TY - GEN
T1 - Segmentation of drosophila RNAi fluorescence images using level sets
AU - Xiong, Guanglei
AU - Zhou, Xiaobo
AU - Ji, Liang
AU - Bradley, Pamela
AU - Perrimon, Norbert
AU - Wong, Stephen
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - Deformable model
KW - Geodesic active contour
KW - Geometric active contour
KW - Image segmentation
KW - Level sets
UR - http://www.scopus.com/inward/record.url?scp=77951117024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951117024&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2006.312365
DO - 10.1109/ICIP.2006.312365
M3 - Conference contribution
AN - SCOPUS:77951117024
SN - 1424404819
SN - 9781424404810
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 73
EP - 76
BT - 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
T2 - 2006 IEEE International Conference on Image Processing, ICIP 2006
Y2 - 8 October 2006 through 11 October 2006
ER -