Electronic microscopy has been used for morphology evaluation of different materials structures. However, microscopy results may be affected by several factors. Image processing methods can be used to correct and improve the quality of these results. In this paper we propose an algorithm based on starlets to perform the segmentation of scanning electron microscopy images. An application is presented in order to locate gold nanoparticles in natural rubber membranes. In this application, our method showed accuracy greater than 85 for all test images. Results given by this method will be used in future studies, to computationally estimate the density distribution of gold nanoparticles in natural rubber samples and to predict reduction kinetics of gold nanoparticles at different time periods.
Image segmentation is often used in microscopy images that are currently segmented to obtain features such as number of cells @cite_18 , separation of overlapped particles @cite_7 and skull-stripping of mouse brain @cite_14 .
@cite_18 @cite_14 @cite_7
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A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Small animal magnetic resonance microscopy (MRM) has been widely used today in computational neuroanatomy. Accurate identification of brain tissue in a mouse MRM is a critical fundamental step in neuroimaging processing, which is given less attention. This study presents an automatic skull-stripping technique based on template models and histogram analysis. Results were evaluated by calculating the Jaccard similarity index (JSI) and boundary concordance ratio (BCR) between the automatically segmented and manually traced brain volumes. Results demonstrate that this technique accurately extracts the brain volume (mean JSI = 97.1 , BC = 94.4 ). The brain extraction method presented in this study will greatly facilitate analysis of neuroimaging studies of rodent animals in neurodegenerative diseases. Microsc. Res. Tech. 2013.  2012 Wiley Periodicals, Inc. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles.