3D analysis of SEM images of corrosion casting using adaptive stereo matching

The corrosion casting method represents one of the most widely used technique to study the 3D microvascularization of many tissues, both in their normal and pathological conditions. For a long time this technique was used only to perform a qualitative evaluation of the images obtained by scanning electron microscopy (SEM). A quantitative evaluation of vascular parameters (e.g., interbranching and intervascular distances, angle measurements, lengths and diameters) was lacking, mainly because of the difficulties found in the measurement performed on 2D SEM images. Then, some authors reported a quantitative method based on the analyses of stereo-pair images that allowed precise morphometric measurements. To visualize the specimens in 3D, it was necessary to use red-green glasses. In this article we describe a new approach by which we can automatically obtain a 3D reconstruction of vascular cast specimen's surface directly from stereo-images. Moreover, we developed a software that performed micrometric measurements on the 3D construct generated from the stereo-pictures. In conclusion, implementing together these two softwares and applying them to corrosion casting samples made it possible to render in 3D the surface of corrosion cast as well as make quantitative measurements on the corrosion casts.     

A vision-based, 3D reconstruction technique for scanning electron microscopy: direct comparison with atomic force microscopy

High-resolution, detailed 3D reconstructions of biological specimens obtained from scanning electron microscopy stereo-micrographs and proprietary software were compared with Tapping-Mode AFM datasets of the same fields. The reconstruction software implements several original solutions including a neural adaptive point-matching technique, the ability to build an irregular triangulated mesh rather than a regular orthogonal grid, and the ability to re-map one of the original images exactly onto the reconstructed surface. The technique was applied to human nerve tissue to obtain 1,424 x 968-pixel, texture-mapped datasets, which were subsequently compared against 512 x 512-pixel AFM datasets from the same viewfields. Accounting for the inherent differences of the two techniques, direct comparison revealed an excellent visual match. The correspondence was also quantified by calculating the cross-correlation coefficient between corresponding altimetric profiles in SEM and AFM data, which consistently exceeded a figure of 0.9, with a rate of point mismatch in the order of 0.01%. Research is still underway to improve the robustness of the technique when applied to arbitrary images     

Comparison of the multilayer perceptron with neuro-fuzzy techniquesin the estimation of cover class mixture in remotely sensed data

Mixed pixels are a major source of inconvenience in the classification of remotely sensed data. This paper compares MLP with so-called neuro-fuzzy algorithms in the estimation of pixel component cover classes. Two neuro-fuzzy networks are selected from the literature as representatives of soft classifiers featuring different combinations of fuzzy set-theoretic principles with neural network learning mechanisms. These networks are: 1) the fuzzy multilayer perceptron (FMLP) and 2) a two-stage hybrid (TSH) learning neural network whose unsupervised first stage consists of the fully self-organizing simplified adaptive resonance theory (FOSART) clustering model, FMLP, TSH, and MLP are compared on CLASSITEST, a standard set of synthetic images where per-pixel proportions of cover class mixtures are known a priori. Results are assessed by means of evaluation tools specifically developed for the comparison of soft classifiers. Experimental results show that classification accuracies of FMLP and TSH are comparable, whereas TSH is faster to train than FMLP. On the other hand, FMLP and TSW outperform MLP when little prior knowledge is available for training the network, i.e., when no fuzzy training sites, describing intermediate label assignments, are available     

Effect of Gastric Digestion pH on Iron, Zinc, and Calcium Dialyzability from Preterm and Term Starting Infant Formulas

ABSTRACT: Iron, zinc, and calcium dialyzability from preterm and term starting infant formulas were determined after in vitro digestion, using special gastric conditions prevailing in preterm and newborn infants. Mineral dialyzability was studied using pH 2.0,3.5, and 4.5 for gastric digestion. The effect of gastric pH was more important on iron dialyzability (FeD) and zinc dialyzability (ZnD) than on calcium dialyzability (CaD). The effect on iron dialyzability was remarkable in fluid formulations with high digestibility: FeD was 18% to 20% when gastric digestion was made at pH 2, decreasing to 3% when made at pH 4.5. In most powder infant formulas with the lowest digestibility, FeD remained close to 10% despite variations in gastric digestion pH. Percent zinc dialyzability (ZnD%) steadily decreased when gastric digestion pH increased. At each pH, percent iron dialyzability (FeD%) and ZnD% from human milk were higher than those from infant formulas. Evaluation of mineral dialyzability from these infant formulas, using a gastric digestion pH prevailing in preterm and newborn infants, can provide valuable information on mineral availability.     

Computer-assisted diagnosis of postmenopausal osteoporosis using a fuzzy expert system shell

This paper shows how the AMDIS (Automated Medical Diagnosis with Intelligent Systems) integrated system can be employed to build a fuzzy medical expert system in the domain of postmenopausal osteoporosis. The fundamental aims of the expert system are to standardize knowledge and support physicians in the early detection of postmenopausal osteoporosis. A wide range of diagnostic situations has been considered for both categories of the disease, with judgments that range from disease is excluded to disease is definite. The salient aspects of the approach are the use of fuzzy logic as an analytic language for the representation and manipulation of knowledge and strategies and the integration of structured interview techniques and learning-by-example to address the knowledge acquisition task.     

Effects of roasting and boiling of quinoa,kiwicha and kañiwa on composition and availability of minerals in vitro

BACKGOUND: Andean indigenous crops such as quinoa (Chenopodium quinoa), kiwicha (Amaranthus caudatus) and kañiwa (Chenopodium pallidicaule) seeds are good sources of minerals (calcium and iron). Little is known, however, about mineral bioavailability in these grains. Thus the aim of the present study was to determine the iron, calcium and zinc potential availability in raw, roasted and boiled quinoa, kañiwa and kiwicha seeds. Potential availability was estimated by dialyzability. RESULTS: These seeds are good sources of phenolic compounds and kañiwa of dietary fiber. Their calcium, zinc and iron content is higher than in common cereals. In general, roasting did not significantly affect mineral dialyzability. Conversely, in boiled grains there was an increase in dialyzability of zinc and, in the case of kañiwa, also in iron and calcium dialyzability. CONCLUSION: Taking into account the high content of minerals in Andean grains, the potential contribution of these minerals would not differ considerably from that of wheat flour. Further studies are required to research the effect of extrusion on mineral availability in Andean grains. Copyright © 2010 Society of Chemical Industry     

Fuzzy contextual classification of multisource remote sensingimages

The authors' objective has been to model satellite image classification as a cognitive process, providing a procedure that mimics the rich interaction of human activity in solving classification problems. The key features of this approach are the definition of a knowledge-based classification methodology designed to integrate contextual information into a multisource classification scheme, together with a fuzzy knowledge representation framework to model the overall process in a form that closely resembles the mental representation of human experts. An application for the identification of the glacier equilibrium line in two different zones of the Italian Alps has been developed to evaluate the performance of their methodology in a real domain where class discrimination requires the simultaneous use of contextual and multisource information. Numerical results are provided and compared with those obtained by a conventional classification procedure. The advantages of the approach, as seen in the experimental context, are examined