Volume : 09, Issue : 10, October – 2022

Title:

94.AN OVERVIEW PROPER SAMPLING FOR PERIPHERAL BLOOD SMEAR FOR ACCURATE DIAGNOSIS

Authors :

Murad Eidhah Alharthi, Ali Ayidh Alharthi, Rami Abduallah Althobaiti , Salma Nasser Assiri, Nada Nasser Asiri, Noora Nasser Asiri, Mohammad Hussein Althaqafi , Hussain Ali Hussain Sumayli, Bandar Hemeed Almalki, Afnan aziz azab

Abstract :

The peripheral blood smear (PBS) is a laboratory procedure that examines the cytology of peripheral blood cells spread on a slide. Despite its simplicity, PBF (Peripheral Blood Film) is crucial in the assessment and identification of several clinical disorders. This essay emphasizes the fundamental principles and creative aspects underlying the PBF. The text discusses the laboratory applications, clinical indications, and interpretations of many clinical disorders. Although there have been advancements in automating haematology and using molecular approaches, the peripheral blood film (PBF) continues to be a crucial diagnostic tool for haematologists. The haemato-pathologist should ensure that a high-quality smear is obtained, followed by a comprehensive examination and accurate interpretation that aligns with the patient’s clinical condition. Clinicians should be well-informed about the clinical usefulness and correct implementation of the reports in patient treatment. A manual inspection of the peripheral blood smear (PBS) is now conducted on a subset of samples that are submitted for automated full cell count.

Cite This Article:

Please cite this article in press Please cite this article in Murad Eidhah Alharthi et al, An Overview Proper Sampling For Peripheral Blood Smear For Accurate Diagnosis, Indo Am. J. P. Sci, 2022; 09 (10).

References:

1. Bain BJ. Diagnosis from the blood Smear. N Engl J Med. 2005;353:498–507.
2. Gulati GL, Alomari M, Kocher W, Schwarting R. Criteria for Blood Smear Review.
3. Schaefer M, Rowan RM. The Clinical relevance of nucleated red cell counts. Sysmex Journal International . 2000;10(2):59–63.
4. Tefferi A, Hanson CA, Inwards DJ. How to Interpret and Pursue an Abnormal Complete Blood Cell Count in Adults. Mayo Clin Proc. 2005;80(7):923–936.
5. Berend Houwen B. Blood film preparation and staining procedures. Laboratory Haematology. 2000;6: 1–7.
6. Gulati G, Song J, Florea AD, Gong J. Purpose and criteria for blood smear scan, blood smear examination, and blood smear review. Ann Lab Med. 2013;33:1–7.
7. Bain BJ. Diagnosis from the blood smear. N Engl J Med. 2005;353:498–507.
8. Pratumvinit B, Wongkrajang P, Reesukumal K, Klinbua C, Niamjoy P. Validation and optimization of criteria for manual smear review following automated blood cell analysis in a large university hospital. Arch Pathol Lab Med. 2013;137:408–14.
9. Peterson P, Blomberg DJ, Rabinovitch A, et al. Physician review of the peripheral blood smear: when and why. An opinion. Lab Hematol. 2001;7:175–9.
10. Acharya V, Kumar P. Identification and red blood cell automated counting from blood smear images using computer-aided system. Med Biol Eng Comput. 2018;56(3):483–489.
11. Tkachuk DC, Hirschmann JV. Approach to the microscopic evaluation of blood and bone marrow. In: Tkachuk DC, Hirschmann JV , editors. Wintrobe Atlas of Clinical Haematology. Lippincott: Williams & Wilkins; 2007.
12. Munster M. The role of the peripheral blood smear in the modern haematology laboratory. SEED haematology. Sysmex. 2013. Feb, Available at http://www.sysmex_ europe.com/…/SEED/sysmex_pdf .
13. General stains. In: Barrie Sims. The science of laboratory diagnosis. 2nd Edition 2005.
14. Riley RS, James GW, Sommer S, Martin MJ. How to prepare and interpret peripheral blood smears. Available at http://www.pathology.vcu.edu/education/pathlab/pages/haemato path/pbs.html .
15. Prasad K, Winter J, Bhat UM, Acharya RV, Prabhu GK. Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images. J Digit Imaging. 2012;25(4):542–549.
16. Prasad MN, Prasad K, Navya K (2018) Color transfer method for efficient enhancement of color images and its application to peripheral blood smear analysis. In: International conference on recent trends in image processing and pattern recognition, pp 134–142. Springer
17. Bhavnani LA, Jaliya UK, Joshi MJ. Segmentation and counting of WBCs and RBCs from microscopic blood sample images. Int J Image Graph Signal Process. 2016;8(11):2016.
18. Maji P, Mandal A, Ganguly M, Saha S (2015) An automated method for counting and characterizing red blood cells using mathematical morphology. In: 2015 Eighth international conference on advances in pattern recognition (ICAPR), pp 1–6.
19. Di Ruberto C, Loddo A, Putzu L (2019) A region proposal approach for cells detection and counting from microscopic blood images. In: International conference on image analysis and processing, pp 47–58. Springer
20. Di Ruberto C, Loddo A, Putzu L. Detection of red and white blood cells from microscopic blood images using a region proposal approach. Comput Biol Med. 2020;116:103530.
21. Sharif JM, Miswan M, Ngadi M, Salam MSH, bin Abdul Jamil MM (2012) Red blood cell segmentation using masking and Watershed algorithm: a preliminary study. In: 2012 International conference on biomedical engineering (ICoBE), pp 258–262.
22. Biswas S, Ghoshal D. Blood cell detection using thresholding estimation based watershed transformation with sobel filter in frequency domain. Proced Comput Sci. 2016;89:651–657.
23. Habibzadeh M, Krzyzak A, Fevens T (2011) Application of pattern recognition techniques for the analysis of thin blood smear images. Journal of Medical Informatics and Technologies 18 (2011)
24. Cruz D, Jennifer C, Castor LC, Mendoza CMT, Jay BA, Jane LSC, Brian PTB et al (2017) Determination of blood components (WBCs, RBCs, and platelets) count in microscopic images using image processing and analysis. In: 2017 IEEE 9th International conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM), pp 1–7.
25. Mahmood NH, Lim PC, Mazalan SM, Razak MAA. Blood cells extraction using color based segmentation technique. Int J Life Sci Biotechnol Pharma Res. 2013;2(2):2250–3137.
26. Mahmood NH, Mansor MA. Red blood cells estimation using Hough transform technique. Signal Image Process. 2012;3(2):53.
27. Sarrafzadeh O, Dehnavi AM, Rabbani H, Ghane N, Talebi A (2015) Circlet based framework for red blood cells segmentation and counting. In: 2015 IEEE workshop on signal processing systems (SiPS), pp 1–6.
28. Das BK, Jha KK, Dutta HS (2014) A new approach for segmentation and identification of disease affected blood cells. In: 2014 International conference on intelligent computing applications, pp 208–212.
29. Poomcokrak J, Neatpisarnvanit C (2008) Red blood cells extraction and counting. In: The 3rd international symposium on biomedical engineering, pp 199–203.
30. Al-Hafiz F, Al-Megren S, Kurdi H. Red blood cell segmentation by thresholding and Canny detector. Procedia Comput Sci. 2018;141:327–334.
31. Abbas N, Mohamad D, et al. Microscopic RGB color images enhancement for blood cells segmentation in YCbCr color space for k-means clustering. J Theor Appl Inf Technol. 2013;55(1):117–125.
32. Wei X, Cao Y, Fu G, Wang Y. A counting method for complex overlapping erythrocytes-based microscopic imaging. J Innov Opt Health Sci. 2015;8(06):1550033.
33. Savkare S, Narote S (2015) Blood cell segmentation from microscopic blood images. In: 2015 International conference on information processing (ICIP), pp 502–505.