Structural analysis of a UAV wing through image processing and FEM

Analisi strutturale dell’ala di un UAV (velivolo senza pilota a bordo), sviluppata usando varie metodologie: misurazioni sperimentali statiche e dinamiche, e simulazioni numeriche con l’utilizzo di programmi agli elementi finiti. L’analisi statica è stata a sua volta portata avanti seguendo due differenti metodi: la classica e diretta determinazione degli spostamenti mediante l’utilizzo di un catetometro e un metodo visivo, basato sull’elaborazione di immagini e sviluppato appositamente a tale scopo in ambiente Matlab. Oltre a ciò è stata svolta anche una analisi FEM volta a valutare l’errore che si ottiene affrontando il problema con uno studio numerico. Su tale modello FEM è stata svolta anche una analisi di tipo dinamico con lo scopo di confrontare tali dati con i dati derivanti da un test dinamico sperimentale per ottenere informazioni utili per una seguente possibile analisi aeroelastica.

Impact of optic media opacities and image compression on quantitative analysis of optical coherence tomography

To analyze the impact of opacities in the optical pathway and image compression of 32-bit raw data to 8-bit jpg images on quantified optical coherence tomography (OCT) image analysis.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).

Training Bayesian networks for image segmentation

We are concerned with the problem of image segmentation in which each pixel is assigned to one of a predefined finite number of classes. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of segmentations. Markov Random Fields (MRFs) have been used to incorporate some of this prior knowledge, but this not entirely satisfactory as inference in MRFs is NP-hard. The multiscale quadtree model of Bouman and Shapiro (1994) is an attractive alternative, as this is a tree-structured belief network in which inference can be carried out in linear time (Pearl 1988). It is an hierarchical model where the bottom-level nodes are pixels, and higher levels correspond to downsampled versions of the image. The conditional-probability tables (CPTs) in the belief network encode the knowledge of how the levels interact. In this paper we discuss two methods of learning the CPTs given training data, using (a) maximum likelihood and the EM algorithm and (b) emphconditional maximum likelihood (CML). Segmentations obtained using networks trained by CML show a statistically-significant improvement in performance on synthetic images. We also demonstrate the methods on a real-world outdoor-scene segmentation task.

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.

Quantification of Clay Dispersion in Nanocomposites of Styrenic Polymers

Polymer-clay nanocomposites are materials with many interesting structures, properties, and potential applications. Microstructural evaluation of a nanocomposite is not an easy task, as clay may form hierarchical structures which may look different when observed at various magnifications under a microscope, and also as the concepts of ""intercalation"" and ""exfoliation"" are not self-sufficient to describe its morphology. In this work polymer-clay nanocomposites of polystyrene and two styrene-containing block copolymers (styrene-butadiene-styrene and styrene-ethylene/butylene-styrene) were prepared using three different techniques. Clay dispersion was evaluated by a recently developed microscopy image analysis procedure, combining the analysis of optical and transmission electron micrographs, and the characterization was complemented by X-ray diffraction and rheological measurements. The results showed better clay dispersion for both block copolymers nanocomposites, mainly due to their molecular architectures. Moreover, the techniques which showed the best results involved mixing the materials in a solvent medium. POLYM. ENG. SCI., 50:257-267, 2010. (C) 2009 Society of Plastics Engineers

Proteomic analysis of normal and malignant prostate tissue to identify novel proteins lost in cancer

BACKGROUND. Alterations of important protein pathways, including loss of prostate secretory granules, and disruption of the prostatic secretory pathway have been identified as early events in malignancy. In this study, proteomics was used to map the differences in protein expression between normal and malignant prostate tissues and to identify and analyze differentially expressed proteins in human prostate tissue with particular regard to the proteins lost in malignancy. METHODS. Small quantities of normal and malignant prostate tissue were taken fresh from 34 radical prostatectomy cases. After histological examination, proteins were solubilized from selected tissues and separated using two-dimensional electrophoresis. Using image analysis, the proteome of normal and malignant tissues were mapped and differentially expressed proteins (present in normal and absent in malignant tissue) were identified and subsequently analyzed using peptide mass finger printing and N-terminal sequencing. Western blotting and immunohistochemistry were performed to examine expression profiles and tissue localization of candidate proteins. RESULTS. Comparison of protein maps of normal and malignant prostate were used to identify 20 proteins which were lost in malignant transformation, including prostate specific antigen (PSA), alpha-l antichymotrypsin (ACT), haptoglobin, and lactoylglutathione lyase. Three of the 20 had not previously been reported in human prostate tissue (Ubiquitin-like NEDD8, calponin, and a follistatin-related protein). Western blotting confirmed differences in the expression profiles of NEDD8 and calponin, and immunohistochemistry demonstrated differences in the cellular localization of these two proteins in normal and malignant prostate glands. CONCLUSIONS. The expression of NEDD8, calponin, and the follistatin-related protein in normal prostate tissues is a novel finding and the role of these important functional proteins in normal prostate and their loss or reduced expression in prostate malignancy warrants further investigations. (C) 2002 Wiley-Liss, Inc.

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.

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.

Analysis of enamel microbiopsies in shed primary teeth by Scanning Electron Microscopy (SEM) and Polarizing Microscopy (PM)

The aims of this study were 1) to verify how close to the theoretically presumed areas are the areas of enamel microbiopsies carried out in vivo or in exfoliated teeth; 2) to test whether the etching solution penetrates beyond the tape borders: 3) to test whether the etching solution demineralizes the enamel in depth. 24 shed upper primary central incisors were randomly divided into two groups: the Rehydrated Teeth Group and the Dry Teeth Group. An enamel microbiopsy was performed, and the enamel microbiopsies were then analyzed by Scanning Electron Microscopy (SEMI) and Polarizing Microscopy (PM). Quantitative birefringence measurements were performed. The ""true"" etched area was determined by measuring the etched enamel using the NIH Image analysis program. Enamel birefringence was compared using the paired t test. There was a statistically significant difference when the etched areas in the Rehydrated teeth were compared with those of the Dry teeth (p = 0.04). The etched areas varied from -11.6% to 73.5% of the presumed area in the Rehydrated teeth, and from 6.6% to 61.3% in the Dry teeth. The mean percentage of variation in each group could be used as a correction factor for the etched area. Analysis of PM pictures shows no evidence of in-depth enamel demineralization by the etching solution. No statistically significant differences in enamel birefringence were observed between values underneath and outside the microbiopsy area in the same tooth, showing that no mineral loss occurred below the enamel superficial layer. Our data showed no evidence of in-depth enamel demineralization by the etching solution used in the enamel microbiopsy proposed for primary enamel. This study also showed a variation in the measured diameter of the enamel microbiopsy in nineteen teeth out of twenty four, indicating that in most cases the etching solution penetrated beyond the tape borders. (C) 2009 Elsevier B.V. All rights reserved.

On-line analysis of froth surface in coal and mineral flotation using JKFrothCam

The personal computer revolution has resulted in the widespread availability of low-cost image analysis hardware. At the same time, new graphic file formats have made it possible to handle and display images at resolutions beyond the capability of the human eye. Consequently, there has been a significant research effort in recent years aimed at making use of these hardware and software technologies for flotation plant monitoring. Computer-based vision technology is now moving out of the research laboratory and into the plant to become a useful means of monitoring and controlling flotation performance at the cell level. This paper discusses the metallurgical parameters that influence surface froth appearance and examines the progress that has been made in image analysis of flotation froths. The texture spectrum and pixel tracing techniques developed at the Julius Kruttschnitt Mineral Research Centre are described in detail. The commercial implementation, JKFrothCam, is one of a number of froth image analysis systems now reaching maturity. In plants where it is installed, JKFrothCam has shown a number of performance benefits. Flotation runs more consistently, meeting product specifications while maintaining high recoveries. The system has also shown secondary benefits in that reagent costs have been significantly reduced as a result of improved flotation control. (C) 2002 Elsevier Science B.V. All rights reserved.

Methods for automatic and assisted image annotation

Personal memories composed of digital pictures are very popular at the moment. To retrieve these media items annotation is required. During the last years, several approaches have been proposed in order to overcome the image annotation problem. This paper presents our proposals to address this problem. Automatic and semi-automatic learning methods for semantic concepts are presented. The automatic method is based on semantic concepts estimated using visual content, context metadata and audio information. The semi-automatic method is based on results provided by a computer game. The paper describes our proposals and presents their evaluations.

Cluster Analysis of Business Data

This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.

Cluster analysis using affinity aoefficient in order to identify religious beliefs profiles

International Scientific Forum, ISF 2013, ISF 2013, 12-14 December 2013, Tirana.