Recursive water flow: A shape decomposition approach for cell clump splitting

We propose a hierarchical method for splitting cell clumps into individual cells which we call Recursive Water Flow (rwf). For the segmentation of cells in histological images we first apply foreground segmentation leading to connected regions of clumping cells. rwf defines one-dimensional cost-functions along paths on the skeleton of these regions. In particular, we collect split-relevant information along geodesic paths between skeleton end-points. This framework allows to exploit combined shape and intensity information for identifying optimal split positions and recursively decomposing the clumps into single distinct cells. Results show that our framework helps to improve cell segmentation performance in contrast to splitting based on local geometrical clues.

[1]  Hui Kong,et al.  Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting , 2011, IEEE Transactions on Medical Imaging.

[2]  Gyan Bhanot,et al.  Expectation–Maximization-Driven Geodesic Active Contour With Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology , 2010, IEEE Transactions on Biomedical Engineering.

[3]  Cigdem Demir,et al.  Attributed Relational Graphs for Cell Nucleus Segmentation in Fluorescence Microscopy Images , 2013, IEEE Transactions on Medical Imaging.

[4]  Hui Wang,et al.  Clump splitting via bottleneck detection and shape classification , 2012, Pattern Recognit..

[5]  Lin Yang,et al.  Automatic Image Analysis of Histopathology Specimens Using Concave Vertex Graph , 2008, MICCAI.

[6]  A. Madabhushi,et al.  Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.

[7]  Olli Yli-Harja,et al.  A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search , 2013, Pattern Recognit..

[8]  Xin Li,et al.  2D Shape Decomposition Based on Combined Skeleton-Boundary Features , 2008, ISVC.

[9]  A. Ruifrok,et al.  Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.

[10]  H. Irshad,et al.  Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential , 2014, IEEE Reviews in Biomedical Engineering.

[11]  X Xiao,et al.  Adaptive striping watershed segmentation method for processing microscopic images of overlapping irregular‐shaped and multicentre particles , 2015, Journal of microscopy.

[12]  Sim Heng Ong,et al.  A rule-based approach for robust clump splitting , 2006, Pattern Recognit..

[13]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Changming Sun,et al.  Clustered nuclei splitting via curvature information and gray‐scale distance transform , 2015, Journal of microscopy.