ICIP 2006, Atlanta, GA

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Paper Detail

Session:Biomedical Image Segmentation
Time:Monday, October 9, 10:20 - 10:40
Presentation: Lecture
Topic: Biomedical Imaging: Biomedical image segmentation and quantitative analysis
Authors: Guanglei Xiong; Tsinghua University 
 Xiaobo Zhou; Harvard Medical School 
 Liang Ji; Tsinghua University 
 Pamela Bradley; Harvard Medical School 
 Norbert Perrimon; Harvard Medical School 
 Stephen T. C. Wong; Harvard Medical School 
Abstract: Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Robust automated segmentation of the large volumes of output images generated from image-based screening is much needed for data analyses. In this paper, we propose a new automated segmentation technique to fill the void. The technique consists of two steps: nuclei and cytoplasm segmentation. In the former step, nuclei are extracted, labeled and used as starting points for the latter. A new force obtained from rough segmentation is introduced into the classical level set curve evolution to improve the performance for odd shapes, such as spiky or ruffly cells. A scheme of preventing curves from crossing is proposed to treat the difficulty of segmenting touching cells. We apply it to three types of drosophila cells in RNAi fluorescence images. In all cases, greater than 92% accuracy is obtained.