Dijital Mikroskop Altında Alınan Kan Hücresi Görüntülerinden Beyaz Kan Hücrelerinin Algılanması ve Sınıflandırılması

Abstract

Kan yapısında bulunan beyaz kan hücrelerinin sayısı, yapısı ve şekli klinik açıdan önemli bilgilere ulaşmamızı sağlamaktadır. Bu bilgilere ulaşmak için alınan mikroskop görüntüleri incelenmekte ve elde edilen bulgular doktora iletilmektedir. Ancak uzmanlar tarafından manuel olarak yapılan bu işlemler yorucu ve zaman kaybına sebebiyet vermektedir. Bu sebeplerden dolayı otomatik olarak hücrelerin belirlenmesi ve hangi sınıfa ait olduğunun tespit edilmesi, işlemleri hızlandıracak ve daha fazla verinin incelenebilmesine olanak sağlayacaktır. Araştırmacıların çoğu hücre sayımı ve algılanması üzerine çeşitli metodolojiler kullanmaktadırlar. Bu makalemizde kullanılan metodolojiler üzerinde duracağız. Amaç, daha fazla doğruluk elde etmek için bu metodolojileri incelemek ve gelecekteki araştırmalara yön vermektir. 

Keywords

• Beyaz kan hücreleri (BKH),Görüntü işleme,segmantasyon,sınıflandırma

References

1. [1] Medically reviewed by Deborah Weatherspoon, PhD, RN, CRNA, COI on March 6, 2017- Written by Valencia Higuera
2. [2] T. Rosyadi, A. Arif, Nopriadi, B. Achmad, Faridah, "Classification of Leukocyte Images Using K-Means Clustering Based on Geometry Features" in 6th International Annual Engineering Seminar (InAES), Yogyakarta, Indonesia, 2016.
3. [3] Salem N.M. Segmentation of white blood cells from microscopic images using K-means clustering; Proceedings of the 31st IEEE National Radio Science Conference (NRSC); Cairo, Egypt. 28–30 April 2014; pp. 371–376.
4. [4] Paul Mooney, different cell types for detect and classify blood cell subtypes, Kaggle.
5. [5] N. Sinha, A. G. Ramakrishnan, "Automation of differential blood count", TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, vol. 2, pp. 547-551, 2003.
6. [6] J. M. Sharif, M. F. Miswan, M. A. Nagdi, Sah Hj Salman, "Red Blood Cell Segmentation Using Masking and Watershed Algorithm: A Preliminary Study", EEE International Conference on Biomedical Engineering, 2012. And the related images from; N. Abbas, D. Mohamad "Mıcroscopıc Rgb Color Images Enhancement For Blood Cells Segmentatıon In Ycbcr Color Space For K-Means Clusterıng ", JATIT10th September 2013. Vol. 55 No.1.[7] Dai Chunni, Liu Jingao, "Spectral Feature Extraction of Blood Cells Based on Hyperspectral Data", IEEE 9th International Conference on Natural Computation, Shenyang, China, 2013. and the related image from; F. Leyuan, Nanjun He, Shutao Li, Antonio J. Plaza, , and Javier Plaza ü.
7. [8] Mohmed A. Mohmed Mostafa, Far Behrouz, "A Fast Technique for White Blood Cell Nuclei Automated Segmentation Based on Gram-Schmidt Orthogonalization", IEEE 24th International Conference on Tool with Artificial Intelligence, 2012 and the related image from; A New Approach to White Blood Cell Nucleus Segmentation Based on Gram-Schmidt Orthogonalization
8. [9] Biplab Kanti Das, Krishna Kumar Jha, Himadri Sekhar Dutta, "A New approach for Segmentation and Identification of Disease Affected Blood Cells", IEEE International Conference on Intelligent Computing Applications, 2014.

9. [10] J. Ge, Z. Gong, Jun Liu, J. Nguyen, Zongyi Yang, Chen Wang and Yu Sun, "A Aystem for Counting Fetal and Maternal Red Blood Cells", IEEE Transaction on Biomedical Engineering, vol. 61, no. 12, pp. , 2014 and the related image from; “A Comparative Study of White Blood cells Segmentation using Otsu Threshold and Watershed Transformation”, researchgate.
10. [11] J. Theerapattanakul, J. Plodpai, C. Pintavirooj, "An Efficient Method for Segmentation Step of Automated White Blood Cell Classification", pp. 0-7803, 2004.
11. [12] J. Al-Muhairy, M. Al-Assaf Automatic “White Blood Cell Segmentation Based On Image Proccessing”, researchgate, january 2005.
12. [13] S. Kareem, R. C. S. Morling, I. Kale, "A Novel Method to Count the Red Blood Cells in Thin Blood Films", IEEE, 2011
13. [14] http://en.wikipedia.org/wiki/Image_segmentation
14. [15] D. Kaur, Y. Kaur, “Various Image Segmentation Techniques: A Review”, IJCSMC, Vol. 3, Issue. 5, Mohali, India, May 2014.
15. [16] Jiang, K., Q.X. Jiang and Y. Xiong, 2003. A Novel White Blood Cell Segmentation Scheme Using Scale-Space Filtering and Watershed Clustering. Mach. Learning Cybernetics, 5: 2820-2825 and the related image from; Z. Liu, J. Liu, “Segmentation of White Blood Cells through Nucleus Mark Watershed Operations and Mean Shift Clustering” Published online, Basel, 8 september 2015.
16. [17] Sinha, N. and A.G. Ramakrishnan, 2003. Automation of differential blood count. Proceedings of the Conference on Convergent Technologies for Asia-Pacific Region, Oct. 15-17, Banglore, India, pp: 547-551 and the related image from; C. Zhang, X. Xiao “White Blood Cell Segmentation by Color-Space-Based K-Means Clustering” ncbi, september 1, 2014.
17. [18] Theera-Umpon, N., 2005. Patch-based white blood cell nucleus segmentation using fuzzy clustering. ECTI Transa. Electrical Eng. Electr. Commun., 3: 15-19 and the related image from; T. Karthikeyan, N. Poornima “Microscopic Image Segmentation Using Fuzzy C Means For Leukemia Diagnosis” semanticscholar, published 2017.
18. [19] Ramin Soltanzadeh, Hossein Rabbani, And Ardeshir Talebi2,” Extraction Of Nucleolus Candidate Zone In white Blood Cells Of Peripheral Blood Smear Images Using Curvelet Transform”, In Biomedical Engineering Department, Medical Image And Signal Processing Research Center
19. [20] M. Sobhy, M. Salem and Mohamed El Dosoky A Comparative Study of White Blood cells Segmentation using Otsu Threshold and Watershed Transformation
20. [21] White blood cell segmentation for fresh blood smear images, Published in: 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS).
21. [22] F. Sadeghian, Z. Seman, A. R. Ramli, B. H. A. Kahar, and M. I. Saripan," A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing", Biological Procedures Online, Vol. 11, No. 1, 2009.
22. [23] L. B. Dorini, R. Minetto, and N. J. Leite, "Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis", IEEE Journal of Biomedical and Health Informatics, Vol. 17, No. 1, 2013.
23. [24] Bhagyashri G Patil, Prof. Sanjeev N.Jain , “Cancer Cells Detection Using Digital Image Processing Methods” in International Journal of Latest Trends in Engineering and Technology”.
24. [25] A. Khashman, E. Al-Zgoul, “Image Segmentation of Blood Cells in Leukemia Patients” in RECENT ADVANCES in COMPUTER ENGINEERING and APPLICATIONSISBN: 978-960-474-151-9, and Ms. Minal D. Joshi, Prof. Atul H. Karode, Prof. S.R.Suralkar, “White Blood Cells Segmentation and Classification to Detect Acute Leukemia” in International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 2, Issue 3, May – June 2013
25. [26] Tara.Saikumar, B.K.Anoop, P.S.Murthy in “Robust Adaptive Threshold Algorithm based on Kernel Fuzzy Clustering on Image segmentation” in Natarajan Meghanathan, et al. (Eds): ITCS, SIP, JSE-2012, CS & IT 04, pp. 99–103, 2012. © CS & IT-CSCP 2012
26. [27] A. Gautam, P. Singh, B. Raman, H. Bhadauria, "Automatic Classification of Leukocytes using Morphological Features and Naive Bayes Classifier", IEEE Region 10 Conference (TENCON), 2016.
27. [28] P. S. Hiremath, P. Bannigidad, and S. Geeta, "Automated Identification and Classification of White Blood Cells (Leukocytes) in Digital Microscopic Images", IJCA Special Issue on "Recent Trends in Image Processing and Pattern Recognition", 2010. [29] W. Yu, C. Yang, L. Zhang, H. Shen, Y. Xia, J. Sha, "Automatic Classification of Leukocytes Using Deep Neural Network", IEEE 12th International Conference on ASIC (ASICON), 2017.
28. [30] J. Macawile and at all, “White blood cell classification and counting using convolutional neural network” 2018 3rd International Conference on Control and Robotics Engineering (ICCRE), Nagoya, Japan, April 2018.
29. [31] J. Laosai, K. Chamnongthai, “Acute leukemia classification by using SVM and K-Means clustering” International Electrical Engineering Congress (iEECON), Chonburi, Thailand, 2014.
30. [32] S. F. Bikhet, A. M. Darwish, H. A. Tolba, and S. I. Shaheen, “Segmentation and classification of white blood cells,” in Proceedings of the IEEE Interntional Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 2259–2261, June 2000. View at Scopus
31. [33] Morphological Granulometric Features of Nucleus in Automatic Bone Marrow White Blood Cell Classification Nipon Theera-Umpon, Senior Member, IEEE, and Sompong Dhompongsa, may 2007.
32. [34] G. Ongun, U. Halici, K. Leblebicioglu, V. Atalay, M. Beksac, and S. Beksac, “Feature extraction and classification of blood cells for an automated differential blood count system,” in Proc. Int. Joint Conf. Neural Netw., Washington, DC, 2001, pp. 2461–2466.
33. [35] White Blood Cell Classification based on the Combination of Eigen Cell and Parametric Feature Detection Published in: 2006 1ST IEEE Conference on Industrial Electronics and Applications
34. [36] S. Manik ; M. Saini ; N.Vadera, “Counting and classification of white blood cell using Artificial Neural Network (ANN)”, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, July 2016.
35. [37] Mu-Chun Su, Chun-Yen Cheng, Pa-Chun Wang; A Neural-Network-Based Approach to White Blood Cell.