson's disease and has been associated with disturbed brain opioid transmission. However, so far the results of clinical and preclinical studies on the effects of opioids agonists and antagonists have been contradictory at best. Prodynorphin mRNA levels correlate well with the severity of dyskinesia in animal models of Parkinson's disease; however the identities of the actual neuroactive opioid effectors in their target basal ganglia output structures have not yet been determined. For the first time MALDI-TOF imaging mass spectrometry (IMS was used for unbiased assessment and topographical elucidation of prodynorphin-derived peptides in the substantia nigra of a unilateral rat model of Parkinson's disease and L-DOPA induced dyskinesia. Nigral levels of dynorphin B and alpha-neoendorphin strongly correlated with the severity of dyskinesia. Even if dynorphin peptide levels were elevated in both the medial and lateral part of the substantia nigra, MALDI IMS analysis revealed that the most prominent changes were localized to the lateral part of the substantia nigra. MALDI IMS is advantageous compared with traditional molecular methods, such as radioimmunoassay, in that neither the molecular identity analyzed, nor the specific localization needs to be predetermined. Indeed, MALDI IMS revealed that the bioconverted metabolite leu-enkephalin-arg also correlated positively with severity of dyskinesia. Multiplexing DynB and leu-enkephalin-arg ion images revealed small (0.25 by 0.5 mm nigral subregions with complementing ion intensities, indicating localized peptide release followed by bioconversion. The nigral dynorphins associated with L-DOPA-induced dyskinesia were not those with high affinity to kappa opioid receptors, but consisted of shorter peptides, mainly dynorphin B and alpha-neoendorphin that are known to bind and activate mu and delta opioid receptors

An Efficient Evolutionary Based Method For Image Segmentation

OpenAIRE

Aslanzadeh, Roohollah; Qazanfari, Kazem; Rahmati, Mohammad

2017-01-01

The goal of this paper is to present a new efficient image segmentation method based on evolutionary computation which is a model inspired from human behavior. Based on this model, a four layer process for image segmentation is proposed using the split/merge approach. In the first layer, an image is split into numerous regions using the watershed algorithm. In the second layer, a co-evolutionary process is applied to form centers of finals segments by merging similar primary regions. In the t...

StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images

Energy Technology Data Exchange (ETDEWEB)

De Backer, A.; Bos, K.H.W. van den [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp (Belgium); Van den Broek, W. [AG Strukturforschung/Elektronenmikroskopie, Institut für Physik, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin (Germany); Sijbers, J. [iMinds-Vision Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk (Belgium); Van Aert, S., E-mail: sandra.vanaert@uantwerpen.be [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp (Belgium)

2016-12-15

An efficient model-based estimation algorithm is introduced to quantify the atomic column positions and intensities from atomic resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for overlap between neighbouring columns, enabling the analysis of a large field of view. For this algorithm, the accuracy and precision with which measurements for the atomic column positions and scattering cross-sections from annular dark field (ADF) STEM images can be estimated, has been investigated. The highest attainable precision is reached even for low dose images. Furthermore, the advantages of the model-based approach taking into account overlap between neighbouring columns are highlighted. This is done for the estimation of the distance between two neighbouring columns as a function of their distance and for the estimation of the scattering cross-section which is compared to the integrated intensity from a Voronoi cell. To provide end-users this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license. - Highlights: • An efficient model-based method for quantitative electron microscopy is introduced. • Images are modelled as a superposition of 2D Gaussian peaks. • Overlap between neighbouring columns is taken into account. • Structure parameters can be obtained with the highest precision and accuracy. • StatSTEM, auser friendly program (GNU public license) is developed.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

Science.gov (United States)

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Physics-based shape matching for intraoperative image guidance

Energy Technology Data Exchange (ETDEWEB)

Suwelack, Stefan, E-mail: suwelack@kit.edu; Röhl, Sebastian; Bodenstedt, Sebastian; Reichard, Daniel; Dillmann, Rüdiger; Speidel, Stefanie [Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Adenauerring 2, Karlsruhe 76131 (Germany); Santos, Thiago dos; Maier-Hein, Lena [Computer-assisted Interventions, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120 (Germany); Wagner, Martin; Wünscher, Josephine; Kenngott, Hannes; Müller, Beat P. [General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 110, Heidelberg 69120 (Germany)

2014-11-01

Purpose: Soft-tissue deformations can severely degrade the validity of preoperative planning data during computer assisted interventions. Intraoperative imaging such as stereo endoscopic, time-of-flight or, laser range scanner data can be used to compensate these movements. In this context, the intraoperative surface has to be matched to the preoperative model. The shape matching is especially challenging in the intraoperative setting due to noisy sensor data, only partially visible surfaces, ambiguous shape descriptors, and real-time requirements. Methods: A novel physics-based shape matching (PBSM) approach to register intraoperatively acquired surface meshes to preoperative planning data is proposed. The key idea of the method is to describe the nonrigid registration process as an electrostatic–elastic problem, where an elastic body (preoperative model) that is electrically charged slides into an oppositely charged rigid shape (intraoperative surface). It is shown that the corresponding energy functional can be efficiently solved using the finite element (FE) method. It is also demonstrated how PBSM can be combined with rigid registration schemes for robust nonrigid registration of arbitrarily aligned surfaces. Furthermore, it is shown how the approach can be combined with landmark based methods and outline its application to image guidance in laparoscopic interventions. Results: A profound analysis of the PBSM scheme based on in silico and phantom data is presented. Simulation studies on several liver models show that the approach is robust to the initial rigid registration and to parameter variations. The studies also reveal that the method achieves submillimeter registration accuracy (mean error between 0.32 and 0.46 mm). An unoptimized, single core implementation of the approach achieves near real-time performance (2 TPS, 7–19 s total registration time). It outperforms established methods in terms of speed and accuracy. Furthermore, it is shown that the

pyBSM: A Python package for modeling imaging systems

Science.gov (United States)

LeMaster, Daniel A.; Eismann, Michael T.

2017-05-01

There are components that are common to all electro-optical and infrared imaging system performance models. The purpose of the Python Based Sensor Model (pyBSM) is to provide open source access to these functions for other researchers to build upon. Specifically, pyBSM implements much of the capability found in the ERIM Image Based Sensor Model (IBSM) V2.0 along with some improvements. The paper also includes two use-case examples. First, performance of an airborne imaging system is modeled using the General Image Quality Equation (GIQE). The results are then decomposed into factors affecting noise and resolution. Second, pyBSM is paired with openCV to evaluate performance of an algorithm used to detect objects in an image.

Achilles tendons from decorin- and biglycan-null mouse models have inferior mechanical and structural properties predicted by an image-based empirical damage model.

Science.gov (United States)

Gordon, J A; Freedman, B R; Zuskov, A; Iozzo, R V; Birk, D E; Soslowsky, L J

2015-07-16

Achilles tendons are a common source of pain and injury, and their pathology may originate from aberrant structure function relationships. Small leucine rich proteoglycans (SLRPs) influence mechanical and structural properties in a tendon-specific manner. However, their roles in the Achilles tendon have not been defined. The objective of this study was to evaluate the mechanical and structural differences observed in mouse Achilles tendons lacking class I SLRPs; either decorin or biglycan. In addition, empirical modeling techniques based on mechanical and image-based measures were employed. Achilles tendons from decorin-null (Dcn(-/-)) and biglycan-null (Bgn(-/-)) C57BL/6 female mice (N=102) were used. Each tendon underwent a dynamic mechanical testing protocol including simultaneous polarized light image capture to evaluate both structural and mechanical properties of each Achilles tendon. An empirical damage model was adapted for application to genetic variation and for use with image based structural properties to predict tendon dynamic mechanical properties. We found that Achilles tendons lacking decorin and biglycan had inferior mechanical and structural properties that were age dependent; and that simple empirical models, based on previously described damage models, were predictive of Achilles tendon dynamic modulus in both decorin- and biglycan-null mice. Copyright © 2015 Elsevier Ltd. All rights reserved.

Applying an animal model to quantify the uncertainties of an image-based 4D-CT algorithm

International Nuclear Information System (INIS)

Pierce, Greg; Battista, Jerry; Wang, Kevin; Lee, Ting-Yim

2012-01-01

The purpose of this paper is to use an animal model to quantify the spatial displacement uncertainties and test the fundamental assumptions of an image-based 4D-CT algorithm in vivo. Six female Landrace cross pigs were ventilated and imaged using a 64-slice CT scanner (GE Healthcare) operating in axial cine mode. The breathing amplitude pattern of the pigs was varied by periodically crimping the ventilator gas return tube during the image acquisition. The image data were used to determine the displacement uncertainties that result from matching CT images at the same respiratory phase using normalized cross correlation (NCC) as the matching criteria. Additionally, the ability to match the respiratory phase of a 4.0 cm subvolume of the thorax to a reference subvolume using only a single overlapping 2D slice from the two subvolumes was tested by varying the location of the overlapping matching image within the subvolume and examining the effect this had on the displacement relative to the reference volume. The displacement uncertainty resulting from matching two respiratory images using NCC ranged from 0.54 ± 0.10 mm per match to 0.32 ± 0.16 mm per match in the lung of the animal. The uncertainty was found to propagate in quadrature, increasing with number of NCC matches performed. In comparison, the minimum displacement achievable if two respiratory images were matched perfectly in phase ranged from 0.77 ± 0.06 to 0.93 ± 0.06 mm in the lung. The assumption that subvolumes from separate cine scan could be matched by matching a single overlapping 2D image between to subvolumes was validated. An in vivo animal model was developed to test an image-based 4D-CT algorithm. The uncertainties associated with using NCC to match the respiratory phase of two images were quantified and the assumption that a 4.0 cm 3D subvolume can by matched in respiratory phase by matching a single 2D image from the 3D subvolume was validated. The work in this paper shows the image-based 4D

Despeckling Polsar Images Based on Relative Total Variation Model

Science.gov (United States)

Jiang, C.; He, X. F.; Yang, L. J.; Jiang, J.; Wang, D. Y.; Yuan, Y.

2018-04-01

Relatively total variation (RTV) algorithm, which can effectively decompose structure information and texture in image, is employed in extracting main structures of the image. However, applying the RTV directly to polarimetric SAR (PolSAR) image filtering will not preserve polarimetric information. A new RTV approach based on the complex Wishart distribution is proposed considering the polarimetric properties of PolSAR. The proposed polarization RTV (PolRTV) algorithm can be used for PolSAR image filtering. The L-band Airborne SAR (AIRSAR) San Francisco data is used to demonstrate the effectiveness of the proposed algorithm in speckle suppression, structural information preservation, and polarimetric property preservation.

Live imaging-based model selection reveals periodic regulation of the stochastic G1/S phase transition in vertebrate axial development.

Directory of Open Access Journals (Sweden)

Mayu Sugiyama

2014-12-01

Full Text Available In multicellular organism development, a stochastic cellular response is observed, even when a population of cells is exposed to the same environmental conditions. Retrieving the spatiotemporal regulatory mode hidden in the heterogeneous cellular behavior is a challenging task. The G1/S transition observed in cell cycle progression is a highly stochastic process. By taking advantage of a fluorescence cell cycle indicator, Fucci technology, we aimed to unveil a hidden regulatory mode of cell cycle progression in developing zebrafish. Fluorescence live imaging of Cecyil, a zebrafish line genetically expressing Fucci, demonstrated that newly formed notochordal cells from the posterior tip of the embryonic mesoderm exhibited the red (G1 fluorescence signal in the developing notochord. Prior to their initial vacuolation, these cells showed a fluorescence color switch from red to green, indicating G1/S transitions. This G1/S transition did not occur in a synchronous manner, but rather exhibited a stochastic process, since a mixed population of red and green cells was always inserted between newly formed red (G1 notochordal cells and vacuolating green cells. We termed this mixed population of notochordal cells, the G1/S transition window. We first performed quantitative analyses of live imaging data and a numerical estimation of the probability of the G1/S transition, which demonstrated the existence of a posteriorly traveling regulatory wave of the G1/S transition window. To obtain a better understanding of this regulatory mode, we constructed a mathematical model and performed a model selection by comparing the results obtained from the models with those from the experimental data. Our analyses demonstrated that the stochastic G1/S transition window in the notochord travels posteriorly in a periodic fashion, with doubled the periodicity of the neighboring paraxial mesoderm segmentation. This approach may have implications for the characterization of

Joint model of motion and anatomy for PET image reconstruction

International Nuclear Information System (INIS)

Qiao Feng; Pan Tinsu; Clark, John W. Jr.; Mawlawi, Osama

2007-01-01

Anatomy-based positron emission tomography (PET) image enhancement techniques have been shown to have the potential for improving PET image quality. However, these techniques assume an accurate alignment between the anatomical and the functional images, which is not always valid when imaging the chest due to respiratory motion. In this article, we present a joint model of both motion and anatomical information by integrating a motion-incorporated PET imaging system model with an anatomy-based maximum a posteriori image reconstruction algorithm. The mismatched anatomical information due to motion can thus be effectively utilized through this joint model. A computer simulation and a phantom study were conducted to assess the efficacy of the joint model, whereby motion and anatomical information were either modeled separately or combined. The reconstructed images in each case were compared to corresponding reference images obtained using a quadratic image prior based maximum a posteriori reconstruction algorithm for quantitative accuracy. Results of these studies indicated that while modeling anatomical information or motion alone improved the PET image quantitation accuracy, a larger improvement in accuracy was achieved when using the joint model. In the computer simulation study and using similar image noise levels, the improvement in quantitation accuracy compared to the reference images was 5.3% and 19.8% when using anatomical or motion information alone, respectively, and 35.5% when using the joint model. In the phantom study, these results were 5.6%, 5.8%, and 19.8%, respectively. These results suggest that motion compensation is important in order to effectively utilize anatomical information in chest imaging using PET. The joint motion-anatomy model presented in this paper provides a promising solution to this problem

FITTING OF PARAMETRIC BUILDING MODELS TO OBLIQUE AERIAL IMAGES

Directory of Open Access Journals (Sweden)

U. S. Panday

2012-09-01

Full Text Available In literature and in photogrammetric workstations many approaches and systems to automatically reconstruct buildings from remote sensing data are described and available. Those building models are being used for instance in city modeling or in cadastre context. If a roof overhang is present, the building walls cannot be estimated correctly from nadir-view aerial images or airborne laser scanning (ALS data. This leads to inconsistent building outlines, which has a negative influence on visual impression, but more seriously also represents a wrong legal boundary in the cadaster. Oblique aerial images as opposed to nadir-view images reveal greater detail, enabling to see different views of an object taken from different directions. Building walls are visible from oblique images directly and those images are used for automated roof overhang estimation in this research. A fitting algorithm is employed to find roof parameters of simple buildings. It uses a least squares algorithm to fit projected wire frames to their corresponding edge lines extracted from the images. Self-occlusion is detected based on intersection result of viewing ray and the planes formed by the building whereas occlusion from other objects is detected using an ALS point cloud. Overhang and ground height are obtained by sweeping vertical and horizontal planes respectively. Experimental results are verified with high resolution ortho-images, field survey, and ALS data. Planimetric accuracy of 1cm mean and 5cm standard deviation was obtained, while buildings' orientation were accurate to mean of 0.23° and standard deviation of 0.96° with ortho-image. Overhang parameters were aligned to approximately 10cm with field survey. The ground and roof heights were accurate to mean of – 9cm and 8cm with standard deviations of 16cm and 8cm with ALS respectively. The developed approach reconstructs 3D building models well in cases of sufficient texture. More images should be acquired for

Non-rigid image registration using bone growth model

DEFF Research Database (Denmark)

Bro-Nielsen, Morten; Gramkow, Claus; Kreiborg, Sven

1997-01-01

Non-rigid registration has traditionally used physical models like elasticity and fluids. These models are very seldom valid models of the difference between the registered images. This paper presents a non-rigid registration algorithm, which uses a model of bone growth as a model of the change...... between time sequence images of the human mandible. By being able to register the images, this paper at the same time contributes to the validation of the growth model, which is based on the currently available medical theories and knowledge...

Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

Directory of Open Access Journals (Sweden)

Yidong Tang

2016-01-01

Full Text Available The sparse representation based classifier (SRC and its kernel version (KSRC have been employed for hyperspectral image (HSI classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

Satellite-based ET estimation using Landsat 8 images and SEBAL model

Directory of Open Access Journals (Sweden)

Bruno Bonemberger da Silva

Full Text Available ABSTRACT Estimation of evapotranspiration is a key factor to achieve sustainable water management in irrigated agriculture because it represents water use of crops. Satellite-based estimations provide advantages compared to direct methods as lysimeters especially when the objective is to calculate evapotranspiration at a regional scale. The present study aimed to estimate the actual evapotranspiration (ET at a regional scale, using Landsat 8 - OLI/TIRS images and complementary data collected from a weather station. SEBAL model was used in South-West Paraná, region composed of irrigated and dry agricultural areas, native vegetation and urban areas. Five Landsat 8 images, row 223 and path 78, DOY 336/2013, 19/2014, 35/2014, 131/2014 and 195/2014 were used, from which ET at daily scale was estimated as a residual of the surface energy balance to produce ET maps. The steps for obtain ET using SEBAL include radiometric calibration, calculation of the reflectance, surface albedo, vegetation indexes (NDVI, SAVI and LAI and emissivity. These parameters were obtained based on the reflective bands of the orbital sensor with temperature surface estimated from thermal band. The estimated ET values in agricultural areas, native vegetation and urban areas using SEBAL algorithm were compatible with those shown in the literature and ET errors between the ET estimates from SEBAL model and Penman Monteith FAO 56 equation were less than or equal to 1.00 mm day-1.

Detection of the Typical Pulse Condition on Cun-Guan-Chi Based on Image Sensor

Directory of Open Access Journals (Sweden)

Aihua ZHANG

2014-02-01

Full Text Available In order to simulate the diagnosis by feeling the pulse with Traditional Chinese Medicine, a device based on CCD was designed to detect the pulse image of Cun-Guan-Chi. Using the MM-3 pulse model as experimental subject, the synchronous pulse image data of some typical pulse condition were collected by this device on Cun-Guan-Chi. The typical pulses include the normal pulse, the slippery pulse, the slow pulse and the soft pulse. According to the lens imaging principle, the pulse waves were extracted by using the area method, then the 3D pulse condition image was restructured and some features were extracted including the period, the frequency, the width, and the length. The slippery pulse data of pregnant women were collected by this device, and the pulse images were analyzed. The results are consistent based on comparing the features of the slippery pulse model with the slippery pulse of pregnant women. This study overcame shortages of the existing detection device such as the few detecting parts and the limited information, and more comprehensive 3D pulse condition information could be obtained. This work laid a foundation for realizing the objective diagnosis and revealing the comprehensive information of the pulse.

NEPHRUS: model of intelligent multilayers expert system for evaluation of the renal system based on scintigraphic images analysis

International Nuclear Information System (INIS)

Silva, Jose W.E. da; Schirru, Roberto; Boasquevisque, Edson M.

1997-01-01

This work develops a prototype for the system model based on Artificial Intelligence devices able to perform functions related to scintigraphic image analysis of the urinary system. Criteria used by medical experts for analysis images obtained with 99m Tc+DTPA and/or 99m Tc+DMSA were modeled and a multi resolution diagnosis technique was implemented. Special attention was given to the programs user interface design. Human Factor Engineering techniques were considered so as to ally friendliness and robustness. Results obtained using Artificial Neural Networks for the qualitative image analysis and the knowledge model constructed shows the feasibility of Artificial Intelligence implementation that use 'inherent' abilities of each technique in the resolution of diagnosis image analysis problems. (author). 12 refs., 2 figs., 2 tabs

Methodological challenges of optical tweezers-based X-ray fluorescence imaging of biological model organisms at synchrotron facilities.

Science.gov (United States)

Vergucht, Eva; Brans, Toon; Beunis, Filip; Garrevoet, Jan; Bauters, Stephen; De Rijcke, Maarten; Deruytter, David; Janssen, Colin; Riekel, Christian; Burghammer, Manfred; Vincze, Laszlo

2015-07-01

Recently, a radically new synchrotron radiation-based elemental imaging approach for the analysis of biological model organisms and single cells in their natural in vivo state was introduced. The methodology combines optical tweezers (OT) technology for non-contact laser-based sample manipulation with synchrotron radiation confocal X-ray fluorescence (XRF) microimaging for the first time at ESRF-ID13. The optical manipulation possibilities and limitations of biological model organisms, the OT setup developments for XRF imaging and the confocal XRF-related challenges are reported. In general, the applicability of the OT-based setup is extended with the aim of introducing the OT XRF methodology in all research fields where highly sensitive in vivo multi-elemental analysis is of relevance at the (sub)micrometre spatial resolution level.

Innovative biomagnetic imaging sensors for breast cancer: A model-based study

International Nuclear Information System (INIS)

Deng, Y.; Golkowski, M.

2012-01-01

Breast cancer is a serious potential health problem for all women and is the second leading cause of cancer deaths in the United States. The current screening procedures and imaging techniques, including x-ray mammography, clinical biopsy, ultrasound imaging, and magnetic resonance imaging, provide only 73% accuracy in detecting breast cancer. This gives the impetus to explore alternate techniques for imaging the breast and detecting early stage tumors. Among the complementary methods, the noninvasive biomagnetic breast imaging is attractive and promising, because both ionizing radiation and breast compressions that the prevalent x-ray mammography suffers from are avoided. It furthermore offers very high contrast because of the significant electromagnetic properties' differences between the cancerous, benign, and normal breast tissues. In this paper, a hybrid and accurate modeling tool for biomagnetic breast imaging is developed, which couples the electromagnetic and ultrasonic energies, and initial validations between the model predication and experimental findings are conducted.

AN IMAGE-BASED TECHNIQUE FOR 3D BUILDING RECONSTRUCTION USING MULTI-VIEW UAV IMAGES

Directory of Open Access Journals (Sweden)

F. Alidoost

2015-12-01

Full Text Available Nowadays, with the development of the urban areas, the automatic reconstruction of the buildings, as an important objects of the city complex structures, became a challenging topic in computer vision and photogrammetric researches. In this paper, the capability of multi-view Unmanned Aerial Vehicles (UAVs images is examined to provide a 3D model of complex building façades using an efficient image-based modelling workflow. The main steps of this work include: pose estimation, point cloud generation, and 3D modelling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM is applied on UAV images and a dense point cloud is generated. Then, a mesh model of points is calculated using Delaunay 2.5D triangulation and refined to obtain an accurate model of building. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough details of building based on visual assessment.

Voxel-based model construction from colored tomographic images

International Nuclear Information System (INIS)

Loureiro, Eduardo Cesar de Miranda

2002-07-01

This work presents a new approach in the construction of voxel-based phantoms that was implemented to simplify the segmentation process of organs and tissues reducing the time used in this procedure. The segmentation process is performed by painting tomographic images and attributing a different color for each organ or tissue. A voxel-based head and neck phantom was built using this new approach. The way as the data are stored allows an increasing in the performance of the radiation transport code. The program that calculates the radiation transport also works with image files. This capability allows image reconstruction showing isodose areas, under several points of view, increasing the information to the user. Virtual X-ray photographs can also be obtained allowing that studies could be accomplished looking for the radiographic techniques optimization assessing, at the same time, the doses in organs and tissues. The accuracy of the program here presented, called MCvoxEL, that implements this new approach, was tested by comparison to results from two modern and well-supported Monte Carlo codes. Dose conversion factors for parallel X-ray exposure were also calculated. (author)

New Details of the Human Corneal Limbus Revealed With Second Harmonic Generation Imaging.

Science.gov (United States)

Park, Choul Yong; Lee, Jimmy K; Zhang, Cheng; Chuck, Roy S

2015-09-01

To report novel findings of the human corneal limbus by using second harmonic generation (SHG) imaging. Corneal limbus was imaged by using an inverted two-photon excitation fluorescence microscope. Laser (Ti:Sapphire) was tuned at 850 nm for two-photon excitation. Backscatter signals of SHG and autofluorescence (AF) were collected through a 425/30-nm emission filter and a 525/45-emission filter, respectively. Multiple, consecutive, and overlapping image stacks (z-stack) were acquired for the corneal limbal area. Two novel collagen structures were revealed by SHG imaging at the limbus: an anterior limbal cribriform layer and presumed anchoring fibers. Anterior limbal cribriform layer is an intertwined reticular collagen architecture just beneath the limbal epithelial niche and is located between the peripheral cornea and Tenon's/scleral tissue. Autofluorescence imaging revealed high vascularity in this structure. Central to the anterior limbal cribriform layer, radial strands of collagen were found to connect the peripheral cornea to the limbus. These presumed anchoring fibers have both collagen and elastin and were found more extensively in the superficial layers than deep layer and were absent in very deep limbus near Schlemm's canal. By using SHG imaging, new details of the collagen architecture of human corneal limbal area were elucidated. High resolution images with volumetric analysis revealed two novel collagen structures.

Graphene-based ultrasonic detector for photoacoustic imaging

Science.gov (United States)

Yang, Fan; Song, Wei; Zhang, Chonglei; Fang, Hui; Min, Changjun; Yuan, Xiaocong

2018-03-01

Taking advantage of optical absorption imaging contrast, photoacoustic imaging technology is able to map the volumetric distribution of the optical absorption properties within biological tissues. Unfortunately, traditional piezoceramics-based transducers used in most photoacoustic imaging setups have inadequate frequency response, resulting in both poor depth resolution and inaccurate quantification of the optical absorption information. Instead of the piezoelectric ultrasonic transducer, we develop a graphene-based optical sensor for detecting photoacoustic pressure. The refractive index in the coupling medium is modulated due to photoacoustic pressure perturbation, which creates the variation of the polarization-sensitive optical absorption property of the graphene. As a result, the photoacoustic detection is realized through recording the reflectance intensity difference of polarization light. The graphene-based detector process an estimated noise-equivalentpressure (NEP) sensitivity of 550 Pa over 20-MHz bandwidth with a nearby linear pressure response from 11.0 kPa to 53.0 kPa. Further, a graphene-based photoacoustic microscopy is built, and non-invasively reveals the microvascular anatomy in mouse ears label-freely.

Non-Rigid Contour-Based Registration of Cell Nuclei in 2-D Live Cell Microscopy Images Using a Dynamic Elasticity Model.

Science.gov (United States)

Sorokin, Dmitry V; Peterlik, Igor; Tektonidis, Marco; Rohr, Karl; Matula, Pavel

2018-01-01

The analysis of the pure motion of subnuclear structures without influence of the cell nucleus motion and deformation is essential in live cell imaging. In this paper, we propose a 2-D contour-based image registration approach for compensation of nucleus motion and deformation in fluorescence microscopy time-lapse sequences. The proposed approach extends our previous approach, which uses a static elasticity model to register cell images. Compared with that scheme, the new approach employs a dynamic elasticity model for the forward simulation of nucleus motion and deformation based on the motion of its contours. The contour matching process is embedded as a constraint into the system of equations describing the elastic behavior of the nucleus. This results in better performance in terms of the registration accuracy. Our approach was successfully applied to real live cell microscopy image sequences of different types of cells including image data that was specifically designed and acquired for evaluation of cell image registration methods. An experimental comparison with the existing contour-based registration methods and an intensity-based registration method has been performed. We also studied the dependence of the results on the choice of method parameters.

Trajectory-based morphological operators: a model for efficient image processing.

Science.gov (United States)

Jimeno-Morenilla, Antonio; Pujol, Francisco A; Molina-Carmona, Rafael; Sánchez-Romero, José L; Pujol, Mar

2014-01-01

Mathematical morphology has been an area of intensive research over the last few years. Although many remarkable advances have been achieved throughout these years, there is still a great interest in accelerating morphological operations in order for them to be implemented in real-time systems. In this work, we present a new model for computing mathematical morphology operations, the so-called morphological trajectory model (MTM), in which a morphological filter will be divided into a sequence of basic operations. Then, a trajectory-based morphological operation (such as dilation, and erosion) is defined as the set of points resulting from the ordered application of the instant basic operations. The MTM approach allows working with different structuring elements, such as disks, and from the experiments, it can be extracted that our method is independent of the structuring element size and can be easily applied to industrial systems and high-resolution images.

{sup 1}H NMR-based metabolic profiling reveals inherent biological variation in yeast and nematode model systems

Energy Technology Data Exchange (ETDEWEB)

Szeto, Samuel S. W.; Reinke, Stacey N.; Lemire, Bernard D., E-mail: bernard.lemire@ualberta.ca [University of Alberta, Department of Biochemistry, School of Molecular and Systems Medicine (Canada)

2011-04-15

The application of metabolomics to human and animal model systems is poised to provide great insight into our understanding of disease etiology and the metabolic changes that are associated with these conditions. However, metabolomic studies have also revealed that there is significant, inherent biological variation in human samples and even in samples from animal model systems where the animals are housed under carefully controlled conditions. This inherent biological variability is an important consideration for all metabolomics analyses. In this study, we examined the biological variation in {sup 1}H NMR-based metabolic profiling of two model systems, the yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans. Using relative standard deviations (RSD) as a measure of variability, our results reveal that both model systems have significant amounts of biological variation. The C. elegans metabolome possesses greater metabolic variance with average RSD values of 29 and 39%, depending on the food source that was used. The S. cerevisiae exometabolome RSD values ranged from 8% to 12% for the four strains examined. We also determined whether biological variation occurs between pairs of phenotypically identical yeast strains. Multivariate statistical analysis allowed us to discriminate between pair members based on their metabolic phenotypes. Our results highlight the variability of the metabolome that exists even for less complex model systems cultured under defined conditions. We also highlight the efficacy of metabolic profiling for defining these subtle metabolic alterations.

A Novel 3D Imaging Method for Airborne Downward-Looking Sparse Array SAR Based on Special Squint Model

Directory of Open Access Journals (Sweden)

Xiaozhen Ren

2014-01-01

Full Text Available Three-dimensional (3D imaging technology based on antenna array is one of the most important 3D synthetic aperture radar (SAR high resolution imaging modes. In this paper, a novel 3D imaging method is proposed for airborne down-looking sparse array SAR based on the imaging geometry and the characteristic of echo signal. The key point of the proposed algorithm is the introduction of a special squint model in cross track processing to obtain accurate focusing. In this special squint model, point targets with different cross track positions have different squint angles at the same range resolution cell, which is different from the conventional squint SAR. However, after theory analysis and formulation deduction, the imaging procedure can be processed with the uniform reference function, and the phase compensation factors and algorithm realization procedure are demonstrated in detail. As the method requires only Fourier transform and multiplications and thus avoids interpolations, it is computationally efficient. Simulations with point scatterers are used to validate the method.

A dynamic model-based approach to motion and deformation tracking of prosthetic valves from biplane x-ray images.

Science.gov (United States)

Wagner, Martin G; Hatt, Charles R; Dunkerley, David A P; Bodart, Lindsay E; Raval, Amish N; Speidel, Michael A

2018-04-16

Transcatheter aortic valve replacement (TAVR) is a minimally invasive procedure in which a prosthetic heart valve is placed and expanded within a defective aortic valve. The device placement is commonly performed using two-dimensional (2D) fluoroscopic imaging. Within this work, we propose a novel technique to track the motion and deformation of the prosthetic valve in three dimensions based on biplane fluoroscopic image sequences. The tracking approach uses a parameterized point cloud model of the valve stent which can undergo rigid three-dimensional (3D) transformation and different modes of expansion. Rigid elements of the model are individually rotated and translated in three dimensions to approximate the motions of the stent. Tracking is performed using an iterative 2D-3D registration procedure which estimates the model parameters by minimizing the mean-squared image values at the positions of the forward-projected model points. Additionally, an initialization technique is proposed, which locates clusters of salient features to determine the initial position and orientation of the model. The proposed algorithms were evaluated based on simulations using a digital 4D CT phantom as well as experimentally acquired images of a prosthetic valve inside a chest phantom with anatomical background features. The target registration error was 0.12 ± 0.04 mm in the simulations and 0.64 ± 0.09 mm in the experimental data. The proposed algorithm could be used to generate 3D visualization of the prosthetic valve from two projections. In combination with soft-tissue sensitive-imaging techniques like transesophageal echocardiography, this technique could enable 3D image guidance during TAVR procedures. © 2018 American Association of Physicists in Medicine.

Device model for pixelless infrared image up-converters based on polycrystalline graphene heterostructures

Science.gov (United States)

Ryzhii, V.; Shur, M. S.; Ryzhii, M.; Karasik, V. E.; Otsuji, T.

2018-01-01

We developed a device model for pixelless converters of far/mid-infrared radiation (FIR/MIR) images into near-infrared/visible (NIR/VIR) images. These converters use polycrystalline graphene layers (PGLs) immersed in the van der Waals materials integrated with a light emitting diode (LED). The PGL serves as an element of the PGL infrared photodetector (PGLIP) sensitive to the incoming FIR/MIR due to the interband absorption. The spatially non-uniform photocurrent generated in the PGLIP repeats (mimics) the non-uniform distribution (image) created by the incident FIR/MIR. The injection of the nonuniform photocurrent into the LED active layer results in the nonuniform NIR/VIR image reproducing the FIR/MIR image. The PGL and the entire layer structure are not deliberately partitioned into pixels. We analyze the characteristics of such pixelless PGLIP-LED up-converters and show that their image contrast transfer function and the up-conversion efficiency depend on the PGL lateral resistivity. The up-converter exhibits high photoconductive gain and conversion efficiency when the lateral resistivity is sufficiently high. Several teams have successfully demonstrated the large area PGLs with the resistivities varying in a wide range. Such layers can be used in the pixelless PGLIP-LED image up-converters. The PGLIP-LED image up-converters can substantially surpass the image up-converters based on the quantum-well infrared photodetector integrated with the LED. These advantages are due to the use of the interband FIR/NIR absorption and a high photoconductive gain in the GLIPs.

Visual guidance of forward flight in hummingbirds reveals control based on image features instead of pattern velocity.

Science.gov (United States)

Dakin, Roslyn; Fellows, Tyee K; Altshuler, Douglas L

2016-08-02

Information about self-motion and obstacles in the environment is encoded by optic flow, the movement of images on the eye. Decades of research have revealed that flying insects control speed, altitude, and trajectory by a simple strategy of maintaining or balancing the translational velocity of images on the eyes, known as pattern velocity. It has been proposed that birds may use a similar algorithm but this hypothesis has not been tested directly. We examined the influence of pattern velocity on avian flight by manipulating the motion of patterns on the walls of a tunnel traversed by Anna's hummingbirds. Contrary to prediction, we found that lateral course control is not based on regulating nasal-to-temporal pattern velocity. Instead, birds closely monitored feature height in the vertical axis, and steered away from taller features even in the absence of nasal-to-temporal pattern velocity cues. For vertical course control, we observed that birds adjusted their flight altitude in response to upward motion of the horizontal plane, which simulates vertical descent. Collectively, our results suggest that birds avoid collisions using visual cues in the vertical axis. Specifically, we propose that birds monitor the vertical extent of features in the lateral visual field to assess distances to the side, and vertical pattern velocity to avoid collisions with the ground. These distinct strategies may derive from greater need to avoid collisions in birds, compared with small insects.

Interactive classification and content-based retrieval of tissue images

Science.gov (United States)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

LINE-BASED MULTI-IMAGE MATCHING FOR FAÇADE RECONSTRUCTION

Directory of Open Access Journals (Sweden)

T. A. Teo

2012-07-01

Full Text Available This research integrates existing LOD 2 building models and multiple close-range images for façade structural lines extraction. The major works are orientation determination and multiple image matching. In the orientation determination, Speeded Up Robust Features (SURF is applied to extract tie points automatically. Then, tie points and control points are combined for block adjustment. An object-based multi-images matching is proposed to extract the façade structural lines. The 2D lines in image space are extracted by Canny operator followed by Hough transform. The role of LOD 2 building models is to correct the tilt displacement of image from different views. The wall of LOD 2 model is also used to generate hypothesis planes for similarity measurement. Finally, average normalized cross correlation is calculated to obtain the best location in object space. The test images are acquired by a nonmetric camera Nikon D2X. The total number of image is 33. The experimental results indicate that the accuracy of orientation determination is about 1 pixel from 2515 tie points and 4 control points. It also indicates that line-based matching is more flexible than point-based matching.

Crowdsourcing Based 3d Modeling

Science.gov (United States)

Somogyi, A.; Barsi, A.; Molnar, B.; Lovas, T.

2016-06-01

Web-based photo albums that support organizing and viewing the users' images are widely used. These services provide a convenient solution for storing, editing and sharing images. In many cases, the users attach geotags to the images in order to enable using them e.g. in location based applications on social networks. Our paper discusses a procedure that collects open access images from a site frequently visited by tourists. Geotagged pictures showing the image of a sight or tourist attraction are selected and processed in photogrammetric processing software that produces the 3D model of the captured object. For the particular investigation we selected three attractions in Budapest. To assess the geometrical accuracy, we used laser scanner and DSLR as well as smart phone photography to derive reference values to enable verifying the spatial model obtained from the web-album images. The investigation shows how detailed and accurate models could be derived applying photogrammetric processing software, simply by using images of the community, without visiting the site.

A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

Science.gov (United States)

Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

Lung region extraction based on the model information and the inversed MIP method by using chest CT images

International Nuclear Information System (INIS)

Tomita, Toshihiro; Miguchi, Ryosuke; Okumura, Toshiaki; Yamamoto, Shinji; Matsumoto, Mitsuomi; Tateno, Yukio; Iinuma, Takeshi; Matsumoto, Toru.

1997-01-01

We developed a lung region extraction method based on the model information and the inversed MIP method in the Lung Cancer Screening CT (LSCT). Original model is composed of typical 3-D lung contour lines, a body axis, an apical point, and a convex hull. First, the body axis. the apical point, and the convex hull are automatically extracted from the input image Next, the model is properly transformed to fit to those of input image by the affine transformation. Using the same affine transformation coefficients, typical lung contour lines are also transferred, which correspond to rough contour lines of input image. Experimental results applied for 68 samples showed this method quite promising. (author)

Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

Science.gov (United States)

Folkert, Michael R.; Setton, Jeremy; Apte, Aditya P.; Grkovski, Milan; Young, Robert J.; Schöder, Heiko; Thorstad, Wade L.; Lee, Nancy Y.; Deasy, Joseph O.; Oh, Jung Hun

2017-07-01

In this study, we investigate the use of imaging feature-based outcomes research (‘radiomics’) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC)  =  0.65 (p  =  0.004), 0.73 (p  =  0.026), and 0.66 (p  =  0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC  =  0.68 (p  =  0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC  =  0.60 (p  =  0.092) and 0.65 (p  =  0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained

New variational image decomposition model for simultaneously denoising and segmenting optical coherence tomography images

International Nuclear Information System (INIS)

Duan, Jinming; Bai, Li; Tench, Christopher; Gottlob, Irene; Proudlock, Frank

2015-01-01

Optical coherence tomography (OCT) imaging plays an important role in clinical diagnosis and monitoring of diseases of the human retina. Automated analysis of optical coherence tomography images is a challenging task as the images are inherently noisy. In this paper, a novel variational image decomposition model is proposed to decompose an OCT image into three components: the first component is the original image but with the noise completely removed; the second contains the set of edges representing the retinal layer boundaries present in the image; and the third is an image of noise, or in image decomposition terms, the texture, or oscillatory patterns of the original image. In addition, a fast Fourier transform based split Bregman algorithm is developed to improve computational efficiency of solving the proposed model. Extensive experiments are conducted on both synthesised and real OCT images to demonstrate that the proposed model outperforms the state-of-the-art speckle noise reduction methods and leads to accurate retinal layer segmentation. (paper)

Modeling human faces with multi-image photogrammetry

Science.gov (United States)

D'Apuzzo, Nicola

2002-03-01

Modeling and measurement of the human face have been increasing by importance for various purposes. Laser scanning, coded light range digitizers, image-based approaches and digital stereo photogrammetry are the used methods currently employed in medical applications, computer animation, video surveillance, teleconferencing and virtual reality to produce three dimensional computer models of the human face. Depending on the application, different are the requirements. Ours are primarily high accuracy of the measurement and automation in the process. The method presented in this paper is based on multi-image photogrammetry. The equipment, the method and results achieved with this technique are here depicted. The process is composed of five steps: acquisition of multi-images, calibration of the system, establishment of corresponding points in the images, computation of their 3-D coordinates and generation of a surface model. The images captured by five CCD cameras arranged in front of the subject are digitized by a frame grabber. The complete system is calibrated using a reference object with coded target points, which can be measured fully automatically. To facilitate the establishment of correspondences in the images, texture in the form of random patterns can be projected from two directions onto the face. The multi-image matching process, based on a geometrical constrained least squares matching algorithm, produces a dense set of corresponding points in the five images. Neighborhood filters are then applied on the matching results to remove the errors. After filtering the data, the three-dimensional coordinates of the matched points are computed by forward intersection using the results of the calibration process; the achieved mean accuracy is about 0.2 mm in the sagittal direction and about 0.1 mm in the lateral direction. The last step of data processing is the generation of a surface model from the point cloud and the application of smooth filters. Moreover, a

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

Science.gov (United States)

Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

2017-12-01

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

WE-E-17A-01: Characterization of An Imaging-Based Model of Tumor Angiogenesis

International Nuclear Information System (INIS)

Adhikarla, V; Jeraj, R

2014-01-01

Purpose: Understanding the transient dynamics of tumor oxygenation is important when evaluating tumor-vasculature response to anti-angiogenic therapies. An imaging-based tumor-vasculature model was used to elucidate factors that affect these dynamics. Methods: Tumor growth depends on its doubling time (Td). Hypoxia increases pro-angiogenic factor (VEGF) concentration which is modeled to reduce vessel perfusion, attributing to its effect of increasing vascular permeability. Perfused vessel recruitment depends on the existing perfused vasculature, VEGF concentration and maximum VEGF concentration (VEGFmax) for vessel dysfunction. A convolution-based algorithm couples the tumor to the normal tissue vessel density (VD-nt). The parameters are benchmarked to published pre-clinical data and a sensitivity study evaluating the changes in the peak and time to peak tumor oxygenation characterizes them. The model is used to simulate changes in hypoxia and proliferation PET imaging data obtained using [Cu- 61]Cu-ATSM and [F-18]FLT respectively. Results: Td and VD-nt were found to be the most influential on peak tumor pO2 while VEGFmax was marginally influential. A +20 % change in Td, VD-nt and VEGFmax resulted in +50%, +25% and +5% increase in peak pO2. In contrast, Td was the most influential on the time to peak oxygenation with VD-nt and VEGFmax playing marginal roles. A +20% change in Td, VD-nt and VEGFmax increased the time to peak pO2 by +50%, +5% and +0%. A −20% change in the above parameters resulted in comparable decreases in the peak and time to peak pO2. Model application to the PET data was able to demonstrate the voxel-specific changes in hypoxia of the imaged tumor. Conclusion: Tumor-specific doubling time and vessel density are important parameters to be considered when evaluating hypoxia transients. While the current model simulates the oxygen dynamics of an untreated tumor, incorporation of therapeutic effects can make the model a potent tool for analyzing

Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images

Science.gov (United States)

Sánchez, Clara I.; Hornero, Roberto; Mayo, Agustín; García, María

2009-02-01

Diabetic Retinopathy is one of the leading causes of blindness and vision defects in developed countries. An early detection and diagnosis is crucial to avoid visual complication. Microaneurysms are the first ocular signs of the presence of this ocular disease. Their detection is of paramount importance for the development of a computer-aided diagnosis technique which permits a prompt diagnosis of the disease. However, the detection of microaneurysms in retinal images is a difficult task due to the wide variability that these images usually present in screening programs. We propose a statistical approach based on mixture model-based clustering and logistic regression which is robust to the changes in the appearance of retinal fundus images. The method is evaluated on the public database proposed by the Retinal Online Challenge in order to obtain an objective performance measure and to allow a comparative study with other proposed algorithms.

Anti-cancer agents in Saudi Arabian herbals revealed by automated high-content imaging

KAUST Repository

Hajjar, Dina

2017-06-13

Natural products have been used for medical applications since ancient times. Commonly, natural products are structurally complex chemical compounds that efficiently interact with their biological targets, making them useful drug candidates in cancer therapy. Here, we used cell-based phenotypic profiling and image-based high-content screening to study the mode of action and potential cellular targets of plants historically used in Saudi Arabia\\'s traditional medicine. We compared the cytological profiles of fractions taken from Juniperus phoenicea (Arar), Anastatica hierochuntica (Kaff Maryam), and Citrullus colocynthis (Hanzal) with a set of reference compounds with established modes of action. Cluster analyses of the cytological profiles of the tested compounds suggested that these plants contain possible topoisomerase inhibitors that could be effective in cancer treatment. Using histone H2AX phosphorylation as a marker for DNA damage, we discovered that some of the compounds induced double-strand DNA breaks. Furthermore, chemical analysis of the active fraction isolated from Juniperus phoenicea revealed possible anti-cancer compounds. Our results demonstrate the usefulness of cell-based phenotypic screening of natural products to reveal their biological activities.

Single-Image Super-Resolution Based on Rational Fractal Interpolation.

Science.gov (United States)

Zhang, Yunfeng; Fan, Qinglan; Bao, Fangxun; Liu, Yifang; Zhang, Caiming

2018-08-01

This paper presents a novel single-image super-resolution (SR) procedure, which upscales a given low-resolution (LR) input image to a high-resolution image while preserving the textural and structural information. First, we construct a new type of bivariate rational fractal interpolation model and investigate its analytical properties. This model has different forms of expression with various values of the scaling factors and shape parameters; thus, it can be employed to better describe image features than current interpolation schemes. Furthermore, this model combines the advantages of rational interpolation and fractal interpolation, and its effectiveness is validated through theoretical analysis. Second, we develop a single-image SR algorithm based on the proposed model. The LR input image is divided into texture and non-texture regions, and then, the image is interpolated according to the characteristics of the local structure. Specifically, in the texture region, the scaling factor calculation is the critical step. We present a method to accurately calculate scaling factors based on local fractal analysis. Extensive experiments and comparisons with the other state-of-the-art methods show that our algorithm achieves competitive performance, with finer details and sharper edges.

Physics-based deformable organisms for medical image analysis

Science.gov (United States)

Hamarneh, Ghassan; McIntosh, Chris

2005-04-01

Previously, "Deformable organisms" were introduced as a novel paradigm for medical image analysis that uses artificial life modelling concepts. Deformable organisms were designed to complement the classical bottom-up deformable models methodologies (geometrical and physical layers), with top-down intelligent deformation control mechanisms (behavioral and cognitive layers). However, a true physical layer was absent and in order to complete medical image segmentation tasks, deformable organisms relied on pure geometry-based shape deformations guided by sensory data, prior structural knowledge, and expert-generated schedules of behaviors. In this paper we introduce the use of physics-based shape deformations within the deformable organisms framework yielding additional robustness by allowing intuitive real-time user guidance and interaction when necessary. We present the results of applying our physics-based deformable organisms, with an underlying dynamic spring-mass mesh model, to segmenting and labelling the corpus callosum in 2D midsagittal magnetic resonance images.

Pixel-based meshfree modelling of skeletal muscles.

Science.gov (United States)

Chen, Jiun-Shyan; Basava, Ramya Rao; Zhang, Yantao; Csapo, Robert; Malis, Vadim; Sinha, Usha; Hodgson, John; Sinha, Shantanu

2016-01-01

This paper introduces the meshfree Reproducing Kernel Particle Method (RKPM) for 3D image-based modeling of skeletal muscles. This approach allows for construction of simulation model based on pixel data obtained from medical images. The material properties and muscle fiber direction obtained from Diffusion Tensor Imaging (DTI) are input at each pixel point. The reproducing kernel (RK) approximation allows a representation of material heterogeneity with smooth transition. A multiphase multichannel level set based segmentation framework is adopted for individual muscle segmentation using Magnetic Resonance Images (MRI) and DTI. The application of the proposed methods for modeling the human lower leg is demonstrated.

Some practical considerations in finite element-based digital image correlation

KAUST Repository

Wang, Bo

2015-04-20

As an alternative to subset-based digital image correlation (DIC), finite element-based (FE-based) DIC method has gained increasing attention in the experimental mechanics community. However, the literature survey reveals that some important issues have not been well addressed in the published literatures. This work therefore aims to point out a few important considerations in the practical algorithm implementation of the FE-based DIC method, along with simple but effective solutions that can effectively tackle these issues. First, to better accommodate the possible intensity variations of the deformed images practically occurred in real experiments, a robust zero-mean normalized sum of squared difference criterion, instead of the commonly used sum of squared difference criterion, is introduced to quantify the similarity between reference and deformed elements in FE-based DIC. Second, to reduce the bias error induced by image noise and imperfect intensity interpolation, low-pass filtering of the speckle images with a 5×5 pixels Gaussian filter prior to correlation analysis, is presented. Third, to ensure the iterative calculation of FE-based DIC converges correctly and rapidly, an efficient subset-based DIC method, instead of simple integer-pixel displacement searching, is used to provide accurate initial guess of deformation for each calculation point. Also, the effects of various convergence criteria on the efficiency and accuracy of FE-based DIC are carefully examined, and a proper convergence criterion is recommended. The efficacy of these solutions is verified by numerical and real experiments. The results reveal that the improved FE-based DIC offers evident advantages over existing FE-based DIC method in terms of accuracy and efficiency. © 2015 Elsevier Ltd. All rights reserved.

Comparing orbiter and rover image-based mapping of an ancient sedimentary environment, Aeolis Palus, Gale crater, Mars

Science.gov (United States)

Stack, Kathryn M.; Edwards, Christopher; Grotzinger, J. P.; Gupta, S.; Sumner, D.; Edgar, Lauren; Fraeman, A.; Jacob, S.; LeDeit, L.; Lewis, K.W.; Rice, M.S.; Rubin, D.; Calef, F.; Edgett, K.; Williams, R.M.E.; Williford, K.H.

2016-01-01

This study provides the first systematic comparison of orbital facies maps with detailed ground-based geology observations from the Mars Science Laboratory (MSL) Curiosity rover to examine the validity of geologic interpretations derived from orbital image data. Orbital facies maps were constructed for the Darwin, Cooperstown, and Kimberley waypoints visited by the Curiosity rover using High Resolution Imaging Science Experiment (HiRISE) images. These maps, which represent the most detailed orbital analysis of these areas to date, were compared with rover image-based geologic maps and stratigraphic columns derived from Curiosity’s Mast Camera (Mastcam) and Mars Hand Lens Imager (MAHLI). Results show that bedrock outcrops can generally be distinguished from unconsolidated surficial deposits in high-resolution orbital images and that orbital facies mapping can be used to recognize geologic contacts between well-exposed bedrock units. However, process-based interpretations derived from orbital image mapping are difficult to infer without known regional context or observable paleogeomorphic indicators, and layer-cake models of stratigraphy derived from orbital maps oversimplify depositional relationships as revealed from a rover perspective. This study also shows that fine-scale orbital image-based mapping of current and future Mars landing sites is essential for optimizing the efficiency and science return of rover surface operations.

A foreground object features-based stereoscopic image visual comfort assessment model

Science.gov (United States)

Jin, Xin; Jiang, G.; Ying, H.; Yu, M.; Ding, S.; Peng, Z.; Shao, F.

2014-11-01

Since stereoscopic images provide observers with both realistic and discomfort viewing experience, it is necessary to investigate the determinants of visual discomfort. By considering that foreground object draws most attention when human observing stereoscopic images. This paper proposes a new foreground object based visual comfort assessment (VCA) metric. In the first place, a suitable segmentation method is applied to disparity map and then the foreground object is ascertained as the one having the biggest average disparity. In the second place, three visual features being average disparity, average width and spatial complexity of foreground object are computed from the perspective of visual attention. Nevertheless, object's width and complexity do not consistently influence the perception of visual comfort in comparison with disparity. In accordance with this psychological phenomenon, we divide the whole images into four categories on the basis of different disparity and width, and exert four different models to more precisely predict its visual comfort in the third place. Experimental results show that the proposed VCA metric outperformance other existing metrics and can achieve a high consistency between objective and subjective visual comfort scores. The Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are over 0.84 and 0.82, respectively.

Minimal Camera Networks for 3D Image Based Modeling of Cultural Heritage Objects

Science.gov (United States)

Alsadik, Bashar; Gerke, Markus; Vosselman, George; Daham, Afrah; Jasim, Luma

2014-01-01

3D modeling of cultural heritage objects like artifacts, statues and buildings is nowadays an important tool for virtual museums, preservation and restoration. In this paper, we introduce a method to automatically design a minimal imaging network for the 3D modeling of cultural heritage objects. This becomes important for reducing the image capture time and processing when documenting large and complex sites. Moreover, such a minimal camera network design is desirable for imaging non-digitally documented artifacts in museums and other archeological sites to avoid disturbing the visitors for a long time and/or moving delicate precious objects to complete the documentation task. The developed method is tested on the Iraqi famous statue “Lamassu”. Lamassu is a human-headed winged bull of over 4.25 m in height from the era of Ashurnasirpal II (883–859 BC). Close-range photogrammetry is used for the 3D modeling task where a dense ordered imaging network of 45 high resolution images were captured around Lamassu with an object sample distance of 1 mm. These images constitute a dense network and the aim of our study was to apply our method to reduce the number of images for the 3D modeling and at the same time preserve pre-defined point accuracy. Temporary control points were fixed evenly on the body of Lamassu and measured by using a total station for the external validation and scaling purpose. Two network filtering methods are implemented and three different software packages are used to investigate the efficiency of the image orientation and modeling of the statue in the filtered (reduced) image networks. Internal and external validation results prove that minimal image networks can provide highly accurate records and efficiency in terms of visualization, completeness, processing time (>60% reduction) and the final accuracy of 1 mm. PMID:24670718

Minimal camera networks for 3D image based modeling of cultural heritage objects.

Science.gov (United States)

Alsadik, Bashar; Gerke, Markus; Vosselman, George; Daham, Afrah; Jasim, Luma

2014-03-25

3D modeling of cultural heritage objects like artifacts, statues and buildings is nowadays an important tool for virtual museums, preservation and restoration. In this paper, we introduce a method to automatically design a minimal imaging network for the 3D modeling of cultural heritage objects. This becomes important for reducing the image capture time and processing when documenting large and complex sites. Moreover, such a minimal camera network design is desirable for imaging non-digitally documented artifacts in museums and other archeological sites to avoid disturbing the visitors for a long time and/or moving delicate precious objects to complete the documentation task. The developed method is tested on the Iraqi famous statue "Lamassu". Lamassu is a human-headed winged bull of over 4.25 m in height from the era of Ashurnasirpal II (883-859 BC). Close-range photogrammetry is used for the 3D modeling task where a dense ordered imaging network of 45 high resolution images were captured around Lamassu with an object sample distance of 1 mm. These images constitute a dense network and the aim of our study was to apply our method to reduce the number of images for the 3D modeling and at the same time preserve pre-defined point accuracy. Temporary control points were fixed evenly on the body of Lamassu and measured by using a total station for the external validation and scaling purpose. Two network filtering methods are implemented and three different software packages are used to investigate the efficiency of the image orientation and modeling of the statue in the filtered (reduced) image networks. Internal and external validation results prove that minimal image networks can provide highly accurate records and efficiency in terms of visualization, completeness, processing time (>60% reduction) and the final accuracy of 1 mm.

Interpretation of medical images by model guided analysis

International Nuclear Information System (INIS)

Karssemeijer, N.

1989-01-01

Progress in the development of digital pictorial information systems stimulates a growing interest in the use of image analysis techniques in medicine. Especially when precise quantitative information is required the use of fast and reproducable computer analysis may be more appropriate than relying on visual judgement only. Such quantitative information can be valuable, for instance, in diagnostics or in irradiation therapy planning. As medical images are mostly recorded in a prescribed way, human anatomy guarantees a common image structure for each particular type of exam. In this thesis it is investigated how to make use of this a priori knowledge to guide image analysis. For that purpose models are developed which are suited to capture common image structure. The first part of this study is devoted to an analysis of nuclear medicine images of myocardial perfusion. In ch. 2 a model of these images is designed in order to represent characteristic image properties. It is shown that for these relatively simple images a compact symbolic description can be achieved, without significant loss of diagnostically importance of several image properties. Possibilities for automatic interpretation of more complex images is investigated in the following chapters. The central topic is segmentation of organs. Two methods are proposed and tested on a set of abdominal X-ray CT scans. Ch. 3 describes a serial approach based on a semantic network and the use of search areas. Relational constraints are used to guide the image processing and to classify detected image segments. In teh ch.'s 4 and 5 a more general parallel approach is utilized, based on a markov random field image model. A stochastic model used to represent prior knowledge about the spatial arrangement of organs is implemented as an external field. (author). 66 refs.; 27 figs.; 6 tabs

Image Re-Ranking Based on Topic Diversity.

Science.gov (United States)

Qian, Xueming; Lu, Dan; Wang, Yaxiong; Zhu, Li; Tang, Yuan Yan; Wang, Meng

2017-08-01

Social media sharing Websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then, the community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr data set and NUS-Wide data sets show the effectiveness of the proposed approach.

Cryptanalysis of a chaos-based image encryption algorithm

International Nuclear Information System (INIS)

Cokal, Cahit; Solak, Ercan

2009-01-01

A chaos-based image encryption algorithm was proposed in [Z.-H. Guan, F. Huang, W. Guan, Phys. Lett. A 346 (2005) 153]. In this Letter, we analyze the security weaknesses of the proposal. By applying chosen-plaintext and known-plaintext attacks, we show that all the secret parameters can be revealed

Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging.

Science.gov (United States)

Liu, Fang; Jang, Hyungseok; Kijowski, Richard; Bradshaw, Tyler; McMillan, Alan B

2018-02-01

Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existing MR imaging-based AC approaches with CT-based AC. Results Deep MRAC provides an accurate pseudo CT scan with a mean Dice coefficient of 0.971 ± 0.005 for air, 0.936 ± 0.011 for soft tissue, and 0.803 ± 0.021 for bone. Furthermore, deep MRAC provides good PET results, with average errors of less than 1% in most brain regions. Significantly lower PET reconstruction errors were realized with deep MRAC (-0.7% ± 1.1) compared with Dixon-based soft-tissue and air segmentation (-5.8% ± 3.1) and anatomic CT-based template registration (-4.8% ± 2.2). Conclusion The authors developed an automated approach that allows generation of discrete-valued pseudo CT scans (soft tissue, bone, and air) from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging. This deep learning approach for MR imaging-based AC provided reduced PET reconstruction error relative to a CT-based standard within the brain compared

CROWDSOURCING BASED 3D MODELING

Directory of Open Access Journals (Sweden)

A. Somogyi

2016-06-01

Full Text Available Web-based photo albums that support organizing and viewing the users’ images are widely used. These services provide a convenient solution for storing, editing and sharing images. In many cases, the users attach geotags to the images in order to enable using them e.g. in location based applications on social networks. Our paper discusses a procedure that collects open access images from a site frequently visited by tourists. Geotagged pictures showing the image of a sight or tourist attraction are selected and processed in photogrammetric processing software that produces the 3D model of the captured object. For the particular investigation we selected three attractions in Budapest. To assess the geometrical accuracy, we used laser scanner and DSLR as well as smart phone photography to derive reference values to enable verifying the spatial model obtained from the web-album images. The investigation shows how detailed and accurate models could be derived applying photogrammetric processing software, simply by using images of the community, without visiting the site.

Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modeling: Application to Neurodegenerative Diseases

Directory of Open Access Journals (Sweden)

Francisco J. Martinez-Murcia

2017-11-01

Full Text Available The rise of neuroimaging in research and clinical practice, together with the development of new machine learning techniques has strongly encouraged the Computer Aided Diagnosis (CAD of different diseases and disorders. However, these algorithms are often tested in proprietary datasets to which the access is limited and, therefore, a direct comparison between CAD procedures is not possible. Furthermore, the sample size is often small for developing accurate machine learning methods. Multi-center initiatives are currently a very useful, although limited, tool in the recruitment of large populations and standardization of CAD evaluation. Conversely, we propose a brain image synthesis procedure intended to generate a new image set that share characteristics with an original one. Our system focuses on nuclear imaging modalities such as PET or SPECT brain images. We analyze the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF estimator. Once the model has been built, we can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space. The system has been evaluated on different functional neuroimaging datasets assessing the: resemblance of the synthetic images with the original ones, the differences between them, their generalization ability and the independence of the synthetic dataset with respect to the original. The synthetic images maintain the differences between groups found at the original dataset, with no significant differences when comparing them to real-world samples. Furthermore, they featured a similar performance and generalization capability to that of the original dataset. These results prove that these images are suitable for standardizing the evaluation of CAD pipelines, and providing data augmentation in machine learning systems -e.g. in deep

Construction of In Vivo Fluorescent Imaging of Echinococcus granulosus in a Mouse Model.

Science.gov (United States)

Wang, Sibo; Yang, Tao; Zhang, Xuyong; Xia, Jie; Guo, Jun; Wang, Xiaoyi; Hou, Jixue; Zhang, Hongwei; Chen, Xueling; Wu, Xiangwei

2016-06-01

Human hydatid disease (cystic echinococcosis, CE) is a chronic parasitic infection caused by the larval stage of the cestode Echinococcus granulosus. As the disease mainly affects the liver, approximately 70% of all identified CE cases are detected in this organ. Optical molecular imaging (OMI), a noninvasive imaging technique, has never been used in vivo with the specific molecular markers of CE. Thus, we aimed to construct an in vivo fluorescent imaging mouse model of CE to locate and quantify the presence of the parasites within the liver noninvasively. Drug-treated protoscolices were monitored after marking by JC-1 dye in in vitro and in vivo studies. This work describes for the first time the successful construction of an in vivo model of E. granulosus in a small living experimental animal to achieve dynamic monitoring and observation of multiple time points of the infection course. Using this model, we quantified and analyzed labeled protoscolices based on the intensities of their red and green fluorescence. Interestingly, the ratio of red to green fluorescence intensity not only revealed the location of protoscolices but also determined the viability of the parasites in vivo and in vivo tests. The noninvasive imaging model proposed in this work will be further studied for long-term detection and observation and may potentially be widely utilized in susceptibility testing and therapeutic effect evaluation.

Validation of model-based brain shift correction in neurosurgery via intraoperative magnetic resonance imaging: preliminary results

Science.gov (United States)

Luo, Ma; Frisken, Sarah F.; Weis, Jared A.; Clements, Logan W.; Unadkat, Prashin; Thompson, Reid C.; Golby, Alexandra J.; Miga, Michael I.

2017-03-01

The quality of brain tumor resection surgery is dependent on the spatial agreement between preoperative image and intraoperative anatomy. However, brain shift compromises the aforementioned alignment. Currently, the clinical standard to monitor brain shift is intraoperative magnetic resonance (iMR). While iMR provides better understanding of brain shift, its cost and encumbrance is a consideration for medical centers. Hence, we are developing a model-based method that can be a complementary technology to address brain shift in standard resections, with resource-intensive cases as referrals for iMR facilities. Our strategy constructs a deformation `atlas' containing potential deformation solutions derived from a biomechanical model that account for variables such as cerebrospinal fluid drainage and mannitol effects. Volumetric deformation is estimated with an inverse approach that determines the optimal combinatory `atlas' solution fit to best match measured surface deformation. Accordingly, preoperative image is updated based on the computed deformation field. This study is the latest development to validate our methodology with iMR. Briefly, preoperative and intraoperative MR images of 2 patients were acquired. Homologous surface points were selected on preoperative and intraoperative scans as measurement of surface deformation and used to drive the inverse problem. To assess the model accuracy, subsurface shift of targets between preoperative and intraoperative states was measured and compared to model prediction. Considering subsurface shift above 3 mm, the proposed strategy provides an average shift correction of 59% across 2 cases. While further improvements in both the model and ability to validate with iMR are desired, the results reported are encouraging.

Cryptanalysis on an image block encryption algorithm based on spatiotemporal chaos

International Nuclear Information System (INIS)

Wang Xing-Yuan; He Guo-Xiang

2012-01-01

An image block encryption scheme based on spatiotemporal chaos has been proposed recently. In this paper, we analyse the security weakness of the proposal. The main problem of the original scheme is that the generated keystream remains unchanged for encrypting every image. Based on the flaws, we demonstrate a chosen plaintext attack for revealing the equivalent keys with only 6 pairs of plaintext/ciphertext used. Finally, experimental results show the validity of our attack. (general)

Voxel-based statistical analysis of cerebral glucose metabolism in the rat cortical deafness model by 3D reconstruction of brain from autoradiographic images

Energy Technology Data Exchange (ETDEWEB)

Lee, Jae Sung; Park, Kwang Suk [Seoul National University College of Medicine, Department of Nuclear Medicine, 28 Yungun-Dong, Chongno-Ku, Seoul (Korea); Seoul National University College of Medicine, Department of Biomedical Engineering, Seoul (Korea); Ahn, Soon-Hyun; Oh, Seung Ha; Kim, Chong Sun; Chung, June-Key; Lee, Myung Chul [Seoul National University College of Medicine, Department of Otolaryngology, Head and Neck Surgery, Seoul (Korea); Lee, Dong Soo; Jeong, Jae Min [Seoul National University College of Medicine, Department of Nuclear Medicine, 28 Yungun-Dong, Chongno-Ku, Seoul (Korea)

2005-06-01

Animal models of cortical deafness are essential for investigation of the cerebral glucose metabolism in congenital or prelingual deafness. Autoradiographic imaging is mainly used to assess the cerebral glucose metabolism in rodents. In this study, procedures for the 3D voxel-based statistical analysis of autoradiographic data were established to enable investigations of the within-modal and cross-modal plasticity through entire areas of the brain of sensory-deprived animals without lumping together heterogeneous subregions within each brain structure into a large region of interest. Thirteen 2-[1-{sup 14}C]-deoxy-D-glucose autoradiographic images were acquired from six deaf and seven age-matched normal rats (age 6-10 weeks). The deafness was induced by surgical ablation. For the 3D voxel-based statistical analysis, brain slices were extracted semiautomatically from the autoradiographic images, which contained the coronal sections of the brain, and were stacked into 3D volume data. Using principal axes matching and mutual information maximization algorithms, the adjacent coronal sections were co-registered using a rigid body transformation, and all sections were realigned to the first section. A study-specific template was composed and the realigned images were spatially normalized onto the template. Following count normalization, voxel-wise t tests were performed to reveal the areas with significant differences in cerebral glucose metabolism between the deaf and the control rats. Continuous and clear edges were detected in each image after registration between the coronal sections, and the internal and external landmarks extracted from the spatially normalized images were well matched, demonstrating the reliability of the spatial processing procedures. Voxel-wise t tests showed that the glucose metabolism in the bilateral auditory cortices of the deaf rats was significantly (P<0.001) lower than that in the controls. There was no significantly reduced metabolism in

Voxel-based statistical analysis of cerebral glucose metabolism in the rat cortical deafness model by 3D reconstruction of brain from autoradiographic images

International Nuclear Information System (INIS)

Lee, Jae Sung; Park, Kwang Suk; Ahn, Soon-Hyun; Oh, Seung Ha; Kim, Chong Sun; Chung, June-Key; Lee, Myung Chul; Lee, Dong Soo; Jeong, Jae Min

2005-01-01

Animal models of cortical deafness are essential for investigation of the cerebral glucose metabolism in congenital or prelingual deafness. Autoradiographic imaging is mainly used to assess the cerebral glucose metabolism in rodents. In this study, procedures for the 3D voxel-based statistical analysis of autoradiographic data were established to enable investigations of the within-modal and cross-modal plasticity through entire areas of the brain of sensory-deprived animals without lumping together heterogeneous subregions within each brain structure into a large region of interest. Thirteen 2-[1- 14 C]-deoxy-D-glucose autoradiographic images were acquired from six deaf and seven age-matched normal rats (age 6-10 weeks). The deafness was induced by surgical ablation. For the 3D voxel-based statistical analysis, brain slices were extracted semiautomatically from the autoradiographic images, which contained the coronal sections of the brain, and were stacked into 3D volume data. Using principal axes matching and mutual information maximization algorithms, the adjacent coronal sections were co-registered using a rigid body transformation, and all sections were realigned to the first section. A study-specific template was composed and the realigned images were spatially normalized onto the template. Following count normalization, voxel-wise t tests were performed to reveal the areas with significant differences in cerebral glucose metabolism between the deaf and the control rats. Continuous and clear edges were detected in each image after registration between the coronal sections, and the internal and external landmarks extracted from the spatially normalized images were well matched, demonstrating the reliability of the spatial processing procedures. Voxel-wise t tests showed that the glucose metabolism in the bilateral auditory cortices of the deaf rats was significantly (P<0.001) lower than that in the controls. There was no significantly reduced metabolism in any

Video-based noncooperative iris image segmentation.

Science.gov (United States)

Du, Yingzi; Arslanturk, Emrah; Zhou, Zhi; Belcher, Craig

2011-02-01

In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.

Predicting 3D pose in partially overlapped X-ray images of knee prostheses using model-based Roentgen stereophotogrammetric analysis (RSA).

Science.gov (United States)

Hsu, Chi-Pin; Lin, Shang-Chih; Shih, Kao-Shang; Huang, Chang-Hung; Lee, Chian-Her

2014-12-01

After total knee replacement, the model-based Roentgen stereophotogrammetric analysis (RSA) technique has been used to monitor the status of prosthetic wear, misalignment, and even failure. However, the overlap of the prosthetic outlines inevitably increases errors in the estimation of prosthetic poses due to the limited amount of available outlines. In the literature, quite a few studies have investigated the problems induced by the overlapped outlines, and manual adjustment is still the mainstream. This study proposes two methods to automate the image processing of overlapped outlines prior to the pose registration of prosthetic models. The outline-separated method defines the intersected points and segments the overlapped outlines. The feature-recognized method uses the point and line features of the remaining outlines to initiate registration. Overlap percentage is defined as the ratio of overlapped to non-overlapped outlines. The simulated images with five overlapping percentages are used to evaluate the robustness and accuracy of the proposed methods. Compared with non-overlapped images, overlapped images reduce the number of outlines available for model-based RSA calculation. The maximum and root mean square errors for a prosthetic outline are 0.35 and 0.04 mm, respectively. The mean translation and rotation errors are 0.11 mm and 0.18°, respectively. The errors of the model-based RSA results are increased when the overlap percentage is beyond about 9%. In conclusion, both outline-separated and feature-recognized methods can be seamlessly integrated to automate the calculation of rough registration. This can significantly increase the clinical practicability of the model-based RSA technique.

Gallbladder shape extraction from ultrasound images using active contour models.

Science.gov (United States)

Ciecholewski, Marcin; Chochołowicz, Jakub

2013-12-01

Gallbladder function is routinely assessed using ultrasonographic (USG) examinations. In clinical practice, doctors very often analyse the gallbladder shape when diagnosing selected disorders, e.g. if there are turns or folds of the gallbladder, so extracting its shape from USG images using supporting software can simplify a diagnosis that is often difficult to make. The paper describes two active contour models: the edge-based model and the region-based model making use of a morphological approach, both designed for extracting the gallbladder shape from USG images. The active contour models were applied to USG images without lesions and to those showing specific disease units, namely, anatomical changes like folds and turns of the gallbladder as well as polyps and gallstones. This paper also presents modifications of the edge-based model, such as the method for removing self-crossings and loops or the method of dampening the inflation force which moves nodes if they approach the edge being determined. The user is also able to add a fragment of the approximated edge beyond which neither active contour model will move if this edge is incomplete in the USG image. The modifications of the edge-based model presented here allow more precise results to be obtained when extracting the shape of the gallbladder from USG images than if the morphological model is used. © 2013 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

A neural network detection model of spilled oil based on the texture analysis of SAR image

Science.gov (United States)

An, Jubai; Zhu, Lisong

2006-01-01

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

Wavefront analysis for plenoptic camera imaging

International Nuclear Information System (INIS)

Luan Yin-Sen; Xu Bing; Yang Ping; Tang Guo-Mao

2017-01-01

The plenoptic camera is a single lens stereo camera which can retrieve the direction of light rays while detecting their intensity distribution. In this paper, to reveal more truths of plenoptic camera imaging, we present the wavefront analysis for the plenoptic camera imaging from the angle of physical optics but not from the ray tracing model of geometric optics. Specifically, the wavefront imaging model of a plenoptic camera is analyzed and simulated by scalar diffraction theory and the depth estimation is redescribed based on physical optics. We simulate a set of raw plenoptic images of an object scene, thereby validating the analysis and derivations and the difference between the imaging analysis methods based on geometric optics and physical optics are also shown in simulations. (paper)

HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

Directory of Open Access Journals (Sweden)

Qingjiao Sun

2016-01-01

Full Text Available Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR pathological image enhancement method based on improved bias field correction and guided image filter (GIF. Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.

Mapping Fire Severity Using Imaging Spectroscopy and Kernel Based Image Analysis

Science.gov (United States)

Prasad, S.; Cui, M.; Zhang, Y.; Veraverbeke, S.

2014-12-01

Improved spatial representation of within-burn heterogeneity after wildfires is paramount to effective land management decisions and more accurate fire emissions estimates. In this work, we demonstrate feasibility and efficacy of airborne imaging spectroscopy (hyperspectral imagery) for quantifying wildfire burn severity, using kernel based image analysis techniques. Two different airborne hyperspectral datasets, acquired over the 2011 Canyon and 2013 Rim fire in California using the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) sensor, were used in this study. The Rim Fire, covering parts of the Yosemite National Park started on August 17, 2013, and was the third largest fire in California's history. Canyon Fire occurred in the Tehachapi mountains, and started on September 4, 2011. In addition to post-fire data for both fires, half of the Rim fire was also covered with pre-fire images. Fire severity was measured in the field using Geo Composite Burn Index (GeoCBI). The field data was utilized to train and validate our models, wherein the trained models, in conjunction with imaging spectroscopy data were used for GeoCBI estimation wide geographical regions. This work presents an approach for using remotely sensed imagery combined with GeoCBI field data to map fire scars based on a non-linear (kernel based) epsilon-Support Vector Regression (e-SVR), which was used to learn the relationship between spectra and GeoCBI in a kernel-induced feature space. Classification of healthy vegetation versus fire-affected areas based on morphological multi-attribute profiles was also studied. The availability of pre- and post-fire imaging spectroscopy data over the Rim Fire provided a unique opportunity to evaluate the performance of bi-temporal imaging spectroscopy for assessing post-fire effects. This type of data is currently constrained because of limited airborne acquisitions before a fire, but will become widespread with future spaceborne sensors such as those on

POLARIZATION IMAGING AND SCATTERING MODEL OF CANCEROUS LIVER TISSUES

Directory of Open Access Journals (Sweden)

DONGZHI LI

2013-07-01

Full Text Available We apply different polarization imaging techniques for cancerous liver tissues, and compare the relative contrasts for difference polarization imaging (DPI, degree of polarization imaging (DOPI and rotating linear polarization imaging (RLPI. Experimental results show that a number of polarization imaging parameters are capable of differentiating cancerous cells in isotropic liver tissues. To analyze the contrast mechanism of the cancer-sensitive polarization imaging parameters, we propose a scattering model containing two types of spherical scatterers and carry on Monte Carlo simulations based on this bi-component model. Both the experimental and Monte Carlo simulated results show that the RLPI technique can provide a good imaging contrast of cancerous tissues. The bi-component scattering model provides a useful tool to analyze the contrast mechanism of polarization imaging of cancerous tissues.

Solid models for CT/MR image display

International Nuclear Information System (INIS)

ManKovich, N.J.; Yue, A.; Kioumehr, F.; Ammirati, M.; Turner, S.

1991-01-01

Medical imaging can now take wider advantage of Computer-Aided-Manufacturing through rapid prototyping technologies (RPT) such as stereolithography, laser sintering, and laminated object manufacturing to directly produce solid models of patient anatomy from processed CT and MR images. While conventional surgical planning relies on consultation with the radiologist combined with direct reading and measurement of CT and MR studies, 3-D surface and volumetric display workstations are providing a more easily interpretable view of patient anatomy. RPT can provide the surgeon with a life size model of patient anatomy constructed layer by layer with full internal detail. The authors have developed a prototype image processing and model fabrication system based on stereolithography, which provides the neurosurgeon with models of the skull base. Parallel comparison of the mode with the original thresholded CT data and with a CRT displayed surface rendering showed that both have an accuracy of >99.6 percent. The measurements on the surface rendered display proved more difficult to exactly locate and yielded a standard deviation of 2.37 percent. This paper presents an accuracy study and discusses ways of assessing the quality of neurosurgical plans when 3-D models re made available as planning tools

Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image

International Nuclear Information System (INIS)

Wang Huan; Guo Xiuhua; Jia Zhongwei; Li Hongkai; Liang Zhigang; Li Kuncheng; He Qian

2010-01-01

Purpose: To introduce multilevel binomial logistic prediction model-based computer-aided diagnostic (CAD) method of small solitary pulmonary nodules (SPNs) diagnosis by combining patient and image characteristics by textural features of CT image. Materials and methods: Describe fourteen gray level co-occurrence matrix textural features obtained from 2171 benign and malignant small solitary pulmonary nodules, which belongs to 185 patients. Multilevel binomial logistic model is applied to gain these initial insights. Results: Five texture features, including Inertia, Entropy, Correlation, Difference-mean, Sum-Entropy, and age of patients own aggregating character on patient-level, which are statistically different (P < 0.05) between benign and malignant small solitary pulmonary nodules. Conclusion: Some gray level co-occurrence matrix textural features are efficiently descriptive features of CT image of small solitary pulmonary nodules, which can profit diagnosis of earlier period lung cancer if combined patient-level characteristics to some extent.

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Directory of Open Access Journals (Sweden)

Ye Yao

2018-04-01

Full Text Available Computer-generated graphics (CGs are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1 the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2 the proposed method with three HPFs achieves 100% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.

Blind compressed sensing image reconstruction based on alternating direction method

Science.gov (United States)

Liu, Qinan; Guo, Shuxu

2018-04-01

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

Science.gov (United States)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

Development of acoustic model-based iterative reconstruction technique for thick-concrete imaging

Science.gov (United States)

Almansouri, Hani; Clayton, Dwight; Kisner, Roger; Polsky, Yarom; Bouman, Charles; Santos-Villalobos, Hector

2016-02-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well's health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.1

Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Energy Technology Data Exchange (ETDEWEB)

Almansouri, Hani [Purdue University; Clayton, Dwight A [ORNL; Kisner, Roger A [ORNL; Polsky, Yarom [ORNL; Bouman, Charlie [Purdue University; Santos-Villalobos, Hector J [ORNL

2016-01-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well's health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.

Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Energy Technology Data Exchange (ETDEWEB)

Almansouri, Hani [Purdue University; Clayton, Dwight A [ORNL; Kisner, Roger A [ORNL; Polsky, Yarom [ORNL; Bouman, Charlie [Purdue University; Santos-Villalobos, Hector J [ORNL

2015-01-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well s health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.

Image Quality Improvement on OpenGL-Based Animations by Using CUDA Architecture

Directory of Open Access Journals (Sweden)

Taner UÇKAN

2016-04-01

Full Text Available 2D or 3D rendering technology is used for graphically modelling many physical phenomena occurring in real life by means of the computers. On the other hand, the ever-increasing intensity of the graphics applications require that the image quality of the so-called modellings is enhanced and they are performed more quickly. In this direction, a new software and hardware-based architecture called CUDA has been introduced by Nvidia at the end of 2006. Thanks to this architecture, larger number of graphics processors has started contributing towards the parallel solutions of the general-purpose problems. In this study, this new parallel computing architecture is taken into consideration and an animation application consisting of humanoid robots with different behavioral characteristics is developed using the OpenGL library in C++. This animation is initially implemented on a single serial CPU and then parallelized using the CUDA architecture. Eventually, the serial and the parallel versions of the same animation are compared against each other on the basis of the number of image frames per second. The results reveal that the parallel application is by far the best yielding high quality images.

CT radiation dose and image quality optimization using a porcine model.

Science.gov (United States)

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2013-01-01

To evaluate potential radiation dose savings and resultant image quality effects with regard to optimization of commonly performed computed tomography (CT) studies derived from imaging a porcine (pig) model. Imaging protocols for 4 clinical CT suites were developed based on the lowest milliamperage and kilovoltage, the highest pitch that could be set from current imaging protocol parameters, or both. This occurred before significant changes in noise, contrast, and spatial resolution were measured objectively on images produced from a quality assurance CT phantom. The current and derived phantom protocols were then applied to scan a porcine model for head, abdomen, and chest CT studies. Further optimized protocols were developed based on the same methodology as in the phantom study. The optimization achieved with respect to radiation dose and image quality was evaluated following data collection of radiation dose recordings and image quality review. Relative visual grading analysis of image quality criteria adapted from the European guidelines on radiology quality criteria for CT were used for studies completed with both the phantom-based or porcine-derived imaging protocols. In 5 out of 16 experimental combinations, the current clinical protocol was maintained. In 2 instances, the phantom protocol reduced radiation dose by 19% to 38%. In the remaining 9 instances, the optimization based on the porcine model further reduced radiation dose by 17% to 38%. The porcine model closely reflects anatomical structures in humans, allowing the grading of anatomical criteria as part of image quality review without radiation risks to human subjects. This study demonstrates that using a porcine model to evaluate CT optimization resulted in more radiation dose reduction than when imaging protocols were tested solely on quality assurance phantoms.

IMAGE DESCRIPTIONS FOR SKETCH BASED IMAGE RETRIEVAL

OpenAIRE

SAAVEDRA RONDO, JOSE MANUEL; SAAVEDRA RONDO, JOSE MANUEL

2008-01-01

Due to the massive use of Internet together with the proliferation of media devices, content based image retrieval has become an active discipline in computer science. A common content based image retrieval approach requires that the user gives a regular image (e.g, a photo) as a query. However, having a regular image as query may be a serious problem. Indeed, people commonly use an image retrieval system because they do not count on the desired image. An easy alternative way t...

Vocal Tract Images Reveal Neural Representations of Sensorimotor Transformation During Speech Imitation

Science.gov (United States)

Carey, Daniel; Miquel, Marc E.; Evans, Bronwen G.; Adank, Patti; McGettigan, Carolyn

2017-01-01

Abstract Imitating speech necessitates the transformation from sensory targets to vocal tract motor output, yet little is known about the representational basis of this process in the human brain. Here, we address this question by using real-time MR imaging (rtMRI) of the vocal tract and functional MRI (fMRI) of the brain in a speech imitation paradigm. Participants trained on imitating a native vowel and a similar nonnative vowel that required lip rounding. Later, participants imitated these vowels and an untrained vowel pair during separate fMRI and rtMRI runs. Univariate fMRI analyses revealed that regions including left inferior frontal gyrus were more active during sensorimotor transformation (ST) and production of nonnative vowels, compared with native vowels; further, ST for nonnative vowels activated somatomotor cortex bilaterally, compared with ST of native vowels. Using test representational similarity analysis (RSA) models constructed from participants’ vocal tract images and from stimulus formant distances, we found that RSA searchlight analyses of fMRI data showed either type of model could be represented in somatomotor, temporal, cerebellar, and hippocampal neural activation patterns during ST. We thus provide the first evidence of widespread and robust cortical and subcortical neural representation of vocal tract and/or formant parameters, during prearticulatory ST. PMID:28334401

A prospective gating method to acquire a diverse set of free-breathing CT images for model-based 4DCT

Science.gov (United States)

O'Connell, D.; Ruan, D.; Thomas, D. H.; Dou, T. H.; Lewis, J. H.; Santhanam, A.; Lee, P.; Low, D. A.

2018-02-01

Breathing motion modeling requires observation of tissues at sufficiently distinct respiratory states for proper 4D characterization. This work proposes a method to improve sampling of the breathing cycle with limited imaging dose. We designed and tested a prospective free-breathing acquisition protocol with a simulation using datasets from five patients imaged with a model-based 4DCT technique. Each dataset contained 25 free-breathing fast helical CT scans with simultaneous breathing surrogate measurements. Tissue displacements were measured using deformable image registration. A correspondence model related tissue displacement to the surrogate. Model residual was computed by comparing predicted displacements to image registration results. To determine a stopping criteria for the prospective protocol, i.e. when the breathing cycle had been sufficiently sampled, subsets of N scans where 5  ⩽  N  ⩽  9 were used to fit reduced models for each patient. A previously published metric was employed to describe the phase coverage, or ‘spread’, of the respiratory trajectories of each subset. Minimum phase coverage necessary to achieve mean model residual within 0.5 mm of the full 25-scan model was determined and used as the stopping criteria. Using the patient breathing traces, a prospective acquisition protocol was simulated. In all patients, phase coverage greater than the threshold necessary for model accuracy within 0.5 mm of the 25 scan model was achieved in six or fewer scans. The prospectively selected respiratory trajectories ranked in the (97.5  ±  4.2)th percentile among subsets of the originally sampled scans on average. Simulation results suggest that the proposed prospective method provides an effective means to sample the breathing cycle with limited free-breathing scans. One application of the method is to reduce the imaging dose of a previously published model-based 4DCT protocol to 25% of its original value while

Image based SAR product simulation for analysis

Science.gov (United States)

Domik, G.; Leberl, F.

1987-01-01

SAR product simulation serves to predict SAR image gray values for various flight paths. Input typically consists of a digital elevation model and backscatter curves. A new method is described of product simulation that employs also a real SAR input image for image simulation. This can be denoted as 'image-based simulation'. Different methods to perform this SAR prediction are presented and advantages and disadvantages discussed. Ascending and descending orbit images from NASA's SIR-B experiment were used for verification of the concept: input images from ascending orbits were converted into images from a descending orbit; the results are compared to the available real imagery to verify that the prediction technique produces meaningful image data.

WE-D-303-02: Applications of Volumetric Images Generated with a Respiratory Motion Model Based On An External Surrogate Signal

International Nuclear Information System (INIS)

Hurwitz, M; Williams, C; Dhou, S; Lewis, J; Mishra, P

2015-01-01

Purpose: Respiratory motion can vary significantly over the course of simulation and treatment. Our goal is to use volumetric images generated with a respiratory motion model to improve the definition of the internal target volume (ITV) and the estimate of delivered dose. Methods: Ten irregular patient breathing patterns spanning 35 seconds each were incorporated into a digital phantom. Ten images over the first five seconds of breathing were used to emulate a 4DCT scan, build the ITV, and generate a patient-specific respiratory motion model which correlated the measured trajectories of markers placed on the patients’ chests with the motion of the internal anatomy. This model was used to generate volumetric images over the subsequent thirty seconds of breathing. The increase in the ITV taking into account the full 35 seconds of breathing was assessed with ground-truth and model-generated images. For one patient, a treatment plan based on the initial ITV was created and the delivered dose was estimated using images from the first five seconds as well as ground-truth and model-generated images from the next 30 seconds. Results: The increase in the ITV ranged from 0.2 cc to 6.9 cc for the ten patients based on ground-truth information. The model predicted this increase in the ITV with an average error of 0.8 cc. The delivered dose to the tumor (D95) changed significantly from 57 Gy to 41 Gy when estimated using 5 seconds and 30 seconds, respectively. The model captured this effect, giving an estimated D95 of 44 Gy. Conclusion: A respiratory motion model generating volumetric images of the internal patient anatomy could be useful in estimating the increase in the ITV due to irregular breathing during simulation and in assessing delivered dose during treatment. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc. and Radiological Society of North America Research Scholar Grant #RSCH1206

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

Science.gov (United States)

Papandreou, George; Chen, Liang-Chieh; Yuille, Alan L.

2015-01-01

The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting. Key ingredient of the proposed model is a compact dictionary of mini-epitomes, learned in an unsupervised fashion on a large collection of images. The use of epitomes allows us to explicitly account for photometric and position variability in image appearance. We show that this flexibility considerably increases the capacity of the dictionary to accurately approximate the appearance of image patches and support recognition tasks. For image classification, we develop histogram-based image encoding methods tailored to the epitomic representation, as well as an “epitomic footprint” encoding which is easy to visualize and highlights the generative nature of our model. We discuss in detail computational aspects and develop efficient algorithms to make the model scalable to large tasks. The proposed techniques are evaluated with experiments on the challenging PASCAL VOC 2007 image classification benchmark. PMID:26321859

SQL based cardiovascular ultrasound image classification.

Science.gov (United States)

Nandagopalan, S; Suryanarayana, Adiga B; Sudarshan, T S B; Chandrashekar, Dhanalakshmi; Manjunath, C N

2013-01-01

This paper proposes a novel method to analyze and classify the cardiovascular ultrasound echocardiographic images using Naïve-Bayesian model via database OLAP-SQL. Efficient data mining algorithms based on tightly-coupled model is used to extract features. Three algorithms are proposed for classification namely Naïve-Bayesian Classifier for Discrete variables (NBCD) with SQL, NBCD with OLAP-SQL, and Naïve-Bayesian Classifier for Continuous variables (NBCC) using OLAP-SQL. The proposed model is trained with 207 patient images containing normal and abnormal categories. Out of the three proposed algorithms, a high classification accuracy of 96.59% was achieved from NBCC which is better than the earlier methods.

A simple method for detecting tumor in T2-weighted MRI brain images. An image-based analysis

International Nuclear Information System (INIS)

Lau, Phooi-Yee; Ozawa, Shinji

2006-01-01

The objective of this paper is to present a decision support system which uses a computer-based procedure to detect tumor blocks or lesions in digitized medical images. The authors developed a simple method with a low computation effort to detect tumors on T2-weighted Magnetic Resonance Imaging (MRI) brain images, focusing on the connection between the spatial pixel value and tumor properties from four different perspectives: cases having minuscule differences between two images using a fixed block-based method, tumor shape and size using the edge and binary images, tumor properties based on texture values using spatial pixel intensity distribution controlled by a global discriminate value, and the occurrence of content-specific tumor pixel for threshold images. Measurements of the following medical datasets were performed: different time interval images, and different brain disease images on single and multiple slice images. Experimental results have revealed that our proposed technique incurred an overall error smaller than those in other proposed methods. In particular, the proposed method allowed decrements of false alarm and missed alarm errors, which demonstrate the effectiveness of our proposed technique. In this paper, we also present a prototype system, known as PCB, to evaluate the performance of the proposed methods by actual experiments, comparing the detection accuracy and system performance. (author)

VIP-Man: An image-based whole-body adult male model constructed from color photographs of the visible human project for multi-particle Monte Carlo calculations

International Nuclear Information System (INIS)

Xu, X.G.; Chao, T.C.; Bozkurt, A.

2000-01-01

Human anatomical models have been indispensable to radiation protection dosimetry using Monte Carlo calculations. Existing MIRD-based mathematical models are easy to compute and standardize, but they are simplified and crude compared to human anatomy. This article describes the development of an image-based whole-body model, called VIP-Man, using transversal color photographic images obtained from the National Library of Medicine's Visible Human Project for Monte Carlo organ dose calculations involving photons, electron, neutrons, and protons. As the first of a series of papers on dose calculations based on VIP-Man, this article provides detailed information about how to construct an image-based model, as well as how to adopt it into well-tested Monte Carlo codes, EGS4, MCNP4B, and MCNPX

Polarization-dependent Imaging Contrast (PIC) mapping reveals nanocrystal orientation patterns in carbonate biominerals

Energy Technology Data Exchange (ETDEWEB)

Gilbert, Pupa U.P.A., E-mail: pupa@physics.wisc.edu [University of Wisconsin-Madison, Departments of Physics and Chemistry, Madison, WI 53706 (United States)

2012-10-15

Highlights: Black-Right-Pointing-Pointer Nanocrystal orientation shown by Polarization-dependent Imaging Contrast (PIC) maps. Black-Right-Pointing-Pointer PIC-mapping of carbonate biominerals reveals their ultrastructure at the nanoscale. Black-Right-Pointing-Pointer The formation mechanisms of biominerals is discovered by PIC-mapping using PEEM. -- Abstract: Carbonate biominerals are one of the most interesting systems a physicist can study. They play a major role in the CO{sub 2} cycle, they master templation, self-assembly, nanofabrication, phase transitions, space filling, crystal nucleation and growth mechanisms. A new imaging modality was introduced in the last 5 years that enables direct observation of the orientation of carbonate single crystals, at the nano- and micro-scale. This is Polarization-dependent Imaging Contrast (PIC) mapping, which is based on X-ray linear dichroism, and uses PhotoElectron Emission spectroMicroscopy (PEEM). Here we present PIC-mapping results from biominerals, including the nacre and prismatic layers of mollusk shells, and sea urchin teeth. We describe various PIC-mapping approaches, and show that these lead to fundamental discoveries on the formation mechanisms of biominerals.

Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

Science.gov (United States)

Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq A

2009-06-01

In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework. The parameters of the distributed causal HMMs are estimated by extending the classical 1-D training and classification algorithms to multiple dimensions. The proposed extension to arbitrary causal, multidimensional HMMs allows state transitions that are dependent on all causal neighbors. We, thus, extend three fundamental algorithms to multidimensional causal systems, i.e., 1) expectation-maximization (EM), 2) general forward-backward (GFB), and 3) Viterbi algorithms. In the simulations, we choose to limit ourselves to a noncausal 2-D model whose noncausality is along a single dimension, in order to significantly reduce the computational complexity. Simulation results demonstrate the superior performance, higher accuracy rate, and applicability of the proposed noncausal HMM framework to image and video classification.

A new level set model for cell image segmentation

Science.gov (United States)

Ma, Jing-Feng; Hou, Kai; Bao, Shang-Lian; Chen, Chun

2011-02-01

In this paper we first determine three phases of cell images: background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.

RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

Directory of Open Access Journals (Sweden)

Z. Wang

2017-09-01

Full Text Available In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

Simulation of seagrass bed mapping by satellite images based on the radiative transfer model

Science.gov (United States)

Sagawa, Tatsuyuki; Komatsu, Teruhisa

2015-06-01

Seagrass and seaweed beds play important roles in coastal marine ecosystems. They are food sources and habitats for many marine organisms, and influence the physical, chemical, and biological environment. They are sensitive to human impacts such as reclamation and pollution. Therefore, their management and preservation are necessary for a healthy coastal environment. Satellite remote sensing is a useful tool for mapping and monitoring seagrass beds. The efficiency of seagrass mapping, seagrass bed classification in particular, has been evaluated by mapping accuracy using an error matrix. However, mapping accuracies are influenced by coastal environments such as seawater transparency, bathymetry, and substrate type. Coastal management requires sufficient accuracy and an understanding of mapping limitations for monitoring coastal habitats including seagrass beds. Previous studies are mainly based on case studies in specific regions and seasons. Extensive data are required to generalise assessments of classification accuracy from case studies, which has proven difficult. This study aims to build a simulator based on a radiative transfer model to produce modelled satellite images and assess the visual detectability of seagrass beds under different transparencies and seagrass coverages, as well as to examine mapping limitations and classification accuracy. Our simulations led to the development of a model of water transparency and the mapping of depth limits and indicated the possibility for seagrass density mapping under certain ideal conditions. The results show that modelling satellite images is useful in evaluating the accuracy of classification and that establishing seagrass bed monitoring by remote sensing is a reliable tool.

Development and tests of a mouse voxel model dor MCNPX based on Digimouse images

Energy Technology Data Exchange (ETDEWEB)

Melo M, B.; Ferreira F, C. [Centro de Desenvolvimento da Tecnologia Nuclear / CNEN, Pte. Antonio Carlos No. 6627, Belo Horizonte 31270-901, Minas Gerais (Brazil); Garcia de A, I.; Machado T, B.; Passos Ribeiro de C, T., E-mail: bmm@cdtn.br [Universidade Federal de Minas Gerais, Departamento de Engenharia Nuclear, Pte. Antonio Carlos 6627, Belo Horizonte 31270-901, Minas Gerais (Brazil)

2015-10-15

Mice have been widely used in experimental protocols involving ionizing radiation. Biological effects (Be) induced by radiation can compromise studies results. Good estimates of mouse whole body and organs absorbed dose could provide valuable information to researchers. The aim of this study was to create and test a new voxel phantom for mice dosimetry from -Digimouse- project images. Micro CT images from Digimouse project were used in this work. Corel PHOTOPAINT software was utilized in segmentation process. The three-dimensional (3-D) model assembly and its voxel size manipulation were performed by Image J. SISCODES was used to adapt the model to run in MCNPX Monte Carlo code. The resulting model was called DM{sub B}RA. The volume and mass of segmented organs were compared with data available in literature. For the preliminary tests the heart was considered the source organ. Photons of diverse energies were simulated and Saf values obtained through F6:p and + F6 MCNPX tallies. The results were compared with reference data. 3-D picturing of absorbed doses patterns and relative errors distribution were generated by a C++ -in house- made program and visualized through Amide software. The organ masses of DM{sub B}RA correlated well with two models that were based on same set of images. However some organs, like eyes and adrenals, skeleton and brain showed large discrepancies. Segmentation of an identical image set by different persons and/or methods can result significant organ masses variations. We believe that the main causes of these differences were: i) operator dependent subjectivity in the definition of organ limits during the segmentation processes; and i i) distinct voxel dimensions between evaluated models. Lack of reference data for mice models construction and dosimetry was detected. Comparison with other models originated from different mice strains also demonstrated that the anatomical and size variability can be significant. Use of + F6 tally for mouse

Development and tests of a mouse voxel model dor MCNPX based on Digimouse images

International Nuclear Information System (INIS)

Melo M, B.; Ferreira F, C.; Garcia de A, I.; Machado T, B.; Passos Ribeiro de C, T.

2015-10-01

Mice have been widely used in experimental protocols involving ionizing radiation. Biological effects (Be) induced by radiation can compromise studies results. Good estimates of mouse whole body and organs absorbed dose could provide valuable information to researchers. The aim of this study was to create and test a new voxel phantom for mice dosimetry from -Digimouse- project images. Micro CT images from Digimouse project were used in this work. Corel PHOTOPAINT software was utilized in segmentation process. The three-dimensional (3-D) model assembly and its voxel size manipulation were performed by Image J. SISCODES was used to adapt the model to run in MCNPX Monte Carlo code. The resulting model was called DM B RA. The volume and mass of segmented organs were compared with data available in literature. For the preliminary tests the heart was considered the source organ. Photons of diverse energies were simulated and Saf values obtained through F6:p and + F6 MCNPX tallies. The results were compared with reference data. 3-D picturing of absorbed doses patterns and relative errors distribution were generated by a C++ -in house- made program and visualized through Amide software. The organ masses of DM B RA correlated well with two models that were based on same set of images. However some organs, like eyes and adrenals, skeleton and brain showed large discrepancies. Segmentation of an identical image set by different persons and/or methods can result significant organ masses variations. We believe that the main causes of these differences were: i) operator dependent subjectivity in the definition of organ limits during the segmentation processes; and i i) distinct voxel dimensions between evaluated models. Lack of reference data for mice models construction and dosimetry was detected. Comparison with other models originated from different mice strains also demonstrated that the anatomical and size variability can be significant. Use of + F6 tally for mouse phantoms

Contrast-based sensorless adaptive optics for retinal imaging.

Science.gov (United States)

Zhou, Xiaolin; Bedggood, Phillip; Bui, Bang; Nguyen, Christine T O; He, Zheng; Metha, Andrew

2015-09-01

Conventional adaptive optics ophthalmoscopes use wavefront sensing methods to characterize ocular aberrations for real-time correction. However, there are important situations in which the wavefront sensing step is susceptible to difficulties that affect the accuracy of the correction. To circumvent these, wavefront sensorless adaptive optics (or non-wavefront sensing AO; NS-AO) imaging has recently been developed and has been applied to point-scanning based retinal imaging modalities. In this study we show, for the first time, contrast-based NS-AO ophthalmoscopy for full-frame in vivo imaging of human and animal eyes. We suggest a robust image quality metric that could be used for any imaging modality, and test its performance against other metrics using (physical) model eyes.

A review of anisotropic conductivity models of brain white matter based on diffusion tensor imaging.

Science.gov (United States)

Wu, Zhanxiong; Liu, Yang; Hong, Ming; Yu, Xiaohui

2018-06-01

The conductivity of brain tissues is not only essential for electromagnetic source estimation (ESI), but also a key reflector of the brain functional changes. Different from the other brain tissues, the conductivity of whiter matter (WM) is highly anisotropic and a tensor is needed to describe it. The traditional electrical property imaging methods, such as electrical impedance tomography (EIT) and magnetic resonance electrical impedance tomography (MREIT), usually fail to image the anisotropic conductivity tensor of WM with high spatial resolution. The diffusion tensor imaging (DTI) is a newly developed technique that can fulfill this purpose. This paper reviews the existing anisotropic conductivity models of WM based on the DTI and discusses their advantages and disadvantages, as well as identifies opportunities for future research on this subject. It is crucial to obtain the linear conversion coefficient between the eigenvalues of anisotropic conductivity tensor and diffusion tensor, since they share the same eigenvectors. We conclude that the electrochemical model is suitable for ESI analysis because the conversion coefficient can be directly obtained from the concentration of ions in extracellular liquid and that the volume fraction model is appropriate to study the influence of WM structural changes on electrical conductivity. Graphical abstract ᅟ.

The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction

DEFF Research Database (Denmark)

Sabuncu, Mert R.; Van Leemput, Koen

2012-01-01

This paper presents the relevance voxel machine (RVoxM), a dedicated Bayesian model for making predictions based on medical imaging data. In contrast to the generic machine learning algorithms that have often been used for this purpose, the method is designed to utilize a small number of spatially...

Quantitative Assessment of Optical Coherence Tomography Imaging Performance with Phantom-Based Test Methods And Computational Modeling

Science.gov (United States)

Agrawal, Anant

Optical coherence tomography (OCT) is a powerful medical imaging modality that uniquely produces high-resolution cross-sectional images of tissue using low energy light. Its clinical applications and technological capabilities have grown substantially since its invention about twenty years ago, but efforts have been limited to develop tools to assess performance of OCT devices with respect to the quality and content of acquired images. Such tools are important to ensure information derived from OCT signals and images is accurate and consistent, in order to support further technology development, promote standardization, and benefit public health. The research in this dissertation investigates new physical and computational models which can provide unique insights into specific performance characteristics of OCT devices. Physical models, known as phantoms, are fabricated and evaluated in the interest of establishing standardized test methods to measure several important quantities relevant to image quality. (1) Spatial resolution is measured with a nanoparticle-embedded phantom and model eye which together yield the point spread function under conditions where OCT is commonly used. (2) A multi-layered phantom is constructed to measure the contrast transfer function along the axis of light propagation, relevant for cross-sectional imaging capabilities. (3) Existing and new methods to determine device sensitivity are examined and compared, to better understand the detection limits of OCT. A novel computational model based on the finite-difference time-domain (FDTD) method, which simulates the physics of light behavior at the sub-microscopic level within complex, heterogeneous media, is developed to probe device and tissue characteristics influencing the information content of an OCT image. This model is first tested in simple geometric configurations to understand its accuracy and limitations, then a highly realistic representation of a biological cell, the retinal

Ground-based infrared surveys: imaging the thermal fields at volcanoes and revealing the controlling parameters.

Science.gov (United States)

Pantaleo, Michele; Walter, Thomas

2013-04-01

Temperature monitoring is a widespread procedure in the frame of volcano hazard monitoring. Indeed temperature changes are expected to reflect changes in volcanic activity. We propose a new approach, within the thermal monitoring, which is meant to shed light on the parameters controlling the fluid pathways and the fumarole sites by using infrared measurements. Ground-based infrared cameras allow one to remotely image the spatial distribution, geometric pattern and amplitude of fumarole fields on volcanoes at metre to centimetre resolution. Infrared mosaics and time series are generated and interpreted, by integrating geological field observations and modeling, to define the setting of the volcanic degassing system at shallow level. We present results for different volcano morphologies and show that lithology, structures and topography control the appearance of fumarole field by the creation of permeability contrasts. We also show that the relative importance of those parameters is site-dependent. Deciphering the setting of the degassing system is essential for hazard assessment studies because it would improve our understanding on how the system responds to endogenous or exogenous modification.

Hyperspectral Imaging Coupled with Random Frog and Calibration Models for Assessment of Total Soluble Solids in Mulberries

Directory of Open Access Journals (Sweden)

Yan-Ru Zhao

2015-01-01

Full Text Available Chemometrics methods coupled with hyperspectral imaging technology in visible and near infrared (Vis/NIR region (380–1030 nm were introduced to assess total soluble solids (TSS in mulberries. Hyperspectral images of 310 mulberries were acquired by hyperspectral reflectance imaging system (512 bands and their corresponding TSS contents were measured by a Brix meter. Random frog (RF method was used to select important wavelengths from the full wavelengths. TSS values in mulberry fruits were predicted by partial least squares regression (PLSR and least-square support vector machine (LS-SVM models based on full wavelengths and the selected important wavelengths. The optimal PLSR model with 23 important wavelengths was employed to visualise the spatial distribution of TSS in tested samples, and TSS concentrations in mulberries were revealed through the TSS spatial distribution. The results declared that hyperspectral imaging is promising for determining the spatial distribution of TSS content in mulberry fruits, which provides a reference for detecting the internal quality of fruits.

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.

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Gordonov, Simon; Hwang, Mun Kyung; Wells, Alan; Gertler, Frank B; Lauffenburger, Douglas A; Bathe, Mark

2016-01-01

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.

Discrete imaging models for three-dimensional optoacoustic tomography using radially symmetric expansion functions.

Science.gov (United States)

Wang, Kun; Schoonover, Robert W; Su, Richard; Oraevsky, Alexander; Anastasio, Mark A

2014-05-01

Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an emerging computed biomedical imaging modality that exploits optical contrast and ultrasonic detection principles. Iterative image reconstruction algorithms that are based on discrete imaging models are actively being developed for OAT due to their ability to improve image quality by incorporating accurate models of the imaging physics, instrument response, and measurement noise. In this work, we investigate the use of discrete imaging models based on Kaiser-Bessel window functions for iterative image reconstruction in OAT. A closed-form expression for the pressure produced by a Kaiser-Bessel function is calculated, which facilitates accurate computation of the system matrix. Computer-simulation and experimental studies are employed to demonstrate the potential advantages of Kaiser-Bessel function-based iterative image reconstruction in OAT.

HVS-based medical image compression

Energy Technology Data Exchange (ETDEWEB)

Kai Xie [Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai (China)]. E-mail: xie_kai2001@sjtu.edu.cn; Jie Yang [Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai (China); Min Zhuyue [CREATIS-CNRS Research Unit 5515 and INSERM Unit 630, 69621 Villeurbanne (France); Liang Lixiao [Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai (China)

2005-07-01

Introduction: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. Methods: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model. Results: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. Conclusions: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time.

HVS-based medical image compression

International Nuclear Information System (INIS)

Kai Xie; Jie Yang; Min Zhuyue; Liang Lixiao

2005-01-01

Introduction: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. Methods: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model. Results: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. Conclusions: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time

IMAGE TO POINT CLOUD METHOD OF 3D-MODELING

Directory of Open Access Journals (Sweden)

A. G. Chibunichev

2012-07-01

Full Text Available This article describes the method of constructing 3D models of objects (buildings, monuments based on digital images and a point cloud obtained by terrestrial laser scanner. The first step is the automated determination of exterior orientation parameters of digital image. We have to find the corresponding points of the image and point cloud to provide this operation. Before the corresponding points searching quasi image of point cloud is generated. After that SIFT algorithm is applied to quasi image and real image. SIFT algorithm allows to find corresponding points. Exterior orientation parameters of image are calculated from corresponding points. The second step is construction of the vector object model. Vectorization is performed by operator of PC in an interactive mode using single image. Spatial coordinates of the model are calculated automatically by cloud points. In addition, there is automatic edge detection with interactive editing available. Edge detection is performed on point cloud and on image with subsequent identification of correct edges. Experimental studies of the method have demonstrated its efficiency in case of building facade modeling.

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

Science.gov (United States)

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

2014-07-01

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

Band co-registration modeling of LAPAN-A3/IPB multispectral imager based on satellite attitude

Science.gov (United States)

Hakim, P. R.; Syafrudin, A. H.; Utama, S.; Jayani, A. P. S.

2018-05-01

One of significant geometric distortion on images of LAPAN-A3/IPB multispectral imager is co-registration error between each color channel detector. Band co-registration distortion usually can be corrected by using several approaches, which are manual method, image matching algorithm, or sensor modeling and calibration approach. This paper develops another approach to minimize band co-registration distortion on LAPAN-A3/IPB multispectral image by using supervised modeling of image matching with respect to satellite attitude. Modeling results show that band co-registration error in across-track axis is strongly influenced by yaw angle, while error in along-track axis is fairly influenced by both pitch and roll angle. Accuracy of the models obtained is pretty good, which lies between 1-3 pixels error for each axis of each pair of band co-registration. This mean that the model can be used to correct the distorted images without the need of slower image matching algorithm, nor the laborious effort needed in manual approach and sensor calibration. Since the calculation can be executed in order of seconds, this approach can be used in real time quick-look image processing in ground station or even in satellite on-board image processing.

Image-based compound profiling reveals a dual inhibitor of tyrosine kinase and microtubule polymerization

OpenAIRE

Tanabe, Kenji

2016-01-01

Small-molecule compounds are widely used as biological research tools and therapeutic drugs. Therefore, uncovering novel targets of these compounds should provide insights that are valuable in both basic and clinical studies. I developed a method for image-based compound profiling by quantitating the effects of compounds on signal transduction and vesicle trafficking of epidermal growth factor receptor (EGFR). Using six signal transduction molecules and two markers of vesicle trafficking, 570...

CT/FMT dual-model imaging of breast cancer based on peptide-lipid nanoparticles

Science.gov (United States)

Xu, Guoqiang; Lin, Qiaoya; Lian, Lichao; Qian, Yuan; Lu, Lisen; Zhang, Zhihong

2016-03-01

Breast cancer is one of the most harmful cancers in human. Its early diagnosis is expected to improve the patients' survival rate. X-ray computed tomography (CT) has been widely used in tumor detection for obtaining three-dimentional information. Fluorescence Molecular Tomography (FMT) imaging combined with near-infrared fluorescent dyes provides a powerful tool for the acquisition of molecular biodistribution information in deep tissues. Thus, the combination of CT and FMT imaging modalities allows us to better differentiate diseased tissues from normal tissues. Here we developed a tumor-targeting nanoparticle for dual-modality imaging based on a biocompatible HDL-mimicking peptide-phospholipid scaffold (HPPS) nanocarrier. By incorporation of CT contrast agents (iodinated oil) and far-infrared fluorescent dyes (DiR-BOA) into the hydrophobic core of HPPS, we obtained the FMT and CT signals simultaneously. Increased accumulation of the nanoparticles in the tumor lesions was achieved through the effect of the tumor-targeting peptide on the surface of nanoparticle. It resulted in excellent contrast between lesions and normal tissues. Together, the abilities to sensitively separate the lesions from adjacent normal tissues with the aid of a FMT/CT dual-model imaging approach make the targeting nanoparticles a useful tool for the diagnostics of breast cancer.

A New Approach to Image-Based Estimation of Food Volume

Directory of Open Access Journals (Sweden)

Hamid Hassannejad

2017-06-01

Full Text Available A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people’s health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user’s smartphone. From such a video, six frames are selected based on the pictures’ viewpoints as determined by the smartphone’s orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92 % on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s.

Pixel-based meshfree modelling of skeletal muscles

OpenAIRE

Chen, Jiun-Shyan; Basava, Ramya Rao; Zhang, Yantao; Csapo, Robert; Malis, Vadim; Sinha, Usha; Hodgson, John; Sinha, Shantanu

2015-01-01

This paper introduces the meshfree Reproducing Kernel Particle Method (RKPM) for 3D image-based modeling of skeletal muscles. This approach allows for construction of simulation model based on pixel data obtained from medical images. The material properties and muscle fiber direction obtained from Diffusion Tensor Imaging (DTI) are input at each pixel point. The reproducing kernel (RK) approximation allows a representation of material heterogeneity with smooth transition. A ...

Model-Based Normalization of a Fractional-Crystal Collimator for Small-Animal PET Imaging.

Science.gov (United States)

Li, Yusheng; Matej, Samuel; Karp, Joel S; Metzler, Scott D

2017-05-01

Previously, we proposed to use a coincidence collimator to achieve fractional-crystal resolution in PET imaging. We have designed and fabricated a collimator prototype for a small-animal PET scanner, A-PET. To compensate for imperfections in the fabricated collimator prototype, collimator normalization, as well as scanner normalization, is required to reconstruct quantitative and artifact-free images. In this study, we develop a normalization method for the collimator prototype based on the A-PET normalization using a uniform cylinder phantom. We performed data acquisition without the collimator for scanner normalization first, and then with the collimator from eight different rotation views for collimator normalization. After a reconstruction without correction, we extracted the cylinder parameters from which we generated expected emission sinograms. Single scatter simulation was used to generate the scattered sinograms. We used the least-squares method to generate the normalization coefficient for each LOR based on measured, expected and scattered sinograms. The scanner and collimator normalization coefficients were factorized by performing two normalizations separately. The normalization methods were also verified using experimental data acquired from A-PET with and without the collimator. In summary, we developed a model-base collimator normalization that can significantly reduce variance and produce collimator normalization with adequate statistical quality within feasible scan time.

Dictionary-based image reconstruction for superresolution in integrated circuit imaging.

Science.gov (United States)

Cilingiroglu, T Berkin; Uyar, Aydan; Tuysuzoglu, Ahmet; Karl, W Clem; Konrad, Janusz; Goldberg, Bennett B; Ünlü, M Selim

2015-06-01

Resolution improvement through signal processing techniques for integrated circuit imaging is becoming more crucial as the rapid decrease in integrated circuit dimensions continues. Although there is a significant effort to push the limits of optical resolution for backside fault analysis through the use of solid immersion lenses, higher order laser beams, and beam apodization, signal processing techniques are required for additional improvement. In this work, we propose a sparse image reconstruction framework which couples overcomplete dictionary-based representation with a physics-based forward model to improve resolution and localization accuracy in high numerical aperture confocal microscopy systems for backside optical integrated circuit analysis. The effectiveness of the framework is demonstrated on experimental data.

Cloud top structure of Venus revealed by Subaru/COMICS mid-infrared images

Science.gov (United States)

Sato, T. M.; Sagawa, H.; Kouyama, T.; Mitsuyama, K.; Satoh, T.; Ohtsuki, S.; Ueno, M.; Kasaba, Y.; Nakamura, M.; Imamura, T.

2014-11-01

We have investigated the cloud top structure of Venus by analyzing ground-based images taken at the mid-infrared wavelengths of 8.66 μm and 11.34 μm. Venus at a solar phase angle of ∼90°, with the morning terminator in view, was observed by the Cooled Mid-Infrared Camera and Spectrometer (COMICS), mounted on the 8.2-m Subaru Telescope, during the period October 25-29, 2007. The disk-averaged brightness temperatures for the observation period are ∼230 K and ∼238 K at 8.66 μm and 11.34 μm, respectively. The obtained images with good signal-to-noise ratio and with high spatial resolution (∼200 km at the sub-observer point) provide several important findings. First, we present observational evidence, for the first time, of the possibility that the westward rotation of the polar features (the hot polar spots and the surrounding cold collars) is synchronized between the northern and southern hemispheres. Second, after high-pass filtering, the images reveal that streaks and mottled and patchy patterns are distributed over the entire disk, with typical amplitudes of ∼0.5 K, and vary from day to day. The detected features, some of which are similar to those seen in past UV images, result from inhomogeneities of both the temperature and the cloud top altitude. Third, the equatorial center-to-limb variations of brightness temperatures have a systematic day-night asymmetry, except those on October 25, that the dayside brightness temperatures are higher than the nightside brightness temperatures by 0-4 K under the same viewing geometry. Such asymmetry would be caused by the propagation of the migrating semidiurnal tide. Finally, by applying the lapse rates deduced from previous studies, we demonstrate that the equatorial center-to-limb curves in the two spectral channels give access to two parameters: the cloud scale height H and the cloud top altitude zc. The acceptable models for data on October 25 are obtained at H = 2.4-4.3 km and zc = 66-69 km; this supports

Mobile object retrieval in server-based image databases

Science.gov (United States)

Manger, D.; Pagel, F.; Widak, H.

2013-05-01

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

Virtual Reality Model of the Three-Dimensional Anatomy of the Cavernous Sinus Based on a Cadaveric Image and Dissection.

Science.gov (United States)

Qian, Zeng-Hui; Feng, Xu; Li, Yang; Tang, Ke

2018-01-01

Studying the three-dimensional (3D) anatomy of the cavernous sinus is essential for treating lesions in this region with skull base surgeries. Cadaver dissection is a conventional method that has insurmountable flaws with regard to understanding spatial anatomy. The authors' research aimed to build an image model of the cavernous sinus region in a virtual reality system to precisely, individually and objectively elucidate the complete and local stereo-anatomy. Computed tomography and magnetic resonance imaging scans were performed on 5 adult cadaver heads. Latex mixed with contrast agent was injected into the arterial system and then into the venous system. Computed tomography scans were performed again following the 2 injections. Magnetic resonance imaging scans were performed again after the cranial nerves were exposed. Image data were input into a virtual reality system to establish a model of the cavernous sinus. Observation results of the image models were compared with those of the cadaver heads. Visualization of the cavernous sinus region models built using the virtual reality system was good for all the cadavers. High resolutions were achieved for the images of different tissues. The observed results were consistent with those of the cadaver head. The spatial architecture and modality of the cavernous sinus were clearly displayed in the 3D model by rotating the model and conveniently changing its transparency. A 3D virtual reality model of the cavernous sinus region is helpful for globally and objectively understanding anatomy. The observation procedure was accurate, convenient, noninvasive, and time and specimen saving.

Canny edge-based deformable image registration.

Science.gov (United States)

Kearney, Vasant; Huang, Yihui; Mao, Weihua; Yuan, Baohong; Tang, Liping

2017-02-07

This work focuses on developing a 2D Canny edge-based deformable image registration (Canny DIR) algorithm to register in vivo white light images taken at various time points. This method uses a sparse interpolation deformation algorithm to sparsely register regions of the image with strong edge information. A stability criterion is enforced which removes regions of edges that do not deform in a smooth uniform manner. Using a synthetic mouse surface ground truth model, the accuracy of the Canny DIR algorithm was evaluated under axial rotation in the presence of deformation. The accuracy was also tested using fluorescent dye injections, which were then used for gamma analysis to establish a second ground truth. The results indicate that the Canny DIR algorithm performs better than rigid registration, intensity corrected Demons, and distinctive features for all evaluation matrices and ground truth scenarios. In conclusion Canny DIR performs well in the presence of the unique lighting and shading variations associated with white-light-based image registration.

Superpixel-based segmentation of glottal area from videolaryngoscopy images

Science.gov (United States)

Turkmen, H. Irem; Albayrak, Abdulkadir; Karsligil, M. Elif; Kocak, Ismail

2017-11-01

Segmentation of the glottal area with high accuracy is one of the major challenges for the development of systems for computer-aided diagnosis of vocal-fold disorders. We propose a hybrid model combining conventional methods with a superpixel-based segmentation approach. We first employed a superpixel algorithm to reveal the glottal area by eliminating the local variances of pixels caused by bleedings, blood vessels, and light reflections from mucosa. Then, the glottal area was detected by exploiting a seeded region-growing algorithm in a fully automatic manner. The experiments were conducted on videolaryngoscopy images obtained from both patients having pathologic vocal folds as well as healthy subjects. Finally, the proposed hybrid approach was compared with conventional region-growing and active-contour model-based glottal area segmentation algorithms. The performance of the proposed method was evaluated in terms of segmentation accuracy and elapsed time. The F-measure, true negative rate, and dice coefficients of the hybrid method were calculated as 82%, 93%, and 82%, respectively, which are superior to the state-of-art glottal-area segmentation methods. The proposed hybrid model achieved high success rates and robustness, making it suitable for developing a computer-aided diagnosis system that can be used in clinical routines.

Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model

Science.gov (United States)

Ma, Ling; Lu, Guolan; Wang, Dongsheng; Wang, Xu; Chen, Zhuo Georgia; Muller, Susan; Chen, Amy; Fei, Baowei

2017-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.

Bas-Relief Modeling from Normal Images with Intuitive Styles.

Science.gov (United States)

Ji, Zhongping; Ma, Weiyin; Sun, Xianfang

2014-05-01

Traditional 3D model-based bas-relief modeling methods are often limited to model-dependent and monotonic relief styles. This paper presents a novel method for digital bas-relief modeling with intuitive style control. Given a composite normal image, the problem discussed in this paper involves generating a discontinuity-free depth field with high compression of depth data while preserving or even enhancing fine details. In our framework, several layers of normal images are composed into a single normal image. The original normal image on each layer is usually generated from 3D models or through other techniques as described in this paper. The bas-relief style is controlled by choosing a parameter and setting a targeted height for them. Bas-relief modeling and stylization are achieved simultaneously by solving a sparse linear system. Different from previous work, our method can be used to freely design bas-reliefs in normal image space instead of in object space, which makes it possible to use any popular image editing tools for bas-relief modeling. Experiments with a wide range of 3D models and scenes show that our method can effectively generate digital bas-reliefs.

A novel airport extraction model based on saliency region detection for high spatial resolution remote sensing images

Science.gov (United States)

Lv, Wen; Zhang, Libao; Zhu, Yongchun

2017-06-01

The airport is one of the most crucial traffic facilities in military and civil fields. Automatic airport extraction in high spatial resolution remote sensing images has many applications such as regional planning and military reconnaissance. Traditional airport extraction strategies usually base on prior knowledge and locate the airport target by template matching and classification, which will cause high computation complexity and large costs of computing resources for high spatial resolution remote sensing images. In this paper, we propose a novel automatic airport extraction model based on saliency region detection, airport runway extraction and adaptive threshold segmentation. In saliency region detection, we choose frequency-tuned (FT) model for computing airport saliency using low level features of color and luminance that is easy and fast to implement and can provide full-resolution saliency maps. In airport runway extraction, Hough transform is adopted to count the number of parallel line segments. In adaptive threshold segmentation, the Otsu threshold segmentation algorithm is proposed to obtain more accurate airport regions. The experimental results demonstrate that the proposed model outperforms existing saliency analysis models and shows good performance in the extraction of the airport.

Compressive sensing based ptychography image encryption

Science.gov (United States)

Rawat, Nitin

2015-09-01

A compressive sensing (CS) based ptychography combined with an optical image encryption is proposed. The diffraction pattern is recorded through ptychography technique further compressed by non-uniform sampling via CS framework. The system requires much less encrypted data and provides high security. The diffraction pattern as well as the lesser measurements of the encrypted samples serves as a secret key which make the intruder attacks more difficult. Furthermore, CS shows that the linearly projected few random samples have adequate information for decryption with a dramatic volume reduction. Experimental results validate the feasibility and effectiveness of our proposed technique compared with the existing techniques. The retrieved images do not reveal any information with the original information. In addition, the proposed system can be robust even with partial encryption and under brute-force attacks.

A new level set model for cell image segmentation

International Nuclear Information System (INIS)

Ma Jing-Feng; Chen Chun; Hou Kai; Bao Shang-Lian

2011-01-01

In this paper we first determine three phases of cell images: background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing. (cross-disciplinary physics and related areas of science and technology)

Integral equation models for image restoration: high accuracy methods and fast algorithms

International Nuclear Information System (INIS)

Lu, Yao; Shen, Lixin; Xu, Yuesheng

2010-01-01

Discrete models are consistently used as practical models for image restoration. They are piecewise constant approximations of true physical (continuous) models, and hence, inevitably impose bottleneck model errors. We propose to work directly with continuous models for image restoration aiming at suppressing the model errors caused by the discrete models. A systematic study is conducted in this paper for the continuous out-of-focus image models which can be formulated as an integral equation of the first kind. The resulting integral equation is regularized by the Lavrentiev method and the Tikhonov method. We develop fast multiscale algorithms having high accuracy to solve the regularized integral equations of the second kind. Numerical experiments show that the methods based on the continuous model perform much better than those based on discrete models, in terms of PSNR values and visual quality of the reconstructed images

IMAGE ANALYSIS BASED ON EDGE DETECTION TECHNIQUES

Institute of Scientific and Technical Information of China (English)

纳瑟; 刘重庆

2002-01-01

A method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. An initial segmented result is obtained based on K-means clustering technique and the minimum distance. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. The gradient values are calculated and then the watershed technique is used. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The DIS map is obtained. This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. In MRF model,gray level l, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The segmentation results are improved by using watershed algorithm. After all pixels of the segmented regions are processed, a map of primitive region with edges is generated. The edge map is obtained using a merge process based on averaged intensity mean values. A common edge detectors that work on (MRF) segmented image are used and the results are compared. The segmentation and edge detection result is one closed boundary per actual region in the image.

Fast dictionary-based reconstruction for diffusion spectrum imaging.

Science.gov (United States)

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F; Yendiki, Anastasia; Wald, Lawrence L; Adalsteinsson, Elfar

2013-11-01

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.

Saccades to future ball location reveal memory-based prediction in a virtual-reality interception task.

Science.gov (United States)

Diaz, Gabriel; Cooper, Joseph; Rothkopf, Constantin; Hayhoe, Mary

2013-01-16

Despite general agreement that prediction is a central aspect of perception, there is relatively little evidence concerning the basis on which visual predictions are made. Although both saccadic and pursuit eye-movements reveal knowledge of the future position of a moving visual target, in many of these studies targets move along simple trajectories through a fronto-parallel plane. Here, using a naturalistic and racquet-based interception task in a virtual environment, we demonstrate that subjects make accurate predictions of visual target motion, even when targets follow trajectories determined by the complex dynamics of physical interactions and the head and body are unrestrained. Furthermore, we found that, following a change in ball elasticity, subjects were able to accurately adjust their prebounce predictions of the ball's post-bounce trajectory. This suggests that prediction is guided by experience-based models of how information in the visual image will change over time.

Molecular–Genetic Imaging: A Nuclear Medicine–Based Perspective

Directory of Open Access Journals (Sweden)

Ronald G. Blasberg

2002-07-01

Full Text Available Molecular imaging is a relatively new discipline, which developed over the past decade, initially driven by in situ reporter imaging technology. Noninvasive in vivo molecular–genetic imaging developed more recently and is based on nuclear (positron emission tomography [PET], gamma camera, autoradiography imaging as well as magnetic resonance (MR and in vivo optical imaging. Molecular–genetic imaging has its roots in both molecular biology and cell biology, as well as in new imaging technologies. The focus of this presentation will be nuclear-based molecular–genetic imaging, but it will comment on the value and utility of combining different imaging modalities. Nuclear-based molecular imaging can be viewed in terms of three different imaging strategies: (1 “indirect” reporter gene imaging; (2 “direct” imaging of endogenous molecules; or (3 “surrogate” or “bio-marker” imaging. Examples of each imaging strategy will be presented and discussed. The rapid growth of in vivo molecular imaging is due to the established base of in vivo imaging technologies, the established programs in molecular and cell biology, and the convergence of these disciplines. The development of versatile and sensitive assays that do not require tissue samples will be of considerable value for monitoring molecular–genetic and cellular processes in animal models of human disease, as well as for studies in human subjects in the future. Noninvasive imaging of molecular–genetic and cellular processes will complement established ex vivo molecular–biological assays that require tissue sampling, and will provide a spatial as well as a temporal dimension to our understanding of various diseases and disease processes.

Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

Directory of Open Access Journals (Sweden)

Inhye Yoon

2015-03-01

Full Text Available Since incoming light to an unmanned aerial vehicle (UAV platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i image segmentation based on geometric classes; (ii generation of the context-adaptive transmission map; and (iii intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

Wavelength-adaptive dehazing using histogram merging-based classification for UAV images.

Science.gov (United States)

Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

2015-03-19

Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

Automated quantification and sizing of unbranched filamentous cyanobacteria by model based object oriented image analysis

OpenAIRE

Zeder, M; Van den Wyngaert, S; Köster, O; Felder, K M; Pernthaler, J

2010-01-01

Quantification and sizing of filamentous cyanobacteria in environmental samples or cultures are time-consuming and are often performed by using manual or semiautomated microscopic analysis. Automation of conventional image analysis is difficult because filaments may exhibit great variations in length and patchy autofluorescence. Moreover, individual filaments frequently cross each other in microscopic preparations, as deduced by modeling. This paper describes a novel approach based on object-...

TH-C-18A-06: Combined CT Image Quality and Radiation Dose Monitoring Program Based On Patient Data to Assess Consistency of Clinical Imaging Across Scanner Models

International Nuclear Information System (INIS)

Christianson, O; Winslow, J; Samei, E

2014-01-01

Purpose: One of the principal challenges of clinical imaging is to achieve an ideal balance between image quality and radiation dose across multiple CT models. The number of scanners and protocols at large medical centers necessitates an automated quality assurance program to facilitate this objective. Therefore, the goal of this work was to implement an automated CT image quality and radiation dose monitoring program based on actual patient data and to use this program to assess consistency of protocols across CT scanner models. Methods: Patient CT scans are routed to a HIPPA compliant quality assurance server. CTDI, extracted using optical character recognition, and patient size, measured from the localizers, are used to calculate SSDE. A previously validated noise measurement algorithm determines the noise in uniform areas of the image across the scanned anatomy to generate a global noise level (GNL). Using this program, 2358 abdominopelvic scans acquired on three commercial CT scanners were analyzed. Median SSDE and GNL were compared across scanner models and trends in SSDE and GNL with patient size were used to determine the impact of differing automatic exposure control (AEC) algorithms. Results: There was a significant difference in both SSDE and GNL across scanner models (9–33% and 15–35% for SSDE and GNL, respectively). Adjusting all protocols to achieve the same image noise would reduce patient dose by 27–45% depending on scanner model. Additionally, differences in AEC methodologies across vendors resulted in disparate relationships of SSDE and GNL with patient size. Conclusion: The difference in noise across scanner models indicates that protocols are not optimally matched to achieve consistent image quality. Our results indicated substantial possibility for dose reduction while achieving more consistent image appearance. Finally, the difference in AEC methodologies suggests the need for size-specific CT protocols to minimize variability in image

TH-C-18A-06: Combined CT Image Quality and Radiation Dose Monitoring Program Based On Patient Data to Assess Consistency of Clinical Imaging Across Scanner Models

Energy Technology Data Exchange (ETDEWEB)

Christianson, O; Winslow, J; Samei, E [Duke University Medical Center, Durham, NC (United States)

2014-06-15

Purpose: One of the principal challenges of clinical imaging is to achieve an ideal balance between image quality and radiation dose across multiple CT models. The number of scanners and protocols at large medical centers necessitates an automated quality assurance program to facilitate this objective. Therefore, the goal of this work was to implement an automated CT image quality and radiation dose monitoring program based on actual patient data and to use this program to assess consistency of protocols across CT scanner models. Methods: Patient CT scans are routed to a HIPPA compliant quality assurance server. CTDI, extracted using optical character recognition, and patient size, measured from the localizers, are used to calculate SSDE. A previously validated noise measurement algorithm determines the noise in uniform areas of the image across the scanned anatomy to generate a global noise level (GNL). Using this program, 2358 abdominopelvic scans acquired on three commercial CT scanners were analyzed. Median SSDE and GNL were compared across scanner models and trends in SSDE and GNL with patient size were used to determine the impact of differing automatic exposure control (AEC) algorithms. Results: There was a significant difference in both SSDE and GNL across scanner models (9–33% and 15–35% for SSDE and GNL, respectively). Adjusting all protocols to achieve the same image noise would reduce patient dose by 27–45% depending on scanner model. Additionally, differences in AEC methodologies across vendors resulted in disparate relationships of SSDE and GNL with patient size. Conclusion: The difference in noise across scanner models indicates that protocols are not optimally matched to achieve consistent image quality. Our results indicated substantial possibility for dose reduction while achieving more consistent image appearance. Finally, the difference in AEC methodologies suggests the need for size-specific CT protocols to minimize variability in image

Fingerprint Image Enhancement Based on Second Directional Derivative of the Digital Image

Directory of Open Access Journals (Sweden)

Onnia Vesa

2002-01-01

Full Text Available This paper presents a novel approach of fingerprint image enhancement that relies on detecting the fingerprint ridges as image regions where the second directional derivative of the digital image is positive. A facet model is used in order to approximate the derivatives at each image pixel based on the intensity values of pixels located in a certain neighborhood. We note that the size of this neighborhood has a critical role in achieving accurate enhancement results. Using neighborhoods of various sizes, the proposed algorithm determines several candidate binary representations of the input fingerprint pattern. Subsequently, an output binary ridge-map image is created by selecting image zones, from the available binary image candidates, according to a MAP selection rule. Two public domain collections of fingerprint images are used in order to objectively assess the performance of the proposed fingerprint image enhancement approach.

Construction of 3D MR image-based computer models of pathologic hearts, augmented with histology and optical fluorescence imaging to characterize action potential propagation.

Science.gov (United States)

Pop, Mihaela; Sermesant, Maxime; Liu, Garry; Relan, Jatin; Mansi, Tommaso; Soong, Alan; Peyrat, Jean-Marc; Truong, Michael V; Fefer, Paul; McVeigh, Elliot R; Delingette, Herve; Dick, Alexander J; Ayache, Nicholas; Wright, Graham A

2012-02-01

Cardiac computer models can help us understand and predict the propagation of excitation waves (i.e., action potential, AP) in healthy and pathologic hearts. Our broad aim is to develop accurate 3D MR image-based computer models of electrophysiology in large hearts (translatable to clinical applications) and to validate them experimentally. The specific goals of this paper were to match models with maps of the propagation of optical AP on the epicardial surface using large porcine hearts with scars, estimating several parameters relevant to macroscopic reaction-diffusion electrophysiological models. We used voltage-sensitive dyes to image AP in large porcine hearts with scars (three specimens had chronic myocardial infarct, and three had radiofrequency RF acute scars). We first analyzed the main AP waves' characteristics: duration (APD) and propagation under controlled pacing locations and frequencies as recorded from 2D optical images. We further built 3D MR image-based computer models that have information derived from the optical measures, as well as morphologic MRI data (i.e., myocardial anatomy, fiber directions and scar definition). The scar morphology from MR images was validated against corresponding whole-mount histology. We also compared the measured 3D isochronal maps of depolarization to simulated isochrones (the latter replicating precisely the experimental conditions), performing model customization and 3D volumetric adjustments of the local conductivity. Our results demonstrated that mean APD in the border zone (BZ) of the infarct scars was reduced by ~13% (compared to ~318 ms measured in normal zone, NZ), but APD did not change significantly in the thin BZ of the ablation scars. A generic value for velocity ratio (1:2.7) in healthy myocardial tissue was derived from measured values of transverse and longitudinal conduction velocities relative to fibers direction (22 cm/s and 60 cm/s, respectively). The model customization and 3D volumetric

A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM in ultrasound images.

Directory of Open Access Journals (Sweden)

Bo-I Chuang

Full Text Available Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians' opinions.

Horror Image Recognition Based on Context-Aware Multi-Instance Learning.

Science.gov (United States)

Li, Bing; Xiong, Weihua; Wu, Ou; Hu, Weiming; Maybank, Stephen; Yan, Shuicheng

2015-12-01

Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the fuzzy support vector machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on the tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large-scale image sets collected from the Internet.

Segmentation of laser range radar images using hidden Markov field models

International Nuclear Information System (INIS)

Pucar, P.

1993-01-01

Segmentation of images in the context of model based stochastic techniques is connected with high, very often unpracticle computational complexity. The objective with this thesis is to take the models used in model based image processing, simplify and use them in suboptimal, but not computationally demanding algorithms. Algorithms that are essentially one-dimensional, and their extensions to two dimensions are given. The model used in this thesis is the well known hidden Markov model. Estimation of the number of hidden states from observed data is a problem that is addressed. The state order estimation problem is of general interest and is not specifically connected to image processing. An investigation of three state order estimation techniques for hidden Markov models is given. 76 refs

Comparison of Subset-Based Local and Finite Element-Based Global Digital Image Correlation

KAUST Repository

Pan, Bing; Wang, B.; Lubineau, Gilles; Moussawi, Ali

2015-01-01

Digital image correlation (DIC) techniques require an image matching algorithm to register the same physical points represented in different images. Subset-based local DIC and finite element-based (FE-based) global DIC are the two primary image matching methods that have been extensively investigated and regularly used in the field of experimental mechanics. Due to its straightforward implementation and high efficiency, subset-based local DIC has been used in almost all commercial DIC packages. However, it is argued by some researchers that FE-based global DIC offers better accuracy because of the enforced continuity between element nodes. We propose a detailed performance comparison between these different DIC algorithms both in terms of measurement accuracy and computational efficiency. Then, by measuring displacements of the same calculation points using the same calculation algorithms (e.g., correlation criterion, initial guess estimation, subpixel interpolation, optimization algorithm and convergence conditions) and identical calculation parameters (e.g., subset or element size), the performances of subset-based local DIC and two FE-based global DIC approaches are carefully compared in terms of measurement error and computational efficiency using both numerical tests and real experiments. A detailed examination of the experimental results reveals that, when subset (element) size is not very small and the local deformation within a subset (element) can be well approximated by the shape function used, standard subset-based local DIC approach not only provides better results in measured displacements, but also demonstrates much higher computation efficiency. However, several special merits of FE-based global DIC approaches are indicated.

Comparison of Subset-Based Local and Finite Element-Based Global Digital Image Correlation

KAUST Repository

Pan, Bing

2015-02-12

Digital image correlation (DIC) techniques require an image matching algorithm to register the same physical points represented in different images. Subset-based local DIC and finite element-based (FE-based) global DIC are the two primary image matching methods that have been extensively investigated and regularly used in the field of experimental mechanics. Due to its straightforward implementation and high efficiency, subset-based local DIC has been used in almost all commercial DIC packages. However, it is argued by some researchers that FE-based global DIC offers better accuracy because of the enforced continuity between element nodes. We propose a detailed performance comparison between these different DIC algorithms both in terms of measurement accuracy and computational efficiency. Then, by measuring displacements of the same calculation points using the same calculation algorithms (e.g., correlation criterion, initial guess estimation, subpixel interpolation, optimization algorithm and convergence conditions) and identical calculation parameters (e.g., subset or element size), the performances of subset-based local DIC and two FE-based global DIC approaches are carefully compared in terms of measurement error and computational efficiency using both numerical tests and real experiments. A detailed examination of the experimental results reveals that, when subset (element) size is not very small and the local deformation within a subset (element) can be well approximated by the shape function used, standard subset-based local DIC approach not only provides better results in measured displacements, but also demonstrates much higher computation efficiency. However, several special merits of FE-based global DIC approaches are indicated.

Content-Based Image Retrial Based on Hadoop

Directory of Open Access Journals (Sweden)

DongSheng Yin

2013-01-01

Full Text Available Generally, time complexity of algorithms for content-based image retrial is extremely high. In order to retrieve images on large-scale databases efficiently, a new way for retrieving based on Hadoop distributed framework is proposed. Firstly, a database of images features is built by using Speeded Up Robust Features algorithm and Locality-Sensitive Hashing and then perform the search on Hadoop platform in a parallel way specially designed. Considerable experimental results show that it is able to retrieve images based on content on large-scale cluster and image sets effectively.

Multi-band Image Registration Method Based on Fourier Transform

Institute of Scientific and Technical Information of China (English)

庹红娅; 刘允才

2004-01-01

This paper presented a registration method based on Fourier transform for multi-band images which is involved in translation and small rotation. Although different band images differ a lot in the intensity and features,they contain certain common information which we can exploit. A model was given that the multi-band images have linear correlations under the least-square sense. It is proved that the coefficients have no effect on the registration progress if two images have linear correlations. Finally, the steps of the registration method were proposed. The experiments show that the model is reasonable and the results are satisfying.

A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images.

Science.gov (United States)

Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun

2017-06-22

With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency.

Least-squares model-based halftoning

Science.gov (United States)

Pappas, Thrasyvoulos N.; Neuhoff, David L.

1992-08-01

A least-squares model-based approach to digital halftoning is proposed. It exploits both a printer model and a model for visual perception. It attempts to produce an 'optimal' halftoned reproduction, by minimizing the squared error between the response of the cascade of the printer and visual models to the binary image and the response of the visual model to the original gray-scale image. Conventional methods, such as clustered ordered dither, use the properties of the eye only implicitly, and resist printer distortions at the expense of spatial and gray-scale resolution. In previous work we showed that our printer model can be used to modify error diffusion to account for printer distortions. The modified error diffusion algorithm has better spatial and gray-scale resolution than conventional techniques, but produces some well known artifacts and asymmetries because it does not make use of an explicit eye model. Least-squares model-based halftoning uses explicit eye models and relies on printer models that predict distortions and exploit them to increase, rather than decrease, both spatial and gray-scale resolution. We have shown that the one-dimensional least-squares problem, in which each row or column of the image is halftoned independently, can be implemented with the Viterbi's algorithm. Unfortunately, no closed form solution can be found in two dimensions. The two-dimensional least squares solution is obtained by iterative techniques. Experiments show that least-squares model-based halftoning produces more gray levels and better spatial resolution than conventional techniques. We also show that the least- squares approach eliminates the problems associated with error diffusion. Model-based halftoning can be especially useful in transmission of high quality documents using high fidelity gray-scale image encoders. As we have shown, in such cases halftoning can be performed at the receiver, just before printing. Apart from coding efficiency, this approach

Discriminative Projection Selection Based Face Image Hashing

Science.gov (United States)

Karabat, Cagatay; Erdogan, Hakan

Face image hashing is an emerging method used in biometric verification systems. In this paper, we propose a novel face image hashing method based on a new technique called discriminative projection selection. We apply the Fisher criterion for selecting the rows of a random projection matrix in a user-dependent fashion. Moreover, another contribution of this paper is to employ a bimodal Gaussian mixture model at the quantization step. Our simulation results on three different databases demonstrate that the proposed method has superior performance in comparison to previously proposed random projection based methods.

Modelling of chromatic contrast for retrieval of wallpaper images

OpenAIRE

Gao, Xiaohong W.; Wang, Yuanlei; Qian, Yu; Gao, Alice

2015-01-01

Colour remains one of the key factors in presenting an object and consequently has been widely applied in retrieval of images based on their visual contents. However, a colour appearance changes with the change of viewing surroundings, the phenomenon that has not been paid attention yet while performing colour-based image retrieval. To comprehend this effect, in this paper, a chromatic contrast model, CAMcc, is developed for the application of retrieval of colour intensive images, cementing t...

Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

Directory of Open Access Journals (Sweden)

Huiyan Jiang

2013-01-01

Full Text Available A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

Detecting ship targets in spaceborne infrared image based on modeling radiation anomalies

Science.gov (United States)

Wang, Haibo; Zou, Zhengxia; Shi, Zhenwei; Li, Bo

2017-09-01

Using infrared imaging sensors to detect ship target in the ocean environment has many advantages compared to other sensor modalities, such as better thermal sensitivity and all-weather detection capability. We propose a new ship detection method by modeling radiation anomalies for spaceborne infrared image. The proposed method can be decomposed into two stages, where in the first stage, a test infrared image is densely divided into a set of image patches and the radiation anomaly of each patch is estimated by a Gaussian Mixture Model (GMM), and thereby target candidates are obtained from anomaly image patches. In the second stage, target candidates are further checked by a more discriminative criterion to obtain the final detection result. The main innovation of the proposed method is inspired by the biological mechanism that human eyes are sensitive to the unusual and anomalous patches among complex background. The experimental result on short wavelength infrared band (1.560 - 2.300 μm) and long wavelength infrared band (10.30 - 12.50 μm) of Landsat-8 satellite shows the proposed method achieves a desired ship detection accuracy with higher recall than other classical ship detection methods.

Agent-based modeling of autophagy reveals emergent regulatory behavior of spatio-temporal autophagy dynamics.

Science.gov (United States)

Börlin, Christoph S; Lang, Verena; Hamacher-Brady, Anne; Brady, Nathan R

2014-09-10

Autophagy is a vesicle-mediated pathway for lysosomal degradation, essential under basal and stressed conditions. Various cellular components, including specific proteins, protein aggregates, organelles and intracellular pathogens, are targets for autophagic degradation. Thereby, autophagy controls numerous vital physiological and pathophysiological functions, including cell signaling, differentiation, turnover of cellular components and pathogen defense. Moreover, autophagy enables the cell to recycle cellular components to metabolic substrates, thereby permitting prolonged survival under low nutrient conditions. Due to the multi-faceted roles for autophagy in maintaining cellular and organismal homeostasis and responding to diverse stresses, malfunction of autophagy contributes to both chronic and acute pathologies. We applied a systems biology approach to improve the understanding of this complex cellular process of autophagy. All autophagy pathway vesicle activities, i.e. creation, movement, fusion and degradation, are highly dynamic, temporally and spatially, and under various forms of regulation. We therefore developed an agent-based model (ABM) to represent individual components of the autophagy pathway, subcellular vesicle dynamics and metabolic feedback with the cellular environment, thereby providing a framework to investigate spatio-temporal aspects of autophagy regulation and dynamic behavior. The rules defining our ABM were derived from literature and from high-resolution images of autophagy markers under basal and activated conditions. Key model parameters were fit with an iterative method using a genetic algorithm and a predefined fitness function. From this approach, we found that accurate prediction of spatio-temporal behavior required increasing model complexity by implementing functional integration of autophagy with the cellular nutrient state. The resulting model is able to reproduce short-term autophagic flux measurements (up to 3�

Brain MR image segmentation based on an improved active contour model.

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Xiangrui Meng

Full Text Available It is often a difficult task to accurately segment brain magnetic resonance (MR images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%.

Hyperspectral imaging technology for revealing the original handwritings covered by the same inks

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Yuanyuan Lian

2017-01-01

Full Text Available This manuscript presents a preliminary investigation on the applicability of hyperspectral imaging technology for nondestructive and rapid analysis to reveal covered original handwritings. The hyperspectral imager Nuance-Macro was used to collect the reflected light signature of inks from the overlapping parts. The software Nuance1p46 was used to analyze the reflected light signature of inks which shows the covered original handwritings. Different types of black/blue ballpoint pen inks and black/blue gel pen inks were chosen for sample preparation. From the hyperspectral images examined, the covered original handwritings of application were revealed in 90.5%, 69.1%, 49.5%, and 78.6% of the cases. Further, the correlation between the revealing effect and spectral characteristics of the reflected light of inks at the overlapping parts was interpreted through theoretical analysis and experimental verification. The results indicated that when the spectral characteristics of the reflected light of inks at the overlapping parts were the same or very similar to that of the ink that was used to cover the original handwriting, the original handwriting could not be shown. On the contrary, when the spectral characteristics of the reflected light of inks at the overlapping parts were different to that of the ink that was used to cover the original handwriting, the original handwriting was revealed.

Using a method based on Potts Model to segment a micro-CT image stack of trabecular bones of femoral region

Energy Technology Data Exchange (ETDEWEB)

Andrade, Pedro H.A. de; Cabral, Manuela O.M., E-mail: andrade.pha@gmail.com [Universidade Federal de Pernambuco (DEN/UFPE), Recife, PE (Brazil). Departamento de Engenharia Nuclear; Vieira, Jose W.; Correia, Filipe L. de B., E-mail: jose.wilson59@uol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia de Pernambuco (IFPE), Recife, PE (Brazil); Lima, Fernando R. De A., E-mail: falima@cnen.gov.br [Centro Regional de Ciencias Nucleares do Nordeste (CRCN-NE/CNEN-PE), Recife, PE (brazil)

2015-07-01

Exposure Computational Models are composed basically of an anthropomorphic phantom, a Monte Carlo (MC) code, and an algorithm simulator of the radioactive source. Tomographic phantoms are developed from medical images and must be pre-processed and segmented before being coupled to a MC code (which simulates the interaction of radiation with matter). This work presents a methodology used for treatment of micro-CT images stack of a femur, obtained from a 30 year old female skeleton provided by the Imaging Laboratory for Anthropology of the University of Bristol, UK. These images contain resolution of 60 micrometers and from these a block containing only 160 x 60 x 160 pixels of trabecular tissues and bone marrow was cut and saved as ⁎.sgi file (header + ⁎.raw file). The Grupo de Dosimetria Numerica (Recife-PE, Brazil) developed a software named Digital Image Processing (DIP), in which a method for segmentation based on a physical model for particle interaction known as Potts Model (or q-Ising) was implemented. This model analyzes the statistical dependence between sites in a network. In Potts Model, when the values of spin variables at neighboring sites are identical, it is assigned an 'energy of interaction' between them. Otherwise, it is said that the sites do not interact. Making an analogy between network sites and the pixels of a digital image and, moreover, between the spins variables and the intensity of the gray scale, it was possible to apply this model to obtain texture descriptors and segment the image. (author)

Using a method based on Potts Model to segment a micro-CT image stack of trabecular bones of femoral region

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