Journal of KIISE:Computing Practices and Letters (한국정보과학회논문지:컴퓨팅의 실제 및 레터)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Robust Method for Automatic Segmentation and Recognition of Apoptosis Cell

Apoptosis 세포의 자동화된 분할 및 인식을 위한 강인한 방법

  • 류해릉 (조선대학교 정보통신공학과) ;
  • 신영숙 (조선대학교 정보통신공학과)
  • Published : 2009.06.15
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Abstract

In this paper we propose an image-based approach, which is different from the traditional flow cytometric method to detect shape of apoptosis cells. This method can overcome the defects of cytometry and give precise recognition of apoptosis cells. In this work K-means clustering was used to do the rough segmentation and an active contour model, called 'snake' was used to do the precise edge detection. And then some features were extracted including physical feature, shape descriptor and texture features of the apoptosis cells. Finally a Mahalanobis distance classifier classifies the segmentation images as apoptosis and non-apoptosis cell.

본 연구는 Apoptosis세포들의 형상을 검출하기 위하여 전통적인 세포측정법과는 다른 영상기반 접근법을 제안한다. 이 방법은 세포측정 법의 단점을 극복하고 Apoptosis 세포들을 정확하게 인식할 수 있다. 본 연구에서 K-means 군집화 방법이 Apoptosis 세포의 거시적인 분할을 행하는 데 사용되었으며, '스네이코'라고 불리는 액티브 윤곽선 모델이 정밀한 경계선 검출을 위해 사용되었다. 그리고 Apoptosis세포들의 물리적 특징, 형태적 특징 그리고 무늬특징들을 포함하는 몇가지 특징들이 추출되었다. 마지막으로 Mahalanobis 거리 분류기가 Apoptosis세포와 비Apoptosis 세포로서 분할영상들을 분류한다.

Keywords

References

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