Level Set Segmentation of Cellular Images Based on Topological Dependence.
ABSTRACT Segmentation of cellular images presents a challenging task for computer vision, especially when the cells of irregular shapes
clump together. Level set methods can segment cells with irregular shapes when signal-to-noise ratio is low, however they
could not effectively segment cells that are clumping together. We perform topological analysis on the zero level sets to
enable effective segmentation of clumped cells. Geometrical shapes and intensities are important information for segmentation
of cells. We assimilated them in our approach and hence we are able to gain from the advantages of level sets while circumventing
its shortcoming. Validation on a data set of 4916 neural cells shows that our method is 93.3 ±0.6% accurate.
SourceAvailable from: Simon Liao[Show abstract] [Hide abstract]
ABSTRACT: In this research, we have concentrated on trajectory extraction based on image segmentation and data association in order to provide an economic and complete solution for rapid microfluidic cell migration experiments. We applied re-gion scalable active contour model to segment the individual cells and then employed the ellipse fitting technique to process touching cells. Subsequently, we have also introduced a topology based technique to associate the cells between consecutive frames. This scheme achieves satisfactory segmentation and tracking results on the datasets acquired by our microfluidic platform.Engineering 01/2013; 5(10):226-232. DOI:10.4236/eng.2013.510B047
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ABSTRACT: Cell detection and segmentation in microscopy images are essential for automated cell behavior analysis including cell shape analysis and cell tracking. Robust cell detection in high-density and low-contrast images is still challenging since cells often touch and partially overlap, forming a cell cluster with blurry intercellular boundaries. In such cases, current methods tend to detect multiple cells as a cluster. If the control parameters are adjusted to separate the touching cells, other problems often occur: a single cell may be segmented into several regions, and cells in low-intensity regions may not be detected. To solve these problems, we first detect redundant candidate regions, which include many false positives but in turn very few false negatives, by allowing candidate regions to overlap with each other. Next, the score for how likely the candidate region contains the main part of a single cell is computed for each cell candidate using supervised learning. Then we select an optimal set of cell regions from the redundant regions under non-overlapping constraints, where each selected region looks like a single cell and the selected regions do not overlap. We formulate this problem of optimal region selection as a binary linear programming problem under non-overlapping constraints. This binary linear programming maximizes the sum of the weighted scores of the selected regions, where a region's score represents how likely it is that the region corresponds to a single cell as determined by using cell appearance features.We demonstrated the effectiveness of our method for several types of cells in microscopy images. Our method performed better than five representative methods, achieving an F-measure of over 0.9 for all data sets. Experimental application of the proposed method to 3D images demonstrated that also works well for 3D cell detection.IEEE Transactions on Medical Imaging 01/2015; DOI:10.1109/TMI.2015.2391095 · 3.80 Impact Factor
Conference Paper: CRF-driven multi-compartment geometric model[Show abstract] [Hide abstract]
ABSTRACT: We present a hybrid framework for segmenting structures consisting of distinct inter-connected parts. We combine the robustness of Conditional Random Fields in appearance classification with the shape constraints of geometric models and the relative part topology constraints that multi-compartment modeling provides. We demonstrate the performance of our method in cell segmentation from fluorescent microscopic images, where the compartments of interest are the cell nucleus, cytoplasm, and the negative hypothesis (background). We compare our results with the most relevant model- and appearance-based segmentation methods.Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on; 01/2013