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Image Processing, IEEE Transactions on

Issue 9 • Date Sept. 2005

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Displaying Results 1 - 24 of 24
  • Table of contents

    Publication Year: 2005 , Page(s): c1 - c4
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  • IEEE Transactions on Image Processing publication information

    Publication Year: 2005 , Page(s): c2
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  • Guest Editorial

    Publication Year: 2005 , Page(s): 1233 - 1236
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  • The role of photon statistics in fluorescence anisotropy imaging

    Publication Year: 2005 , Page(s): 1237 - 1245
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1836 KB) |  | HTML iconHTML  

    Anisotropy imaging can be used to image resonance energy transfer between pairs of identical fluorophores and, thus, constitutes a powerful tool for monitoring protein homo-association in living single cells. The requirement for only a single fluorophore significantly simplifies biological preparation and interpretation. We use quantitative methods for the acquisition and image processing of anisotropy data that return the expected error of the anisotropy per pixel based on photon statistics. The analysis methods include calibration procedures and allow for a balance in spatial, anisotropy, and temporal resolution. They are featured here with anisotropy images of fluorescent calibration beads and enhanced green fluorescent protein complexes in live cells. View full abstract»

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  • An adaptive multirate algorithm for acquisition of fluorescence microscopy data sets

    Publication Year: 2005 , Page(s): 1246 - 1253
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (926 KB) |  | HTML iconHTML  

    We propose an algorithm for adaptive efficient acquisition of fluorescence microscopy data sets using a multirate (MR) approach. We simulate acquisition as part of a larger system for protein classification based on their subcellular location patterns and, thus, strive to maintain the achieved level of classification accuracy as much as possible. This problem is similar to image compression but unique due to additional restrictions, namely causality; we have access only to the information scanned up to that point. While we do want to acquire fewer samples with as low distortion as possible to achieve compression, our goal is to do so while affecting the overall classification accuracy as little as possible. We achieve this by using an adaptive MR scanning scheme which samples the regions of the image area that hold the most pertinent information. Our results show that we can achieve significant compression which we can then use to acquire faster or to increase space resolution of our data set, all while minimally affecting the classification accuracy of the entire system. View full abstract»

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  • Deconvolution methods for mitigation of transverse blurring in optical coherence tomography

    Publication Year: 2005 , Page(s): 1254 - 1264
    Cited by:  Papers (15)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1840 KB) |  | HTML iconHTML  

    Imaging resolution in optical coherence tomography (OCT) is a key determinant for acquiring clinically useful optical biopsies of tissues. In contrast to light or confocal microscopy, the axial and transverse resolutions in OCT are independent and each can be analyzed individually. A method for mitigating transverse blurring and the apparent loss of transverse resolution in OCT by means of Gaussian beam deconvolution is presented. Such a method provides better representation of a specimen by using known physical parameters of a lens. To implement this method, deconvolution algorithms based on a focal-dependent kernel are investigated. First, the direct inverse problem is investigated using two types of regularization, truncated singular value decomposition, and Tikhonov. Second, an iterative expectation maximization algorithm, the Richardson-Lucy algorithm, with a beam-width-dependent iteration scheme is developed. A dynamically iterative Richardson-Lucy algorithm can reduce transverse blurring by providing an improvement in the transverse point-spread-function for sparse scattering samples in regions up to two times larger than the confocal region of the lens. These deblurring improvements inside and outside of the confocal region, which are validated experimentally, are possible without introducing new optical imaging hardware or acquiring multiple images of the same specimen. Implementation of this method in sparse scattering specimens, such as engineered tissues, has the potential to improve cellular detection and categorization. View full abstract»

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  • An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue

    Publication Year: 2005 , Page(s): 1265 - 1276
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3991 KB) |  | HTML iconHTML  

    The automatic segmentation of nuclei in confocal reflectance images of cervical tissue is an important goal toward developing less expensive cervical precancer detection methods. Since in vivo confocal reflectance microscopy is an emerging technology for cancer detection, no prior work has been reported on the automatic segmentation of in vivo confocal reflectance images. However, prior work has shown that nuclear size and nuclear-to-cytoplasmic ratio can determine the presence or extent of cervical precancer. Thus, segmenting nuclei in confocal images will aid in cervical precancer detection. Successful segmentation of images of any type can be significantly enhanced by the introduction of accurate image models. To enable a deeper understanding of confocal reflectance microscopy images of cervical tissue, and to supply a basis for parameter selection in a classification algorithm, we have developed a model that accounts for the properties of the imaging system and of the tissues. Using our model in conjunction with a powerful image enhancement tool (anisotropic median-diffusion), appropriate statistical image modeling of spatial interactions (Gaussian Markov random fields), and a Bayesian framework for classification-segmentation, we have developed an effective algorithm for automatically segmenting nuclei in confocal images of cervical tissue. We have applied our algorithm to an extensive set of cervical images and have found that it detects 90% of hand-segmented nuclei with an average of 6 false positives per frame. View full abstract»

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  • Subspace-based prototyping and classification of chromosome images

    Publication Year: 2005 , Page(s): 1277 - 1287
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1277 KB) |  | HTML iconHTML  

    Chromosomes are essential genomic information carriers. Chromosome classification constitutes an important part of routine clinical and cancer cytogenetics analysis. Cytogeneticists perform visual interpretation of banded chromosome images according to the diagrammatic models of various chromosome types known as the ideograms, which mimic artists' depiction of the chromosomes. In this paper, we present a subspace-based approach for automated prototyping and classification of chromosome images. We show that 1) prototype chromosome images can be quantitatively synthesized from a subspace to objectively represent the chromosome images of a given type or population, and 2) the transformation coefficients (or projected coordinate values of sample chromosomes) in the subspace can be utilized as the extracted feature measurements for classification purposes. We examine in particular the formation of three well-known subspaces, namely the ones derived from principal component analysis (PCA), Fisher's linear discriminant analysis, and the discrete cosine transform (DCT). These subspaces are implemented and evaluated for prototyping two-dimensional (2-D) images and for classification of both 2-D images and one-dimensional profiles of chromosomes. Experimental results show that previously unseen prototype chromosome images of high visual quality can be synthesized using the proposed subspace-based method, and that PCA and the DCT significantly outperform the well-known benchmark technique of weighted density distribution functions in classifying 2-D chromosome images. View full abstract»

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  • Automated evaluation of her-2/neu status in breast tissue from fluorescent in situ hybridization images

    Publication Year: 2005 , Page(s): 1288 - 1299
    Cited by:  Papers (17)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1015 KB) |  | HTML iconHTML  

    The evaluation of fluorescent in situ hybridization (FISH) images is one of the most widely used methods to determine Her-2/neu status of breast samples, a valuable prognostic indicator. Conventional evaluation is a difficult task since it involves manual counting of dots in multiple images. In this paper, we present a multistage algorithm for the automated classification of FISH images from breast carcinomas. The algorithm focuses not only on the detection of FISH dots per image, but also on combining results from multiple images taken from a slice for overall case classification. The algorithm includes mainly two stages for nuclei and dot detection respectively. The dot segmentation consists of a top-hat filtering stage followed by template matching to separate real signals from noise. Nuclei segmentation includes a nonlinearity correction step, global thresholding to identify candidate regions, and a geometric rule to distinguish between holes within a nucleus and holes between nuclei. Finally, the marked watershed transform is used to segment cell nuclei with markers detected as regional maxima of the distance transform. Combining the two stages allows the measurement of FISH signals ratio per cell nucleus and the collective classification of cases as positive or negative. The system was evaluated with receiver operating characteristic analysis and the results were encouraging for the further development of this method. View full abstract»

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  • A tool for the quantitative spatial analysis of complex cellular systems

    Publication Year: 2005 , Page(s): 1300 - 1313
    Cited by:  Papers (6)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2033 KB) |  | HTML iconHTML  

    Spatial events largely determine the biology of cells, tissues, and organs. In this paper, we present a tool for the quantitative spatial analysis of heterogeneous cell populations, and we show experimental validation of this tool using both artificial and real (mammary gland tissue) data, in two and three dimensions. We present the refined relative neighborhood graph as a means to establish neighborhood between cells in an image while modeling the topology of the tissue. Then, we introduce the M function as a method to quantitatively evaluate the existence of spatial patterns within one cell population or the relationship between the spatial distributions of multiple cell populations. Finally, we show a number of examples that demonstrate the feasibility of our approach. View full abstract»

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  • An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms

    Publication Year: 2005 , Page(s): 1314 - 1323
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1834 KB) |  | HTML iconHTML  

    Electron tomography allows for the determination of the three-dimensional structures of cells and tissues at resolutions significantly higher than that which is possible with optical microscopy. Electron tomograms contain, in principle, vast amounts of information on the locations and architectures of large numbers of subcellular assemblies and organelles. The development of reliable quantitative approaches for the analysis of features in tomograms is an important problem, and a challenging prospect due to the low signal-to-noise ratios that are inherent to biological electron microscopic images. This is, in part, a consequence of the tremendous complexity of biological specimens. We report on a new method for the automated segmentation of HIV particles and selected cellular compartments in electron tomograms recorded from fixed, plastic-embedded sections derived from HIV-infected human macrophages. Individual features in the tomogram are segmented using a novel robust algorithm that finds their boundaries as global minimal surfaces in a metric space defined by image features. The optimization is carried out in a transformed spherical domain with the center an interior point of the particle of interest, providing a proper setting for the fast and accurate minimization of the segmentation energy. This method provides tools for the semi-automated detection and statistical evaluation of HIV particles at different stages of assembly in the cells and presents opportunities for correlation with biochemical markers of HIV infection. The segmentation algorithm developed here forms the basis of the automated analysis of electron tomograms and will be especially useful given the rapid increases in the rate of data acquisition. It could also enable studies of much larger data sets, such as those which might be obtained from the tomographic analysis of HIV-infected cells from studies of large populations. View full abstract»

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  • Automatic ultrastructure segmentation of reconstructed CryoEM maps of icosahedral viruses

    Publication Year: 2005 , Page(s): 1324 - 1337
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3231 KB) |  | HTML iconHTML  

    We present an automatic algorithm to segment all the local and global asymmetric units of a three-dimensional density map of icosahedral viruses. This approach is readily applicable to the structural analysis of a broad range of virus structures that are reconstructed using cryo-electron microscopy (cryo-EM) technique. Our algorithm includes three major steps operating on the three dimensional density map: the detection of critical points of the volumetric density function, the detection of global and local symmetry axes, and, finally, the boundary segmentation of all the asymmetric units. We demonstrate the efficacy of our algorithm and report our results on several experimental volumetric datasets, consisting of both reconstructed cryo-EM molecular density maps taken from the European Bioinformatics Institute archive, as well our own synthetically generated (blurred) maps calculated from X-ray resolution molecular structural data taken from the Protein Data Bank. View full abstract»

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  • Automatic selection of parameters for vessel/neurite segmentation algorithms

    Publication Year: 2005 , Page(s): 1338 - 1350
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1021 KB) |  | HTML iconHTML  

    An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage against its conciseness. It enables nonexpert users to select parameter settings objectively, without knowledge of underlying algorithms, broadening the applicability of the segmentation algorithm, and delivering higher morphometric accuracy. It enables adaptation of parameters across batches of images. It simplifies the user interface to just one optional parameter and reduces the cost of technical support. Finally, the method is modular, extensible, and amenable to parallel computation. The method is applied to 223 images of human retinas and cultured neurons, from four different sources, using a single segmentation algorithm with eight parameters. Improvements in segmentation quality compared to default settings using 1000 iterations ranged from 4.7%-21%. Paired t-tests showed that improvements are statistically significant (p<0.0005). Most of the improvement occurred in the first 44 iterations. Improvements in description lengths and agreement with the ground truth were strongly correlated (ρ=0.78). View full abstract»

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  • Object type recognition for automated analysis of protein subcellular location

    Publication Year: 2005 , Page(s): 1351 - 1359
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (546 KB) |  | HTML iconHTML  

    The new field of location proteomics seeks to provide a comprehensive, objective characterization of the subcellular locations of all proteins expressed in a given cell type. Previous work has demonstrated that automated classifiers can recognize the patterns of all major subcellular organelles and structures in fluorescence microscope images with high accuracy. However, since some proteins may be present in more than one organelle, this paper addresses a more difficult task: recognizing a pattern that is a mixture of two or more fundamental patterns. The approach utilizes an object-based image model, in which each image of a location pattern is represented by a set of objects of distinct, learned types. Using a two-stage approach in which object types are learned and then cell-level features are calculated based on the object types, the basic location patterns were well recognized. Given the object types, a multinomial mixture model was built to recognize mixture patterns. Under appropriate conditions, synthetic mixture patterns can be decomposed with over 80% accuracy, which, for the first time, shows that the problem of computationally decomposing subcellular patterns into fundamental organelle patterns can be solved. View full abstract»

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  • Toward automatic phenotyping of developing embryos from videos

    Publication Year: 2005 , Page(s): 1360 - 1371
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2418 KB) |  | HTML iconHTML  

    We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images. View full abstract»

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  • Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics

    Publication Year: 2005 , Page(s): 1372 - 1383
    Cited by:  Papers (104)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2252 KB) |  | HTML iconHTML  

    We present a new, robust, computational procedure for tracking fluorescent markers in time-lapse microscopy. The algorithm is optimized for finding the time-trajectory of single particles in very noisy dynamic (two- or three-dimensional) image sequences. It proceeds in three steps. First, the images are aligned to compensate for the movement of the biological structure under investigation. Second, the particle's signature is enhanced by applying a Mexican hat filter, which we show to be the optimal detector of a Gaussian-like spot in 1/ω 2 noise. Finally, the optimal trajectory of the particle is extracted by applying a dynamic programming optimization procedure. We have used this software, which is implemented as a Java plug-in for the public-domain ImageJ software, to track the movement of chromosomal loci within nuclei of budding yeast cells. Besides reducing trajectory analysis time by several 100-fold, we achieve high reproducibility and accuracy of tracking. The application of the method to yeast chromatin dynamics reveals different classes of constraints on mobility of telomeres, reflecting differences in nuclear envelope association. The generic nature of the software allows application to a variety of similar biological imaging tasks that require the extraction and quantitation of a moving particle's trajectory. View full abstract»

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  • Single quantum dot tracking based on perceptual Grouping using minimal paths in a spatiotemporal volume

    Publication Year: 2005 , Page(s): 1384 - 1395
    Cited by:  Papers (46)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1197 KB) |  | HTML iconHTML  

    Semiconductor quantum dots (QDs) are new fluorescent probes with great promise for ultrasensitive biological imaging. When detected at the single-molecule level, QD-tagged molecules can be observed and tracked in the membrane of live cells over unprecedented durations. The motion of these individual molecules, recorded in sequences of fluorescence images, can reveal aspects of the dynamics of cellular processes that remain hidden in conventional ensemble imaging. Due to QD complex optical properties, such as fluorescence intermittency, the quantitative analysis of these sequences is, however, challenging and requires advanced algorithms. We present here a novel approach, which, instead of a frame by frame analysis, is based on perceptual grouping in a spatiotemporal volume. By applying a detection process based on an image fluorescence model, we first obtain an unstructured set of points. Individual molecular trajectories are then considered as minimal paths in a Riemannian metric derived from the fluorescence image stack. These paths are computed with a variant of the fast marching method and few parameters are required. We demonstrate the ability of our algorithm to track intermittent objects both in sequences of synthetic data and in experimental measurements obtained with individual QD-tagged receptors in the membrane of live neurons. While developed for tracking QDs, this method can, however, be used with any fluorescent probes. View full abstract»

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  • Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces

    Publication Year: 2005 , Page(s): 1396 - 1410
    Cited by:  Papers (95)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4027 KB) |  | HTML iconHTML  

    Cell migrations and deformations play essential roles in biological processes, such as parasite invasion, immune response, embryonic development, and cancer. We describe a fully automatic segmentation and tracking method designed to enable quantitative analyses of cellular shape and motion from dynamic three-dimensional microscopy data. The method uses multiple active surfaces with or without edges, coupled by a penalty for overlaps, and a volume conservation constraint that improves outlining of cell/cell boundaries. Its main advantages are robustness to low signal-to-noise ratios and the ability to handle multiple cells that may touch, divide, enter, or leave the observation volume. We give quantitative validation results based on synthetic images and show two examples of applications to real biological data. View full abstract»

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  • IEEE Transactions on Image Processing Edics

    Publication Year: 2005 , Page(s): 1411
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  • IEEE Transactions on Image Processing Information for authors

    Publication Year: 2005 , Page(s): 1412 - 1413
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  • International Conference on Image Processing (ICIP 2006)

    Publication Year: 2005 , Page(s): 1414
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  • 2006 IEEE International Conference on Multimedia and Expo (ICME)

    Publication Year: 2005 , Page(s): 1415
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  • 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'06)

    Publication Year: 2005 , Page(s): 1416
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  • IEEE Signal Processing Society Information

    Publication Year: 2005 , Page(s): c3
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Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

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Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003