Red blood cell segmentation using masking and watershed algorithm: A preliminary study

2012 International Conference on Biomedical Engineering, ICoBE 2012

Volumn , Issue , 2012, Pages 258-262

  Sharif, J M ^a   Miswan, M F ^a   Ngadi, M A ^a   Salam, Md Sah Hj ^a   Jamil, Muhammad Mahadi Bin Abdul ^b  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

^b UNIVERSITI TUN HUSSEIN ONN MALAYSIA   (Malaysia)

Author keywords

analyzing method; blood cell counting; blood cell disorder; counting application; Segmentation

Indexed keywords

ANALYZING METHOD; AUTOMATED COUNTING; BLOOD CELL IMAGES; BLOOD CELLS; BLOOD SMEARS; CELL HANDLING; COLOR CONVERSIONS; HUMAN BLOOD CELLS; IMPROVED VISUALIZATION; KEY TECHNOLOGIES; MORPHOLOGICAL OPERATOR; OVERLAPPING CELLS; RED BLOOD CELL; SMALL OBJECTS; WATER-SHED ALGORITHM; WHITE BLOOD CELLS;

ALGORITHMS; BIOMEDICAL ENGINEERING; BLOOD; IMAGE SEGMENTATION; LANDFORMS; WATERSHEDS;

CELLS;

EID: 84860673145     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICoBE.2012.6179016     Document Type: Conference Paper

References (15)

1. Heather Darbo, The Blood Smear (What, Why and How), LVT, VTS (Emergency and Critical Care) Animal Emergency & Specialty Services at http://www.animalemergencycenter.com/education.html
2. G.P.M Priyankara, O.W Seneviratne, R.K.O.H Silva et.al (2006). An extensible computer vision application for blood cell recognition and analysis, Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka.
3. Su, M.-j., et al (2008). A new method for blood cell image segmentation and counting based on PCNN and autowave in Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on 2008.
4. Q. A. Salih, A. R. Ramli, R. Mahmud et.al (2004). 3D Visualization for Blood Cells Analysis versus Edge Detection. The Internet Journal of Medical Technology. 2004 Volume 1 Number 2
5. F. Sadeghian, Z. Seman, A.R. Ramli et.al (2009). A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing, Shulin Li (ed.), Biological Procedures Online, Volume 11, Number 1.
6. E. Montseny, P. Sobrevilla, S. Romani (2004). A Fuzzy Approach to White Blood Cells Segmentation in Color Bone Marrow Images, FUZZIEEE 2004, Budapest, Hungary.
7. S. Chinwaraphat et.al (2008). A Modified Fuzzy Clustering For White Blood Cell Segmentation, The 3rd International Symposium on Biomedical Engineering (ISBME 2008).
8. K. Jiang, Q.M. Liao, Y. Xiong (2006). A novel white blood cell segmentation scheme based on feature space clustering, Soft Comput (2006) 10: 12-19.
9. P.S.Hiremath, P. Bannigidad, S. Geeta (2010). Automated Identification and Classification of White Blood Cells (Leukocytes) in Digital Microscopic Images, IJCA Special Issue on "Recent Trends in Image Processing and Pattern Recognition" RTIPPR, 2010.
10. Nasution, AMT, EK Suryaningtyas (2008). Comparison of Red Blood Cells Counting using two Algorithms: Connected Component Labeling and Backprojection of Artificial Neural Network, PhotonicsGlobal@Singapore, 2008. IPGC 2008. IEEE
11. K.S. Kim, P.K. Kim, J.J. Song et.al (2000). Analyzing Blood Cell Image to Distinguish Its Abnormalities. MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia.
12. Adnan Khashman, Esam Al-zgoul (2009). Image segmentation of blood cell in Leukemia patients. N. CYPRUS, Recent Advances in Computer Engineering and Applications.
13. S. S. Savkare, S. P. Narote (2011). Automatic Classification of Normal and Infected Blood Cells for Parasitemia Detection, IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.2, February 2011.
14. G. Diaz, A. Manzanera (2010). Automatic Analysis of Microscopic Images in Hematological Cytology Applications, Clinical Technologies: Concepts, Methodologies, Tools and Applications (3 vol).
15. M.F. Miswan, J.M. Sharif, M.A. Ngadi et. al (2011). An Overview: Segmentation Method for Blood Cell Disorders, BIOMED 2011, IFMBE Proceedings 35, pp. 596-599, 2011.