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Sample records for images detects regular

  1. Selection of regularization parameter for l1-regularized damage detection

    Science.gov (United States)

    Hou, Rongrong; Xia, Yong; Bao, Yuequan; Zhou, Xiaoqing

    2018-06-01

    The l1 regularization technique has been developed for structural health monitoring and damage detection through employing the sparsity condition of structural damage. The regularization parameter, which controls the trade-off between data fidelity and solution size of the regularization problem, exerts a crucial effect on the solution. However, the l1 regularization problem has no closed-form solution, and the regularization parameter is usually selected by experience. This study proposes two strategies of selecting the regularization parameter for the l1-regularized damage detection problem. The first method utilizes the residual and solution norms of the optimization problem and ensures that they are both small. The other method is based on the discrepancy principle, which requires that the variance of the discrepancy between the calculated and measured responses is close to the variance of the measurement noise. The two methods are applied to a cantilever beam and a three-story frame. A range of the regularization parameter, rather than one single value, can be determined. When the regularization parameter in this range is selected, the damage can be accurately identified even for multiple damage scenarios. This range also indicates the sensitivity degree of the damage identification problem to the regularization parameter.

  2. Automatic Constraint Detection for 2D Layout Regularization

    KAUST Repository

    Jiang, Haiyong; Nan, Liangliang; Yan, Dongming; Dong, Weiming; Zhang, Xiaopeng; Wonka, Peter

    2015-01-01

    plans or images, such as floor plans and facade images, and for the improvement of user created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing

  3. Automatic Constraint Detection for 2D Layout Regularization.

    Science.gov (United States)

    Jiang, Haiyong; Nan, Liangliang; Yan, Dong-Ming; Dong, Weiming; Zhang, Xiaopeng; Wonka, Peter

    2016-08-01

    In this paper, we address the problem of constraint detection for layout regularization. The layout we consider is a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important in digitizing plans or images, such as floor plans and facade images, and in the improvement of user-created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm that automatically detects constraints. We evaluate the proposed framework using a variety of input layouts from different applications. Our results demonstrate that our method has superior performance to the state of the art.

  4. Automatic Constraint Detection for 2D Layout Regularization

    KAUST Repository

    Jiang, Haiyong

    2015-09-18

    In this paper, we address the problem of constraint detection for layout regularization. As layout we consider a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important for digitizing plans or images, such as floor plans and facade images, and for the improvement of user created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate the layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm to automatically detect constraints. In our results, we evaluate the proposed framework on a variety of input layouts from different applications, which demonstrates our method has superior performance to the state of the art.

  5. EIT image reconstruction with four dimensional regularization.

    Science.gov (United States)

    Dai, Tao; Soleimani, Manuchehr; Adler, Andy

    2008-09-01

    Electrical impedance tomography (EIT) reconstructs internal impedance images of the body from electrical measurements on body surface. The temporal resolution of EIT data can be very high, although the spatial resolution of the images is relatively low. Most EIT reconstruction algorithms calculate images from data frames independently, although data are actually highly correlated especially in high speed EIT systems. This paper proposes a 4-D EIT image reconstruction for functional EIT. The new approach is developed to directly use prior models of the temporal correlations among images and 3-D spatial correlations among image elements. A fast algorithm is also developed to reconstruct the regularized images. Image reconstruction is posed in terms of an augmented image and measurement vector which are concatenated from a specific number of previous and future frames. The reconstruction is then based on an augmented regularization matrix which reflects the a priori constraints on temporal and 3-D spatial correlations of image elements. A temporal factor reflecting the relative strength of the image correlation is objectively calculated from measurement data. Results show that image reconstruction models which account for inter-element correlations, in both space and time, show improved resolution and noise performance, in comparison to simpler image models.

  6. Fractional Regularization Term for Variational Image Registration

    Directory of Open Access Journals (Sweden)

    Rafael Verdú-Monedero

    2009-01-01

    Full Text Available Image registration is a widely used task of image analysis with applications in many fields. Its classical formulation and current improvements are given in the spatial domain. In this paper a regularization term based on fractional order derivatives is formulated. This term is defined and implemented in the frequency domain by translating the energy functional into the frequency domain and obtaining the Euler-Lagrange equations which minimize it. The new regularization term leads to a simple formulation and design, being applicable to higher dimensions by using the corresponding multidimensional Fourier transform. The proposed regularization term allows for a real gradual transition from a diffusion registration to a curvature registration which is best suited to some applications and it is not possible in the spatial domain. Results with 3D actual images show the validity of this approach.

  7. Bayesian regularization of diffusion tensor images

    DEFF Research Database (Denmark)

    Frandsen, Jesper; Hobolth, Asger; Østergaard, Leif

    2007-01-01

    Diffusion tensor imaging (DTI) is a powerful tool in the study of the course of nerve fibre bundles in the human brain. Using DTI, the local fibre orientation in each image voxel can be described by a diffusion tensor which is constructed from local measurements of diffusion coefficients along...... several directions. The measured diffusion coefficients and thereby the diffusion tensors are subject to noise, leading to possibly flawed representations of the three dimensional fibre bundles. In this paper we develop a Bayesian procedure for regularizing the diffusion tensor field, fully utilizing...

  8. Multiview Hessian regularization for image annotation.

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    Liu, Weifeng; Tao, Dacheng

    2013-07-01

    The rapid development of computer hardware and Internet technology makes large scale data dependent models computationally tractable, and opens a bright avenue for annotating images through innovative machine learning algorithms. Semisupervised learning (SSL) therefore received intensive attention in recent years and was successfully deployed in image annotation. One representative work in SSL is Laplacian regularization (LR), which smoothes the conditional distribution for classification along the manifold encoded in the graph Laplacian, however, it is observed that LR biases the classification function toward a constant function that possibly results in poor generalization. In addition, LR is developed to handle uniformly distributed data (or single-view data), although instances or objects, such as images and videos, are usually represented by multiview features, such as color, shape, and texture. In this paper, we present multiview Hessian regularization (mHR) to address the above two problems in LR-based image annotation. In particular, mHR optimally combines multiple HR, each of which is obtained from a particular view of instances, and steers the classification function that varies linearly along the data manifold. We apply mHR to kernel least squares and support vector machines as two examples for image annotation. Extensive experiments on the PASCAL VOC'07 dataset validate the effectiveness of mHR by comparing it with baseline algorithms, including LR and HR.

  9. SAR image regularization with fast approximate discrete minimization.

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    Denis, Loïc; Tupin, Florence; Darbon, Jérôme; Sigelle, Marc

    2009-07-01

    Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automatic interpretation of images a challenging task and noise reduction is often a prerequisite for successful use of classical image processing algorithms. Numerous approaches have been proposed to filter speckle noise. Markov random field (MRF) modelization provides a convenient way to express both data fidelity constraints and desirable properties of the filtered image. In this context, total variation minimization has been extensively used to constrain the oscillations in the regularized image while preserving its edges. Speckle noise follows heavy-tailed distributions, and the MRF formulation leads to a minimization problem involving nonconvex log-likelihood terms. Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically possible, is not achievable on large images required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (namely the alpha -expansion) is too heavy specially when considering joint regularization of several images. We show that a satisfying solution can be reached, in few iterations, by performing a graph-cut-based combinatorial exploration of large trial moves. This algorithm is applied to joint regularization of the amplitude and interferometric phase in urban area SAR images.

  10. Wavelet domain image restoration with adaptive edge-preserving regularization.

    Science.gov (United States)

    Belge, M; Kilmer, M E; Miller, E L

    2000-01-01

    In this paper, we consider a wavelet based edge-preserving regularization scheme for use in linear image restoration problems. Our efforts build on a collection of mathematical results indicating that wavelets are especially useful for representing functions that contain discontinuities (i.e., edges in two dimensions or jumps in one dimension). We interpret the resulting theory in a statistical signal processing framework and obtain a highly flexible framework for adapting the degree of regularization to the local structure of the underlying image. In particular, we are able to adapt quite easily to scale-varying and orientation-varying features in the image while simultaneously retaining the edge preservation properties of the regularizer. We demonstrate a half-quadratic algorithm for obtaining the restorations from observed data.

  11. Low-Complexity Regularization Algorithms for Image Deblurring

    KAUST Repository

    Alanazi, Abdulrahman

    2016-11-01

    Image restoration problems deal with images in which information has been degraded by blur or noise. In practice, the blur is usually caused by atmospheric turbulence, motion, camera shake, and several other mechanical or physical processes. In this study, we present two regularization algorithms for the image deblurring problem. We first present a new method based on solving a regularized least-squares (RLS) problem. This method is proposed to find a near-optimal value of the regularization parameter in the RLS problems. Experimental results on the non-blind image deblurring problem are presented. In all experiments, comparisons are made with three benchmark methods. The results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and structural similarity, as well as the visual quality of the deblurred images. To reduce the complexity of the proposed algorithm, we propose a technique based on the bootstrap method to estimate the regularization parameter in low and high-resolution images. Numerical results show that the proposed technique can effectively reduce the computational complexity of the proposed algorithms. In addition, for some cases where the point spread function (PSF) is separable, we propose using a Kronecker product so as to reduce the computations. Furthermore, in the case where the image is smooth, it is always desirable to replace the regularization term in the RLS problems by a total variation term. Therefore, we propose a novel method for adaptively selecting the regularization parameter in a so-called square root regularized total variation (SRTV). Experimental results demonstrate that our proposed method outperforms the other benchmark methods when applied to smooth images in terms of PSNR, SSIM and the restored image quality. In this thesis, we focus on the non-blind image deblurring problem, where the blur kernel is assumed to be known. However, we developed algorithms that also work

  12. Poisson image reconstruction with Hessian Schatten-norm regularization.

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    Lefkimmiatis, Stamatios; Unser, Michael

    2013-11-01

    Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupted by Poisson noise. In this paper, we propose an efficient framework for Poisson image reconstruction, under a regularization approach, which depends on matrix-valued regularization operators. In particular, the employed regularizers involve the Hessian as the regularization operator and Schatten matrix norms as the potential functions. For the solution of the problem, we propose two optimization algorithms that are specifically tailored to the Poisson nature of the noise. These algorithms are based on an augmented-Lagrangian formulation of the problem and correspond to two variants of the alternating direction method of multipliers. Further, we derive a link that relates the proximal map of an l(p) norm with the proximal map of a Schatten matrix norm of order p. This link plays a key role in the development of one of the proposed algorithms. Finally, we provide experimental results on natural and biological images for the task of Poisson image deblurring and demonstrate the practical relevance and effectiveness of the proposed framework.

  13. Iterative Method of Regularization with Application of Advanced Technique for Detection of Contours

    International Nuclear Information System (INIS)

    Niedziela, T.; Stankiewicz, A.

    2000-01-01

    This paper proposes a novel iterative method of regularization with application of an advanced technique for detection of contours. To eliminate noises, the properties of convolution of functions are utilized. The method can be accomplished in a simple neural cellular network, which creates the possibility of extraction of contours by automatic image recognition equipment. (author)

  14. Image deblurring using a perturbation-basec regularization approach

    KAUST Repository

    Alanazi, Abdulrahman

    2017-11-02

    The image restoration problem deals with images in which information has been degraded by blur or noise. In this work, we present a new method for image deblurring by solving a regularized linear least-squares problem. In the proposed method, a synthetic perturbation matrix with a bounded norm is forced into the discrete ill-conditioned model matrix. This perturbation is added to enhance the singular-value structure of the matrix and hence to provide an improved solution. A method is proposed to find a near-optimal value of the regularization parameter for the proposed approach. To reduce the computational complexity, we present a technique based on the bootstrapping method to estimate the regularization parameter for both low and high-resolution images. Experimental results on the image deblurring problem are presented. Comparisons are made with three benchmark methods and the results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and SSIM values.

  15. Image deblurring using a perturbation-basec regularization approach

    KAUST Repository

    Alanazi, Abdulrahman; Ballal, Tarig; Masood, Mudassir; Al-Naffouri, Tareq Y.

    2017-01-01

    The image restoration problem deals with images in which information has been degraded by blur or noise. In this work, we present a new method for image deblurring by solving a regularized linear least-squares problem. In the proposed method, a synthetic perturbation matrix with a bounded norm is forced into the discrete ill-conditioned model matrix. This perturbation is added to enhance the singular-value structure of the matrix and hence to provide an improved solution. A method is proposed to find a near-optimal value of the regularization parameter for the proposed approach. To reduce the computational complexity, we present a technique based on the bootstrapping method to estimate the regularization parameter for both low and high-resolution images. Experimental results on the image deblurring problem are presented. Comparisons are made with three benchmark methods and the results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and SSIM values.

  16. Graph Regularized Auto-Encoders for Image Representation.

    Science.gov (United States)

    Yiyi Liao; Yue Wang; Yong Liu

    2017-06-01

    Image representation has been intensively explored in the domain of computer vision for its significant influence on the relative tasks such as image clustering and classification. It is valuable to learn a low-dimensional representation of an image which preserves its inherent information from the original image space. At the perspective of manifold learning, this is implemented with the local invariant idea to capture the intrinsic low-dimensional manifold embedded in the high-dimensional input space. Inspired by the recent successes of deep architectures, we propose a local invariant deep nonlinear mapping algorithm, called graph regularized auto-encoder (GAE). With the graph regularization, the proposed method preserves the local connectivity from the original image space to the representation space, while the stacked auto-encoders provide explicit encoding model for fast inference and powerful expressive capacity for complex modeling. Theoretical analysis shows that the graph regularizer penalizes the weighted Frobenius norm of the Jacobian matrix of the encoder mapping, where the weight matrix captures the local property in the input space. Furthermore, the underlying effects on the hidden representation space are revealed, providing insightful explanation to the advantage of the proposed method. Finally, the experimental results on both clustering and classification tasks demonstrate the effectiveness of our GAE as well as the correctness of the proposed theoretical analysis, and it also suggests that GAE is a superior solution to the current deep representation learning techniques comparing with variant auto-encoders and existing local invariant methods.

  17. Multilinear Graph Embedding: Representation and Regularization for Images.

    Science.gov (United States)

    Chen, Yi-Lei; Hsu, Chiou-Ting

    2014-02-01

    Given a set of images, finding a compact and discriminative representation is still a big challenge especially when multiple latent factors are hidden in the way of data generation. To represent multifactor images, although multilinear models are widely used to parameterize the data, most methods are based on high-order singular value decomposition (HOSVD), which preserves global statistics but interprets local variations inadequately. To this end, we propose a novel method, called multilinear graph embedding (MGE), as well as its kernelization MKGE to leverage the manifold learning techniques into multilinear models. Our method theoretically links the linear, nonlinear, and multilinear dimensionality reduction. We also show that the supervised MGE encodes informative image priors for image regularization, provided that an image is represented as a high-order tensor. From our experiments on face and gait recognition, the superior performance demonstrates that MGE better represents multifactor images than classic methods, including HOSVD and its variants. In addition, the significant improvement in image (or tensor) completion validates the potential of MGE for image regularization.

  18. Multiview vector-valued manifold regularization for multilabel image classification.

    Science.gov (United States)

    Luo, Yong; Tao, Dacheng; Xu, Chang; Xu, Chao; Liu, Hong; Wen, Yonggang

    2013-05-01

    In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g., pedestrian, bicycle, and tree) and is properly characterized by multiple visual features (e.g., color, texture, and shape). Currently, available tools ignore either the label relationship or the view complementarily. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multilabel structure in the output space, we introduce multiview vector-valued manifold regularization (MV(3)MR) to integrate multiple features. MV(3)MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conduct extensive experiments on two challenging, but popular, datasets, PASCAL VOC' 07 and MIR Flickr, and validate the effectiveness of the proposed MV(3)MR for image classification.

  19. Real time QRS complex detection using DFA and regular grammar.

    Science.gov (United States)

    Hamdi, Salah; Ben Abdallah, Asma; Bedoui, Mohamed Hedi

    2017-02-28

    The sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration. The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks' amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed. Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.

  20. Manifold regularization for sparse unmixing of hyperspectral images.

    Science.gov (United States)

    Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin

    2016-01-01

    Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

  1. Extended -Regular Sequence for Automated Analysis of Microarray Images

    Directory of Open Access Journals (Sweden)

    Jin Hee-Jeong

    2006-01-01

    Full Text Available Microarray study enables us to obtain hundreds of thousands of expressions of genes or genotypes at once, and it is an indispensable technology for genome research. The first step is the analysis of scanned microarray images. This is the most important procedure for obtaining biologically reliable data. Currently most microarray image processing systems require burdensome manual block/spot indexing work. Since the amount of experimental data is increasing very quickly, automated microarray image analysis software becomes important. In this paper, we propose two automated methods for analyzing microarray images. First, we propose the extended -regular sequence to index blocks and spots, which enables a novel automatic gridding procedure. Second, we provide a methodology, hierarchical metagrid alignment, to allow reliable and efficient batch processing for a set of microarray images. Experimental results show that the proposed methods are more reliable and convenient than the commercial tools.

  2. A general framework for regularized, similarity-based image restoration.

    Science.gov (United States)

    Kheradmand, Amin; Milanfar, Peyman

    2014-12-01

    Any image can be represented as a function defined on a weighted graph, in which the underlying structure of the image is encoded in kernel similarity and associated Laplacian matrices. In this paper, we develop an iterative graph-based framework for image restoration based on a new definition of the normalized graph Laplacian. We propose a cost function, which consists of a new data fidelity term and regularization term derived from the specific definition of the normalized graph Laplacian. The normalizing coefficients used in the definition of the Laplacian and associated regularization term are obtained using fast symmetry preserving matrix balancing. This results in some desired spectral properties for the normalized Laplacian such as being symmetric, positive semidefinite, and returning zero vector when applied to a constant image. Our algorithm comprises of outer and inner iterations, where in each outer iteration, the similarity weights are recomputed using the previous estimate and the updated objective function is minimized using inner conjugate gradient iterations. This procedure improves the performance of the algorithm for image deblurring, where we do not have access to a good initial estimate of the underlying image. In addition, the specific form of the cost function allows us to render the spectral analysis for the solutions of the corresponding linear equations. In addition, the proposed approach is general in the sense that we have shown its effectiveness for different restoration problems, including deblurring, denoising, and sharpening. Experimental results verify the effectiveness of the proposed algorithm on both synthetic and real examples.

  3. An algorithm for total variation regularized photoacoustic imaging

    DEFF Research Database (Denmark)

    Dong, Yiqiu; Görner, Torsten; Kunis, Stefan

    2014-01-01

    Recovery of image data from photoacoustic measurements asks for the inversion of the spherical mean value operator. In contrast to direct inversion methods for specific geometries, we consider a semismooth Newton scheme to solve a total variation regularized least squares problem. During the iter......Recovery of image data from photoacoustic measurements asks for the inversion of the spherical mean value operator. In contrast to direct inversion methods for specific geometries, we consider a semismooth Newton scheme to solve a total variation regularized least squares problem. During...... the iteration, each matrix vector multiplication is realized in an efficient way using a recently proposed spectral discretization of the spherical mean value operator. All theoretical results are illustrated by numerical experiments....

  4. EIT Imaging Regularization Based on Spectral Graph Wavelets.

    Science.gov (United States)

    Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Vauhkonen, Marko; Wolf, Gerhard; Mueller-Lisse, Ullrich; Moeller, Knut

    2017-09-01

    The objective of electrical impedance tomographic reconstruction is to identify the distribution of tissue conductivity from electrical boundary conditions. This is an ill-posed inverse problem usually solved under the finite-element method framework. In previous studies, standard sparse regularization was used for difference electrical impedance tomography to achieve a sparse solution. However, regarding elementwise sparsity, standard sparse regularization interferes with the smoothness of conductivity distribution between neighboring elements and is sensitive to noise. As an effect, the reconstructed images are spiky and depict a lack of smoothness. Such unexpected artifacts are not realistic and may lead to misinterpretation in clinical applications. To eliminate such artifacts, we present a novel sparse regularization method that uses spectral graph wavelet transforms. Single-scale or multiscale graph wavelet transforms are employed to introduce local smoothness on different scales into the reconstructed images. The proposed approach relies on viewing finite-element meshes as undirected graphs and applying wavelet transforms derived from spectral graph theory. Reconstruction results from simulations, a phantom experiment, and patient data suggest that our algorithm is more robust to noise and produces more reliable images.

  5. Regularized image denoising based on spectral gradient optimization

    International Nuclear Information System (INIS)

    Lukić, Tibor; Lindblad, Joakim; Sladoje, Nataša

    2011-01-01

    Image restoration methods, such as denoising, deblurring, inpainting, etc, are often based on the minimization of an appropriately defined energy function. We consider energy functions for image denoising which combine a quadratic data-fidelity term and a regularization term, where the properties of the latter are determined by a used potential function. Many potential functions are suggested for different purposes in the literature. We compare the denoising performance achieved by ten different potential functions. Several methods for efficient minimization of regularized energy functions exist. Most are only applicable to particular choices of potential functions, however. To enable a comparison of all the observed potential functions, we propose to minimize the objective function using a spectral gradient approach; spectral gradient methods put very weak restrictions on the used potential function. We present and evaluate the performance of one spectral conjugate gradient and one cyclic spectral gradient algorithm, and conclude from experiments that both are well suited for the task. We compare the performance with three total variation-based state-of-the-art methods for image denoising. From the empirical evaluation, we conclude that denoising using the Huber potential (for images degraded by higher levels of noise; signal-to-noise ratio below 10 dB) and the Geman and McClure potential (for less noisy images), in combination with the spectral conjugate gradient minimization algorithm, shows the overall best performance

  6. Bias correction for magnetic resonance images via joint entropy regularization.

    Science.gov (United States)

    Wang, Shanshan; Xia, Yong; Dong, Pei; Luo, Jianhua; Huang, Qiu; Feng, Dagan; Li, Yuanxiang

    2014-01-01

    Due to the imperfections of the radio frequency (RF) coil or object-dependent electrodynamic interactions, magnetic resonance (MR) images often suffer from a smooth and biologically meaningless bias field, which causes severe troubles for subsequent processing and quantitative analysis. To effectively restore the original signal, this paper simultaneously exploits the spatial and gradient features of the corrupted MR images for bias correction via the joint entropy regularization. With both isotropic and anisotropic total variation (TV) considered, two nonparametric bias correction algorithms have been proposed, namely IsoTVBiasC and AniTVBiasC. These two methods have been applied to simulated images under various noise levels and bias field corruption and also tested on real MR data. The test results show that the proposed two methods can effectively remove the bias field and also present comparable performance compared to the state-of-the-art methods.

  7. Mixture models with entropy regularization for community detection in networks

    Science.gov (United States)

    Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

    2018-04-01

    Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

  8. Prospective regularization design in prior-image-based reconstruction

    International Nuclear Information System (INIS)

    Dang, Hao; Siewerdsen, Jeffrey H; Stayman, J Webster

    2015-01-01

    Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in

  9. Beamforming Through Regularized Inverse Problems in Ultrasound Medical Imaging.

    Science.gov (United States)

    Szasz, Teodora; Basarab, Adrian; Kouame, Denis

    2016-12-01

    Beamforming (BF) in ultrasound (US) imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum (DAS) is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance (MV) method and its variants, which overcome the drawbacks of DAS, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform BF in US imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: 1) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and 2) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right tradeoff between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with in vivo examples of carotid and thyroid. Compared with DAS, MV, and two other recently published BF techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.

  10. A soft double regularization approach to parametric blind image deconvolution.

    Science.gov (United States)

    Chen, Li; Yap, Kim-Hui

    2005-05-01

    This paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures. Conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information. This paper attempts to address this issue by assessing the relevance of parametric blur information, and incorporating the knowledge into the parametric double regularization (PDR) scheme. The PDR method assumes that the actual blur satisfies up to a certain degree of parametric structure, as there are many well-known parametric blurs in practical applications. Further, it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available. A manifold soft parametric modeling technique is proposed to generate the blur manifolds, and estimate the fuzzy blur structure. The PDR scheme involves the development of the meaningful cost function, the estimation of blur support and structure, and the optimization of the cost function. Experimental results show that it is effective in restoring degraded images under different environments.

  11. Iterative reconstruction for x-ray computed tomography using prior-image induced nonlocal regularization.

    Science.gov (United States)

    Zhang, Hua; Huang, Jing; Ma, Jianhua; Bian, Zhaoying; Feng, Qianjin; Lu, Hongbing; Liang, Zhengrong; Chen, Wufan

    2014-09-01

    Repeated X-ray computed tomography (CT) scans are often required in several specific applications such as perfusion imaging, image-guided biopsy needle, image-guided intervention, and radiotherapy with noticeable benefits. However, the associated cumulative radiation dose significantly increases as comparison with that used in the conventional CT scan, which has raised major concerns in patients. In this study, to realize radiation dose reduction by reducing the X-ray tube current and exposure time (mAs) in repeated CT scans, we propose a prior-image induced nonlocal (PINL) regularization for statistical iterative reconstruction via the penalized weighted least-squares (PWLS) criteria, which we refer to as "PWLS-PINL". Specifically, the PINL regularization utilizes the redundant information in the prior image and the weighted least-squares term considers a data-dependent variance estimation, aiming to improve current low-dose image quality. Subsequently, a modified iterative successive overrelaxation algorithm is adopted to optimize the associative objective function. Experimental results on both phantom and patient data show that the present PWLS-PINL method can achieve promising gains over the other existing methods in terms of the noise reduction, low-contrast object detection, and edge detail preservation.

  12. 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.

  13. The regularized monotonicity method: detecting irregular indefinite inclusions

    DEFF Research Database (Denmark)

    Garde, Henrik; Staboulis, Stratos

    2018-01-01

    inclusions, where the conductivity distribution has both more and less conductive parts relative to the background conductivity; one such method is the monotonicity method of Harrach, Seo, and Ullrich. We formulate the method for irregular indefinite inclusions, meaning that we make no regularity assumptions...

  14. Phantom experiments using soft-prior regularization EIT for breast cancer imaging.

    Science.gov (United States)

    Murphy, Ethan K; Mahara, Aditya; Wu, Xiaotian; Halter, Ryan J

    2017-06-01

    A soft-prior regularization (SR) electrical impedance tomography (EIT) technique for breast cancer imaging is described, which shows an ability to accurately reconstruct tumor/inclusion conductivity values within a dense breast model investigated using a cylindrical and a breast-shaped tank. The SR-EIT method relies on knowing the spatial location of a suspicious lesion initially detected from a second imaging modality. Standard approaches (using Laplace smoothing and total variation regularization) without prior structural information are unable to accurately reconstruct or detect the tumors. The soft-prior approach represents a very significant improvement to these standard approaches, and has the potential to improve conventional imaging techniques, such as automated whole breast ultrasound (AWB-US), by providing electrical property information of suspicious lesions to improve AWB-US's ability to discriminate benign from cancerous lesions. Specifically, the best soft-regularization technique found average absolute tumor/inclusion errors of 0.015 S m -1 for the cylindrical test and 0.055 S m -1 and 0.080 S m -1 for the breast-shaped tank for 1.8 cm and 2.5 cm inclusions, respectively. The standard approaches were statistically unable to distinguish the tumor from the mammary gland tissue. An analysis of false tumors (benign suspicious lesions) provides extra insight into the potential and challenges EIT has for providing clinically relevant information. The ability to obtain accurate conductivity values of a suspicious lesion (>1.8 cm) detected from another modality (e.g. AWB-US) could significantly reduce false positives and result in a clinically important technology.

  15. Backtracking-Based Iterative Regularization Method for Image Compressive Sensing Recovery

    Directory of Open Access Journals (Sweden)

    Lingjun Liu

    2017-01-01

    Full Text Available This paper presents a variant of the iterative shrinkage-thresholding (IST algorithm, called backtracking-based adaptive IST (BAIST, for image compressive sensing (CS reconstruction. For increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity. To add back more details, the BAIST method backtracks to the previous noisy image using L2 norm minimization, i.e., minimizing the Euclidean distance between the current solution and the previous ones. Through this modification, the BAIST method achieves superior performance while maintaining the low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive regularizor to automatically detect the sparsity level of an image. Experimental results show that our algorithm outperforms the original IST method and several excellent CS techniques.

  16. 4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging.

    Science.gov (United States)

    Reilhac, Anthonin; Charil, Arnaud; Wimberley, Catriona; Angelis, Georgios; Hamze, Hasar; Callaghan, Paul; Garcia, Marie-Paule; Boisson, Frederic; Ryder, Will; Meikle, Steven R; Gregoire, Marie-Claude

    2015-09-01

    Quantitative measurements in dynamic PET imaging are usually limited by the poor counting statistics particularly in short dynamic frames and by the low spatial resolution of the detection system, resulting in partial volume effects (PVEs). In this work, we present a fast and easy to implement method for the restoration of dynamic PET images that have suffered from both PVE and noise degradation. It is based on a weighted least squares iterative deconvolution approach of the dynamic PET image with spatial and temporal regularization. Using simulated dynamic [(11)C] Raclopride PET data with controlled biological variations in the striata between scans, we showed that the restoration method provides images which exhibit less noise and better contrast between emitting structures than the original images. In addition, the method is able to recover the true time activity curve in the striata region with an error below 3% while it was underestimated by more than 20% without correction. As a result, the method improves the accuracy and reduces the variability of the kinetic parameter estimates calculated from the corrected images. More importantly it increases the accuracy (from less than 66% to more than 95%) of measured biological variations as well as their statistical detectivity. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  17. Handheld microwave bomb-detecting imaging system

    Science.gov (United States)

    Gorwara, Ashok; Molchanov, Pavlo

    2017-05-01

    Proposed novel imaging technique will provide all weather high-resolution imaging and recognition capability for RF/Microwave signals with good penetration through highly scattered media: fog, snow, dust, smoke, even foliage, camouflage, walls and ground. Image resolution in proposed imaging system is not limited by diffraction and will be determined by processor and sampling frequency. Proposed imaging system can simultaneously cover wide field of view, detect multiple targets and can be multi-frequency, multi-function. Directional antennas in imaging system can be close positioned and installed in cell phone size handheld device, on small aircraft or distributed around protected border or object. Non-scanning monopulse system allows dramatically decrease in transmitting power and at the same time provides increased imaging range by integrating 2-3 orders more signals than regular scanning imaging systems.

  18. EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

    Science.gov (United States)

    Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos

    2015-01-01

    Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

  19. HIERARCHICAL REGULARIZATION OF POLYGONS FOR PHOTOGRAMMETRIC POINT CLOUDS OF OBLIQUE IMAGES

    Directory of Open Access Journals (Sweden)

    L. Xie

    2017-05-01

    Full Text Available Despite the success of multi-view stereo (MVS reconstruction from massive oblique images in city scale, only point clouds and triangulated meshes are available from existing MVS pipelines, which are topologically defect laden, free of semantical information and hard to edit and manipulate interactively in further applications. On the other hand, 2D polygons and polygonal models are still the industrial standard. However, extraction of the 2D polygons from MVS point clouds is still a non-trivial task, given the fact that the boundaries of the detected planes are zigzagged and regularities, such as parallel and orthogonal, cannot preserve. Aiming to solve these issues, this paper proposes a hierarchical polygon regularization method for the photogrammetric point clouds from existing MVS pipelines, which comprises of local and global levels. After boundary points extraction, e.g. using alpha shapes, the local level is used to consolidate the original points, by refining the orientation and position of the points using linear priors. The points are then grouped into local segments by forward searching. In the global level, regularities are enforced through a labeling process, which encourage the segments share the same label and the same label represents segments are parallel or orthogonal. This is formulated as Markov Random Field and solved efficiently. Preliminary results are made with point clouds from aerial oblique images and compared with two classical regularization methods, which have revealed that the proposed method are more powerful in abstracting a single building and is promising for further 3D polygonal model reconstruction and GIS applications.

  20. Image-guided regularization level set evolution for MR image segmentation and bias field correction.

    Science.gov (United States)

    Wang, Lingfeng; Pan, Chunhong

    2014-01-01

    Magnetic resonance (MR) image segmentation is a crucial step in surgical and treatment planning. In this paper, we propose a level-set-based segmentation method for MR images with intensity inhomogeneous problem. To tackle the initialization sensitivity problem, we propose a new image-guided regularization to restrict the level set function. The maximum a posteriori inference is adopted to unify segmentation and bias field correction within a single framework. Under this framework, both the contour prior and the bias field prior are fully used. As a result, the image intensity inhomogeneity can be well solved. Extensive experiments are provided to evaluate the proposed method, showing significant improvements in both segmentation and bias field correction accuracies as compared with other state-of-the-art approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

  1. Detecting regularities in soccer dynamics: A T-pattern approach

    Directory of Open Access Journals (Sweden)

    Valentino Zurloni

    2014-01-01

    Full Text Available La dinámica del juego en partidos de fútbol profesional es un fenómeno complejo que no ha estado resuelto de forma óptima a través delas vías tradicionales que han pretendido la cuantificación en deportes de equipo. El objetivo de este estudio es el de detectar la dinámica existente mediante un análisis de patrones temporales. Específicamente, se pretenden revelar las estructuras ocultas pero estables que subyacen a las situaciones interactivas que determinan las acciones de ataque en el fútbol. El planteamiento metodológico se basa en un diseño observacional, y con apoyo de registros digitales y análisis informatizados. Los datos se analizaron mediante el programa Theme 6 beta, el cual permite detectar la estructura temporaly secuencial de las series de datos, poniendo de manifiesto patrones que regular o irregularmente ocurren repetidamente en un período de observación. El Theme ha detectado muchos patrones temporales (T-patterns en los partidos de fútbol analizados. Se hallaron notables diferencias entre los partidos ganados y perdidos. El número de distintos T-patterns detectados fue mayor para los partidos perdidos, y menor para los ganados, mientras que el número de eventos codificados fue similar. El programa Theme y los T-patterns mejoran las posibilidades investigadoras respecto a un análisis de rendimiento basado en la frecuencia, y hacen que esta metodología sea eficaz para la investigación y constituya un apoyo procedimental en el análisis del deporte. Nuestros resultados indican que se requieren posteriores investigaciones relativas a posibles conexiones entre la detección de estas estructuras temporales y las observaciones humanas respecto al rendimiento en el fútbol. Este planteamiento sería un apoyo tanto para los miembros de los equipos como para los entrenadores, permitiendo alcanzar una mejor comprensión de la dinámica del juego y aportando una información que no ofrecen los métodos tradicionales.

  2. Regularization iteration imaging algorithm for electrical capacitance tomography

    Science.gov (United States)

    Tong, Guowei; Liu, Shi; Chen, Hongyan; Wang, Xueyao

    2018-03-01

    The image reconstruction method plays a crucial role in real-world applications of the electrical capacitance tomography technique. In this study, a new cost function that simultaneously considers the sparsity and low-rank properties of the imaging targets is proposed to improve the quality of the reconstruction images, in which the image reconstruction task is converted into an optimization problem. Within the framework of the split Bregman algorithm, an iterative scheme that splits a complicated optimization problem into several simpler sub-tasks is developed to solve the proposed cost function efficiently, in which the fast-iterative shrinkage thresholding algorithm is introduced to accelerate the convergence. Numerical experiment results verify the effectiveness of the proposed algorithm in improving the reconstruction precision and robustness.

  3. Efficient moving target analysis for inverse synthetic aperture radar images via joint speeded-up robust features and regular moment

    Science.gov (United States)

    Yang, Hongxin; Su, Fulin

    2018-01-01

    We propose a moving target analysis algorithm using speeded-up robust features (SURF) and regular moment in inverse synthetic aperture radar (ISAR) image sequences. In our study, we first extract interest points from ISAR image sequences by SURF. Different from traditional feature point extraction methods, SURF-based feature points are invariant to scattering intensity, target rotation, and image size. Then, we employ a bilateral feature registering model to match these feature points. The feature registering scheme can not only search the isotropic feature points to link the image sequences but also reduce the error matching pairs. After that, the target centroid is detected by regular moment. Consequently, a cost function based on correlation coefficient is adopted to analyze the motion information. Experimental results based on simulated and real data validate the effectiveness and practicability of the proposed method.

  4. SNAPSHOT SPECTRAL AND COLOR IMAGING USING A REGULAR DIGITAL CAMERA WITH A MONOCHROMATIC IMAGE SENSOR

    Directory of Open Access Journals (Sweden)

    J. Hauser

    2017-10-01

    Full Text Available Spectral imaging (SI refers to the acquisition of the three-dimensional (3D spectral cube of spatial and spectral data of a source object at a limited number of wavelengths in a given wavelength range. Snapshot spectral imaging (SSI refers to the instantaneous acquisition (in a single shot of the spectral cube, a process suitable for fast changing objects. Known SSI devices exhibit large total track length (TTL, weight and production costs and relatively low optical throughput. We present a simple SSI camera based on a regular digital camera with (i an added diffusing and dispersing phase-only static optical element at the entrance pupil (diffuser and (ii tailored compressed sensing (CS methods for digital processing of the diffused and dispersed (DD image recorded on the image sensor. The diffuser is designed to mix the spectral cube data spectrally and spatially and thus to enable convergence in its reconstruction by CS-based algorithms. In addition to performing SSI, this SSI camera is capable to perform color imaging using a monochromatic or gray-scale image sensor without color filter arrays.

  5. Total variation regularization in measurement and image space for PET reconstruction

    KAUST Repository

    Burger, M

    2014-09-18

    © 2014 IOP Publishing Ltd. The aim of this paper is to test and analyse a novel technique for image reconstruction in positron emission tomography, which is based on (total variation) regularization on both the image space and the projection space. We formulate our variational problem considering both total variation penalty terms on the image and on an idealized sinogram to be reconstructed from a given Poisson distributed noisy sinogram. We prove existence, uniqueness and stability results for the proposed model and provide some analytical insight into the structures favoured by joint regularization. For the numerical solution of the corresponding discretized problem we employ the split Bregman algorithm and extensively test the approach in comparison to standard total variation regularization on the image. The numerical results show that an additional penalty on the sinogram performs better on reconstructing images with thin structures.

  6. Novelty detection in dermatological images

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel

    2003-01-01

    The problem of novelty detection is considered for at set of dermatological image data. Different points of view are analyzed in detail. First, novelty detection is treated as a contextual classification problem. Different learning phases can be detected when the sample size is increased. The det......The problem of novelty detection is considered for at set of dermatological image data. Different points of view are analyzed in detail. First, novelty detection is treated as a contextual classification problem. Different learning phases can be detected when the sample size is increased...

  7. Image super-resolution reconstruction based on regularization technique and guided filter

    Science.gov (United States)

    Huang, De-tian; Huang, Wei-qin; Gu, Pei-ting; Liu, Pei-zhong; Luo, Yan-min

    2017-06-01

    In order to improve the accuracy of sparse representation coefficients and the quality of reconstructed images, an improved image super-resolution algorithm based on sparse representation is presented. In the sparse coding stage, the autoregressive (AR) regularization and the non-local (NL) similarity regularization are introduced to improve the sparse coding objective function. A group of AR models which describe the image local structures are pre-learned from the training samples, and one or several suitable AR models can be adaptively selected for each image patch to regularize the solution space. Then, the image non-local redundancy is obtained by the NL similarity regularization to preserve edges. In the process of computing the sparse representation coefficients, the feature-sign search algorithm is utilized instead of the conventional orthogonal matching pursuit algorithm to improve the accuracy of the sparse coefficients. To restore image details further, a global error compensation model based on weighted guided filter is proposed to realize error compensation for the reconstructed images. Experimental results demonstrate that compared with Bicubic, L1SR, SISR, GR, ANR, NE + LS, NE + NNLS, NE + LLE and A + (16 atoms) methods, the proposed approach has remarkable improvement in peak signal-to-noise ratio, structural similarity and subjective visual perception.

  8. Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning

    OpenAIRE

    Lai, Rongjie; Li, Jia

    2017-01-01

    Low-rank structures play important role in recent advances of many problems in image science and data science. As a natural extension of low-rank structures for data with nonlinear structures, the concept of the low-dimensional manifold structure has been considered in many data processing problems. Inspired by this concept, we consider a manifold based low-rank regularization as a linear approximation of manifold dimension. This regularization is less restricted than the global low-rank regu...

  9. Progressive image denoising through hybrid graph Laplacian regularization: a unified framework.

    Science.gov (United States)

    Liu, Xianming; Zhai, Deming; Zhao, Debin; Zhai, Guangtao; Gao, Wen

    2014-04-01

    Recovering images from corrupted observations is necessary for many real-world applications. In this paper, we propose a unified framework to perform progressive image recovery based on hybrid graph Laplacian regularized regression. We first construct a multiscale representation of the target image by Laplacian pyramid, then progressively recover the degraded image in the scale space from coarse to fine so that the sharp edges and texture can be eventually recovered. On one hand, within each scale, a graph Laplacian regularization model represented by implicit kernel is learned, which simultaneously minimizes the least square error on the measured samples and preserves the geometrical structure of the image data space. In this procedure, the intrinsic manifold structure is explicitly considered using both measured and unmeasured samples, and the nonlocal self-similarity property is utilized as a fruitful resource for abstracting a priori knowledge of the images. On the other hand, between two successive scales, the proposed model is extended to a projected high-dimensional feature space through explicit kernel mapping to describe the interscale correlation, in which the local structure regularity is learned and propagated from coarser to finer scales. In this way, the proposed algorithm gradually recovers more and more image details and edges, which could not been recovered in previous scale. We test our algorithm on one typical image recovery task: impulse noise removal. Experimental results on benchmark test images demonstrate that the proposed method achieves better performance than state-of-the-art algorithms.

  10. Sparse regularization for EIT reconstruction incorporating structural information derived from medical imaging.

    Science.gov (United States)

    Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Mueller-Lisse, Ullrich; Moeller, Knut

    2016-06-01

    Electrical impedance tomography (EIT) reconstructs the conductivity distribution of a domain using electrical data on its boundary. This is an ill-posed inverse problem usually solved on a finite element mesh. For this article, a special regularization method incorporating structural information of the targeted domain is proposed and evaluated. Structural information was obtained either from computed tomography images or from preliminary EIT reconstructions by a modified k-means clustering. The proposed regularization method integrates this structural information into the reconstruction as a soft constraint preferring sparsity in group level. A first evaluation with Monte Carlo simulations indicated that the proposed solver is more robust to noise and the resulting images show fewer artifacts. This finding is supported by real data analysis. The structure based regularization has the potential to balance structural a priori information with data driven reconstruction. It is robust to noise, reduces artifacts and produces images that reflect anatomy and are thus easier to interpret for physicians.

  11. Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing.

    Science.gov (United States)

    Elmoataz, Abderrahim; Lezoray, Olivier; Bougleux, Sébastien

    2008-07-01

    We introduce a nonlocal discrete regularization framework on weighted graphs of the arbitrary topologies for image and manifold processing. The approach considers the problem as a variational one, which consists of minimizing a weighted sum of two energy terms: a regularization one that uses a discrete weighted p-Dirichlet energy and an approximation one. This is the discrete analogue of recent continuous Euclidean nonlocal regularization functionals. The proposed formulation leads to a family of simple and fast nonlinear processing methods based on the weighted p-Laplace operator, parameterized by the degree p of regularity, the graph structure and the graph weight function. These discrete processing methods provide a graph-based version of recently proposed semi-local or nonlocal processing methods used in image and mesh processing, such as the bilateral filter, the TV digital filter or the nonlocal means filter. It works with equal ease on regular 2-D and 3-D images, manifolds or any data. We illustrate the abilities of the approach by applying it to various types of images, meshes, manifolds, and data represented as graphs.

  12. Gravitational lensing and ghost images in the regular Bardeen no-horizon spacetimes

    International Nuclear Information System (INIS)

    Schee, Jan; Stuchlík, Zdeněk

    2015-01-01

    We study deflection of light rays and gravitational lensing in the regular Bardeen no-horizon spacetimes. Flatness of these spacetimes in the central region implies existence of interesting optical effects related to photons crossing the gravitational field of the no-horizon spacetimes with low impact parameters. These effects occur due to existence of a critical impact parameter giving maximal deflection of light rays in the Bardeen no-horizon spacetimes. We give the critical impact parameter in dependence on the specific charge of the spacetimes, and discuss 'ghost' direct and indirect images of Keplerian discs, generated by photons with low impact parameters. The ghost direct images can occur only for large inclination angles of distant observers, while ghost indirect images can occur also for small inclination angles. We determine the range of the frequency shift of photons generating the ghost images and determine distribution of the frequency shift across these images. We compare them to those of the standard direct images of the Keplerian discs. The difference of the ranges of the frequency shift on the ghost and direct images could serve as a quantitative measure of the Bardeen no-horizon spacetimes. The regions of the Keplerian discs giving the ghost images are determined in dependence on the specific charge of the no-horizon spacetimes. For comparison we construct direct and indirect (ordinary and ghost) images of Keplerian discs around Reissner-Nördström naked singularities demonstrating a clear qualitative difference to the ghost direct images in the regular Bardeen no-horizon spacetimes. The optical effects related to the low impact parameter photons thus give clear signature of the regular Bardeen no-horizon spacetimes, as no similar phenomena could occur in the black hole or naked singularity spacetimes. Similar direct ghost images have to occur in any regular no-horizon spacetimes having nearly flat central region

  13. Regularized Fractional Power Parameters for Image Denoising Based on Convex Solution of Fractional Heat Equation

    Directory of Open Access Journals (Sweden)

    Hamid A. Jalab

    2014-01-01

    Full Text Available The interest in using fractional mask operators based on fractional calculus operators has grown for image denoising. Denoising is one of the most fundamental image restoration problems in computer vision and image processing. This paper proposes an image denoising algorithm based on convex solution of fractional heat equation with regularized fractional power parameters. The performances of the proposed algorithms were evaluated by computing the PSNR, using different types of images. Experiments according to visual perception and the peak signal to noise ratio values show that the improvements in the denoising process are competent with the standard Gaussian filter and Wiener filter.

  14. Block matching sparsity regularization-based image reconstruction for incomplete projection data in computed tomography

    Science.gov (United States)

    Cai, Ailong; Li, Lei; Zheng, Zhizhong; Zhang, Hanming; Wang, Linyuan; Hu, Guoen; Yan, Bin

    2018-02-01

    In medical imaging many conventional regularization methods, such as total variation or total generalized variation, impose strong prior assumptions which can only account for very limited classes of images. A more reasonable sparse representation frame for images is still badly needed. Visually understandable images contain meaningful patterns, and combinations or collections of these patterns can be utilized to form some sparse and redundant representations which promise to facilitate image reconstructions. In this work, we propose and study block matching sparsity regularization (BMSR) and devise an optimization program using BMSR for computed tomography (CT) image reconstruction for an incomplete projection set. The program is built as a constrained optimization, minimizing the L1-norm of the coefficients of the image in the transformed domain subject to data observation and positivity of the image itself. To solve the program efficiently, a practical method based on the proximal point algorithm is developed and analyzed. In order to accelerate the convergence rate, a practical strategy for tuning the BMSR parameter is proposed and applied. The experimental results for various settings, including real CT scanning, have verified the proposed reconstruction method showing promising capabilities over conventional regularization.

  15. A visibility-based approach using regularization for imaging-spectroscopy in solar X-ray astronomy

    Energy Technology Data Exchange (ETDEWEB)

    Prato, M; Massone, A M; Piana, M [CNR - INFM LAMIA, Via Dodecaneso 33 1-16146 Genova (Italy); Emslie, A G [Department of Physics, Oklahoma State University, Stillwater, OK 74078 (United States); Hurford, G J [Space Sciences Laboratory, University of California at Berkeley, 8 Gauss Way, Berkeley, CA 94720-7450 (United States); Kontar, E P [Department of Physics and Astronomy, The University, Glasgow G12 8QQ, Scotland (United Kingdom); Schwartz, R A [CUA - Catholic University and LSSP at NASA Goddard Space Flight Center, code 671.1 Greenbelt, MD 20771 (United States)], E-mail: massone@ge.infm.it

    2008-11-01

    The Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) is a nine-collimators satellite detecting X-rays and {gamma}-rays emitted by the Sun during flares. As the spacecraft rotates, imaging information is encoded as rapid time-variations of the detected flux. We recently proposed a method for the construction of electron flux maps at different electron energies from sets of count visibilities (i.e., direct, calibrated measurements of specific Fourier components of the source spatial structure) measured by RHESSI. The method requires the application of regularized inversion for the synthesis of electron visibility spectra and of imaging techniques for the reconstruction of two-dimensional electron flux maps. The method, already tested on real events registered by RHESSI, is validated in this paper by means of simulated realistic data.

  16. Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy

    Energy Technology Data Exchange (ETDEWEB)

    O’Shea, Tuathan P., E-mail: tuathan.oshea@icr.ac.uk; Bamber, Jeffrey C.; Harris, Emma J. [Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS foundation Trust, Sutton, London SM2 5PT (United Kingdom)

    2016-01-15

    Purpose: Ultrasound-based motion estimation is an expanding subfield of image-guided radiation therapy. Although ultrasound can detect tissue motion that is a fraction of a millimeter, its accuracy is variable. For controlling linear accelerator tracking and gating, ultrasound motion estimates must remain highly accurate throughout the imaging sequence. This study presents a temporal regularization method for correlation-based template matching which aims to improve the accuracy of motion estimates. Methods: Liver ultrasound sequences (15–23 Hz imaging rate, 2.5–5.5 min length) from ten healthy volunteers under free breathing were used. Anatomical features (blood vessels) in each sequence were manually annotated for comparison with normalized cross-correlation based template matching. Five sequences from a Siemens Acuson™ scanner were used for algorithm development (training set). Results from incremental tracking (IT) were compared with a temporal regularization method, which included a highly specific similarity metric and state observer, known as the α–β filter/similarity threshold (ABST). A further five sequences from an Elekta Clarity™ system were used for validation, without alteration of the tracking algorithm (validation set). Results: Overall, the ABST method produced marked improvements in vessel tracking accuracy. For the training set, the mean and 95th percentile (95%) errors (defined as the difference from manual annotations) were 1.6 and 1.4 mm, respectively (compared to 6.2 and 9.1 mm, respectively, for IT). For each sequence, the use of the state observer leads to improvement in the 95% error. For the validation set, the mean and 95% errors for the ABST method were 0.8 and 1.5 mm, respectively. Conclusions: Ultrasound-based motion estimation has potential to monitor liver translation over long time periods with high accuracy. Nonrigid motion (strain) and the quality of the ultrasound data are likely to have an impact on tracking

  17. Functional dissociation between regularity encoding and deviance detection along the auditory hierarchy.

    Science.gov (United States)

    Aghamolaei, Maryam; Zarnowiec, Katarzyna; Grimm, Sabine; Escera, Carles

    2016-02-01

    Auditory deviance detection based on regularity encoding appears as one of the basic functional properties of the auditory system. It has traditionally been assessed with the mismatch negativity (MMN) long-latency component of the auditory evoked potential (AEP). Recent studies have found earlier correlates of deviance detection based on regularity encoding. They occur in humans in the first 50 ms after sound onset, at the level of the middle-latency response of the AEP, and parallel findings of stimulus-specific adaptation observed in animal studies. However, the functional relationship between these different levels of regularity encoding and deviance detection along the auditory hierarchy has not yet been clarified. Here we addressed this issue by examining deviant-related responses at different levels of the auditory hierarchy to stimulus changes varying in their degree of deviation regarding the spatial location of a repeated standard stimulus. Auditory stimuli were presented randomly from five loudspeakers at azimuthal angles of 0°, 12°, 24°, 36° and 48° during oddball and reversed-oddball conditions. Middle-latency responses and MMN were measured. Our results revealed that middle-latency responses were sensitive to deviance but not the degree of deviation, whereas the MMN amplitude increased as a function of deviance magnitude. These findings indicated that acoustic regularity can be encoded at the level of the middle-latency response but that it takes a higher step in the auditory hierarchy for deviance magnitude to be encoded, thus providing a functional dissociation between regularity encoding and deviance detection along the auditory hierarchy. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  18. Marker Detection in Aerial Images

    KAUST Repository

    Alharbi, Yazeed

    2017-04-09

    The problem that the thesis is trying to solve is the detection of small markers in high-resolution aerial images. Given a high-resolution image, the goal is to return the pixel coordinates corresponding to the center of the marker in the image. The marker has the shape of two triangles sharing a vertex in the middle, and it occupies no more than 0.01% of the image size. An improvement on the Histogram of Oriented Gradients (HOG) is proposed, eliminating the majority of baseline HOG false positives for marker detection. The improvement is guided by the observation that standard HOG description struggles to separate markers from negatives patches containing an X shape. The proposed method alters intensities with the aim of altering gradients. The intensity-dependent gradient alteration leads to more separation between filled and unfilled shapes. The improvement is used in a two-stage algorithm to achieve high recall and high precision in detection of markers in aerial images. In the first stage, two classifiers are used: one to quickly eliminate most of the uninteresting parts of the image, and one to carefully select the marker among the remaining interesting regions. Interesting regions are selected by scanning the image with a fast classifier trained on the HOG features of markers in all rotations and scales. The next classifier is more precise and uses our method to eliminate the majority of the false positives of standard HOG. In the second stage, detected markers are tracked forward and backward in time. Tracking is needed to detect extremely blurred or distorted markers that are missed by the previous stage. The algorithm achieves 94% recall with minimal user guidance. An average of 30 guesses are given per image; the user verifies for each whether it is a marker or not. The brute force approach would return 100,000 guesses per image.

  19. Videokymography. Imaging and quantification of regular and irregular vocal fold vibrations

    NARCIS (Netherlands)

    Schutte, HK; Svec, JG; Sram, F; McCafferty, G; Coman, W; Carroll, R

    1996-01-01

    A newly developed imaging technique makes it possible to observe the vocal fold vibration pattern also under unstable conditions. In contrast to stroboscopy, which strongly relies on the regularity of the vibration under study videokymography enables the study of irregular patterns as well. The

  20. Manifold regularized multitask learning for semi-supervised multilabel image classification.

    Science.gov (United States)

    Luo, Yong; Tao, Dacheng; Geng, Bo; Xu, Chao; Maybank, Stephen J

    2013-02-01

    It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

  1. Image segmentation with a novel regularized composite shape prior based on surrogate study

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu [The Department of Radiation Oncology, University of California, Los Angeles, California 90095 (United States)

    2016-05-15

    Purpose: Incorporating training into image segmentation is a good approach to achieve additional robustness. This work aims to develop an effective strategy to utilize shape prior knowledge, so that the segmentation label evolution can be driven toward the desired global optimum. Methods: In the variational image segmentation framework, a regularization for the composite shape prior is designed to incorporate the geometric relevance of individual training data to the target, which is inferred by an image-based surrogate relevance metric. Specifically, this regularization is imposed on the linear weights of composite shapes and serves as a hyperprior. The overall problem is formulated in a unified optimization setting and a variational block-descent algorithm is derived. Results: The performance of the proposed scheme is assessed in both corpus callosum segmentation from an MR image set and clavicle segmentation based on CT images. The resulted shape composition provides a proper preference for the geometrically relevant training data. A paired Wilcoxon signed rank test demonstrates statistically significant improvement of image segmentation accuracy, when compared to multiatlas label fusion method and three other benchmark active contour schemes. Conclusions: This work has developed a novel composite shape prior regularization, which achieves superior segmentation performance than typical benchmark schemes.

  2. Image segmentation with a novel regularized composite shape prior based on surrogate study

    International Nuclear Information System (INIS)

    Zhao, Tingting; Ruan, Dan

    2016-01-01

    Purpose: Incorporating training into image segmentation is a good approach to achieve additional robustness. This work aims to develop an effective strategy to utilize shape prior knowledge, so that the segmentation label evolution can be driven toward the desired global optimum. Methods: In the variational image segmentation framework, a regularization for the composite shape prior is designed to incorporate the geometric relevance of individual training data to the target, which is inferred by an image-based surrogate relevance metric. Specifically, this regularization is imposed on the linear weights of composite shapes and serves as a hyperprior. The overall problem is formulated in a unified optimization setting and a variational block-descent algorithm is derived. Results: The performance of the proposed scheme is assessed in both corpus callosum segmentation from an MR image set and clavicle segmentation based on CT images. The resulted shape composition provides a proper preference for the geometrically relevant training data. A paired Wilcoxon signed rank test demonstrates statistically significant improvement of image segmentation accuracy, when compared to multiatlas label fusion method and three other benchmark active contour schemes. Conclusions: This work has developed a novel composite shape prior regularization, which achieves superior segmentation performance than typical benchmark schemes.

  3. Crack detection using image processing

    International Nuclear Information System (INIS)

    Moustafa, M.A.A

    2010-01-01

    This thesis contains five main subjects in eight chapters and two appendices. The first subject discus Wiener filter for filtering images. In the second subject, we examine using different methods, as Steepest Descent Algorithm (SDA) and the Wavelet Transformation, to detect and filling the cracks, and it's applications in different areas as Nano technology and Bio-technology. In third subject, we attempt to find 3-D images from 1-D or 2-D images using texture mapping with Open Gl under Visual C ++ language programming. The fourth subject consists of the process of using the image warping methods for finding the depth of 2-D images using affine transformation, bilinear transformation, projective mapping, Mosaic warping and similarity transformation. More details about this subject will be discussed below. The fifth subject, the Bezier curves and surface, will be discussed in details. The methods for creating Bezier curves and surface with unknown distribution, using only control points. At the end of our discussion we will obtain the solid form, using the so called NURBS (Non-Uniform Rational B-Spline); which depends on: the degree of freedom, control points, knots, and an evaluation rule; and is defined as a mathematical representation of 3-D geometry that can accurately describe any shape from a simple 2-D line, circle, arc, or curve to the most complex 3-D organic free-form surface or (solid) which depends on finding the Bezier curve and creating family of curves (surface), then filling in between to obtain the solid form. Another representation for this subject is concerned with building 3D geometric models from physical objects using image-based techniques. The advantage of image techniques is that they require no expensive equipment; we use NURBS, subdivision surface and mesh for finding the depth of any image with one still view or 2D image. The quality of filtering depends on the way the data is incorporated into the model. The data should be treated with

  4. Photoacoustic Imaging in Oxygen Detection

    Directory of Open Access Journals (Sweden)

    Fei Cao

    2017-12-01

    Full Text Available Oxygen level, including blood oxygen saturation (sO2 and tissue oxygen partial pressure (pO2, are crucial physiological parameters in life science. This paper reviews the importance of these two parameters and the detection methods for them, focusing on the application of photoacoustic imaging in this scenario. sO2 is traditionally detected with optical spectra-based methods, and has recently been proven uniquely efficient by using photoacoustic methods. pO2, on the other hand, is typically detected by PET, MRI, or pure optical approaches, yet with limited spatial resolution, imaging frame rate, or penetration depth. Great potential has also been demonstrated by employing photoacoustic imaging to overcome the existing limitations of the aforementioned techniques.

  5. Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning.

    Science.gov (United States)

    Huang, Yawen; Shao, Ling; Frangi, Alejandro F

    2018-03-01

    Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions. In this paper, we propose a weakly coupled and geometry co-regularized joint dictionary learning method to address the problem of cross-modality synthesis while considering the fact that collecting the large amounts of training data is often impractical. Our learning stage requires only a few registered multi-modality image pairs as training data. To employ both paired images and a large set of unpaired data, a cross-modality image matching criterion is proposed. Then, we propose a unified model by integrating such a criterion into the joint dictionary learning and the observed common feature space for associating cross-modality data for the purpose of synthesis. Furthermore, two regularization terms are added to construct robust sparse representations. Our experimental results demonstrate superior performance of the proposed model over state-of-the-art methods.

  6. A Class of Manifold Regularized Multiplicative Update Algorithms for Image Clustering.

    Science.gov (United States)

    Yang, Shangming; Yi, Zhang; He, Xiaofei; Li, Xuelong

    2015-12-01

    Multiplicative update algorithms are important tools for information retrieval, image processing, and pattern recognition. However, when the graph regularization is added to the cost function, different classes of sample data may be mapped to the same subspace, which leads to the increase of data clustering error rate. In this paper, an improved nonnegative matrix factorization (NMF) cost function is introduced. Based on the cost function, a class of novel graph regularized NMF algorithms is developed, which results in a class of extended multiplicative update algorithms with manifold structure regularization. Analysis shows that in the learning, the proposed algorithms can efficiently minimize the rank of the data representation matrix. Theoretical results presented in this paper are confirmed by simulations. For different initializations and data sets, variation curves of cost functions and decomposition data are presented to show the convergence features of the proposed update rules. Basis images, reconstructed images, and clustering results are utilized to present the efficiency of the new algorithms. Last, the clustering accuracies of different algorithms are also investigated, which shows that the proposed algorithms can achieve state-of-the-art performance in applications of image clustering.

  7. Travel time tomography with local image regularization by sparsity constrained dictionary learning

    Science.gov (United States)

    Bianco, M.; Gerstoft, P.

    2017-12-01

    We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

  8. Application of regularization technique in image super-resolution algorithm via sparse representation

    Science.gov (United States)

    Huang, De-tian; Huang, Wei-qin; Huang, Hui; Zheng, Li-xin

    2017-11-01

    To make use of the prior knowledge of the image more effectively and restore more details of the edges and structures, a novel sparse coding objective function is proposed by applying the principle of the non-local similarity and manifold learning on the basis of super-resolution algorithm via sparse representation. Firstly, the non-local similarity regularization term is constructed by using the similar image patches to preserve the edge information. Then, the manifold learning regularization term is constructed by utilizing the locally linear embedding approach to enhance the structural information. The experimental results validate that the proposed algorithm has a significant improvement compared with several super-resolution algorithms in terms of the subjective visual effect and objective evaluation indices.

  9. Detecting regular sound changes in linguistics as events of concerted evolution.

    Science.gov (United States)

    Hruschka, Daniel J; Branford, Simon; Smith, Eric D; Wilkins, Jon; Meade, Andrew; Pagel, Mark; Bhattacharya, Tanmoy

    2015-01-05

    Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family. Linguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular sound change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations. We demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  10. An algorithmic framework for Mumford–Shah regularization of inverse problems in imaging

    International Nuclear Information System (INIS)

    Hohm, Kilian; Weinmann, Andreas; Storath, Martin

    2015-01-01

    The Mumford–Shah model is a very powerful variational approach for edge preserving regularization of image reconstruction processes. However, it is algorithmically challenging because one has to deal with a non-smooth and non-convex functional. In this paper, we propose a new efficient algorithmic framework for Mumford–Shah regularization of inverse problems in imaging. It is based on a splitting into specific subproblems that can be solved exactly. We derive fast solvers for the subproblems which are key for an efficient overall algorithm. Our method neither requires a priori knowledge of the gray or color levels nor of the shape of the discontinuity set. We demonstrate the wide applicability of the method for different modalities. In particular, we consider the reconstruction from Radon data, inpainting, and deconvolution. Our method can be easily adapted to many further imaging setups. The relevant condition is that the proximal mapping of the data fidelity can be evaluated a within reasonable time. In other words, it can be used whenever classical Tikhonov regularization is possible. (paper)

  11. An interior-point method for total variation regularized positron emission tomography image reconstruction

    Science.gov (United States)

    Bai, Bing

    2012-03-01

    There has been a lot of work on total variation (TV) regularized tomographic image reconstruction recently. Many of them use gradient-based optimization algorithms with a differentiable approximation of the TV functional. In this paper we apply TV regularization in Positron Emission Tomography (PET) image reconstruction. We reconstruct the PET image in a Bayesian framework, using Poisson noise model and TV prior functional. The original optimization problem is transformed to an equivalent problem with inequality constraints by adding auxiliary variables. Then we use an interior point method with logarithmic barrier functions to solve the constrained optimization problem. In this method, a series of points approaching the solution from inside the feasible region are found by solving a sequence of subproblems characterized by an increasing positive parameter. We use preconditioned conjugate gradient (PCG) algorithm to solve the subproblems directly. The nonnegativity constraint is enforced by bend line search. The exact expression of the TV functional is used in our calculations. Simulation results show that the algorithm converges fast and the convergence is insensitive to the values of the regularization and reconstruction parameters.

  12. Iterative choice of the optimal regularization parameter in TV image deconvolution

    International Nuclear Information System (INIS)

    Sixou, B; Toma, A; Peyrin, F; Denis, L

    2013-01-01

    We present an iterative method for choosing the optimal regularization parameter for the linear inverse problem of Total Variation image deconvolution. This approach is based on the Morozov discrepancy principle and on an exponential model function for the data term. The Total Variation image deconvolution is performed with the Alternating Direction Method of Multipliers (ADMM). With a smoothed l 2 norm, the differentiability of the value of the Lagrangian at the saddle point can be shown and an approximate model function obtained. The choice of the optimal parameter can be refined with a Newton method. The efficiency of the method is demonstrated on a blurred and noisy bone CT cross section

  13. Sky Detection in Hazy Image.

    Science.gov (United States)

    Song, Yingchao; Luo, Haibo; Ma, Junkai; Hui, Bin; Chang, Zheng

    2018-04-01

    Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions.

  14. Application of image editing software for forensic detection of image ...

    African Journals Online (AJOL)

    Application of image editing software for forensic detection of image. ... The image editing software's available today is apt for creating visually compelling and sophisticated fake images, ... EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT

  15. Assessment of prior image induced nonlocal means regularization for low-dose CT reconstruction: Change in anatomy.

    Science.gov (United States)

    Zhang, Hao; Ma, Jianhua; Wang, Jing; Moore, William; Liang, Zhengrong

    2017-09-01

    Repeated computed tomography (CT) scans are prescribed for some clinical applications such as lung nodule surveillance. Several studies have demonstrated that incorporating a high-quality prior image into the reconstruction of subsequent low-dose CT (LDCT) acquisitions can either improve image quality or reduce data fidelity requirements. Our proposed previous normal-dose image induced nonlocal means (ndiNLM) regularization method for LDCT is an example of such a method. However, one major concern with prior image based methods is that they might produce false information when the prior image and the current LDCT image show different structures (for example, if a lung nodule emerges, grows, shrinks, or disappears over time). This study aims to assess the performance of the ndiNLM regularization method in situations with change in anatomy. We incorporated the ndiNLM regularization into the statistical image reconstruction (SIR) framework for reconstruction of subsequent LDCT images. Because of its patch-based search mechanism, a rough registration between the prior image and the current LDCT image is adequate for the SIR-ndiNLM method. We assessed the performance of the SIR-ndiNLM method in lung nodule surveillance for two different scenarios: (a) the nodule was not found in a baseline exam but appears in a follow-up LDCT scan; (b) the nodule was present in a baseline exam but disappears in a follow-up LDCT scan. We further investigated the effect of nodule size on the performance of the SIR-ndiNLM method. We found that a relatively large search-window (e.g., 33 × 33) should be used for the SIR-ndiNLM method to account for misalignment between the prior image and the current LDCT image, and to ensure that enough similar patches can be found in the prior image. With proper selection of other parameters, experimental results with two patient datasets demonstrated that the SIR-ndiNLM method did not miss true nodules nor introduce false nodules in the lung nodule

  16. Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images.

    Science.gov (United States)

    Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

    2015-05-22

    In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.

  17. From Matched Spatial Filtering towards the Fused Statistical Descriptive Regularization Method for Enhanced Radar Imaging

    Directory of Open Access Journals (Sweden)

    Shkvarko Yuriy

    2006-01-01

    Full Text Available We address a new approach to solve the ill-posed nonlinear inverse problem of high-resolution numerical reconstruction of the spatial spectrum pattern (SSP of the backscattered wavefield sources distributed over the remotely sensed scene. An array or synthesized array radar (SAR that employs digital data signal processing is considered. By exploiting the idea of combining the statistical minimum risk estimation paradigm with numerical descriptive regularization techniques, we address a new fused statistical descriptive regularization (SDR strategy for enhanced radar imaging. Pursuing such an approach, we establish a family of the SDR-related SSP estimators, that encompass a manifold of existing beamforming techniques ranging from traditional matched filter to robust and adaptive spatial filtering, and minimum variance methods.

  18. The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2007-01-01

    This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted...... to observations that show little change, i.e., for which the sum of squared, standardized MAD variates is small, and small weights are assigned to observations for which the sum is large. Like the original MAD method, the iterative extension is invariant to linear (affine) transformations of the original...... an agricultural region in Kenya, and hyperspectral airborne HyMap data from a small rural area in southeastern Germany are given. The latter case demonstrates the need for regularization....

  19. Regularized non-stationary morphological reconstruction algorithm for weak signal detection in microseismic monitoring: methodology

    Science.gov (United States)

    Huang, Weilin; Wang, Runqiu; Chen, Yangkang

    2018-05-01

    Microseismic signal is typically weak compared with the strong background noise. In order to effectively detect the weak signal in microseismic data, we propose a mathematical morphology based approach. We decompose the initial data into several morphological multiscale components. For detection of weak signal, a non-stationary weighting operator is proposed and introduced into the process of reconstruction of data by morphological multiscale components. The non-stationary weighting operator can be obtained by solving an inversion problem. The regularized non-stationary method can be understood as a non-stationary matching filtering method, where the matching filter has the same size as the data to be filtered. In this paper, we provide detailed algorithmic descriptions and analysis. The detailed algorithm framework, parameter selection and computational issue for the regularized non-stationary morphological reconstruction (RNMR) method are presented. We validate the presented method through a comprehensive analysis through different data examples. We first test the proposed technique using a synthetic data set. Then the proposed technique is applied to a field project, where the signals induced from hydraulic fracturing are recorded by 12 three-component geophones in a monitoring well. The result demonstrates that the RNMR can improve the detectability of the weak microseismic signals. Using the processed data, the short-term-average over long-term average picking algorithm and Geiger's method are applied to obtain new locations of microseismic events. In addition, we show that the proposed RNMR method can be used not only in microseismic data but also in reflection seismic data to detect the weak signal. We also discussed the extension of RNMR from 1-D to 2-D or a higher dimensional version.

  20. Buried object detection in GPR images

    Science.gov (United States)

    Paglieroni, David W; Chambers, David H; Bond, Steven W; Beer, W. Reginald

    2014-04-29

    A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

  1. The L0 Regularized Mumford-Shah Model for Bias Correction and Segmentation of Medical Images.

    Science.gov (United States)

    Duan, Yuping; Chang, Huibin; Huang, Weimin; Zhou, Jiayin; Lu, Zhongkang; Wu, Chunlin

    2015-11-01

    We propose a new variant of the Mumford-Shah model for simultaneous bias correction and segmentation of images with intensity inhomogeneity. First, based on the model of images with intensity inhomogeneity, we introduce an L0 gradient regularizer to model the true intensity and a smooth regularizer to model the bias field. In addition, we derive a new data fidelity using the local intensity properties to allow the bias field to be influenced by its neighborhood. Second, we use a two-stage segmentation method, where the fast alternating direction method is implemented in the first stage for the recovery of true intensity and bias field and a simple thresholding is used in the second stage for segmentation. Different from most of the existing methods for simultaneous bias correction and segmentation, we estimate the bias field and true intensity without fixing either the number of the regions or their values in advance. Our method has been validated on medical images of various modalities with intensity inhomogeneity. Compared with the state-of-art approaches and the well-known brain software tools, our model is fast, accurate, and robust with initializations.

  2. A regularized relaxed ordered subset list-mode reconstruction algorithm and its preliminary application to undersampling PET imaging

    International Nuclear Information System (INIS)

    Cao, Xiaoqing; Xie, Qingguo; Xiao, Peng

    2015-01-01

    List mode format is commonly used in modern positron emission tomography (PET) for image reconstruction due to certain special advantages. In this work, we proposed a list mode based regularized relaxed ordered subset (LMROS) algorithm for static PET imaging. LMROS is able to work with regularization terms which can be formulated as twice differentiable convex functions. Such a versatility would make LMROS a convenient and general framework for fulfilling different regularized list mode reconstruction methods. LMROS was applied to two simulated undersampling PET imaging scenarios to verify its effectiveness. Convex quadratic function, total variation constraint, non-local means and dictionary learning based regularization methods were successfully realized for different cases. The results showed that the LMROS algorithm was effective and some regularization methods greatly reduced the distortions and artifacts caused by undersampling. (paper)

  3. Regularization by Functions of Bounded Variation and Applications to Image Enhancement

    International Nuclear Information System (INIS)

    Casas, E.; Kunisch, K.; Pola, C.

    1999-01-01

    Optimization problems regularized by bounded variation seminorms are analyzed. The optimality system is obtained and finite-dimensional approximations of bounded variation function spaces as well as of the optimization problems are studied. It is demonstrated that the choice of the vector norm in the definition of the bounded variation seminorm is of special importance for approximating subspaces consisting of piecewise constant functions. Algorithms based on a primal-dual framework that exploit the structure of these nondifferentiable optimization problems are proposed. Numerical examples are given for denoising of blocky images with very high noise

  4. Dynamic PET image reconstruction integrating temporal regularization associated with respiratory motion correction for applications in oncology

    Science.gov (United States)

    Merlin, Thibaut; Visvikis, Dimitris; Fernandez, Philippe; Lamare, Frédéric

    2018-02-01

    Respiratory motion reduces both the qualitative and quantitative accuracy of PET images in oncology. This impact is more significant for quantitative applications based on kinetic modeling, where dynamic acquisitions are associated with limited statistics due to the necessity of enhanced temporal resolution. The aim of this study is to address these drawbacks, by combining a respiratory motion correction approach with temporal regularization in a unique reconstruction algorithm for dynamic PET imaging. Elastic transformation parameters for the motion correction are estimated from the non-attenuation-corrected PET images. The derived displacement matrices are subsequently used in a list-mode based OSEM reconstruction algorithm integrating a temporal regularization between the 3D dynamic PET frames, based on temporal basis functions. These functions are simultaneously estimated at each iteration, along with their relative coefficients for each image voxel. Quantitative evaluation has been performed using dynamic FDG PET/CT acquisitions of lung cancer patients acquired on a GE DRX system. The performance of the proposed method is compared with that of a standard multi-frame OSEM reconstruction algorithm. The proposed method achieved substantial improvements in terms of noise reduction while accounting for loss of contrast due to respiratory motion. Results on simulated data showed that the proposed 4D algorithms led to bias reduction values up to 40% in both tumor and blood regions for similar standard deviation levels, in comparison with a standard 3D reconstruction. Patlak parameter estimations on reconstructed images with the proposed reconstruction methods resulted in 30% and 40% bias reduction in the tumor and lung region respectively for the Patlak slope, and a 30% bias reduction for the intercept in the tumor region (a similar Patlak intercept was achieved in the lung area). Incorporation of the respiratory motion correction using an elastic model along with a

  5. Bayesian image reconstruction for improving detection performance of muon tomography.

    Science.gov (United States)

    Wang, Guobao; Schultz, Larry J; Qi, Jinyi

    2009-05-01

    Muon tomography is a novel technology that is being developed for detecting high-Z materials in vehicles or cargo containers. Maximum likelihood methods have been developed for reconstructing the scattering density image from muon measurements. However, the instability of maximum likelihood estimation often results in noisy images and low detectability of high-Z targets. In this paper, we propose using regularization to improve the image quality of muon tomography. We formulate the muon reconstruction problem in a Bayesian framework by introducing a prior distribution on scattering density images. An iterative shrinkage algorithm is derived to maximize the log posterior distribution. At each iteration, the algorithm obtains the maximum a posteriori update by shrinking an unregularized maximum likelihood update. Inverse quadratic shrinkage functions are derived for generalized Laplacian priors and inverse cubic shrinkage functions are derived for generalized Gaussian priors. Receiver operating characteristic studies using simulated data demonstrate that the Bayesian reconstruction can greatly improve the detection performance of muon tomography.

  6. Bayesian estimation of regularization and atlas building in diffeomorphic image registration.

    Science.gov (United States)

    Zhang, Miaomiao; Singh, Nikhil; Fletcher, P Thomas

    2013-01-01

    This paper presents a generative Bayesian model for diffeomorphic image registration and atlas building. We develop an atlas estimation procedure that simultaneously estimates the parameters controlling the smoothness of the diffeomorphic transformations. To achieve this, we introduce a Monte Carlo Expectation Maximization algorithm, where the expectation step is approximated via Hamiltonian Monte Carlo sampling on the manifold of diffeomorphisms. An added benefit of this stochastic approach is that it can successfully solve difficult registration problems involving large deformations, where direct geodesic optimization fails. Using synthetic data generated from the forward model with known parameters, we demonstrate the ability of our model to successfully recover the atlas and regularization parameters. We also demonstrate the effectiveness of the proposed method in the atlas estimation problem for 3D brain images.

  7. A Novel Kernel-Based Regularization Technique for PET Image Reconstruction

    Directory of Open Access Journals (Sweden)

    Abdelwahhab Boudjelal

    2017-06-01

    Full Text Available Positron emission tomography (PET is an imaging technique that generates 3D detail of physiological processes at the cellular level. The technique requires a radioactive tracer, which decays and releases a positron that collides with an electron; consequently, annihilation photons are emitted, which can be measured. The purpose of PET is to use the measurement of photons to reconstruct the distribution of radioisotopes in the body. Currently, PET is undergoing a revamp, with advancements in data measurement instruments and the computing methods used to create the images. These computer methods are required to solve the inverse problem of “image reconstruction from projection”. This paper proposes a novel kernel-based regularization technique for maximum-likelihood expectation-maximization ( κ -MLEM to reconstruct the image. Compared to standard MLEM, the proposed algorithm is more robust and is more effective in removing background noise, whilst preserving the edges; this suppresses image artifacts, such as out-of-focus slice blur.

  8. Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

    International Nuclear Information System (INIS)

    Xu, Qiaofeng; Sawatzky, Alex; Anastasio, Mark A.; Yang, Deshan; Tan, Jun

    2016-01-01

    Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated

  9. Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

    Science.gov (United States)

    Xu, Qiaofeng; Yang, Deshan; Tan, Jun; Sawatzky, Alex; Anastasio, Mark A.

    2016-01-01

    Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated

  10. An investigation on the effects of brand equity, trust, image and customer satisfaction on regular insurance firm customers’ loyalty

    Directory of Open Access Journals (Sweden)

    Hamid Reza Saeednia

    2014-03-01

    Full Text Available Brand plays essential role on the success of most organizations and it has been considered as organizational assets. Therefore, brand management is important in today’s structure of organizations. A good brand helps gain new customer and future preferences, which leads to customer retention. Brand loyalty is one of the most important components of brand management. It can raise firm’s market share and it has close relationship with firm’s return of investment and profits. This research tries to answer this question and finds out more about the relationship between customer satisfaction, trust, brand equity, brand image and customer loyalty. The study uses a sample of 384 regular customers who use insurance services in Iran. Using Pearson correlation ratio as well as structural equation modeling, the study has detected positive and meaningful relationship between brand equity and other factors such as customer satisfaction, trust, etc.

  11. Electron paramagnetic resonance image reconstruction with total variation and curvelets regularization

    Science.gov (United States)

    Durand, Sylvain; Frapart, Yves-Michel; Kerebel, Maud

    2017-11-01

    Spatial electron paramagnetic resonance imaging (EPRI) is a recent method to localize and characterize free radicals in vivo or in vitro, leading to applications in material and biomedical sciences. To improve the quality of the reconstruction obtained by EPRI, a variational method is proposed to inverse the image formation model. It is based on a least-square data-fidelity term and the total variation and Besov seminorm for the regularization term. To fully comprehend the Besov seminorm, an implementation using the curvelet transform and the L 1 norm enforcing the sparsity is proposed. It allows our model to reconstruct both image where acquisition information are missing and image with details in textured areas, thus opening possibilities to reduce acquisition times. To implement the minimization problem using the algorithm developed by Chambolle and Pock, a thorough analysis of the direct model is undertaken and the latter is inverted while avoiding the use of filtered backprojection (FBP) and of non-uniform Fourier transform. Numerical experiments are carried out on simulated data, where the proposed model outperforms both visually and quantitatively the classical model using deconvolution and FBP. Improved reconstructions on real data, acquired on an irradiated distal phalanx, were successfully obtained.

  12. Regular Discrete Cosine Transform and its Application to Digital Images Representation

    Directory of Open Access Journals (Sweden)

    Yuri A. Gadzhiev

    2011-11-01

    Full Text Available Discrete cosine transform dct-i, unlike dct-ii, does not concentrate the energy of a transformed vector sufficiently well, so it is not used practically for the purposes of digital image compression. By performing regular normalization of the basic cosine transform matrix, we obtain a discrete cosine transform which has the same cosine basis as dct-i, coincides as dct-i with its own inverse transform, but unlike dct-i, it does not reduce the proper ability of cosine transform to the energy concentration. In this paper we consider briefly the properties of this transform, its possible integer implementation for the case of 8x8-matrix, its applications to the image itself and to the preliminary rgb colour space transformations, further more we investigate some models of quantization, perform an experiment for the estimation of the level of digital images compression and the quality achieved by use of this transform. This experiment shows that the transform can be sufficiently effective for practical use, but the question of its comparative effectiveness with respect to dct-ii remains open.

  13. Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization.

    Science.gov (United States)

    Liao, Shu; Gao, Yaozong; Shi, Yinghuan; Yousuf, Ambereen; Karademir, Ibrahim; Oto, Aytekin; Shen, Dinggang

    2013-01-01

    Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.

  14. Defect detection and sizing in ultrasonic imaging

    International Nuclear Information System (INIS)

    Moysan, J.; Benoist, P.; Chapuis, N.; Magnin, I.

    1991-01-01

    This paper introduces imaging processing developed with the SPARTACUS system in the field of ultrasonic testing. The aim of the imaging processing is to detect and to separate defects echoes from background noise. Image segmentation and particularities of ultrasonic images are the base of studied methods. 4 figs.; 6 refs [fr

  15. Analysis of sea-surface radar signatures by means of wavelet-based edge detection and detection of regularities; Analyse von Radarsignaturen der Meeresoberflaeche mittels auf Wavelets basierender Kantenerkennung und Regularitaetsbestimmung

    Energy Technology Data Exchange (ETDEWEB)

    Wolff, U. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Gewaesserphysik

    2000-07-01

    The derivation and implementation of an algorithm for edge detection in images and for the detection of the Lipschitz regularity in edge points are described. The method is based on the use of the wavelet transform for edge detection at different resolutions. The Lipschitz regularity is a measure that characterizes the edges. The description of the derivation is first performed in one dimension. The approach of Mallat is formulated consistently and proved. Subsequently, the two-dimensional case is addressed, for which the derivation, as well as the description of the algorithm, is analogous. The algorithm is applied to detect edges in nautical radar images using images collected at the island of Sylt. The edges discernible in the images and the Lipschitz values provide information about the position and nature of spatial variations in the depth of the seafloor. By comparing images from different periods of measurement, temporal changes in the bottom structures can be localized at different resolutions and interpreted. The method is suited to the monitoring of coastal areas. It is an inexpensive way to observe long-term changes in the seafloor character. Thus, the results of this technique may be used by the authorities responsible for coastal protection to decide whether measures should be taken or not. (orig.)

  16. Factors influencing detail detectability in radiologic imaging

    International Nuclear Information System (INIS)

    Gurvich, A.M.

    1985-01-01

    The detectability of various details is estimated quantitatively from the essential technical parameters of the imaging system and additional influencing factors including viewing of the image. The analysis implies the formation of the input radiation distribution (contrast formation, influence of kVp). Noise, image contrast (gamma), modulation transfer function and contrast threshold of the observer are of different influence on details of different size. Thus further optimization of imaging systems and their adaption to specific imaging tasks are facilitated

  17. Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR Technique in EIT: A Simulation Study

    Directory of Open Access Journals (Sweden)

    Tushar Kanti Bera

    2011-06-01

    Full Text Available A Block Matrix based Multiple Regularization (BMMR technique is proposed for improving conductivity image quality in EIT. The response matrix (JTJ has been partitioned into several sub-block matrices and the highest eigenvalue of each sub-block matrices has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domain with circular inhomogeneity and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR and modified Levenberg-Marquardt Regularization (LMR methods. It is observed that the BMMR technique reduces the projection error and solution error and improves the conductivity reconstruction in EIT. Result show that the BMMR method also improves the image contrast and inhomogeneity conductivity profile and hence the reconstructed image quality is enhanced. ;doi:10.5617/jeb.170 J Electr Bioimp, vol. 2, pp. 33-47, 2011

  18. Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks

    Directory of Open Access Journals (Sweden)

    Shuihua Wang

    2015-01-01

    Full Text Available Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer’s disease, Parkinson’s diseases, and autism. In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby. We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines.

  19. From regular text to artistic writing and artworks: Fourier statistics of images with low and high aesthetic appeal

    Directory of Open Access Journals (Sweden)

    Tamara eMelmer

    2013-04-01

    Full Text Available The spatial characteristics of letters and their influence on readability and letter identification have been intensely studied during the last decades. There have been few studies, however, on statistical image properties that reflect more global aspects of text, for example properties that may relate to its aesthetic appeal. It has been shown that natural scenes and a large variety of visual artworks possess a scale-invariant Fourier power spectrum that falls off linearly with increasing frequency in log-log plots. We asked whether images of text share this property. As expected, the Fourier spectrum of images of regular typed or handwritten text is highly anisotropic, i.e. the spectral image properties in vertical, horizontal and oblique orientations differ. Moreover, the spatial frequency spectra of text images are not scale invariant in any direction. The decline is shallower in the low-frequency part of the spectrum for text than for aesthetic artworks, whereas, in the high-frequency part, it is steeper. These results indicate that, in general, images of regular text contain less global structure (low spatial frequencies relative to fine detail (high spatial frequencies than images of aesthetics artworks. Moreover, we studied images of text with artistic claim (ornate print and calligraphy and ornamental art. For some measures, these images assume average values intermediate between regular text and aesthetic artworks. Finally, to answer the question of whether the statistical properties measured by us are universal amongst humans or are subject to intercultural differences, we compared images from three different cultural backgrounds (Western, East Asian and Arabic. Results for different categories (regular text, aesthetic writing, ornamental art and fine art were similar across cultures.

  20. From regular text to artistic writing and artworks: Fourier statistics of images with low and high aesthetic appeal

    Science.gov (United States)

    Melmer, Tamara; Amirshahi, Seyed A.; Koch, Michael; Denzler, Joachim; Redies, Christoph

    2013-01-01

    The spatial characteristics of letters and their influence on readability and letter identification have been intensely studied during the last decades. There have been few studies, however, on statistical image properties that reflect more global aspects of text, for example properties that may relate to its aesthetic appeal. It has been shown that natural scenes and a large variety of visual artworks possess a scale-invariant Fourier power spectrum that falls off linearly with increasing frequency in log-log plots. We asked whether images of text share this property. As expected, the Fourier spectrum of images of regular typed or handwritten text is highly anisotropic, i.e., the spectral image properties in vertical, horizontal, and oblique orientations differ. Moreover, the spatial frequency spectra of text images are not scale-invariant in any direction. The decline is shallower in the low-frequency part of the spectrum for text than for aesthetic artworks, whereas, in the high-frequency part, it is steeper. These results indicate that, in general, images of regular text contain less global structure (low spatial frequencies) relative to fine detail (high spatial frequencies) than images of aesthetics artworks. Moreover, we studied images of text with artistic claim (ornate print and calligraphy) and ornamental art. For some measures, these images assume average values intermediate between regular text and aesthetic artworks. Finally, to answer the question of whether the statistical properties measured by us are universal amongst humans or are subject to intercultural differences, we compared images from three different cultural backgrounds (Western, East Asian, and Arabic). Results for different categories (regular text, aesthetic writing, ornamental art, and fine art) were similar across cultures. PMID:23554592

  1. ℓ1/2-norm regularized nonnegative low-rank and sparse affinity graph for remote sensing image segmentation

    Science.gov (United States)

    Tian, Shu; Zhang, Ye; Yan, Yiming; Su, Nan

    2016-10-01

    Segmentation of real-world remote sensing images is a challenge due to the complex texture information with high heterogeneity. Thus, graph-based image segmentation methods have been attracting great attention in the field of remote sensing. However, most of the traditional graph-based approaches fail to capture the intrinsic structure of the feature space and are sensitive to noises. A ℓ-norm regularization-based graph segmentation method is proposed to segment remote sensing images. First, we use the occlusion of the random texture model (ORTM) to extract the local histogram features. Then, a ℓ-norm regularized low-rank and sparse representation (LNNLRS) is implemented to construct a ℓ-regularized nonnegative low-rank and sparse graph (LNNLRS-graph), by the union of feature subspaces. Moreover, the LNNLRS-graph has a high ability to discriminate the manifold intrinsic structure of highly homogeneous texture information. Meanwhile, the LNNLRS representation takes advantage of the low-rank and sparse characteristics to remove the noises and corrupted data. Last, we introduce the LNNLRS-graph into the graph regularization nonnegative matrix factorization to enhance the segmentation accuracy. The experimental results using remote sensing images show that when compared to five state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.

  2. Medical Image Tamper Detection Based on Passive Image Authentication.

    Science.gov (United States)

    Ulutas, Guzin; Ustubioglu, Arda; Ustubioglu, Beste; V Nabiyev, Vasif; Ulutas, Mustafa

    2017-12-01

    Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient's history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission. It decreases visual quality of the medical image and can cause false diagnosis. The proposed method uses passive image authentication mechanism to detect the tampered regions on medical images. Structural texture information is obtained from the medical image by using local binary pattern rotation invariant (LBPROT) to make the keypoint extraction techniques more successful. Keypoints on the texture image are obtained with scale invariant feature transform (SIFT). Tampered regions are detected by the method by matching the keypoints. The method improves the keypoint-based passive image authentication mechanism (they do not detect tampering when the smooth region is used for covering an object) by using LBPROT before keypoint extraction because smooth regions also have texture information. Experimental results show that the method detects tampered regions on the medical images even if the forged image has undergone some attacks (Gaussian blurring/additive white Gaussian noise) or the forged regions are scaled/rotated before pasting.

  3. The patterning of retinal horizontal cells: normalizing the regularity index enhances the detection of genomic linkage

    Directory of Open Access Journals (Sweden)

    Patrick W. Keeley

    2014-10-01

    Full Text Available Retinal neurons are often arranged as non-random distributions called mosaics, as their somata minimize proximity to neighboring cells of the same type. The horizontal cells serve as an example of such a mosaic, but little is known about the developmental mechanisms that underlie their patterning. To identify genes involved in this process, we have used three different spatial statistics to assess the patterning of the horizontal cell mosaic across a panel of genetically distinct recombinant inbred strains. To avoid the confounding effect cell density, which varies two-fold across these different strains, we computed the real/random regularity ratio, expressing the regularity of a mosaic relative to a randomly distributed simulation of similarly sized cells. To test whether this latter statistic better reflects the variation in biological processes that contribute to horizontal cell spacing, we subsequently compared the genetic linkage for each of these two traits, the regularity index and the real/random regularity ratio, each computed from the distribution of nearest neighbor (NN distances and from the Voronoi domain (VD areas. Finally, we compared each of these analyses with another index of patterning, the packing factor. Variation in the regularity indexes, as well as their real/random regularity ratios, and the packing factor, mapped quantitative trait loci (QTL to the distal ends of Chromosomes 1 and 14. For the NN and VD analyses, we found that the degree of linkage was greater when using the real/random regularity ratio rather than the respective regularity index. Using informatic resources, we narrow the list of prospective genes positioned at these two intervals to a small collection of six genes that warrant further investigation to determine their potential role in shaping the patterning of the horizontal cell mosaic.

  4. A Variational Approach to the Denoising of Images Based on Different Variants of the TV-Regularization

    International Nuclear Information System (INIS)

    Bildhauer, Michael; Fuchs, Martin

    2012-01-01

    We discuss several variants of the TV-regularization model used in image recovery. The proposed alternatives are either of nearly linear growth or even of linear growth, but with some weak ellipticity properties. The main feature of the paper is the investigation of the analytic properties of the corresponding solutions.

  5. Application of Fourier-wavelet regularized deconvolution for improving image quality of free space propagation x-ray phase contrast imaging.

    Science.gov (United States)

    Zhou, Zhongxing; Gao, Feng; Zhao, Huijuan; Zhang, Lixin

    2012-11-21

    New x-ray phase contrast imaging techniques without using synchrotron radiation confront a common problem from the negative effects of finite source size and limited spatial resolution. These negative effects swamp the fine phase contrast fringes and make them almost undetectable. In order to alleviate this problem, deconvolution procedures should be applied to the blurred x-ray phase contrast images. In this study, three different deconvolution techniques, including Wiener filtering, Tikhonov regularization and Fourier-wavelet regularized deconvolution (ForWaRD), were applied to the simulated and experimental free space propagation x-ray phase contrast images of simple geometric phantoms. These algorithms were evaluated in terms of phase contrast improvement and signal-to-noise ratio. The results demonstrate that the ForWaRD algorithm is most appropriate for phase contrast image restoration among above-mentioned methods; it can effectively restore the lost information of phase contrast fringes while reduce the amplified noise during Fourier regularization.

  6. Ghost imaging with bucket detection and point detection

    Science.gov (United States)

    Zhang, De-Jian; Yin, Rao; Wang, Tong-Biao; Liao, Qing-Hua; Li, Hong-Guo; Liao, Qinghong; Liu, Jiang-Tao

    2018-04-01

    We experimentally investigate ghost imaging with bucket detection and point detection in which three types of illuminating sources are applied: (a) pseudo-thermal light source; (b) amplitude modulated true thermal light source; (c) amplitude modulated laser source. Experimental results show that the quality of ghost images reconstructed with true thermal light or laser beam is insensitive to the usage of bucket or point detector, however, the quality of ghost images reconstructed with pseudo-thermal light in bucket detector case is better than that in point detector case. Our theoretical analysis shows that the reason for this is due to the first order transverse coherence of the illuminating source.

  7. Detecting violations of temporal regularities in waking and sleeping two-month-old infants

    NARCIS (Netherlands)

    Otte, R.A.; Winkler, I.; Braeken, M.A.K.A.; Stekelenburg, J.J.; van der Stelt, O.; Van den Bergh, B.R.H.

    2013-01-01

    Correctly processing rapid sequences of sounds is essential for developmental milestones, such as language acquisition. We investigated the sensitivity of two-month-old infants to violations of a temporal regularity, by recording event-related brain potentials (ERPs) in an auditory oddball paradigm

  8. Target Detection Using an AOTF Hyperspectral Imager

    Science.gov (United States)

    Cheng, L-J.; Mahoney, J.; Reyes, F.; Suiter, H.

    1994-01-01

    This paper reports results of a recent field experiment using a prototype system to evaluate the acousto-optic tunable filter polarimetric hyperspectral imaging technology for target detection applications.

  9. Ultrasonic Detection Using Correlation Images (Preprint)

    National Research Council Canada - National Science Library

    Cepel, Raini; Ho, K. C; Rinker, Brett A; Palmer, Donald D; Neal, Steven P

    2006-01-01

    .... In this paper, we describe an amplitude independent approach for imaging and detection based on the similarity of adjacent signals, quantified by the correlation coefficient calculated between A-scans...

  10. Efficient operator splitting algorithm for joint sparsity-regularized SPIRiT-based parallel MR imaging reconstruction.

    Science.gov (United States)

    Duan, Jizhong; Liu, Yu; Jing, Peiguang

    2018-02-01

    Self-consistent parallel imaging (SPIRiT) is an auto-calibrating model for the reconstruction of parallel magnetic resonance imaging, which can be formulated as a regularized SPIRiT problem. The Projection Over Convex Sets (POCS) method was used to solve the formulated regularized SPIRiT problem. However, the quality of the reconstructed image still needs to be improved. Though methods such as NonLinear Conjugate Gradients (NLCG) can achieve higher spatial resolution, these methods always demand very complex computation and converge slowly. In this paper, we propose a new algorithm to solve the formulated Cartesian SPIRiT problem with the JTV and JL1 regularization terms. The proposed algorithm uses the operator splitting (OS) technique to decompose the problem into a gradient problem and a denoising problem with two regularization terms, which is solved by our proposed split Bregman based denoising algorithm, and adopts the Barzilai and Borwein method to update step size. Simulation experiments on two in vivo data sets demonstrate that the proposed algorithm is 1.3 times faster than ADMM for datasets with 8 channels. Especially, our proposal is 2 times faster than ADMM for the dataset with 32 channels. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Rhabdomyolysis detected by bone imaging

    International Nuclear Information System (INIS)

    Sanders, J.A.

    1989-01-01

    Rhabdomyolysis involves necrosis of skeletal muscle and may arise from multiple conditions both traumatic and nontraumatic. Bone imaging with Technetium-99m phosphates is a very sensitive indicator of acute muscle damage and may be used to visualize the extent of rhabdomyolysis and its resolution. A case of alcohol-induced rhabdomyolysis is presented

  12. Algorithms for boundary detection in radiographic images

    International Nuclear Information System (INIS)

    Gonzaga, Adilson; Franca, Celso Aparecido de

    1996-01-01

    Edge detecting techniques applied to radiographic digital images are discussed. Some algorithms have been implemented and the results are displayed to enhance boundary or hide details. An algorithm applied in a pre processed image with contrast enhanced is proposed and the results are discussed

  13. Bistatic Forward Scattering Radar Detection and Imaging

    Directory of Open Access Journals (Sweden)

    Hu Cheng

    2016-06-01

    Full Text Available Forward Scattering Radar (FSR is a special type of bistatic radar that can implement image detection, imaging, and identification using the forward scattering signals provided by the moving targets that cross the baseline between the transmitter and receiver. Because the forward scattering effect has a vital significance in increasing the targets’ Radar Cross Section (RCS, FSR is quite advantageous for use in counter stealth detection. This paper first introduces the front line technology used in forward scattering RCS, FSR detection, and Shadow Inverse Synthetic Aperture Radar (SISAR imaging and key problems such as the statistical characteristics of forward scattering clutter, accurate parameter estimation, and multitarget discrimination are then analyzed. Subsequently, the current research progress in FSR detection and SISAR imaging are described in detail, including the theories and experiments. In addition, with reference to the BeiDou navigation satellite, the results of forward scattering experiments in civil aircraft detection are shown. Finally, this paper considers future developments in FSR target detection and imaging and presents a new, promising technique for stealth target detection.

  14. Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy

    Science.gov (United States)

    Nabizadeh, Nooshin; John, Nigel

    2014-03-01

    Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.

  15. Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

    Science.gov (United States)

    Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

    2015-01-01

    Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

  16. SU-E-I-93: Improved Imaging Quality for Multislice Helical CT Via Sparsity Regularized Iterative Image Reconstruction Method Based On Tensor Framelet

    International Nuclear Information System (INIS)

    Nam, H; Guo, M; Lee, K; Li, R; Xing, L; Gao, H

    2014-01-01

    Purpose: Inspired by compressive sensing, sparsity regularized iterative reconstruction method has been extensively studied. However, its utility pertinent to multislice helical 4D CT for radiotherapy with respect to imaging quality, dose, and time has not been thoroughly addressed. As the beginning of such an investigation, this work carries out the initial comparison of reconstructed imaging quality between sparsity regularized iterative method and analytic method through static phantom studies using a state-of-art 128-channel multi-slice Siemens helical CT scanner. Methods: In our iterative method, tensor framelet (TF) is chosen as the regularization method for its superior performance from total variation regularization in terms of reduced piecewise-constant artifacts and improved imaging quality that has been demonstrated in our prior work. On the other hand, X-ray transforms and its adjoints are computed on-the-fly through GPU implementation using our previous developed fast parallel algorithms with O(1) complexity per computing thread. For comparison, both FDK (approximate analytic method) and Katsevich algorithm (exact analytic method) are used for multislice helical CT image reconstruction. Results: The phantom experimental data with different imaging doses were acquired using a state-of-art 128-channel multi-slice Siemens helical CT scanner. The reconstructed image quality was compared between TF-based iterative method, FDK and Katsevich algorithm with the quantitative analysis for characterizing signal-to-noise ratio, image contrast, and spatial resolution of high-contrast and low-contrast objects. Conclusion: The experimental results suggest that our tensor framelet regularized iterative reconstruction algorithm improves the helical CT imaging quality from FDK and Katsevich algorithm for static experimental phantom studies that have been performed

  17. Detection of latent prints by Raman imaging

    Science.gov (United States)

    Lewis, Linda Anne [Andersonville, TN; Connatser, Raynella Magdalene [Knoxville, TN; Lewis, Sr., Samuel Arthur

    2011-01-11

    The present invention relates to a method for detecting a print on a surface, the method comprising: (a) contacting the print with a Raman surface-enhancing agent to produce a Raman-enhanced print; and (b) detecting the Raman-enhanced print using a Raman spectroscopic method. The invention is particularly directed to the imaging of latent fingerprints.

  18. Color image fusion for concealed weapon detection

    NARCIS (Netherlands)

    Toet, A.

    2003-01-01

    Recent advances in passive and active imaging sensor technology offer the potential to detect weapons that are concealed underneath a person's clothing or carried along in bags. Although the concealed weapons can sometimes easily be detected, it can be difficult to perceive their context, due to the

  19. Image denoising based on noise detection

    Science.gov (United States)

    Jiang, Yuanxiang; Yuan, Rui; Sun, Yuqiu; Tian, Jinwen

    2018-03-01

    Because of the noise points in the images, any operation of denoising would change the original information of non-noise pixel. A noise detection algorithm based on fractional calculus was proposed to denoise in this paper. Convolution of the image was made to gain direction gradient masks firstly. Then, the mean gray was calculated to obtain the gradient detection maps. Logical product was made to acquire noise position image next. Comparisons in the visual effect and evaluation parameters after processing, the results of experiment showed that the denoising algorithms based on noise were better than that of traditional methods in both subjective and objective aspects.

  20. 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.

  1. Early skin tumor detection from microscopic images through image processing

    International Nuclear Information System (INIS)

    Siddiqi, A.A.; Narejo, G.B.; Khan, A.M.

    2017-01-01

    The research is done to provide appropriate detection technique for skin tumor detection. The work is done by using the image processing toolbox of MATLAB. Skin tumors are unwanted skin growth with different causes and varying extent of malignant cells. It is a syndrome in which skin cells mislay the ability to divide and grow normally. Early detection of tumor is the most important factor affecting the endurance of a patient. Studying the pattern of the skin cells is the fundamental problem in medical image analysis. The study of skin tumor has been of great interest to the researchers. DIP (Digital Image Processing) allows the use of much more complex algorithms for image processing, and hence, can offer both more sophisticated performance at simple task, and the implementation of methods which would be impossibly by analog means. It allows much wider range of algorithms to be applied to the input data and can avoid problems such as build up of noise and signal distortion during processing. The study shows that few works has been done on cellular scale for the images of skin. This research allows few checks for the early detection of skin tumor using microscopic images after testing and observing various algorithms. After analytical evaluation the result has been observed that the proposed checks are time efficient techniques and appropriate for the tumor detection. The algorithm applied provides promising results in lesser time with accuracy. The GUI (Graphical User Interface) that is generated for the algorithm makes the system user friendly. (author)

  2. Image Denoising And Segmentation Approchto Detect Tumor From BRAINMRI Images

    Directory of Open Access Journals (Sweden)

    Shanta Rangaswamy

    2018-04-01

    Full Text Available The detection of the Brain Tumor is a challenging problem, due to the structure of the Tumor cells in the brain. This project presents a systematic method that enhances the detection of brain tumor cells and to analyze functional structures by training and classification of the samples in SVM and tumor cell segmentation of the sample using DWT algorithm. From the input MRI Images collected, first noise is removed from MRI images by applying wiener filtering technique. In image enhancement phase, all the color components of MRI Images will be converted into gray scale image and make the edges clear in the image to get better identification and improvised quality of the image. In the segmentation phase, DWT on MRI Image to segment the grey-scale image is performed. During the post-processing, classification of tumor is performed by using SVM classifier. Wiener Filter, DWT, SVM Segmentation strategies were used to find and group the tumor position in the MRI filtered picture respectively. An essential perception in this work is that multi arrange approach utilizes various leveled classification strategy which supports execution altogether. This technique diminishes the computational complexity quality in time and memory. This classification strategy works accurately on all images and have achieved the accuracy of 93%.

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

    Directory of Open Access Journals (Sweden)

    Ahmed Elazab

    2015-01-01

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

  4. Smart CMOS image sensor for lightning detection and imaging.

    Science.gov (United States)

    Rolando, Sébastien; Goiffon, Vincent; Magnan, Pierre; Corbière, Franck; Molina, Romain; Tulet, Michel; Bréart-de-Boisanger, Michel; Saint-Pé, Olivier; Guiry, Saïprasad; Larnaudie, Franck; Leone, Bruno; Perez-Cuevas, Leticia; Zayer, Igor

    2013-03-01

    We present a CMOS image sensor dedicated to lightning detection and imaging. The detector has been designed to evaluate the potentiality of an on-chip lightning detection solution based on a smart sensor. This evaluation is performed in the frame of the predevelopment phase of the lightning detector that will be implemented in the Meteosat Third Generation Imager satellite for the European Space Agency. The lightning detection process is performed by a smart detector combining an in-pixel frame-to-frame difference comparison with an adjustable threshold and on-chip digital processing allowing an efficient localization of a faint lightning pulse on the entire large format array at a frequency of 1 kHz. A CMOS prototype sensor with a 256×256 pixel array and a 60 μm pixel pitch has been fabricated using a 0.35 μm 2P 5M technology and tested to validate the selected detection approach.

  5. Automatic detection of blurred images in UAV image sets

    Science.gov (United States)

    Sieberth, Till; Wackrow, Rene; Chandler, Jim H.

    2016-12-01

    Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. Images with known blur are processed digitally to determine a quantifiable measure of image blur. The algorithm is required to process UAV images fast and reliably to relieve the operator from detecting blurred images manually. The newly developed method makes it possible to detect blur caused by linear camera displacement and is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of

  6. Comic image understanding based on polygon detection

    Science.gov (United States)

    Li, Luyuan; Wang, Yongtao; Tang, Zhi; Liu, Dong

    2013-01-01

    Comic image understanding aims to automatically decompose scanned comic page images into storyboards and then identify the reading order of them, which is the key technique to produce digital comic documents that are suitable for reading on mobile devices. In this paper, we propose a novel comic image understanding method based on polygon detection. First, we segment a comic page images into storyboards by finding the polygonal enclosing box of each storyboard. Then, each storyboard can be represented by a polygon, and the reading order of them is determined by analyzing the relative geometric relationship between each pair of polygons. The proposed method is tested on 2000 comic images from ten printed comic series, and the experimental results demonstrate that it works well on different types of comic images.

  7. Automatic crop row detection from UAV images

    DEFF Research Database (Denmark)

    Midtiby, Henrik; Rasmussen, Jesper

    are considered weeds. We have used a Sugar beet field as a case for evaluating the proposed crop detection method. The suggested image processing consists of: 1) locating vegetation regions in the image by thresholding the excess green image derived from the orig- inal image, 2) calculate the Hough transform......Images from Unmanned Aerial Vehicles can provide information about the weed distribution in fields. A direct way is to quantify the amount of vegetation present in different areas of the field. The limitation of this approach is that it includes both crops and weeds in the reported num- bers. To get...... of the segmented image 3) determine the dominating crop row direction by analysing output from the Hough transform and 4) use the found crop row direction to locate crop rows....

  8. Experiment Design Regularization-Based Hardware/Software Codesign for Real-Time Enhanced Imaging in Uncertain Remote Sensing Environment

    Directory of Open Access Journals (Sweden)

    Castillo Atoche A

    2010-01-01

    Full Text Available A new aggregated Hardware/Software (HW/SW codesign approach to optimization of the digital signal processing techniques for enhanced imaging with real-world uncertain remote sensing (RS data based on the concept of descriptive experiment design regularization (DEDR is addressed. We consider the applications of the developed approach to typical single-look synthetic aperture radar (SAR imaging systems operating in the real-world uncertain RS scenarios. The software design is aimed at the algorithmic-level decrease of the computational load of the large-scale SAR image enhancement tasks. The innovative algorithmic idea is to incorporate into the DEDR-optimized fixed-point iterative reconstruction/enhancement procedure the convex convergence enforcement regularization via constructing the proper multilevel projections onto convex sets (POCS in the solution domain. The hardware design is performed via systolic array computing based on a Xilinx Field Programmable Gate Array (FPGA XC4VSX35-10ff668 and is aimed at implementing the unified DEDR-POCS image enhancement/reconstruction procedures in a computationally efficient multi-level parallel fashion that meets the (near real-time image processing requirements. Finally, we comment on the simulation results indicative of the significantly increased performance efficiency both in resolution enhancement and in computational complexity reduction metrics gained with the proposed aggregated HW/SW co-design approach.

  9. MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering

    Energy Technology Data Exchange (ETDEWEB)

    Adluru, Ganesh, E-mail: gadluru@gmail.com; Anderson, Jeffrey [UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108 (United States); Gur, Yaniv [IBM Almaden Research Center, San Jose, California 95120 (United States); Chen, Liyong; Feinberg, David [Advanced MRI Technologies, Sebastpool, California, 95472 (United States); DiBella, Edward V. R. [UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108 and Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112 (United States)

    2015-08-15

    Purpose: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. Methods: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. Results: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. Conclusions: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.

  10. MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering

    International Nuclear Information System (INIS)

    Adluru, Ganesh; Anderson, Jeffrey; Gur, Yaniv; Chen, Liyong; Feinberg, David; DiBella, Edward V. R.

    2015-01-01

    Purpose: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. Methods: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. Results: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. Conclusions: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions

  11. Blind Methods for Detecting Image Fakery

    Czech Academy of Sciences Publication Activity Database

    Mahdian, Babak; Saic, Stanislav

    2010-01-01

    Roč. 25, č. 4 (2010), s. 18-24 ISSN 0885-8985 R&D Projects: GA ČR GA102/08/0470 Institutional research plan: CEZ:AV0Z10750506 Keywords : Image forensics * Image Fakery * Forgery detection * Authentication Subject RIV: BD - Theory of Information Impact factor: 0.179, year: 2010 http://library.utia.cas.cz/separaty/2010/ZOI/saic-0343316.pdf

  12. Employing image processing techniques for cancer detection using microarray images.

    Science.gov (United States)

    Dehghan Khalilabad, Nastaran; Hassanpour, Hamid

    2017-02-01

    Microarray technology is a powerful genomic tool for simultaneously studying and analyzing the behavior of thousands of genes. The analysis of images obtained from this technology plays a critical role in the detection and treatment of diseases. The aim of the current study is to develop an automated system for analyzing data from microarray images in order to detect cancerous cases. The proposed system consists of three main phases, namely image processing, data mining, and the detection of the disease. The image processing phase performs operations such as refining image rotation, gridding (locating genes) and extracting raw data from images the data mining includes normalizing the extracted data and selecting the more effective genes. Finally, via the extracted data, cancerous cell is recognized. To evaluate the performance of the proposed system, microarray database is employed which includes Breast cancer, Myeloid Leukemia and Lymphomas from the Stanford Microarray Database. The results indicate that the proposed system is able to identify the type of cancer from the data set with an accuracy of 95.45%, 94.11%, and 100%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Mean magnetic susceptibility regularized susceptibility tensor imaging (MMSR-STI) for estimating orientations of white matter fibers in human brain.

    Science.gov (United States)

    Li, Xu; van Zijl, Peter C M

    2014-09-01

    An increasing number of studies show that magnetic susceptibility in white matter fibers is anisotropic and may be described by a tensor. However, the limited head rotation possible for in vivo human studies leads to an ill-conditioned inverse problem in susceptibility tensor imaging (STI). Here we suggest the combined use of limiting the susceptibility anisotropy to white matter and imposing morphology constraints on the mean magnetic susceptibility (MMS) for regularizing the STI inverse problem. The proposed MMS regularized STI (MMSR-STI) method was tested using computer simulations and in vivo human data collected at 3T. The fiber orientation estimated from both the STI and MMSR-STI methods was compared to that from diffusion tensor imaging (DTI). Computer simulations show that the MMSR-STI method provides a more accurate estimation of the susceptibility tensor than the conventional STI approach. Similarly, in vivo data show that use of the MMSR-STI method leads to a smaller difference between the fiber orientation estimated from STI and DTI for most selected white matter fibers. The proposed regularization strategy for STI can improve estimation of the susceptibility tensor in white matter. © 2014 Wiley Periodicals, Inc.

  14. Hemorrhage detection in MRI brain images using images features

    Science.gov (United States)

    Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

    2013-11-01

    The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

  15. Regularization Techniques for ECG Imaging during Atrial Fibrillation: a Computational Study

    Directory of Open Access Journals (Sweden)

    Carlos Figuera

    2016-10-01

    Full Text Available The inverse problem of electrocardiography is usually analyzed during stationary rhythms. However, the performance of the regularization methods under fibrillatory conditions has not been fully studied. In this work, we assessed different regularization techniques during atrial fibrillation (AF for estimating four target parameters, namely, epicardial potentials, dominant frequency (DF, phase maps, and singularity point (SP location. We use a realistic mathematical model of atria and torso anatomy with three different electrical activity patterns (i.e. sinus rhythm, simple AF and complex AF. Body surface potentials (BSP were simulated using Boundary Element Method and corrupted with white Gaussian noise of different powers. Noisy BSPs were used to obtain the epicardial potentials on the atrial surface, using fourteen different regularization techniques. DF, phase maps and SP location were computed from estimated epicardial potentials. Inverse solutions were evaluated using a set of performance metrics adapted to each clinical target. For the case of SP location, an assessment methodology based on the spatial mass function of the SP location and four spatial error metrics was proposed. The role of the regularization parameter for Tikhonov-based methods, and the effect of noise level and imperfections in the knowledge of the transfer matrix were also addressed. Results showed that the Bayes maximum-a-posteriori method clearly outperforms the rest of the techniques but requires a priori information about the epicardial potentials. Among the purely non-invasive techniques, Tikhonov-based methods performed as well as more complex techniques in realistic fibrillatory conditions, with a slight gain between 0.02 and 0.2 in terms of the correlation coefficient. Also, the use of a constant regularization parameter may be advisable since the performance was similar to that obtained with a variable parameter (indeed there was no difference for the zero

  16. Label-Informed Non-negative Matrix Factorization with Manifold Regularization for Discriminative Subnetwork Detection.

    Science.gov (United States)

    Watanabe, Takanori; Tunc, Birkan; Parker, Drew; Kim, Junghoon; Verma, Ragini

    2016-10-01

    In this paper, we present a novel method for obtaining a low dimensional representation of a complex brain network that: (1) can be interpreted in a neurobiologically meaningful way, (2) emphasizes group differences by accounting for label information, and (3) captures the variation in disease subtypes/severity by respecting the intrinsic manifold structure underlying the data. Our method is a supervised variant of non-negative matrix factorization (NMF), and achieves dimensionality reduction by extracting an orthogonal set of subnetworks that are interpretable, reconstructive of the original data, and also discriminative at the group level. In addition, the method includes a manifold regularizer that encourages the low dimensional representations to be smooth with respect to the intrinsic geometry of the data, allowing subjects with similar disease-severity to share similar network representations. While the method is generalizable to other types of non-negative network data, in this work we have used structural connectomes (SCs) derived from diffusion data to identify the cortical/subcortical connections that have been disrupted in abnormal neurological state. Experiments on a traumatic brain injury (TBI) dataset demonstrate that our method can identify subnetworks that can reliably classify TBI from controls and also reveal insightful connectivity patterns that may be indicative of a biomarker.

  17. Ultrasound Imaging Methods for Breast Cancer Detection

    NARCIS (Netherlands)

    Ozmen, N.

    2014-01-01

    The main focus of this thesis is on modeling acoustic wavefield propagation and implementing imaging algorithms for breast cancer detection using ultrasound. As a starting point, we use an integral equation formulation, which can be used to solve both the forward and inverse problems. This thesis

  18. Regularized Pre-image Estimation for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed...

  19. Input Space Regularization Stabilizes Pre-images for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2009-01-01

    Solution of the pre-image problem is key to efficient nonlinear de-noising using kernel Principal Component Analysis. Pre-image estimation is inherently ill-posed for typical kernels used in applications and consequently the most widely used estimation schemes lack stability. For de...

  20. Handbook of particle detection and imaging

    CERN Document Server

    Buvat, Irène

    2012-01-01

    The handbook centers on detection techniques in the field of particle physics, medical imaging and related subjects. It is structured into three parts. The first one is dealing with basic ideas of particle detectors, followed by applications of these devices in high energy physics and other fields. In the last part the large field of medical imaging using similar detection techniques is described. The different chapters of the book are written by world experts in their field. Clear instructions on the detection techniques and principles in terms of relevant operation parameters for scientists and graduate students are given.Detailed tables and diagrams will make this a very useful handbook for the application of these techniques in many different fields like physics, medicine, biology and other areas of natural science.

  1. Scalable Track Detection in SAR CCD Images

    Energy Technology Data Exchange (ETDEWEB)

    Chow, James G [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Quach, Tu-Thach [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-03-01

    Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988, up fr om 0.907 obtained by the current state-of-the-art method.

  2. Handbook of particle detection and imaging

    Energy Technology Data Exchange (ETDEWEB)

    Grupen, Claus [Siegen Univ. (Germany). Fachbereich 7 - Physik; Buvat, Irene (eds.) [Paris 7 et 11 Univ., Orsay (France). IMNC-UMR 8165 CNRS

    2012-07-01

    The handbook centers on detection techniques in the field of particle physics, medical imaging and related subjects. It is structured into three parts. The first one is dealing with basic ideas of particle detectors, followed by applications of these devices in high energy physics and other fields. In the last part the large field of medical imaging using similar detection techniques is described. The different chapters of the book are written by world experts in their field. Clear instructions on the detection techniques and principles in terms of relevant operation parameters for scientists and graduate students are given. Detailed tables and diagrams will make this a very useful handbook for the application of these techniques in many different fields like physics, medicine, biology and other areas of natural science. (orig.)

  3. Pornographic image detection with Gabor filters

    Science.gov (United States)

    Durrell, Kevan; Murray, Daniel J. C.

    2002-04-01

    across on the Internet. Identifying this type of pornographic pictures of low image quality poses particular challenges for any detection software. This paper will address some of the challenges and hurdles we faced in designing and carrying out our experiments. The paper will also discuss the main results of our experiments, as well as some confounds that, at present, limit the effectiveness of our approach to identifying pornographic images, and some directions that may be taken in future research.

  4. Detection and recognition of road markings in panoramic images

    Science.gov (United States)

    Li, Cheng; Creusen, Ivo; Hazelhoff, Lykele; de With, Peter H. N.

    2015-03-01

    Detection of road lane markings is attractive for practical applications such as advanced driver assistance systems and road maintenance. This paper proposes a system to detect and recognize road lane markings in panoramic images. The system can be divided into four stages. First, an inverse perspective mapping is applied to the original panoramic image to generate a top-view road view, in which the potential road markings are segmented based on their intensity difference compared to the surrounding pixels. Second, a feature vector of each potential road marking segment is extracted by calculating the Euclidean distance between the center and the boundary at regular angular steps. Third, the shape of each segment is classified using a Support Vector Machine (SVM). Finally, by modeling the lane markings, previous falsely detected segments can be rejected based on their orientation and position relative to the lane markings. Our experiments show that the system is promising and is capable of recognizing 93%, 95% and 91% of striped line segments, blocks and arrows respectively, as well as 94% of the lane markings.

  5. Imaging inflammatory acne: lesion detection and tracking

    Science.gov (United States)

    Cula, Gabriela O.; Bargo, Paulo R.; Kollias, Nikiforos

    2010-02-01

    It is known that effectiveness of acne treatment increases when the lesions are detected earlier, before they could progress into mature wound-like lesions, which lead to scarring and discoloration. However, little is known about the evolution of acne from early signs until after the lesion heals. In this work we computationally characterize the evolution of inflammatory acne lesions, based on analyzing cross-polarized images that document acne-prone facial skin over time. Taking skin images over time, and being able to follow skin features in these images present serious challenges, due to change in the appearance of skin, difficulty in repositioning the subject, involuntary movement such as breathing. A computational technique for automatic detection of lesions by separating the background normal skin from the acne lesions, based on fitting Gaussian distributions to the intensity histograms, is presented. In order to track and quantify the evolution of lesions, in terms of the degree of progress or regress, we designed a study to capture facial skin images from an acne-prone young individual, followed over the course of 3 different time points. Based on the behavior of the lesions between two consecutive time points, the automatically detected lesions are classified in four categories: new lesions, resolved lesions (i.e. lesions that disappear completely), lesions that are progressing, and lesions that are regressing (i.e. lesions in the process of healing). The classification our methods achieve correlates well with visual inspection of a trained human grader.

  6. Detection of rheumatoid arthritis using infrared imaging

    Science.gov (United States)

    Frize, Monique; Adéa, Cynthia; Payeur, Pierre; Di Primio, Gina; Karsh, Jacob; Ogungbemile, Abiola

    2011-03-01

    Rheumatoid arthritis (RA) is an inflammatory disease causing pain, swelling, stiffness, and loss of function in joints; it is difficult to diagnose in early stages. An early diagnosis and treatment can delay the onset of severe disability. Infrared (IR) imaging offers a potential approach to detect changes in degree of inflammation. In 18 normal subjects and 13 patients diagnosed with Rheumatoid Arthritis (RA), thermal images were collected from joints of hands, wrists, palms, and knees. Regions of interest (ROIs) were manually selected from all subjects and all parts imaged. For each subject, values were calculated from the temperature measurements: Mode/Max, Median/Max, Min/Max, Variance, Max-Min, (Mode-Mean), and Mean/Min. The data sets did not have a normal distribution, therefore non parametric tests (Kruskal-Wallis and Ranksum) were applied to assess if the data from the control group and the patient group were significantly different. Results indicate that: (i) thermal images can be detected on patients with the disease; (ii) the best joints to image are the metacarpophalangeal joints of the 2nd and 3rd fingers and the knees; the difference between the two groups was significant at the 0.05 level; (iii) the best calculations to differentiate between normal subjects and patients with RA are the Mode/Max, Variance, and Max-Min. We concluded that it is possible to reliably detect RA in patients using IR imaging. Future work will include a prospective study of normal subjects and patients that will compare IR results with Magnetic Resonance (MR) analysis.

  7. PCB Fault Detection Using Image Processing

    Science.gov (United States)

    Nayak, Jithendra P. R.; Anitha, K.; Parameshachari, B. D., Dr.; Banu, Reshma, Dr.; Rashmi, P.

    2017-08-01

    The importance of the Printed Circuit Board inspection process has been magnified by requirements of the modern manufacturing environment where delivery of 100% defect free PCBs is the expectation. To meet such expectations, identifying various defects and their types becomes the first step. In this PCB inspection system the inspection algorithm mainly focuses on the defect detection using the natural images. Many practical issues like tilt of the images, bad light conditions, height at which images are taken etc. are to be considered to ensure good quality of the image which can then be used for defect detection. Printed circuit board (PCB) fabrication is a multidisciplinary process, and etching is the most critical part in the PCB manufacturing process. The main objective of Etching process is to remove the exposed unwanted copper other than the required circuit pattern. In order to minimize scrap caused by the wrongly etched PCB panel, inspection has to be done in early stage. However, all of the inspections are done after the etching process where any defective PCB found is no longer useful and is simply thrown away. Since etching process costs 0% of the entire PCB fabrication, it is uneconomical to simply discard the defective PCBs. In this paper a method to identify the defects in natural PCB images and associated practical issues are addressed using Software tools and some of the major types of single layer PCB defects are Pattern Cut, Pin hole, Pattern Short, Nick etc., Therefore the defects should be identified before the etching process so that the PCB would be reprocessed. In the present approach expected to improve the efficiency of the system in detecting the defects even in low quality images

  8. Stamp Detection in Color Document Images

    DEFF Research Database (Denmark)

    Micenkova, Barbora; van Beusekom, Joost

    2011-01-01

    , moreover, it can be imprinted with a variable quality and rotation. Previous methods were restricted to detection of stamps of particular shapes or colors. The method presented in the paper includes segmentation of the image by color clustering and subsequent classification of candidate solutions...... by geometrical and color-related features. The approach allows for differentiation of stamps from other color objects in the document such as logos or texts. For the purpose of evaluation, a data set of 400 document images has been collected, annotated and made public. With the proposed method, recall of 83...

  9. Intelligent Image Segment for Material Composition Detection

    Directory of Open Access Journals (Sweden)

    Liang Xiaodan

    2017-01-01

    Full Text Available In the process of material composition detection, the image analysis is an inevitable problem. Multilevel thresholding based OTSU method is one of the most popular image segmentation techniques. How, with the increase of the number of thresholds, the computing time increases exponentially. To overcome this problem, this paper proposed an artificial bee colony algorithm with a two-level topology. This improved artificial bee colony algorithm can quickly find out the suitable thresholds and nearly no trap into local optimal. The test results confirm it good performance.

  10. Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging Based on a Riemannian Manifold Approach.

    Science.gov (United States)

    Baust, Maximilian; Weinmann, Andreas; Wieczorek, Matthias; Lasser, Tobias; Storath, Martin; Navab, Nassir

    2016-08-01

    In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorporates the measured DWIs (using an inverse problem setup) and which measures the nearness of neighboring tensors in the manifold. To approach this functional, we propose generalized forward- backward splitting algorithms which combine an explicit and several implicit steps performed on a decomposition of the functional. We validate the performance of the derived algorithms on synthetic and real DTI data. In particular, we work on real 3D data. To our knowledge, the present paper describes the first approach to TV regularization in a combined manifold and inverse problem setup.

  11. Automated image based prominent nucleoli detection.

    Science.gov (United States)

    Yap, Choon K; Kalaw, Emarene M; Singh, Malay; Chong, Kian T; Giron, Danilo M; Huang, Chao-Hui; Cheng, Li; Law, Yan N; Lee, Hwee Kuan

    2015-01-01

    Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

  12. Automated image based prominent nucleoli detection

    Directory of Open Access Journals (Sweden)

    Choon K Yap

    2015-01-01

    Full Text Available Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

  13. Imaging, object detection, and change detection with a polarized multistatic GPR array

    Science.gov (United States)

    Beer, N. Reginald; Paglieroni, David W.

    2015-07-21

    A polarized detection system performs imaging, object detection, and change detection factoring in the orientation of an object relative to the orientation of transceivers. The polarized detection system may operate on one of several modes of operation based on whether the imaging, object detection, or change detection is performed separately for each transceiver orientation. In combined change mode, the polarized detection system performs imaging, object detection, and change detection separately for each transceiver orientation, and then combines changes across polarizations. In combined object mode, the polarized detection system performs imaging and object detection separately for each transceiver orientation, and then combines objects across polarizations and performs change detection on the result. In combined image mode, the polarized detection system performs imaging separately for each transceiver orientation, and then combines images across polarizations and performs object detection followed by change detection on the result.

  14. Spectral imaging for contamination detection in food

    DEFF Research Database (Denmark)

    Carstensen, Jens Michael

    application of the technique is finding anomalies I supposedly homogeneous matter or homogeneous mixtures. This application occurs frequently in the food industry when different types of contamination are to be detected. Contaminants could be e.g. foreign matter, process-induced toxins, and microbiological...... spoilage. Many of these contaminants may be detected in the wavelength range visible to normal silicium-based camera sensors i.e. 350-1050 nm with proper care during sample preparation, sample presentation, image acquisition and analysis. This presentation will give an introduction to the techniques behind...

  15. Optical tomographic imaging for breast cancer detection

    Science.gov (United States)

    Cong, Wenxiang; Intes, Xavier; Wang, Ge

    2017-09-01

    Diffuse optical breast imaging utilizes near-infrared (NIR) light propagation through tissues to assess the optical properties of tissues for the identification of abnormal tissue. This optical imaging approach is sensitive, cost-effective, and does not involve any ionizing radiation. However, the image reconstruction of diffuse optical tomography (DOT) is a nonlinear inverse problem and suffers from severe illposedness due to data noise, NIR light scattering, and measurement incompleteness. An image reconstruction method is proposed for the detection of breast cancer. This method splits the image reconstruction problem into the localization of abnormal tissues and quantification of absorption variations. The localization of abnormal tissues is performed based on a well-posed optimization model, which can be solved via a differential evolution optimization method to achieve a stable reconstruction. The quantification of abnormal absorption is then determined in localized regions of relatively small extents, in which a potential tumor might be. Consequently, the number of unknown absorption variables can be greatly reduced to overcome the underdetermined nature of DOT. Numerical simulation experiments are performed to verify merits of the proposed method, and the results show that the image reconstruction method is stable and accurate for the identification of abnormal tissues, and robust against the measurement noise of data.

  16. TU-CD-BRA-12: Coupling PET Image Restoration and Segmentation Using Variational Method with Multiple Regularizations

    Energy Technology Data Exchange (ETDEWEB)

    Li, L; Tan, S [Huazhong University of Science and Technology, Wuhan, Hubei (China); Lu, W [University of Maryland School of Medicine, Baltimore, MD (United States)

    2015-06-15

    Purpose: To propose a new variational method which couples image restoration with tumor segmentation for PET images using multiple regularizations. Methods: Partial volume effect (PVE) is a major degrading factor impacting tumor segmentation accuracy in PET imaging. The existing segmentation methods usually need to take prior calibrations to compensate PVE and they are highly system-dependent. Taking into account that image restoration and segmentation can promote each other and they are tightly coupled, we proposed a variational method to solve the two problems together. Our method integrated total variation (TV) semi-blind deconvolution and Mumford-Shah (MS) segmentation. The TV norm was used on edges to protect the edge information, and the L{sub 2} norm was used to avoid staircase effect in the no-edge area. The blur kernel was constrained to the Gaussian model parameterized by its variance and we assumed that the variances in the X-Y and Z directions are different. The energy functional was iteratively optimized by an alternate minimization algorithm. Segmentation performance was tested on eleven patients with non-Hodgkin’s lymphoma, and evaluated by Dice similarity index (DSI) and classification error (CE). For comparison, seven other widely used methods were also tested and evaluated. Results: The combination of TV and L{sub 2} regularizations effectively improved the segmentation accuracy. The average DSI increased by around 0.1 than using either the TV or the L{sub 2} norm. The proposed method was obviously superior to other tested methods. It has an average DSI and CE of 0.80 and 0.41, while the FCM method — the second best one — has only an average DSI and CE of 0.66 and 0.64. Conclusion: Coupling image restoration and segmentation can handle PVE and thus improves tumor segmentation accuracy in PET. Alternate use of TV and L2 regularizations can further improve the performance of the algorithm. This work was supported in part by National Natural

  17. Improving image quality in Electrical Impedance Tomography (EIT using Projection Error Propagation-based Regularization (PEPR technique: A simulation study

    Directory of Open Access Journals (Sweden)

    Tushar Kanti Bera

    2011-03-01

    Full Text Available A Projection Error Propagation-based Regularization (PEPR method is proposed and the reconstructed image quality is improved in Electrical Impedance Tomography (EIT. A projection error is produced due to the misfit of the calculated and measured data in the reconstruction process. The variation of the projection error is integrated with response matrix in each iterations and the reconstruction is carried out in EIDORS. The PEPR method is studied with the simulated boundary data for different inhomogeneity geometries. Simulated results demonstrate that the PEPR technique improves image reconstruction precision in EIDORS and hence it can be successfully implemented to increase the reconstruction accuracy in EIT.>doi:10.5617/jeb.158 J Electr Bioimp, vol. 2, pp. 2-12, 2011

  18. Sparse coded image super-resolution using K-SVD trained dictionary based on regularized orthogonal matching pursuit.

    Science.gov (United States)

    Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

    2015-01-01

    Image super-resolution (SR) plays a vital role in medical imaging that allows a more efficient and effective diagnosis process. Usually, diagnosing is difficult and inaccurate from low-resolution (LR) and noisy images. Resolution enhancement through conventional interpolation methods strongly affects the precision of consequent processing steps, such as segmentation and registration. Therefore, we propose an efficient sparse coded image SR reconstruction technique using a trained dictionary. We apply a simple and efficient regularized version of orthogonal matching pursuit (ROMP) to seek the coefficients of sparse representation. ROMP has the transparency and greediness of OMP and the robustness of the L1-minization that enhance the dictionary learning process to capture feature descriptors such as oriented edges and contours from complex images like brain MRIs. The sparse coding part of the K-SVD dictionary training procedure is modified by substituting OMP with ROMP. The dictionary update stage allows simultaneously updating an arbitrary number of atoms and vectors of sparse coefficients. In SR reconstruction, ROMP is used to determine the vector of sparse coefficients for the underlying patch. The recovered representations are then applied to the trained dictionary, and finally, an optimization leads to high-resolution output of high-quality. Experimental results demonstrate that the super-resolution reconstruction quality of the proposed scheme is comparatively better than other state-of-the-art schemes.

  19. Optical motion detection using image partitioning

    International Nuclear Information System (INIS)

    Hessel, K.R.; Stalker, K.T.; McCarthy, A.E.

    1976-08-01

    An optical system for surveillance or intrusion detection, based upon image partitioning, is proposed. The scene of interest is imaged onto a checkerboard pattern of transmissive and reflective areas and the transmitted and reflected light components are measured by detectors. Changes in the scene disturb the light balance and can cause an alarm indication. Several system configurations are proposed. Measurements and computer simulations are used to determine the operating characteristics of the several configurations. Depth of focus problems at the patterned reflector is the primary concern. Noise considerations determine the theoretical limitation of system performance and are analyzed in some detail. Indications are that, under good scene radiance conditions, a change in the scene of approximately one part in 10 3 is detectable with a signal-to-noise ratio sufficient for a false alarm rate of one every few months

  20. Detection of jet contrails from satellite images

    Science.gov (United States)

    Meinert, Dieter

    1994-02-01

    In order to investigate the influence of modern technology on the world climate it is important to have automatic detection methods for man-induced parameters. In this case the influence of jet contrails on the greenhouse effect shall be investigated by means of images from polar orbiting satellites. Current methods of line recognition and amplification cannot distinguish between contrails and rather sharp edges of natural cirrus or noise. They still rely on human control. Through the combination of different methods from cloud physics, image comparison, pattern recognition, and artificial intelligence we try to overcome this handicap. Here we will present the basic methods applied to each image frame, and list preliminary results derived this way.

  1. Novelty detection for breast cancer image classification

    Science.gov (United States)

    Cichosz, Pawel; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold

    2016-09-01

    Using classification learning algorithms for medical applications may require not only refined model creation techniques and careful unbiased model evaluation, but also detecting the risk of misclassification at the time of model application. This is addressed by novelty detection, which identifies instances for which the training set is not sufficiently representative and for which it may be safer to restrain from classification and request a human expert diagnosis. The paper investigates two techniques for isolated instance identification, based on clustering and one-class support vector machines, which represent two different approaches to multidimensional outlier detection. The prediction quality for isolated instances in breast cancer image data is evaluated using the random forest algorithm and found to be substantially inferior to the prediction quality for non-isolated instances. Each of the two techniques is then used to create a novelty detection model which can be combined with a classification model and used at the time of prediction to detect instances for which the latter cannot be reliably applied. Novelty detection is demonstrated to improve random forest prediction quality and argued to deserve further investigation in medical applications.

  2. Single image super-resolution via regularized extreme learning regression for imagery from microgrid polarimeters

    Science.gov (United States)

    Sargent, Garrett C.; Ratliff, Bradley M.; Asari, Vijayan K.

    2017-08-01

    The advantage of division of focal plane imaging polarimeters is their ability to obtain temporally synchronized intensity measurements across a scene; however, they sacrifice spatial resolution in doing so due to their spatially modulated arrangement of the pixel-to-pixel polarizers and often result in aliased imagery. Here, we propose a super-resolution method based upon two previously trained extreme learning machines (ELM) that attempt to recover missing high frequency and low frequency content beyond the spatial resolution of the sensor. This method yields a computationally fast and simple way of recovering lost high and low frequency content from demosaicing raw microgrid polarimetric imagery. The proposed method outperforms other state-of-the-art single-image super-resolution algorithms in terms of structural similarity and peak signal-to-noise ratio.

  3. Regularization of DT-MR images using a successive Fermat median filtering method.

    Science.gov (United States)

    Kwon, Kiwoon; Kim, Dongyoun; Kim, Sunghee; Park, Insung; Jeong, Jaewon; Kim, Taehwan; Hong, Cheolpyo; Han, Bongsoo

    2008-05-21

    Tractography using diffusion tensor magnetic resonance imaging (DT-MRI) is a method to determine the architecture of axonal fibers in the central nervous system by computing the direction of greatest diffusion in the white matter of the brain. To reduce the noise in DT-MRI measurements, a tensor-valued median filter, which is reported to be denoising and structure preserving in the tractography, is applied. In this paper, we proposed the successive Fermat (SF) method, successively using Fermat point theory for a triangle contained in the two-dimensional plane, as a median filtering method. We discussed the error analysis and numerical study about the SF method for phantom and experimental data. By considering the computing time and the image quality aspects of the numerical study simultaneously, we showed that the SF method is much more efficient than the simple median (SM) and gradient descents (GD) methods.

  4. Regularization of DT-MR images using a successive Fermat median filtering method

    International Nuclear Information System (INIS)

    Kwon, Kiwoon; Kim, Dongyoun; Kim, Sunghee; Park, Insung; Jeong, Jaewon; Kim, Taehwan; Hong, Cheolpyo; Han, Bongsoo

    2008-01-01

    Tractography using diffusion tensor magnetic resonance imaging (DT-MRI) is a method to determine the architecture of axonal fibers in the central nervous system by computing the direction of greatest diffusion in the white matter of the brain. To reduce the noise in DT-MRI measurements, a tensor-valued median filter, which is reported to be denoising and structure preserving in the tractography, is applied. In this paper, we proposed the successive Fermat (SF) method, successively using Fermat point theory for a triangle contained in the two-dimensional plane, as a median filtering method. We discussed the error analysis and numerical study about the SF method for phantom and experimental data. By considering the computing time and the image quality aspects of the numerical study simultaneously, we showed that the SF method is much more efficient than the simple median (SM) and gradient descents (GD) methods

  5. Regularization of DT-MR images using a successive Fermat median filtering method

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Kiwoon; Kim, Dongyoun; Kim, Sunghee; Park, Insung; Jeong, Jaewon; Kim, Taehwan [Department of Biomedical Engineering, Yonsei University, Wonju, 220-710 (Korea, Republic of); Hong, Cheolpyo; Han, Bongsoo [Department of Radiological Science, Yonsei University, Wonju, 220-710 (Korea, Republic of)], E-mail: bshan@yonsei.ac.kr

    2008-05-21

    Tractography using diffusion tensor magnetic resonance imaging (DT-MRI) is a method to determine the architecture of axonal fibers in the central nervous system by computing the direction of greatest diffusion in the white matter of the brain. To reduce the noise in DT-MRI measurements, a tensor-valued median filter, which is reported to be denoising and structure preserving in the tractography, is applied. In this paper, we proposed the successive Fermat (SF) method, successively using Fermat point theory for a triangle contained in the two-dimensional plane, as a median filtering method. We discussed the error analysis and numerical study about the SF method for phantom and experimental data. By considering the computing time and the image quality aspects of the numerical study simultaneously, we showed that the SF method is much more efficient than the simple median (SM) and gradient descents (GD) methods.

  6. Image change detection systems, methods, and articles of manufacture

    Science.gov (United States)

    Jones, James L.; Lassahn, Gordon D.; Lancaster, Gregory D.

    2010-01-05

    Aspects of the invention relate to image change detection systems, methods, and articles of manufacture. According to one aspect, a method of identifying differences between a plurality of images is described. The method includes loading a source image and a target image into memory of a computer, constructing source and target edge images from the source and target images to enable processing of multiband images, displaying the source and target images on a display device of the computer, aligning the source and target edge images, switching displaying of the source image and the target image on the display device, to enable identification of differences between the source image and the target image.

  7. Regularization and Bayesian methods for inverse problems in signal and image processing

    CERN Document Server

    Giovannelli , Jean-François

    2015-01-01

    The focus of this book is on "ill-posed inverse problems". These problems cannot be solved only on the basis of observed data. The building of solutions involves the recognition of other pieces of a priori information. These solutions are then specific to the pieces of information taken into account. Clarifying and taking these pieces of information into account is necessary for grasping the domain of validity and the field of application for the solutions built.  For too long, the interest in these problems has remained very limited in the signal-image community. However, the community has si

  8. Harmonic Auto-Regularization for Non Rigid Groupwise Registration in Cardiac Magnetic Resonance Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Sanz-Estebanez, S.; Royuela-del-Val, J.; Sevilla, T.; Revilla-Orodea, A.; Aja-Fernandez, S.; Alberola-Lopez, C.

    2016-07-01

    In this paper we present a new approach for non rigid groupwise registration of cardiac magnetic resonance images by means of free-form deformations, imposing a prior harmonic deformation assumption. The procedure proposes a primal-dual framework for solving an equality constrained minimization problem, which allows an automatic estimate of the trade-off between image fidelity and the Laplacian smoothness terms for each iteration. The method has been applied to both a 4D extended cardio-torso phantom and to a set of voluntary patients. The accuracy of the method has been measured for the synthetic experiment as the difference in modulus between the estimated displacement field and the ground truth; as for the real data, we have calculated the Dice coefficient between the contour manual delineations provided by two cardiologists at end systolic phase and those provided by them at end diastolic phase and, consequently propagated by the registration algorithm to the systolic instant. The automatic procedure turns out to be competitive in motion compensation with other methods even though their parameters have been previously set for optimal performance in different scenarios. (Author)

  9. Application and Analysis of Wavelet Transform in Image Edge Detection

    Institute of Scientific and Technical Information of China (English)

    Jianfang gao[1

    2016-01-01

    For the image processing technology, technicians have been looking for a convenient and simple detection method for a long time, especially for the innovation research on image edge detection technology. Because there are a lot of original information at the edge during image processing, thus, we can get the real image data in terms of the data acquisition. The usage of edge is often in the case of some irregular geometric objects, and we determine the contour of the image by combining with signal transmitted data. At the present stage, there are different algorithms in image edge detection, however, different types of algorithms have divergent disadvantages so It is diffi cult to detect the image changes in a reasonable range. We try to use wavelet transformation in image edge detection, making full use of the wave with the high resolution characteristics, and combining multiple images, in order to improve the accuracy of image edge detection.

  10. Grid-texture mechanisms in human vision: Contrast detection of regular sparse micro-patterns requires specialist templates.

    Science.gov (United States)

    Baker, Daniel H; Meese, Tim S

    2016-07-27

    Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50-100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.

  11. Detecting jaundice by using digital image processing

    Science.gov (United States)

    Castro-Ramos, J.; Toxqui-Quitl, C.; Villa Manriquez, F.; Orozco-Guillen, E.; Padilla-Vivanco, A.; Sánchez-Escobar, JJ.

    2014-03-01

    When strong Jaundice is presented, babies or adults should be subject to clinical exam like "serum bilirubin" which can cause traumas in patients. Often jaundice is presented in liver disease such as hepatitis or liver cancer. In order to avoid additional traumas we propose to detect jaundice (icterus) in newborns or adults by using a not pain method. By acquiring digital images in color, in palm, soles and forehead, we analyze RGB attributes and diffuse reflectance spectra as the parameter to characterize patients with either jaundice or not, and we correlate that parameters with the level of bilirubin. By applying support vector machine we distinguish between healthy and sick patients.

  12. A-Track: Detecting Moving Objects in FITS images

    Science.gov (United States)

    Atay, T.; Kaplan, M.; Kilic, Y.; Karapinar, N.

    2017-04-01

    A-Track is a fast, open-source, cross-platform pipeline for detecting moving objects (asteroids and comets) in sequential telescope images in FITS format. The moving objects are detected using a modified line detection algorithm.

  13. An improved computing method for the image edge detection

    Institute of Scientific and Technical Information of China (English)

    Gang Wang; Liang Xiao; Anzhi He

    2007-01-01

    The framework of detecting the image edge based on the sub-pixel multi-fractal measure (SPMM) is presented. The measure is defined, which gives the sub-pixel local distribution of the image gradient. The more precise singularity exponent of every pixel can be obtained by performing the SPMM analysis on the image. Using the singularity exponents and the multi-fractal spectrum of the image, the image can be segmented into a series of sets with different singularity exponents, thus the image edge can be detected automatically and easily. The simulation results show that the SPMM has higher quality factor in the image edge detection.

  14. Optimal Scale Edge Detection Utilizing Noise within Images

    Directory of Open Access Journals (Sweden)

    Adnan Khashman

    2003-04-01

    Full Text Available Edge detection techniques have common problems that include poor edge detection in low contrast images, speed of recognition and high computational cost. An efficient solution to the edge detection of objects in low to high contrast images is scale space analysis. However, this approach is time consuming and computationally expensive. These expenses can be marginally reduced if an optimal scale is found in scale space edge detection. This paper presents a new approach to detecting objects within images using noise within the images. The novel idea is based on selecting one optimal scale for the entire image at which scale space edge detection can be applied. The selection of an ideal scale is based on the hypothesis that "the optimal edge detection scale (ideal scale depends on the noise within an image". This paper aims at providing the experimental evidence on the relationship between the optimal scale and the noise within images.

  15. Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

    Directory of Open Access Journals (Sweden)

    Ramesh Kumar Lama

    2017-01-01

    Full Text Available Alzheimer’s disease (AD is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR images to discriminate AD, mild cognitive impairment (MCI, and healthy control (HC subjects using a support vector machine (SVM, an import vector machine (IVM, and a regularized extreme learning machine (RELM. The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

  16. Troubles detected during regular inspection of No.1 plant in Oi Power Station, Kansai Electric Power Co., Inc

    International Nuclear Information System (INIS)

    1990-01-01

    No. 1 plant in Oi Power Station, Kansai Electric Power Co., Inc. is a PWR plant with rated output of 1175 MW, and its regular inspection is carried out since August 14, 1989. When eddy current flaw detection inspection was carried out on the total number (11426 except already plugged tubes) of the heating tubes of steam generators, significant indication was observed in tube supporting plate part of 279 tubes, at the boundary of tube plate expanded part of 34 tubes, and in the tube plate expanded part of 99 tubes, 411 heating tubes in total (all on high temperature side). Consequently, it was decided to repair 367 tubes using sleeves, and to plug other 44 tubes. Besides, among the heating tubes plugged in the past, it was decided to remove plugs from 161 tubes, and by repairing them with sleeves, to use them again. Total number of heating tubes 13552 (3388 tubes x 4 steam generators), Number of plugged tubes 2009 (decrease by 117 this time), Ratio of plugging 14.8%. (K.I.)

  17. Image Processing Methods Usable for Object Detection on the Chessboard

    Directory of Open Access Journals (Sweden)

    Beran Ladislav

    2016-01-01

    Full Text Available Image segmentation and object detection is challenging problem in many research. Although many algorithms for image segmentation have been invented, there is no simple algorithm for image segmentation and object detection. Our research is based on combination of several methods for object detection. The first method suitable for image segmentation and object detection is colour detection. This method is very simply, but there is problem with different colours. For this method it is necessary to have precisely determined colour of segmented object before all calculations. In many cases it is necessary to determine this colour manually. Alternative simply method is method based on background removal. This method is based on difference between reference image and detected image. In this paper several methods suitable for object detection are described. Thisresearch is focused on coloured object detection on chessboard. The results from this research with fusion of neural networks for user-computer game checkers will be applied.

  18. Detect Image Tamper by Semi-Fragile Digital Watermarking

    Institute of Scientific and Technical Information of China (English)

    LIUFeilong; WANGYangsheng

    2004-01-01

    To authenticate the integrity of image while resisting some valid image processing such as JPEG compression, a semi-fragile image watermarking is described. Image name, one of the image features, has been used as the key of pseudo-random function to generate the special watermarks for the different image. Watermarks are embedded by changing the relationship between the blocks' DCT DC coefficients, and the image tamper are detected with the relationship of these DCT DC coefficients.Experimental results show that the proposed technique can resist JPEG compression, and detect image tamper in the meantime.

  19. Surface Distresses Detection of Pavement Based on Digital Image Processing

    OpenAIRE

    Ouyang , Aiguo; Luo , Chagen; Zhou , Chao

    2010-01-01

    International audience; Pavement crack is the main form of early diseases of pavement. The use of digital photography to record pavement images and subsequent crack detection and classification has undergone continuous improvements over the past decade. Digital image processing has been applied to detect the pavement crack for its advantages of large amount of information and automatic detection. The applications of digital image processing in pavement crack detection, distresses classificati...

  20. A combined use of multispectral and SAR images for ship detection and characterization through object based image analysis

    Science.gov (United States)

    Aiello, Martina; Gianinetto, Marco

    2017-10-01

    Marine routes represent a huge portion of commercial and human trades, therefore surveillance, security and environmental protection themes are gaining increasing importance. Being able to overcome the limits imposed by terrestrial means of monitoring, ship detection from satellite has recently prompted a renewed interest for a continuous monitoring of illegal activities. This paper describes an automatic Object Based Image Analysis (OBIA) approach to detect vessels made of different materials in various sea environments. The combined use of multispectral and SAR images allows for a regular observation unrestricted by lighting and atmospheric conditions and complementarity in terms of geographic coverage and geometric detail. The method developed adopts a region growing algorithm to segment the image in homogeneous objects, which are then classified through a decision tree algorithm based on spectral and geometrical properties. Then, a spatial analysis retrieves the vessels' position, length and heading parameters and a speed range is associated. Optimization of the image processing chain is performed by selecting image tiles through a statistical index. Vessel candidates are detected over amplitude SAR images using an adaptive threshold Constant False Alarm Rate (CFAR) algorithm prior the object based analysis. Validation is carried out by comparing the retrieved parameters with the information provided by the Automatic Identification System (AIS), when available, or with manual measurement when AIS data are not available. The estimation of length shows R2=0.85 and estimation of heading R2=0.92, computed as the average of R2 values obtained for both optical and radar images.

  1. Improving thoracic four-dimensional cone-beam CT reconstruction with anatomical-adaptive image regularization (AAIR)

    International Nuclear Information System (INIS)

    Shieh, Chun-Chien; Kipritidis, John; O'Brien, Ricky T; Cooper, Benjamin J; Keall, Paul J; Kuncic, Zdenka

    2015-01-01

    Total-variation (TV) minimization reconstructions can significantly reduce noise and streaks in thoracic four-dimensional cone-beam computed tomography (4D CBCT) images compared to the Feldkamp–Davis–Kress (FDK) algorithm currently used in practice. TV minimization reconstructions are, however, prone to over-smoothing anatomical details and are also computationally inefficient. The aim of this study is to demonstrate a proof of concept that these disadvantages can be overcome by incorporating the general knowledge of the thoracic anatomy via anatomy segmentation into the reconstruction. The proposed method, referred as the anatomical-adaptive image regularization (AAIR) method, utilizes the adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS) framework, but introduces an additional anatomy segmentation step in every iteration. The anatomy segmentation information is implemented in the reconstruction using a heuristic approach to adaptively suppress over-smoothing at anatomical structures of interest. The performance of AAIR depends on parameters describing the weighting of the anatomy segmentation prior and segmentation threshold values. A sensitivity study revealed that the reconstruction outcome is not sensitive to these parameters as long as they are chosen within a suitable range. AAIR was validated using a digital phantom and a patient scan and was compared to FDK, ASD-POCS and the prior image constrained compressed sensing (PICCS) method. For the phantom case, AAIR reconstruction was quantitatively shown to be the most accurate as indicated by the mean absolute difference and the structural similarity index. For the patient case, AAIR resulted in the highest signal-to-noise ratio (i.e. the lowest level of noise and streaking) and the highest contrast-to-noise ratios for the tumor and the bony anatomy (i.e. the best visibility of anatomical details). Overall, AAIR was much less prone to over-smoothing anatomical details compared to ASD-POCS and

  2. Automatic detection of NIL defects using microscopy and image processing

    KAUST Repository

    Pietroy, David; Gereige, Issam; Gourgon, Cé cile

    2013-01-01

    patterns, sticking. In this paper, microscopic imaging combined to a specific processing algorithm is used to detect numerically defects in printed patterns. Results obtained for 1D and 2D imprinted gratings with different microscopic image magnifications

  3. Detecting aircrafts from satellite images using saliency and conical ...

    Indian Academy of Sciences (India)

    Samik Banerjee

    automatically detect all kinds of interesting targets in satellite images. .... which is used for text and image categorization, has been also introduced for object ...... 3.4 GHz processor, 32 GB RAM and Windows 7 (64 bit). Operating System. 6.

  4. Imaging methods for detection of infectious foci

    International Nuclear Information System (INIS)

    Couret, I.; Rossi, M.; Weinemann, P.; Moretti, J.L.

    1993-01-01

    Several tracers can be used for imaging infection. None is a worthwhile agent for all infectious foci, but each one has preferential applications, depending on its uptake mechanism by the infectious and/or inflammatory focus. Autologous leucocytes labeled in vitro with indium-111 (In-111) or with technetium-99-hexamethylpropyleneamine oxime (Tc-99m HMPAO) were applied with success in the detection of peripheral bone infection, focal vascular graft infection and inflammatory bowel disease. Labeling with In-111 is of interest in chronic bone infection, while labeling with Tc-99m HMPAO gets the advantage of a better dosimetry and imaging. The interest of in vivo labeled leucocytes with a Tc-99m labeled monoclonal antigranulocyte antibody anti-NCA 95 (BW 250/183) was proved in the same principal type of infectious foci than in vitro labeled leucocytes. Sites of chronic infection in the spine and the pelvis, whether active or healed, appear as photopenic defects on both in vitro labeled leucocytes and Tc-99m monoclonal antigranulocyte antibody (BW 250/183) scintigraphies. With gallium-67 results showed a high sensitivity with a low specificity. This tracer demonstrated good performance to delineate foci of infectious spondylitis. In-111 and Tc-99m labeled polyclonal human immunoglobulin (HIG) was applied with success in the assessment of various infectious foci, particularly in chronic sepsis. As labeled leucocytes, labeled HIG showed cold defects in infectious sepsis of the spine. Research in nuclear medicine is very active in the development of more specific tracers of infection, mainly involved in Tc-99m or In-111 labeled chemotactic peptides, antigranulocyte antibody fragments, antibiotic derivatives and interleukins. (authors). 70 refs

  5. Theoretical Analysis of Penalized Maximum-Likelihood Patlak Parametric Image Reconstruction in Dynamic PET for Lesion Detection.

    Science.gov (United States)

    Yang, Li; Wang, Guobao; Qi, Jinyi

    2016-04-01

    Detecting cancerous lesions is a major clinical application of emission tomography. In a previous work, we studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by first reconstructing a sequence of dynamic PET images, and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in Patlak parametric images. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize detection performance. The proposed method is validated using computer-based Monte Carlo simulations. Good agreements between the theoretical predictions and the Monte Carlo results are observed. Both theoretical predictions and Monte Carlo simulation results show the benefit of the indirect and direct methods under optimized regularization parameters in dynamic PET reconstruction for lesion detection, when compared with the conventional static PET reconstruction.

  6. The technique of Cerenkov ring image detection

    International Nuclear Information System (INIS)

    Langerveld, D.

    1990-01-01

    Charged particles with an energy between 2 GeV and 25 GeV can be identified in the DELPHI barrel RICH detector by using the technique of Cerenkov ring image detection. The method of identification is based on a determination of the Cerenkov angle by measuring the positions of the emitted Cerenkov photons to high precision in a photon detector. The resolution in the photon that can be obtained depends mainly on the chromatic dispersion in the radiators and on the resolution in the photon detector is used in the barrel RICH in combination with two radiators. The photon detector consists of 48 drift tubes, constructed from quarz plates, each equipped with a wire chamber at the end. The drift gas with which the tubes are filled contains a small admixture of TMAE vapour from which the Cerenkov photons can liberate photoelectrons. It is shown in this thesis that an efficient photon detection and an accurate localization of the photon conversion points is possible. The spatial resolution of the photon detector is determind by the resolution of the wire chambe, the accuracy of the drift measurement, the distortions in the paths of the drifting electrons. The resolution of the wire chamber has been measured to be 0.8 mm in the x- and 1.7 mm in the y-coordinate. The error in the z-coordinate introduced by the drift time measurement is 0.2 mm. The distortions in the paths of the drifting electrons have been measured both in the x and y-direction. The longitudinal and transverse diffusion coefficients have been measured as a function of the field strength for two different drift gas mixtures. (author). 96 refs.; 61 figs.; 11 tabs

  7. Human Body Image Edge Detection Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    李勇; 付小莉

    2003-01-01

    Human dresses are different in thousands way.Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method or gray method to detect human body image edges can't obtain satisfactory results because of false detections and missed detections. According to tte peculiarity of human body image, dyadic wavelet transform of cubic spline is successfully applied to detect the face and profile edges of human body image and Mallat algorithm is used in the wavelet decomposition in this paper.

  8. Acousto-Mechanical Imaging for Breast Cancer Detection

    National Research Council Canada - National Science Library

    Emelianov, Stanislav Y

    2002-01-01

    The underlying hypothesis of our study is that quantitative breast elasticity imaging is possible and provides unique information, which could increase the detection, characterization and monitoring...

  9. Acousto-Mechanical Imaging for Breast Cancer Detection

    National Research Council Canada - National Science Library

    Emelianov, Stanislav Y

    2003-01-01

    The underlying hypothesis of our study is that quantitative breast elasticity imaging is possible and provides unique information, which could increase the detection, characterization and monitoring...

  10. Effect of image quality on calcification detection in digital mammography

    Energy Technology Data Exchange (ETDEWEB)

    Warren, Lucy M.; Mackenzie, Alistair; Cooke, Julie; Given-Wilson, Rosalind M.; Wallis, Matthew G.; Chakraborty, Dev P.; Dance, David R.; Bosmans, Hilde; Young, Kenneth C. [National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford GU2 7XX, United Kingdom and Department of Physics, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH (United Kingdom); Jarvis Breast Screening and Diagnostic Centre, Guildford GU1 1LJ (United Kingdom); Department of Radiology, St. George' s Healthcare NHS Trust, Tooting, London SW17 0QT (United Kingdom); Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, United Kingdom and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 0QQ (United Kingdom); Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15210 (United States); National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford GU2 7XX, United Kingdom and Department of Physics, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH (United Kingdom); University Hospitals Leuven, Herestraat 49, 3000 Leuven (Belgium); National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford GU2 7XX, United Kingdom and Department of Physics, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH (United Kingdom)

    2012-06-15

    Purpose: This study aims to investigate if microcalcification detection varies significantly when mammographic images are acquired using different image qualities, including: different detectors, dose levels, and different image processing algorithms. An additional aim was to determine how the standard European method of measuring image quality using threshold gold thickness measured with a CDMAM phantom and the associated limits in current EU guidelines relate to calcification detection. Methods: One hundred and sixty two normal breast images were acquired on an amorphous selenium direct digital (DR) system. Microcalcification clusters extracted from magnified images of slices of mastectomies were electronically inserted into half of the images. The calcification clusters had a subtle appearance. All images were adjusted using a validated mathematical method to simulate the appearance of images from a computed radiography (CR) imaging system at the same dose, from both systems at half this dose, and from the DR system at quarter this dose. The original 162 images were processed with both Hologic and Agfa (Musica-2) image processing. All other image qualities were processed with Agfa (Musica-2) image processing only. Seven experienced observers marked and rated any identified suspicious regions. Free response operating characteristic (FROC) and ROC analyses were performed on the data. The lesion sensitivity at a nonlesion localization fraction (NLF) of 0.1 was also calculated. Images of the CDMAM mammographic test phantom were acquired using the automatic setting on the DR system. These images were modified to the additional image qualities used in the observer study. The images were analyzed using automated software. In order to assess the relationship between threshold gold thickness and calcification detection a power law was fitted to the data. Results: There was a significant reduction in calcification detection using CR compared with DR: the alternative FROC

  11. Automatic system for detecting pornographic images

    Science.gov (United States)

    Ho, Kevin I. C.; Chen, Tung-Shou; Ho, Jun-Der

    2002-09-01

    Due to the dramatic growth of network and multimedia technology, people can more easily get variant information by using Internet. Unfortunately, it also makes the diffusion of illegal and harmful content much easier. So, it becomes an important topic for the Internet society to protect and safeguard Internet users from these content that may be encountered while surfing on the Net, especially children. Among these content, porno graphs cause more serious harm. Therefore, in this study, we propose an automatic system to detect still colour porno graphs. Starting from this result, we plan to develop an automatic system to search porno graphs or to filter porno graphs. Almost all the porno graphs possess one common characteristic that is the ratio of the size of skin region and non-skin region is high. Based on this characteristic, our system first converts the colour space from RGB colour space to HSV colour space so as to segment all the possible skin-colour regions from scene background. We also apply the texture analysis on the selected skin-colour regions to separate the skin regions from non-skin regions. Then, we try to group the adjacent pixels located in skin regions. If the ratio is over a given threshold, we can tell if the given image is a possible porno graph. Based on our experiment, less than 10% of non-porno graphs are classified as pornography, and over 80% of the most harmful porno graphs are classified correctly.

  12. Fake currency detection using image processing

    Science.gov (United States)

    Agasti, Tushar; Burand, Gajanan; Wade, Pratik; Chitra, P.

    2017-11-01

    The advancement of color printing technology has increased the rate of fake currency note printing and duplicating the notes on a very large scale. Few years back, the printing could be done in a print house, but now anyone can print a currency note with maximum accuracy using a simple laser printer. As a result the issue of fake notes instead of the genuine ones has been increased very largely. India has been unfortunately cursed with the problems like corruption and black money. And counterfeit of currency notes is also a big problem to it. This leads to design of a system that detects the fake currency note in a less time and in a more efficient manner. The proposed system gives an approach to verify the Indian currency notes. Verification of currency note is done by the concepts of image processing. This article describes extraction of various features of Indian currency notes. MATLAB software is used to extract the features of the note. The proposed system has got advantages like simplicity and high performance speed. The result will predict whether the currency note is fake or not.

  13. Object detection from images obtained through underwater turbulence medium

    Science.gov (United States)

    Furhad, Md. Hasan; Tahtali, Murat; Lambert, Andrew

    2017-09-01

    Imaging through underwater experiences severe distortions due to random fluctuations of temperature and salinity in water, which produces underwater turbulence through diffraction limited blur. Lights reflecting from objects perturb and attenuate contrast, making the recognition of objects of interest difficult. Thus, the information available for detecting underwater objects of interest becomes a challenging task as they have inherent confusion among the background, foreground and other image properties. In this paper, a saliency-based approach is proposed to detect the objects acquired through an underwater turbulent medium. This approach has drawn attention among a wide range of computer vision applications, such as image retrieval, artificial intelligence, neuro-imaging and object detection. The image is first processed through a deblurring filter. Next, a saliency technique is used on the image for object detection. In this step, a saliency map that highlights the target regions is generated and then a graph-based model is proposed to extract these target regions for object detection.

  14. A survey of landmine detection using hyperspectral imaging

    Science.gov (United States)

    Makki, Ihab; Younes, Rafic; Francis, Clovis; Bianchi, Tiziano; Zucchetti, Massimo

    2017-02-01

    Hyperspectral imaging is a trending technique in remote sensing that finds its application in many different areas, such as agriculture, mapping, target detection, food quality monitoring, etc. This technique gives the ability to remotely identify the composition of each pixel of the image. Therefore, it is a natural candidate for the purpose of landmine detection, thanks to its inherent safety and fast response time. In this paper, we will present the results of several studies that employed hyperspectral imaging for the purpose of landmine detection, discussing the different signal processing techniques used in this framework for hyperspectral image processing and target detection. Our purpose is to highlight the progresses attained in the detection of landmines using hyperspectral imaging and to identify possible perspectives for future work, in order to achieve a better detection in real-time operation mode.

  15. Improved image quality and detectability of hypovascular liver metastases on DECT with different adjusted window settings

    Energy Technology Data Exchange (ETDEWEB)

    Altenbernd, Jens; Forsting, Michael; Lauenstein, Thomas; Wetter, Axel [Duisburg-Essen Univ., Essen (Germany). Dept. of Diagnostic and Interventional Radiology and Neuroradiology

    2017-03-15

    To investigate dual-energy CT of hypovascular liver metastases (LMs) with special focus on window settings (WSs). The aim of the study is to investigate the extent to which adapted WSs and the low-energy images of DECT improve the visibility especially of smaller LMs. 30 patients with LMs of colorectal cancer were investigated with DECT of the liver. In each patient contrast-enhanced DECT imaging with portal-venous delay was performed. The total number, mean number and conspicuity (1= excellent - 5 = poor) of LMs were documented on 80-kVp images and virtual 120-kVp images with different WSs (25/200 HU, 50/200, 75/200 HU, 25/350 HU, 50/350 HU, 75/350 HU, 25/500 HU, 50/500 HU, 75/500 HU). The attenuation (HU) of LMs and several anatomic regions and the background noise on 80 kVp images and virtual 120 kVp images were documented. Signal (liver)/noise and liver/LM ratio (SNR/LLMR) were calculated. The total number of LMs depending on size (<1cm, 1-2cm, >2cm) on 80 kVp images and virtual 120 kVp images with previously investigated best and regular WSs were documented. The highest total number, mean number per patient and total number of LMs <1cm were detected with the WS 25/350 HU on 80kVp images (7.0; p = 0.02/218; p = 0.01/64;p<0.001) compared to the WS 75/200 HU on virtual 120 kVp images and the regular WS 50/350 HU on 80 kVp images and virtual 120 kVp images. The best conspicuity of LMs on 80 kVp images was documented with the WS 25/350 HU compared to the best WS on virtual 120 kVp images with 75/200 HU (1.2 vs. 2.5; p = 0.01). HU of normal liver, aorta, SNR and LLMR differed significantly between 80 kVp images and virtual 120 kVp images (128.1 vs. 93.6; < 0.05/192.8 vs. 131.4; < 0.05/10.3 vs. 8.1; p < 0.05/2.8 vs. 2.1; p < 0.05). Low kVp images of DECT datasets are more precise in detecting hypovascular liver metastases than virtual 120 kVp images. Dedicated window settings have a relevant influence on conspicuity.

  16. Microwave Imaging for Breast Cancer Detection

    DEFF Research Database (Denmark)

    Rubæk, Tonny; Fhager, Andreas; Jensen, Peter Damsgaard

    2011-01-01

    Still more research groups are promoting microwave imaging as a viable supplement or substitution to more conventional imaging modalities. A widespread approach for microwave imaging of the breast is tomographic imaging in which one seeks to reconstruct the distributions of permittivity and condu......Still more research groups are promoting microwave imaging as a viable supplement or substitution to more conventional imaging modalities. A widespread approach for microwave imaging of the breast is tomographic imaging in which one seeks to reconstruct the distributions of permittivity...... and conductivity in the breast. In this paper two nonlinear tomographic algorithms are compared – one is a single-frequency algorithm and the other is a time-domain algorithm....

  17. Generative adversarial networks for anomaly detection in images

    OpenAIRE

    Batiste Ros, Guillem

    2018-01-01

    Anomaly detection is used to identify abnormal observations that don t follow a normal pattern. Inthis work, we use the power of Generative Adversarial Networks in sampling from image distributionsto perform anomaly detection with images and to identify local anomalous segments within thisimages. Also, we explore potential application of this method to support pathological analysis ofbiological tissues

  18. Low contrast detectability for color patterns variation of display images

    International Nuclear Information System (INIS)

    Ogura, Akio

    1998-01-01

    In recent years, the radionuclide images are acquired in digital form and displayed with false colors for signal intensity. This color scales for signal intensity have various patterns. In this study, low contrast detectability was compared the performance of gray scale cording with three color scales: the hot color scale, prism color scale and stripe color scale. SPECT images of brain phantom were displayed using four color patterns. These printed images and display images were evaluated with ROC analysis. Display images were indicated higher detectability than printed images. The hot scale and gray scale images indicated better Az of ROC than prism scale images because the prism scale images showed higher false positive rate. (author)

  19. Effect of image quality on calcification detection in digital mammography.

    Science.gov (United States)

    Warren, Lucy M; Mackenzie, Alistair; Cooke, Julie; Given-Wilson, Rosalind M; Wallis, Matthew G; Chakraborty, Dev P; Dance, David R; Bosmans, Hilde; Young, Kenneth C

    2012-06-01

    This study aims to investigate if microcalcification detection varies significantly when mammographic images are acquired using different image qualities, including: different detectors, dose levels, and different image processing algorithms. An additional aim was to determine how the standard European method of measuring image quality using threshold gold thickness measured with a CDMAM phantom and the associated limits in current EU guidelines relate to calcification detection. One hundred and sixty two normal breast images were acquired on an amorphous selenium direct digital (DR) system. Microcalcification clusters extracted from magnified images of slices of mastectomies were electronically inserted into half of the images. The calcification clusters had a subtle appearance. All images were adjusted using a validated mathematical method to simulate the appearance of images from a computed radiography (CR) imaging system at the same dose, from both systems at half this dose, and from the DR system at quarter this dose. The original 162 images were processed with both Hologic and Agfa (Musica-2) image processing. All other image qualities were processed with Agfa (Musica-2) image processing only. Seven experienced observers marked and rated any identified suspicious regions. Free response operating characteristic (FROC) and ROC analyses were performed on the data. The lesion sensitivity at a nonlesion localization fraction (NLF) of 0.1 was also calculated. Images of the CDMAM mammographic test phantom were acquired using the automatic setting on the DR system. These images were modified to the additional image qualities used in the observer study. The images were analyzed using automated software. In order to assess the relationship between threshold gold thickness and calcification detection a power law was fitted to the data. There was a significant reduction in calcification detection using CR compared with DR: the alternative FROC (AFROC) area decreased from

  20. Effect of image quality on calcification detection in digital mammography

    International Nuclear Information System (INIS)

    Warren, Lucy M.; Mackenzie, Alistair; Cooke, Julie; Given-Wilson, Rosalind M.; Wallis, Matthew G.; Chakraborty, Dev P.; Dance, David R.; Bosmans, Hilde; Young, Kenneth C.

    2012-01-01

    Purpose: This study aims to investigate if microcalcification detection varies significantly when mammographic images are acquired using different image qualities, including: different detectors, dose levels, and different image processing algorithms. An additional aim was to determine how the standard European method of measuring image quality using threshold gold thickness measured with a CDMAM phantom and the associated limits in current EU guidelines relate to calcification detection. Methods: One hundred and sixty two normal breast images were acquired on an amorphous selenium direct digital (DR) system. Microcalcification clusters extracted from magnified images of slices of mastectomies were electronically inserted into half of the images. The calcification clusters had a subtle appearance. All images were adjusted using a validated mathematical method to simulate the appearance of images from a computed radiography (CR) imaging system at the same dose, from both systems at half this dose, and from the DR system at quarter this dose. The original 162 images were processed with both Hologic and Agfa (Musica-2) image processing. All other image qualities were processed with Agfa (Musica-2) image processing only. Seven experienced observers marked and rated any identified suspicious regions. Free response operating characteristic (FROC) and ROC analyses were performed on the data. The lesion sensitivity at a nonlesion localization fraction (NLF) of 0.1 was also calculated. Images of the CDMAM mammographic test phantom were acquired using the automatic setting on the DR system. These images were modified to the additional image qualities used in the observer study. The images were analyzed using automated software. In order to assess the relationship between threshold gold thickness and calcification detection a power law was fitted to the data. Results: There was a significant reduction in calcification detection using CR compared with DR: the alternative FROC

  1. Advanced concepts in multi-dimensional radiation detection and imaging

    International Nuclear Information System (INIS)

    Vetter, Kai; Barnowski, Ross; Pavlovsky, Ryan; Haefner, Andy; Torii, Tatsuo; Shikaze, Yoshiaki; Sanada, Yukihisa

    2016-01-01

    Recent developments in the detector fabrication, signal readout, and data processing enable new concepts in radiation detection that are relevant for applications ranging from fundamental physics to medicine as well as nuclear security and safety. We present recent progress in multi-dimensional radiation detection and imaging in the Berkeley Applied Nuclear Physics program. It is based on the ability to reconstruct scenes in three dimensions and fuse it with gamma-ray image information. We are using the High-Efficiency Multimode Imager HEMI in its Compton imaging mode and combining it with contextual sensors such as the Microsoft Kinect or visual cameras. This new concept of volumetric imaging or scene data fusion provides unprecedented capabilities in radiation detection and imaging relevant for the detection and mapping of radiological and nuclear materials. This concept brings us one step closer to the seeing the world with gamma-ray eyes. (author)

  2. Image processing based detection of lung cancer on CT scan images

    Science.gov (United States)

    Abdillah, Bariqi; Bustamam, Alhadi; Sarwinda, Devvi

    2017-10-01

    In this paper, we implement and analyze the image processing method for detection of lung cancer. Image processing techniques are widely used in several medical problems for picture enhancement in the detection phase to support the early medical treatment. In this research we proposed a detection method of lung cancer based on image segmentation. Image segmentation is one of intermediate level in image processing. Marker control watershed and region growing approach are used to segment of CT scan image. Detection phases are followed by image enhancement using Gabor filter, image segmentation, and features extraction. From the experimental results, we found the effectiveness of our approach. The results show that the best approach for main features detection is watershed with masking method which has high accuracy and robust.

  3. Land cover change detection in West Jilin using ETM+ images

    Institute of Scientific and Technical Information of China (English)

    Edward M.Osei,Jr.; ZHOU Yun-xuan

    2004-01-01

    In order to assess the information content and accuracy ofLandsat ETM+ digital images in land cover change detection,change-detection techniques of image differencing,normalized difference vegetation index,principal components analysis and tasseled-cap transformation were applied to yield 13 images. These images were thresholded into change and no change areas. The thresholded images were then checked in terms of various accuracies. The experiment results show that kappa coefficients of the 13 images range from 48.05 ~78.09. Different images do detect different types of changes. Images associated with changes in the near-infrared-reflectance or greenness detects crop-type changes and changes between vegetative and non-vegetative features. A unique means of using only Landsat imagery without reference data for the assessment of change in arid land are presented. Images of 12th June, 2000 and 2nd June, 2002 are used to validate the means. Analyses of standard accuracy and spatial agreement are performed to compare the new images (hereafter called "change images" ) representing the change between the two dates. Spatial agreement evaluates the conformity in the classified "change pixels" and "no-change pixels" at the same location on different change images and comprehensively examines the different techniques. This method would enable authorities to monitor land degradation efficiently and accurately.

  4. Digital Correlation based on Wavelet Transform for Image Detection

    International Nuclear Information System (INIS)

    Barba, L; Vargas, L; Torres, C; Mattos, L

    2011-01-01

    In this work is presented a method for the optimization of digital correlators to improve the characteristic detection on images using wavelet transform as well as subband filtering. It is proposed an approach of wavelet-based image contrast enhancement in order to increase the performance of digital correlators. The multiresolution representation is employed to improve the high frequency content of images taken into account the input contrast measured for the original image. The energy of correlation peaks and discrimination level of several objects are improved with this technique. To demonstrate the potentiality in extracting characteristics using the wavelet transform, small objects inside reference images are detected successfully.

  5. Near-infrared Mueller matrix imaging for colonic cancer detection

    Science.gov (United States)

    Wang, Jianfeng; Zheng, Wei; Lin, Kan; Huang, Zhiwei

    2016-03-01

    Mueller matrix imaging along with polar decomposition method was employed for the colonic cancer detection by polarized light in the near-infrared spectral range (700-1100 nm). A high-speed (colonic tissues (i.e., normal and caner) were acquired. Polar decomposition was further implemented on the 16 images to derive the diattentuation, depolarization, and the retardance images. The decomposed images showed clear margin between the normal and cancerous colon tissue samples. The work shows the potential of near-infrared Mueller matrix imaging for the early diagnosis and detection of malignant lesions in the colon.

  6. l0 regularization based on a prior image incorporated non-local means for limited-angle X-ray CT reconstruction.

    Science.gov (United States)

    Zhang, Lingli; Zeng, Li; Guo, Yumeng

    2018-03-15

    Restricted by the scanning environment in some CT imaging modalities, the acquired projection data are usually incomplete, which may lead to a limited-angle reconstruction problem. Thus, image quality usually suffers from the slope artifacts. The objective of this study is to first investigate the distorted domains of the reconstructed images which encounter the slope artifacts and then present a new iterative reconstruction method to address the limited-angle X-ray CT reconstruction problem. The presented framework of new method exploits the structural similarity between the prior image and the reconstructed image aiming to compensate the distorted edges. Specifically, the new method utilizes l0 regularization and wavelet tight framelets to suppress the slope artifacts and pursue the sparsity. New method includes following 4 steps to (1) address the data fidelity using SART; (2) compensate for the slope artifacts due to the missed projection data using the prior image and modified nonlocal means (PNLM); (3) utilize l0 regularization to suppress the slope artifacts and pursue the sparsity of wavelet coefficients of the transformed image by using iterative hard thresholding (l0W); and (4) apply an inverse wavelet transform to reconstruct image. In summary, this method is referred to as "l0W-PNLM". Numerical implementations showed that the presented l0W-PNLM was superior to suppress the slope artifacts while preserving the edges of some features as compared to the commercial and other popular investigative algorithms. When the image to be reconstructed is inconsistent with the prior image, the new method can avoid or minimize the distorted edges in the reconstructed images. Quantitative assessments also showed that applying the new method obtained the highest image quality comparing to the existing algorithms. This study demonstrated that the presented l0W-PNLM yielded higher image quality due to a number of unique characteristics, which include that (1) it utilizes

  7. Detecting content adaptive scaling of images for forensic applications

    Science.gov (United States)

    Fillion, Claude; Sharma, Gaurav

    2010-01-01

    Content-aware resizing methods have recently been developed, among which, seam-carving has achieved the most widespread use. Seam-carving's versatility enables deliberate object removal and benign image resizing, in which perceptually important content is preserved. Both types of modifications compromise the utility and validity of the modified images as evidence in legal and journalistic applications. It is therefore desirable that image forensic techniques detect the presence of seam-carving. In this paper we address detection of seam-carving for forensic purposes. As in other forensic applications, we pose the problem of seam-carving detection as the problem of classifying a test image in either of two classes: a) seam-carved or b) non-seam-carved. We adopt a pattern recognition approach in which a set of features is extracted from the test image and then a Support Vector Machine based classifier, trained over a set of images, is utilized to estimate which of the two classes the test image lies in. Based on our study of the seam-carving algorithm, we propose a set of intuitively motivated features for the detection of seam-carving. Our methodology for detection of seam-carving is then evaluated over a test database of images. We demonstrate that the proposed method provides the capability for detecting seam-carving with high accuracy. For images which have been reduced 30% by benign seam-carving, our method provides a classification accuracy of 91%.

  8. Effects of image processing on the detective quantum efficiency

    Science.gov (United States)

    Park, Hye-Suk; Kim, Hee-Joung; Cho, Hyo-Min; Lee, Chang-Lae; Lee, Seung-Wan; Choi, Yu-Na

    2010-04-01

    Digital radiography has gained popularity in many areas of clinical practice. This transition brings interest in advancing the methodologies for image quality characterization. However, as the methodologies for such characterizations have not been standardized, the results of these studies cannot be directly compared. The primary objective of this study was to standardize methodologies for image quality characterization. The secondary objective was to evaluate affected factors to Modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE) according to image processing algorithm. Image performance parameters such as MTF, NPS, and DQE were evaluated using the international electro-technical commission (IEC 62220-1)-defined RQA5 radiographic techniques. Computed radiography (CR) images of hand posterior-anterior (PA) for measuring signal to noise ratio (SNR), slit image for measuring MTF, white image for measuring NPS were obtained and various Multi-Scale Image Contrast Amplification (MUSICA) parameters were applied to each of acquired images. In results, all of modified images were considerably influence on evaluating SNR, MTF, NPS, and DQE. Modified images by the post-processing had higher DQE than the MUSICA=0 image. This suggests that MUSICA values, as a post-processing, have an affect on the image when it is evaluating for image quality. In conclusion, the control parameters of image processing could be accounted for evaluating characterization of image quality in same way. The results of this study could be guided as a baseline to evaluate imaging systems and their imaging characteristics by measuring MTF, NPS, and DQE.

  9. Adaptive regularization

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Rasmussen, Carl Edward; Svarer, C.

    1994-01-01

    Regularization, e.g., in the form of weight decay, is important for training and optimization of neural network architectures. In this work the authors provide a tool based on asymptotic sampling theory, for iterative estimation of weight decay parameters. The basic idea is to do a gradient desce...

  10. Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

    Energy Technology Data Exchange (ETDEWEB)

    Gang, Grace J. [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9, Canada and Department of Biomedical Engineering, Johns Hopkins University, Baltimore Maryland 21205 (Canada); Stayman, J. Webster; Zbijewski, Wojciech [Department of Biomedical Engineering, Johns Hopkins University, Baltimore Maryland 21205 (United States); Siewerdsen, Jeffrey H., E-mail: jeff.siewerdsen@jhu.edu [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9, Canada and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205 (United States)

    2014-08-15

    Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. Methods: Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according toFessler [“Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography,” IEEE Trans. Image Process. 5(3), 493–506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. Results: Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP

  11. Edge-detect interpolation for direct digital periapical images

    International Nuclear Information System (INIS)

    Song, Nam Kyu; Koh, Kwang Joon

    1998-01-01

    The purpose of this study was to aid in the use of the digital images by edge-detect interpolation for direct digital periapical images using edge-deted interpolation. This study was performed by image processing of 20 digital periapical images; pixel replication, linear non-interpolation, linear interpolation, and edge-sensitive interpolation. The obtained results were as follows ; 1. Pixel replication showed blocking artifact and serious image distortion. 2. Linear interpolation showed smoothing effect on the edge. 3. Edge-sensitive interpolation overcame the smoothing effect on the edge and showed better image.

  12. Crack Length Detection by Digital Image Processing

    DEFF Research Database (Denmark)

    Lyngbye, Janus; Brincker, Rune

    1990-01-01

    It is described how digital image processing is used for measuring the length of fatigue cracks. The system is installed in a Personal Computer equipped with image processing hardware and performs automated measuring on plane metal specimens used in fatigue testing. Normally one can not achieve...... a resolution better then that of the image processing equipment. To overcome this problem an extrapolation technique is used resulting in a better resolution. The system was tested on a specimen loaded with different loads. The error σa was less than 0.031 mm, which is of the same size as human measuring...

  13. Crack Detection by Digital Image Processing

    DEFF Research Database (Denmark)

    Lyngbye, Janus; Brincker, Rune

    It is described how digital image processing is used for measuring the length of fatigue cracks. The system is installed in a Personal, Computer equipped with image processing hardware and performs automated measuring on plane metal specimens used in fatigue testing. Normally one can not achieve...... a resolution better than that of the image processing equipment. To overcome this problem an extrapolation technique is used resulting in a better resolution. The system was tested on a specimen loaded with different loads. The error σa was less than 0.031 mm, which is of the same size as human measuring...

  14. Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

    Science.gov (United States)

    Sentana, I. W. B.; Jawas, N.; Asri, S. A.

    2018-01-01

    Lung nodule is an early indicator of some lung diseases, including lung cancer. In Computed Tomography (CT) based image, nodule is known as a shape that appears brighter than lung surrounding. This research aim to develop an application that automatically detect lung nodule in CT images. There are some steps in algorithm such as image acquisition and conversion, image binarization, lung segmentation, blob detection, and classification. Data acquisition is a step to taking image slice by slice from the original *.dicom format and then each image slices is converted into *.tif image format. Binarization that tailoring Otsu algorithm, than separated the background and foreground part of each image slices. After removing the background part, the next step is to segment part of the lung only so the nodule can localized easier. Once again Otsu algorithm is use to detect nodule blob in localized lung area. The final step is tailoring Support Vector Machine (SVM) to classify the nodule. The application has succeed detecting near round nodule with a certain threshold of size. Those detecting result shows drawback in part of thresholding size and shape of nodule that need to enhance in the next part of the research. The algorithm also cannot detect nodule that attached to wall and Lung Chanel, since it depend the searching only on colour differences.

  15. Motion Detection in Ultrasound Image-Sequences Using Tensor Voting

    Science.gov (United States)

    Inba, Masafumi; Yanagida, Hirotaka; Tamura, Yasutaka

    2008-05-01

    Motion detection in ultrasound image sequences using tensor voting is described. We have been developing an ultrasound imaging system adopting a combination of coded excitation and synthetic aperture focusing techniques. In our method, frame rate of the system at distance of 150 mm reaches 5000 frame/s. Sparse array and short duration coded ultrasound signals are used for high-speed data acquisition. However, many artifacts appear in the reconstructed image sequences because of the incompleteness of the transmitted code. To reduce the artifacts, we have examined the application of tensor voting to the imaging method which adopts both coded excitation and synthetic aperture techniques. In this study, the basis of applying tensor voting and the motion detection method to ultrasound images is derived. It was confirmed that velocity detection and feature enhancement are possible using tensor voting in the time and space of simulated ultrasound three-dimensional image sequences.

  16. Camouflage target detection via hyperspectral imaging plus information divergence measurement

    Science.gov (United States)

    Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Ji, Yiqun; Shen, Weimin

    2016-01-01

    Target detection is one of most important applications in remote sensing. Nowadays accurate camouflage target distinction is often resorted to spectral imaging technique due to its high-resolution spectral/spatial information acquisition ability as well as plenty of data processing methods. In this paper, hyper-spectral imaging technique together with spectral information divergence measure method is used to solve camouflage target detection problem. A self-developed visual-band hyper-spectral imaging device is adopted to collect data cubes of certain experimental scene before spectral information divergences are worked out so as to discriminate target camouflage and anomaly. Full-band information divergences are measured to evaluate target detection effect visually and quantitatively. Information divergence measurement is proved to be a low-cost and effective tool for target detection task and can be further developed to other target detection applications beyond spectral imaging technique.

  17. Borehole imaging tool detects well bore fractures

    International Nuclear Information System (INIS)

    Ma, T.A.; Bigelow, E.L.

    1993-01-01

    This paper reports on borehole imaging data which can provide high quality geological and petrophysical information to improve fracture identification, dip computations, and lithology determinations in a well bore. The ability to visually quantify the area of a borehole wall occupied by fractures and vugs enhances reservoir characterization and well completion operations. The circumferential borehole imaging log (CBIL) instrument is an acoustic logging device designed to produce a map of the entire borehole wall. The visual images can confirm computed dips and the geological features related to dip. Borehole geometry, including breakout, are accurately described by complete circumferential caliper measurements, which is important information for drilling and completion engineers. In may reservoirs, the images can identify porosity type, bedding characteristics, and petrophysical parameters

  18. High resolution PET breast imager with improved detection efficiency

    Science.gov (United States)

    Majewski, Stanislaw

    2010-06-08

    A highly efficient PET breast imager for detecting lesions in the entire breast including those located close to the patient's chest wall. The breast imager includes a ring of imaging modules surrounding the imaged breast. Each imaging module includes a slant imaging light guide inserted between a gamma radiation sensor and a photodetector. The slant light guide permits the gamma radiation sensors to be placed in close proximity to the skin of the chest wall thereby extending the sensitive region of the imager to the base of the breast. Several types of photodetectors are proposed for use in the detector modules, with compact silicon photomultipliers as the preferred choice, due to its high compactness. The geometry of the detector heads and the arrangement of the detector ring significantly reduce dead regions thereby improving detection efficiency for lesions located close to the chest wall.

  19. A Review of Imaging Methods for Prostate Cancer Detection

    Directory of Open Access Journals (Sweden)

    Saradwata Sarkar

    2016-01-01

    Full Text Available Imaging is playing an increasingly important role in the detection of prostate cancer (PCa. This review summarizes the key imaging modalities–multiparametric ultrasound (US, multiparametric magnetic resonance imaging (MRI, MRI-US fusion imaging, and positron emission tomography (PET imaging–-used in the diagnosis and localization of PCa. Emphasis is laid on the biological and functional characteristics of tumors that rationalize the use of a specific imaging technique. Changes to anatomical architecture of tissue can be detected by anatomical grayscale US and T2-weighted MRI. Tumors are known to progress through angiogenesis–-a fact exploited by Doppler and contrast-enhanced US and dynamic contrast-enhanced MRI. The increased cellular density of tumors is targeted by elastography and diffusion-weighted MRI. PET imaging employs several different radionuclides to target the metabolic and cellular activities during tumor growth. Results from studies using these various imaging techniques are discussed and compared.

  20. Adapting Local Features for Face Detection in Thermal Image

    Directory of Open Access Journals (Sweden)

    Chao Ma

    2017-11-01

    Full Text Available A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses. We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

  1. Cascaded image analysis for dynamic crack detection in material testing

    Science.gov (United States)

    Hampel, U.; Maas, H.-G.

    Concrete probes in civil engineering material testing often show fissures or hairline-cracks. These cracks develop dynamically. Starting at a width of a few microns, they usually cannot be detected visually or in an image of a camera imaging the whole probe. Conventional image analysis techniques will detect fissures only if they show a width in the order of one pixel. To be able to detect and measure fissures with a width of a fraction of a pixel at an early stage of their development, a cascaded image analysis approach has been developed, implemented and tested. The basic idea of the approach is to detect discontinuities in dense surface deformation vector fields. These deformation vector fields between consecutive stereo image pairs, which are generated by cross correlation or least squares matching, show a precision in the order of 1/50 pixel. Hairline-cracks can be detected and measured by applying edge detection techniques such as a Sobel operator to the results of the image matching process. Cracks will show up as linear discontinuities in the deformation vector field and can be vectorized by edge chaining. In practical tests of the method, cracks with a width of 1/20 pixel could be detected, and their width could be determined at a precision of 1/50 pixel.

  2. Familiarization and Detection of Green Monopropellants Image

    Science.gov (United States)

    Coan, Mary R.

    2015-01-01

    Ammonium dinitramide (ADN) and hydroxyl ammonium nitrate (HAN) are green monopropellants which will be appearing at Kennedy Space Center (KSC) for processing in the next few years. These are relatively safe replacements for hydrazine as a monopropellant; however, little is known about methods of leak detection, vapor scrubbing, air emissions, or cleanup that will be required for safe and environmentally benign operations at KSC. The goal of this work is to develop leak detection and related technologies for the two new green monopropellants.

  3. VISION BASED OBSTACLE DETECTION IN UAV IMAGING

    Directory of Open Access Journals (Sweden)

    S. Badrloo

    2017-08-01

    Full Text Available Detecting and preventing incidence with obstacles is crucial in UAV navigation and control. Most of the common obstacle detection techniques are currently sensor-based. Small UAVs are not able to carry obstacle detection sensors such as radar; therefore, vision-based methods are considered, which can be divided into stereo-based and mono-based techniques. Mono-based methods are classified into two groups: Foreground-background separation, and brain-inspired methods. Brain-inspired methods are highly efficient in obstacle detection; hence, this research aims to detect obstacles using brain-inspired techniques, which try to enlarge the obstacle by approaching it. A recent research in this field, has concentrated on matching the SIFT points along with, SIFT size-ratio factor and area-ratio of convex hulls in two consecutive frames to detect obstacles. This method is not able to distinguish between near and far obstacles or the obstacles in complex environment, and is sensitive to wrong matched points. In order to solve the above mentioned problems, this research calculates the dist-ratio of matched points. Then, each and every point is investigated for Distinguishing between far and close obstacles. The results demonstrated the high efficiency of the proposed method in complex environments.

  4. Effects of image processing on the detective quantum efficiency

    Energy Technology Data Exchange (ETDEWEB)

    Park, Hye-Suk; Kim, Hee-Joung; Cho, Hyo-Min; Lee, Chang-Lae; Lee, Seung-Wan; Choi, Yu-Na [Yonsei University, Wonju (Korea, Republic of)

    2010-02-15

    The evaluation of image quality is an important part of digital radiography. The modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE) are widely accepted measurements of the digital radiographic system performance. However, as the methodologies for such characterization have not been standardized, it is difficult to compare directly reported the MTF, NPS, and DQE results. In this study, we evaluated the effect of an image processing algorithm for estimating the MTF, NPS, and DQE. The image performance parameters were evaluated using the international electro-technical commission (IEC 62220-1)-defined RQA5 radiographic techniques. Computed radiography (CR) posterior-anterior (PA) images of a hand for measuring the signal to noise ratio (SNR), the slit images for measuring the MTF, and the white images for measuring the NPS were obtained, and various multi-Scale image contrast amplification (MUSICA) factors were applied to each of the acquired images. All of the modifications of the images obtained by using image processing had a considerable influence on the evaluated image quality. In conclusion, the control parameters of image processing can be accounted for evaluating characterization of image quality in same way. The results of this study should serve as a baseline for based on evaluating imaging systems and their imaging characteristics by MTF, NPS, and DQE measurements.

  5. Effects of image processing on the detective quantum efficiency

    International Nuclear Information System (INIS)

    Park, Hye-Suk; Kim, Hee-Joung; Cho, Hyo-Min; Lee, Chang-Lae; Lee, Seung-Wan; Choi, Yu-Na

    2010-01-01

    The evaluation of image quality is an important part of digital radiography. The modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE) are widely accepted measurements of the digital radiographic system performance. However, as the methodologies for such characterization have not been standardized, it is difficult to compare directly reported the MTF, NPS, and DQE results. In this study, we evaluated the effect of an image processing algorithm for estimating the MTF, NPS, and DQE. The image performance parameters were evaluated using the international electro-technical commission (IEC 62220-1)-defined RQA5 radiographic techniques. Computed radiography (CR) posterior-anterior (PA) images of a hand for measuring the signal to noise ratio (SNR), the slit images for measuring the MTF, and the white images for measuring the NPS were obtained, and various multi-Scale image contrast amplification (MUSICA) factors were applied to each of the acquired images. All of the modifications of the images obtained by using image processing had a considerable influence on the evaluated image quality. In conclusion, the control parameters of image processing can be accounted for evaluating characterization of image quality in same way. The results of this study should serve as a baseline for based on evaluating imaging systems and their imaging characteristics by MTF, NPS, and DQE measurements.

  6. Natural-pose hand detection in low-resolution images

    Directory of Open Access Journals (Sweden)

    Nyan Bo Bo1

    2009-07-01

    Full Text Available Robust real-time hand detection and tracking in video sequences would enable many applications in areas as diverse ashuman-computer interaction, robotics, security and surveillance, and sign language-based systems. In this paper, we introducea new approach for detecting human hands that works on single, cluttered, low-resolution images. Our prototype system, whichis primarily intended for security applications in which the images are noisy and low-resolution, is able to detect hands as smallas 2424 pixels in cluttered scenes. The system uses grayscale appearance information to classify image sub-windows as eithercontaining or not containing a human hand very rapidly at the cost of a high false positive rate. To improve on the false positiverate of the main classifier without affecting its detection rate, we introduce a post-processor system that utilizes the geometricproperties of skin color blobs. When we test our detector on a test image set containing 106 hands, 92 of those hands aredetected (86.8% detection rate, with an average false positive rate of 1.19 false positive detections per image. The rapiddetection speed, the high detection rate of 86.8%, and the low false positive rate together ensure that our system is useable asthe main detector in a diverse variety of applications requiring robust hand detection and tracking in low-resolution, clutteredscenes.

  7. Video library for video imaging detection at intersection stop lines.

    Science.gov (United States)

    2010-04-01

    The objective of this activity was to record video that could be used for controlled : evaluation of video image vehicle detection system (VIVDS) products and software upgrades to : existing products based on a list of conditions that might be diffic...

  8. Incorporating Spatial Information for Microaneurysm Detection in Retinal Images

    Directory of Open Access Journals (Sweden)

    Mohamed M. Habib

    2017-06-01

    Full Text Available The presence of microaneurysms(MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR. This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method.

  9. Ferritin protein imaging and detection by magnetic force microscopy.

    Science.gov (United States)

    Hsieh, Chiung-Wen; Zheng, Bin; Hsieh, Shuchen

    2010-03-14

    Magnetic force microscopy was used to image and detect ferritin proteins and the strength of the magnetic signal is discussed, revealing a large workable lift height between the magnetic tip and the ferritin sample.

  10. Renal fine structures detected by NMR imaging

    International Nuclear Information System (INIS)

    Zilch, H.G.

    1986-01-01

    A significantly improved image quality is achieved by the technique described, as compared to the magnetic resonance data obtained so far. The detailed analysis of the kidney goes as deep as into anatomic fine structures, and there is reason to hope for far better diagnostic details. (orig.) [de

  11. DWT-SATS Based Detection of Image Region Cloning

    OpenAIRE

    Michael Zimba

    2014-01-01

    A duplicated image region may be subjected to a number of attacks such as noise addition, compression, reflection, rotation, and scaling with the intention of either merely mating it to its targeted neighborhood or preventing its detection. In this paper, we present an effective and robust method of detecting duplicated regions inclusive of those affected by the various attacks. In order to reduce the dimension of the image, the proposed algorithm firstly performs discrete wavelet transform, ...

  12. Image recognition on raw and processed potato detection: a review

    Science.gov (United States)

    Qi, Yan-nan; Lü, Cheng-xu; Zhang, Jun-ning; Li, Ya-shuo; Zeng, Zhen; Mao, Wen-hua; Jiang, Han-lu; Yang, Bing-nan

    2018-02-01

    Objective: Chinese potato staple food strategy clearly pointed out the need to improve potato processing, while the bottleneck of this strategy is technology and equipment of selection of appropriate raw and processed potato. The purpose of this paper is to summarize the advanced raw and processed potato detection methods. Method: According to consult research literatures in the field of image recognition based potato quality detection, including the shape, weight, mechanical damage, germination, greening, black heart, scab potato etc., the development and direction of this field were summarized in this paper. Result: In order to obtain whole potato surface information, the hardware was built by the synchronous of image sensor and conveyor belt to achieve multi-angle images of a single potato. Researches on image recognition of potato shape are popular and mature, including qualitative discrimination on abnormal and sound potato, and even round and oval potato, with the recognition accuracy of more than 83%. Weight is an important indicator for potato grading, and the image classification accuracy presents more than 93%. The image recognition of potato mechanical damage focuses on qualitative identification, with the main affecting factors of damage shape and damage time. The image recognition of potato germination usually uses potato surface image and edge germination point. Both of the qualitative and quantitative detection of green potato have been researched, currently scab and blackheart image recognition need to be operated using the stable detection environment or specific device. The image recognition of processed potato mainly focuses on potato chips, slices and fries, etc. Conclusion: image recognition as a food rapid detection tool have been widely researched on the area of raw and processed potato quality analyses, its technique and equipment have the potential for commercialization in short term, to meet to the strategy demand of development potato as

  13. Sonar Image Enhancements for Improved Detection of Sea Mines

    DEFF Research Database (Denmark)

    Jespersen, Karl; Sørensen, Helge Bjarup Dissing; Zerr, Benoit

    1999-01-01

    In this paper, five methods for enhancing sonar images prior to automatic detection of sea mines are investigated. Two of the methods have previously been published in connection with detection systems and serve as reference. The three new enhancement approaches are variance stabilizing log...... transform, nonlinear filtering, and pixel averaging for speckle reduction. The effect of the enhancement step is tested by using the full prcessing chain i.e. enhancement, detection and thresholding to determine the number of detections and false alarms. Substituting different enhancement algorithms...... in the processing chain gives a precise measure of the performance of the enhancement stage. The test is performed using a sonar image database with images ranging from very simple to very complex. The result of the comparison indicates that the new enhancement approaches improve the detection performance....

  14. Image enhancement using thermal-visible fusion for human detection

    Science.gov (United States)

    Zaihidee, Ezrinda Mohd; Hawari Ghazali, Kamarul; Zuki Saleh, Mohd

    2017-09-01

    An increased interest in detecting human beings in video surveillance system has emerged in recent years. Multisensory image fusion deserves more research attention due to the capability to improve the visual interpretability of an image. This study proposed fusion techniques for human detection based on multiscale transform using grayscale visual light and infrared images. The samples for this study were taken from online dataset. Both images captured by the two sensors were decomposed into high and low frequency coefficients using Stationary Wavelet Transform (SWT). Hence, the appropriate fusion rule was used to merge the coefficients and finally, the final fused image was obtained by using inverse SWT. From the qualitative and quantitative results, the proposed method is more superior than the two other methods in terms of enhancement of the target region and preservation of details information of the image.

  15. Automatic food detection in egocentric images using artificial intelligence technology

    Science.gov (United States)

    Our objective was to develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable devic...

  16. Detection of Defects of BGA by Tomography Imaging

    Directory of Open Access Journals (Sweden)

    Tetsuhiro SUMIMOTO

    2005-08-01

    Full Text Available To improve a cost performance and the reliability of PC boards, an inspection of BGA is required in the surface mount process. Types of defects at BGA solder joints are solder bridges, missing connections, solder voids, open connections and miss-registrations of parts. As we can find mostly solder bridges in these defects, we pick up this to detect solder bridge in a production line. The problems of image analysis for the detection of defects at BGA solder joints are the detection accuracy and image processing time according to a line speed of production. To get design data for the development of the inspection system, which can be used easily in the surface mount process, it is important to develop image analysis techniques based on the X-ray image data. We attempt to detect the characteristics of the defects of BGA based on an image analysis. Using the X-ray penetration equipment, we have captured images of an IC package to search an abnormal BGA. Besides, in order to get information in detail of an abnormal BGA, we tried to capture the tomographic images utilizing the latest imaging techniques.

  17. Detection of Fusarium in single wheat kernels using spectral Imaging

    NARCIS (Netherlands)

    Polder, G.; Heijden, van der G.W.A.M.; Waalwijk, C.; Young, I.T.

    2005-01-01

    Fusarium head blight (FHB) is a harmful fungal disease that occurs in small grains. Non-destructive detection of this disease is traditionally done using spectroscopy or image processing. In this paper the combination of these two in the form of spectral imaging is evaluated. Transmission spectral

  18. Detection of meniscal injuries using MR imaging

    International Nuclear Information System (INIS)

    Lotysch, M.; Mink, J.H.; Levy, T.; Schwartz, A.; Crues, J.V. III.

    1986-01-01

    Eighty-six knees studied by MR imaging were treated operatively and the surgical-radiologic conditions were reviewed using the following grading system of meniscal signal: grade 1, globular signal within the meniscus that does not involve an articular margin; grade 2, linear signal within the meniscus that does not extend to an articular surface; grade 3, linear or globular signal extending to an articular surface. This grading system was applied to evaluate 86 knees (172 menisci). Eleven menisci had been treated operatively at an earlier period and were excluded. Of 92 grade 1 or 2 menisci, 88 were normal at surgery. Of 69 grade 3 minisci, lesions were associated with meniscal tears in 68 at surgery. MR imaging is an accurate method of evaluating meniscal surgery

  19. A phantom design for assessment of detectability in PET imaging

    International Nuclear Information System (INIS)

    Wollenweber, Scott D.; Alessio, Adam M.; Kinahan, Paul E.

    2016-01-01

    Purpose: The primary clinical role of positron emission tomography (PET) imaging is the detection of anomalous regions of 18 F-FDG uptake, which are often indicative of malignant lesions. The goal of this work was to create a task-configurable fillable phantom for realistic measurements of detectability in PET imaging. Design goals included simplicity, adjustable feature size, realistic size and contrast levels, and inclusion of a lumpy (i.e., heterogeneous) background. Methods: The detection targets were hollow 3D-printed dodecahedral nylon features. The exostructure sphere-like features created voids in a background of small, solid non-porous plastic (acrylic) spheres inside a fillable tank. The features filled at full concentration while the background concentration was reduced due to filling only between the solid spheres. Results: Multiple iterations of feature size and phantom construction were used to determine a configuration at the limit of detectability for a PET/CT system. A full-scale design used a 20 cm uniform cylinder (head-size) filled with a fixed pattern of features at a contrast of approximately 3:1. Known signal-present and signal-absent PET sub-images were extracted from multiple scans of the same phantom and with detectability in a challenging (i.e., useful) range. These images enabled calculation and comparison of the quantitative observer detectability metrics between scanner designs and image reconstruction methods. The phantom design has several advantages including filling simplicity, wall-less contrast features, the control of the detectability range via feature size, and a clinically realistic lumpy background. Conclusions: This phantom provides a practical method for testing and comparison of lesion detectability as a function of imaging system, acquisition parameters, and image reconstruction methods and parameters.

  20. Robust simultaneous detection of coronary borders in complex images

    International Nuclear Information System (INIS)

    Sonka, M.; Winniford, M.D.; Collins, S.M.

    1995-01-01

    Visual estimation of coronary obstruction severity from angiograms suffers from poor inter- and intraobserver reproducibility and is often inaccurate. In spite of the widely recognized limitations of visual analysis, automated methods have not found widespread clinical use, in part because they too frequently fail to accurately identify vessel borders. The authors have developed a robust method for simultaneous detection of left and right coronary borders that is suitable for analysis of complex images with poor contrast, nearby or overlapping structures, or branching vessels. The reliability of the simultaneous border detection method and that of their previously reported conventional border detection method were tested in 130 complex images, selected because conventional automated border detection might be expected to fail. Conventional analysis failed to yield acceptable borders in 65/130 or 50% of images. Simultaneous border detection was much more robust (p < .001) and failed in only 15/130 or 12% of complex images. Simultaneous border detection identified stenosis diameters that correlated significantly better with observer-derived stenosis diameters than did diameters obtained with conventional border detection (p < 0.001). Simultaneous detection of left and right coronary borders is highly robust and has substantial promise for enhancing the utility of quantitative coronary angiography in the clinical setting

  1. Analytic 3D image reconstruction using all detected events

    International Nuclear Information System (INIS)

    Kinahan, P.E.; Rogers, J.G.

    1988-11-01

    We present the results of testing a previously presented algorithm for three-dimensional image reconstruction that uses all gamma-ray coincidence events detected by a PET volume-imaging scanner. By using two iterations of an analytic filter-backprojection method, the algorithm is not constrained by the requirement of a spatially invariant detector point spread function, which limits normal analytic techniques. Removing this constraint allows the incorporation of all detected events, regardless of orientation, which improves the statistical quality of the final reconstructed image

  2. Application of image processing technology in yarn hairiness detection

    Directory of Open Access Journals (Sweden)

    Guohong ZHANG

    2016-02-01

    Full Text Available Digital image processing technology is one of the new methods for yarn detection, which can realize the digital characterization and objective evaluation of yarn appearance. This paper overviews the current status of development and application of digital image processing technology used for yarn hairiness evaluation, and analyzes and compares the traditional detection methods and this new developed method. Compared with the traditional methods, the image processing technology based method is more objective, fast and accurate, which is the vital development trend of the yarn appearance evaluation.

  3. INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION

    Directory of Open Access Journals (Sweden)

    T.-A. Teo

    2016-06-01

    Full Text Available The image quality plays an important role for Unmanned Aerial Vehicle (UAV’s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS, which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear method to degree-of-motion-blur (bmotion based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM classifier and extracted features (i.e. image information, POS data, blinear and bmotion to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.

  4. Automatic correspondence detection in mammogram and breast tomosynthesis images

    Science.gov (United States)

    Ehrhardt, Jan; Krüger, Julia; Bischof, Arpad; Barkhausen, Jörg; Handels, Heinz

    2012-02-01

    Two-dimensional mammography is the major imaging modality in breast cancer detection. A disadvantage of mammography is the projective nature of this imaging technique. Tomosynthesis is an attractive modality with the potential to combine the high contrast and high resolution of digital mammography with the advantages of 3D imaging. In order to facilitate diagnostics and treatment in the current clinical work-flow, correspondences between tomosynthesis images and previous mammographic exams of the same women have to be determined. In this paper, we propose a method to detect correspondences in 2D mammograms and 3D tomosynthesis images automatically. In general, this 2D/3D correspondence problem is ill-posed, because a point in the 2D mammogram corresponds to a line in the 3D tomosynthesis image. The goal of our method is to detect the "most probable" 3D position in the tomosynthesis images corresponding to a selected point in the 2D mammogram. We present two alternative approaches to solve this 2D/3D correspondence problem: a 2D/3D registration method and a 2D/2D mapping between mammogram and tomosynthesis projection images with a following back projection. The advantages and limitations of both approaches are discussed and the performance of the methods is evaluated qualitatively and quantitatively using a software phantom and clinical breast image data. Although the proposed 2D/3D registration method can compensate for moderate breast deformations caused by different breast compressions, this approach is not suitable for clinical tomosynthesis data due to the limited resolution and blurring effects perpendicular to the direction of projection. The quantitative results show that the proposed 2D/2D mapping method is capable of detecting corresponding positions in mammograms and tomosynthesis images automatically for 61 out of 65 landmarks. The proposed method can facilitate diagnosis, visual inspection and comparison of 2D mammograms and 3D tomosynthesis images for

  5. Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning

    Science.gov (United States)

    Leavesley, Silas J.; Deal, Joshua; Hill, Shante; Martin, Will A.; Lall, Malvika; Lopez, Carmen; Rider, Paul F.; Rich, Thomas C.; Boudreaux, Carole W.

    2018-02-01

    Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices - likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image. We have demonstrated that hyperspectral imaging approaches which scan the fluorescence excitation spectrum can provide increased signal strength and faster imaging, compared to traditional emission-scanning approaches. We have also demonstrated that excitation-scanning approaches may be able to detect spectral differences between colonic adenomas and adenocarcinomas and normal mucosa in flash-frozen tissues. Here, we report feasibility results from using excitation-scanning hyperspectral imaging to screen pairs of fresh tumoral and nontumoral colorectal tissues. Tissues were imaged using a novel hyperspectral imaging fluorescence excitation scanning microscope, sampling a wavelength range of 360-550 nm, at 5 nm increments. Image data were corrected to achieve a NIST-traceable flat spectral response. Image data were then analyzed using a range of supervised and unsupervised classification approaches within ENVI software (Harris Geospatial Solutions). Supervised classification resulted in >99% accuracy for single-patient image data, but only 64% accuracy for multi-patient classification (n=9 to date), with the drop in accuracy due to increased false-positive detection rates. Hence, initial data indicate that this approach may be a viable detection approach, but that larger patient sample sizes need to be evaluated and the effects of inter-patient variability studied.

  6. On the detection of pornographic digital images

    Science.gov (United States)

    Schettini, Raimondo; Brambilla, Carla; Cusano, Claudio; Ciocca, Gianluigi

    2003-06-01

    The paper addresses the problem of distinguishing between pornographic and non-pornographic photographs, for the design of semantic filters for the web. Both, decision forests of trees built according to CART (Classification And Regression Trees) methodology and Support Vectors Machines (SVM), have been used to perform the classification. The photographs are described by a set of low-level features, features that can be automatically computed simply on gray-level and color representation of the image. The database used in our experiments contained 1500 photographs, 750 of which labeled as pornographic on the basis of the independent judgement of several viewers.

  7. Automatic detection of regions of interest in mammographic images

    Science.gov (United States)

    Cheng, Erkang; Ling, Haibin; Bakic, Predrag R.; Maidment, Andrew D. A.; Megalooikonomou, Vasileios

    2011-03-01

    This work is a part of our ongoing study aimed at comparing the topology of anatomical branching structures with the underlying image texture. Detection of regions of interest (ROIs) in clinical breast images serves as the first step in development of an automated system for image analysis and breast cancer diagnosis. In this paper, we have investigated machine learning approaches for the task of identifying ROIs with visible breast ductal trees in a given galactographic image. Specifically, we have developed boosting based framework using the AdaBoost algorithm in combination with Haar wavelet features for the ROI detection. Twenty-eight clinical galactograms with expert annotated ROIs were used for training. Positive samples were generated by resampling near the annotated ROIs, and negative samples were generated randomly by image decomposition. Each detected ROI candidate was given a confidences core. Candidate ROIs with spatial overlap were merged and their confidence scores combined. We have compared three strategies for elimination of false positives. The strategies differed in their approach to combining confidence scores by summation, averaging, or selecting the maximum score.. The strategies were compared based upon the spatial overlap with annotated ROIs. Using a 4-fold cross-validation with the annotated clinical galactographic images, the summation strategy showed the best performance with 75% detection rate. When combining the top two candidates, the selection of maximum score showed the best performance with 96% detection rate.

  8. Assessment and Development of Microwave Imaging for Breast Cancer Detection

    DEFF Research Database (Denmark)

    Jensen, Peter Damsgaard

    At the Technical University of Denmark (DTU), a 3D tomographic microwave imaging system is currently being developed with the aim of using nonlinear microwave imaging for breast-cancer detection. The imaging algorithm used in the system is based on an iterative Newton-type scheme. In this algorithm...... used in the microwave tomographic imaging system is presented. Non-linear microwave tomographic imaging of the breast is a challenging computational problem. The breast is heterogeneous and contains several high-contrast and lossy regions, resulting in large differences in the measured signal levels....... This implies that special care must be taken when the imaging problem is formulated. Under such conditions, microwave imaging systems will most often be considerably more sensitive to changes in the electromagnetic properties in certain regions of the breast. The result is that the parameters might...

  9. THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

    Directory of Open Access Journals (Sweden)

    O. Akcay

    2017-05-01

    Full Text Available A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER and Speeded Up Robust Features (SURF algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

  10. Salient man-made structure detection in infrared images

    Science.gov (United States)

    Li, Dong-jie; Zhou, Fu-gen; Jin, Ting

    2013-09-01

    Target detection, segmentation and recognition is a hot research topic in the field of image processing and pattern recognition nowadays, among which salient area or object detection is one of core technologies of precision guided weapon. Many theories have been raised in this paper; we detect salient objects in a series of input infrared images by using the classical feature integration theory and Itti's visual attention system. In order to find the salient object in an image accurately, we present a new method to solve the edge blur problem by calculating and using the edge mask. We also greatly improve the computing speed by improving the center-surround differences method. Unlike the traditional algorithm, we calculate the center-surround differences through rows and columns separately. Experimental results show that our method is effective in detecting salient object accurately and rapidly.

  11. Roi Detection and Vessel Segmentation in Retinal Image

    Science.gov (United States)

    Sabaz, F.; Atila, U.

    2017-11-01

    Diabetes disrupts work by affecting the structure of the eye and afterwards leads to loss of vision. Depending on the stage of disease that called diabetic retinopathy, there are sudden loss of vision and blurred vision problems. Automated detection of vessels in retinal images is a useful study to diagnose eye diseases, disease classification and other clinical trials. The shape and structure of the vessels give information about the severity of the disease and the stage of the disease. Automatic and fast detection of vessels allows for a quick diagnosis of the disease and the treatment process to start shortly. ROI detection and vessel extraction methods for retinal image are mentioned in this study. It is shown that the Frangi filter used in image processing can be successfully used in detection and extraction of vessels.

  12. ROI DETECTION AND VESSEL SEGMENTATION IN RETINAL IMAGE

    Directory of Open Access Journals (Sweden)

    F. Sabaz

    2017-11-01

    Full Text Available Diabetes disrupts work by affecting the structure of the eye and afterwards leads to loss of vision. Depending on the stage of disease that called diabetic retinopathy, there are sudden loss of vision and blurred vision problems. Automated detection of vessels in retinal images is a useful study to diagnose eye diseases, disease classification and other clinical trials. The shape and structure of the vessels give information about the severity of the disease and the stage of the disease. Automatic and fast detection of vessels allows for a quick diagnosis of the disease and the treatment process to start shortly. ROI detection and vessel extraction methods for retinal image are mentioned in this study. It is shown that the Frangi filter used in image processing can be successfully used in detection and extraction of vessels.

  13. Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

    International Nuclear Information System (INIS)

    Samala, Ravi K; Chan, Heang-Ping; Lu, Yao; Hadjiiski, Lubomir M; Wei, Jun; Helvie, Mark A

    2014-01-01

    This paper describes a new approach to detect microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) via its planar projection (PPJ) image. With IRB approval, two-view (cranio-caudal and mediolateral oblique views) DBTs of human subject breasts were obtained with a GE GEN2 prototype DBT system that acquires 21 projection angles spanning 60° in 3° increments. A data set of 307 volumes (154 human subjects) was divided by case into independent training (127 with MCs) and test sets (104 with MCs and 76 free of MCs). A simultaneous algebraic reconstruction technique with multiscale bilateral filtering (MSBF) regularization was used to enhance microcalcifications and suppress noise. During the MSBF regularized reconstruction, the DBT volume was separated into high frequency (HF) and low frequency components representing microcalcifications and larger structures. At the final iteration, maximum intensity projection was applied to the regularized HF volume to generate a PPJ image that contained MCs with increased contrast-to-noise ratio (CNR) and reduced search space. High CNR objects in the PPJ image were extracted and labeled as microcalcification candidates. Convolution neural network trained to recognize the image pattern of microcalcifications was used to classify the candidates into true calcifications and tissue structures and artifacts. The remaining microcalcification candidates were grouped into MCs by dynamic conditional clustering based on adaptive CNR threshold and radial distance criteria. False positive (FP) clusters were further reduced using the number of candidates in a cluster, CNR and size of microcalcification candidates. At 85% sensitivity an FP rate of 0.71 and 0.54 was achieved for view- and case-based sensitivity, respectively, compared to 2.16 and 0.85 achieved in DBT. The improvement was significant (p-value = 0.003) by JAFROC analysis. (paper)

  14. Automatic Detection of Vehicles Using Intensity Laser and Anaglyph Image

    Directory of Open Access Journals (Sweden)

    Hideo Araki

    2006-12-01

    Full Text Available In this work is presented a methodology to automatic car detection motion presents in digital aerial image on urban area using intensity, anaglyph and subtracting images. The anaglyph image is used to identify the motion cars on the expose take, because the cars provide red color due the not homology between objects. An implicit model was developed to provide a digital pixel value that has the specific propriety presented early, using the ratio between the RGB color of car object in the anaglyph image. The intensity image is used to decrease the false positive and to do the processing to work into roads and streets. The subtracting image is applied to decrease the false positives obtained due the markings road. The goal of this paper is automatically detect motion cars presents in digital aerial image in urban areas. The algorithm implemented applies normalization on the left and right images and later form the anaglyph with using the translation. The results show the applicability of proposed method and it potentiality on the automatic car detection and presented the performance of proposed methodology.

  15. A Modified Harris Corner Detection for Breast IR Image

    Directory of Open Access Journals (Sweden)

    Chia-Yen Lee

    2014-01-01

    Full Text Available Harris corner detectors, which depend on strong invariance and a local autocorrelation function, display poor detection performance for infrared (IR images with low contrast and nonobvious edges. In addition, feature points detected by Harris corner detectors are clustered due to the numerous nonlocal maxima. This paper proposes a modified Harris corner detector that includes two unique steps for processing IR images in order to overcome the aforementioned problems. Image contrast enhancement based on a generalized form of histogram equalization (HE combined with adjusting the intensity resolution causes false contours on IR images to acquire obvious edges. Adaptive nonmaximal suppression based on eliminating neighboring pixels avoids the clustered features. Preliminary results show that the proposed method can solve the clustering problem and successfully identify the representative feature points of IR breast images.

  16. Canny Edge Detection in Cross-Spectral Fused Images

    Directory of Open Access Journals (Sweden)

    Patricia Suárez

    2017-02-01

    Full Text Available Considering that the images of different spectra provide an ample information that helps a lo in the process of identification and distinction of objects that have unique spectral signatures. In this paper, the use of cross-spectral images in the process of edge detection is evaluated. This study aims to assess the Canny edge detector with two variants. The first relates to the use of merged cross-spectral images and the second the inclusion of morphological filters. To ensure the quality of the data used in this study the GQM (Goal-Question- Metrics, framework, was applied to reduce noise and increase the entropy on images. The metrics obtained in the experiments confirm that the quantity and quality of the detected edges increases significantly after the inclusion of a morphological filter and a channel of near infrared spectrum in the merged images.

  17. ARCOCT: Automatic detection of lumen border in intravascular OCT images.

    Science.gov (United States)

    Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

    2017-11-01

    Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border

  18. Uncertainty Detection for NIF Normal Pointing Images

    International Nuclear Information System (INIS)

    Awwal, A S; Law, C; Ferguson, S W

    2007-01-01

    The National Ignition Facility at the Lawrence Livermore National Laboratory when completed in 2009, will deliver 192-beams aligned precisely at the center of the target chamber producing extreme energy densities and pressures. Video images of laser beams along the beam path are used by automatic alignment algorithms to determine the position of the beams for alignment purposes. However, noise and other optical effects may affect the accuracy of the calculated beam location. Realistic estimation of the uncertainty is necessary to assure that the beam is monitored within the clear optical path. When the uncertainty is above a certain threshold the automated alignment operation is suspended and control of the beam is transferred to a human operator. This work describes our effort to quantify the uncertainty of measurement of the most common alignment beam

  19. Clustered Regularly Interspaced Short Palindromic Repeats/Cas9 Triggered Isothermal Amplification for Site-Specific Nucleic Acid Detection.

    Science.gov (United States)

    Huang, Mengqi; Zhou, Xiaoming; Wang, Huiying; Xing, Da

    2018-02-06

    A novel CRISPR/Cas9 triggered isothermal exponential amplification reaction (CAS-EXPAR) strategy based on CRISPR/Cas9 cleavage and nicking endonuclease (NEase) mediated nucleic acids amplification was developed for rapid and site-specific nucleic acid detection. CAS-EXPAR was primed by the target DNA fragment produced by cleavage of CRISPR/Cas9, and the amplification reaction performed cyclically to generate a large number of DNA replicates which were detected using a real-time fluorescence monitoring method. This strategy that combines the advantages of CRISPR/Cas9 and exponential amplification showed high specificity as well as rapid amplification kinetics. Unlike conventional nucleic acids amplification reactions, CAS-EXPAR does not require exogenous primers, which often cause target-independent amplification. Instead, primers are first generated by Cas9/sgRNA directed site-specific cleavage of target and accumulated during the reaction. It was demonstrated this strategy gave a detection limit of 0.82 amol and showed excellent specificity in discriminating single-base mismatch. Moreover, the applicability of this method to detect DNA methylation and L. monocytogenes total RNA was also verified. Therefore, CAS-EXPAR may provide a new paradigm for efficient nucleic acid amplification and hold the potential for molecular diagnostic applications.

  20. Enhancement and denoising of mammographic images for breast disease detection

    International Nuclear Information System (INIS)

    Yazdani, S.; Yusof, R.; Karimian, A.; Hematian, A.; Yousefi, M.

    2012-01-01

    In these two decades breast cancer is one of the leading cause of death among women. In breast cancer research, Mammographic Image is being assessed as a potential tool for detecting breast disease and investigating response to chemotherapy. In first stage of breast disease discovery, the density measurement of the breast in mammographic images provides very useful information. Because of the importance of the role of mammographic images the need for accurate and robust automated image enhancement techniques is becoming clear. Mammographic images have some disadvantages such as, the high dependence of contrast upon the way the image is acquired, weak distinction in splitting cyst from tumor, intensity non uniformity, the existence of noise, etc. These limitations make problem to detect the typical signs such as masses and microcalcifications. For this reason, denoising and enhancing the quality of mammographic images is very important. The method which is used in this paper is in spatial domain which its input includes high, intermediate and even very low contrast mammographic images based on specialist physician's view, while its output is processed images that show the input images with higher quality, more contrast and more details. In this research, 38 mammographic images have been used. The result of purposed method shows details of abnormal zones and the areas with defects so that specialist could explore these zones more accurately and it could be deemed as an index for cancer diagnosis. In this study, mammographic images are initially converted into digital images and then to increase spatial resolution power, their noise is reduced and consequently their contrast is improved. The results demonstrate effectiveness and efficiency of the proposed methods. (authors)

  1. Edge detection based on computational ghost imaging with structured illuminations

    Science.gov (United States)

    Yuan, Sheng; Xiang, Dong; Liu, Xuemei; Zhou, Xin; Bing, Pibin

    2018-03-01

    Edge detection is one of the most important tools to recognize the features of an object. In this paper, we propose an optical edge detection method based on computational ghost imaging (CGI) with structured illuminations which are generated by an interference system. The structured intensity patterns are designed to make the edge of an object be directly imaged from detected data in CGI. This edge detection method can extract the boundaries for both binary and grayscale objects in any direction at one time. We also numerically test the influence of distance deviations in the interference system on edge extraction, i.e., the tolerance of the optical edge detection system to distance deviation. Hopefully, it may provide a guideline for scholars to build an experimental system.

  2. Airplane detection in remote sensing images using convolutional neural networks

    Science.gov (United States)

    Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

    2018-03-01

    Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

  3. The pre-image problem for Laplacian Eigenmaps utilizing L 1 regularization with applications to data fusion

    International Nuclear Information System (INIS)

    Cloninger, Alexander; Czaja, Wojciech; Doster, Timothy

    2017-01-01

    As the popularity of non-linear manifold learning techniques such as kernel PCA and Laplacian Eigenmaps grows, vast improvements have been seen in many areas of data processing, including heterogeneous data fusion and integration. One problem with the non-linear techniques, however, is the lack of an easily calculable pre-image. Existence of such pre-image would allow visualization of the fused data not only in the embedded space, but also in the original data space. The ability to make such comparisons can be crucial for data analysts and other subject matter experts who are the end users of novel mathematical algorithms. In this paper, we propose a pre-image algorithm for Laplacian Eigenmaps. Our method offers major improvements over existing techniques, which allow us to address the problem of noisy inputs and the issue of how to calculate the pre-image of a point outside the convex hull of training samples; both of which have been overlooked in previous studies in this field. We conclude by showing that our pre-image algorithm, combined with feature space rotations, allows us to recover occluded pixels of an imaging modality based off knowledge of that image measured by heterogeneous modalities. We demonstrate this data recovery on heterogeneous hyperspectral (HS) cameras, as well as by recovering LIDAR measurements from HS data. (paper)

  4. The pre-image problem for Laplacian Eigenmaps utilizing L 1 regularization with applications to data fusion

    Science.gov (United States)

    Cloninger, Alexander; Czaja, Wojciech; Doster, Timothy

    2017-07-01

    As the popularity of non-linear manifold learning techniques such as kernel PCA and Laplacian Eigenmaps grows, vast improvements have been seen in many areas of data processing, including heterogeneous data fusion and integration. One problem with the non-linear techniques, however, is the lack of an easily calculable pre-image. Existence of such pre-image would allow visualization of the fused data not only in the embedded space, but also in the original data space. The ability to make such comparisons can be crucial for data analysts and other subject matter experts who are the end users of novel mathematical algorithms. In this paper, we propose a pre-image algorithm for Laplacian Eigenmaps. Our method offers major improvements over existing techniques, which allow us to address the problem of noisy inputs and the issue of how to calculate the pre-image of a point outside the convex hull of training samples; both of which have been overlooked in previous studies in this field. We conclude by showing that our pre-image algorithm, combined with feature space rotations, allows us to recover occluded pixels of an imaging modality based off knowledge of that image measured by heterogeneous modalities. We demonstrate this data recovery on heterogeneous hyperspectral (HS) cameras, as well as by recovering LIDAR measurements from HS data.

  5. Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images.

    Science.gov (United States)

    Wang, Kang; Jayadev, Chaitra; Nittala, Muneeswar G; Velaga, Swetha B; Ramachandra, Chaithanya A; Bhaskaranand, Malavika; Bhat, Sandeep; Solanki, Kaushal; Sadda, SriniVas R

    2018-03-01

    We examined the sensitivity and specificity of an automated algorithm for detecting referral-warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images. Patients with diabetes were recruited for UWF imaging. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic Retinopathy (ICDR) severity scale was considered as grounds for referral. The software automatically detected DR lesions using the previously trained classifiers and classified each image in the test set as referral-warranted or not warranted. Sensitivity, specificity and the area under the receiver operating curve (AUROC) of the algorithm were computed. The automated algorithm achieved a 91.7%/90.3% sensitivity (95% CI 90.1-93.9/80.4-89.4) with a 50.0%/53.6% specificity (95% CI 31.7-72.8/36.5-71.4) for detecting referral-warranted retinopathy at the patient/eye levels, respectively; the AUROC was 0.873/0.851 (95% CI 0.819-0.922/0.804-0.894). Diabetic retinopathy (DR) lesions were detected from Optos pseudocolour UWF images using an automated algorithm. Images were classified as referral-warranted DR with a high degree of sensitivity and moderate specificity. Automated analysis of UWF images could be of value in DR screening programmes and could allow for more complete and accurate disease staging. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

  6. Novel instrumentation of multispectral imaging technology for detecting tissue abnormity

    Science.gov (United States)

    Yi, Dingrong; Kong, Linghua

    2012-10-01

    Multispectral imaging is becoming a powerful tool in a wide range of biological and clinical studies by adding spectral, spatial and temporal dimensions to visualize tissue abnormity and the underlying biological processes. A conventional spectral imaging system includes two physically separated major components: a band-passing selection device (such as liquid crystal tunable filter and diffraction grating) and a scientific-grade monochromatic camera, and is expensive and bulky. Recently micro-arrayed narrow-band optical mosaic filter was invented and successfully fabricated to reduce the size and cost of multispectral imaging devices in order to meet the clinical requirement for medical diagnostic imaging applications. However the challenging issue of how to integrate and place the micro filter mosaic chip to the targeting focal plane, i.e., the imaging sensor, of an off-shelf CMOS/CCD camera is not reported anywhere. This paper presents the methods and results of integrating such a miniaturized filter with off-shelf CMOS imaging sensors to produce handheld real-time multispectral imaging devices for the application of early stage pressure ulcer (ESPU) detection. Unlike conventional multispectral imaging devices which are bulky and expensive, the resulting handheld real-time multispectral ESPU detector can produce multiple images at different center wavelengths with a single shot, therefore eliminates the image registration procedure required by traditional multispectral imaging technologies.

  7. An efficient method for facial component detection in thermal images

    Science.gov (United States)

    Paul, Michael; Blanik, Nikolai; Blazek, Vladimir; Leonhardt, Steffen

    2015-04-01

    A method to detect certain regions in thermal images of human faces is presented. In this approach, the following steps are necessary to locate the periorbital and the nose regions: First, the face is segmented from the background by thresholding and morphological filtering. Subsequently, a search region within the face, around its center of mass, is evaluated. Automatically computed temperature thresholds are used per subject and image or image sequence to generate binary images, in which the periorbital regions are located by integral projections. Then, the located positions are used to approximate the nose position. It is possible to track features in the located regions. Therefore, these regions are interesting for different applications like human-machine interaction, biometrics and biomedical imaging. The method is easy to implement and does not rely on any training images or templates. Furthermore, the approach saves processing resources due to simple computations and restricted search regions.

  8. Dynamic fluorescence imaging with molecular agents for cancer detection

    Science.gov (United States)

    Kwon, Sun Kuk

    Non-invasive dynamic optical imaging of small animals requires the development of a novel fluorescence imaging modality. Herein, fluorescence imaging is demonstrated with sub-second camera integration times using agents specifically targeted to disease markers, enabling rapid detection of cancerous regions. The continuous-wave fluorescence imaging acquires data with an intensified or an electron-multiplying charge-coupled device. The work presented in this dissertation (i) assessed dose-dependent uptake using dynamic fluorescence imaging and pharmacokinetic (PK) models, (ii) evaluated disease marker availability in two different xenograft tumors, (iii) compared the impact of autofluorescence in fluorescence imaging of near-infrared (NIR) vs. red light excitable fluorescent contrast agents, (iv) demonstrated dual-wavelength fluorescence imaging of angiogenic vessels and lymphatics associated with a xenograft tumor model, and (v) examined dynamic multi-wavelength, whole-body fluorescence imaging with two different fluorescent contrast agents. PK analysis showed that the uptake of Cy5.5-c(KRGDf) in xenograft tumor regions linearly increased with doses of Cy5.5-c(KRGDf) up to 1.5 nmol/mouse. Above 1.5 nmol/mouse, the uptake did not increase with doses, suggesting receptor saturation. Target to background ratio (TBR) and PK analysis for two different tumor cell lines showed that while Kaposi's sarcoma (KS1767) exhibited early and rapid uptake of Cy5.5-c(KRGDf), human melanoma tumors (M21) had non-significant TBR differences and early uptake rates similar to the contralateral normal tissue regions. The differences may be due to different compartment location of the target. A comparison of fluorescence imaging with NIR vs. red light excitable fluorescent dyes demonstrates that NIR dyes are associated with less background signal, enabling rapid tumor detection. In contrast, animals injected with red light excitable fluorescent dyes showed high autofluorescence. Dual

  9. Automatic detection and classification of damage zone(s) for incorporating in digital image correlation technique

    Science.gov (United States)

    Bhattacharjee, Sudipta; Deb, Debasis

    2016-07-01

    Digital image correlation (DIC) is a technique developed for monitoring surface deformation/displacement of an object under loading conditions. This method is further refined to make it capable of handling discontinuities on the surface of the sample. A damage zone is referred to a surface area fractured and opened in due course of loading. In this study, an algorithm is presented to automatically detect multiple damage zones in deformed image. The algorithm identifies the pixels located inside these zones and eliminate them from FEM-DIC processes. The proposed algorithm is successfully implemented on several damaged samples to estimate displacement fields of an object under loading conditions. This study shows that displacement fields represent the damage conditions reasonably well as compared to regular FEM-DIC technique without considering the damage zones.

  10. Animal Detection in Natural Images: Effects of Color and Image Database

    Science.gov (United States)

    Zhu, Weina; Drewes, Jan; Gegenfurtner, Karl R.

    2013-01-01

    The visual system has a remarkable ability to extract categorical information from complex natural scenes. In order to elucidate the role of low-level image features for the recognition of objects in natural scenes, we recorded saccadic eye movements and event-related potentials (ERPs) in two experiments, in which human subjects had to detect animals in previously unseen natural images. We used a new natural image database (ANID) that is free of some of the potential artifacts that have plagued the widely used COREL images. Color and grayscale images picked from the ANID and COREL databases were used. In all experiments, color images induced a greater N1 EEG component at earlier time points than grayscale images. We suggest that this influence of color in animal detection may be masked by later processes when measuring reation times. The ERP results of go/nogo and forced choice tasks were similar to those reported earlier. The non-animal stimuli induced bigger N1 than animal stimuli both in the COREL and ANID databases. This result indicates ultra-fast processing of animal images is possible irrespective of the particular database. With the ANID images, the difference between color and grayscale images is more pronounced than with the COREL images. The earlier use of the COREL images might have led to an underestimation of the contribution of color. Therefore, we conclude that the ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used. PMID:24130744

  11. Multispectral image feature fusion for detecting land mines

    Energy Technology Data Exchange (ETDEWEB)

    Clark, G.A.; Fields, D.J.; Sherwood, R.J. [Lawrence Livermore National Lab., CA (United States)] [and others

    1994-11-15

    Our system fuses information contained in registered images from multiple sensors to reduce the effect of clutter and improve the the ability to detect surface and buried land mines. The sensor suite currently consists if a camera that acquires images in sixible wavelength bands, du, dual-band infrared (5 micron and 10 micron) and ground penetrating radar. Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a variety of physical properties that are more separate in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, holes made by animals and natural processes, etc.) and some artifacts.

  12. Automatic Microaneurysm Detection and Characterization Through Digital Color Fundus Images

    Energy Technology Data Exchange (ETDEWEB)

    Martins, Charles; Veras, Rodrigo; Ramalho, Geraldo; Medeiros, Fatima; Ushizima, Daniela

    2008-08-29

    Ocular fundus images can provide information about retinal, ophthalmic, and even systemic diseases such as diabetes. Microaneurysms (MAs) are the earliest sign of Diabetic Retinopathy, a frequently observed complication in both type 1 and type 2 diabetes. Robust detection of MAs in digital color fundus images is critical in the development of automated screening systems for this kind of disease. Automatic grading of these images is being considered by health boards so that the human grading task is reduced. In this paper we describe segmentation and the feature extraction methods for candidate MAs detection.We show that the candidate MAs detected with the methodology have been successfully classified by a MLP neural network (correct classification of 84percent).

  13. A survey on object detection in optical remote sensing images

    Science.gov (United States)

    Cheng, Gong; Han, Junwei

    2016-07-01

    Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

  14. Detecting pits in tart cherries by hyperspectral transmission imaging

    Science.gov (United States)

    Qin, Jianwei; Lu, Renfu

    2004-11-01

    The presence of pits in processed cherry products causes safety concerns for consumers and imposes potential liability for the food industry. The objective of this research was to investigate a hyperspectral transmission imaging technique for detecting the pit in tart cherries. A hyperspectral imaging system was used to acquire transmission images from individual cherry fruit for four orientations before and after pits were removed over the spectral region between 450 nm and 1,000 nm. Cherries of three size groups (small, intermediate, and large), each with two color classes (light red and dark red) were used for determining the effect of fruit orientation, size, and color on the pit detection accuracy. Additional cherries were studied for the effect of defect (i.e., bruises) on the pit detection. Computer algorithms were developed using the neural network (NN) method to classify the cherries with and without the pit. Two types of data inputs, i.e., single spectra and selected regions of interest (ROIs), were compared. The spectral region between 690 nm and 850 nm was most appropriate for cherry pit detection. The NN with inputs of ROIs achieved higher pit detection rates ranging from 90.6% to 100%, with the average correct rate of 98.4%. Fruit orientation and color had a small effect (less than 1%) on pit detection. Fruit size and defect affected pit detection and their effect could be minimized by training the NN with properly selected cherry samples.

  15. Minimum detectable gas concentration performance evaluation method for gas leak infrared imaging detection systems.

    Science.gov (United States)

    Zhang, Xu; Jin, Weiqi; Li, Jiakun; Wang, Xia; Li, Shuo

    2017-04-01

    Thermal imaging technology is an effective means of detecting hazardous gas leaks. Much attention has been paid to evaluation of the performance of gas leak infrared imaging detection systems due to several potential applications. The minimum resolvable temperature difference (MRTD) and the minimum detectable temperature difference (MDTD) are commonly used as the main indicators of thermal imaging system performance. This paper establishes a minimum detectable gas concentration (MDGC) performance evaluation model based on the definition and derivation of MDTD. We proposed the direct calculation and equivalent calculation method of MDGC based on the MDTD measurement system. We build an experimental MDGC measurement system, which indicates the MDGC model can describe the detection performance of a thermal imaging system to typical gases. The direct calculation, equivalent calculation, and direct measurement results are consistent. The MDGC and the minimum resolvable gas concentration (MRGC) model can effectively describe the performance of "detection" and "spatial detail resolution" of thermal imaging systems to gas leak, respectively, and constitute the main performance indicators of gas leak detection systems.

  16. Automatic detection of NIL defects using microscopy and image processing

    KAUST Repository

    Pietroy, David

    2013-12-01

    Nanoimprint Lithography (NIL) is a promising technology for low cost and large scale nanostructure fabrication. This technique is based on a contact molding-demolding process, that can produce number of defects such as incomplete filling, negative patterns, sticking. In this paper, microscopic imaging combined to a specific processing algorithm is used to detect numerically defects in printed patterns. Results obtained for 1D and 2D imprinted gratings with different microscopic image magnifications are presented. Results are independent on the device which captures the image (optical, confocal or electron microscope). The use of numerical images allows the possibility to automate the detection and to compute a statistical analysis of defects. This method provides a fast analysis of printed gratings and could be used to monitor the production of such structures. © 2013 Elsevier B.V. All rights reserved.

  17. Efficient image duplicated region detection model using sequential block clustering

    Czech Academy of Sciences Publication Activity Database

    Sekeh, M. A.; Maarof, M. A.; Rohani, M. F.; Mahdian, Babak

    2013-01-01

    Roč. 10, č. 1 (2013), s. 73-84 ISSN 1742-2876 Institutional support: RVO:67985556 Keywords : Image forensic * Copy–paste forgery * Local block matching Subject RIV: IN - Informatics, Computer Science Impact factor: 0.986, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/mahdian-efficient image duplicated region detection model using sequential block clustering.pdf

  18. Performance evaluation of spot detection algorithms in fluorescence microscopy images

    CSIR Research Space (South Africa)

    Mabaso, M

    2012-10-01

    Full Text Available triggered the development of a highly sophisticated imaging tool known as fluorescence microscopy. This is used to visualise and study intracellular processes. The use of fluorescence microscopy and a specific staining method make biological molecules... was first used in astronomical applications [2] to detect isotropic objects, and was then introduced to biological applications [3]. Olivio-Marin[3] approached the problem of feature extraction based on undecimated wavelet representation of the image...

  19. Detection of Glaucoma Using Image Processing Techniques: A Critique.

    Science.gov (United States)

    Kumar, B Naveen; Chauhan, R P; Dahiya, Nidhi

    2018-01-01

    The primary objective of this article is to present a summary of different types of image processing methods employed for the detection of glaucoma, a serious eye disease. Glaucoma affects the optic nerve in which retinal ganglion cells become dead, and this leads to loss of vision. The principal cause is the increase in intraocular pressure, which occurs in open-angle and angle-closure glaucoma, the two major types affecting the optic nerve. In the early stages of glaucoma, no perceptible symptoms appear. As the disease progresses, vision starts to become hazy, leading to blindness. Therefore, early detection of glaucoma is needed for prevention. Manual analysis of ophthalmic images is fairly time-consuming and accuracy depends on the expertise of the professionals. Automatic analysis of retinal images is an important tool. Automation aids in the detection, diagnosis, and prevention of risks associated with the disease. Fundus images obtained from a fundus camera have been used for the analysis. Requisite pre-processing techniques have been applied to the image and, depending upon the technique, various classifiers have been used to detect glaucoma. The techniques mentioned in the present review have certain advantages and disadvantages. Based on this study, one can determine which technique provides an optimum result.

  20. Road detection in SAR images using a tensor voting algorithm

    Science.gov (United States)

    Shen, Dajiang; Hu, Chun; Yang, Bing; Tian, Jinwen; Liu, Jian

    2007-11-01

    In this paper, the problem of the detection of road networks in Synthetic Aperture Radar (SAR) images is addressed. Most of the previous methods extract the road by detecting lines and network reconstruction. Traditional algorithms such as MRFs, GA, Level Set, used in the progress of reconstruction are iterative. The tensor voting methodology we proposed is non-iterative, and non-sensitive to initialization. Furthermore, the only free parameter is the size of the neighborhood, related to the scale. The algorithm we present is verified to be effective when it's applied to the road extraction using the real Radarsat Image.

  1. System and method for automated object detection in an image

    Science.gov (United States)

    Kenyon, Garrett T.; Brumby, Steven P.; George, John S.; Paiton, Dylan M.; Schultz, Peter F.

    2015-10-06

    A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.

  2. Detection limits of intraoperative near infrared imaging for tumor resection.

    Science.gov (United States)

    Thurber, Greg M; Figueiredo, Jose-Luiz; Weissleder, Ralph

    2010-12-01

    The application of fluorescent molecular imaging to surgical oncology is a developing field with the potential to reduce morbidity and mortality. However, the detection thresholds and other requirements for successful intervention remain poorly understood. Here we modeled and experimentally validated depth and size of detection of tumor deposits, trade-offs in coverage and resolution of areas of interest, and required pharmacokinetics of probes based on differing levels of tumor target presentation. Three orthotopic tumor models were imaged by widefield epifluorescence and confocal microscopes, and the experimental results were compared with pharmacokinetic models and light scattering simulations to determine detection thresholds. Widefield epifluorescence imaging can provide sufficient contrast to visualize tumor margins and detect tumor deposits 3-5  mm deep based on labeled monoclonal antibodies at low objective magnification. At higher magnification, surface tumor deposits at cellular resolution are detectable at TBR ratios achieved with highly expressed antigens. A widefield illumination system with the capability for macroscopic surveying and microscopic imaging provides the greatest utility for varying surgical goals. These results have implications for system and agent designs, which ultimately should aid complete resection in most surgical beds and provide real-time feedback to obtain clean margins. © 2010 Wiley-Liss, Inc.

  3. Optic disc detection and boundary extraction in retinal images.

    Science.gov (United States)

    Basit, A; Fraz, Muhammad Moazam

    2015-04-10

    With the development of digital image processing, analysis and modeling techniques, automatic retinal image analysis is emerging as an important screening tool for early detection of ophthalmologic disorders such as diabetic retinopathy and glaucoma. In this paper, a robust method for optic disc detection and extraction of the optic disc boundary is proposed to help in the development of computer-assisted diagnosis and treatment of such ophthalmic disease. The proposed method is based on morphological operations, smoothing filters, and the marker controlled watershed transform. Internal and external markers are used to first modify the gradient magnitude image and then the watershed transformation is applied on this modified gradient magnitude image for boundary extraction. This method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. The proposed method has optic disc detection success rate of 100%, 100%, 100% and 98.9% for the DRIVE, Shifa, CHASE_DB1, and DIARETDB1 databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 61.88%, 70.96%, 45.61%, and 54.69% for these databases, respectively, which are higher than currents methods.

  4. Pedestrian detection from thermal images: A sparse representation based approach

    Science.gov (United States)

    Qi, Bin; John, Vijay; Liu, Zheng; Mita, Seiichi

    2016-05-01

    Pedestrian detection, a key technology in computer vision, plays a paramount role in the applications of advanced driver assistant systems (ADASs) and autonomous vehicles. The objective of pedestrian detection is to identify and locate people in a dynamic environment so that accidents can be avoided. With significant variations introduced by illumination, occlusion, articulated pose, and complex background, pedestrian detection is a challenging task for visual perception. Different from visible images, thermal images are captured and presented with intensity maps based objects' emissivity, and thus have an enhanced spectral range to make human beings perceptible from the cool background. In this study, a sparse representation based approach is proposed for pedestrian detection from thermal images. We first adopted the histogram of sparse code to represent image features and then detect pedestrian with the extracted features in an unimodal and a multimodal framework respectively. In the unimodal framework, two types of dictionaries, i.e. joint dictionary and individual dictionary, are built by learning from prepared training samples. In the multimodal framework, a weighted fusion scheme is proposed to further highlight the contributions from features with higher separability. To validate the proposed approach, experiments were conducted to compare with three widely used features: Haar wavelets (HWs), histogram of oriented gradients (HOG), and histogram of phase congruency (HPC) as well as two classification methods, i.e. AdaBoost and support vector machine (SVM). Experimental results on a publicly available data set demonstrate the superiority of the proposed approach.

  5. Lung Nodule Detection in CT Images using Neuro Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    M. Usman Akram

    2013-07-01

    Full Text Available Automated lung cancer detection using computer aided diagnosis (CAD is an important area in clinical applications. As the manual nodule detection is very time consuming and costly so computerized systems can be helpful for this purpose. In this paper, we propose a computerized system for lung nodule detection in CT scan images. The automated system consists of two stages i.e. lung segmentation and enhancement, feature extraction and classification. The segmentation process will result in separating lung tissue from rest of the image, and only the lung tissues under examination are considered as candidate regions for detecting malignant nodules in lung portion. A feature vector for possible abnormal regions is calculated and regions are classified using neuro fuzzy classifier. It is a fully automatic system that does not require any manual intervention and experimental results show the validity of our system.

  6. Detection of electrophysiology catheters in noisy fluoroscopy images.

    Science.gov (United States)

    Franken, Erik; Rongen, Peter; van Almsick, Markus; ter Haar Romeny, Bart

    2006-01-01

    Cardiac catheter ablation is a minimally invasive medical procedure to treat patients with heart rhythm disorders. It is useful to know the positions of the catheters and electrodes during the intervention, e.g. for the automatization of cardiac mapping. Our goal is therefore to develop a robust image analysis method that can detect the catheters in X-ray fluoroscopy images. Our method uses steerable tensor voting in combination with a catheter-specific multi-step extraction algorithm. The evaluation on clinical fluoroscopy images shows that especially the extraction of the catheter tip is successful and that the use of tensor voting accounts for a large increase in performance.

  7. Near field ice detection using infrared based optical imaging technology

    Science.gov (United States)

    Abdel-Moati, Hazem; Morris, Jonathan; Zeng, Yousheng; Corie, Martin Wesley; Yanni, Victor Garas

    2018-02-01

    If not detected and characterized, icebergs can potentially pose a hazard to oil and gas exploration, development and production operations in arctic environments as well as commercial shipping channels. In general, very large bergs are tracked and predicted using models or satellite imagery. Small and medium bergs are detectable using conventional marine radar. As icebergs decay they shed bergy bits and growlers, which are much smaller and more difficult to detect. Their low profile above the water surface, in addition to occasional relatively high seas, makes them invisible to conventional marine radar. Visual inspection is the most common method used to detect bergy bits and growlers, but the effectiveness of visual inspections is reduced by operator fatigue and low light conditions. The potential hazard from bergy bits and growlers is further increased by short detection range (<1 km). As such, there is a need for robust and autonomous near-field detection of such smaller icebergs. This paper presents a review of iceberg detection technology and explores applications for infrared imagers in the field. Preliminary experiments are performed and recommendations are made for future work, including a proposed imager design which would be suited for near field ice detection.

  8. An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery

    CSIR Research Space (South Africa)

    Mdakane, Lizwe W

    2017-06-01

    Full Text Available Oil slick events caused due to bilge leakage/dumps from ships and from other anthropogenic sources pose a threat to the aquatic ecosystem and need to be monitored on a regular basis. An automatic image-segmentation-based framework to detect oil...

  9. Scintillator Based Coded-Aperture Imaging for Neutron Detection

    International Nuclear Information System (INIS)

    Hayes, Sean-C.; Gamage, Kelum-A-A.

    2013-06-01

    In this paper we are going to assess the variations of neutron images using a series of Monte Carlo simulations. We are going to study neutron images of the same neutron source with different source locations, using a scintillator based coded-aperture system. The Monte Carlo simulations have been conducted making use of the EJ-426 neutron scintillator detector. This type of detector has a low sensitivity to gamma rays and is therefore of particular use in a system with a source that emits a mixed radiation field. From the use of different source locations, several neutron images have been produced, compared both qualitatively and quantitatively for each case. This allows conclusions to be drawn on how suited the scintillator based coded-aperture neutron imaging system is to detecting various neutron source locations. This type of neutron imaging system can be easily used to identify and locate nuclear materials precisely. (authors)

  10. An introduction to microwave imaging for breast cancer detection

    CERN Document Server

    Conceição, Raquel Cruz; O'Halloran, Martin

    2016-01-01

    This book collates past and current research on one of the most promising emerging modalities for breast cancer detection. Readers will discover how, as a standalone technology or in conjunction with another modality, microwave imaging has the potential to provide reliable, safe and comfortable breast exams at low cost. Current breast imaging modalities include X- ray, Ultrasound, Magnetic Resonance Imaging, and Positron Emission Tomography. Each of these methods suffers from limitations, including poor sensitivity or specificity, high cost, patient discomfort, and exposure to potentially harmful ionising radiation. Microwave breast imaging is based on a contrast in the dielectric properties of breast tissue that exists at microwave frequencies. The book begins by considering the anatomy and dielectric properties of the breast, contrasting historical and recent studies. Next, radar-based breast imaging algorithms are discussed, encompassing both early-stage artefact removal, and data independent and adaptive ...

  11. HDR IMAGING FOR FEATURE DETECTION ON DETAILED ARCHITECTURAL SCENES

    Directory of Open Access Journals (Sweden)

    G. Kontogianni

    2015-02-01

    Full Text Available 3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

  12. Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms

    Science.gov (United States)

    Benmoussat, M. S.; Guillaume, M.; Caulier, Y.; Spinnler, K.

    2013-11-01

    A fully-automatic approach based on the use of induction thermography and detection algorithms is proposed to inspect industrial metallic parts containing different surface and sub-surface anomalies such as open cracks, open and closed notches with different sizes and depths. A practical experimental setup is developed, where lock-in and pulsed thermography (LT and PT, respectively) techniques are used to establish a dataset of thermal images for three different mockups. Data cubes are constructed by stacking up the temporal sequence of thermogram images. After the reduction of the data space dimension by means of denoising and dimensionality reduction methods; anomaly detection algorithms are applied on the reduced data cubes. The dimensions of the reduced data spaces are automatically calculated with arbitrary criterion. The results show that, when reduced data cubes are used, the anomaly detection algorithms originally developed for hyperspectral data, the well-known Reed and Xiaoli Yu detector (RX) and the regularized adaptive RX (RARX), give good detection performances for both surface and sub-surface defects in a non-supervised way.

  13. Community detection for fluorescent lifetime microscopy image segmentation

    Science.gov (United States)

    Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Achilefu, Samuel; Nussinov, Zohar

    2014-03-01

    Multiresolution community detection (CD) method has been suggested in a recent work as an efficient method for performing unsupervised segmentation of fluorescence lifetime (FLT) images of live cell images containing fluorescent molecular probes.1 In the current paper, we further explore this method in FLT images of ex vivo tissue slices. The image processing problem is framed as identifying clusters with respective average FLTs against a background or "solvent" in FLT imaging microscopy (FLIM) images derived using NIR fluorescent dyes. We have identified significant multiresolution structures using replica correlations in these images, where such correlations are manifested by information theoretic overlaps of the independent solutions ("replicas") attained using the multiresolution CD method from different starting points. In this paper, our method is found to be more efficient than a current state-of-the-art image segmentation method based on mixture of Gaussian distributions. It offers more than 1:25 times diversity based on Shannon index than the latter method, in selecting clusters with distinct average FLTs in NIR FLIM images.

  14. Computerized detection of lacunar infarcts in brain MR images

    International Nuclear Information System (INIS)

    Uchiyama, Yoshikazu; Matsui, Atsushi; Yokoyama, Ryujiro

    2007-01-01

    Asymptomatic lacunar infarcts are often found in the Brain Dock. The presence of asymptomatic lacunar infarcts increases the risk of serious cerebral infarction. Thus, it is an important task for radiologists and/or neurosurgeons to detect asymptomatic lacunar infarctions in MRI images. However, it is difficult for radiologists and/or neurosurgeons to identify lacunar infarcts correctly in MRI images, because it is hard to distinguish between lacunar infarcts and enlarged Virchow-Robin space. Therefore, the purpose of our study was to develop a computer-aided diagnosis scheme for detection of lacunar infarctions in order to assist radiologists and/or neurosurgeons' interpretation as a ''second opinion.'' Our database consisted of 1143 T2-weighted MR images and 1143 T1-weighted MR images, which were selected from 132 patients. First, we segmented the cerebral parenchyma region by use of a region growing technique. The white-tophat transformation was then applied for enhancement of lacunar infarcts. The multiple-phase binarization was used for identifying initial candidates of lacunar infarcts. For removal of false positives (FPs), 12 features were determined in each of the initial candidates in T2 and T1-weighted MR images. The rule-based schemes and an artificial neural network with these features were used for distinguishing between lacunar infarcts and FPs. The sensitivity of detection of lacunar infarcts was 96.8% (90/93) with 0.69 (737/1063) FP per image. This computerized method may be useful for radiologists and/or neurosurgeons in detecting lacunar infracts in MRI images. (author)

  15. CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    K.-Y. Lee

    2016-06-01

    Full Text Available Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012 uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+ and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate

  16. Cloud Detection of Optical Satellite Images Using Support Vector Machine

    Science.gov (United States)

    Lee, Kuan-Yi; Lin, Chao-Hung

    2016-06-01

    Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection

  17. Extended image differencing for change detection in UAV video mosaics

    Science.gov (United States)

    Saur, Günter; Krüger, Wolfgang; Schumann, Arne

    2014-03-01

    Change detection is one of the most important tasks when using unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. We address changes of short time scale, i.e. the observations are taken in time distances from several minutes up to a few hours. Each observation is a short video sequence acquired by the UAV in near-nadir view and the relevant changes are, e.g., recently parked or moved vehicles. In this paper we extend our previous approach of image differencing for single video frames to video mosaics. A precise image-to-image registration combined with a robust matching approach is needed to stitch the video frames to a mosaic. Additionally, this matching algorithm is applied to mosaic pairs in order to align them to a common geometry. The resulting registered video mosaic pairs are the input of the change detection procedure based on extended image differencing. A change mask is generated by an adaptive threshold applied to a linear combination of difference images of intensity and gradient magnitude. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed size of shadows, and compression or transmission artifacts. The special effects of video mosaicking such as geometric distortions and artifacts at moving objects have to be considered, too. In our experiments we analyze the influence of these effects on the change detection results by considering several scenes. The results show that for video mosaics this task is more difficult than for single video frames. Therefore, we extended the image registration by estimating an elastic transformation using a thin plate spline approach. The results for mosaics are comparable to that of single video frames and are useful for interactive image exploitation due to a larger scene coverage.

  18. Automatic food detection in egocentric images using artificial intelligence technology.

    Science.gov (United States)

    Jia, Wenyan; Li, Yuecheng; Qu, Ruowei; Baranowski, Thomas; Burke, Lora E; Zhang, Hong; Bai, Yicheng; Mancino, Juliet M; Xu, Guizhi; Mao, Zhi-Hong; Sun, Mingui

    2018-03-26

    To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

  19. Pedestrian detection in infrared image using HOG and Autoencoder

    Science.gov (United States)

    Chen, Tianbiao; Zhang, Hao; Shi, Wenjie; Zhang, Yu

    2017-11-01

    In order to guarantee the safety of driving at night, vehicle-mounted night vision system was used to detect pedestrian in front of cars and send alarm to prevent the potential dangerous. To decrease the false positive rate (FPR) and increase the true positive rate (TPR), a pedestrian detection method based on HOG and Autoencoder (HOG+Autoencoder) was presented. Firstly, the HOG features of input images were computed and encoded by Autoencoder. Then the encoded features were classified by Softmax. In the process of training, Autoencoder was trained unsupervised. Softmax was trained with supervision. Autoencoder and Softmax were stacked into a model and fine-tuned by labeled images. Experiment was conducted to compare the detection performance between HOG and HOG+Autoencoder, using images collected by vehicle-mounted infrared camera. There were 80000 images for training set and 20000 for the testing set, with a rate of 1:3 between positive and negative images. The result shows that when TPR is 95%, FPR of HOG+Autoencoder is 0.4%, while the FPR of HOG is 5% with the same TPR.

  20. Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.

    Science.gov (United States)

    Ahmad, Asif; Asif, Amina; Rajpoot, Nasir; Arif, Muhammad; Minhas, Fayyaz Ul Amir Afsar

    2017-11-21

    Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for detection of cellular nuclei in hematoxylin and eosin stained histopathology images. Our proposed approach is based on kernelized correlation filters. Correlation filters have been widely used in object detection and tracking applications but their strength has not been explored in the medical imaging domain up till now. Our experimental results show that the proposed scheme gives state of the art accuracy and can learn complex nuclear morphologies. Like deep learning approaches, the proposed filters do not require engineering of image features as they can operate directly on histopathology images without significant preprocessing. However, unlike deep learning methods, the large-margin correlation filters developed in this work are interpretable, computationally efficient and do not require specialized or expensive computing hardware. A cloud based webserver of the proposed method and its python implementation can be accessed at the following URL: http://faculty.pieas.edu.pk/fayyaz/software.html#corehist .

  1. Semi-supervised rail defect detection from imbalanced image data

    NARCIS (Netherlands)

    Hajizadeh, S.; Nunez Vicencio, Alfredo; Tax, D.M.J.; Acarman, Tankut

    2016-01-01

    Rail defect detection by video cameras has recently gained much attention in both
    academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semisupervised learning

  2. Detection in Urban Scenario Using Combined Airborne Imaging Sensors

    NARCIS (Netherlands)

    Renhorn, I.; Axelsson, M.; Benoist, K.W.; Bourghys, D.; Boucher, Y.; Xavier Briottet, X.; Sergio De CeglieD, S. De; Dekker, R.J.; Dimmeler, A.; Dost, R.; Friman, O.; Kåsen, I.; Maerker, J.; Persie, M. van; Resta, S.; Schwering, P.B.W.; Shimoni, M.; Vegard Haavardsholm, T.

    2012-01-01

    The EDA project “Detection in Urban scenario using Combined Airborne imaging Sensors” (DUCAS) is in progress. The aim of the project is to investigate the potential benefit of combined high spatial and spectral resolution airborne imagery for several defense applications in the urban area. The

  3. Detection in Urban Scenario using Combined Airborne Imaging Sensors

    NARCIS (Netherlands)

    Renhorn, I.; Axelsson, M.; Benoist, K.W.; Bourghys, D.; Boucher, Y.; Xavier Briottet, X.; Sergio De CeglieD, S. De; Dekker, R.J.; Dimmeler, A.; Dost, R.; Friman, O.; Kåsen, I.; Maerker, J.; Persie, M. van; Resta, S.; Schwering, P.B.W.; Shimoni, M.; Vegard Haavardsholm, T.

    2012-01-01

    The EDA project “Detection in Urban scenario using Combined Airborne imaging Sensors” (DUCAS) is in progress. The aim of the project is to investigate the potential benefit of combined high spatial and spectral resolution airborne imagery for several defense applications in the urban area. The

  4. Study of cosmic ray nuclei detection by an image calorimeter

    Energy Technology Data Exchange (ETDEWEB)

    Casolino, M.; Sparvoli, R.; Morselli, A.; Picozza, P. [Rome Univ. `Tor Vergata` (Italy)]|[INFN, Sezione Univ. `Tor Vergata` Rome (Italy); Ozerov, Yu.V.; Zemskov, V.M.; Zverev, V.G.; Galper, A.M. [Moscow Engineering Physics Institute, Moscow (Russian Federation); Carlson, P. [Royal Institute of Technology, Stockholm (Sweden); Fuglesang, C. [ESA-EAC, Cologne (Germany)

    1995-09-01

    It is shown that a cosmic gamma-ray telescope made of a multilayer silicon tracker and a imaging CsI calorimeter, is capable of identifying cosmic ray nuclei. The telescope charge resolution is estimated around 4% independently of charge. Simulation methods are used to determine the telescope properties for nuclei detection.

  5. Figure detection and part label extraction from patent drawing images

    CSIR Research Space (South Africa)

    Cronje, J

    2012-11-01

    Full Text Available threshold. Many old patent images contain a lot of salt and pepper noise. A connected component algorithm is performed and if the number of very small components detected are more than 30% of the total number of components, a dilate and erode process...

  6. Ocular melanoma: Detection using iodine-123-iodoamphetamine and SPECT imaging

    International Nuclear Information System (INIS)

    Dewey, S.H.; Leonard, J.C.

    1990-01-01

    Uptake of iodine-123-iodoamphetamine has been demonstrated in malignant melanoma using planar imaging techniques and has been used to detect an ocular melanoma at 12 hr postinjection. Using SPECT technique, an ocular melanoma is identified in a 64-yr-old male at 1 hr postinjection

  7. Detection of rheumatoid arthritis in humans by fluorescence imaging

    Science.gov (United States)

    Ebert, Bernd; Dziekan, Thomas; Weissbach, Carmen; Mahler, Marianne; Schirner, Michael; Berliner, Birgitt; Bauer, Daniel; Voigt, Jan; Berliner, Michael; Bahner, Malte L.; Macdonald, Rainer

    2010-02-01

    The blood pool agent indo-cyanine green (ICG) has been investigated in a prospective clinical study for detection of rheumatoid arthritis using fluorescence imaging. Temporal behavior as well as spatial distribution of fluorescence intensity are suited to differentiate healthy and inflamed finger joints after i.v. injection of an ICG bolus.

  8. Spectral autofluorescence imaging of the retina for drusen detection

    Science.gov (United States)

    Foubister, James J.; Gorman, Alistair; Harvey, Andy; Hemert, Jano van

    2018-02-01

    The presence and characteristics of drusen in retinal images, namely their size, location, and distribution, can be used to aid in the diagnosis and monitoring of Age Related Macular Degeneration (AMD); one of the leading causes for blindness in the elderly population. Current imaging techniques are effective at determining the presence and number of drusen, but fail when it comes to classifying their size and form. These distinctions are important for correctly characterising the disease, especially in the early stages where the development of just one larger drusen can indicate progression. Another challenge for automated detection is in distinguishing them from other retinal features, such as cotton wool spots. We describe the development of a multi-spectral scanning-laser ophthalmoscope that records images of retinal autofluorescence (AF) in four spectral bands. This will offer the potential to detect drusen with improved contrast based on spectral discrimination for automated classification. The resulting improved specificity and sensitivity for their detection offers more reliable characterisation of AMD. We present proof of principle images prior to further system optimisation and clinical trials for assessment of enhanced detection of drusen.

  9. Improving HOG with image segmentation: application to human detection

    NARCIS (Netherlands)

    Salas, Y.S.; Bermudez, D.V.; Peña, A.M.L.; Gomez, D.G.; Gevers, T.

    2012-01-01

    In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of

  10. Convolutional neural network features based change detection in satellite images

    Science.gov (United States)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  11. Detection of microaneurysms in retinal images using an ensemble classifier

    Directory of Open Access Journals (Sweden)

    M.M. Habib

    2017-01-01

    Full Text Available This paper introduces, and reports on the performance of, a novel combination of algorithms for automated microaneurysm (MA detection in retinal images. The presence of MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR which is one of the leading causes of blindness amongst the working age population. An extensive survey of the literature is presented and current techniques in the field are summarised. The proposed technique first detects an initial set of candidates using a Gaussian Matched Filter and then classifies this set to reduce the number of false positives. A Tree Ensemble classifier is used with a set of 70 features (the most commons features in the literature. A new set of 32 MA groundtruth images (with a total of 256 labelled MAs based on images from the MESSIDOR dataset is introduced as a public dataset for benchmarking MA detection algorithms. We evaluate our algorithm on this dataset as well as another public dataset (DIARETDB1 v2.1 and compare it against the best available alternative. Results show that the proposed classifier is superior in terms of eliminating false positive MA detection from the initial set of candidates. The proposed method achieves an ROC score of 0.415 compared to 0.2636 achieved by the best available technique. Furthermore, results show that the classifier model maintains consistent performance across datasets, illustrating the generalisability of the classifier and that overfitting does not occur.

  12. Image Registration-Based Bolt Loosening Detection of Steel Joints

    Science.gov (United States)

    2018-01-01

    Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts. PMID:29597264

  13. Image Registration-Based Bolt Loosening Detection of Steel Joints.

    Science.gov (United States)

    Kong, Xiangxiong; Li, Jian

    2018-03-28

    Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts.

  14. DETECTION OF BARCHAN DUNES IN HIGH RESOLUTION SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    M. A. Azzaoui

    2016-06-01

    Full Text Available Barchan dunes are the fastest moving sand dunes in the desert. We developed a process to detect barchans dunes on High resolution satellite images. It consisted of three steps, we first enhanced the image using histogram equalization and noise reduction filters. Then, the second step proceeds to eliminate the parts of the image having a texture different from that of the barchans dunes. Using supervised learning, we tested a coarse to fine textural analysis based on Kolomogorov Smirnov test and Youden’s J-statistic on co-occurrence matrix. As an output we obtained a mask that we used in the next step to reduce the search area. In the third step we used a gliding window on the mask and check SURF features with SVM to get barchans dunes candidates. Detected barchans dunes were considered as the fusion of overlapping candidates. The results of this approach were very satisfying in processing time and precision.

  15. Wear Detection of Drill Bit by Image-based Technique

    Science.gov (United States)

    Sukeri, Maziyah; Zulhilmi Paiz Ismadi, Mohd; Rahim Othman, Abdul; Kamaruddin, Shahrul

    2018-03-01

    Image processing for computer vision function plays an essential aspect in the manufacturing industries for the tool condition monitoring. This study proposes a dependable direct measurement method to measure the tool wear using image-based analysis. Segmentation and thresholding technique were used as the means to filter and convert the colour image to binary datasets. Then, the edge detection method was applied to characterize the edge of the drill bit. By using cross-correlation method, the edges of original and worn drill bits were correlated to each other. Cross-correlation graphs were able to detect the difference of the worn edge despite small difference between the graphs. Future development will focus on quantifying the worn profile as well as enhancing the sensitivity of the technique.

  16. Neutron imaging integrated circuit and method for detecting neutrons

    Science.gov (United States)

    Nagarkar, Vivek V.; More, Mitali J.

    2017-12-05

    The present disclosure provides a neutron imaging detector and a method for detecting neutrons. In one example, a method includes providing a neutron imaging detector including plurality of memory cells and a conversion layer on the memory cells, setting one or more of the memory cells to a first charge state, positioning the neutron imaging detector in a neutron environment for a predetermined time period, and reading a state change at one of the memory cells, and measuring a charge state change at one of the plurality of memory cells from the first charge state to a second charge state less than the first charge state, where the charge state change indicates detection of neutrons at said one of the memory cells.

  17. Software Analysis of Mining Images for Objects Detection

    Directory of Open Access Journals (Sweden)

    Jan Tomecek

    2013-11-01

    Full Text Available The contribution is dealing with the development of the new module of robust FOTOMNG system for editing images from a video or miningimage from measurements for subsequent improvement of detection of required objects in the 2D image. The generated module allows create a finalhigh-quality picture by combination of multiple images with the search objects. We can combine input data according to the parameters or basedon reference frames. Correction of detected 2D objects is also part of this module. The solution is implemented intoFOTOMNG system and finishedwork has been tested in appropriate frames, which were validated core functionality and usability. Tests confirmed the function of each part of themodule, its accuracy and implications of integration.

  18. Textureless Macula Swelling Detection with Multiple Retinal Fundus Images

    Energy Technology Data Exchange (ETDEWEB)

    Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Tobin Jr, Kenneth William [ORNL; Grisan, Enrico [University of Padua, Padua, Italy; Favaro, Paolo [Heriot-Watt University, Edinburgh; Ruggeri, Alfredo [University of Padua, Padua, Italy; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2010-01-01

    Retinal fundus images acquired with non-mydriatic digital fundus cameras are a versatile tool for the diagnosis of various retinal diseases. Because of the ease of use of newer camera models and their relatively low cost, these cameras can be employed by operators with limited training for telemedicine or Point-of-Care applications. We propose a novel technique that uses uncalibrated multiple-view fundus images to analyse the swelling of the macula. This innovation enables the detection and quantitative measurement of swollen areas by remote ophthalmologists. This capability is not available with a single image and prone to error with stereo fundus cameras. We also present automatic algorithms to measure features from the reconstructed image which are useful in Point-of-Care automated diagnosis of early macular edema, e.g., before the appearance of exudation. The technique presented is divided into three parts: first, a preprocessing technique simultaneously enhances the dark microstructures of the macula and equalises the image; second, all available views are registered using non-morphological sparse features; finally, a dense pyramidal optical flow is calculated for all the images and statistically combined to build a naiveheight- map of the macula. Results are presented on three sets of synthetic images and two sets of real world images. These preliminary tests show the ability to infer a minimum swelling of 300 microns and to correlate the reconstruction with the swollen location.

  19. Image re-sampling detection through a novel interpolation kernel.

    Science.gov (United States)

    Hilal, Alaa

    2018-06-01

    Image re-sampling involved in re-size and rotation transformations is an essential element block in a typical digital image alteration. Fortunately, traces left from such processes are detectable, proving that the image has gone a re-sampling transformation. Within this context, we present in this paper two original contributions. First, we propose a new re-sampling interpolation kernel. It depends on five independent parameters that controls its amplitude, angular frequency, standard deviation, and duration. Then, we demonstrate its capacity to imitate the same behavior of the most frequent interpolation kernels used in digital image re-sampling applications. Secondly, the proposed model is used to characterize and detect the correlation coefficients involved in re-sampling transformations. The involved process includes a minimization of an error function using the gradient method. The proposed method is assessed over a large database of 11,000 re-sampled images. Additionally, it is implemented within an algorithm in order to assess images that had undergone complex transformations. Obtained results demonstrate better performance and reduced processing time when compared to a reference method validating the suitability of the proposed approaches. Copyright © 2018 Elsevier B.V. All rights reserved.

  20. Image edges detection through B-Spline filters

    International Nuclear Information System (INIS)

    Mastropiero, D.G.

    1997-01-01

    B-Spline signal processing was used to detect the edges of a digital image. This technique is based upon processing the image in the Spline transform domain, instead of doing so in the space domain (classical processing). The transformation to the Spline transform domain means finding out the real coefficients that makes it possible to interpolate the grey levels of the original image, with a B-Spline polynomial. There exist basically two methods of carrying out this interpolation, which produces the existence of two different Spline transforms: an exact interpolation of the grey values (direct Spline transform), and an approximated interpolation (smoothing Spline transform). The latter results in a higher smoothness of the gray distribution function defined by the Spline transform coefficients, and is carried out with the aim of obtaining an edge detection algorithm which higher immunity to noise. Finally the transformed image was processed in order to detect the edges of the original image (the gradient method was used), and the results of the three methods (classical, direct Spline transform and smoothing Spline transform) were compared. The results were that, as expected, the smoothing Spline transform technique produced a detection algorithm more immune to external noise. On the other hand the direct Spline transform technique, emphasizes more the edges, even more than the classical method. As far as the consuming time is concerned, the classical method is clearly the fastest one, and may be applied whenever the presence of noise is not important, and whenever edges with high detail are not required in the final image. (author). 9 refs., 17 figs., 1 tab

  1. Supervised detection of exoplanets in high-contrast imaging sequences

    Science.gov (United States)

    Gomez Gonzalez, C. A.; Absil, O.; Van Droogenbroeck, M.

    2018-06-01

    Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on unsupervised learning techniques, such as low-rank approximations, for generating a model point spread function (PSF) and subtracting the residual starlight and speckle noise. Aims: In order to maximize the detection rate of HCI instruments and survey campaigns, advanced algorithms with higher sensitivities to faint companions are needed, especially for the speckle-dominated innermost region of the images. Methods: We propose a reformulation of the exoplanet detection task (for ADI sequences) that builds on well-established machine learning techniques to take HCI post-processing from an unsupervised to a supervised learning context. In this new framework, we present algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). We test these algorithms on real ADI datasets from VLT/NACO and VLT/SPHERE HCI instruments. We then assess their performances by injecting fake companions and using receiver operating characteristic analysis. This is done in comparison with state-of-the-art ADI algorithms, such as ADI principal component analysis (ADI-PCA). Results: This study shows the improved sensitivity versus specificity trade-off of the proposed supervised detection approach. At the diffraction limit, SODINN improves the true positive rate by a factor ranging from 2 to 10 (depending on the dataset and angular separation) with respect to ADI-PCA when working at the same false-positive level. Conclusions: The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. In addition, it offers the possibility of re-processing existing HCI databases to maximize their scientific return and potentially improve

  2. NQR: From imaging to explosives and drugs detection

    International Nuclear Information System (INIS)

    Osan, Tristan M.; Cerioni, Lucas M.C.; Forguez, Jose; Olle, Juan M.; Pusiol, Daniel J.

    2007-01-01

    The main aim of this work is to present an overview of the nuclear quadrupole resonance (NQR) spectroscopy capabilities for solid state imaging and detection of illegal substances, such as explosives and drugs. We briefly discuss the evolution of different NQR imaging techniques, in particular those involving spatial encoding which permit conservation of spectroscopic information. It has been shown that plastic explosives and other forbidden substances cannot be easily detected by means of conventional inspection techniques, such as those based on conventional X-ray technology. For this kind of applications, the experimental results show that the information inferred from NQR spectroscopy provides excellent means to perform volumetric and surface detection of dangerous explosive and drug compounds

  3. UNFOLDED REGULAR AND SEMI-REGULAR POLYHEDRA

    Directory of Open Access Journals (Sweden)

    IONIŢĂ Elena

    2015-06-01

    Full Text Available This paper proposes a presentation unfolding regular and semi-regular polyhedra. Regular polyhedra are convex polyhedra whose faces are regular and equal polygons, with the same number of sides, and whose polyhedral angles are also regular and equal. Semi-regular polyhedra are convex polyhedra with regular polygon faces, several types and equal solid angles of the same type. A net of a polyhedron is a collection of edges in the plane which are the unfolded edges of the solid. Modeling and unfolding Platonic and Arhimediene polyhedra will be using 3dsMAX program. This paper is intended as an example of descriptive geometry applications.

  4. Recent development of fluorescent imaging for specific detection of tumors

    International Nuclear Information System (INIS)

    Nakata, Eiji; Morii, Takashi; Uto, Yoshihiro; Hori, Hitoshi

    2011-01-01

    Increasing recent studies on fluorescent imaging for specific detection of tumors are described here on strategies of molecular targeting, metabolic specificity and hypoxic circumstance. There is described an instance of a conjugate of antibody and pH-activable fluorescent ligand, which specifically binds to the tumor cells, is internalized in the cellular lysozomes where their pH is low, and then is activated to become fluorescent only in viable tumor cells. For the case of metabolic specificity, excessive loading of the precursor (5-aminolevulinic acid) of protoporphyrin IX (ppIX), due to their low activity to convert ppIX to heme B, results in making tumors observable in red as ppIX emits fluorescence (red, 585 nm) when excited by blue ray of 410 nm. Similarly, imaging with indocyanine green which is accumulated in hepatoma cells is reported in success in detection of small lesion and metastasis when the dye is administered during operation. Reductive reactions exceed in tumor hypoxic conditions, of which feature is usable for imaging. Conjugates of nitroimidazole and fluorescent dye are reported to successfully image tumors by nitro reduction. Authors' UTX-12 is a non-fluorescent nitroaromatic derivative of pH-sensitive fluorescent dye seminaphtharhodafluor (SNARF), and is designed for the nitro group, the hypoxia-responding sensor, to be reduced in tumor hypoxic conditions and then for the aromatic moiety to be cleaved to release free SNARF. Use of hypoxia-inducible factor-1 (HIF-1) for imaging has been also reported in many. As above, studies on fluorescent imaging for specific detection of tumors are mostly at fundamental step but its future is conceivably promising along with advances in other technology like fluorescent endoscopy and multimodal imaging. (author)

  5. Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

    International Nuclear Information System (INIS)

    Shekhar, Raj; Lei, Peng; Castro-Pareja, Carlos R.; Plishker, William L.; D'Souza, Warren D.

    2007-01-01

    Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the

  6. Infective endocarditis detection through SPECT/CT images digital processing

    Science.gov (United States)

    Moreno, Albino; Valdés, Raquel; Jiménez, Luis; Vallejo, Enrique; Hernández, Salvador; Soto, Gabriel

    2014-03-01

    Infective endocarditis (IE) is a difficult-to-diagnose pathology, since its manifestation in patients is highly variable. In this work, it was proposed a semiautomatic algorithm based on SPECT images digital processing for the detection of IE using a CT images volume as a spatial reference. The heart/lung rate was calculated using the SPECT images information. There were no statistically significant differences between the heart/lung rates values of a group of patients diagnosed with IE (2.62+/-0.47) and a group of healthy or control subjects (2.84+/-0.68). However, it is necessary to increase the study sample of both the individuals diagnosed with IE and the control group subjects, as well as to improve the images quality.

  7. Chagas Parasite Detection in Blood Images Using AdaBoost

    Directory of Open Access Journals (Sweden)

    Víctor Uc-Cetina

    2015-01-01

    Full Text Available The Chagas disease is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. Visual detection of such parasite through microscopic inspection is a tedious and time-consuming task. In this paper, we provide an AdaBoost learning solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup. With this method, we get 100% and 93.25% of sensitivity and specificity, respectively. A ROC comparison with the method most commonly used for the detection of malaria parasites based on support vector machines (SVM is also provided. Our experimental work shows mainly two things: (1 Chagas parasites can be detected automatically using machine learning methods with high accuracy and (2 AdaBoost + SVM provides better overall detection performance than AdaBoost or SVMs alone. Such results are the best ones known so far for the problem of automatic detection of Chagas parasites through the use of machine learning, computer vision, and image processing methods.

  8. Automatic detection of radioactive fixations in oncology PET images

    International Nuclear Information System (INIS)

    Tomei-Le-Digarcher, Sandrine

    2009-01-01

    Therapeutic follow-up of patients with cancer is nowadays of main interest in research. Positron Emission Tomography (PET) appears to become a reference exam for monitoring treatment of cancers, particular in lymphoma. This PhD thus deals on the development of a computer aided detection (CAD) tool focused on hardly visible tumors for whole-body 3D PET images. To achieve such a goal, we proposed an approach based on the combination of two classifiers, the Linear Discriminant Analysis (LDA) and the Support Vector Machines, associated with wavelet image features. Each classifier gives a 3D score map quantifying the probability of its voxels to correspond to a tumor. We proposed a 3D evaluation strategy based on the use of simulated images giving the targeted tumor characteristic gold standard. Such database was developed in this PhD from hundred Monte Carlo simulations of the Zuba phantom. It includes hundred images presenting 375 spherical tumors of calibrated contrasts. Results of the CAD obtained from the binary detection maps are promising. They open the perspective of enriching the binary information generally given to the clinician with parametric indices quantifying the pertinence of each detected tumor. (author)

  9. Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

    Science.gov (United States)

    Ortega-Terol, Damian; Hernandez-Lopez, David; Ballesteros, Rocio; Gonzalez-Aguilera, Diego

    2017-10-15

    Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.

  10. Selective Detection of Neurotransmitters by Fluorescence and Chemiluminescence Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Ziqiang Wang; Edward S. Yeung

    2001-08-06

    In recent years, luminescence imaging has been widely employed in neurochemical analysis. It has a number of advantages for the study of neuronal and other biological cells: (1) a particular molecular species or cellular constituent can be selectively visualized in the presence of a large excess of other species in a heterogeneous environment; (2) low concentration detection limits can be achieved because of the inherent sensitivity associated with fluorescence and chemiluminescence; (3) low excitation intensities can be used so that long-term observation can be realized while the viability of the specimen is preserved; and (4) excellent spatial resolution can be obtained with the light microscope so subcellular compartments can be identified. With good sensitivity, temporal and spatial resolution, the flux of ions and molecules and the distribution and dynamics of intracellular species can be measured in real time with specific luminescence probes, substrates, or with native fluorescence. A noninvasive detection scheme based on glutamate dehydrogenase (GDH) enzymatic assay combined with microscopy was developed to measure the glutamate release in cultured cells from the central nervous system (CNS). The enzyme reaction is very specific and sensitive. The detection limit with CCD imaging is down to {micro}M levels of glutamate with reasonable response time. They also found that chemiluminescence associated with the ATP-dependent reaction between luciferase and luciferin can be used to image ATP at levels down to 10 nM in the millisecond time scale. Similar imaging experiments should be feasible in a broad spectrum of biological systems.

  11. Signature detection and matching for document image retrieval.

    Science.gov (United States)

    Zhu, Guangyu; Zheng, Yefeng; Doermann, David; Jaeger, Stefan

    2009-11-01

    As one of the most pervasive methods of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. However, detection and segmentation of free-form objects such as signatures from clustered background is currently an open document analysis problem. In this paper, we focus on two fundamental problems in signature-based document image retrieval. First, we propose a novel multiscale approach to jointly detecting and segmenting signatures from document images. Rather than focusing on local features that typically have large variations, our approach captures the structural saliency using a signature production model and computes the dynamic curvature of 2D contour fragments over multiple scales. This detection framework is general and computationally tractable. Second, we treat the problem of signature retrieval in the unconstrained setting of translation, scale, and rotation invariant nonrigid shape matching. We propose two novel measures of shape dissimilarity based on anisotropic scaling and registration residual error and present a supervised learning framework for combining complementary shape information from different dissimilarity metrics using LDA. We quantitatively study state-of-the-art shape representations, shape matching algorithms, measures of dissimilarity, and the use of multiple instances as query in document image retrieval. We further demonstrate our matching techniques in offline signature verification. Extensive experiments using large real-world collections of English and Arabic machine-printed and handwritten documents demonstrate the excellent performance of our approaches.

  12. The use of image morphing to improve the detection of tumors in emission imaging

    International Nuclear Information System (INIS)

    Dykstra, C.; Greer, K.; Jaszczak, R.; Celler, A.

    1999-01-01

    Two of the limitations on the utility of SPECT and planar scintigraphy for the non-invasive detection of carcinoma are the small sizes of many tumors and the possible low contrast between tumor uptake and background. This is particularly true for breast imaging. Use of some form of image processing can improve the visibility of tumors which are at the limit of hardware resolution. Smoothing, by some form of image averaging, either during or post-reconstruction, is widely used to reduce noise and thereby improve the detectability of regions of elevated activity. However, smoothing degrades resolution and, by averaging together closely spaced noise, may make noise look like a valid region of increased uptake. Image morphing by erosion and dilation does not average together image values; it instead selectively removes small features and irregularities from an image without changing the larger features. Application of morphing to emission images has shown that it does not, therefore, degrade resolution and does not always degrade contrast. For these reasons it may be a better method of image processing for noise removal in some images. In this paper the authors present a comparison of the effects of smoothing and morphing using breast and liver studies

  13. Animal detection in natural images: effects of color and image database.

    Directory of Open Access Journals (Sweden)

    Weina Zhu

    Full Text Available The visual system has a remarkable ability to extract categorical information from complex natural scenes. In order to elucidate the role of low-level image features for the recognition of objects in natural scenes, we recorded saccadic eye movements and event-related potentials (ERPs in two experiments, in which human subjects had to detect animals in previously unseen natural images. We used a new natural image database (ANID that is free of some of the potential artifacts that have plagued the widely used COREL images. Color and grayscale images picked from the ANID and COREL databases were used. In all experiments, color images induced a greater N1 EEG component at earlier time points than grayscale images. We suggest that this influence of color in animal detection may be masked by later processes when measuring reation times. The ERP results of go/nogo and forced choice tasks were similar to those reported earlier. The non-animal stimuli induced bigger N1 than animal stimuli both in the COREL and ANID databases. This result indicates ultra-fast processing of animal images is possible irrespective of the particular database. With the ANID images, the difference between color and grayscale images is more pronounced than with the COREL images. The earlier use of the COREL images might have led to an underestimation of the contribution of color. Therefore, we conclude that the ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used.

  14. A Brazing Defect Detection Using an Ultrasonic Infrared Imaging Inspection

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Jai Wan; Choi, Young Soo; Jung, Seung Ho; Jung, Hyun Kyu [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2007-10-15

    When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material in real time. In this paper a realtime detection of the brazing defect of thin Inconel plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (23 kHz) ultrasonic transducer was used to infuse the welded Inconel plates with a short pulse of sound for 280 ms. The ultrasonic source has a maximum power of 2 kW. The surface temperature of the area under inspection is imaged by an infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the bound between the two faces of the Inconel plates near the defective brazing point and heated up highly, are observed. And the weak thermal signal is observed at the defect position of brazed plate also. Using the image processing technology such as background subtraction average and image enhancement using histogram equalization, the position of defective brazing regions in the thin Inconel plates can be located certainly

  15. Spectral differential imaging detection of planets about nearby stars

    International Nuclear Information System (INIS)

    Smith, W.H.

    1987-01-01

    Direct ground-based optical imaging of planets in orbit about nearby stars may be accomplished by spectral differential imaging using multiple passband acoustooptic filters with a CCD. This technique provides two essential results. First, it provides a means to modulate the stellar flux reflected from a planet while leaving the flux from the star and other sources in the same field of view unmodulated. Second, spectral differential imaging enables the CCD detector to achieve a sufficiently high dynamic range to locate planets near a star in spite of an integrated brightness differential of 5 x 10 8 . Spectral differential imaging at nearby diffraction limited imaging conditions with telescope apodization can reduce the time to conduct a sensitive planetary search to a few hours in some cases. The feasibility of this idea is discussed here and shown to provide, in principle, the discrimination and sensitivity to detect a Jovian-class planet about stars at distances of about 10 parsecs. The detection of brown dwarfs is shown to be feasible as well. 31 references

  16. Aerial Images and Convolutional Neural Network for Cotton Bloom Detection.

    Science.gov (United States)

    Xu, Rui; Li, Changying; Paterson, Andrew H; Jiang, Yu; Sun, Shangpeng; Robertson, Jon S

    2017-01-01

    Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of -4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.

  17. Gamma-ray detection and Compton camera image reconstruction with application to hadron therapy

    International Nuclear Information System (INIS)

    Frandes, M.

    2010-09-01

    detection chain from Monte Carlo simulations to reconstruction of individual events, and finally to image reconstruction. A list-mode Maximum-Likelihood Expectation-Maximization (MLEM) algorithm was adopted to perform image reconstruction in conjunction with the imaging response, which has to depict the complex behavior of the detector. Modeling the imaging response requires complex calculations, considering the incident angle, all measured energies, the Compton scatter angle in the first interaction, the direction of scattered electron (when measured). In the simplest form, each event response is described by Compton cone profiles. The shapes of the profiles are approximated by 1D Gaussian distributions. A strong correlation was observed between pattern of the reconstructed high-energy gamma events, and location of the Bragg peak. The performance of the imaging technique illustrated by the HTI is a function of the detector performance in terms of detection efficiency, spatial and energy resolution, acquisition time, and the algorithms used to reconstruct the gamma-ray activity. Thus beside optimizations of the imaging system, the applied imaging algorithm has a high influence on the final reconstructed images. The HTI reconstructed images are corrupted by noise due to the low photon counts recorded, the uncertainties induced by finite energy resolution, Doppler broadening, the limited model used to estimate the imaging response, and the artifacts generated when iterating the MLEM algorithm. This noise is spatially varying and signal-dependent, representing a major obstacle for information extraction. Thus image de-noising techniques were investigated. A Wavelet based multi-resolution strategy of list-mode MLEM Regularization (WREM) was developed to reconstruct Compton images. At each iteration, a threshold-based processing step was integrated. The noise variance was estimated at each scale of the wavelet decomposition as the median value of the coefficients from the high

  18. Counterfeit Electronics Detection Using Image Processing and Machine Learning

    Science.gov (United States)

    Asadizanjani, Navid; Tehranipoor, Mark; Forte, Domenic

    2017-01-01

    Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

  19. Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images

    Directory of Open Access Journals (Sweden)

    Akara Sopharak

    2013-07-01

    Full Text Available Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed.

  20. Counterfeit Electronics Detection Using Image Processing and Machine Learning

    International Nuclear Information System (INIS)

    Asadizanjani, Navid; Tehranipoor, Mark; Forte, Domenic

    2017-01-01

    Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit. (paper)

  1. Edge detection in digital images using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Marjan Kuchaki Rafsanjani

    2015-11-01

    Full Text Available Ant Colony Optimization (ACO is an optimization algorithm inspired by the behavior of real ant colonies to approximate the solutions of difficult optimization problems. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed approach is based on the distribution of ants on an image; ants try to find possible edges by using a state transition function. Experimental results show that the proposed method compared to standard edge detectors is less sensitive to Gaussian noise and gives finer details and thinner edges when compared to earlier ant-based approaches.

  2. Risk stratification of patients with combined acute pulmonary embolism and pulmonary hypertension using dynamic and regular pulmonary perfusion imaging

    International Nuclear Information System (INIS)

    Wang Xuemei; Wang Jing; Li Guohua; Wang Xiangcheng; Zhang Kaixiu; Liu Caiping

    2010-01-01

    Objective: To stratify the risks of patients with acute pulmonary embolism (APE) and pulmonary hypertension (PH) by dynamic pulmonary perfusion imaging (DPPI) and pulmonary perfusion imaging (PPI). Methods: From October 2007 to February 2009, 20 healthy volunteers (12 males, 8 females; mean age =48.47±13.47 years) and 31 APE patients (21 males, 10 females; mean age =47.68±18.06 years; from October 2007 to July 2009) were included in the study. DPPI and PPI were performed in all subjects. Percentage of perfusion defect scores (PPDs%) were calculated by semi-quantitative analysis of PPI. Risk levels were defined according to PPDs% calculated from PPI: normal (PPDs% =0); very low risk (0 60%). Lung equilibrium time (LET) was calculated on region of interest (ROI) drawn over DPPI. Clinical risk was scored by Aujesky method.The t-test, ANOVA and correlation analysis were used with SPSS 13.0 software. Results: (1) LET in healthy volunteers and APE patients was (12.18±3.28) and (32.90±14.29) s respectively (t = 6.81, P<0.01). (2) The correlation coefficient, coefficient of determination between LET and PPDs% in APE patients were 0.93 and 0.87, respectively. The correlation coefficient between LET and clinical risk score was 0.86. (3) The mean LET of APE patients in very low risk (n =5), low risk (n = 12), moderate risk (n=9), high risk (n=4) and very high risk groups (n=1) were (19.59 ±0.04), (25.03±0.08), (36.07±0.10), (57.15±0.06) and (70±0.00) s, respectively. There was significant difference among APE patients with different risk levels (F =16.78, P<0.01). Conclusions: (1) DPPI was a reliable, convenient and non-invasive method for the evaluation of PH in APE. (2) Combined LET of DPPI and PPDs% of PPI was valuable for risk stratification and prognosis estimation in APE patients. (authors)

  3. Evidential analysis of difference images for change detection of multitemporal remote sensing images

    Science.gov (United States)

    Chen, Yin; Peng, Lijuan; Cremers, Armin B.

    2018-03-01

    In this article, we develop two methods for unsupervised change detection in multitemporal remote sensing images based on Dempster-Shafer's theory of evidence (DST). In most unsupervised change detection methods, the probability of difference image is assumed to be characterized by mixture models, whose parameters are estimated by the expectation maximization (EM) method. However, the main drawback of the EM method is that it does not consider spatial contextual information, which may entail rather noisy detection results with numerous spurious alarms. To remedy this, we firstly develop an evidence theory based EM method (EEM) which incorporates spatial contextual information in EM by iteratively fusing the belief assignments of neighboring pixels to the central pixel. Secondly, an evidential labeling method in the sense of maximizing a posteriori probability (MAP) is proposed in order to further enhance the detection result. It first uses the parameters estimated by EEM to initialize the class labels of a difference image. Then it iteratively fuses class conditional information and spatial contextual information, and updates labels and class parameters. Finally it converges to a fixed state which gives the detection result. A simulated image set and two real remote sensing data sets are used to evaluate the two evidential change detection methods. Experimental results show that the new evidential methods are comparable to other prevalent methods in terms of total error rate.

  4. MULTI-TEMPORAL CLASSIFICATION AND CHANGE DETECTION USING UAV IMAGES

    Directory of Open Access Journals (Sweden)

    S. Makuti

    2018-05-01

    Full Text Available In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tests: colour features (HSV, textural features (GLCM and 3D geometric features. For classification purposes Conditional Random Field (CRF has been used: the unary potential was determined using the Random Forest algorithm while the pairwise potential was defined by the fully connected CRF. In the performed tests, different feature configurations and settings have been considered to assess the performance of these methods in such challenging task. Experimental results showed that the post-classification approach outperforms the pre-classification change detection method. This was analysed using the overall accuracy, where by post classification have an accuracy of up to 62.6 % and the pre classification change detection have an accuracy of 46.5 %. These results represent a first useful indication for future works and developments.

  5. Pipeline Defects Detection Using MFL Signals and Self Quotient Image

    International Nuclear Information System (INIS)

    Kim, Min Ho; Choi, Doo Hyun; Rho, Yong Woo

    2010-01-01

    Defects positioning of underground gas pipelines using MFL(magnetic flux leakage) inspection which is one of non-destructive evaluation techniques is proposed in this paper. MFL signals acquired from MFL PIG(pipeline inspection gauge) have nonlinearity and distortion caused by various extemal disturbances. SQI(self quotient image), a compensation technique for nonlinearity and distortion of MFL signal, is used to correct positioning of pipeline defects. Through the experiments using artificial defects carved in the KOGAS pipeline simulation facility, it is found that the performance of proposed defect detection is greatly improved compared to that of the conventional DCT(discrete cosine transform) coefficients based detection

  6. Detecting brain tumor in pathological slides using hyperspectral imaging.

    Science.gov (United States)

    Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M; Sarmiento, Roberto

    2018-02-01

    Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.

  7. Near-infrared imaging spectroscopy for counterfeit drug detection

    Science.gov (United States)

    Arnold, Thomas; De Biasio, Martin; Leitner, Raimund

    2011-06-01

    Pharmaceutical counterfeiting is a significant issue in the healthcare community as well as for the pharmaceutical industry worldwide. The use of counterfeit medicines can result in treatment failure or even death. A rapid screening technique such as near infrared (NIR) spectroscopy could aid in the search for and identification of counterfeit drugs. This work presents a comparison of two laboratory NIR imaging systems and the chemometric analysis of the acquired spectroscopic image data. The first imaging system utilizes a NIR liquid crystal tuneable filter and is designed for the investigation of stationary objects. The second imaging system utilizes a NIR imaging spectrograph and is designed for the fast analysis of moving objects on a conveyor belt. Several drugs in form of tablets and capsules were analyzed. Spectral unmixing techniques were applied to the mixed reflectance spectra to identify constituent parts of the investigated drugs. The results show that NIR spectroscopic imaging can be used for contact-less detection and identification of a variety of counterfeit drugs.

  8. Coded aperture imaging system for nuclear fuel motion detection

    International Nuclear Information System (INIS)

    Stalker, K.T.; Kelly, J.G.

    1980-01-01

    A Coded Aperature Imaging System (CAIS) has been developed at Sandia National Laboratories to image the motion of nuclear fuel rods undergoing tests simulating accident conditions within a liquid metal fast breeder reactor. The tests require that the motion of the test fuel be monitored while it is immersed in a liquid sodium coolant precluding the use of normal optical means of imaging. However, using the fission gamma rays emitted by the fuel itself and coded aperture techniques, images with 1.5 mm radial and 5 mm axial resolution have been attained. Using an electro-optical detection system coupled to a high speed motion picture camera a time resolution of one millisecond can be achieved. This paper will discuss the application of coded aperture imaging to the problem, including the design of the one-dimensional Fresnel zone plate apertures used and the special problems arising from the reactor environment and use of high energy gamma ray photons to form the coded image. Also to be discussed will be the reconstruction techniques employed and the effect of various noise sources on system performance. Finally, some experimental results obtained using the system will be presented

  9. Diffusion-weighted MR imaging for detection of extrahepatic cholangiocarcinoma

    International Nuclear Information System (INIS)

    Cui, Xing-Yu; Chen, Hong-Wei; Cai, Song; Bao, Jian; Tang, Qun-Feng; Wu, Li-Yuan; Fang, Xiang-Ming

    2012-01-01

    Objectives: To measure the sensitivity of diffusion-weighted imaging (DWI) and determine the most appropriate b value for DWI; to explore the correlation between the apparent diffusion coefficient (ADC) value and the degree of extrahepatic cholangiocarcinoma differentiation. Methods: Preoperative diffusion-weighted imaging and magnetic resonance examinations were performed for 31 patients with extrahepatic cholangiocarcinoma. Tumor ADC values were measured, and the signal-to-noise ratio, contrast-to-noise ratio, and signal-intensity ratio between the diffusion-weighted images with various b values as well as the T2-weighted images were calculated. Pathologically confirmed patients were pathologically graded to compare the ADC value with different b values of tumor at different degrees of differentiation, and the results were statistically analyzed by using the Friedman test. Results: A total of 29 cases of extrahepatic cholangiocarcinoma were detected by DWI. As the b value increased, tumor signal-to-noise ratio and contrast-to-noise ratio between the tumor and normal liver gradually decreased, but the tumor signal-intensity ratio gradually increased. When b = 800 s/mm 2 , contrast-to-noise ratio between tumor and normal liver, tumor signal-intensity ratio, and tumor signal-to-noise ratio of diffusion-weighted images were all higher than those of T2-weighted images; the differences were statistically significant (P 2 was the best in DWI of extrahepatic cholangiocarcinoma; the lesion ADC value declined as the degree of cancerous tissue differentiation decreased.

  10. An Algorithm for Pedestrian Detection in Multispectral Image Sequences

    Science.gov (United States)

    Kniaz, V. V.; Fedorenko, V. V.

    2017-05-01

    The growing interest for self-driving cars provides a demand for scene understanding and obstacle detection algorithms. One of the most challenging problems in this field is the problem of pedestrian detection. Main difficulties arise from a diverse appearances of pedestrians. Poor visibility conditions such as fog and low light conditions also significantly decrease the quality of pedestrian detection. This paper presents a new optical flow based algorithm BipedDetet that provides robust pedestrian detection on a single-borad computer. The algorithm is based on the idea of simplified Kalman filtering suitable for realization on modern single-board computers. To detect a pedestrian a synthetic optical flow of the scene without pedestrians is generated using slanted-plane model. The estimate of a real optical flow is generated using a multispectral image sequence. The difference of the synthetic optical flow and the real optical flow provides the optical flow induced by pedestrians. The final detection of pedestrians is done by the segmentation of the difference of optical flows. To evaluate the BipedDetect algorithm a multispectral dataset was collected using a mobile robot.

  11. Evaluation of radiographic imaging techniques in lung nodule detection

    International Nuclear Information System (INIS)

    Ho, J.T.; Kruger, R.A.

    1989-01-01

    Dual-energy radiography appears to be the most effective technique to address bone superposition that compromises conventional chest radiography. A dual-energy, single-exposure, film-based technique was compared with a dual-energy, dual-exposure technique and conventional chest radiography in a simulated lung nodule detection study. Observers detected more nodules on images produced by dual-energy techniques than on images produced by conventional chest radiography. The difference between dual-energy and conventional chest radiography is statistically significant and the difference between dual-energy, dual-exposure and single-exposure techniques is statistically insignificant. The single-exposure technique has the potential to replace the dual-exposure technique in future clinical application

  12. Myocardial perfusion imaging for detection of silent myocardial ischemia

    International Nuclear Information System (INIS)

    Beller, G.A.

    1988-01-01

    Despite the widespread use of the exercise stress test in diagnosing asymptomatic myocardial ischemia, exercise radionuclide imaging remains useful for detecting silent ischemia in numerous patient populations, including those who are totally asymptomatic, those who have chronic stable angina, those who have recovered from an episode of unstable angina or an uncomplicated myocardial infarction, and those who have undergone angioplasty or received thrombolytic therapy. Studies show that thallium scintigraphy is more sensitive than exercise electrocardiography in detecting ischemia, i.e., in part, because perfusion defects occur more frequently than ST depression and before angina in the ischemic cascade. Thallium-201 scintigraphy can be performed to differentiate a true- from a false-positive exercise electrocardiographic test in patients with exercise-induced ST depression and no angina. The development of technetium-labeled isonitriles may improve the accuracy of myocardial perfusion imaging. 11 references

  13. MR imaging for detection of trampoline injuries in children.

    Science.gov (United States)

    Hauth, E; Jaeger, H; Luckey, P; Beer, M

    2017-01-18

    The recreational use of trampolines is an increasingly popular activity among children and adolescents. Several studies reported about radiological findings in trampoline related injuries in children. The following publication presents our experience with MRI for detection of trampoline injuries in children. 20 children (mean 9.2 years, range: 4-15 years) who had undergone an MRI study for detection of suspected trampoline injuries within one year were included. 9/20 (45%) children had a radiograph as the first imaging modality in conjunction with primary care. In 11/20 (55%) children MR imaging was performed as the first modality. MR imaging was performed on two 1.5 T scanners with 60 and 70 cm bore design respectively without sedation. In 9/20 (45%) children the injury mechanism was a collision with another child. 7/20 (35%) children experienced leg pain several hours to one day after using the trampoline without acute accident and 4/20 (20%) children described a fall from the trampoline to the ground. All plain radiographs were performed in facilities outside the study centre and all were classified as having no pathological findings. In contrast, MR imaging detected injuries in 15/20 (75%) children. Lower extremity injuries were the most common findings, observed in 12/15 (80%) children. Amongst these, injuries of the ankle and foot were diagnosed in 7/15 (47%) patients. Fractures of the proximal tibial metaphysis were observed in 3/15 children. One child had developed a thoracic vertebral fracture. The two remaining children experienced injuries to the sacrum and a soft tissue injury of the thumb respectively. Seven children described clinical symptoms without an overt accident. Here, fractures of the proximal tibia were observed in 2 children, a hip joint effusion in another 2, and an injury of the ankle and foot in 1 child. There were no associated spinal cord injuries, no fracture dislocations, no vascular injuries and no head and neck injuries. In the

  14. SQUID-detected magnetic resonance imaging in microtesla magnetic fields

    International Nuclear Information System (INIS)

    McDermott, Robert; Kelso, Nathan; Lee, SeungKyun; Moessle, Michael; Mueck, Michael; Myers, Whittier; Haken, Bernard ten; Seton, H.C.; Trabesinger, Andreas H.; Pines, Alex; Clarke, John

    2003-01-01

    We describe studies of nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance imaging (MRI) of liquid samples at room temperature in microtesla magnetic fields. The nuclear spins are prepolarized in a strong transient field. The magnetic signals generated by the precessing spins, which range in frequency from tens of Hz to several kHz, are detected by a low-transition temperature dc SQUID (Superconducting QUantum Interference Device) coupled to an untuned, superconducting flux transformer configured as an axial gradiometer. The combination of prepolarization and frequency-independent detector sensitivity results in a high signal-to-noise ratio and high spectral resolution (∼1 Hz) even in grossly inhomogeneous magnetic fields. In the NMR experiments, the high spectral resolution enables us to detect the 10-Hz splitting of the spectrum of protons due to their scalar coupling to a 31P nucleus. Furthermore, the broadband detection scheme combined with a non-resonant field-reversal spin echo allows the simultaneous observation of signals from protons and 31P nuclei, even though their NMR resonance frequencies differ by a factor of 2.5. We extend our methodology to MRI in microtesla fields, where the high spectral resolution translates into high spatial resolution. We demonstrate two-dimensional images of a mineral oil phantom and slices of peppers, with a spatial resolution of about 1 mm. We also image an intact pepper using slice selection, again with 1-mm resolution. In further experiments we demonstrate T1-contrast imaging of a water phantom, some parts of which were doped with a paramagnetic salt to reduce the longitudinal relaxation time T1. Possible applications of this MRI technique include screening for tumors and integration with existing multichannel SQUID systems for brain imaging

  15. Detection of cracks on concrete surfaces by hyperspectral image processing

    Science.gov (United States)

    Santos, Bruno O.; Valença, Jonatas; Júlio, Eduardo

    2017-06-01

    All large infrastructures worldwide must have a suitable monitoring and maintenance plan, aiming to evaluate their behaviour and predict timely interventions. In the particular case of concrete infrastructures, the detection and characterization of crack patterns is a major indicator of their structural response. In this scope, methods based on image processing have been applied and presented. Usually, methods focus on image binarization followed by applications of mathematical morphology to identify cracks on concrete surface. In most cases, publications are focused on restricted areas of concrete surfaces and in a single crack. On-site, the methods and algorithms have to deal with several factors that interfere with the results, namely dirt and biological colonization. Thus, the automation of a procedure for on-site characterization of crack patterns is of great interest. This advance may result in an effective tool to support maintenance strategies and interventions planning. This paper presents a research based on the analysis and processing of hyper-spectral images for detection and classification of cracks on concrete structures. The objective of the study is to evaluate the applicability of several wavelengths of the electromagnetic spectrum for classification of cracks in concrete surfaces. An image survey considering highly discretized wavelengths between 425 nm and 950 nm was performed on concrete specimens, with bandwidths of 25 nm. The concrete specimens were produced with a crack pattern induced by applying a load with displacement control. The tests were conducted to simulate usual on-site drawbacks. In this context, the surface of the specimen was subjected to biological colonization (leaves and moss). To evaluate the results and enhance crack patterns a clustering method, namely k-means algorithm, is being applied. The research conducted allows to define the suitability of using clustering k-means algorithm combined with hyper-spectral images highly

  16. IMAGE PROCESSING FOR DETECTION OF ORAL WHITE SPONGE NEVUS LESIONS

    Directory of Open Access Journals (Sweden)

    Rajdeep Mitra

    2016-12-01

    Full Text Available White Sponge Nevus is a rear hereditary disease in human causes incurable white lesions in oral mucosa. Appropriate history, clinical examination along with biopsy and cytological studies are helpful for diagnosis of this disorder. Identification can also be made in alternative way by applying image processing technique using Watershed segmentation with MATLAB software. The applied techniques are effective and reliable for early accurate detection of the disease as alternative of expertise clinical and time taking laboratory investigations.

  17. Automated rice leaf disease detection using color image analysis

    Science.gov (United States)

    Pugoy, Reinald Adrian D. L.; Mariano, Vladimir Y.

    2011-06-01

    In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

  18. LBP based detection of intestinal motility in WCE images

    Science.gov (United States)

    Gallo, Giovanni; Granata, Eliana

    2011-03-01

    In this research study, a system to support medical analysis of intestinal contractions by processing WCE images is presented. Small intestine contractions are among the motility patterns which reveal many gastrointestinal disorders, such as functional dyspepsia, paralytic ileus, irritable bowel syndrome, bacterial overgrowth. The images have been obtained using the Wireless Capsule Endoscopy (WCE) technique, a patented, video colorimaging disposable capsule. Manual annotation of contractions is an elaborating task, since the recording device of the capsule stores about 50,000 images and contractions might represent only the 1% of the whole video. In this paper we propose the use of Local Binary Pattern (LBP) combined with the powerful textons statistics to find the frames of the video related to contractions. We achieve a sensitivity of about 80% and a specificity of about 99%. The achieved high detection accuracy of the proposed system has provided thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in endoscopy.

  19. A holistic image segmentation framework for cloud detection and extraction

    Science.gov (United States)

    Shen, Dan; Xu, Haotian; Blasch, Erik; Horvath, Gregory; Pham, Khanh; Zheng, Yufeng; Ling, Haibin; Chen, Genshe

    2013-05-01

    Atmospheric clouds are commonly encountered phenomena affecting visual tracking from air-borne or space-borne sensors. Generally clouds are difficult to detect and extract because they are complex in shape and interact with sunlight in a complex fashion. In this paper, we propose a clustering game theoretic image segmentation based approach to identify, extract, and patch clouds. In our framework, the first step is to decompose a given image containing clouds. The problem of image segmentation is considered as a "clustering game". Within this context, the notion of a cluster is equivalent to a classical equilibrium concept from game theory, as the game equilibrium reflects both the internal and external (e.g., two-player) cluster conditions. To obtain the evolutionary stable strategies, we explore three evolutionary dynamics: fictitious play, replicator dynamics, and infection and immunization dynamics (InImDyn). Secondly, we use the boundary and shape features to refine the cloud segments. This step can lower the false alarm rate. In the third step, we remove the detected clouds and patch the empty spots by performing background recovery. We demonstrate our cloud detection framework on a video clip provides supportive results.

  20. In Vivo Dual Fluorescence Imaging to Detect Joint Destruction.

    Science.gov (United States)

    Cho, Hongsik; Bhatti, Fazal-Ur-Rehman; Lee, Sangmin; Brand, David D; Yi, Ae-Kyung; Hasty, Karen A

    2016-10-01

    Diagnosis of cartilage damage in early stages of arthritis is vital to impede the progression of disease. In this regard, considerable progress has been made in near-infrared fluorescence (NIRF) optical imaging technique. Arthritis can develop due to various mechanisms but one of the main contributors is the production of matrix metalloproteinases (MMPs), enzymes that can degrade components of the extracellular matrix. Especially, MMP-1 and MMP-13 have main roles in rheumatoid arthritis and osteoarthritis because they enhance collagen degradation in the process of arthritis. We present here a novel NIRF imaging strategy that can be used to determine the activity of MMPs and cartilage damage simultaneously by detection of exposed type II collagen in cartilage tissue. In this study, retro-orbital injection of mixed fluorescent dyes, MMPSense 750 FAST (MMP750) dye and Alexa Fluor 680 conjugated monoclonal mouse antibody immune-reactive to type II collagen, was administered in the arthritic mice. Both dyes were detected with different intensity according to degree of joint destruction in the animal. Thus, our dual fluorescence imaging method can be used to detect cartilage damage as well as MMP activity simultaneously in early stage arthritis. © 2016 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

  1. Multiparametric magnetic resonance imaging in the detection of prostate cancer

    International Nuclear Information System (INIS)

    Durmus, T.; Baur, A.; Hamm, B.

    2014-01-01

    Prostate cancer is the most common malignancy in men, but only about 10 % of patients die from that cancer. Recent studies suggest that not all patients benefit from a radical therapeutic approach. When prostate cancer is suspected, magnetic resonance imaging (MRI) can make an important contribution to cancer localization within the prostate. Many studies show that T2-weighted morphologic imaging should be supplemented by multiparametric MRI techniques including diffusion-weighted imaging, contrast-enhanced sequences, and MR spectroscopy. This approach detects aggressive prostate cancer with high sensitivity and specificity. The findings of multiparametric MRI additionally contribute information to the assessment of cancer aggressiveness. The use of these multiparametric MRI techniques will gain an increasing role in the clinical management of prostate cancer patients. They can help in establishing a definitive diagnosis with a minimum of invasiveness and may also contribute to optimal individualized treatment. This review article presents the different techniques of multiparametric MRI and discusses their contribution to the detection of prostate cancer. Moreover, this review outlines an objective approach to image interpretation and structured reporting of MRI findings using the PI-RADS criteria. The review concludes with an outline of approaches to prostate biopsy on the basis of MRI (transrectal ultrasound, direct MRI guidance of tissue sampling, and MRI-ultrasound fusion biopsy) and emerging future uses of MRI in the planning of focal treatment options and in the active surveillance of patients diagnosed with prostate cancer. (orig.)

  2. Coordinate-invariant regularization

    International Nuclear Information System (INIS)

    Halpern, M.B.

    1987-01-01

    A general phase-space framework for coordinate-invariant regularization is given. The development is geometric, with all regularization contained in regularized DeWitt Superstructures on field deformations. Parallel development of invariant coordinate-space regularization is obtained by regularized functional integration of the momenta. As representative examples of the general formulation, the regularized general non-linear sigma model and regularized quantum gravity are discussed. copyright 1987 Academic Press, Inc

  3. Edge Detection on Images of Pseudoimpedance Section Supported by Context and Adaptive Transformation Model Images

    Directory of Open Access Journals (Sweden)

    Kawalec-Latała Ewa

    2014-03-01

    Full Text Available Most of underground hydrocarbon storage are located in depleted natural gas reservoirs. Seismic survey is the most economical source of detailed subsurface information. The inversion of seismic section for obtaining pseudoacoustic impedance section gives the possibility to extract detailed subsurface information. The seismic wavelet parameters and noise briefly influence the resolution. Low signal parameters, especially long signal duration time and the presence of noise decrease pseudoimpedance resolution. Drawing out from measurement or modelled seismic data approximation of distribution of acoustic pseuoimpedance leads us to visualisation and images useful to stratum homogeneity identification goal. In this paper, the improvement of geologic section image resolution by use of minimum entropy deconvolution method before inversion is applied. The author proposes context and adaptive transformation of images and edge detection methods as a way to increase the effectiveness of correct interpretation of simulated images. In the paper, the edge detection algorithms using Sobel, Prewitt, Robert, Canny operators as well as Laplacian of Gaussian method are emphasised. Wiener filtering of image transformation improving rock section structure interpretation pseudoimpedance matrix on proper acoustic pseudoimpedance value, corresponding to selected geologic stratum. The goal of the study is to develop applications of image transformation tools to inhomogeneity detection in salt deposits.

  4. Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

    Science.gov (United States)

    Alexandridis, Thomas K; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

    2017-09-01

    In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

  5. Various imaging methods in the detection of small hepatomas

    International Nuclear Information System (INIS)

    Nakatsuka, Haruki; Kaminou, Toshio; Takemoto, Kazumasa; Takashima, Sumio; Kobayashi, Nobuyuki; Nakamura, Kenji; Onoyama, Yasuto; Kurioka, Naruto

    1985-01-01

    Fifty-one patients with small hepatomas under 5 cm in diameter were studied to compare the detectability of various imaging methods. Positive finding was obtained in 50 % of the patients by scintigraphy, in 74 % by ultrasonography and in 79 % by CT during screening tests. Rate of detection in retrospective analysis, after the site of the tumor had been known, were 73 %, 93 % and 87 % respectively. Rate of detection was 92 % by celiac arteriography and 98 % by selective hepatic arteriography. In 21 patients, who had the tumor under 3 cm, the rate was 32 % for scintigraphy, 74 % for ultrasonography and 65 % for CT during screening, whereas it was 58 %, 84 % and 75 % retrospectively. By celiac arteriography, it was 85 %, and by hepatic arteriography, 95 %. Rate of detection of small hepatomas in screening tests differed remarkably from that in retrospective analysis. No single method of imaging can disclose reliably the presense of small hepatoma, therefore more than one method should be used in screening. (author)

  6. A new imaging technique for detecting interstellar communications

    Science.gov (United States)

    Vallerga, John; Welsh, Barry; Kotze, Marissa; Siegmund, Oswald

    2017-01-01

    We report on a unique detection methodology using the Berkeley Visible Image Tube (BVIT) mounted on the 10m Southern African Large Telescope (SALT) to search for laser pulses originating in communications from advanced extraterrestrial (ET) civilizations residing on nearby Earth-like planets located within their habitability zones. The detection technique assumes that ET communicates through high powered pulsed lasers with pulse durations on the order of 5 nanoseconds, the signals thereby being brighter than that of the host star within this very short period of time. Our technique turns down the gain of the optically sensitive photon counting microchannel plate detector such that ~30 photons are required in a 5ns window to generate an imaged event. Picking a priori targets with planets in the habitable zone substantially reduces the false alarm rate. Interplanetary communication by optical masers was first postulated by Schwartz and Townes in 1961. Under the assumption that ET has access to a 10 m class telescope operated as a transmitter then we could detect lasers with a similar power to that of the Livermore Laboratory laser (~1.8Mj per pulse), to a distance of ~ 1000 pc. In this talk we present the results of 2400 seconds of BVIT observations on the SALT of the star Wolf 1061, which is known to harbor an Earth-sized exoplanet located in the habitability zone. At this distance (4.3 pc), BVIT on SALT could detect a 48 joule per pulse laser, now commercially available as tabletop devices.

  7. Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images.

    Science.gov (United States)

    Emam, Mahmoud; Han, Qi; Zhang, Hongli

    2018-01-01

    In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision-recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods. © 2017 American Academy of Forensic Sciences.

  8. An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images.

    Science.gov (United States)

    Guo, Yanen; Xu, Xiaoyin; Wang, Yuanyuan; Wang, Yaming; Xia, Shunren; Yang, Zhong

    2014-08-01

    Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images. © 2014 Wiley Periodicals, Inc.

  9. Prostate cancer detection from model-free T1-weighted time series and diffusion imaging

    Science.gov (United States)

    Haq, Nandinee F.; Kozlowski, Piotr; Jones, Edward C.; Chang, Silvia D.; Goldenberg, S. Larry; Moradi, Mehdi

    2015-03-01

    The combination of Dynamic Contrast Enhanced (DCE) images with diffusion MRI has shown great potential in prostate cancer detection. The parameterization of DCE images to generate cancer markers is traditionally performed based on pharmacokinetic modeling. However, pharmacokinetic models make simplistic assumptions about the tissue perfusion process, require the knowledge of contrast agent concentration in a major artery, and the modeling process is sensitive to noise and fitting instabilities. We address this issue by extracting features directly from the DCE T1-weighted time course without modeling. In this work, we employed a set of data-driven features generated by mapping the DCE T1 time course to its principal component space, along with diffusion MRI features to detect prostate cancer. The optimal set of DCE features is extracted with sparse regularized regression through a Least Absolute Shrinkage and Selection Operator (LASSO) model. We show that when our proposed features are used within the multiparametric MRI protocol to replace the pharmacokinetic parameters, the area under ROC curve is 0.91 for peripheral zone classification and 0.87 for whole gland classification. We were able to correctly classify 32 out of 35 peripheral tumor areas identified in the data when the proposed features were used with support vector machine classification. The proposed feature set was used to generate cancer likelihood maps for the prostate gland.

  10. Noise and contrast detection in computed tomography images

    International Nuclear Information System (INIS)

    Faulkner, K.; Moores, B.M.

    1984-01-01

    A discrete representation of the reconstruction process is used in an analysis of noise in computed tomography (CT) images. This model is consistent with the method of data collection in actual machines. An expression is derived which predicts the variance on the measured linear attenuation coefficient of a single pixel in an image. The dependence of the variance on various CT scanner design parameters such as pixel size, slice width, scan time, number of detectors, etc., is then described. The variation of noise with sampling area is theoretically explained. These predictions are in good agreement with a set of experimental measurements made on a range of CT scanners. The equivalent sampling aperture of the CT process is determined and the effect of the reconstruction filter on the variance of the linear attenuation coefficient is also noted, in particular, the choice and its consequences for reconstructed images and noise behaviour. The theory has been extended to include contrast detail behaviour, and these predictions compare favourably with experimental measurements. The theory predicts that image smoothing will have little effect on the contrast-detail detectability behaviour of reconstructed images. (author)

  11. Text Detection in Natural Scene Images by Stroke Gabor Words.

    Science.gov (United States)

    Yi, Chucai; Tian, Yingli

    2011-01-01

    In this paper, we propose a novel algorithm, based on stroke components and descriptive Gabor filters, to detect text regions in natural scene images. Text characters and strings are constructed by stroke components as basic units. Gabor filters are used to describe and analyze the stroke components in text characters or strings. We define a suitability measurement to analyze the confidence of Gabor filters in describing stroke component and the suitability of Gabor filters on an image window. From the training set, we compute a set of Gabor filters that can describe principle stroke components of text by their parameters. Then a K -means algorithm is applied to cluster the descriptive Gabor filters. The clustering centers are defined as Stroke Gabor Words (SGWs) to provide a universal description of stroke components. By suitability evaluation on positive and negative training samples respectively, each SGW generates a pair of characteristic distributions of suitability measurements. On a testing natural scene image, heuristic layout analysis is applied first to extract candidate image windows. Then we compute the principle SGWs for each image window to describe its principle stroke components. Characteristic distributions generated by principle SGWs are used to classify text or nontext windows. Experimental results on benchmark datasets demonstrate that our algorithm can handle complex backgrounds and variant text patterns (font, color, scale, etc.).

  12. Image analysis and machine learning for detecting malaria.

    Science.gov (United States)

    Poostchi, Mahdieh; Silamut, Kamolrat; Maude, Richard J; Jaeger, Stefan; Thoma, George

    2018-04-01

    Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis. Published by Elsevier Inc.

  13. Molecular Ultrasound Imaging for the Detection of Neural Inflammation

    Science.gov (United States)

    Volz, Kevin R.

    Molecular imaging is a form of nanotechnology that enables the noninvasive examination of biological processes in vivo. Radiopharmaceutical agents are used to selectively target biochemical markers, which permits their detection and evaluation. Early visualization of molecular variations indicative of pathophysiological processes can aid in patient diagnoses and management decisions. Molecular imaging is performed by introducing molecular probes into the body. Molecular probes are often contrast agents that have been nanoengineered to selectively target and tether to molecules, enabling their radiologic identification. Ultrasound contrast agents have been demonstrated as an effective method of detecting perfusion at the tissue level. Through a nanoengineering process, ultrasound contrast agents can be targeted to specific molecules, thereby extending ultrasound's capabilities from the tissue to molecular level. Molecular ultrasound, or targeted contrast enhanced ultrasound (TCEUS), has recently emerged as a popular molecular imaging technique due to its ability to provide real-time anatomical and functional information in the absence of ionizing radiation. However, molecular ultrasound represents a novel form of molecular imaging, and consequently remains largely preclinical. A review of the TCEUS literature revealed multiple preclinical studies demonstrating its success in detecting inflammation in a variety of tissues. Although, a gap was identified in the existing evidence, as TCEUS effectiveness for detection of neural inflammation in the spinal cord was unable to be uncovered. This gap in knowledge, coupled with the profound impacts that this TCEUS application could have clinically, provided rationale for its exploration, and use as contributory evidence for the molecular ultrasound body of literature. An animal model that underwent a contusive spinal cord injury was used to establish preclinical evidence of TCEUS to detect neural inflammation. Imaging was

  14. Automatic fog detection for public safety by using camera images

    Science.gov (United States)

    Pagani, Giuliano Andrea; Roth, Martin; Wauben, Wiel

    2017-04-01

    Fog and reduced visibility have considerable impact on the performance of road, maritime, and aeronautical transportation networks. The impact ranges from minor delays to more serious congestions or unavailability of the infrastructure and can even lead to damage or loss of lives. Visibility is traditionally measured manually by meteorological observers using landmarks at known distances in the vicinity of the observation site. Nowadays, distributed cameras facilitate inspection of more locations from one remote monitoring center. The main idea is, however, still deriving the visibility or presence of fog by an operator judging the scenery and the presence of landmarks. Visibility sensors are also used, but they are rather costly and require regular maintenance. Moreover, observers, and in particular sensors, give only visibility information that is representative for a limited area. Hence the current density of visibility observations is insufficient to give detailed information on the presence of fog. Cameras are more and more deployed for surveillance and security reasons in cities and for monitoring traffic along main transportation ways. In addition to this primary use of cameras, we consider cameras as potential sensors to automatically identify low visibility conditions. The approach that we follow is to use machine learning techniques to determine the presence of fog and/or to make an estimation of the visibility. For that purpose a set of features are extracted from the camera images such as the number of edges, brightness, transmission of the image dark channel, fractal dimension. In addition to these image features, we also consider meteorological variables such as wind speed, temperature, relative humidity, and dew point as additional features to feed the machine learning model. The results obtained with a training and evaluation set consisting of 10-minute sampled images for two KNMI locations over a period of 1.5 years by using decision trees methods

  15. Streak detection and analysis pipeline for optical images

    Science.gov (United States)

    Virtanen, J.; Granvik, M.; Torppa, J.; Muinonen, K.; Poikonen, J.; Lehti, J.; Säntti, T.; Komulainen, T.; Flohrer, T.

    2014-07-01

    We describe a novel data processing and analysis pipeline for optical observations of moving objects, either of natural (asteroids, meteors) or artificial origin (satellites, space debris). The monitoring of the space object populations requires reliable acquisition of observational data to support the development and validation of population models, and to build and maintain catalogues of orbital elements. The orbital catalogues are, in turn, needed for the assessment of close approaches (for asteroids, with the Earth; for satellites, with each other) and for the support of contingency situations or launches. For both types of populations, there is also increasing interest to detect fainter objects corresponding to the small end of the size distribution. We focus on the low signal-to-noise (SNR) detection of objects with high angular velocities, resulting in long and faint object trails, or streaks, in the optical images. The currently available, mature image processing algorithms for detection and astrometric reduction of optical data cover objects that cross the sensor field-of-view comparably slowly, and, particularly for satellites, within a rather narrow, predefined range of angular velocities. By applying specific tracking techniques, the objects appear point-like or as short trails in the exposures. However, the general survey scenario is always a 'track-before-detect' problem, resulting in streaks of arbitrary lengths. Although some considerations for low-SNR processing of streak-like features are available in the current image processing and computer vision literature, algorithms are not readily available yet. In the ESA-funded StreakDet (Streak detection and astrometric reduction) project, we develop and evaluate an automated processing pipeline applicable to single images (as compared to consecutive frames of the same field) obtained with any observing scenario, including space-based surveys and both low- and high-altitude populations. The algorithmic

  16. Detection of hypercholesterolemia using hyperspectral imaging of human skin

    Science.gov (United States)

    Milanic, Matija; Bjorgan, Asgeir; Larsson, Marcus; Strömberg, Tomas; Randeberg, Lise L.

    2015-07-01

    Hypercholesterolemia is characterized by high blood levels of cholesterol and is associated with increased risk of atherosclerosis and cardiovascular disease. Xanthelasma is a subcutaneous lesion appearing in the skin around the eyes. Xanthelasma is related to hypercholesterolemia. Identifying micro-xanthelasma can thereforeprovide a mean for early detection of hypercholesterolemia and prevent onset and progress of disease. The goal of this study was to investigate spectral and spatial characteristics of hypercholesterolemia in facial skin. Optical techniques like hyperspectral imaging (HSI) might be a suitable tool for such characterization as it simultaneously provides high resolution spatial and spectral information. In this study a 3D Monte Carlo model of lipid inclusions in human skin was developed to create hyperspectral images in the spectral range 400-1090 nm. Four lesions with diameters 0.12-1.0 mm were simulated for three different skin types. The simulations were analyzed using three algorithms: the Tissue Indices (TI), the two layer Diffusion Approximation (DA), and the Minimum Noise Fraction transform (MNF). The simulated lesions were detected by all methods, but the best performance was obtained by the MNF algorithm. The results were verified using data from 11 volunteers with known cholesterol levels. The face of the volunteers was imaged by a LCTF system (400- 720 nm), and the images were analyzed using the previously mentioned algorithms. The identified features were then compared to the known cholesterol levels of the subjects. Significant correlation was obtained for the MNF algorithm only. This study demonstrates that HSI can be a promising, rapid modality for detection of hypercholesterolemia.

  17. Infrared thermal imaging for automated detection of diabetic foot complications.

    Science.gov (United States)

    van Netten, Jaap J; van Baal, Jeff G; Liu, Chanjuan; van der Heijden, Ferdi; Bus, Sicco A

    2013-09-01

    Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had diffuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). No differences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with diffuse complications, mean temperature differences of >3 °C between ipsilateral and contralateral foot were found. With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or diffuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings. © 2013 Diabetes Technology Society.

  18. On the detection of early osteoarthritis by quantitative microscopic imaging

    Science.gov (United States)

    Mittelstaedt, Daniel John

    Articular cartilage is a thin layer of connective tissue that protects the ends of bones in diarthroidal joints. Cartilage distributes mechanical forces during daily movement throughout its unique depth-dependent structure. The extracellular matrix (ECM) of cartilage primarily contains water, collagen, and glycosaminoglycan (GAG). The collagen fibers are intertwined with negatively charged GAG and surround the cells (i.e. chondrocytes) in cartilage. Degradation to the ECM reduces the load bearing properties of cartilage which can be initiated by injury (e.g. anterior cruciate ligament (ACL) rupture) or disease (e.g. osteoarthritis (OA)). Magnetic resonance imaging (MRI) and x-ray computed tomography (CT) are noninvasive imaging techniques that are increasingly being used in the clinical detection of cartilage degradation. The aim of the first project in this dissertation was to quantify and compare the depth-dependent GAG concentration from healthy and biochemically degraded humeral ex vivo articular cartilage using quantitative contrast enhanced micro-computed tomography (qCECT) at high resolution. The second project in this dissertation was aimed to measure the topographical and depth-dependent GAG concentration using qCECT and delayed gadolinium enhanced magnetic resonance imaging of cartilage (dGEMRIC) from the medial tibia cartilage three weeks after unilateral ACL transection which is an animal model of OA (i.e. modified Pond-Nuki model). These GAG measurements were correlated with a biochemical method, inductively couple plasma optical emission spectrometry, to compare the degradation on the medial tibia between the OA and contralateral cartilage. The third project in this dissertation used the same cartilage specimens as in project two to investigate the change in T2 due to OA and the effect on T2 from a contrast agent. Furthermore, the change in T2 relaxation was investigated from static unconfined compression with correlations by biomechanical

  19. Automated drusen detection in retinal images using analytical modelling algorithms

    Directory of Open Access Journals (Sweden)

    Manivannan Ayyakkannu

    2011-07-01

    Full Text Available Abstract Background Drusen are common features in the ageing macula associated with exudative Age-Related Macular Degeneration (ARMD. They are visible in retinal images and their quantitative analysis is important in the follow up of the ARMD. However, their evaluation is fastidious and difficult to reproduce when performed manually. Methods This article proposes a methodology for Automatic Drusen Deposits Detection and quantification in Retinal Images (AD3RI by using digital image processing techniques. It includes an image pre-processing method to correct the uneven illumination and to normalize the intensity contrast with smoothing splines. The drusen detection uses a gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. The detected drusen are then fitted by Modified Gaussian functions, producing a model of the image that is used to evaluate the affected area. Twenty two images were graded by eight experts, with the aid of a custom made software and compared with AD3RI. This comparison was based both on the total area and on the pixel-to-pixel analysis. The coefficient of variation, the intraclass correlation coefficient, the sensitivity, the specificity and the kappa coefficient were calculated. Results The ground truth used in this study was the experts' average grading. In order to evaluate the proposed methodology three indicators were defined: AD3RI compared to the ground truth (A2G; each expert compared to the other experts (E2E and a standard Global Threshold method compared to the ground truth (T2G. The results obtained for the three indicators, A2G, E2E and T2G, were: coefficient of variation 28.8 %, 22.5 % and 41.1 %, intraclass correlation coefficient 0.92, 0.88 and 0.67, sensitivity 0.68, 0.67 and 0.74, specificity 0.96, 0.97 and 0.94, and kappa coefficient 0.58, 0.60 and 0.49, respectively. Conclusions The gradings produced by AD3RI obtained an agreement

  20. Simultaneous macula detection and optic disc boundary segmentation in retinal fundus images

    Science.gov (United States)

    Girard, Fantin; Kavalec, Conrad; Grenier, Sébastien; Ben Tahar, Houssem; Cheriet, Farida

    2016-03-01

    The optic disc (OD) and the macula are important structures in automatic diagnosis of most retinal diseases inducing vision defects such as glaucoma, diabetic or hypertensive retinopathy and age-related macular degeneration. We propose a new method to detect simultaneously the macula and the OD boundary. First, the color fundus images are processed to compute several maps highlighting the different anatomical structures such as vessels, the macula and the OD. Then, macula candidates and OD candidates are found simultaneously and independently using seed detectors identified on the corresponding maps. After selecting a set of macula/OD pairs, the top candidates are sent to the OD segmentation method. The segmentation method is based on local K-means applied to color coordinates in polar space followed by a polynomial fitting regularization step. Pair scores are updated, resulting in the final best macula/OD pair. The method was evaluated on two public image databases: ONHSD and MESSIDOR. The results show an overlapping area of 0.84 on ONHSD and 0.90 on MESSIDOR, which is better than recent state of the art methods. Our segmentation method is robust to contrast and illumination problems and outputs the exact boundary of the OD, not just a circular or elliptical model. The macula detection has an accuracy of 94%, which again outperforms other macula detection methods. This shows that combining the OD and macula detections improves the overall accuracy. The computation time for the whole process is 6.4 seconds, which is faster than other methods in the literature.

  1. Automatic Shadow Detection and Removal from a Single Image.

    Science.gov (United States)

    Khan, Salman H; Bennamoun, Mohammed; Sohel, Ferdous; Togneri, Roberto

    2016-03-01

    We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.

  2. An Accurate Framework for Arbitrary View Pedestrian Detection in Images

    Science.gov (United States)

    Fan, Y.; Wen, G.; Qiu, S.

    2018-01-01

    We consider the problem of detect pedestrian under from images collected under various viewpoints. This paper utilizes a novel framework called locality-constrained affine subspace coding (LASC). Firstly, the positive training samples are clustered into similar entities which represent similar viewpoint. Then Principal Component Analysis (PCA) is used to obtain the shared feature of each viewpoint. Finally, the samples that can be reconstructed by linear approximation using their top- k nearest shared feature with a small error are regarded as a correct detection. No negative samples are required for our method. Histograms of orientated gradient (HOG) features are used as the feature descriptors, and the sliding window scheme is adopted to detect humans in images. The proposed method exploits the sparse property of intrinsic information and the correlations among the multiple-views samples. Experimental results on the INRIA and SDL human datasets show that the proposed method achieves a higher performance than the state-of-the-art methods in form of effect and efficiency.

  3. Multi-sensor radiation detection, imaging, and fusion

    Energy Technology Data Exchange (ETDEWEB)

    Vetter, Kai [Department of Nuclear Engineering, University of California, Berkeley, CA 94720 (United States); Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)

    2016-01-01

    Glenn Knoll was one of the leaders in the field of radiation detection and measurements and shaped this field through his outstanding scientific and technical contributions, as a teacher, his personality, and his textbook. His Radiation Detection and Measurement book guided me in my studies and is now the textbook in my classes in the Department of Nuclear Engineering at UC Berkeley. In the spirit of Glenn, I will provide an overview of our activities at the Berkeley Applied Nuclear Physics program reflecting some of the breadth of radiation detection technologies and their applications ranging from fundamental studies in physics to biomedical imaging and to nuclear security. I will conclude with a discussion of our Berkeley Radwatch and Resilient Communities activities as a result of the events at the Dai-ichi nuclear power plant in Fukushima, Japan more than 4 years ago. - Highlights: • .Electron-tracking based gamma-ray momentum reconstruction. • .3D volumetric and 3D scene fusion gamma-ray imaging. • .Nuclear Street View integrates and associates nuclear radiation features with specific objects in the environment. • Institute for Resilient Communities combines science, education, and communities to minimize impact of disastrous events.

  4. Automated detection of diabetic retinopathy in retinal images

    Directory of Open Access Journals (Sweden)

    Carmen Valverde

    2016-01-01

    Full Text Available Diabetic retinopathy (DR is a disease with an increasing prevalence and the main cause of blindness among working-age population. The risk of severe vision loss can be significantly reduced by timely diagnosis and treatment. Systematic screening for DR has been identified as a cost-effective way to save health services resources. Automatic retinal image analysis is emerging as an important screening tool for early DR detection, which can reduce the workload associated to manual grading as well as save diagnosis costs and time. Many research efforts in the last years have been devoted to developing automatic tools to help in the detection and evaluation of DR lesions. However, there is a large variability in the databases and evaluation criteria used in the literature, which hampers a direct comparison of the different studies. This work is aimed at summarizing the results of the available algorithms for the detection and classification of DR pathology. A detailed literature search was conducted using PubMed. Selected relevant studies in the last 10 years were scrutinized and included in the review. Furthermore, we will try to give an overview of the available commercial software for automatic retinal image analysis.

  5. Automated detection of diabetic retinopathy in retinal images.

    Science.gov (United States)

    Valverde, Carmen; Garcia, Maria; Hornero, Roberto; Lopez-Galvez, Maria I

    2016-01-01

    Diabetic retinopathy (DR) is a disease with an increasing prevalence and the main cause of blindness among working-age population. The risk of severe vision loss can be significantly reduced by timely diagnosis and treatment. Systematic screening for DR has been identified as a cost-effective way to save health services resources. Automatic retinal image analysis is emerging as an important screening tool for early DR detection, which can reduce the workload associated to manual grading as well as save diagnosis costs and time. Many research efforts in the last years have been devoted to developing automatic tools to help in the detection and evaluation of DR lesions. However, there is a large variability in the databases and evaluation criteria used in the literature, which hampers a direct comparison of the different studies. This work is aimed at summarizing the results of the available algorithms for the detection and classification of DR pathology. A detailed literature search was conducted using PubMed. Selected relevant studies in the last 10 years were scrutinized and included in the review. Furthermore, we will try to give an overview of the available commercial software for automatic retinal image analysis.

  6. Improving PET spatial resolution and detectability for prostate cancer imaging

    International Nuclear Information System (INIS)

    Bal, H; Guerin, L; Casey, M E; Conti, M; Eriksson, L; Michel, C; Fanti, S; Pettinato, C; Adler, S; Choyke, P

    2014-01-01

    Prostate cancer, one of the most common forms of cancer among men, can benefit from recent improvements in positron emission tomography (PET) technology. In particular, better spatial resolution, lower noise and higher detectability of small lesions could be greatly beneficial for early diagnosis and could provide a strong support for guiding biopsy and surgery. In this article, the impact of improved PET instrumentation with superior spatial resolution and high sensitivity are discussed, together with the latest development in PET technology: resolution recovery and time-of-flight reconstruction. Using simulated cancer lesions, inserted in clinical PET images obtained with conventional protocols, we show that visual identification of the lesions and detectability via numerical observers can already be improved using state of the art PET reconstruction methods. This was achieved using both resolution recovery and time-of-flight reconstruction, and a high resolution image with 2 mm pixel size. Channelized Hotelling numerical observers showed an increase in the area under the LROC curve from 0.52 to 0.58. In addition, a relationship between the simulated input activity and the area under the LROC curve showed that the minimum detectable activity was reduced by more than 23%. (paper)

  7. Data Fusion and Fuzzy Clustering on Ratio Images for Change Detection in Synthetic Aperture Radar Images

    Directory of Open Access Journals (Sweden)

    Wenping Ma

    2014-01-01

    Full Text Available The unsupervised approach to change detection via synthetic aperture radar (SAR images becomes more and more popular. The three-step procedure is the most widely used procedure, but it does not work well with the Yellow River Estuary dataset obtained by two synthetic aperture radars. The difference of the two radars in imaging techniques causes severe noise, which seriously affects the difference images generated by a single change detector in step two, producing the difference image. To deal with problem, we propose a change detector to fuse the log-ratio (LR and the mean-ratio (MR images by a context independent variable behavior (CIVB operator and can utilize the complement information in two ratio images. In order to validate the effectiveness of the proposed change detector, the change detector will be compared with three other change detectors, namely, the log-ratio (LR, mean-ratio (MR, and the wavelet-fusion (WR operator, to deal with three datasets with different characteristics. The four operators are applied not only in a widely used three-step procedure but also in a new approach. The experiments show that the false alarms and overall errors of change detection are greatly reduced, and the kappa and KCC are improved a lot. And its superiority can also be observed visually.

  8. Early Detection of Diabetic Retinopathy in Fluorescent Angiography Retinal Images Using Image Processing Methods

    Directory of Open Access Journals (Sweden)

    Meysam Tavakoli

    2010-12-01

    Full Text Available Introduction: Diabetic retinopathy (DR is the single largest cause of sight loss and blindness in the working age population of Western countries; it is the most common cause of blindness in adults between 20 and 60 years of age. Early diagnosis of DR is critical for preventing vision loss so early detection of microaneurysms (MAs as the first signs of DR is important. This paper addresses the automatic detection of MAs in fluorescein angiography fundus images, which plays a key role in computer assisted diagnosis of DR, a serious and frequent eye disease. Material and Methods: The algorithm can be divided into three main steps. The first step or pre-processing was for background normalization and contrast enhancement of the image. The second step aimed at detecting landmarks, i.e., all patterns possibly corresponding to vessels and the optic nerve head, which was achieved using a local radon transform. Then, MAs were extracted, which were used in the final step to automatically classify candidates into real MA and other objects. A database of 120 fluorescein angiography fundus images was used to train and test the algorithm. The algorithm was compared to manually obtained gradings of those images. Results: Sensitivity of diagnosis for DR was 94%, with specificity of 75%, and sensitivity of precise microaneurysm localization was 92%, at an average number of 8 false positives per image. Discussion and Conclusion: Sensitivity and specificity of this algorithm make it one of the best methods in this field. Using local radon transform in this algorithm eliminates the noise sensitivity for microaneurysm detection in retinal image analysis.

  9. Multivariate image analysis of laser-induced photothermal imaging used for detection of caries tooth

    Science.gov (United States)

    El-Sherif, Ashraf F.; Abdel Aziz, Wessam M.; El-Sharkawy, Yasser H.

    2010-08-01

    Time-resolved photothermal imaging has been investigated to characterize tooth for the purpose of discriminating between normal and caries areas of the hard tissue using thermal camera. Ultrasonic thermoelastic waves were generated in hard tissue by the absorption of fiber-coupled Q-switched Nd:YAG laser pulses operating at 1064 nm in conjunction with a laser-induced photothermal technique used to detect the thermal radiation waves for diagnosis of human tooth. The concepts behind the use of photo-thermal techniques for off-line detection of caries tooth features were presented by our group in earlier work. This paper illustrates the application of multivariate image analysis (MIA) techniques to detect the presence of caries tooth. MIA is used to rapidly detect the presence and quantity of common caries tooth features as they scanned by the high resolution color (RGB) thermal cameras. Multivariate principal component analysis is used to decompose the acquired three-channel tooth images into a two dimensional principal components (PC) space. Masking score point clusters in the score space and highlighting corresponding pixels in the image space of the two dominant PCs enables isolation of caries defect pixels based on contrast and color information. The technique provides a qualitative result that can be used for early stage caries tooth detection. The proposed technique can potentially be used on-line or real-time resolved to prescreen the existence of caries through vision based systems like real-time thermal camera. Experimental results on the large number of extracted teeth as well as one of the thermal image panoramas of the human teeth voltanteer are investigated and presented.

  10. Ice Sheet Change Detection by Satellite Image Differencing

    Science.gov (United States)

    Bindschadler, Robert A.; Scambos, Ted A.; Choi, Hyeungu; Haran, Terry M.

    2010-01-01

    Differencing of digital satellite image pairs highlights subtle changes in near-identical scenes of Earth surfaces. Using the mathematical relationships relevant to photoclinometry, we examine the effectiveness of this method for the study of localized ice sheet surface topography changes using numerical experiments. We then test these results by differencing images of several regions in West Antarctica, including some where changes have previously been identified in altimeter profiles. The technique works well with coregistered images having low noise, high radiometric sensitivity, and near-identical solar illumination geometry. Clouds and frosts detract from resolving surface features. The ETM(plus) sensor on Landsat-7, ALI sensor on EO-1, and MODIS sensor on the Aqua and Terra satellite platforms all have potential for detecting localized topographic changes such as shifting dunes, surface inflation and deflation features associated with sub-glacial lake fill-drain events, or grounding line changes. Availability and frequency of MODIS images favor this sensor for wide application, and using it, we demonstrate both qualitative identification of changes in topography and quantitative mapping of slope and elevation changes.

  11. Snapshot imaging Fraunhofer line discriminator for detection of plant fluorescence

    Science.gov (United States)

    Gupta Roy, S.; Kudenov, M. W.

    2015-05-01

    Non-invasive quantification of plant health is traditionally accomplished using reflectance based metrics, such as the normalized difference vegetative index (NDVI). However, measuring plant fluorescence (both active and passive) to determine photochemistry of plants has gained importance. Due to better cost efficiency, lower power requirements, and simpler scanning synchronization, detecting passive fluorescence is preferred over active fluorescence. In this paper, we propose a high speed imaging approach for measuring passive plant fluorescence, within the hydrogen alpha Fraunhofer line at ~656 nm, using a Snapshot Imaging Fraunhofer Line Discriminator (SIFOLD). For the first time, the advantage of snapshot imaging for high throughput Fraunhofer Line Discrimination (FLD) is cultivated by our system, which is based on a multiple-image Fourier transform spectrometer and a spatial heterodyne interferometer (SHI). The SHI is a Sagnac interferometer, which is dispersion compensated using blazed diffraction gratings. We present data and techniques for calibrating the SIFOLD to any particular wavelength. This technique can be applied to quantify plant fluorescence at low cost and reduced complexity of data collection.

  12. Proton detected 13C imaging. Implementation and development

    International Nuclear Information System (INIS)

    Hudson, A.M.J.

    1999-08-01

    The work described in this thesis has been undertaken by the author, except where indicated by reference, within the Magnetic Resonance Centre at the University of Nottingham during the period from October 1996 to March 1999. This thesis documents the implementation and development of an imaging technique used to generate proton detected nuclear magnetic resonance images, representing the density of carbon-13 nuclear spins. The technique uses rotating frame, liquid state coherent polarisation transfer to generate carbon-13 edited proton signal. The dual resonant radio frequency (RF) coils used to create the homogeneous RF fields, intrinsic to the polarisation transfer are described, along with a consideration of their properties. Single resonant structures are also investigated, and a microscopy coil suitable for side access imaging studies is presented. By the use of field plotting methods, the main magnet field homogeneity was optimised, and the resonator RF fields were characterised. This provided the information for a theoretical evaluation of the efficacy of this implementation of the cyclic cross polarisation sequence, which was confirmed experimentally. Applications of the cyclic cross polarisation imaging scheme are presented, along with a novel variant of the sequence, used for mapping the RF field within samples with a low magnetogyric ratio. (author)

  13. Detecting crop population growth using chlorophyll fluorescence imaging.

    Science.gov (United States)

    Wang, Heng; Qian, Xiangjie; Zhang, Lan; Xu, Sailong; Li, Haifeng; Xia, Xiaojian; Dai, Liankui; Xu, Liang; Yu, Jingquan; Liu, Xu

    2017-12-10

    For both field and greenhouse crops, it is challenging to evaluate their growth information on a large area over a long time. In this work, we developed a chlorophyll fluorescence imaging-based system for crop population growth information detection. Modular design was used to make the system provide high-intensity uniform illumination. This system can perform modulated chlorophyll fluorescence induction kinetics measurement and chlorophyll fluorescence parameter imaging over a large area of up to 45  cm×34  cm. The system can provide different lighting intensity by modulating the duty cycle of its control signal. Results of continuous monitoring of cucumbers in nitrogen deficiency show the system can reduce the judge error of crop physiological status and improve monitoring efficiency. Meanwhile, the system is promising in high throughput application scenarios.

  14. Imaging and detection of mines from acoustic measurements

    Science.gov (United States)

    Witten, Alan J.; DiMarzio, Charles A.; Li, Wen; McKnight, Stephen W.

    1999-08-01

    A laboratory-scale acoustic experiment is described where a buried target, a hockey puck cut in half, is shallowly buried in a sand box. To avoid the need for source and receiver coupling to the host sand, an acoustic wave is generated in the subsurface by a pulsed laser suspended above the air-sand interface. Similarly, an airborne microphone is suspended above this interface and moved in unison with the laser. After some pre-processing of the data, reflections for the target, although weak, could clearly be identified. While the existence and location of the target can be determined by inspection of the data, its unique shape can not. Since target discrimination is important in mine detection, a 3D imaging algorithm was applied to the acquired acoustic data. This algorithm yielded a reconstructed image where the shape of the target was resolved.

  15. Transillumination and HDR Imaging for Proximal Caries Detection.

    Science.gov (United States)

    Lederer, A; Kunzelmann, K H; Hickel, R; Litzenburger, F

    2018-02-01

    The purpose was to develop an in vitro model for the validation of near-infrared transillumination (NIRT) for proximal caries detection, to enhance NIRT with high-dynamic-range imaging (HDRI), and to compare both methods, using micro-computed tomography (µCT) as a reference standard. Both proximal surfaces of 53 healthy or decayed permanent human teeth were examined using the Diagnocam (DC) (KaVo) and NIRT with HDRI (NIRT-HDRI). NIRT was combined with HDRI to improve the diagnostic performance by reducing under- and overexposed image areas. For NIRT-HDRI, an exposure series was captured and merged into a single HDR image. A classification was applied according to lesion depth. All surfaces were assessed twice by 2 trained examiners, and additionally with µCT for validation. The Kappa statistic was used to calculate inter-rater reliability and agreement between DC and NIRT-HDRI. Inter-rater reliability (weighted Kappa, wκ) showed very good agreement for the DC (0.90) and NIRT-HDRI (0.96). The overall agreement (wκ) was almost perfect (0.85). In the individual categories (0 to 4), the agreement (simple Kappa) ranged from almost perfect (category 4) to moderate (1 and 2) to substantial (categories 0 and 3). Sensitivity and specificity of sound surfaces, enamel, and dentin caries ranged from 0.57 to 0.99 and were similar for both methods in the different categories. NIRT-HDRI had a higher sensitivity for sound surfaces and enamel caries, as well as a higher specificity for dentin caries. Regarding the obtained images, HDRI allowed for the detection of caries within a greater range of luminance levels, resulting in a more detailed visualization of structures without under- or overexposure. However, HDRI this did not improve the diagnostics significantly. Distinguishing between a processed demineralized enamel and dentin lesions appears to be a problem specific to NIRT and cannot be balanced using HDRI.

  16. Texture alteration detection in bitemporal images of lesions with psoriasis

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    The objective of this work is to explore the feasibility of quantifying textural change between pairs of segmented patterns without registering them. The Multi-variate Alteration Detection (M.A.D.) Transform is applied to a texture model constructed with the data of segmented psoriasis lesions im...... images. The texture model is Haralick's co-occurrence matrix, which is computed and normalized for each single band with the equalized data of a given lesion. The contribution of each single color band to the textural change is analyzed....

  17. Detection system using scintillating optical fibers and image tube readout

    International Nuclear Information System (INIS)

    Alspector, J.; Borenstein, S.

    1979-01-01

    The hodoscope subgroup has studied a detection system consisting of bundles of optical fibers with readout via image tubes. The basic building block is an optical fiber with a scintillator inner core. The inner core has refractive index n/sub o/ (1.58 for plastic scintillator), and the outer sheath has a low index (approx. 1.4). Light is created in the core by passage of a particle track; if the light strikes the sheath at an angle greater than the critical angle phi/sub c/, it is trapped in the fiber until it finds its way to the photon detector

  18. Object detection and recognition in digital images theory and practice

    CERN Document Server

    Cyganek, Boguslaw

    2013-01-01

    Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications.

  19. Computer-assisted detection of epileptiform focuses on SPECT images

    Science.gov (United States)

    Grzegorczyk, Dawid; Dunin-Wąsowicz, Dorota; Mulawka, Jan J.

    2010-09-01

    Epilepsy is a common nervous system disease often related to consciousness disturbances and muscular spasm which affects about 1% of the human population. Despite major technological advances done in medicine in the last years there was no sufficient progress towards overcoming it. Application of advanced statistical methods and computer image analysis offers the hope for accurate detection and later removal of an epileptiform focuses which are the cause of some types of epilepsy. The aim of this work was to create a computer system that would help to find and diagnose disorders of blood circulation in the brain This may be helpful for the diagnosis of the epileptic seizures onset in the brain.

  20. Myocardial perfusion imaging for the detection of coronary heart disease

    International Nuclear Information System (INIS)

    Strauss, H.W.; Cook, D.J.; Bailey, I.; Rouleau, J.; Pitt, B.

    1976-01-01

    A method of myocardial perfusion imaging using 201 Tl is described. Thallium is able to substitute for potassium in biological systems including transport by the sodium--potassium ATP-ase system. The high extraction efficiency of the heart for 201 Tl offers a method whereby a tracer may be administered intravenously and is concentrated to a significant degree by the heart. However, only about 3 to 4 percent of the dose administered lodges in the myocardium. Experiments with dogs indicated that the regional distribution of Tl in the heart reflects the regional distribution of blood flow. The goal is to develop a procedure that can detect those patients with significant disease prior to the onset of a catastrophic event and studies are being undertaken to improve the sensitivity of the method for the detection of smaller lesions in the myocardium

  1. Magnetic resonance imaging of living systems by remote detection

    Science.gov (United States)

    Wemmer, David; Pines, Alexander; Bouchard, Louis; Xu, Shoujun; Harel, Elad; Budker, Dmitry; Lowery, Thomas; Ledbetter, Micah

    2013-10-29

    A novel approach to magnetic resonance imaging is disclosed. Blood flowing through a living system is prepolarized, and then encoded. The polarization can be achieved using permanent or superconducting magnets. The polarization may be carried out upstream of the region to be encoded or at the place of encoding. In the case of an MRI of a brain, polarization of flowing blood can be effected by placing a magnet over a section of the body such as the heart upstream of the head. Alternatively, polarization and encoding can be effected at the same location. Detection occurs at a remote location, using a separate detection device such as an optical atomic magnetometer, or an inductive Faraday coil. The detector may be placed on the surface of the skin next to a blood vessel such as a jugular vein carrying blood away from the encoded region.

  2. Detecting unresolved binary stars in Euclid VIS images

    Science.gov (United States)

    Kuntzer, T.; Courbin, F.

    2017-10-01

    Measuring a weak gravitational lensing signal to the level required by the next generation of space-based surveys demands exquisite reconstruction of the point-spread function (PSF). However, unresolved binary stars can significantly distort the PSF shape. In an effort to mitigate this bias, we aim at detecting unresolved binaries in realistic Euclid stellar populations. We tested methods in numerical experiments where (I) the PSF shape is known to Euclid requirements across the field of view; and (II) the PSF shape is unknown. We drew simulated catalogues of PSF shapes for this proof-of-concept paper. Following the Euclid survey plan, the objects were observed four times. We propose three methods to detect unresolved binary stars. The detection is based on the systematic and correlated biases between exposures of the same object. One method is a simple correlation analysis, while the two others use supervised machine-learning algorithms (random forest and artificial neural network). In both experiments, we demonstrate the ability of our methods to detect unresolved binary stars in simulated catalogues. The performance depends on the level of prior knowledge of the PSF shape and the shape measurement errors. Good detection performances are observed in both experiments. Full complexity, in terms of the images and the survey design, is not included, but key aspects of a more mature pipeline are discussed. Finding unresolved binaries in objects used for PSF reconstruction increases the quality of the PSF determination at arbitrary positions. We show, using different approaches, that we are able to detect at least binary stars that are most damaging for the PSF reconstruction process. The code corresponding to the algorithms used in this work and all scripts to reproduce the results are publicly available from a GitHub repository accessible via http://lastro.epfl.ch/software

  3. Detection of Organophosphorus Pesticides with Colorimetry and Computer Image Analysis.

    Science.gov (United States)

    Li, Yanjie; Hou, Changjun; Lei, Jincan; Deng, Bo; Huang, Jing; Yang, Mei

    2016-01-01

    Organophosphorus pesticides (OPs) represent a very important class of pesticides that are widely used in agriculture because of their relatively high-performance and moderate environmental persistence, hence the sensitive and specific detection of OPs is highly significant. Based on the inhibitory effect of acetylcholinesterase (AChE) induced by inhibitors, including OPs and carbamates, a colorimetric analysis was used for detection of OPs with computer image analysis of color density in CMYK (cyan, magenta, yellow and black) color space and non-linear modeling. The results showed that there was a gradually weakened trend of yellow intensity with the increase of the concentration of dichlorvos. The quantitative analysis of dichlorvos was achieved by Artificial Neural Network (ANN) modeling, and the results showed that the established model had a good predictive ability between training sets and predictive sets. Real cabbage samples containing dichlorvos were detected by colorimetry and gas chromatography (GC), respectively. The results showed that there was no significant difference between colorimetry and GC (P > 0.05). The experiments of accuracy, precision and repeatability revealed good performance for detection of OPs. AChE can also be inhibited by carbamates, and therefore this method has potential applications in real samples for OPs and carbamates because of high selectivity and sensitivity.

  4. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Jean Marie Vianney Kinani

    2017-01-01

    Full Text Available We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR and fluid-attenuated inversion recovery (FLAIR images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

  5. Imaging monitoring techniques applications in the transient gratings detection

    Science.gov (United States)

    Zhao, Qing-ming

    2009-07-01

    Experimental studies of Degenerate four-wave mixing (DFWM) in iodine vapor at atmospheric pressure and 0℃ and 25℃ are reported. The Laser-induced grating (LIG) studies are carried out by generating the thermal grating using a pulsed, narrow bandwidth, dye laser .A new image processing system for detecting forward DFWM spectroscopy on iodine vapor is reported. This system is composed of CCD camera, imaging processing card and the related software. With the help of the detecting system, phase matching can be easily achieved in the optical arrangement by crossing the two pumps and the probe as diagonals linking opposite corners of a rectangular box ,and providing a way to position the PhotoMultiplier Tube (PMT) . Also it is practical to know the effect of the pointing stability on the optical path by monitoring facula changing with the laser beam pointing and disturbs of the environment. Finally the effects of Photostability of dye laser on the ration of signal to noise in DFWM using forward geometries have been investigated in iodine vapor. This system makes it feasible that the potential application of FG-DFWM is used as a diagnostic tool in combustion research and environment monitoring.

  6. BUILDING DETECTION USING AERIAL IMAGES AND DIGITAL SURFACE MODELS

    Directory of Open Access Journals (Sweden)

    J. Mu

    2017-05-01

    Full Text Available In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.

  7. Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection

    Directory of Open Access Journals (Sweden)

    Bianca Regeling

    2016-08-01

    Full Text Available Hyperspectral imaging (HSI is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.

  8. DEEP LEARNING AND IMAGE PROCESSING FOR AUTOMATED CRACK DETECTION AND DEFECT MEASUREMENT IN UNDERGROUND STRUCTURES

    Directory of Open Access Journals (Sweden)

    F. Panella

    2018-05-01

    Full Text Available This work presents the combination of Deep-Learning (DL and image processing to produce an automated cracks recognition and defect measurement tool for civil structures. The authors focus on tunnel civil structures and survey and have developed an end to end tool for asset management of underground structures. In order to maintain the serviceability of tunnels, regular inspection is needed to assess their structural status. The traditional method of carrying out the survey is the visual inspection: simple, but slow and relatively expensive and the quality of the output depends on the ability and experience of the engineer as well as on the total workload (stress and tiredness may influence the ability to observe and record information. As a result of these issues, in the last decade there is the desire to automate the monitoring using new methods of inspection. The present paper has the goal of combining DL with traditional image processing to create a tool able to detect, locate and measure the structural defect.

  9. Time integration and statistical regulation applied to mobile objects detection in a sequence of images

    International Nuclear Information System (INIS)

    Letang, Jean-Michel

    1993-01-01

    This PhD thesis deals with the detection of moving objects in monocular image sequences. The first section presents the inherent problems of motion analysis in real applications. We propose a method robust to perturbations frequently encountered during acquisition of outdoor scenes. It appears three main directions for investigations, all of them pointing out the importance of the temporal axis, which is a specific dimension for motion analysis. In the first part, the image sequence is considered as a set of temporal signals. The temporal multi-scale decomposition enables the characterization of various dynamical behaviors of the objects being in the scene at a given instant. A second module integrates motion information. This elementary trajectography of moving objects provides a temporal prediction map, giving a confidence level of motion presence. Interactions between both sets of data are expressed within a statistical regularization. Markov random field models supply a formal framework to convey a priori knowledge of the primitives to be evaluated. A calibration method with qualitative boxes is presented to estimate model parameters. Our approach requires only simple computations and leads to a rather fast algorithm, that we evaluate in the last section over various typical sequences. (author) [fr

  10. Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Conradsen, Knut; Simpson, James J.

    1998-01-01

    type analyses of simple difference images. Case studies with AHVRR and Landsat MSS data using simple linear stretching and masking of the change images show the usefulness of the new MAD and MAF/MAD change detection schemes. Ground truth observations confirm the detected changes. A simple simulation...

  11. Regularization design for high-quality cone-beam CT of intracranial hemorrhage using statistical reconstruction

    Science.gov (United States)

    Dang, H.; Stayman, J. W.; Xu, J.; Sisniega, A.; Zbijewski, W.; Wang, X.; Foos, D. H.; Aygun, N.; Koliatsos, V. E.; Siewerdsen, J. H.

    2016-03-01

    Intracranial hemorrhage (ICH) is associated with pathologies such as hemorrhagic stroke and traumatic brain injury. Multi-detector CT is the current front-line imaging modality for detecting ICH (fresh blood contrast 40-80 HU, down to 1 mm). Flat-panel detector (FPD) cone-beam CT (CBCT) offers a potential alternative with a smaller scanner footprint, greater portability, and lower cost potentially well suited to deployment at the point of care outside standard diagnostic radiology and emergency room settings. Previous studies have suggested reliable detection of ICH down to 3 mm in CBCT using high-fidelity artifact correction and penalized weighted least-squared (PWLS) image reconstruction with a post-artifact-correction noise model. However, ICH reconstructed by traditional image regularization exhibits nonuniform spatial resolution and noise due to interaction between the statistical weights and regularization, which potentially degrades the detectability of ICH. In this work, we propose three regularization methods designed to overcome these challenges. The first two compute spatially varying certainty for uniform spatial resolution and noise, respectively. The third computes spatially varying regularization strength to achieve uniform "detectability," combining both spatial resolution and noise in a manner analogous to a delta-function detection task. Experiments were conducted on a CBCT test-bench, and image quality was evaluated for simulated ICH in different regions of an anthropomorphic head. The first two methods improved the uniformity in spatial resolution and noise compared to traditional regularization. The third exhibited the highest uniformity in detectability among all methods and best overall image quality. The proposed regularization provides a valuable means to achieve uniform image quality in CBCT of ICH and is being incorporated in a CBCT prototype for ICH imaging.

  12. Automated Detection of Firearms and Knives in a CCTV Image

    Science.gov (United States)

    Grega, Michał; Matiolański, Andrzej; Guzik, Piotr; Leszczuk, Mikołaj

    2016-01-01

    Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims. PMID:26729128

  13. LANDSAT-8 OPERATIONAL LAND IMAGER CHANGE DETECTION ANALYSIS

    Directory of Open Access Journals (Sweden)

    W. Pervez

    2017-05-01

    Full Text Available This paper investigated the potential utility of Landsat-8 Operational Land Imager (OLI for change detection analysis and mapping application because of its superior technical design to previous Landsat series. The OLI SVM classified data was successfully classified with regard to all six test classes (i.e., bare land, built-up land, mixed trees, bushes, dam water and channel water. OLI support vector machine (SVM classified data for the four seasons (i.e., spring, autumn, winter, and summer was used to change detection results of six cases: (1 winter to spring which resulted reduction in dam water mapping and increases of bushes; (2 winter to summer which resulted reduction in dam water mapping and increase of vegetation; (3 winter to autumn which resulted increase in dam water mapping; (4 spring to summer which resulted reduction of vegetation and shallow water; (5 spring to autumn which resulted decrease of vegetation; and (6 summer to autumn which resulted increase of bushes and vegetation . OLI SVM classified data resulted higher overall accuracy and kappa coefficient and thus found suitable for change detection analysis.

  14. Automated Detection of Firearms and Knives in a CCTV Image

    Directory of Open Access Journals (Sweden)

    Michał Grega

    2016-01-01

    Full Text Available Closed circuit television systems (CCTV are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

  15. Automated Detection of Firearms and Knives in a CCTV Image.

    Science.gov (United States)

    Grega, Michał; Matiolański, Andrzej; Guzik, Piotr; Leszczuk, Mikołaj

    2016-01-01

    Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

  16. LAKE ICE DETECTION IN LOW-RESOLUTION OPTICAL SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    M. Tom

    2018-05-01

    Full Text Available Monitoring and analyzing the (decreasing trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m–1000 m satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal. Only the cloud-free (clean pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM. We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.

  17. Lake Ice Detection in Low-Resolution Optical Satellite Images

    Science.gov (United States)

    Tom, M.; Kälin, U.; Sütterlin, M.; Baltsavias, E.; Schindler, K.

    2018-05-01

    Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m-1000 m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.

  18. Algorithms for detection of objects in image sequences captured from an airborne imaging system

    Science.gov (United States)

    Kasturi, Rangachar; Camps, Octavia; Tang, Yuan-Liang; Devadiga, Sadashiva; Gandhi, Tarak

    1995-01-01

    This research was initiated as a part of the effort at the NASA Ames Research Center to design a computer vision based system that can enhance the safety of navigation by aiding the pilots in detecting various obstacles on the runway during critical section of the flight such as a landing maneuver. The primary goal is the development of algorithms for detection of moving objects from a sequence of images obtained from an on-board video camera. Image regions corresponding to the independently moving objects are segmented from the background by applying constraint filtering on the optical flow computed from the initial few frames of the sequence. These detected regions are tracked over subsequent frames using a model based tracking algorithm. Position and velocity of the moving objects in the world coordinate is estimated using an extended Kalman filter. The algorithms are tested using the NASA line image sequence with six static trucks and a simulated moving truck and experimental results are described. Various limitations of the currently implemented version of the above algorithm are identified and possible solutions to build a practical working system are investigated.

  19. Semiautomated analysis of embryoscope images: Using localized variance of image intensity to detect embryo developmental stages.

    Science.gov (United States)

    Mölder, Anna; Drury, Sarah; Costen, Nicholas; Hartshorne, Geraldine M; Czanner, Silvester

    2015-02-01

    Embryo selection in in vitro fertilization (IVF) treatment has traditionally been done manually using microscopy at intermittent time points during embryo development. Novel technique has made it possible to monitor embryos using time lapse for long periods of time and together with the reduced cost of data storage, this has opened the door to long-term time-lapse monitoring, and large amounts of image material is now routinely gathered. However, the analysis is still to a large extent performed manually, and images are mostly used as qualitative reference. To make full use of the increased amount of microscopic image material, (semi)automated computer-aided tools are needed. An additional benefit of automation is the establishment of standardization tools for embryo selection and transfer, making decisions more transparent and less subjective. Another is the possibility to gather and analyze data in a high-throughput manner, gathering data from multiple clinics and increasing our knowledge of early human embryo development. In this study, the extraction of data to automatically select and track spatio-temporal events and features from sets of embryo images has been achieved using localized variance based on the distribution of image grey scale levels. A retrospective cohort study was performed using time-lapse imaging data derived from 39 human embryos from seven couples, covering the time from fertilization up to 6.3 days. The profile of localized variance has been used to characterize syngamy, mitotic division and stages of cleavage, compaction, and blastocoel formation. Prior to analysis, focal plane and embryo location were automatically detected, limiting precomputational user interaction to a calibration step and usable for automatic detection of region of interest (ROI) regardless of the method of analysis. The results were validated against the opinion of clinical experts. © 2015 International Society for Advancement of Cytometry. © 2015 International

  20. From Pixels to Region: A Salient Region Detection Algorithm for Location-Quantification Image

    Directory of Open Access Journals (Sweden)

    Mengmeng Zhang

    2014-01-01

    Full Text Available Image saliency detection has become increasingly important with the development of intelligent identification and machine vision technology. This process is essential for many image processing algorithms such as image retrieval, image segmentation, image recognition, and adaptive image compression. We propose a salient region detection algorithm for full-resolution images. This algorithm analyzes the randomness and correlation of image pixels and pixel-to-region saliency computation mechanism. The algorithm first obtains points with more saliency probability by using the improved smallest univalue segment assimilating nucleus operator. It then reconstructs the entire saliency region detection by taking these points as reference and combining them with image spatial color distribution, as well as regional and global contrasts. The results for subjective and objective image saliency detection show that the proposed algorithm exhibits outstanding performance in terms of technology indices such as precision and recall rates.

  1. First photon detection in transillumination imaging: A theoretical evaluation

    International Nuclear Information System (INIS)

    Behin-Ain, Setayesh

    2003-01-01

    This thesis is a theoretical evaluation of the (single) first photon detection (FPD) technique as a limiting case of time-resolved transillumination imaging (TI) for diagnostic purposes. It combines analytic and Monte Carlo (MC) simulation methods to derive the single photon statistics and to solve the radiative transfer equation (RTE) for a given source-medium-detector geometry. In order to efficiently simulate very early arriving photons, an Indeterministic Monte Carlo (IMC) technique based on path integrals is devised and validated. The IMC extends controlled MC techniques to accelerate and enhance the probability of detecting shorter trajectories thereby improving the statistics. The IMC technique provides a tool for the construction of a temporal point spread function (TPSF) of the emerging photons for the entire time scale. It is then used to predict the spatial resolution of these systems for shorter (sub-100 picosecond) time scales. The calculation of the TPSF at short time scales for a pulse made incident onto the medium enables the mathematical derivation of the temporal probability density functions (p.d.f.) for the first arriving photon, f 1 (t). This facilitates the investigation of a first photon detection (FPD) system as applied to a diagnostic TI configuration. A FPD system produces a signal representing f 1 (t) from which the mean transit time of the first arriving photon, t-bar 1 , may then be estimated for a sequence of incident pulses at each scan position. By rectilinear scanning across the medium, a two-dimensional (2-D) map of t-bar 1 can be created and displayed as a gray scale image. The application of FPD to TI is evaluated assuming an ideal detector capable of detecting the first arriving photon with 100% efficiency (infinite extinction coefficient). However, a model for a FPD system corresponding to a nonideal (single first photon) detector is also considered through the evaluation of the p.d.f. for the later (first, second,...) arriving

  2. Impulse radar imaging system for concealed object detection

    Science.gov (United States)

    Podd, F. J. W.; David, M.; Iqbal, G.; Hussain, F.; Morris, D.; Osakue, E.; Yeow, Y.; Zahir, S.; Armitage, D. W.; Peyton, A. J.

    2013-10-01

    -to-noise parameter to determine how the frequencies contained in the echo dataset are normalised. The chosen image reconstruction algorithm is based on the back-projection method. The algorithm was implemented in MATLAB and uses a pre-calculated sensitivity matrix to increase the computation speed. The results include both 2D and 3D image datasets. The 3D datasets were obtained by scanning the dual sixteen element linear antenna array over the test object. The system has been tested on both humans and mannequin test objects. The front surface of an object placed on the human/mannequin torso is clearly visible, but its presence is also seen from a tell-tale imaging characteristic. This characteristic is caused by a reduction in the wave velocity as the electromagnetic radiation passes through the object, and manifests as an indentation in the reconstructed image that is readily identifiable. The prototype system has been shown to easily detect a 12 mm x 30 mm x70 mm plastic object concealed under clothing.

  3. Extracardiac findings detected by cardiac magnetic resonance imaging

    International Nuclear Information System (INIS)

    Wyttenbach, Rolf; Medioni, Nathalie; Santini, Paolo; Vock, Peter; Szucs-Farkas, Zsolt

    2012-01-01

    To determine the prevalence and importance of extracardiac findings (ECF) in patients undergoing clinical CMR and to test the hypothesis that the original CMR reading focusing on the heart may underestimate extracardiac abnormalities. 401 consecutive patients (mean age 53 years) underwent CMR at 1.5 T. Main indications were ischaemic heart disease (n = 183) and cardiomyopathy (n = 164). All CMR sequences, including scout images, were reviewed with specific attention to ECF in a second reading by the same radiologist who performed the first clinical reading. Potentially significant findings were defined as abnormalities requiring additional clinical or radiological follow-up. 250 incidental ECF were detected, of which 84 (34%) had potentially significant ECF including bronchial carcinoma (n = 1), lung consolidation (n = 7) and abdominal abnormalities. In 166 CMR studies (41%) non-significant ECF were detected. The number of ECF identified at second versus first reading was higher for significant (84 vs. 47) and non-significant (166 vs. 36) findings (P < 0.00001). About one fifth of patients undergoing CMR were found to have potentially significant ECF requiring additional work-up. The second dedicated reading detected significantly more ECF compared with the first clinical reading emphasising the importance of active search for extracardiac abnormalities when evaluating CMR studies. circle Many patients undergoing cardiac MR have significant extracardiac findings (ECF) circle These impact on management and require additional work-up. circle Wide review of scout and cine sequences will detect most ECFs. circle Education of radiologists is important to identify ECFs on CMR studies. (orig.)

  4. SQUID-Detected Magnetic Resonance Imaging in MicroteslaFields

    Energy Technology Data Exchange (ETDEWEB)

    Moessle, Michael; Hatridge, Michael; Clarke, John

    2006-08-14

    amplitude in MRI using laser polarized noble gases such as {sup 3}He or {sup 129}Xe (10-12). Hyperpolarized gases were used successfully to image the human lung in fields on the order of several mT (13-15). To overcome the sensitivity loss of Faraday detection at low frequencies, ultrasensitive magnetometers based on the Superconducting QUantum Interference Device (SQUID) (16) are used to detect NMR and MRI signals (17-24). Recently, SQUID-based MRI systems capable of acquiring in vivo images have appeared. For example, in the 10-mT system of Seton et al. (18) signals are coupled to a SQUID via a superconducting tuned circuit, while Clarke and coworkers (22, 25, 26) developed a system at 132 {micro}T with an untuned input circuit coupled to a SQUID. In a quite different approach, atomic magnetometers have been used recently to detect the magnetization (27) and NMR signal (28) of hyperpolarized gases. This technique could potentially be used for low-field MRI in the future. The goal of this review is to summarize the current state-of-the-art of MRI in microtesla fields detected with SQUIDs. The principles of SQUIDs and NMR are briefly reviewed. We show that very narrow NMR linewidths can be achieved in low magnetic fields that are quite inhomogeneous, with illustrative examples from spectroscopy. After describing our ultralow-field MRI system, we present a variety of images. We demonstrate that in microtesla fields the longitudinal relaxation T{sub 1} is much more material dependent than is the case in high fields; this results in a substantial improvement in 'T{sub 1}-weighted contrast imaging'. After outlining the first attempts to combine microtesla NMR with magnetoencephalography (MEG) (29), we conclude with a discussion of future directions.

  5. Surface regions of illusory images are detected with a slower processing speed than those of luminance-defined images.

    Science.gov (United States)

    Mihaylova, Milena; Manahilov, Velitchko

    2010-11-24

    Research has shown that the processing time for discriminating illusory contours is longer than for real contours. We know, however, little whether the visual processes, associated with detecting regions of illusory surfaces, are also slower as those responsible for detecting luminance-defined images. Using a speed-accuracy trade-off (SAT) procedure, we measured accuracy as a function of processing time for detecting illusory Kanizsa-type and luminance-defined squares embedded in 2D static luminance noise. The data revealed that the illusory images were detected at slower processing speed than the real images, while the points in time, when accuracy departed from chance, were not significantly different for both stimuli. The classification images for detecting illusory and real squares showed that observers employed similar detection strategies using surface regions of the real and illusory squares. The lack of significant differences between the x-intercepts of the SAT functions for illusory and luminance-modulated stimuli suggests that the detection of surface regions of both images could be based on activation of a single mechanism (the dorsal magnocellular visual pathway). The slower speed for detecting illusory images as compared to luminance-defined images could be attributed to slower processes of filling-in of regions of illusory images within the dorsal pathway.

  6. Detection and characterization with short TI inversion recovery MR imaging

    Energy Technology Data Exchange (ETDEWEB)

    Komata, Kaori (Nippon Medical School, Tokyo (Japan))

    1994-10-01

    Short TI inversion recovery magnetic resonance imaging (STIR-MRI) with spin echo (SE) T1- and T2-weighted images of the pelvis was investigated to evaluate its usefulness in detecting and characterizing endometriosis. Thirty-one women suspected of having the disease were studied in detail. MR findings with and without STIR-MRI were correlated with the results of laparotomy (27 women) and laparoscopy (4 women). Surgery revealed endometriosis in 29 women (17 ovarian chocolate cysts, 22 intestinal adhesions, 14 cul-de-sac obliterations and 12 adenomyosis). The other two women did not have endometriosis (uterine prolapse in one and submucosal leiomyoma in one). An ovarian chocolate cyst was diagnosed when a T1-elongated lesion showed shading, loculus or a low intensity rim on SE MR images, and a low intensity rim on STIR-MRI. Only 12 of the 17 chocolate cysts and neither of the two hemorrhagic corpus lutein cysts were correctly diagnosed on SE MR images, whereas 18 of these 19 cysts were correctly diagnosed because of the low intensity rim on STIR-MRI. In the pathological analysis, the rim was found to be a fibrous capsule and there were many macrophages which phagocytized hemosiderin. For the assessment of ovarian chocolate cysts, accuracy improved from 63.2% to 94.7%. As for the adhesion between the intestine and the uterus, specificity improved from 61.9% to 90.5% and accuracy improved from 67.7% to 93.5% when STRI-MRI was used. For the assessment of the cul-de-sac obliteration, accuracy improved from 67.7% to 83.8% although [chi][sup 2] analysis showed no significance. The major factors for the improved accuracy with STIR-MRI are the decrease of the motion artifact owing to the suppression of the fat signal, decreased chemical shift artifact and accurate differentiation of fat from hemorrhagic component. Therefore, STIR-MRI is a useful and reliable procedure and should be used together with SE T1-, T2-weighted images for the assessment of endometriosis. (author).

  7. Detection and characterization with short TI inversion recovery MR imaging

    International Nuclear Information System (INIS)

    Komata, Kaori

    1994-01-01

    Short TI inversion recovery magnetic resonance imaging (STIR-MRI) with spin echo (SE) T1- and T2-weighted images of the pelvis was investigated to evaluate its usefulness in detecting and characterizing endometriosis. Thirty-one women suspected of having the disease were studied in detail. MR findings with and without STIR-MRI were correlated with the results of laparotomy (27 women) and laparoscopy (4 women). Surgery revealed endometriosis in 29 women (17 ovarian chocolate cysts, 22 intestinal adhesions, 14 cul-de-sac obliterations and 12 adenomyosis). The other two women did not have endometriosis (uterine prolapse in one and submucosal leiomyoma in one). An ovarian chocolate cyst was diagnosed when a T1-elongated lesion showed shading, loculus or a low intensity rim on SE MR images, and a low intensity rim on STIR-MRI. Only 12 of the 17 chocolate cysts and neither of the two hemorrhagic corpus lutein cysts were correctly diagnosed on SE MR images, whereas 18 of these 19 cysts were correctly diagnosed because of the low intensity rim on STIR-MRI. In the pathological analysis, the rim was found to be a fibrous capsule and there were many macrophages which phagocytized hemosiderin. For the assessment of ovarian chocolate cysts, accuracy improved from 63.2% to 94.7%. As for the adhesion between the intestine and the uterus, specificity improved from 61.9% to 90.5% and accuracy improved from 67.7% to 93.5% when STRI-MRI was used. For the assessment of the cul-de-sac obliteration, accuracy improved from 67.7% to 83.8% although χ 2 analysis showed no significance. The major factors for the improved accuracy with STIR-MRI are the decrease of the motion artifact owing to the suppression of the fat signal, decreased chemical shift artifact and accurate differentiation of fat from hemorrhagic component. Therefore, STIR-MRI is a useful and reliable procedure and should be used together with SE T1-, T2-weighted images for the assessment of endometriosis. (author)

  8. Military efforts in nanosensors, 3D printing, and imaging detection

    Science.gov (United States)

    Edwards, Eugene; Booth, Janice C.; Roberts, J. Keith; Brantley, Christina L.; Crutcher, Sihon H.; Whitley, Michael; Kranz, Michael; Seif, Mohamed; Ruffin, Paul

    2017-04-01

    A team of researchers and support organizations, affiliated with the Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC), has initiated multidiscipline efforts to develop nano-based structures and components for advanced weaponry, aviation, and autonomous air/ground systems applications. The main objective of this research is to exploit unique phenomena for the development of novel technology to enhance warfighter capabilities and produce precision weaponry. The key technology areas that the authors are exploring include nano-based sensors, analysis of 3D printing constituents, and nano-based components for imaging detection. By integrating nano-based devices, structures, and materials into weaponry, the Army can revolutionize existing (and future) weaponry systems by significantly reducing the size, weight, and cost. The major research thrust areas include the development of carbon nanotube sensors to detect rocket motor off-gassing; the application of current methodologies to assess materials used for 3D printing; and the assessment of components to improve imaging seekers. The status of current activities, associated with these key areas and their implementation into AMRDEC's research, is outlined in this paper. Section #2 outlines output data, graphs, and overall evaluations of carbon nanotube sensors placed on a 16 element chip and exposed to various environmental conditions. Section #3 summarizes the experimental results of testing various materials and resulting components that are supplementary to additive manufacturing/fused deposition modeling (FDM). Section #4 recapitulates a preliminary assessment of the optical and electromechanical components of seekers in an effort to propose components and materials that can work more effectively.

  9. Detection of pulmonary nodules on lung X-ray images. Studies on multi-resolutional filter and energy subtraction images

    International Nuclear Information System (INIS)

    Sawada, Akira; Sato, Yoshinobu; Kido, Shoji; Tamura, Shinichi

    1999-01-01

    The purpose of this work is to prove the effectiveness of an energy subtraction image for the detection of pulmonary nodules and the effectiveness of multi-resolutional filter on an energy subtraction image to detect pulmonary nodules. Also we study influential factors to the accuracy of detection of pulmonary nodules from viewpoints of types of images, types of digital filters and types of evaluation methods. As one type of images, we select an energy subtraction image, which removes bones such as ribs from the conventional X-ray image by utilizing the difference of X-ray absorption ratios at different energy between bones and soft tissue. Ribs and vessels are major causes of CAD errors in detection of pulmonary nodules and many researches have tried to solve this problem. So we select conventional X-ray images and energy subtraction X-ray images as types of images, and at the same time select ∇ 2 G (Laplacian of Guassian) filter, Min-DD (Minimum Directional Difference) filter and our multi-resolutional filter as types of digital filters. Also we select two evaluation methods and prove the effectiveness of an energy subtraction image, the effectiveness of Min-DD filter on a conventional X-ray image and the effectiveness of multi-resolutional filter on an energy subtraction image. (author)

  10. The values of myocardial tomography imaging and gated cardiac blood pool imaging in detecting left ventricular aneurysm

    International Nuclear Information System (INIS)

    Zhu Mei; Pan Zhongyun; Li Jinhui

    1992-01-01

    The sensitivity and specificity of myocardial tomography imaging and gated cardiac blood-pool imaging in detecting LVA were studied in 36 normal subjects and 68 patients with myocardial infarction. The sensitivities of exercise and rest myocardial imaging in detecting LVA were 85% and 77.3% respectively. The specificity of both is 95.5%. The sensitivity of cinema display, phase analysis and left ventricular phase shift in evaluating LVA were 86.7%, 86.7%, 100% respectively. Their specificity were all 100%. It is concluded that blood pool imaging is of choice for the diagnosis of LVA, and that myocardial imaging could also demonstrate LVA during diagnosing myocardial infarction

  11. Radiological imaging detection of tumors localized in fossa cranii posterior.

    Science.gov (United States)

    Kabashi, Serbeze; Muçaj, Sefedin; Ahmetgjekaj, Ilir; Gashi, Sanije; Fazliu, Ilir; Dreshaj, Shemsedin; Shala, Nexhmedin

    2008-01-01

    Intracranial tumors are characterized by a variety imaging aspects and their detection is always a challenge. Clinical application of Magnetic Resonance Imaging (MRI) and Computerized Tomography has provided an earlier detection and treatment of many CNS pathologies. The aim of this study is to estimate the role of CT and MRI in the determination of posterior fossa tumors. During period 2000-2005 in UCCK-Prishtina, 368 patients were diagnosed with intracranial tumors. Fifty-nine of them were found to have tumor localized in fossa crani posterior (FCP) without any significant difference between genders (50.8% female vs. 49.2% male, chi2 test=0.02 p=0.896). The average age of patients with FCP tumors was 33.1 (SD +/- 22.5, rank 1-70). The most of these patients were registered in 2003 (20.3%) whereas the least in 2000 (11.9%). The most affected age-group was 0-9 (25.4%) and 50-59 (23.7%) whereas the incidences was between 30-39 years of age (3.4%). Tumor types that more often were found in young's individuals were: Astrocytomas with a peak incidence in teenagers (average age was 12-year-old SD +/- 7.5, rank 3-23), next was Medulloblastomas (average age was 11-years-old, SD +/- 2.9, rank 6-16 years) and ependymomas (average age was 6.8-years-old, SD +/- 4.6, rank 1-12). Patients with osseous tumors are characterized by older age than median (61.0, SD +/- 4.2, rank 58-64), then metastases (53.0, SD +/- 5.3, rank 45-60) and meningiomas (50.8, SD +/- 7.7, rank 38-63). The overall average mortality was 0.41 cases per 100,000 inhabitants with variations through years from 0.30-0.50/100,000 inhabitants. Comparing with other countries, for some types of FCP tumors, lower morbidity is shown in Kosova, with mean incidence 0.41/100,000. The most frequent tumors in children were medulloblastomas, brainstem gliomas, astrocytomas and ependymomas whereas meningiomas and metastasis were most often found in adults. For FCP tumors detection, MRI had 100% sensitivity, specificity and

  12. Landuse change detection in a surface coal mine area using multi-temporal high resolution satellite images

    Energy Technology Data Exchange (ETDEWEB)

    Demirel, N.; Duzgun, S.; Kemal Emil, M. [Middle East Technical Univ., Ankara (Turkey). Dept. of Mining Engineering

    2010-07-01

    Changes in the landcover and landuse of a mine area can be caused by surface mining activities, exploitation of ore and stripping and dumping overburden. In order to identify the long-term impacts of mining on the environment and land cover, these changes must be continuously monitored. A facility to regularly observe the progress of surface mining and reclamation is important for effective enforcement of mining and environmental regulations. Remote sensing provides a powerful tool to obtain rigorous data and reduce the need for time-consuming and expensive field measurements. The purpose of this study was to conduct post classification change detection for identifying, quantifying, and analyzing the spatial response of landscape due to surface lignite coal mining activities in Goynuk, Bolu, Turkey, from 2004 to 2008. The paper presented the research algorithm which involved acquiring multi temporal high resolution satellite data; preprocessing the data; performing image classification using maximum likelihood classification algorithm and performing accuracy assessment on the classification results; performing post classification change detection algorithm; and analyzing the results. Specifically, the paper discussed the study area, data and methodology, and image preprocessing using radiometric correction. Image classification and change detection were also discussed. It was concluded that the mine and dump area decreased by 192.5 ha from 2004 to 2008 and was caused by the diminishing reserves in the area and decline in the required production. 5 refs., 2 tabs., 4 figs.

  13. Distance-regular graphs

    NARCIS (Netherlands)

    van Dam, Edwin R.; Koolen, Jack H.; Tanaka, Hajime

    2016-01-01

    This is a survey of distance-regular graphs. We present an introduction to distance-regular graphs for the reader who is unfamiliar with the subject, and then give an overview of some developments in the area of distance-regular graphs since the monograph 'BCN'[Brouwer, A.E., Cohen, A.M., Neumaier,

  14. LL-regular grammars

    NARCIS (Netherlands)

    Nijholt, Antinus

    1980-01-01

    Culik II and Cogen introduced the class of LR-regular grammars, an extension of the LR(k) grammars. In this paper we consider an analogous extension of the LL(k) grammars called the LL-regular grammars. The relation of this class of grammars to other classes of grammars will be shown. Any LL-regular

  15. Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images.

    Directory of Open Access Journals (Sweden)

    Izhar Haq

    Full Text Available Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images employs a 3 × 3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG, Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270 × 290 pixels having 24 dB 'salt and pepper' noise, it detected very few (22 false edge pixels, compared to Sobel (1931, Prewitt (2741, LOG (3102, Roberts (1451 and Canny (1045 false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.

  16. The value of diffusion-weighted imaging in combination with T2-weighted imaging for rectal cancer detection

    International Nuclear Information System (INIS)

    Rao Shengxiang; Zeng Mengsu; Chen Caizhong; Li Renchen; Zhang Shujie; Xu Jianming; Hou Yingyong

    2008-01-01

    Objective: To evaluate the clinical value of diffusion-weighted imaging (DWI) in combination with T 2 -weighted imaging (T 2 WI) for the detection of rectal cancer as compared with T 2 WI alone. Materials and methods: Forty-five patients with rectal cancer and 20 without rectal cancer underwent DWI with parallel imaging and T 2 WI on a 1.5 T scanner. Images were independently reviewed by two readers blinded to the results to determine the detectability of rectal cancer. The detectability of T 2 W imaging without and with DW imaging was assessed by means of receiver operating characteristic analysis. The interobserver agreement between the two readers was calculated with kappa statistics. Results: The ROC analysis showed that each of two readers achieved more accurate results with T 2 W imaging combined with DW imaging than with T 2 W imaging alone significantly. The A z values for the two readers for each T 2 WI and T 2 WI combined with DWI were 0.918 versus 0.991 (p = 0.0494), 0.934 versus 0.997 (p = 0.0475), respectively. The values of kappa were 0.934 for T 2 WI and 0.948 for T 2 WI combined with DWI between the two readers. Conclusion: The addition of DW imaging to conventional T 2 W imaging provides better detection of rectal cancer

  17. Multidetector CT of the coronary imaging: assessment of image quality and accuracy in detecting stenoses

    International Nuclear Information System (INIS)

    Huang Meiping; Liu Qishun; Liu Hui; Liang Changhong; Zhang Shaobin

    2006-01-01

    Objective: To evaluate the image quality of 64-multi detector computed tomography (MDCT) and the clinical accuracy in detecting coronary artery lesions. Methods: One hundred and five patients were studied by MDCT. The results were compared with invasive coronary angiography (ICA). Patients were excluded for atrial fibrillation, but not for high heart rate, coronary calcification, or obesity. MDCT was analyzed with regard to image quality and presence of coronary artery lesions. Results: The data evaluation of the image quality was based on a total of 1365 segments (13 coronary segments for each patient), of which 1144 segments were considered to have diagnostic image quality, but 221 segments (16.2%) could not be sufficiently evaluated because of severe calcifications (153 segments) and motion artifacts (68 segments). The median calcium score [Agatston score equivalent (ASE)] was 154 (range 0--1983). 87 of the 105 patients had an ASE of less than 1,000 [median 105 (range 0-994)], and 18 patients had an ASE greater than 1000 [median 1477 (range 1115-1983)]. For detecting lesions with 50% or greater narrowing (without any exclusion criteria), the overall sensitivity, specificity, positive predictive value, and negative predictive value were 85.7%, 97.9%, 93.0%, and 95.5%, respectively. When limiting the number of patients to those with a calcium score of less than 1000 ASE, the threshold- corrected sensitivity for lesions with 50% or greater narrowing was 96.0%; specificity, 98.9%; positive predictive value, 95.3%; and negative predictive value, 99.0%. Conclusion: Our results indicate high quantitative and qualitative diagnostic accuracy of 64-slice MSCT in comparison to QCA in a broad spectrum of patients. (authors)

  18. Coarse to fine aircraft detection from front-looking infrared images

    Science.gov (United States)

    Lin, Jin; Tan, Yihua; Tian, Jinwen

    2018-03-01

    Due to the weak feature and wide angle of long-distance aircraft targeting in the parking apron from front-looking infrared images, there are always false alarms in aircraft targeting detection. This leads to relatively poor reliability for detection results. In this paper, we present a scene-driven coarse-to-fine aircraft target detection method. First, we preprocess the image by combining the sharpened and enhanced images. Second, the region of interest (ROI) is segmented by using the local mean variance of the image and a series of subsequent processing. Then, target candidate areas are located by using the feature of local marginal distributions. Lastly, aircrafts can be detected accurately by a novel aircraft shape filter. Experiments on three infrared image sequences have shown that the presented method is effective and robust in detecting long-distance aircraft from front-looking infrared images and can also improve the reliability of the detection results.

  19. Sub-surface defects detection of by using active thermography and advanced image edge detection

    International Nuclear Information System (INIS)

    Tse, Peter W.; Wang, Gaochao

    2017-01-01

    Active or pulsed thermography is a popular non-destructive testing (NDT) tool for inspecting the integrity and anomaly of industrial equipment. One of the recent research trends in using active thermography is to automate the process in detecting hidden defects. As of today, human effort has still been using to adjust the temperature intensity of the thermo camera in order to visually observe the difference in cooling rates caused by a normal target as compared to that by a sub-surface crack exists inside the target. To avoid the tedious human-visual inspection and minimize human induced error, this paper reports the design of an automatic method that is capable of detecting subsurface defects. The method used the technique of active thermography, edge detection in machine vision and smart algorithm. An infrared thermo-camera was used to capture a series of temporal pictures after slightly heating up the inspected target by flash lamps. Then the Canny edge detector was employed to automatically extract the defect related images from the captured pictures. The captured temporal pictures were preprocessed by a packet of Canny edge detector and then a smart algorithm was used to reconstruct the whole sequences of image signals. During the processes, noise and irrelevant backgrounds exist in the pictures were removed. Consequently, the contrast of the edges of defective areas had been highlighted. The designed automatic method was verified by real pipe specimens that contains sub-surface cracks. After applying such smart method, the edges of cracks can be revealed visually without the need of using manual adjustment on the setting of thermo-camera. With the help of this automatic method, the tedious process in manually adjusting the colour contract and the pixel intensity in order to reveal defects can be avoided. (paper)

  20. Precise Automatic Image Coregistration Tools to Enable Pixel-Level Change Detection, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Automated detection of land cover changes between multitemporal images (i.e., images captured at different times) has long been a goal of the remote sensing...

  1. Regular Expression Pocket Reference

    CERN Document Server

    Stubblebine, Tony

    2007-01-01

    This handy little book offers programmers a complete overview of the syntax and semantics of regular expressions that are at the heart of every text-processing application. Ideal as a quick reference, Regular Expression Pocket Reference covers the regular expression APIs for Perl 5.8, Ruby (including some upcoming 1.9 features), Java, PHP, .NET and C#, Python, vi, JavaScript, and the PCRE regular expression libraries. This concise and easy-to-use reference puts a very powerful tool for manipulating text and data right at your fingertips. Composed of a mixture of symbols and text, regular exp

  2. A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

    Science.gov (United States)

    Shuxin, Li; Zhilong, Zhang; Biao, Li

    2018-01-01

    Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

  3. Photon detection with CMOS sensors for fast imaging

    International Nuclear Information System (INIS)

    Baudot, J.; Dulinski, W.; Winter, M.; Barbier, R.; Chabanat, E.; Depasse, P.; Estre, N.

    2009-01-01

    Pixel detectors employed in high energy physics aim to detect single minimum ionizing particle with micrometric positioning resolution. Monolithic CMOS sensors succeed in this task thanks to a low equivalent noise charge per pixel of around 10 to 15 e - , and a pixel pitch varying from 10 to a few 10 s of microns. Additionally, due to the possibility for integration of some data treatment in the sensor itself, readout times of 100μs have been reached for 100 kilo-pixels sensors. These aspects of CMOS sensors are attractive for applications in photon imaging. For X-rays of a few keV, the efficiency is limited to a few % due to the thin sensitive volume. For visible photons, the back-thinned version of CMOS sensor is sensitive to low intensity sources, of a few hundred photons. When a back-thinned CMOS sensor is combined with a photo-cathode, a new hybrid detector results (EBCMOS) and operates as a fast single photon imager. The first EBCMOS was produced in 2007 and demonstrated single photon counting with low dark current capability in laboratory conditions. It has been compared, in two different biological laboratories, with existing CCD-based 2D cameras for fluorescence microscopy. The current EBCMOS sensitivity and frame rate is comparable to existing EMCCDs. On-going developments aim at increasing this frame rate by, at least, an order of magnitude. We report in conclusion, the first test of a new CMOS sensor, LUCY, which reaches 1000 frames per second.

  4. Learning Rich Features from RGB-D Images for Object Detection and Segmentation

    OpenAIRE

    Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra

    2014-01-01

    In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an av...

  5. Detection of light images by simple tissues as visualized by photosensitized magnetic resonance imaging.

    Directory of Open Access Journals (Sweden)

    Catherine Tempel-Brami

    Full Text Available In this study, we show how light can be absorbed by the body of a living rat due to an injected pigment circulating in the blood stream. This process is then physiologically translated in the tissue into a chemical signature that can be perceived as an image by magnetic resonance imaging (MRI. We previously reported that illumination of an injected photosynthetic bacteriochlorophyll-derived pigment leads to a generation of reactive oxygen species, upon oxygen consumption in the blood stream. Consequently, paramagnetic deoxyhemoglobin accumulating in the illuminated area induces changes in image contrast, detectable by a Blood Oxygen Level Dependent (BOLD-MRI protocol, termed photosensitized (psMRI. Here, we show that laser beam pulses synchronously trigger BOLD-contrast transients in the tissue, allowing representation of the luminous spatiotemporal profile, as a contrast map, on the MR monitor. Regions with enhanced BOLD-contrast (7-61 fold were deduced as illuminated, and were found to overlap with the anatomical location of the incident light. Thus, we conclude that luminous information can be captured and translated by typical oxygen exchange processes in the blood of ordinary tissues, and made visible by psMRI (Fig. 1. This process represents a new channel for communicating environmental light into the body in certain analogy to light absorption by visual pigments in the retina where image perception takes place in the central nervous system. Potential applications of this finding may include: non-invasive intra-operative light guidance and follow-up of photodynamic interventions, determination of light diffusion in opaque tissues for optical imaging and possible assistance to the blind.

  6. Novel MR imaging contrast agents for cancer detection

    Directory of Open Access Journals (Sweden)

    Daryoush Shahbazi-Gahrouei

    2009-05-01

    Full Text Available

    • BACKGROUND: Novel potential MR imaging contrast agents Gd-tetra-carboranylmethoxyphenyl-porphyrin (Gd-TCP, Gd-hematoporphyrin (Gd-H, Gd-DTPA-9.2.27 against melanoma, Gd-DTPA-WM53 against leukemia and Gd-DTPAC595 against breast cancer cells were synthesized and applied to mice with different human cancer cells (melanoma MM-138, leukemia HL-60, breast MCF-7. The relaxivity, the biodistribution, T1 relaxation times, and signal enhancement of the contrast agents are presented and the results are compared.
    • METHODS: After preparation of contrast agents, the animal studies were performed. The cells (2×106 cells were injected subcutaneously in the both flanks of mice. Two to three weeks after tumor plantation, when the tumor diameter was 2-4 mm, mice were injected with the different contrast agents. The animals were sacrificed at 24 hr post IP injection followed by removal of critical organs. The T1 relaxation times and signal intensities of samples were measured using 11.4 T magnetic field and Gd concentration were measured using UV-spectrophotometer.
    • RESULTS: For Gd-H, the percent of Gd localized to the tumors measured by UV-spect was 28, 23 and 21 in leukemia, melanoma and breast cells, respectively. For Gd-TCP this amount was 21%, 18% and 15%, respectively. For Gd-DTPA-9.2.27, Gd-DTPA-WM53 and Gd-DTPA-C595 approximately 35%, 32% and 27% of gadolinium localized to their specific tumor, respectively.
    • CONCLUSION: The specific studied conjugates showed good tumor uptake in the relevant cell lines and low levels of Gd in the liver, kidney and spleen. The studied agents have considerable promise for further diagnosis applications of MR imaging.
    • KEYWORDS: Magnetic Resonance, Imaging, Monoclonal Antibody, Contrast Agents, Gadolinium, Early Detection of Cancer.

  7. Spoofing detection on facial images recognition using LBP and GLCM combination

    Science.gov (United States)

    Sthevanie, F.; Ramadhani, K. N.

    2018-03-01

    The challenge for the facial based security system is how to detect facial image falsification such as facial image spoofing. Spoofing occurs when someone try to pretend as a registered user to obtain illegal access and gain advantage from the protected system. This research implements facial image spoofing detection method by analyzing image texture. The proposed method for texture analysis combines the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) method. The experimental results show that spoofing detection using LBP and GLCM combination achieves high detection rate compared to that of using only LBP feature or GLCM feature.

  8. Detection of regularities in variation in geomechanical behavior of rock mass during multi-roadway preparation and mining of an extraction panel

    Science.gov (United States)

    Tsvetkov, AB; Pavlova, LD; Fryanov, VN

    2018-03-01

    The results of numerical simulation of the stress–strain state in a rock block and surrounding mass mass under multi-roadway preparation to mining are presented. The numerical solutions obtained by the nonlinear modeling and using the constitutive relations of the theory of elasticity are compared. The regularities of the stress distribution in the vicinity of the pillars located in the zone of the abutment pressure of are found.

  9. Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images

    NARCIS (Netherlands)

    Persello, Claudio; Stein, Alfred

    2017-01-01

    This letter investigates fully convolutional networks (FCNs) for the detection of informal settlements in very high resolution (VHR) satellite images. Informal settlements or slums are proliferating in developing countries and their detection and classification provides vital information for

  10. Polarized near-infrared autofluorescence imaging combined with near-infrared diffuse reflectance imaging for improving colonic cancer detection.

    Science.gov (United States)

    Shao, Xiaozhuo; Zheng, Wei; Huang, Zhiwei

    2010-11-08

    We evaluate the diagnostic feasibility of the integrated polarized near-infrared (NIR) autofluorescence (AF) and NIR diffuse reflectance (DR) imaging technique developed for colonic cancer detection. A total of 48 paired colonic tissue specimens (normal vs. cancer) were measured using the integrated NIR DR (850-1100 nm) and NIR AF imaging at the 785 nm laser excitation. The results showed that NIR AF intensities of cancer tissues are significantly lower than those of normal tissues (ppolarization conditions gives a higher diagnostic accuracy (of ~92-94%) compared to non-polarized NIR AF imaging or NIR DR imaging. Further, the ratio imaging of NIR DR to NIR AF with polarization provides the best diagnostic accuracy (of ~96%) among the NIR AF and NIR DR imaging techniques. This work suggests that the integrated NIR AF/DR imaging under polarization condition has the potential to improve the early diagnosis and detection of malignant lesions in the colon.

  11. Brain imaging study of the acute effects of Delta9-tetrahydrocannabinol (THC) on attention and motor coordination in regular users of marijuana.

    Science.gov (United States)

    Weinstein, Aviv; Brickner, Orit; Lerman, Hedva; Greemland, Mazal; Bloch, Miki; Lester, Hava; Chisin, Roland; Mechoulam, Raphael; Bar-Hamburger, Rachel; Freedman, Nanette; Even-Sapir, Einat

    2008-01-01

    Twelve regular users of marijuana underwent two positron emission tomography (PET) scans using [18F] Fluorodeoxyglucose (FDG), one while subject to the effects of 17 mg THC, the other without THC. In both sessions, a virtual reality maze task was performed during the FDG uptake period. When subject to the effects of 17 mg THC, regular marijuana smokers hit the walls more often on the virtual maze task than without THC. Compared to results without THC, 17 mg THC increased brain metabolism during task performance in areas that are associated with motor coordination and attention in the middle and medial frontal cortices and anterior cingulate, and reduced metabolism in areas that are related to visual integration of motion in the occipital lobes. These findings suggest that in regular marijuana users, the immediate effects of marijuana may impact on cognitive-motor skills and brain mechanisms that modulate coordinated movement and driving.

  12. Multisensor Network System for Wildfire Detection Using Infrared Image Processing

    Directory of Open Access Journals (Sweden)

    I. Bosch

    2013-01-01

    Full Text Available This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire, for different probability of false alarm (PFA. The necessity of including decision fusion is thereby demonstrated.

  13. Spot detection and image segmentation in DNA microarray data.

    Science.gov (United States)

    Qin, Li; Rueda, Luis; Ali, Adnan; Ngom, Alioune

    2005-01-01

    Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance.

  14. Digital Image Processing Technique for Breast Cancer Detection

    Science.gov (United States)

    Guzmán-Cabrera, R.; Guzmán-Sepúlveda, J. R.; Torres-Cisneros, M.; May-Arrioja, D. A.; Ruiz-Pinales, J.; Ibarra-Manzano, O. G.; Aviña-Cervantes, G.; Parada, A. González

    2013-09-01

    Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as masses and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for women’s quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. As masses and benign glandular tissue typically appear with low contrast and often very blurred, several computer-aided diagnosis schemes have been developed to support radiologists and internists in their diagnosis. In this article, an approach is proposed to effectively analyze digital mammograms based on texture segmentation for the detection of early stage tumors. The proposed algorithm was tested over several images taken from the digital database for screening mammography for cancer research and diagnosis, and it was found to be absolutely suitable to distinguish masses and microcalcifications from the background tissue using morphological operators and then extract them through machine learning techniques and a clustering algorithm for intensity-based segmentation.

  15. Imaging studies and biomarkers to detect clinically meaningful vesicoureteral reflux

    Directory of Open Access Journals (Sweden)

    Michaella Maloney Prasad

    2017-06-01

    Full Text Available The work-up of a febrile urinary tract infection is generally performed to detect vesicoureteral reflux (VUR and its possible complications. The imaging modalities most commonly used for this purpose are renal-bladder ultrasound, voiding cystourethrogram and dimercapto-succinic acid scan. These studies each contribute valuable information, but carry individual benefits and limitations that may impact their efficacy. Biochemical markers are not commonly used in pediatric urology to diagnose or differentiate high-risk disease, but this is the emerging frontier, which will hopefully change our approach to VUR in the future. As it becomes more apparent that there is tremendous clinical variation within grades of VUR, the need to distinguish clinically significant from insignificant disease grows. The unfortunate truth about VUR is that recommendations for treatment may be inconsistent. Nuances in clinical decision-making will always exist, but opinions for medical versus surgical intervention should be more standardized, based on risk of injury to the kidney.

  16. An image overall complexity evaluation method based on LSD line detection

    Science.gov (United States)

    Li, Jianan; Duan, Jin; Yang, Xu; Xiao, Bo

    2017-04-01

    In the artificial world, whether it is the city's traffic roads or engineering buildings contain a lot of linear features. Therefore, the research on the image complexity of linear information has become an important research direction in digital image processing field. This paper, by detecting the straight line information in the image and using the straight line as the parameter index, establishing the quantitative and accurate mathematics relationship. In this paper, we use LSD line detection algorithm which has good straight-line detection effect to detect the straight line, and divide the detected line by the expert consultation strategy. Then we use the neural network to carry on the weight training and get the weight coefficient of the index. The image complexity is calculated by the complexity calculation model. The experimental results show that the proposed method is effective. The number of straight lines in the image, the degree of dispersion, uniformity and so on will affect the complexity of the image.

  17. Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

    Directory of Open Access Journals (Sweden)

    Biao Wang

    2017-08-01

    Full Text Available Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

  18. Performances of diffusion kurtosis imaging and diffusion tensor imaging in detecting white matter abnormality in schizophrenia

    Directory of Open Access Journals (Sweden)

    Jiajia Zhu

    2015-01-01

    Full Text Available Diffusion kurtosis imaging (DKI is an extension of diffusion tensor imaging (DTI, exhibiting improved sensitivity and specificity in detecting developmental and pathological changes in neural tissues. However, little attention was paid to the performances of DKI and DTI in detecting white matter abnormality in schizophrenia. In this study, DKI and DTI were performed in 94 schizophrenia patients and 91 sex- and age-matched healthy controls. White matter integrity was assessed by fractional anisotropy (FA, mean diffusivity (MD, axial diffusivity (AD, radial diffusivity (RD, mean kurtosis (MK, axial kurtosis (AK and radial kurtosis (RK of DKI and FA, MD, AD and RD of DTI. Group differences in these parameters were compared using tract-based spatial statistics (TBSS (P  AK (20% > RK (3% and RD (37% > FA (24% > MD (21% for DKI, and RD (43% > FA (30% > MD (21% for DTI. DKI-derived diffusion parameters (RD, FA and MD were sensitive to detect abnormality in white matter regions (the corpus callosum and anterior limb of internal capsule with coherent fiber arrangement; however, the kurtosis parameters (MK and AK were sensitive to reveal abnormality in white matter regions (the juxtacortical white matter and corona radiata with complex fiber arrangement. In schizophrenia, the decreased AK suggests axonal damage; however, the increased RD indicates myelin impairment. These findings suggest that diffusion and kurtosis parameters could provide complementary information and they should be jointly used to reveal pathological changes in schizophrenia.

  19. The ship edge feature detection based on high and low threshold for remote sensing image

    Science.gov (United States)

    Li, Xuan; Li, Shengyang

    2018-05-01

    In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

  20. Detection of ossicular chain abnormalities using CT imaging. Comparison of axial and virtual middle ear endoscopic imaging

    International Nuclear Information System (INIS)

    Sakata, Motomichi; Kamagata, Masaki; Harada, Kuniaki; Shirase, Ryuji; Oomoto, Hidechika; Himi, Tetsuo

    2000-01-01

    The purpose of this study was to evaluate the usefulness of axial and three-dimensional imaging (virtual endoscopy) with helical CT for the detection of ossicular chain abnormalities. In 15 patients who had traumatic ossicular dislocation, disruption, and congenital ossicular defect and anomaly, axial helical CT scanning of the temporal bone was performed with GE HSA. Axial and three-dimensional imaging was carried out in normal ears (15 ears) and abnormal ears (10 ears), for the detection of ossicular chain abnormalities. Diagnostic accuracy was evaluated by receiver-operating-characteristic (ROC) curve analysis using a continuous reporting scale. Furthermore, ROC testing was done to determine the sensitivity, specificity, and accuracy of the detection of ossicular chain abnormalities. Diagnostic accuracy in the detection of ossicular chain abnormalities with three-dimensional imaging (A z =0.967, SD=0.022) was not significantly better than that of axial imaging (A z =0.930, SD=0.046); however, the interobserver standard deviation was better for three-dimensional imaging. Three-dimensional imaging resulted in an increase in true positive cases and a decrease in false negatives. Three-dimensional imaging also showed higher sensitivity and accuracy. In the evaluation of ossicular chain abnormalities, three-dimensional imaging (virtual endoscopy) is useful and provides additional information. Three-dimensional imaging may have an important role in diagnostic procedures and/or preoperative evaluation in otology. (author)

  1. Analysis of the development of missile-borne IR imaging detecting technologies

    Science.gov (United States)

    Fan, Jinxiang; Wang, Feng

    2017-10-01

    Today's infrared imaging guiding missiles are facing many challenges. With the development of targets' stealth, new-style IR countermeasures and penetrating technologies as well as the complexity of the operational environments, infrared imaging guiding missiles must meet the higher requirements of efficient target detection, capability of anti-interference and anti-jamming and the operational adaptability in complex, dynamic operating environments. Missileborne infrared imaging detecting systems are constrained by practical considerations like cost, size, weight and power (SWaP), and lifecycle requirements. Future-generation infrared imaging guiding missiles need to be resilient to changing operating environments and capable of doing more with fewer resources. Advanced IR imaging detecting and information exploring technologies are the key technologies that affect the future direction of IR imaging guidance missiles. Infrared imaging detecting and information exploring technologies research will support the development of more robust and efficient missile-borne infrared imaging detecting systems. Novelty IR imaging technologies, such as Infrared adaptive spectral imaging, are the key to effectively detect, recognize and track target under the complicated operating and countermeasures environments. Innovative information exploring techniques for the information of target, background and countermeasures provided by the detection system is the base for missile to recognize target and counter interference, jamming and countermeasure. Modular hardware and software development is the enabler for implementing multi-purpose, multi-function solutions. Uncooled IRFPA detectors and High-operating temperature IRFPA detectors as well as commercial-off-the-shelf (COTS) technology will support the implementing of low-cost infrared imaging guiding missiles. In this paper, the current status and features of missile-borne IR imaging detecting technologies are summarized. The key

  2. Interactive facades analysis and synthesis of semi-regular facades

    KAUST Repository

    AlHalawani, Sawsan; Yang, Yongliang; Liu, Han; Mitra, Niloy J.

    2013-01-01

    Urban facades regularly contain interesting variations due to allowed deformations of repeated elements (e.g., windows in different open or close positions) posing challenges to state-of-the-art facade analysis algorithms. We propose a semi-automatic framework to recover both repetition patterns of the elements and their individual deformation parameters to produce a factored facade representation. Such a representation enables a range of applications including interactive facade images, improved multi-view stereo reconstruction, facade-level change detection, and novel image editing possibilities. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.

  3. Interactive facades analysis and synthesis of semi-regular facades

    KAUST Repository

    AlHalawani, Sawsan

    2013-05-01

    Urban facades regularly contain interesting variations due to allowed deformations of repeated elements (e.g., windows in different open or close positions) posing challenges to state-of-the-art facade analysis algorithms. We propose a semi-automatic framework to recover both repetition patterns of the elements and their individual deformation parameters to produce a factored facade representation. Such a representation enables a range of applications including interactive facade images, improved multi-view stereo reconstruction, facade-level change detection, and novel image editing possibilities. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.

  4. Tomosynthesis Breast Imaging Early Detection and Characterization of Breast Cancer

    National Research Council Canada - National Science Library

    Hamberg, Leena

    2000-01-01

    A digital tomosynthesis mammography method was developed with which to obtain tomographic images of the breast by acquiring a series of low radiation dose images as the x-ray tube moves in an arc above the breast...

  5. Indium-111 chloride imaging in the detection of infected prostheses

    International Nuclear Information System (INIS)

    Sayle, B.A.; Fawcett, H.D.; Wilkey, D.J.; Cierny, G. III; Mader, J.T.

    1985-01-01

    Thirty-three patients with painful joint prostheses and a suspicion of infection were imaged with [ 111 In]chloride. A final diagnosis was established by culture in 19. Of these, 12 were categorized as true positives and three as true negatives. There were two false-positive studies, occurring in patients with knee prostheses. In both, the culture was obtained by aspiration. The sensitivity was 86%, specificity 60%, and accuracy 79%. Seventeen of the proven cases had bone imaging prior to [ 111 In]chloride imaging. All 17 static images were positive and were not helpful in differentiating loosening from infection. Using increased uptake on the blood-pool image as a criteria for infection, the sensitivity was 89%, but the specificity was 0. Adding flow studies made little difference in interpreting the blood-pool images. This study shows that [ 111 In]chloride imaging is more accurate in evaluating infection in prosthesis than bone imaging

  6. Gamma-ray detection and Compton camera image reconstruction with application to hadron therapy; Detection des rayons gamma et reconstruction d'images pour la camera Compton: Application a l'hadrontherapie

    Energy Technology Data Exchange (ETDEWEB)

    Frandes, M.

    2010-09-15

    detection chain from Monte Carlo simulations to reconstruction of individual events, and finally to image reconstruction. A list-mode Maximum-Likelihood Expectation-Maximization (MLEM) algorithm was adopted to perform image reconstruction in conjunction with the imaging response, which has to depict the complex behavior of the detector. Modeling the imaging response requires complex calculations, considering the incident angle, all measured energies, the Compton scatter angle in the first interaction, the direction of scattered electron (when measured). In the simplest form, each event response is described by Compton cone profiles. The shapes of the profiles are approximated by 1D Gaussian distributions. A strong correlation was observed between pattern of the reconstructed high-energy gamma events, and location of the Bragg peak. The performance of the imaging technique illustrated by the HTI is a function of the detector performance in terms of detection efficiency, spatial and energy resolution, acquisition time, and the algorithms used to reconstruct the gamma-ray activity. Thus beside optimizations of the imaging system, the applied imaging algorithm has a high influence on the final reconstructed images. The HTI reconstructed images are corrupted by noise due to the low photon counts recorded, the uncertainties induced by finite energy resolution, Doppler broadening, the limited model used to estimate the imaging response, and the artifacts generated when iterating the MLEM algorithm. This noise is spatially varying and signal-dependent, representing a major obstacle for information extraction. Thus image de-noising techniques were investigated. A Wavelet based multi-resolution strategy of list-mode MLEM Regularization (WREM) was developed to reconstruct Compton images. At each iteration, a threshold-based processing step was integrated. The noise variance was estimated at each scale of the wavelet decomposition as the median value of the coefficients from the high

  7. Unsupervised image segmentation for passive THz broadband images for concealed weapon detection

    Science.gov (United States)

    Ramírez, Mabel D.; Dietlein, Charles R.; Grossman, Erich; Popović, Zoya

    2007-04-01

    This work presents the application of a basic unsupervised classification algorithm for the segmentation of indoor passive Terahertz images. The 30,000 pixel broadband images of a person with concealed weapons under clothing are taken at a range of 0.8-2m over a frequency range of 0.1-1.2THz using single-pixel row-based raster scanning. The spiral-antenna coupled 36x1x0.02μm Nb bridge cryogenic micro-bolometers are developed at NIST-Optoelectronics Division. The antenna is evaporated on a 250μm thick Si substrate with a 4mm diameter hyper-hemispherical Si lens. The NETD of the microbolometer is 125mK at an integration time of 30 ms. The background temperature calibration is performed with a known 25 pixel source above 330 K, and a measured background fluctuation of 200-500mK. Several weapons were concealed under different fabrics: cotton, polyester, windblocker jacket and thermal sweater. Measured temperature contrasts ranged from 0.5-1K for wrinkles in clothing to 5K for a zipper and 8K for the concealed weapon. In order to automate feature detection in the images, some image processing and pattern recognition techniques have been applied and the results are presented here. We show that even simple algorithms, that can potentially be performed in real time, are capable of differentiating between a metal and a dielectric object concealed under clothing. Additionally, we show that pre-processing can reveal low temperature contrast features, such as folds in clothing.

  8. Vitality Detection in Face Images using Second Order Gradient

    OpenAIRE

    Aruni Singh

    2012-01-01

    Spoofing is a very big challenge in biometrics, specially in face image. So many artificial techniques are available to tamper or hide the original face. To ensure the actual presence of live face image in contrast to fake face image this research has been contributed. The intended purpose of proposed approach is also to endorse the biometric authentication, by joining the liveness awareness with Facial Recognition Technology (FRT). In this research 200 dummy face images and 200 real face ima...

  9. Technique Based on Image Pyramid and Bayes Rule for Noise Reduction in Unsupervised Change Detection

    Institute of Scientific and Technical Information of China (English)

    LI Zhi-qiang; HUO hong; FANG Tao; ZHU Ju-lian; GE Wei-li

    2009-01-01

    In this paper, a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is proposed. By using Gaussian pyramid to process two multitemporal images respectively, two image pyramids are constructed. The difference pyramid images are obtained by point-by-point subtraction between the same level images of the two image pyramids. By resizing all difference pyramid images to the size of the original multitemporal image and then making product operator among them, a map being similar to the difference image is obtained. The difference image is generated by point-by-point subtraction between the two multitemporal images directly. At last, the Bayes rule is used to distinguish the changed pixels. Both synthetic and real data sets are used to evaluate the performance of the proposed technique. Experimental results show that the map from the proposed technique is more robust to noise than the difference image.

  10. Helicopter thermal imaging for detecting insect infested cadavers.

    Science.gov (United States)

    Amendt, Jens; Rodner, Sandra; Schuch, Claus-Peter; Sprenger, Heinz; Weidlich, Lars; Reckel, Frank

    2017-09-01

    One of the most common techniques applied for searching living and even dead persons is the FLIR (Forward Looking Infrared) system fixed on an aircraft like e.g. a helicopter, visualizing the thermal patterns emitted from objects in the long-infrared spectrum. However, as body temperature cools down to ambient values within approximately 24h after death, it is common sense that searching for deceased persons can be just applied the first day post-mortem. We postulated that the insect larval masses on a decomposing body generate a heat which can be considerably higher than ambient temperatures for a period of several weeks and that such heat signatures might be used for locating insect infested human remains. We examined the thermal history of two 70 and 90kg heavy pig cadavers for 21days in May and June 2014 in Germany. Adult and immature insects on the carcasses were sampled daily. Temperatures were measured on and inside the cadavers, in selected maggot masses and at the surroundings. Thermal imaging from a helicopter using the FLIR system was performed at three different altitudes up to 1500ft. during seven day-flights and one night-flight. Insect colonization was dominated by blow flies (Diptera: Calliphoridae) which occurred almost immediately after placement of the cadavers. Larvae were noted first on day 2 and infestation of both cadavers was enormous with several thousand larvae each. After day 14 a first wave of post-feeding larvae left the carcasses for pupation. Body temperature of both cadavers ranged between 15°C and 35°C during the first two weeks of the experiment, while body surface temperatures peaked at about 45°C. Maggot masses temperatures reached values up to almost 25°C above ambient temperature. Detection of both cadavers by thermal imaging was possible on seven of the eight helicopter flights until day 21. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

  11. A New Method Based on Two-Stage Detection Mechanism for Detecting Ships in High-Resolution SAR Images

    Directory of Open Access Journals (Sweden)

    Xu Yongli

    2017-01-01

    Full Text Available Ship detection in synthetic aperture radar (SAR remote sensing images, being a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Aiming at the requirements of ship detection in high-resolution SAR images, the accuracy, the intelligent level, a better real-time operation and processing efficiency, The characteristics of ocean background and ship target in high-resolution SAR images were analyzed, we put forward a ship detection algorithm in high-resolution SAR images. The algorithm consists of two detection stages: The first step designs a pre-training classifier based on improved spectral residual visual model to obtain the visual salient regions containing ship targets quickly, then achieve the purpose of probably detection of ships. In the second stage, considering the Bayesian theory of binary hypothesis detection, a local maximum posterior probability (MAP classifier is designed for the classification of pixels. After the parameter estimation and judgment criterion, the classification of pixels are carried out in the target areas to achieve the classification of two types of pixels in the salient regions. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X, Radarsat-2, are used to evaluate the performance of detection methods. Comparing with classical CFAR detection algorithms, experimental results show that the algorithm can achieve a better effect of suppressing false alarms, which caused by the speckle noise and ocean clutter background inhomogeneity. At the same time, the detection speed is increased by 25% to 45%.

  12. Regularization by External Variables

    DEFF Research Database (Denmark)

    Bossolini, Elena; Edwards, R.; Glendinning, P. A.

    2016-01-01

    Regularization was a big topic at the 2016 CRM Intensive Research Program on Advances in Nonsmooth Dynamics. There are many open questions concerning well known kinds of regularization (e.g., by smoothing or hysteresis). Here, we propose a framework for an alternative and important kind of regula......Regularization was a big topic at the 2016 CRM Intensive Research Program on Advances in Nonsmooth Dynamics. There are many open questions concerning well known kinds of regularization (e.g., by smoothing or hysteresis). Here, we propose a framework for an alternative and important kind...

  13. Regular expressions cookbook

    CERN Document Server

    Goyvaerts, Jan

    2009-01-01

    This cookbook provides more than 100 recipes to help you crunch data and manipulate text with regular expressions. Every programmer can find uses for regular expressions, but their power doesn't come worry-free. Even seasoned users often suffer from poor performance, false positives, false negatives, or perplexing bugs. Regular Expressions Cookbook offers step-by-step instructions for some of the most common tasks involving this tool, with recipes for C#, Java, JavaScript, Perl, PHP, Python, Ruby, and VB.NET. With this book, you will: Understand the basics of regular expressions through a

  14. Magnetic resonance imaging in the detection of breast cancer

    International Nuclear Information System (INIS)

    Olcucuoglu, E.; Tuncbilek, I.; Oztekin, P.; Asal, N.; Yilmaz, O.; Kosar, U.

    2012-01-01

    Full text: Purpose: The aim of the study is to state breast Magnetic Resonance Imaging (MRI) diagnostic value of examination of MG (MG), ultrasonography (U.S.) by comparing with the results of a biopsy revealed, and emphasize the value of detecting breast cancer. Materials and methods: 327 patients were included in the breast MRI examination. MG breast MRI and U.S. were performed before the cases, respectively. All tests which are in fact planned no later than two months in between and evaluation were performed by two radiologists. BI-RADS classification was evaluated according to the investigations. As a result of MRI BIRADS 4 and 5 cases that were diagnosed in a biopsy was recommended. Following the recommended BI-RADS 3 biopsies diagnosed as those of the cases were due to the physical examination findings. MG with the results of a biopsy, U.S., and MRI results were compared. Results: The study recommended a biopsy of BIRADS 4 and 5 group, 36 out of 63 cases of breast cancer (32 invasive ductal carcinomas, 2 invasive lobular carcinoma, 1 lymphoma, 1 angiosarcoma) were diagnosed. 16% of patients with BI-RADS 4 group, 94% of BI-RADS 5 group of patients were diagnosed as breast cancer. BI-RADS is a group of breast cancer with axillary adenopathy in a patient with the diagnosis of MRI examination was no diagnostic. False-positive cases in our study were counted for the majority of cases as fibrocystic. Conclusion: MRI sensitivity, specificity, positive predictive value, negative predictive value and accuracy of tests with the highest rates, while the combination of MG and MRI, were found to be the best non-invasive examination methods

  15. Local region power spectrum-based unfocused ship detection method in synthetic aperture radar images

    Science.gov (United States)

    Wei, Xiangfei; Wang, Xiaoqing; Chong, Jinsong

    2018-01-01

    Ships on synthetic aperture radar (SAR) images will be severely defocused and their energy will disperse into numerous resolution cells under long SAR integration time. Therefore, the image intensity of ships is weak and sometimes even overwhelmed by sea clutter on SAR image. Consequently, it is hard to detect the ships from SAR intensity images. A ship detection method based on local region power spectrum of SAR complex image is proposed. Although the energies of the ships are dispersed on SAR intensity images, their spectral energies are rather concentrated or will cause the power spectra of local areas of SAR images to deviate from that of sea surface background. Therefore, the key idea of the proposed method is to detect ships via the power spectra distortion of local areas of SAR images. The local region power spectrum of a moving target on SAR image is analyzed and the way to obtain the detection threshold through the probability density function (pdf) of the power spectrum is illustrated. Numerical P- and L-band airborne SAR ocean data are utilized and the detection results are also illustrated. Results show that the proposed method can well detect the unfocused ships, with a detection rate of 93.6% and a false-alarm rate of 8.6%. Moreover, by comparing with some other algorithms, it indicates that the proposed method performs better under long SAR integration time. Finally, the applicability of the proposed method and the way of parameters selection are also discussed.

  16. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

    Energy Technology Data Exchange (ETDEWEB)

    Qiu, J [Washington University in St Louis, Taian, Shandong (China); Yang, D [Washington University School of Medicine, St Louis, MO (United States)

    2015-06-15

    Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

  17. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

    International Nuclear Information System (INIS)

    Qiu, J; Yang, D

    2015-01-01

    Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

  18. Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network

    Science.gov (United States)

    Liu, Tao; Li, Ying; Cao, Ying; Shen, Qiang

    2017-10-01

    This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.

  19. 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.

  20. Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

    Science.gov (United States)

    Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

    2018-04-01

    In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

  1. Moving object detection in top-view aerial videos improved by image stacking

    Science.gov (United States)

    Teutsch, Michael; Krüger, Wolfgang; Beyerer, Jürgen

    2017-08-01

    Image stacking is a well-known method that is used to improve the quality of images in video data. A set of consecutive images is aligned by applying image registration and warping. In the resulting image stack, each pixel has redundant information about its intensity value. This redundant information can be used to suppress image noise, resharpen blurry images, or even enhance the spatial image resolution as done in super-resolution. Small moving objects in the videos usually get blurred or distorted by image stacking and thus need to be handled explicitly. We use image stacking in an innovative way: image registration is applied to small moving objects only, and image warping blurs the stationary background that surrounds the moving objects. Our video data are coming from a small fixed-wing unmanned aerial vehicle (UAV) that acquires top-view gray-value images of urban scenes. Moving objects are mainly cars but also other vehicles such as motorcycles. The resulting images, after applying our proposed image stacking approach, are used to improve baseline algorithms for vehicle detection and segmentation. We improve precision and recall by up to 0.011, which corresponds to a reduction of the number of false positive and false negative detections by more than 3 per second. Furthermore, we show how our proposed image stacking approach can be implemented efficiently.

  2. 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)

  3. Comparative study of 201Tl reinjection mycoardial imaging and late imaging after reinjection for detecting myocardial viability

    International Nuclear Information System (INIS)

    Lin Jinghui; Chai Xiaofeng; Zhu Mei

    1997-01-01

    PURPOSE: To compare 201 Tl reinjection imaging with late imaging in detecting myocardial viability. METHODS: 62 patients with myocardial infarction underwent 201 Tl exercise, 3∼5 hours redistribution, 16∼35 minutes and 12∼19 hours post 201 Tl reinjection mycoardial tomography imaging. After imaging, percutaneous transluminal coronary angioplasty (PTCA) were performed in 15 patients, and then exercise-redistribution myocardial imaging were repeated. RESULTS: 62 patients had 126 segments of irreversible defects on stress-redistribution imaging, 48 segments showed radioactive filling at 16∼35 minutes post-reinjection. The detecting rate of myocardial viability was 38.1% (48/126). 51 segments presented redistribution on 12∼19 hours late imaging, the detecting rate of myocardial viability was 40.5% (51/126). There were no significant difference in the detecting rate between them (x 2 0.16, P>0.05). But in combination of both methods, there were 62 segments refilling, thereby detecting rate was enhanced to 49.2% (62/126). In 15 patients who had PTCA, out of 17 segments were discovered to be viable before PTCA. After PTCA 12 segments had an improved perfusion of 201 Tl, the positive predictive accuracy was 70.6%. Out of 11 segments were discovered to be infarcted, 9 segments had non-improved 201 Tl perfusion after PTCA, the negative predictive accuracy was 81.8%. CONCLUSION: There were no significant difference in the detecting rate of myocardial viability between 2 '0 1 Tl reinjection and late imaging. In combination of both methods the detecting rate can be enhanced

  4. Improved detection probability of low level light and infrared image fusion system

    Science.gov (United States)

    Luo, Yuxiang; Fu, Rongguo; Zhang, Junju; Wang, Wencong; Chang, Benkang

    2018-02-01

    Low level light(LLL) image contains rich information on environment details, but is easily affected by the weather. In the case of smoke, rain, cloud or fog, much target information will lose. Infrared image, which is from the radiation produced by the object itself, can be "active" to obtain the target information in the scene. However, the image contrast and resolution is bad, the ability of the acquisition of target details is very poor, and the imaging mode does not conform to the human visual habit. The fusion of LLL and infrared image can make up for the deficiency of each sensor and give play to the advantages of single sensor. At first, we show the hardware design of fusion circuit. Then, through the recognition probability calculation of the target(one person) and the background image(trees), we find that the trees detection probability of LLL image is higher than that of the infrared image, and the person detection probability of the infrared image is obviously higher than that of LLL image. The detection probability of fusion image for one person and trees is higher than that of single detector. Therefore, image fusion can significantly enlarge recognition probability and improve detection efficiency.

  5. Clinical Evaluation of Endoscopic Trimodal Imaging for the Detection and Differentiation of Colonic Polyps

    NARCIS (Netherlands)

    van den Broek, Frank J. C.; Fockens, Paul; van Eeden, Susanne; Kara, Mohammed A.; Hardwick, James C. H.; Reitsma, Johannes B.; Dekker, Evelien

    2009-01-01

    Background & Aims: Endoscopic trimodal imaging (ETMI) incorporates high-resolution endoscopy (HRE) and autofluorescence imaging (AFI) for adenoma detection, and narrow-band imaging (NBI) for differentiation of adenomas from nonneoplastic polyps. The aim of this study was to compare AFI with HRE for

  6. Chemical Vapor Detection with a Multispectral Thermal Imager

    National Research Council Canada - National Science Library

    Althouse, Mark L. G; Chang, Chein-I

    1991-01-01

    .... Real-time autonomous detection and alarm is also required. A detection system model by Warren, based on a Gaussian vapor concentration distribution is the basis for detection algorithms. Algorithms recursive in both time and spectral frequency have been derived using Kalman filter theory. Adaptive filtering is used for preprocessing clutter rejection. Various components of the detection system have been tested individually and an integrated system is now being fabricated.

  7. Quantification of photoacoustic microscopy images