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Background: Vulvovaginal candidiasis (VVC) was a common infection associated with lifelong harassment of woman's social and sexual life. The purpose of this study was to describe the species distribution and in vitro antifungal susceptibility of Candida species (Candida spp.) isolated from patients with VVC over 8 years. Methods: Species which isolated from patients with VVC in Peking University First Hospital were identified using chromogenic culture media. Susceptibility to common antifungal agents was determined using agar diffusion method based on CLSI M44-A2 document. SPSS software (version 14.0, Inc., Chicago, IL, USA) was used for statistical analysis, involving statistical description and Chi-square test. Results: The most common strains were Candida (C.) albicans, 80.5% (n = 1775) followed by C. glabrata, 18.1% (n = 400). Nystatin exhibited excellent activity against all species (

The current paper presents a robust image salient region extraction and matching algorithm based on the maximally stable extremal regions in the difference of Gaussian scale space (DoGSS-MSERs) combined with scale-invariant feature transform (SIFT) algorithm and the maximally stable extremal regions (MSERs) algorithm. First, the difference of Gaussian scale space (DoGSS) is constructed using image scale-space theory. The maximally stable extremal regions are then calculated and the stable component is extracted with blur-invariant and scale-invariant property using the stable method in the DoGSS. Finally, the regions are described with a novel region descriptor, thereby achieving matching. The experiments show that the feature regions extracted in the current paper inherit the good properties of SIFT and MSERs (scale-invariant and affine-invariant) and are more stable and more accurate for matching.

Recently, Scale Invariant Feature Transform (SIFT) algorithm is widely used in feature extraction and image matching. However, it has some defects, such as large volume of computational data and low efficiency of image matching. To address these defects, adaptive feature extraction and image matching based on Haar Wavelet Transform and SIFT (AHWT-SIFT) is proposed in this paper. In view of the characteristics of Haar wavelet, the low-frequency components of image can be decomposed adaptively by DWT, which represents the main features of the image and avoids the high-frequency of instability redundant information. Then SIFT is applied in these low-frequency components to extract the feature points. Furthermore, nearest neighbor algorithm is utilized for image matching. The experimental results have shown that the proposed scheme not only retains the general characteristics of SIFT, but the speed and accuracy of feature points matching have been greatly improved.

The denoising principal of the anisotropic diffusion equation is studied. Adaptive filtering of image is realized by combining the improved image structural similarity algorithm and the anisotropic diffusion equation. This algorithm is applied to medical image segmentation. Experimental results show that the improved algorithm has good robustness and advantages in the application of adaptive medical image filtering and segmentation.