Quantitative analysis of vascular structures using image processing

Yi Chun Lin, Pei Ju Chiang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Vascularization, the growth of new blood vessels form the existing vessels, implies many pathological processes and needs to be reasonably quantified. However, most vascular analysis is done manually. This is a tedious and laborious work without consistence. In this paper, we will demonstrate the feasibility of automatic quantification of vascular structures by image processing. To quantify the formation of blood vessels, the following parameters are measured automatically from the processed images: total length of tubes, total number of loops, total tube area, total confluent areas, the number of confluent area and the number of nodal structures. In addition, the obtained number of loops and tube length are compared with the values measured manually. The experimental results show that the highest Hit Rate is 90.3% and the highest False Alarm Rate is 6.675%.

原文English
主出版物標題Proceedings - 2012 4th International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2012
頁面278-283
頁數6
DOIs
出版狀態Published - 2012 十月 17
事件2012 4th International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2012 - Phuket, Thailand
持續時間: 2012 七月 242012 七月 26

出版系列

名字Proceedings - 2012 4th International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2012

Conference

Conference2012 4th International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2012
國家/地區Thailand
城市Phuket
期間12-07-2412-07-26

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦網路與通信

指紋

深入研究「Quantitative analysis of vascular structures using image processing」主題。共同形成了獨特的指紋。

引用此