Visual analysis of image collections

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image-Projection Explorer for Images-a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.

IMAGE, MAGIC AND IMAGINATION: CHALLENGES TO THE ANTHROPOLOGICAL TEXT

What different forms of engagement do image and text allow the spectator/reader? We know that text and image communicate, and that all communication depends on a relationship between those who communicate. The objective of this text is therefore to understand the new possibilities available to an anthropology of the expression of knowledge that makes use of images, such as photographs and films.

Long-Term Astroglial Reaction and Neuronal Plasticity in the Subcortical Visual Pathways After a Complete Ablation of Telencephalon in Pigeons (Columba livia)

This paper analyzes the astroglial and neuronal responses in subtelencephalic structures, following a bilateral ablation of the telencephalon in the Columba livia pigeons. Control birds received a sham operation. Four months later the birds were sacrificed and their brains processed for glial fribillary acid protein (GFAP) and neurofilament immunohistochemistry, markers for astrocytes and neurons, respectively. Computer-assisted image analysis was employed for quantification of the immunoreactive labeling in the nucleus rotundus (N.Rt) and the optic tectum (OT) of the birds. An increased number of GFAP immunoreactive astrocytes were found in several subregions of the N.Rt (p .001), as well as in layers 1, 2cd, 3, and 6 of the OT (p .001) of the lesioned animals. Neurofilament immunoreactivity decreased massively in the entire N.Rt of the lesioned birds; however, remaining neurons with healthy aspect showing large cytoplasm and ramified branches were detected mainly in the periphery of the nucleus. In view of the recently described paracrine neurotrophic properties of the activated astrocytes, the data of the present study may suggest a long-lasting neuroglial interaction in regions of the lesioned bird brain far from injury. Such events may trigger neuronal plasticity in remaining brain structures that may lead spontaneous behavior recovery as the one promoted here even after a massive injury.

Effect of image analysis software on neurofunctional activation during processing of emotional human faces

Functional brain imaging techniques such as functional MRI (fMRI) that allow the in vivo investigation of the human brain have been exponentially employed to address the neurophysiological substrates of emotional processing. Despite the growing number of fMRI studies in the field, when taken separately these individual imaging studies demonstrate contrasting findings and variable pictures, and are unable to definitively characterize the neural networks underlying each specific emotional condition. Different imaging packages, as well as the statistical approaches for image processing and analysis, probably have a detrimental role by increasing the heterogeneity of findings. In particular, it is unclear to what extent the observed neurofunctional response of the brain cortex during emotional processing depends on the fMRI package used in the analysis. In this pilot study, we performed a double analysis of an fMRI dataset using emotional faces. The Statistical Parametric Mapping (SPM) version 2.6 (Wellcome Department of Cognitive Neurology, London, UK) and the XBAM 3.4 (Brain Imaging Analysis Unit, Institute of Psychiatry, Kings College London, UK) programs, which use parametric and non-parametric analysis, respectively, were used to assess our results. Both packages revealed that processing of emotional faces was associated with an increased activation in the brain`s visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex, in the cingulate cortex (anterior and posterior cingulate), and in the dorsolateral and ventrolateral prefrontal cortex. However, blood oxygenation level-dependent (BOLD) response in the temporal regions, insula and putamen was evident in the XBAM analysis but not in the SPM analysis. Overall, SPM and XBAM analyses revealed comparable whole-group brain responses. Further Studies are needed to explore the between-group compatibility of the different imaging packages in other cognitive and emotional processing domains. (C) 2009 Elsevier Ltd. All rights reserved.

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.