Cell Spreading Analysis with Directed Edge Profile-Guided Level Set Active Contours
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Fuzzy Clustering and Active Contours for Histopathology Image Segmentation and Nuclei Detection
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Online phenotype discovery based on minimum classification error model
Pattern Recognition
Level Set Segmentation of Cellular Images Based on Topological Dependence
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Segmentation of Neural Stem/Progenitor Cells Nuclei within 3-D Neurospheres
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Computer Methods and Programs in Biomedicine
Fast globally optimal segmentation of cells in fluorescence microscopy images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Combining 2d and 3d features to classify protein mutants in hela cells
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Automatic assessment of leishmania infection indexes on in vitro macrophage cell cultures
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Automatic analysis of leishmania infected microscopy images via gaussian mixture models
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
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High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems.