Morphology based detection of abnormal red blood cells in peripheral blood smear images

IFMBE Proceedings

Volumn 52, Issue , 2015, Pages 57-60

  Kulasekaran, S ^a   Sheeba, Feminna ^b   Mammen, Joy John ^c   Saivigneshu, B ^b   Mohankumar, S ^b  

^a Healthcare Technology Innovation Centre   (India)

^b MADRAS CHRISTIAN COLLEGE   (India)

^c CHRISTIAN MEDICAL COLLEGE   (India)

Author keywords

Extreme points; Houghman Transform; Morphology; Red blood cells; Watershed

Indexed keywords

BLOOD; CYTOLOGY; FRACTURE; IMAGE SEGMENTATION; MORPHOLOGY; WATERSHEDS;

EXTREME POINTS; INDIVIDUAL CELLS; PATHOLOGICAL CONDITIONS; PERIPHERAL BLOOD SMEARS; RED BLOOD CELL; SEGMENTATION TECHNIQUES; SICKLE-CELL ANEMIA; WATER-SHED ALGORITHM;

CELLS;

EID: 84947432063     PISSN: 16800737     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-319-19452-3_16     Document Type: Conference Paper

References (14)

1. Feminna, S., H.M.T. Thomas, And J. J. Mammen, Segmentation And Reversible Watermarking Of Peripheral Blood Smear Images. Proc. Ieee Conference On Bio Inspired Computing: Theories And Applications 2010. 2: 1373-6
2. Feminna Sheeba, Robinson Thamburaj, Joy J. Mammen, Hannah M. Thomas, Atulya K. Nagar, White Blood Cell Segmentation And Reversible Watermarking, Proc. The IASTED International Symposia on Imaging and Signal Processing in Healthcare and Technology (ISPHT 2011), pp. 63-68
3. Feminna, S., Robinson T., J.J. Mammen, H.M.T. Thomas and Atulya K. Nagar.: Splitting of overlapping cells in peripheral blood smear images by concavity analysis. Combinatorial image analysis lecture notes in computer science DOI: 10.1007/978-3-319-07148-0_21 Volume 8466, (2014) 238-249.
4. Hassan Khajehpour et al, Detection and Segmentation of Erythrocytes in Blood Smear Images Using a Line Operator and Watershed Algorithm, Journal of Medical Signals and Sensors, Vol 3, Issue 3, 2013
5. Ongun, G., et al., An Automated Differential Blood Count System. Proc. of IEEE EMBC 2001.
6. Feminna, S., Robinson, T., Nagar, A.K., Mammen, J.J.: Segmentation of peripheral blood smear images using tissue-like P-Systems. IJNCR–BICTA 2011 Special Issue 3(1), (2012) 16–27
7. Nipon Theera-Umpon, Patch-Based White Blood Cell Nucleus Segmentation using Fuzzy Clustering. ECTI Transactions on El. Eng. and Electronics Communications, 2005. 3: 15-19.
8. Chinwarapath, S., et al., A Modified Fuzzy Clustering for White Blood Cell Segmentation. Proc. of the 3rd Int. Symposium on Biomedical Engineering ISBME 2008,: 356-359.
9. Jameela Ali et al, Isolation and Classification of Red Blood Cells in Anemic Microscopic Images, International Journal of Medical, Health, Biomedical and Pharmaceutical Engineering Vol:8, No:10, 2014.
10. Shagana J. A. Diagnostic Cells in the Peripheral Blood Smear, Journal of Pharmaceutical Sciences and Research,Vol. 6(4), 2014, pp. 213-216, ISSN:0975-1459.
11. Aruna N.S et al, Edge Detection of Sickle Cells in Red Blood Cells, International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5 (3), 2014, pp. 4140-4144, ISSN:0975-964
12. Gonzalez, Woods, Digital Image Processing 3ed (Prentice Hall 2008)
13. Gonzalez, Woods, Eddins, Digital Image Processing using Matlab (Pearson Education 2009
14. Chung-Fang Chien, Yu-Che Cheng, and Ta-Te Lin*, Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan. *Corresponding author: m456@ntu.edu.tw,Robust ellipse detection based on hierarchical image pyramid and Hough transform, Vol. 28, No. 4/April 2011/J. Opt. Soc. Am. A 581