BLOOD CELL SEGMENTATION IMAGES FROM MICROSCOPIC BLOOD IMAGES

Authors

  • A.Boboyarov Samarkand State University
  • R.R.Mixliyev Samarkand State University

Keywords:

cell segmentation; K - average clustering; edge; watershed change

Abstract

Blood cell segmentation is an important innovation for automatic differential blood counting, classification and analysis in clinical examination. Segmentation and recognition in colored blood cell images are two important issues in the field of biomedical cell morphology. This article approaches methods for distinguishing blood cells from microscopically thin blood images. This information is the basis for high-level tasks, such as automatic differential blood count, diagnosis of various diseases such as malaria, babesia, Chagas disease, anemia, leukemia. A system based on cell segmentation is an effective way to segment different blood components such as red blood cells (erythrocytes), white blood cells (wbc). Identification of parasites present in platelets as well as blood cells from images entered for blood count. Input images are captured by connecting a digital camera to a microscope. The resulting images are expanded and segmented using k-means clustering as well as global thresholding. Overlapping cells are separated using sobel edge detector and watershed transform.

References

tobias bergen, dirk steckhan, thomas wittenberg and thorsten zerfab, “segmentation of leukocytes and erythrocytes in blood smear images,” in proc. Engineering in medicine and biology society, ieee, 2008.

dorini, leyza baldo, rodrigo minetto, and neucimar j. Leite. “semiautomatic white blood cell segmentation based on multiscale analysis,” ieee journal of biomedical and health informatics, vol. 17, no.1, pp 250-256, 2013.

man yan, jianyong cai, jiexing gao, lili luo, “k-means cluster algorithm based on color image enhancement for cell segmentation,” in proc. On biomedical engineering and informatics (bmei), ieee, pp 295-299, 2012.

rongtai cai, qingxiang wu, rui zhang, lijuan fan, chengmei ruan, “red blood cell segmentation using active appearance model,” in proc. On signal processing (icsp), vol. 3, pp. 1641-1644. Ieee, 2012.

asli genc-tav, selim aksoy, sevgen onder, “unsupervised segmentation and classi¿cation of cervical cell images,” journal of pattern recognition, vol.45, pp 4151–4168, 2012.

thanatip chankong, nipon theera-umpon, and sansanee auephanwiriyakul, “automatic cervical cell segmentation and classification in pap smears,” journal of computer methods and programs in biomedicine, vol. 113, no.2, pp 539-556, 2014. [7] abbas, naveed, and dzulkifli mohamad, “microscopic rgb color images enhancement for blood cells segmentation in ycbcr color space for k-mean clustering”, journal of theoretical & applied information technology, vol. 54, no. 1, 2013.

dwi anoraganingrum, “cell segmentation with median filter and mathematical morphology operation,” in proc. On image analysis and processing, ieee, 1999.

nasr-isfahani, shirin, atefeh mirsafian, and ali masoudi-nejad., “a new approach for touching cells segmentation,” in proc. On biomedical engineering and informatics, bmei, vol. 1, ieee, 2008.

gonzalez, rafael c., and richard e. Woods, “digital image processing.” Prentice hall (2002): 299-300.

gonzalez, rafael c., and richard e. Woods, “digital image processing using matlab” prentice hall (2002).

world health organization, malaria report, 2014.

s. S. Savkare, s. P. Narote, “automatic system for classification of erythrocytes infected with malaria and identification of parasite’s life stage”, elsevier sciverse science direct, procedia technology, icccs, pp 405-410, 2012.

khan najjed, pervaz h., latif a.k., musharraf a., saniya, “unsupervised identification of malaria parasites using computer vision”, in proc. On comp science and software engineering (jcsse), pp 263-267, 2014.

dpdx laboratory identification of parasite of public health concern, image library and diagnostic procedure. Http://www.dpd.cdc.gov/dpdx.

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Published

2023-04-15

How to Cite

A.Boboyarov, & R.R.Mixliyev. (2023). BLOOD CELL SEGMENTATION IMAGES FROM MICROSCOPIC BLOOD IMAGES. Journal of Academic Research and Trends in Educational Sciences, 2(2), 71–79. Retrieved from http://ijournal.uz/index.php/jartes/article/view/520