Learning cellular texture features in microscopic cancer cell images for automated cell-detection

Autoren Tomas Kazmar
Matej Smid
Margit Fuchs
Birgit Luber
Julian Mattes
Editoren
TitelLearning cellular texture features in microscopic cancer cell images for automated cell-detection
BuchtitelProceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Typin Konferenzband
VerlagIEEE
AbteilungBDA
ISBN978-1-4244-4142-2
MonatAugust
Jahr2010
Seiten49-52
SCCH ID#1032
Abstract

In this paper we present a new approach for automated cell detection in single frames of 2D microscopic phase contrast images of cancer cells which is based on learning cellular texture features. The main challenge addressed in this paper is to deal with clusters of cells where each cell has a rather complex appearance composed of sub-regions with different texture features. Our approach works on two different levels of abstraction. First, we apply statistical learning to learn 6 different types of different local cellular texture features, classify each pixel according to them and we obtain an image partition composed of 6 different pixel categories. Based on this partitioned image we decide in a second step if pre-selected seeds belong to the same cell or not. Experimental results show the high accuracy of the proposed method and especially average precision above 95%.