ADAPTIVE PARAMETER SELECTION FOR GRAPH CUT-BASED SEGMENTATION ON CELL IMAGES
DOI:
https://doi.org/10.5566/ias.1333Keywords:
cell segmentation, energy function, graph cut, parameter selectionAbstract
Graph cut segmentation approach provides a platform for segmenting images in a globally optimised fashion. The graph cut energy function includes a parameter that adjusts its data term and smoothness term relative to each other. However, one of the key challenges in graph cut segmentation is finding a suitable parameter value that suits a given segmentation. A suitable parameter value is desirable in order to avoid image oversegmentation or under-segmentation. To address the problem of trial and error in manual parameter selection, we propose an intuitive and adaptive parameter selection for cell segmentation using graph cut. The greyscale image of the cell is logarithmically transformed to shrink the dynamic range of foreground pixels in order to extract the boundaries of cells. The extracted cell boundary dynamically adjusts and contextualises the parameter value of the graph cut, countering its shrink bias. Experiments suggest that the proposed model outperforms previous cell segmentation approaches.
References
Al-Kofahi Y, Lassoued W, Lee W, Roysam B (2010).
Improved automatic detection and segmentation of
cell nuclei in histopathology images. IEEE T Bio
Med Eng 57:841–52.
Bengtsson E, Lindblad J (1999). Fifty years of
attempts to automate screening for cervical cancer.
JAMIT 17:203–10.
Boykov Y, Jolly MP (2001). Interactive graph cuts
for optimal boundary and region segmentation of
objects in n-d images. In: Proc 8th IEEE Int Conf
on Computer Vision.
Boykov Y, Kolmogorov V (2004). An experimental
comparison of min-cut/max- flow algorithms for
energy minimization in vision. IEEE T Pattern
Anal 26:1124 – 1137.
Candemir S, Akgul YS (2010). Adaptive
regularization parameter for graph cut
segmentation. In: Proc 7th Int Conf on Image
Analysis and Recognition (ICIAR).
Canny J (1986). A computational approach to edge
detection. IEEE T Pattern Anal PAMI-8:679–98.
Chen C, Li H, Zhou X, Wong ST (2008). Constraint
factor graph cut-based active contour method for
automated cellular image segmentation in rnai
screening. Journal of Microsc 230:177–91.
Chen C, Wang E, Ozolek J, Rohde GK (2013). A
flexible and robust approach for segmenting
cell nuclei from 2d microscopy images using
supervised learning and template matching.
Cytometry A 83:495–507.
Coelho LP, Shariff A, Murphy R (2009). Nuclear
segmentation in microscope cell images: A handsegmented
dataset and comparison of algorithms.
In: Proc IEEE Int Symp Biomed Imaging.
Danˇek O, Matula P, Ortiz-De-Sol´orzano C, Mu˜noz
Barrutia A, Maˇska M, Kozubek M (2009).
Segmentation of touching cell nuclei using a twostage
graph cut model. In: Proc of the 16th
Scandinavian Conf on Image Analysis (SCIA).
Berlin, Heidelberg: Springer-Verlag.
Freedman D, Zhang T (2005). Interactive graph cut
based segmentation with shape priors. In: Proc of
IEEE Int Conf on Computer Vistion and Pattern
Recognition Conference (CVPR).
Greig D, Porteous B, Seheult A (1989). Exact
maximum a posteriori estimation for binary
images. J R Stat Soc Series B Stat Methodol
:271–9.
Lang X, Zhu F, Hao Y,Wu Q (2009). Automatic image
segmentation incorporating shape priors via graph
cuts. In: Proc of the Int Conf on Infomation and
Automation (ICIA).
Lin X, Adiga U, Olson K, Guzowski J, Barnes
C, Roysam B (2003). A hybrid 3d watershed
algorithm incorporating gradient cues and object
models for automatic segmentation of nuclei in
confocal image stacks. Cytometry Part A 56A:23–
Peng B, Veksler O (2008). Parameter selection for
graph cut based image segmentation. In: Proc of
the British Machine Vision Conf (BMVC).
Qi J (2014). Dense nuclei segmentation based on graph
cut and convexity concavity analysis. Journal of
Microscopy 253:42–53.
Roy P, Biswas P (2015). A parallel legion algorithm
and cell-based architecture for real time split and
merge video segmentation. Journal of Real Time
Image Processing :1–25.
Vicente S, Kolmogorov V, Rother C (2008). Graph
cut based image segmentation with connectivity
priors. In: Proc of IEEE Int Conf on Computer
Vistion and Pattern Recognition (CVPR).
Wang H, Zhang H (2010). Adaptive shape prior in
graph cut image segmentation. In: Proc of 17th
IEEE Int Conf on Image Processing (ICIP).