2014, Number 1
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Rev Mex Ing Biomed 2014; 35 (1)
Leukocytes Detection, Classification and Counting in Smears of Peripheral Blood
Martínez-Castro J, Reyes-Cadena S, Felipe-Riverón E
Language: English
References: 14
Page: 41-51
PDF size: 589.17 Kb.
ABSTRACT
Using the
k-NN classifier in combination with the first Minkowski
metric, in addition to techniques of digital image processing, we
developed a computational system platform-independent, which is able
to identify, to classify and to count five normal types of leukocytes:
neutrophils, eosinophils, basophils, monocytes and lymphocytes. It is
important to emphasize that this work does not attempt to differentiate
between smears of leukocytes coming from healthy and sick people; this
is because most diseases produce a change in the differential count of
leukocytes rather than in theirs forms. In the other side, the system
could be used in emerging areas such as the topographic hematology
and the chronobiology.
REFERENCES
[1] R.C. Gonzalez and R.E. Woods. Digital Image Processing. Pearson/Prentice Hall, 2008.
[2] Costrarido L. Evaluation Strategies for Medical- Image Analysis and Processing Methodologies. CRC Press, 2013/10/16 2005.
[3] India Department of Electrical Communication Engineering. Indian Institute of Science. Bangalore, editor. Teager Energy Based Blood Cell Segmentation, 2002.
[4] A. Bello. Hematología básica. Prado, 2001.
[5] H Ceelie, R B Dinkelaar, and W van Gelder. Examination of peripheral blood films using automated microscopy; evaluation of diffmaster octavia and cellavision dm96. Journal of Clinical Pathology, 60(1):72–79, 2007.
[6] Qingmin Liao and Yingying Deng. An accurate segmentation method for white blood cell images. In Biomedical Imaging, 2002. Proceedings. 2002 IEEE International Symposium on, pages 245–248, 2002.
[7] Chen Pan, Dong Sun Park, Sook Yoon, and Ju Cheng Yang. Leukocyte image segmentation using simulated visual attention. Expert Syst. Appl., 39 (8):7479– 7494, June 2012.
[8] Robiyanti Adollah, M.Y. Mashor, N.F. Mohd Nasir, H. Rosline, H. Mahsin, and H. Adilah. Blood cell image segmentation: A review. In NoorAzuan Abu Osman, Fatimah Ibrahim, WanAbuBakar Wan Abas, HermanShah Abdul Rahman, and Hua- Nong Ting, editors, 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, volume 21 of IFMBE Proceedings, pages 141–144. Springer Berlin Heidelberg, 2008.
[9] Madhumala Ghosh, Devkumar Das, Chandan Chakraborty, and Ajoy K. Ray. Automated leukocyte recognition using fuzzy divergence. Micron, 41(7):840 – 846, 2010.
[10] Cecilia Di Ruberto, Andrew G. Dempster, Shahid Khan, and Bill Jarra. Analysis of infected blood cell images using morphological operators. Image Vision Comput., 20(2):133–146, 2002.
[11] Ningning Guo, Libo Zeng, and Qiongshui Wu. A method based on multispectral imaging technique for white blood cell segmentation. Comput Biol Med, 37(1):70– 76, Jan 2007.
[12] Mukesh Saraswat, K.V. Arya, and Harish Sharma. Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm and Evolutionary Computation, 11(0):46 – 54, 2013.
[13] Seyed Hamid Rezatofighi and Hamid Soltanian-Zadeh. Automatic recognition of five types of white blood cells in peripheral blood. Computerized Medical Imaging and Graphics, 35(4):333 – 343, 2011.
[14] Fix E. and Hodges J.L. Discriminatory analysis, nonparametric discrimination: Consistency properties. Technical Report 4, USAF School of Aviation Medicine, Randolph Field, Texas, 1951.