title: Image processing and classification algorithm for yeast cell morphology in a microfluidic chip.
abstract: The study of yeast cell morphology requires consistent identification of cell cycle phases based on cell bud size. A computer-based image processing algorithm is designed to automatically classify microscopic images of yeast cells in a microfluidic channel environment. The images were enhanced to reduce background noise, and a robust segmentation algorithm is developed to extract geometrical features including compactness, axis ratio, and bud size. The features are then used for classification, and the accuracy of various machine-learning classifiers is compared. The linear support vector machine, distance-based classification, and k-nearest-neighbor algorithm were the classifiers used in this experiment. The performance of the system under various illumination and focusing conditions were also tested. The results suggest it is possible to automatically classify yeast cells based on their morphological characteristics with noisy and low-contrast images.
authors: University of Waterloo, Department of Mechanical and Mechatronics Engineering, Waterloo, Ontario, N2L 3G1, Canada.
authors: Bo
authors: B
authors: Yang Yu
authors: Caglar
authors: C
authors: Elbuken
authors: Carolyn L
authors: CL
authors: Ren
authors: Jan P
authors: JP
authors: Huissoon
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002453:Cell Cycle; D048430:Cell Shape; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D046210:Microfluidic Analytical Techniques / Q000295:instrumentation* / Q000379:methods; D008853:Microscopy; D012441:Saccharomyces cerevisiae / Q000166:cytology*