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http://dx.doi.org/10.15701/kcgs.2016.22.3.21

Dual Dictionary Learning for Cell Segmentation in Bright-field Microscopy Images  

Lee, Gyuhyun (Ulsan National Institute of Science and Technology)
Quan, Tran Minh (Ulsan National Institute of Science and Technology)
Jeong, Won-Ki (Ulsan National Institute of Science and Technology)
Publication Information
Journal of the Korea Computer Graphics Society / v.22, no.3, 2016 , pp. 21-29 More about this Journal
Abstract
Cell segmentation is an important but time-consuming and laborious task in biological image analysis. An automated, robust, and fast method is required to overcome such burdensome processes. These needs are, however, challenging due to various cell shapes, intensity, and incomplete boundaries. A precise cell segmentation will allow to making a pathological diagnosis of tissue samples. A vast body of literature exists on cell segmentation in microscopy images [1]. The majority of existing work is based on input images and predefined feature models only - for example, using a deformable model to extract edge boundaries in the image. Only a handful of recent methods employ data-driven approaches, such as supervised learning. In this paper, we propose a novel data-driven cell segmentation algorithm for bright-field microscopy images. The proposed method minimizes an energy formula defined by two dictionaries - one is for input images and the other is for their manual segmentation results - and a common sparse code, which aims to find the pixel-level classification by deploying the learned dictionaries on new images. In contrast to deformable models, we do not need to know a prior knowledge of objects. We also employed convolutional sparse coding and Alternating Direction of Multiplier Method (ADMM) for fast dictionary learning and energy minimization. Unlike an existing method [1], our method trains both dictionaries concurrently, and is implemented using the GPU device for faster performance.
Keywords
Dictionary Learning; Image; processing; Cell segmentation; Bright-field image;
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1 H. Irshad, A. Veillard, L. Roux, and D. Racoceanu, "Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential," Biomedical Engineering, IEEE Reviews in, vol. 7, pp. 97-114, 2014.   DOI
2 J. A. Sethian, Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. Cambridge university press, 1999, vol. 3.
3 T. F. Chan and L. A. Vese, "Active contours without edges," Image processing, IEEE transactions on, vol. 10, no. 2, pp. 266-277, 2001.   DOI
4 S. Arslan, T. Ersahin, R. Cetin-Atalay, and C. Gunduz-Demir, "Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images," Medical Imaging, IEEE Transactions on, vol. 32, no. 6, pp. 1121-1131, 2013.   DOI
5 S. Osher and R. Fedkiw, Level set methods and dynamic implicit surfaces. Springer Science & Business Media, 2006, vol. 153.
6 L. Rueda Villegas, R. Alis, M. Lepilliez, and S. Tanguy, "A ghost fluid/level set method for boiling flows and liquid evaporation: Application to the leidenfrost effect," Journal of Computational Physics, vol. 316, pp. 789-813, 2016.   DOI
7 A. Gharipour and A. W.-C. Liew, "Segmentation of cell nuclei in fluorescence microscopy images: An integrated framework using level set segmentation and touching-cell splitting," Pattern Recognition, vol. 58, pp. 1-11, 2016.   DOI
8 Z. Lu, G. Carneiro, and A. P. Bradley, "An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells," Image Processing, IEEE Transactions on, vol. 24, no. 4, pp. 1261-1272, 2015.   DOI
9 X. Qi, F. Xing, D. J. Foran, and L. Yang, "Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set," Biomedical Engineering, IEEE Transactions on, vol. 59, no. 3, pp. 754-765, 2012.   DOI
10 F. Xing and L. Yang, "Fast cell segmentation using scalable sparse manifold learning and affine transform-approximated active contour," in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Springer, 2015, pp. 332-339.
11 M. Aharon, M. Elad, and A. Bruckstein, "K-svd: An algorithm for designing of overcomplete dictionaries for sparse representation technion-israel inst. of technology, 2005," Tech. Ref.
12 H. Bristow, A. Eriksson, and S. Lucey, "Fast convolutional sparse coding," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 391-398.
13 D. L. Donoho, "Compressed sensing," Information Theory, IEEE Transactions on, vol. 52, no. 4, pp. 1289-1306, 2006.   DOI
14 M. Elad and M. Aharon, "Image denoising via learned dictionaries and sparse representation," in Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, vol. 1. IEEE, 2006, pp. 895-900.
15 M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, "Deconvolutional networks," in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010, pp. 2528-2535.
16 B. Wohlberg, "Efficient convolutional sparse coding," in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014, pp. 7173-7177.
17 B.Wohlberg, "Efficient algorithms for convolutional sparse representations," Image Processing, IEEE Transactions on, vol. 25, no. 1, pp. 301-315, 2016.   DOI
18 F. Heide, W. Heidrich, and G. Wetzstein, "Fast and flexible convolutional sparse coding," in Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, 2015, pp. 5135-5143.
19 S. Bahrampour, N. M. Nasrabadi, A. Ray, and W. K. Jenkins, "Multimodal task-driven dictionary learning for image classification," Image Processing, IEEE Transactions on, vol. 25, no. 1, pp. 24-38, 2016.   DOI
20 R. Annunziata and E. Trucco, "Accelerating convolutional sparse coding for curvilinear structures segmentation by refining scird-ts filter banks," 2016.
21 T. Tong, R. Wolz, P. Coupe, J. V. Hajnal, D. Rueckert, A. D. N. Initiative, et al., "Segmentation of mr images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling," NeuroImage, vol. 76, pp. 11-23, 2013.   DOI
22 Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, "Caffe: Convolutional architecture for fast feature embedding," arXiv preprint arXiv:1408.5093, 2014.
23 A. Cardona, S. Saalfeld, S. Preibisch, B. Schmid, A. Cheng, J. Pulokas, P. Tomancak, and V. Hartenstein, "An integrated micro- and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy," PLoS Biology, vol. 8, no. 10, p. e1000502, oct 2010. [Online]. Available: http://dx.doi.org/10.1371/journal.pbio.1000502   DOI