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Complex Images (complex + image)

Distribution by Scientific Domains
Medical Sciences57%

Selected Abstracts

Using dendronal signatures for feature extraction and retrieval

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 4 2000
Luojian Chen
A dendrone is a hierarchical thresholding structure that can be automatically generated from a complex image. The dendrone structure captures the connectedness of objects and subobjects during successive brightness thresholding. Based on connectedness and changes in intensity contours, dendronic representations of objects in images capture the coarse-to-fine unfolding of finer and finer detail, creating a unique signature for target objects that is invariant to lighting, scale, and placement of the object within the image. Subdendrones within the hierarchy are recognizable as objects within the picture. Complex composite images can be autonomously analyzed to determine if they contain the unique dendronic signatures of particular target objects of interest. In this paper, we describe the initial design of the dendronic image characterization environment (DICE) for the generation of dendronic signatures from complex multiband remote imagery. By comparing subdendrones within an image to dendronic signatures of target objects of interest, DICE can be used to match/retrieve target features from a library of composite images. The DICE framework can organize and support a number of alternative object recognition and comparison techniques, depending on the application domain. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol 11, 243,253, 2000 [source]

Automated image-based phenotypic analysis in zebrafish embryos

DEVELOPMENTAL DYNAMICS, Issue 3 2009
Andreas Vogt
Abstract Presently, the zebrafish is the only vertebrate model compatible with contemporary paradigms of drug discovery. Zebrafish embryos are amenable to automation necessary for high-throughput chemical screens, and optical transparency makes them potentially suited for image-based screening. However, the lack of tools for automated analysis of complex images presents an obstacle to using the zebrafish as a high-throughput screening model. We have developed an automated system for imaging and analyzing zebrafish embryos in multi-well plates regardless of embryo orientation and without user intervention. Images of fluorescent embryos were acquired on a high-content reader and analyzed using an artificial intelligence-based image analysis method termed Cognition Network Technology (CNT). CNT reliably detected transgenic fluorescent embryos (Tg(fli1:EGFP)y1) arrayed in 96-well plates and quantified intersegmental blood vessel development in embryos treated with small molecule inhibitors of anigiogenesis. The results demonstrate it is feasible to adapt image-based high-content screening methodology to measure complex whole organism phenotypes. Developmental Dynamics 238:656,663, 2009. © 2009 Wiley-Liss, Inc. [source]

Quantitative Image Analysis in Darmstadt

IMAGING & MICROSCOPY (ELECTRONIC), Issue 3 2007
Konrad Sandau Prof. Dr.
The 14th workshop "Quantitative Image Analysis" has been held at the University of Applied Sciences in Darmstadt on 15 June 2007. Image Analysis works on complex images as 3D-images, massive mosaics and video sequences. [source]

Influence of background and surround on image color matching

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 4 2007
Lidija Mandic
Abstract In this article, the corresponding-color data for complex images reproduced on different media were obtained by simultaneous matching using an adjustment method. In our experiment printed color images and images displayed on a monitor were compared in different viewing conditions. The viewing condition varied in surround relative luminance and background. The experimental data show that surround relative luminance has little influence on color matching between printed and monitor images while changes in background modify color appearance. These results were used to evaluate different chromatic adaptation transforms (CAT). We found that for the same viewing conditions the SHARP transform shows the best agreement between the experimental and predicted data. SHARP transform can not predict accurately corresponding colors for blue and black regions. Therefore, we proposed new CAT that shows better characteristics than other transforms for cyan, green, and black colors and similar characteristics for other colors. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 244,251, 2007 [source]

Labour Party: saved by the modernisers or modernised to be saved?

JOURNAL OF PUBLIC AFFAIRS, Issue 2 2003
Christos Rantavellas
Abstract The paper treats politics as a complex process that embraces actual or potential interactions among constructed meanings of different social actors through various symbolic forms drawing on the specific socio-historical, political context. These symbolic forms can take the form of various kinds from everyday linguistic utterances to complex images and texts. It is suggested that there is a strong interrelationship between ,image' and political discourse and their symbolic value grows as long as they come from consistent communication among all the social actors participating in the political process inside and outside of the political organisation. Two historical examples from the British political landscape,the Labour election defeat in 1987 and the Labour leadership election in 1994,are examined so as to draw some useful remarks concerning the limitations in drawing the line between ,image' and political discourse and among processes considered either internal or external of the party. Copyright © 2003 Henry Stewart Publications [source]

Two-point water-fat imaging with partially-opposed-phase (POP) acquisition: An asymmetric Dixon method,

MAGNETIC RESONANCE IN MEDICINE, Issue 3 2006
Qing-San Xiang
Abstract A novel two-point water-fat imaging method is introduced. In addition to the in-phase acquisition, water and fat magnetization vectors are sampled at partially-opposed-phase (POP) rather than exactly antiparallel as in the original Dixon method. This asymmetric sampling encodes more valuable phase information for identifying water and fat. From the magnitudes of the two complex images, a big and a small chemical component are first robustly obtained pixel by pixel and then used to form two possible error phasor candidates. The true error phasor is extracted from the two error phasor candidates through a simple procedure of regional iterative phasor extraction (RIPE). Finally, least-squares solutions of water and fat are obtained after the extracted error phasor is smoothed and removed from the complex images. For noise behavior, the effective number of signal averages NSA* is typically in the range of 1.87,1.96, very close to the maximum possible value of 2. Compared to earlier approaches, the proposed method is more efficient in data acquisition and straightforward in processing, and the final results are more robust. At both 1.5T and 0.3T, well separated and identified in vivo water and fat images covering a broad range of anatomical regions have been obtained, supporting the clinical utility of the method. Magn Reson Med, 2006. © 2006 Wiley-Liss, Inc. [source]

Statistical models of shape for the analysis of protein spots in two-dimensional electrophoresis gel images

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 6 2003
Mike Rogers
Abstract In image analysis of two-dimensional electrophoresis gels, individual spots need to be identified and quantified. Two classes of algorithms are commonly applied to this task. Parametric methods rely on a model, making strong assumptions about spot appearance, but are often insufficiently flexible to adequately represent all spots that may be present in a gel. Nonparametric methods make no assumptions about spot appearance and consequently impose few constraints on spot detection, allowing more flexibility but reducing robustness when image data is complex. We describe a parametric representation of spot shape that is both general enough to represent unusual spots, and specific enough to introduce constraints on the interpretation of complex images. Our method uses a model of shape based on the statistics of an annotated training set. The model allows new spot shapes, belonging to the same statistical distribution as the training set, to be generated. To represent spot appearance we use the statistically derived shape convolved with a Gaussian kernel, simulating the diffusion process in spot formation. We show that the statistical model of spot appearance and shape is able to fit to image data more closely than the commonly used spot parameterizations based solely on Gaussian and diffusion models. We show that improvements in model fitting are gained without degrading the specificity of the representation. [source]