text: 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.
  @cite_11: 
  @cite_1: Summary Inthispaperanovelparameteroptimizationapproachforcell detection tool and counting cells procedure in phase contrast images are presented. Manual counting of the attached cells in phase contrast images is time-consuming and subjective. For evaluation of electroporation efficiency of attached cells, we often perform manual counting of the cells which is needed to determine the percentage of electroporated cells under different experimental conditions. Here we present an automated cell counting procedure based on novel artificial neuralnetworkoptimizationofImage-basedToolforCounting Nucleialgorithmparameterstofitthetrainingimagesetbased oncountsfromanexpert.Comparingtheresultsofautomated cell counting to user manual counting a 90,31 average agreement was achieved which is reasonably good especially takingintoaccountinter-personerrorwhichcanbeupto10 .
  @cite_8: White blood cell detection is one of the most basic and key steps in the automatic recognition system of white blood cells in microscopic blood images. Its accuracy and stability greatly affect the operating speed and recognition accuracy of the whole system. But there are only a few methods available for cell detection or segmentation due to the complexity of the microscopic images. This paper focuses on this issue. Based on the detailed analysis of the existing two methods-threshold segmentation followed by mathematical morphology (TSMM), and the fuzzy logic method-a new detection algorithm (NDA) based on fuzzy cellular neural networks is proposed. NDA combines the advantages of TSMM and the fuzzy logic method, and overcomes their drawbacks. With NDA, we can detect almost all white blood cells, and the contour of each detected cell is nearly complete. Its adaptability is strong and the running speed is expected to be comparatively high due to the easy hardware implementation of FCN. Experimental results show good performance
summary: There are a number of commercial and free software available for these processing @cite_1 ; however, these tools are focused on some images, and cannot be extended to analysis of other ones @cite_1 @cite_8 . Furthermore, segmentation of nontrivial images is one of the most challenging tasks in image processing: its accuracy determines the success of computational analysis procedures @cite_11 .