A Multi-scale Method for Cell Segmentation in Fluorescence Microscopy Images

Accurate segmentation of cells in fluorescent microscopy images plays a key role in high-throughput applications such as the quantification of protein expression and the study of cell function. Existing cell segmentation methods have drawbacks in terms of inaccurate location of segmentation boundary, misidentification, and inaccurate segmentation of overlapping cells. To address these issues, a novel multi-scale method for cell segmentation in fluorescence microscopy images (MMCS) is proposed in this paper. Our motivation to adopt multi-scale image analysis in the cell segmentation task originates from the basic observation that cells on fluorescence microscope images are often composed of different structures at different scales. In our proposed MMCS, three scales are exploited. At the high scale, noise effects are sufficiently suppressed, and the cell contour is fully smoothed. Then, scale fusion is further performed, that is, the cell contours obtained by segmentation at high, medium, and low scales are averaged, to improve the location accuracy of contour segmentation. To solve the problems of misidentification and cell overlapping, an improved Bradley technique with constraints based on shape and intensity features and region-based fitting of overlapping ellipses technique are also developed and embedded in our multi-scale approach for extracting cell contours at each single scale. The experimental results obtained on a large number of fluorescence microscope images from two data sets show that the proposed MMCS can outperform state-of-the-art methods by a large margin.