Abstract
A probabilistic framework using two random fields, DeTEC (Detection of Touching Elongated Cells) is proposed to detect cells in scanning electron microscopy images with inhomogeneous illumination. The first random field provides a binary segmentation of the image to superpixels that are candidates belonging to cells, and to superpixels that are part of the background, by imposing a prior on the smoothness of the texture features. The second random field selects the superpixels whose boundaries are more likely to form elongated cell walls by imposing a smoothness prior onto the orientations of the boundaries. The method is evaluated on a dataset of Clostridium difficile cell images and is compared to CellDetect.
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References
Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms. In: Proceedings of 18th ACM International Conference on Multimedia, Florence, Italy, pp. 1469–1472 (2010)
Kainz, P.: You should use regression to detect cells. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention, Munich, pp. 276–283 (2015)
Minaee, S., Fotouhi, M., Khalaj, B.: A geometric approach to fully automatic chromosome segmentation. In: Proceedings of IEEE Signal Processing in Medicine and Biology Symposium, Philadelphia, PA, pp. 1–6 (2014)
Wu, B., Nevatia, R.: Detection and segmentation of multiple, partially occluded objects by grouping, merging, assigning part detection responses. Int. J. Comput. Vision 82, 185–204 (2009)
Wayalun, P., Chomphuwiset, P., Laopracha, N., Wanchanthuek, P.: Images enhancement of G-band chromosome using histogram equalization, OTSU thresholding, morphological dilation and flood fill techniques. In: Proceedings 8th International Conference on Computing and Networking Technology, Gueongju, China, pp. 163–168 (2012)
Saiyod, S., Wayalun, P.: A hybrid technique for overlapped chromosome segmentation of G-band mataspread images automatic. In: Proceedings of 4th International Conference on Digital Information and Communication Technology and its Applications, Bangkok, Thailand, pp. 400–404 (2014)
Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in Neural Information Processing Systems, Vancouver, Canada, pp. 1324–1332 (2010)
Fiaschi, L., Koethe, U., Nair, R., Hamprecht, F.A.: Learning to count with regression forest and structured labels. In: Proceedings of 21st International Conference on Pattern Recognition, Tsukuba, Japan, pp. 2685–2688 (2012)
Foroughi, H., Ray, N., Zhang, H.: Robust people counting using sparse representation and random projection. Pattern Recogn. 48, 3038–3052 (2015)
Daněk, O., Matula, P., Ortiz-de Solórzano, C., Muñoz-Barrutia, A., Maška, M., Kozubek, M.: Segmentation of touching cell nuclei using a two-stage graph cut model. In: Proceedings of Scandinavian Conference on Image Analysis, Oslo, Norway, pp. 410–419 (2009)
Keuper, M., Schmidt, T., Rodriguez-Franco, M., Schamel, W., Brox, T., Burkhardt, H., Ronneberger, O.: Hierarchical markov random fields for mast cell segmentation in electron microscopic recordings. In: Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, pp. 973–978 (2011)
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect cells using non-overlapping extremal regions. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, Nice, France, pp. 348–356 (2012)
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect partially overlapping instances. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, pp. 3230–3237 (2013)
Santamaria-Pang, A., Rittscher, J., Gerdes, M., Padfield, D.: Cell segmentation and classification by hierarchical supervised shape ranking. In: Proceedings of IEEE International Symposium on Biomedical Imaging, Brooklyn, NY, pp. 1296–1299 (2015)
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Detecting overlapping instances in microscopy images using extremal region trees. Med. Image Anal. 27, 3–16 (2016)
Zhang, C., Yarkony, J., Hamprecht, F.A.: Cell detection and segmentation using correlation clustering. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention, Boston, MA, pp. 9–16 (2014)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Cambridge, MA, pp. 130–137 (1998)
Mori, G.: Guiding model search using segmentation. In: Proceedings of 10th IEEE International Conference on Computer Vision, Beijing, China, vol. 1, pp. 1417–1423 (2005)
Andres, B., Kappes, J.H., Beier, T., Kothe, U., Hamprecht, F.A.: Probabilistic image segmentation with closedness constraints. In: Proceedings of International Conference on Computer Vision, Barcelona, Spain, pp. 2611–2618 (2011)
Yarkony, J., Ihler, A., Fowlkes, C.C.: Fast planar correlation clustering for image segmentation. In: Proceedings of European Conference on Computer Vision, Florence, Italy, pp. 568–581 (2012)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22, 761–767 (2004)
Plissiti, M.E., Nikou, C.: Overlapping cell nuclei segmentation using a spatially adaptive active physical model. IEEE Trans. Image Process. 21, 4568–4580 (2012)
Karvelis, P., Likas, A., Fotiadis, D.I.: Identifying touching and overlapping chromosomes using the watershed transform and gradient paths. Pattern Recogn. Lett. 31, 2474–2488 (2010)
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This work was supported in part by the Texas Department of State Health Services (Grant #2015-046620) and by the Hugh Roy and Lillie Cranz Cullen Endowment Fund.
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Memariani, A., Nikou, C., Endres, B.T., Bassères, E., Garey, K.W., Kakadiaris, I.A. (2016). DeTEC: Detection of Touching Elongated Cells in SEM Images . In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_27
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DOI: https://doi.org/10.1007/978-3-319-50835-1_27
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