Overlapping white blood cell segmentation and counting on microscopic blood cell images

International Journal on Smart Sensing and Intelligent Systems

Volumn 7, Issue 3, 2014, Pages 1271-1286

  Fatichah, Chastine ^a   Purwitasari, Diana ^a   Hariadi, Victor ^a   Effendy, Faried ^b  

^a DEPARTMENT OF ELECTRICAL ENGINEERING   (Indonesia)

^b AIRLANGGA UNIVERSITY   (Indonesia)

Author keywords

Geometric feature; Mathematical morphology; Microscopic blood cell image; White blood cell segmentation

Indexed keywords

BLOOD; CELLS; CYTOLOGY; DIAGNOSIS; IMAGE SEGMENTATION;

BLOOD CELL COUNTING; BLOOD CELL IDENTIFICATIONS; BLOOD CELL IMAGES; CELL COUNTING; CELL SEGMENTATION; GEOMETRIC FEATURE; MICROSCOPIC BLOOD CELL IMAGE; OVERLAPPING CELLS; WHITE BLOOD CELL SEGMENTATION; WHITE BLOOD CELLS;

MATHEMATICAL MORPHOLOGY;

EID: 84921445030     PISSN: None     EISSN: 11785608     Source Type: Journal    
DOI: 10.21307/ijssis-2017-705     Document Type: Article

References (24)

1. Eom, S., Kim, S., Shin, V., and Ahn, B., Leukocyte Segmentation in Blood Smear Images Using Region-Based Active Contours, Lectures Notes in Computer Science, LNCS(4179), pp.867-876, 2006.
2. Fatichah, C., Tangel, M.L., Widyanto, M.R., Dong, F., Hirota, K., Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 16, No. 1, pp. 76-86, 2012.
3. Yongjin, Y., Xinmei, Z., Zhongfan, X., Research of Image Pre-processing Algorithm Based on FPGA, International Journal On Smart Sensing And Intelligent Systems Vol. 6, No. 4, pp. 1499-1515, September 2013.
4. Case, M., Micheli, M., Arroyo, D., Hillard, J., Kocanda, M., Ultrasonic Blood Flow Sensing Using Doppler Velocimetry, International Journal on Smart Sensing And Intelligent Systems Vol. 6, No. 4, pp.1298-1316, September 2013.
5. Zhang, D., Xue, Y., Ye, X., and Li, Y., Research on Chips' Defect Extraction Based on Image-Matching, International Journal on Smart Sensing And Intelligent Systems Vol. 7, No. 1, pp.321-336, Maret 2014.
6. Archana S. Ghotkar and Dr. Gajanan K. Kharate, Study Of Vision Based Hand Gesture Recognition Using Indian Sign Language, International Journal On Smart Sensing And Intelligent Systems Vol. 7, No. 1, pp. 96-115, Maret 2014.
7. Theera-Umpon, N., White Blood Cell Segmentation and Classification in Microscopic Bone Marrow Images, Springer-Verlag Berlin Heidelberg, pp. 787-796, 2005.
8. Yang, L., Meer, P., and Foran, D. J., Unsupervised Segmentation Based on Robust Estimation and Color Active Contour Models, IEEE Transaction on Information Technology in Biomedicine, Vol. 9, No. 3, pp. 475-86, 2005.
9. Leopoldo, C., Altamirano, Gonzales, J. A., Diaz, R., and Guichard, J. S., Segmentation of Bone Marrow Cell Images for Morphological Classification of Acute Leukemia, Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2010.
10. Dorini, L. B., Minetto, R., and Leite, N. J., White blood cell segmentation using morphological operators and scale-space analysis, Brazilian Symposium on Computer Graphics & Image Processing (SIBGRAPI), pp. 294-304, 2007.
11. Nasr-Isfahani, S., Mirsafian, A., Masoudi-Nejad., A., A New Approach for Touching Cells Segmentation, International Conference on BioMedical Engineering and Informatics, pp. 816-820, 2008.
12. Cloppet, F., and Boucher, A., Segmentation of overlapping/aggregating nuclei cells in biological images, ICPR, pp. 1-4, 2008.
13. Daňek, O., Matula, P., Ortiz-de-Soĺorzano, C., Muñoz-Barrutia, A., Măska, M., and Kozubek, M., Segmentation of Touching Cell Nuclei Using a Two-Stage Graph Cut Model, Springer-Verlag Berlin Heidelberg LNCS 5575, pp. 410-419, 2009.
14. Jung, C., Kim, C., Chae, S.W., Oh, S., Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification, IEEE Transactions on Biomedical Engineering, Vol. 57, No. 12, pp. 2825-2831, 2010.
15. Deng, T. and Heijmans, H., Grey-scale Morphology Based on Fuzzy Logic, Journal of Mathematical Imaging and Vision, Springer Netherlands, vol. 16, no. 2, pp. 155-171, 2002.
16. Hanbury, A. and Serra, J., Mathematical Morphology in the HLS Colour Space, Proceedings of the 12th BMVC British Machine Vision Conference, vol. II, pp. 451-460, 2001.
17. Asano, A., 2008, Granulometry and skeleton, Pattern Information Processing Session 9.
18. R. M. Haralick, S. R. Stenberg, and X. Zhuang, "Image Analysis using Mathematical Morphology", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-9, No. 4, (1987).
19. D. Sinha and E.R. Dougherty, "Fuzzy Mathematical Morphology", Journal of Visual Communication and Image Representation, Vol. 3, No. 3, pp. 286-302, 1992.
20. I. Bloch and H. Maitre, "Fuzzy Mathematical Morphology", Annals of Mathematics and Artificial Intelligence, Vol. 10, pp. 55-84, 1994.
21. I. Bloch and H. Maitre, "Fuzzy Mathematical Morphologies: A comparative study", Pattern Recognition, Vol. 28, No. 9, pp. 1341-1387, 1995.
22. B. DeBaets and E. Kerre, "The fundamentals of fuzzy mathematical morphology part 1: Basic concepts", International Journal of General Systems, Vol. 23, pp. 155-171, 1995.
23. W. Chen, Y. Q. Shi, and G. Xuan, "Identifying computer graphics using HSV color model and statistical moments of characteristic functions", IEEE International Conference on Multimedia and Expo (ICME07), Beijing, China, July 2-5, 2007.
24. Bouchet, A., Pastore, J., and Ballarin, V., "Segmentation of Medical Images using Fuzzy Mathematical Morphology", Journal of Computer Science & Technology, vol. 7, no. 3, pp. 256-262, 2007.