By Topic

On the accurate counting of tumor cells

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Bin Fang ; Singapore-MIT Alliance, Nat. Univ. of Singapore, Singapore ; W. Hsu ; Mong Li Lee

Quantitative analysis of tumor cells is fundamental to pathological studies. Current practices are mostly manual, time-consuming, and tedious, yielding subjective and imprecise results. To understand the behavior of tumor cells, it is critical to have an objective way to count these cells. In addition, these counts must be reproducible and independent of the person performing the count. In this work, we propose a two-stage tumor cell identification strategy. In the first stage, potential tumor cells are segmented automatically using local adaptive thresholding and dynamic water immersion techniques. Unfortunately, due to histological noise in the images, a large number of false identifications are obtained. To improve the accuracy of the identified tumor cells, a second stage of feature rules mining is initiated. Experiment results show that image processing techniques alone are unable to give accurate results for tumor cell counting. However, with the use of features rules, we are able to achieve an identification accuracy of 94.3%.

Published in:

IEEE Transactions on NanoBioscience  (Volume:2 ,  Issue: 2 )