Abstract
The morphological analysis of muscle biopsy helps in diagnosis of neuromuscular disease. The presence, extent, size, shape, and other morphological appearance of the muscle fibres are important indicators for presence or severity of disease. However, estimation of these parameters by simple visual inspection is inaccurate and subjective and manual delineation of individual muscle fibres from muscle biopsy images is time-consuming and tedious. In this study, two automatic segmentation methods are proposed. Both methods operate on fluorescence microscopy images. The first uses a level set framework and the second one a marker-driven watershed transform. In a first stage, mathematical morphology is used to detect the presence of muscle fibres. The result of this step provides requirements for both segmentation methods (initial contour and markers). Experimental results demonstrate that segmentation of watershed detects fibres contours more accurately and with a lower computational cost.
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References
Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological Image Analysis: A Review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)
Dubowitz, V., Sewry, C.A.: Muscle biopsy: a practical approach, 3rd edn. Eselvier (2007)
Castleman, K.R., Chui, L.A., Martin, T.P., Edgerton, V.R.: Quatitative muscle biopsy analysis. Monogr. Clin. Cytol. 9, 101–116 (1984)
Plissiti, M.E., Nikou, C., Charchanti, A.: Watershed-based segmentation of cell nuclei boundaries in Pap smear images. ITAB, art. no. 5687745 (2010)
Li, S., Wu, L., Sun, Y.: Cell image segmentation based on an improved watershed transformation. In: CASoN 2010 , art. no. 5636803, pp. 93–96 (2010)
Harandi, N.M., Sadri, S., Moghaddam, N.A., Amirfattahi, R.: An automated method for segmentation of epithelial cervical cells in images of ThinPrep. J. Med. Syst. Journal 34(6), 1043–1058 (2010)
Bergmeir, C., Garcá Silvente, M., Esquivias López-Cuervo, J., Bentez, J.M.: Segmentation of cervical cell images using mean-shift filtering and morphological operators. In: Proceedings of SPIE, vol. 7623, art. no. 76234C (2010)
Todman, A.G., Claridge, E.: Low-level grouping mechanisms for contour completion. Information Sciences 125(1-4), 19–35 (2000)
Sertel, O., Dogdas, B., Chiu, C.S., Gurcan, M.N.: Microscopic image analysis for quantitative characterization of muscle fiber type composition. Comput. Med. Imag. Grap. 35(7-8), 616–628 (2011)
Kim, Y.J., Brox, T., Feiden, W., Weickert, J.: Fully automated segmentation and morphometrical analysis of muscle fiber images. Cytometry 71(1), 8–15 (2007)
Karen, P., Števanec, M., Smerdu, V., Cvetko, E., Kubnov, L., Eren, I.: Software for muscle fibre type classification and analysis. Eur. J. Histochem. 53(2), 87–95 (2009)
Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (1999)
Osher, S., Sethian, J.A.: Fronts propagating with curvaturedependent speed algorithms based on hamilton-jacobi formulations. J. Comput. Phys. 79, 12–49 (1998)
Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: a new variational formulation. In: Proc. CVPR IEEE (2005)
Meyer, F., Beucher, S.: Morphological segmentation. JVCIR 1(1), 21–46 (1990)
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Sáez, A., Montero-Sánchez, A., Escudero, L.M., Acha, B., Serrano, C. (2012). Segmentation of Muscle Fibres in Fluorescence Microscopy Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_55
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DOI: https://doi.org/10.1007/978-3-642-31298-4_55
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