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.

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.

Morphological analysis of pitting corrosion in AISI 310S austenitic stainless steel after salt spray exposition

This paper aims to study evolution of increase, distribution and classification of pits in 310S austenitic stainless steels obtained in the state as-received and heat-treated under different exposure times in saline. This work applicability has been based on a technique development for morphologic characterization of localized corrosion associated with description aspects of shapes, size and population-specific parameters. Methodology has been consisted in the following steps: specimens preparation, corrosion tests via salt spray in different conditions, microstructural analysis, pits profiles analysis and images analysis, digital processing and image analysis in order to characterize the pits distribution, morphology and size. Results obtained in digital processing and profiles image analysis have been subjected to statistical analysis using median as parameter in the alloy as received and treated. The alloy as received displays the following morphology: hemispheric pits> transition region A> transition region B> irregular> conic. The pits amount in the treated alloy at each exposure time is: transition region B> hemispherical> transition region A> conic> irregular.

Dynamic formation of SEBS copolymer submicrometric structures

Highly ordered A-B-A block copolymer arrangements in the submicrometric scale, resulting from dewetting and solvent evaporation of thin films, have inspired a variety of new applications in the nanometric world. Despite the progress observed in the control of such structures, the intricate scientific phenomena related to regular patterns formation are still not completely elucidated. SEBS is a standard example of a triblock copolymer that forms spontaneously impressive pattern arrangements. From macroscopic thin liquid films of SEBS solution, several physical effects and phenomena act synergistically to achieve well-arranged patterns of stripes and/or droplets. That is, concomitant with dewetting, solvent evaporation, and Marangoni effect, Rayleigh instability and phase separation also play important role in the pattern formation. These two last effects are difficult to be followed experimentally in the nanoscale, which render difficulties to the comprehension of the whole phenomenon. In this paper, we use computational methods for image analysis, which provide quantitative morphometric data of the patterns, specifically comprising stripes fragmentation into droplets. With the help of these computational techniques, we developed an explanation for the final part of the pattern formation, i.e. structural dynamics related to the stripes fragmentation. (C) 2010 Elsevier Ltd. All rights reserved.

Exploratory Approach to the use of Techniques of Regression Analysis and Pooled Analysis in Studies of Consumer Behavior

This paper is part of a large study to assess the adequacy of the use of multivariate statistical techniques in theses and dissertations of some higher education institutions in the area of marketing with theme of consumer behavior from 1997 to 2006. The regression and conjoint analysis are focused on in this paper, two techniques with great potential of use in marketing studies. The objective of this study was to analyze whether the employement of these techniques suits the needs of the research problem presented in as well as to evaluate the level of success in meeting their premisses. Overall, the results suggest the need for more involvement of researchers in the verification of all the theoretical precepts of application of the techniques classified in the category of investigation of dependence among variables.

Microvascular Lymphatic Density Analysis in Cutaneous Regressive and Nonregressive Superficial Spreading Melanomas Using the Lymphatic Marker D2-40

Background: The prognostic significance of spontaneous regression in melanoma, especially thin lesions, has been a controversial issue for the past 20 years, although recent studies suggest that extensive and late regression may be related to worse prognosis. Many data suggest that lymphangiogenesis predicts metastatic spread in melanoma. Methods: We have quantified lymphatic microvascular density (LMVD) in thin (<= 1.0 mm) superficial spreading melanomas comparing regressive and nonregressive melanomas, regressive and nonregressive areas from the same tumor, and early and late histological stages of regression in the same tumor. In addition, we tried to correlate lymphangiogenesis and tumor growth phase. We conducted histological examinations and immunohistochemical analyses using monoclonal antibody D2-40 with subsequent quantification by image analysis of 37 melanomas, 16 regressive and 21 nonregressive (controls). Results: We found higher LMVD in the late stage of regression compared with nonregressive area (internal control) of regressive melanomas. Conclusions: Our study suggest that the late stage of spontaneous regression in thin melanomas may be related to worse prognosis as it showed higher LMVD, and evidence shows that this is related with increased risk of metastatic spread. But this supposition must be confirmed by a longer follow-up for detection of lymph node metastases.

Effects of bilateral adrenalectomy on systemic kainate-induced activation of the nucleus of the solitary tract. Regulation of blood pressure and local neurotransmitters

Glutamatergic transmission through metabotropic and ionotropic receptors, including kainate receptors, plays an important role in the nucleus of the solitary tract (NTS) functions. Glutamate system may interact with several other neurotransmitter systems which might also be influenced by steroid hormones. In the present study we analyzed the ability of systemic kainate to stimulate rat NTS neurons, which was evaluated by c-Fos as a marker of neuronal activation, and also to change the levels of NTS neurotransmitters such as GABA, NPY, CGRP, GAL, NT and NO by means of quantitative immunohistichemistry combined with image analysis. The analysis was also performed in adrenalectomized and kainate stimulated rats in order to evaluate a possible role of adrenal hormones on NTS neurotransmission. Male Wistar rats (3 month-old) were used in the present study. A group of 15 rats was submitted either to bilateral adrenalectomy or sham operation. Forty-eight hours after the surgeries, adrenalectomized rats received a single intraperitoneal injection of kainate (12 mg/kg) and the sham-operated rats were injected either with saline or kainate and sacrificed 8 hours later. The same experimental design was applied in a group of rats in order to register the arterial blood pressure. Systemic kainate decreased the basal values of mean arterial blood pressure (35%) and heart rate (22%) of sham-operated rats, reduction that were maintained in adrenalectomized rats. Kainate triggered a marked elevation of c-Fos positive neurons in the NTS which was 54% counteracted by adrenalectomy. The kainate activated NTS showed changes in the immunoreactive levels of GABA (143% of elevation) and NPY (36% of decrease), which were not modified by previous ablation of adrenal glands. Modulation in the levels of CGRP, GAL and NT immunoreactivities were only observed after kainate in the adrenalectomized rats. Treatments did not alter NOS labeling. It is possible that modulatory function among neurotransmitter systems in the NTS might be influenced by steroid hormones and the implications for central regulation of blood pressure or other visceral regulatory mechanisms control should be further investigated.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Proteomic Analysis of Urine in Rats Chronically Exposed to Fluoride

Urine is an ideal source of materials to search for potential disease-related biomarkers as it is produced by the affected tissues and can be easily obtained by noninvasive methods. 2-DE-based proteomic approach was used to better understand the molecular mechanisms of injury induced by fluoride (F(-)) and define potential biomarkers of dental fluorosis. Three groups of weanling male Wistar rats were treated with drinking water containing 0 (control), 5, or 50 ppm F(-) for 60 days (n = 15/group). During the experimental period, the animals were kept individually in metabolic cages, to analyze the water and food consumption, as well as fecal and urinary F excretion. Urinary proteome profiles were examined using 2-DE and Colloidal Coomassie Brilliant Blue staining. A dose-response regarding F(-) intake and excretion was detected. Quantitative intensity analysis revealed 8, 11, and 8 significantly altered proteins between control vs. 5 ppm F(-), control vs. 50 ppm F(-) and 5 ppm F(-) vs. 50 ppm F(-) groups, respectively. Two proteins regulated by androgens (androgen-regulated 20-KDa protein and 0c-2,1-globulin) and one related to detoxification (aflatoxin-Bl-aldehyde-reductase) were identified by MALDI-TOF-TOF MS/MS. Thus, proteomic analysis can help to better understand the mechanisms underlying F(-) toxicity, even in low doses. 2010 Wiley Periodicals, Inc. J Biochem Mol Toxicol 25:8-14, 2011; View this article online at wileyonlinelibrary.com. DOI 10:1002/jbt.20353

Peptidomic Analysis of Human Cell Lines

Peptides have been proposed to function in intracellular signaling within the cytosol. Although cytosolic peptides are considered to be highly unstable, a large number of peptides have been detected in mouse brain and other biological samples. In the present study, we evaluated the peptidome of three diverse cell lines: SH-SY5Y, MCF7, and HEIC293 cells. A comparison of the peptidomes revealed considerable overlap in the identity of the peptides found in each cell line. The majority of the observed peptides are not derived from the most abundant or least stable proteins in the cell, and approximately half of the cellular peptides correspond to the N- or C- termini of the precursor proteins. Cleavage site analysis revealed a preference for hydrophobic residues in the PI position. Quantitative peptidomic analysis indicated that the levels of most cellular peptides are not altered in response to elevated intracellular calcium, suggesting that calpain is not responsible for their production. The similarity of the peptidomes of the three cell lines and the lack of correlation with the predicted cellular degradome implies the selective formation or retention of these peptides, consistent with the hypothesis that they are functional in the cells.

PIXE analysis of chromium phytoaccumulation by the aquatic macrophytes Eicchornia crassipes

The uptake of hexavalent chromium in free living floating aquatic macrophytes Eicchornia crassipes cultivated in non-toxic chromium-doped hydroponic solutions is presented. A Cr-uptake bioaccumulation experiment was carried out using healthy macrophytes grown in a temperature controlled greenhouse. Six samples of nutrient media and plants were collected during the 23 day experiment. Roots and leaves were acid digested with the addition of an internal Gallium standard, for thin film sample preparation and quantitative Cr analysis by PIXE method. The Cr(6+) mass uptake by the macrophytes reached up to 70% of the initial concentration, comparable to former results and literature data. The Cr-uptake data were described using a non-structural first order kinetic model. Due to low cost and high removal efficiency, living aquatic macrophytes E. crassipes are a viable biosorbent in an artificial wetland of a water effluent treatment plant. (c) 2009 Elsevier B.V. All rights reserved.

Texture analysis using graphs generated by deterministic partially self-avoiding walks

Texture is one of the most important visual attributes for image analysis. It has been widely used in image analysis and pattern recognition. A partially self-avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results. This approach uses walkers (called tourists) to exploit the gray scale image contexts in several levels. Here, we present an approach to generate graphs out of the trajectories produced by the tourist walks. The generated graphs embody important characteristics related to tourist transitivity in the image. Computed from these graphs, the statistical position (degree mean) and dispersion (entropy of two vertices with the same degree) measures are used as texture descriptors. A comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach. (C) 2011 Elsevier Ltd. All rights reserved.

An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation. (C) 2010 Elsevier B.V. All rights reserved.

Multiscale Curvature Analysis of Asphaltic Aggregate Particles

A new method for characterization and analysis of asphaltic mixtures aggregate particles is reported. By relying on multiscale representation of the particles, curvature estimation, and discriminant analysis for optimal separation of the categories of mixtures, a particularly effective and comprehensive methodology is obtained. The potential of the methodology is illustrated with respect to three important types of particles used in asphaltic mixtures, namely basalt, gabbro, and gravel. The obtained results show that gravel particles are markedly distinct from the other two types of particles, with the gabbro category resulting with intermediate geometrical properties. The importance of each considered measurement in the discrimination between the three categories of particles was also quantified in terms of the adopted discriminant analysis.