• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.94-99
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• 2008

Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the region of nucleus is extracted from an image of uterine cervical cytodiagnosis using the HSI model. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy C-means clustering algorithm is employed to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.519-524
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• 2006

Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the region of nucleus is extracted from an image of uterine cervical cytodiagnosis using the fuzzy grey morphology operation. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The enhanced fuzzy ART algorithm is used to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1992-1998
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• 2007

It is important to obtain conn cytodiagnosis to classify background, cytoplasm, and nucleus from the diagnostic image. This study mose an algorithm that detects and classifies carcinoma cells of the uterine cervix in Pap smear using features of cervical cancer. It applies Median filter and Gaussian filter to get noise-removed nucleus area and also applies Kapur method in binarization of the resultant image. We apply 8-directional contour tracking algorithm and stretching technique to identify and revise clustered cells that often hinder to obtain correct analysis. The resulted nucleus area has distinguishable features such as cell size, integration rate, and directional coefficient from normal cells so that we can detect and classify carcinoma cells successfully. The experiment results show that the performance of the algorithm is competitive with human expert.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1699-1704
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• 2009

If detected early enough, cervical cantor may have a good survival rate due to its preneoplastic state. However, the process is so time consuming that a medical expert can handle only a small amount of such examinations. In this paper, we propose a new nucleus extraction algorithm for uterine cervical pap smears in order to mitigate such burdens of medical experts. In the preneoplastic state cytodiagnosis images, it is important to differentiate three main areas - background, cytoplasm and nucleus. Thus, we apply lighting compensation and $3{\times}3$ mask of B channel in order to restore damaged image and remove noises respectively. The cell object is obtained from those clean binarized images with Grossfire algorithm. When there are clusters of cells, the target nucleus can be obtained with repetitive binarization of R channel brightness. In our experiment of using uterine cervical pap smears of 400 magnifications that is common in the diagnostic cytology, our method is able to extract 40 nucleus out of 45 population successfully.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.179-187
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• 2008

In this paper, we apply a set of algorithms to classily normal and cancer nucleus from uterine cervical pap-smear images. First, we use lightening compensation algorithm to restore color images that have defamation through the process of obtaining $1{\times}400$ microscope magnification. Then, we remove the background from images with the histogram distributions of RGB regions. We extract nucleus areas from candidates by applying histogram brightness, Kapur method, and our own 8-direction contour tracing algorithm. Various binarization, cumulative entropy, masking algorithms are used in that process. Then, we are able to recognize normal and cancer nucleus from those areas by using three morphological features - directional information, the size of nucleus, and area ratio - with fuzzy membership functions and deciding rules we devised. The experimental result shows our method has low false recognition rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1153-1158
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• 2006

The classification of the background and cell areas is very important research area because of the ambiguous boundary. In this paper, the region of cell is extracted from an image of uterine cervical cytodiagnosis using the region growing method that increases the region of interest based on similarity between pixels. Segmented image from background and cell areas is binarized using a threshold value. And then 8-directional tracking algorithm for contour lines is applied to extract the cell area. First, the extracted nucleus is transformed to RGB color that is the original image. Second, the K-means clustering algorithm is employed to classify RGB pixels to the R, G, and B channels, respectively. Third, the Hue information of nucleus is extracted from the HSI models that is the transformation of the clustering values in R, G, and B channels. The backpropagation algorithm is employed to classify and identify the normal or abnormal nucleus.