Nucleus Segmentation and Recognition of Uterine Cervical Pop-Smears using Region Growing Technique and Backpropagation Algorithm

영역 확장 기법과 오류 역전파 알고리즘을 이용한 자궁경부 세포진 영역 분할 및 인식

  • Published : 2006.06.01

Abstract

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.

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