title: A multi-stage random forest classifier for phase contrast cell segmentation.
abstract: We present a machine learning based approach to automatically detect and segment cells in phase contrast images. The proposed method consists of a multi-stage classification scheme based on random forest (RF) classifier. Both low level and mid level image features are used to determine meaningful cell regions. Pixel-wise RF classification is first carried out to categorize pixels into 4 classes (dark cell, bright cell, halo artifact, and background) and generate a probability map for cell regions. K-means clustering is then applied on the probability map to group similar pixels into candidate cell regions. Finally, cell validation is performed by another RF to verify the candidate cell regions. The proposed method has been tested on U2-OS human osteosarcoma phase contrast images. The experimental results show better performance of the proposed method with precision 92.96% and recall 96.63% compared to a state-of-the-art segmentation technique.
authors.forename: Ehab
authors.initials: E
authors.lastname: Essa
authors.forename: Xianghua
authors.initials: X
authors.lastname: Xie
authors.forename: Rachel J
authors.initials: RJ
authors.lastname: Errington
authors.forename: Nick
authors.initials: N
authors.lastname: White
mesh_terms: D000465:Algorithms; D016477:Artifacts; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008858:Microscopy, Phase-Contrast; D059629:Signal-To-Noise Ratio