Summary:A new smoothing filter has been developed for noise removal of scanning electron microscopy (SEM) images. We call this the complex hysteresis smoothing (CHS) filter. It is much easier to use for SEM operators than any other conventional smoothing filter, and it rarely produces processing artifacts because it does not utilize a definite mask (which usually has processing parameters of size, shape, weight, and the number of iterations) like a common averaging filter or a complicated filter shape in the Fourier domain. Its criterion for distinguishing noise depends simply on the amplitude of the SEM signal. When applied to several images with different characteristics, it is shown that the present method has a high performance with some original advantages.

No abstract

No abstract

An auto-tuning method for high-angle annular detector dark field scanning transmission electron microscopy (HAADF-STEM) is proposed which corrects the defocus to the optimum Scherzer focus and compensates the astigmatism. Because the method is based on the image contrast transfer function formulated for the HAADF-STEM, the defocus and the astigmatism are accurately measured from input of two different defocus images. The method is designed to work independent of object function in the linear imaging model by analysing the spectral ratio between two Fourier spectra of their images, which is useful for cases where the spectrum of object function is not uniformly spread out over the reciprocal space. The method was preliminarily tested in a Hitachi HD-2000 STEM, and successful results of the auto-tunings from the viewpoint of verification of the algorithm were obtained using general specimens of Au fine particles and a thin section of a semiconductor device.

The statistical method in texture image analysis was applied to area extraction of biological objects from thin section electron microscope images. Four standard estimators defined from the gray level co-occurrence matrix, called 'inverse difference moment,' 'angular second moment,' 'entropy' and 'contrast,' were especially examined using a test pattern consisting of 6 artificial textures and to practical biological thin section images. In the examination on the test pattern, among the estimators the 'contrast' discriminated all textures, but differences of some texture feature levels were small. To clearly discriminate textures, a modified estimator combining 'angular second moment' and 'contrast' was devised. As a result it discriminated all better than the 'contrast.' Electron microscope images used for the image processing are yeast morphological ones showing spherical autophagic bodies in the vacuole. Although the four standard estimators discriminated many organelles, they could not exactly extract images of the autophagic body. However, the modified estimator was able to extract all autophagic bodies from the vacuole image area except for minor points, some of which were not clearly detected by man observation. It as found out that the texture analysis-based method can be used to discriminate slight differences between image textures due to spatial staining granularity.