Research Article
BibTex RIS Cite

Dijital Görüntüler Kullanılarak Kan Grubunun Görüntü İşleme Tabanlı Tespiti

Year 2020, Volume: 7 Issue: 2, 649 - 659, 30.12.2020
https://doi.org/10.35193/bseufbd.646847

Abstract

Kan gruplandırılması, temel tıbbi prosedürlerin çoğu için ilk ve en önemli gerekliliktir. Kan grubunu tespit etmede kullanılan geleneksel teknikte uzman, kan örneğine anti-serum madde karıştırarak kandaki renk ve biçim değişikliğini izler ve kan grubu ile Rh faktörünü belirler. Bu işlem uzman tarafından gün boyunca yapılmaktadır ve yorgunluk, gözden kaçırma gibi sebeplerden dolayı hataya açıktır. Bu çalışmada, görüntü işleme teknikleri kullanılarak kan grubunun otomatik olarak tespit edilmesi amaçlanmıştır. Bunun için gerçek hasta kanlarının dijital görüntüleri üzerinde bir dizi görüntü işleme tekniği uygulanmıştır. Alınan kan görüntüleri üç bölüme ayrıldıktan sonra öncelikle RGB-Gri seviye-Siyah/Beyaz dönüşümü yapılmıştır. Ardından morfolojik işlemler uygulanarak kan bölgesi segmente edilmiş ve kenar sınır çizgileri işaretlenmiştir. Son aşamada ise beyaz piksel yoğunluğu, bölge içerisindeki nesne sayısı ve kenar piksel sayısı tespit edilerek kan grubu ve Rh faktörü belirlenmiştir.

References

  • A. Yamin, F. Imran, U. Akbar, and S. Hassan Tanvir, “Image Processing Based Detection & Classification of Blood Group Using Color Images”, 2017 International Conference of Communication, Computing and Digital Systems (C-CODE) ,2017.
  • D. T. R. Singh, S. Roy, and O. I. Singh, “A New Local Adaptive Thresholding Technique in Binarization,” IJCSI International Journal of Computer Science Issues, vol. 8, no. 6, no.2. Nov, 2011.
  • A. Dada, D. Beck, G. Schmitz."Automation andData Processing in Blood Banking Using the Ortho AutoVue®Innova System", Transfusion Medicine Hemotherapy, vol. 34, pp. 341-346, Sep. 2007.
  • B. A. Myhre, D. McRuer., "Human error - a significant cause of transfusion mortality," Transfusion, vol. 40, pp. 879-885, July 2000.
  • Dr. Derek N., Handbook of Transfusion Medicine, 5th ed., TSO, Norwich, United Kingdom, 2013.
  • S. Rahman, Md. Atifur Rahman, F. Ashraf Khan, S. Binte Shahjahan and K. Nahar, “Blood Group Detection using Image Processing Techniques,” Computer Eng. thesis, Department of Computer Science and Engineering, BRAC University, 24 December 2017.
  • G. Ravindran, T. Joby, M. Pravin, and P. Pandiyan, “Determination and Classification of Blood Types using Image Processing Techniques,” International Journal of Computer Applications, vol. 157, no. 1, pp. 12–16, Jan. 2017.
  • R.A. Rathod and R.A. Pathan, “Determination and Classification of Human Blood Types using SIFT transform and SVM Classifier,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol 5, Issue 11, Nov. 2016.
  • A. Dhande, P. Bhoir and V. Gade, “Identifying the blood group using Image Processing,” International Research Journal of Engineering and Technology, vol 05, Issue 03, Mar. 2018.
  • A. Ferraz, “Automatic system for determining of blood type using imageprocessing techniques,” Sensors & Actuators, vol 172, Issue 1, Dec. 2011.
  • Akshaya P. Sahastrabuddhe, Dr. Sayyad D. Ajij, “Blood group Detection and RBC, WBC Counting: An Image Processing Approach,” International Journal Of Engineering And Computer Science, ISSN: 2319-7242, vol. 5, Issue 10, Oct. 2016.

Determination of Blood Group by Image Processing Using Digital Images

Year 2020, Volume: 7 Issue: 2, 649 - 659, 30.12.2020
https://doi.org/10.35193/bseufbd.646847

Abstract

Blood grouping is the first and most important requirement for most basic medical procedures. The expert in the conventional art used to detect blood type monitors the color and shape change in the blood by mixing anti-serum substance into the blood sample and determines the blood group and Rh factor. This is done by the expert throughout the day and is open to errors due to tiredness and oversight. In this study, it was aimed to determine blood group automatically by using image processing techniques. For this purpose, a series of image processing techniques have been applied on digital images of real patient blood. After the blood samples were divided into three parts, firstly RGB-Gray level-Black/White conversion was performed. Subsequently, morphological procedures were performed, and the blood region was segmented and border lines were marked. In the last stage, the white pixel density, the number of objects in the region, and the number of edge pixels were determined and blood group and Rh factor were determined.

References

  • A. Yamin, F. Imran, U. Akbar, and S. Hassan Tanvir, “Image Processing Based Detection & Classification of Blood Group Using Color Images”, 2017 International Conference of Communication, Computing and Digital Systems (C-CODE) ,2017.
  • D. T. R. Singh, S. Roy, and O. I. Singh, “A New Local Adaptive Thresholding Technique in Binarization,” IJCSI International Journal of Computer Science Issues, vol. 8, no. 6, no.2. Nov, 2011.
  • A. Dada, D. Beck, G. Schmitz."Automation andData Processing in Blood Banking Using the Ortho AutoVue®Innova System", Transfusion Medicine Hemotherapy, vol. 34, pp. 341-346, Sep. 2007.
  • B. A. Myhre, D. McRuer., "Human error - a significant cause of transfusion mortality," Transfusion, vol. 40, pp. 879-885, July 2000.
  • Dr. Derek N., Handbook of Transfusion Medicine, 5th ed., TSO, Norwich, United Kingdom, 2013.
  • S. Rahman, Md. Atifur Rahman, F. Ashraf Khan, S. Binte Shahjahan and K. Nahar, “Blood Group Detection using Image Processing Techniques,” Computer Eng. thesis, Department of Computer Science and Engineering, BRAC University, 24 December 2017.
  • G. Ravindran, T. Joby, M. Pravin, and P. Pandiyan, “Determination and Classification of Blood Types using Image Processing Techniques,” International Journal of Computer Applications, vol. 157, no. 1, pp. 12–16, Jan. 2017.
  • R.A. Rathod and R.A. Pathan, “Determination and Classification of Human Blood Types using SIFT transform and SVM Classifier,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol 5, Issue 11, Nov. 2016.
  • A. Dhande, P. Bhoir and V. Gade, “Identifying the blood group using Image Processing,” International Research Journal of Engineering and Technology, vol 05, Issue 03, Mar. 2018.
  • A. Ferraz, “Automatic system for determining of blood type using imageprocessing techniques,” Sensors & Actuators, vol 172, Issue 1, Dec. 2011.
  • Akshaya P. Sahastrabuddhe, Dr. Sayyad D. Ajij, “Blood group Detection and RBC, WBC Counting: An Image Processing Approach,” International Journal Of Engineering And Computer Science, ISSN: 2319-7242, vol. 5, Issue 10, Oct. 2016.
There are 11 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Hilal Atıcı 0000-0002-1859-8085

Hasan Erdinç Koçer 0000-0002-0799-2140

Saadet Kader 0000-0003-0646-946X

Publication Date December 30, 2020
Submission Date November 14, 2019
Acceptance Date July 8, 2020
Published in Issue Year 2020 Volume: 7 Issue: 2

Cite

APA Atıcı, H., Koçer, H. E., & Kader, S. (2020). Determination of Blood Group by Image Processing Using Digital Images. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 7(2), 649-659. https://doi.org/10.35193/bseufbd.646847