Abstract
This paper proposes image segmentation methods for cell nucleuses and vacuoles in the liver fibrosis tissue images. The novel idea is to segment the objects by extracting the image features to determine the required cell in liver fibrosis images. In the proposed segmentation phase, some image processing methods are applied to segment the objects of nucleuses and vacuoles. Run Length method makes the object regions become obviously and the noises can be suppressed. The morphological opening operation is performed to split connecting objects. For vacuole regions segmentation, the opening operation applies the mode filter to stuff up the dark holes in the objects and keep the completeness of regions. Furthermore, the proposed method uses the Genetic Algorithm to find the most appropriate parameters and weights for the region segmentation. From the experimental results, the proposed method can achieve a good performance on the segmentation of cell nucleuses and vacuoles.
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References
Bataller, R., Brenner, D.A.: Liver fibrosis. The Journal of Clinical Investigation 115(2), 209–218 (2005)
Chan, Y.K., Chang, C.C.: Image matching using run-length feature. Pattern Recognition Letters 22(5), 447–455 (2001)
Chaves-Gonzalez, J.M., Vega-Rodriguez, M.A., Gomez-Pulido, J.A., Sanchez-Perez, J.M.: Detecting Skin in Face Recognition Systems: A Colour Spaces Study. Digital Signal Processing 20(3), 806–823 (2010)
Dai, L., Ji, H., Kong, X.W., Zhang, Y.H.: Antifibrotic effects of ZK14, a novel nitric oxidedonating Biphenyldicarboxylate derivative, on rat HSC-T6 cells and CCl4- induced hepatic fibrosis. Acta Pharmacologica Sinica 31, 27–34 (2010)
Department of Health, Executive Yuan, R.O.C(TAIWAN). 2010 statistics of causes of death 2012 (2012), http://www.doh.gov.tw/ufile/doc/2010-statistics%20of%20cause%20of%20death.pdf
Dillencourt, M., Samet, H., Tamminen, M.: A general approach to connected components labeling for arbitrary image representations. Journal of the ACM 39(2), 253–280 (1992)
Friedman, S.L.: Liver fibrosis- from bench to bedside. Journal of Hepatology (38), 38–53 (2003)
Griffiths, C., Rooney, C., Brock, A.: Leading causes of death in England and Wales -how should we group causes. Health Statistics Quarterly (28), 6–17 (2005)
Huang, D.C., Chen, R.T., Chan, Y.K., Jiang, X.: An automatic indirect immunofluorescence based cell segmentation and counting system. National Digital Library of These and Dissertations in Taiwan (2010)
MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Fifth Berkley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Designs. Springer, New York (1999)
Maulik, U.: Medical Image Segmentation Using Genetic Algorithms. IEEE Transactions on Information Technology in Biomedicine 13(2), 166–173 (2009)
Otsu, N.: A Threshold Selection Method from Gray-Level Histogram. IEEE Transactions on System Man Cybernetics SMC-9(1), 62–66 (1979)
Raghavan, V., Bollmann, P., Jung, G.S.: A critical investigation of recall and precision as measures of retrieval system performance. ACM Transactions on Information Systems 7(3), 205–229 (1989)
Stevenson, R.L., Arce, G.R.: Morphological Filters: Statistics and Further Syntactic Properties. IEEE Transactions on Circuits and Systems CAS-34(11) (1987)
Wikipedia, The Free Encyclopedia, F1 score, http://en.wikipedia.org/wiki/F1_score
Wikipedia, The Free Encyclopedia, Gamma correction, http://en.wikipedia.org/wiki/Gamma_correction
F precision and recall Wikipedia, The Free Encyclopedia, Sensitivity and specificity, http://en.wikipedia.org/wiki/Sensitivity_and_specificity
Yun, Y.S.: Hybrid Genetic Algorithm with Adaptive Local Search Scheme. Computers and Industrial Engineering 51(1), 128–141 (2006)
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Wang, CT., Wang, CL., Chan, YK., Tsai, MH., Wang, YS., Cheng, WY. (2013). Liver Cell Nucleuses and Vacuoles Segmentation by Using Genetic Algorithms for the Tissue Images. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_60
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DOI: https://doi.org/10.1007/978-3-642-38577-3_60
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