Abstract
Cell image segmentation becomes important and yet difficult task in quantitative cytopathology. The main objective is to develop an algorithm to segment and calculate the amount of neutrophils using morphological operators. The current work focuses on extraction of neutrophils from the peripheral blood smear was taken and it’s stained using Leishman stain to obtain differential leukocyte count. The particle analysis is done by extorting the edges to isolate the appropriate elements from the surrounding image after suitable thresholding technique. The preliminary results in this study reveals the potentials of using particle analysis method in cell image segmentation for automation and further used for classifying.
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References
Swedlow, J.R., Eliceiri, K.W.: Open source bioimage informatics for cell biology. Trends Cell Biol. 19(11), 656–660 (2009)
Rittscher, J.: Characterization of biological processes through automated image analysis. Annu. Rev. Biomed. Eng. 12, 315–344 (2010)
Peng, H.: Bioimage informatics: a new area of engineering biology. Bioinformatics 24(17), 1827–1836 (2008)
Adollah, R., Mashor, M.Y., Nasir, N.M., Rosline, H., Mahsin, H., Adilah, H.: Blood cell image segmentation: a review. In: 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, pp. 141–144. Springer, Berlin, Heidelberg (2008)
Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. In: Advanced Algorithmic Approaches to Medical Image Segmentation, pp. 541–558. Springer, London (2002)
Bengtsson, E., Wahlby, C., Lindblad, J.: Robust cell image segmentation methods. Pattern Recogn. Image Anal. 14(2), 157–167 (2004)
Singh, S., Kumar, D.: Red & green blood vessel extraction from the retinal images. IJARCCE, 4(6), June 2015
Mohammed, E.A., Mohamed, M.M.A., Naugler, C., Far, B.H.: Chronic Lymphocytic Leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal Thresholding. In: IEEE International Conference on CCECE 2013 (2013)
Meijering, Erik: Cell segmentation: 50 years down the road. IEEE Signal Process. Mag. 29(5), 140–145 (2012)
Forsberg, D., Monsef, N.: Evaluating cell nuclei segmentation for use on whole-slide images in lung cytology. In: IEEE International Conference on Pattern Recognition (2014)
Wu, Q., Merchant, F.A., Castleman, K.R.: Microscope Image Processing. Academic Press, Burlington (2008)
Klinger, T.: Image processing with Lab VIEW and IMAQ Vision. Prentice Hall Professional, Upper Saddle Rive (2003)
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Vijay Mani Shankar, L., Mahesh, V., Geethanjali, B., Subashini, R. (2019). Automated Segmentation and Computation of the Leukocytes Based on Morphological Operator. In: Hemanth, J., Fernando, X., Lafata, P., Baig, Z. (eds) International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018. ICICI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-030-03146-6_84
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DOI: https://doi.org/10.1007/978-3-030-03146-6_84
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