No abstract available.
Antibodies* ; Coxiella burnetii* ; Coxiella* ; Korea* ; Prevalence*

We can assume a histogram of uterus neck cytoplasm image has three peaks which is consisted of nucleus, cytoplasm and background. We proposed a method for extraction of adaptive thresholding value that is suitable to each various intensity distribution. First, the adaptive thresholding is divided into thresholding of cytoplasm area and nucleus area. The thresholding of cytoplasm area, utilizing whole histogram, extracts thresholding value by using histogram standard deviation which of recognized as a background for each histogram distribution. The classification of nucleus is various in size and has difficulty in precise image extraction because of great difference in intensity in a cell image when using whole histogram distribution. So we suggests 'local thresholding' . In the first place, by using optimal thresholding, we can find nucleus seed area as a mask, and get adaptive thresholding value correct to each histogram distribution by obtaining histogram for each mask. Comparing to other methods that use the same thresholding value for one image, this can effectively extract nucleus and cyotoplasm. Because 'local thresholding' decides most suitable thresholding value for each distribution and characteristics.
Classification ; Cytoplasm ; Masks ; Neck ; Uterus

The region segmentation of the pap-smear image is known to be a difficult and important part in the automatic image recognition system. Both the pixel based methods(thresholding) and the region based methods(split and merge, region growing and edge detection) are widely used for segmentation of the nucleus, cytoplasm and background in the pap-smear images. The pixel based methods are relatively fast, but not accurate, while the region based methods are accurate, but slow. This paper proposes a multistage segmentation strategy which uses thresholding and incremental color clustering methods to reduce computation time while not sacrificing accuracy. Proposed method consists of three stages. The first stage uses global thresholding method to search nucleus blob position, and the second stage employs incremental color clustering with color information. The final stage segments unsuitable nuclei using thresholding method after calculating suitability for each extracted nucleus blob. The proposed segmentation method is tested under various error measures. The experimental results showed that each stage of the proposed method reduced specific error measures: The second stage reduced false negative error and the third stage false positive error.
Cytoplasm