Earticle

초록

영어

BilIN (Biliary INtraepithelial Neoplasm) is a precursor lesion of Intrahepatic CholangioCarcinoma (ICC). It is important to diagnose ICC and distinguish its stages for the prevention of ICC and proper treatment for a patient. BilIN is classified into BilIN-1, BilIN-2, and BilIN-3 by the morphological change and loss of polarity of epithelial cell and the structural abnormality of epithelium. This paper proposes two quantitative features based pathological knowledge for distinguishing stages of BilIN. The first feature is LumenBoundaryAbnomality (LBA) to measure the abnormal structure of epithelium; the second feature is NucleiPolarity for determine loss of polarity of epithelial cells. The experiment performed the stage classification of BilIN using following features; non-epithelial nuclei features, epithelial nuclei features, proposed features, non-epithelial nuclei and proposed features, epithelial nuclei and proposed features. The classification learning algorithm is used back-propagation Artificial Neural Networks. The classification result showed classification accuracies of 35% with non-epithelial features, 40% with epithelial features, 74% with proposed features, and 35% with non-epithelial and proposed features, and 46% with epithelial and proposed features.

목차

Abstract
 1. Introduction
 2. Pathological Characteristics of BilIN
 3. The Proposed Method
  3.1. Normal Lumen Boundary (NLB) Model and Lumen Boundary Abnormality
  3.2. Cytoplasm Length and Nuclei Cellular Polarity
 4. Experiment
  4.1. Experiment Design
  4.2. Results and Discussion
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• JaeWon Song [ Dept. of Computer Science & Information Technology, Inha University, Korea ]
• JuHong Lee [ Dept. of Computer Science & Information Technology, Inha University, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605