Abstract
Connected filtering is a popular strategy that relies on tree-based image representations: for example, one can compute an attribute on each node of the tree and keep only the nodes for which the attribute is sufficiently strong. This operation can be seen as a thresholding of the tree, seen as a graph whose nodes are weighted by the attribute. Rather than being satisfied with a mere thresholding, we propose to expand on this idea, and to apply connected filters on this latest graph. Consequently, the filtering is done not in the space of the image, but on the space of shapes built from the image. Such a processing, that we called shape-based morphology [30], is a generalization of the existing tree-based connected operators. In this paper, two different applications are studied: in the first one, we apply our framework to blood vessels segmentation in retinal images. In the second one, we propose an extension of constrained connectivity. In both cases, quantitative evaluations demonstrate that shape-based filtering, a mere filtering step that we compare to more evolved processings, achieves state-of-the-art results.
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References
Al-Diri, B., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Transactions on Medical Imaging 28(9), 1488–1497 (2009)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. PAMI 33(5), 898–916 (2011)
Breen, E., Jones, R.: Attribute openings, thinnings, and granulometries. CVIU 64(3), 377–389 (1996)
Cao, F., Musé, P., Sur, F.: Extracting meaningful curves from images. JMIV 22, 159–181 (2005)
DRIVE: Digital Retinal Images for Vessel Extraction, http://www.isi.uu.nl/Research/Databases/DRIVE/
Felzenszwalb, P., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59(2), 167–181 (2004)
Guimarães, S.J.F., Cousty, J., Kenmochi, Y., Najman, L.: A hierarchical image segmentation algorithm based on an observation scale. In: Gimel’farb, G., Hancock, E., Imiya, A., Kuijper, A., Kudo, M., Omachi, S., Windeatt, T., Yamada, K. (eds.) SSPR & SPR 2012. LNCS, vol. 7626, pp. 116–125. Springer, Heidelberg (2012)
Hoover, A., Kouznetsova, V., Goldbaum, M.H.: Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging 19, 203–210 (2000)
Jiang, X., Mojon, D.: Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. PAMI 25(1), 131–137 (2003)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. IJCV 1, 321–331 (1987)
Levillain, R., Géraud, T., Najman, L.: Why and how to design a generic and efficient image processing framework: The case of the Milena library. In: Proc. of ICIP, pp. 1941–1944 (2010), http://olena.lrde.epita.fr
Martínez-Pérez, M.E., Hughes, A.D., Stanton, A.V., Thom, S.A., Bharath, A.A., Parker, K.H.: Retinal blood vessel segmentation by means of scale-space analysis and region growing. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 90–97. Springer, Heidelberg (1999)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, vol. 2, pp. 416–423 (July 2001)
Mendonça, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25(9), 1200–1213 (2006)
Monasse, P., Guichard, F.: Fast computation of a contrast-invariant image representation. IEEE Trans. on Image Processing 9(5), 860–872 (2000)
Najman, L.: On the equivalence between hierarchical segmentations and ultrametric watersheds. Journal of Mathematical Imaging and Vision 40, 231–247 (2011)
Najman, L., Cousty, J., Perret, B.: Playing with kruskal: algorithms for morphological trees in edge-weighted graphs. In: Luengo Hendriks, C.L., Borgefors, G., Strand, R. (eds.) ISMM 2013. LNCS, vol. 7883, pp. 135–146. Springer, Heidelberg (2013)
Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. PAMI 18(12), 1163–1173 (1996)
Niemeijer, M., Staal, J.J., van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Fitzpatrick, J.M., Sonka, M. (eds.) SPIE Medical Imaging, vol. 5370, pp. 648–656. SPIE (2004)
Ouzounis, G., Soille, P.: Pattern spectra from partition pyramids and hierarchies. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 108–119. Springer, Heidelberg (2011)
Salembier, P., Serra, J.: Flat zones filtering, connected operators and filters by reconstruction. IEEE Trans. on Image Processing 3(8), 1153–1160 (1995)
Salembier, P., Wilkinson, M.: Connected operators. IEEE Signal Processing Mag. 26(6), 136–157 (2009)
Serra, J.: Image Analysis and Mathematical Morphology, vol. 1. Academic Press, New York (1982)
Soille, P.: Constrained connectivity for hierarchical image decomposition and simplification. PAMI 30(7), 1132–1145 (2008)
Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23(4), 501–509 (2004)
STARE: STructured Analysis of the Retina, http://www.ces.clemson.edu/~ahoover/stare/
Urbach, E.R., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. PAMI 29(2), 272–285 (2007)
Vachier, C., Meyer, F.: Extinction values: A new measurement of persistence. In: IEEE Workshop on Non Linear Signal/Image Processing, pp. 254–257 (1995)
Xu, Y., Géraud, T., Najman, L.: Context-based energy estimator: Application to object segmentation on the tree of shapes. In: ICIP, pp. 1577–1580. IEEE (2012)
Xu, Y., Géraud, T., Najman, L.: Morphological Filtering in Shape Spaces: Applications using Tree-Based Image Representations. In: Proc. of ICPR, pp. 485–488 (2012)
Zana, F., Klein, J.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. ITIP 10(7), 1010–1019 (2001)
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Xu, Y., Géraud, T., Najman, L. (2013). Two Applications of Shape-Based Morphology: Blood Vessels Segmentation and a Generalization of Constrained Connectivity. In: Hendriks, C.L.L., Borgefors, G., Strand, R. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2013. Lecture Notes in Computer Science, vol 7883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38294-9_33
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DOI: https://doi.org/10.1007/978-3-642-38294-9_33
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