Abstract
Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.
Highlights
Cell phase detection is important in stem cell researches and drug planning
The segmented regions obtained by each method can be categorized into 4 types of semantics, that is, True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN)
We proposed a high-accuracy and high-stability cell region extraction method of unstained cells in the microscope image
Summary
Cell phase detection is important in stem cell researches and drug planning. This process is time consuming and sometimes subjective. High throughput cell segmentation is significant for assessing cell phases. The approach of automated cell region extraction, often uses a property that there is a large intensity difference between cell regions and background ones, and separates them by a global thresholding [1], or segments them by using Otsu’s method [2] [3]. The data used in this research has extremely slight intensity difference between the cell areas and the background, so those methods are not effective
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