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Abstract
Blood is a connective tissue in fluid form. Blood cell counting gives vital information about a patient’s health. It is used to evaluate and diagnose diseases such as anaemia, polycythemia, leukemia, thrombocytosis thrombocytopenia, identification of sickle cells etc. It indirectly measures the oxygen carrying capacity of blood.  There are many blood cell counting methods. The oldest is the manual counting which is still considered as the “gold standard†method for counting blood cells. But this method is subjective and the result depends on the technician.  Other method for blood cell counting is by using an automatic hematology analyser. This method gives an accurate blood cell count but the cost of the machine is very high also it cannot identify sickle cells. This paper presents a simple method to count the red blood cells and identify sickle cells using Circular Hough Transform an Image processing technique
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How to Cite
Sreekumar, A., & Bhattacharya, A. (2014). Identification of Sickle Cells from Microscopic Blood Smear Image Using Image Processing. International Journal of Emerging Trends in Science and Technology, 1(05). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/239
References
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8. Ruihu Wang, “Red Blood Cell Classification Through Shape Feature Extraction and PSO-CSVM Classifier Designâ€, Proceedings of 7th International Conference on Advanced Information Management and Systems (ICIPM), Jeju, Nov 29-dec 1 2011, pp 107-111.
2. Guitao Cao, Cai Zhong, Ling Li, Jun Dong, Detection of Red Blood Cell in Urine Micrograph, Proceedings of the 2009 International Conference on Bioinformatics and Biomedical Engineering, Beijing, 11-13 June, 2009, pp 1-4
3. Venkatalakshmi.B, Thilagavathi.K, Automatic Red Blood Cell Counting Using Hough Transform, Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013), Jeju Island, 11-12 April 2013, pp 267-271.
4. S.Kareem, R.C.S Morling and I.Kale, A Novel Method to Count the Red Blood Cells in Thin Blood Filmsâ€, International symposium on circuits and systems, IEEE 2011,Rio de Jenerio, 15-18 May 2011, pp 1021-1024.
5. Heidi Berge, Dale Taylor, Sriram Krishnan, Tania S. Douglas, IMPROVED RED BLOOD CELL COUNTING IN THIN BLOOD SMEARS, 2011 IEEE symposium on Biomedical Imaging, Chicago IL, March 30-April 2, 2011, pp 204-207.
6. J. M. Sharif, M. F. Miswan, M. A. Ngadi, Md Sah Hj Salam, Muhammad Mahadi bin Abdul Jamil, Red Blood Cell Segmentation Using Masking and Watershed Algorithm: A Preliminary Study, 2012 International Conference on Biomedical Engineering (ICoBE),27-28 February 2012,Penang, pp 258-262
7. Rongtai Cai, Qingxiang Wu, Rui Zhang, Lijuan Fan, Chengmei Ruan, “Red Blood Cell Segmentation Using Active Appearance Modelâ€, Proceedings of 11th International Conference on Signal Processing (ICSP), Beijing, 21-25 oct .2012, vol 3, pp 1641-1644.
8. Ruihu Wang, “Red Blood Cell Classification Through Shape Feature Extraction and PSO-CSVM Classifier Designâ€, Proceedings of 7th International Conference on Advanced Information Management and Systems (ICIPM), Jeju, Nov 29-dec 1 2011, pp 107-111.