Investigating the relevance of graph cut parameter on interactive and automatic cell segmentation. (English) Zbl 1431.92095
Summary: Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation fields of application, such as the medical field. The graph cut energy function has a parameter that is tuned to ensure that the output is neither oversegmented (shrink bias) nor undersegmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation. Along this line of research, the graph cut parameter has been leveraged, while in some instances, it has been ignored. Therefore, in this work, the relevance of graph cut parameter on both interactive and automatic cell segmentation is investigated. Statistical analysis, based on F1 score, of three publicly available datasets of cells, suggests that the graph cut parameter plays a significant role in improving the segmentation accuracy of the interactive graph cut than the automatic graph cut.
MSC:
| 92C55 | Biomedical imaging and signal processing |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 05C90 | Applications of graph theory |
References:
| [1] | Massoudi, A.; Sowmya, A.; Mele, K.; Semenovich, D., Employing temporal information for cell segmentation using max-flow/min-cut in phase-contrast video microscopy, Proceedings of International Conference of the IEEE EMBS |
| [2] | Dai, S.; Lu, K.; Dong, J., Lung segmentation with improved graph cuts on chest CT images, Proceedings of 3rd IAPR Asian Conference on Pattern Recognition (ACPR) |
| [3] | Candemir, S.; Akgul, Y. S., Adaptive regularization parameter for graph cut segmentation, Proceedings of Conference on Image Analysis and Recognition (ICIAR) |
| [4] | Freedman, D.; Zhang, T., Interactive graph cut based segmentation with shape priors, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) |
| [5] | Vicente, S.; Kolmogorov, V.; Rother, C., Graph cut based image segmentation with connectivity priors, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
| [6] | Peng, B.; Veksler, O., Parameter selection for graph cut based image segmentation, Proceedings of British Machine Vision Conference (BMVC) |
| [7] | Kirmizigul, D.; Schlesinger, D., Incremental learning in the energy minimisation framework for interactive segmentation, Lecture Notes in Computer Science, 332, 323-332, (2010) · doi:10.1007/978-3-642-15986-2_33 |
| [8] | Wang, T.; Ji, Z.; Sun, Q.; Chen, Q.; Shoudong, H., Image segmentation based on weighting boundary information via graph cut, Journal of Visual Communication and Image Representation, 33, 10-19, (2015) · doi:10.1016/j.jvcir.2015.08.013 |
| [9] | Blake, A.; Rother, C.; Brown, M.; Perez, P.; Torr, P., Interactive image segmentation using an adaptive GMMRF model, Lecture Note in Computer Science, 3021, 428-441, (2004) · Zbl 1098.68730 · doi:10.1007/978-3-540-24670-1_33 |
| [10] | Szummer, M.; Kohli, P.; Hoiem, D., Learning CRFs using graph cuts, Lecture Notes in Computer Science, 5303, 582-595, (2008) · doi:10.1007/978-3-540-88688-4_43 |
| [11] | Nikolas, P. G.; Aggelos, K. K., Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation, IEEE Transactions on Image Processing, 1, 3, 322-336, (1992) · doi:10.1109/83.148606 |
| [12] | Thompson, A. M.; Brown, J. C.; Kay, J. W.; Titterington, D. M., A study of methods of choosing the smoothing parameter in image restoration by regularization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 4, 326-339, (1991) · doi:10.1109/34.88568 |
| [13] | Watzenig, D.; Brandstatter, B.; Holler, G., Adaptive regularization parameter adjustment for reconstruction problems, IEEE Transactions on Magnetics, 40, 2, 1116-1119, (2004) · doi:10.1109/tmag.2004.824557 |
| [14] | Hong, B.; Koo, J.; Dirks, H.; Burger, M., Adaptive regularization in convex composite optimization for variational imaging problems, Proceedings of German Conference on Pattern Recognition |
| [15] | Coelho, L. P.; Shariff, A.; Murphy, R., Nuclear segmentation in microscope cell images: a hand-segmented dataset and comparison of algorithms, Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI) |
| [16] | Al-Kofahi, Y.; Lassoued, W.; Lee, W.; Roysam, B., Improved automatic detection and segmentation of cell nuclei in histopathology images, IEEE Transactions on Biomedical Engineering, 57, 4, 841-852, (2010) · doi:10.1109/tbme.2009.2035102 |
| [17] | Dimopoulos, S.; Christian, E. M.; Fabian, R. Y.; Joerg, S. Y., Accurate cell segmentation in microscopy images using membrane patterns, Bioimage Informatics, 30, 18, 2644-2651, (2014) · doi:10.1093/bioinformatics/btu302 |
| [18] | Kechichian, R.; Gong, H.; Revenu, M.; Lezoray, O.; Desvignes, M., New data model for graph-cut segmentation: application to automatic melanoma delineation, Proceedings of IEEE International Conference on Image Processing (ICIP) |
| [19] | Boykov, Y.; Kolmogorov, V., An experimental comparison of min-cut/max flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 9, 1124-1137, (2004) · doi:10.1109/tpami.2004.60 |
| [20] | Ford, L.; Fulkerson, D. R., Flows in Networks, (1986), Princeton, NJ, USA: Princeton University Press, Princeton, NJ, USA · Zbl 0139.13701 |
| [21] | Boykov, Y.; Jolly, M.-P., Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images, Proceedings of IEEE International Conference on Computer Vision (ICCV) |
| [22] | Oyebode, K. O.; Tapamo, J., Adaptive parameter selection for graph cut-based segmentation on cell images, Image Analysis and Stereology, 35, 1, 29-37, (2016) · Zbl 1379.94009 · doi:10.5566/ias.1333 |
| [23] | Ljosa, V.; Sokolnicki, K. L.; Carpenter, A. E., Annotated high-throughput microscopy image sets for validation, Nature Methods, 10, 5, 6-37, (2012) · doi:10.1038/nmeth0513-445d |
| [24] | Gadde, R.; Yalamanchili, R., Tech Geek, (2011) |
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