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Automatic Segmentation of Acute Leukemia Cells

by A.H. Kandil, O. A. Hassan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 10
Year of Publication: 2016
Authors: A.H. Kandil, O. A. Hassan
10.5120/ijca2016907904

A.H. Kandil, O. A. Hassan . Automatic Segmentation of Acute Leukemia Cells. International Journal of Computer Applications. 133, 10 ( January 2016), 1-8. DOI=10.5120/ijca2016907904

@article{ 10.5120/ijca2016907904,
author = { A.H. Kandil, O. A. Hassan },
title = { Automatic Segmentation of Acute Leukemia Cells },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 10 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number10/23819-2016907904/ },
doi = { 10.5120/ijca2016907904 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:30:46.199479+05:30
%A A.H. Kandil
%A O. A. Hassan
%T Automatic Segmentation of Acute Leukemia Cells
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 10
%P 1-8
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The recognition of the acute Leukemia blast cells in colored microscopic images is a challenging task. Segmentation is the essential step for image analysis and image processing. In this paper, an algorithm is presented that consists of panel selection followed by segmentation using K-means clustering then a refinement process. This algorithm was applied on public dataset designed for testing segmentation techniques. The results were compared with two different segmentation techniques developed by other researchers on the same data set. Our algorithm results in a sensitivity of 97.4 % and specificity of 98.1%. The developed algorithm was tested to another dataset of samples extracted from patients in local hospitals. The algorithm results in sensitivity of 100%, Specificity of 99.747% and accuracy of 99.7617%. The results were approved by expert pathologists.

References
  1. Kekre H, Gharge SM, Sarode TK. Tumor Demarcation in Mammography Images using LBG on Probability Image. International Journal of Computer Applications. 2010 June; 3(8): p. 47-53.
  2. Nadia M. Molecular pathology of cancer Cairo: First edition, National cancer institute Cairo University; 1998.
  3. Asaad NY, Dawoud M. Diagnosis and Prognosis of B-Cell Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma (B-CLL/SLL) and Mantle Cell Lymphoma (MCL). J Egypt Natl Canc Inst. 2005 December; 17(4): p. 279-290.
  4. Bennett JM, Catovsky D, Daniel M, Flandrin G, Galton D, Gralnick HR, et al. Proposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative group. British Journal of Haematology. 1976 January 26 ; 33(4): p. 451-458.
  5. Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood. 2009 Jul 30; 114(5): p. 937-951.
  6. Sabino DMU, da Fontoura Costa L, Rizzatti EG, Zago MA. A texture Approach to Leukocyte recognition. Real time Imaging. 2004 OCT; 10(4): p. 205-216.
  7. Chen Q, Yang X, Petriu EM. Watershed Segmentation for Binary Images with Different Distance Transforms. In Haptic, Audio and Visual Environments and Their Applications, 2004. HAVE 2004. Proceedings. The 3rd IEEE International Workshop; 2004; Ottawa, Ontario, Canada. p. 111-116.
  8. Mohamed MMA, Far B. A Fast Technique for White Blood Cells Nuclei Automatic Segmentation Based on Gram-Schmidt Orthogonalization. In IEEE 24th International Conference on Tools with Artificial Intelligence; 2012. p. 947-952.
  9. Trivedi MM, Bezdek JC. Low-level segmentation of aerial images with fuzzy clustering. IEEE Trans. Syst. Man. Cybern. 1986; 16(4): p. 589-598.
  10. Kim K, Jeon J, Choi W, Kim P, Ho YS. Automatic Cell Classification in Human’s Peripheral Blood Images Based on Morphological image processing. In AI 2001: Advances in Artificial Intelligence.: Springer; 2001. p. 225-236.
  11. Mashor M, Harun NH, Abdullah AA, Rosline H. Improving Colour Image Segmentation on Acute Myelogenous Leukaemia Images Using Contrast Enhancement Techniques. In IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010); 30th November - 2nd December 2010; Kuala Lumpur, Malaysia. p. 246-251.
  12. Mohamed M, Far B. An enhanced threshold based technique for white blood cells nuclei automatic segmentation. In e-Health Networking, Applications and Services (Healthcom), 2012 IEEE 14th International Conference.; 2012; Calgary, Canada. p. 202-207.
  13. Mohammed E, Mohamed MM, Naugler C, Far BH. Chronic lymphocytic leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding. In 26th IEEE Canadian Conference Of Electrical And Computer Engineering (CCECE); 2013; Canada. p. 1-5.
  14. Prasad AS, Latha KS, Rao SK. Separation and counting of blood cells using geometrical features and distance transformed watershed. International Journal of Engineering and Innovative Technology (IJEIT). 2013 August; 3(2).
  15. Kekre H, Thepade SD, Sarode TK, Suryawanshi V. Image Retrieval using Texture Features extracted from GLCM, LBG and KPE. International Journal of Computer Theory and Engineering. 2010 October; 2(5): p. 1793-8201.
  16. Kekre H, Sarode TK, Raul B. Color Image Segmentation using Kekre’s Algorithm for Vector Quantization. International Journal of Computer Science (IJCS). 2008 jan; 3(4): p. 287-292.
  17. Kekre H, Sarode TK, Raul B. Color Image Segmentation using Vector Quantization Techniques Based on Energy Ordering Concept. International Journal of Computing Science and Communication Technologies (IJCSCT). 2009 January; 1(2): p. 164-171.
  18. Kekre H, Sarode T. Two Level Vector Quantization Method for Codebook Generation using Kekre’s Proportionate Error Algorithm. International Journal of Image Processing. 2010; 4(1): p. 1-10.
  19. Kekre H, Sarode TK. New Clustering Algorithm for Vector Quantization using Rotation of Error Vector. (IJCSIS) International Journal of Computer Science and Information Security. 2010; 7(3): p. 159-165.
  20. Kekre H, Gharge SM, Sarode TK. Image Segmentation of Mammographic Images Using Kekre’S Proportionate Error Technique on Probability Images. International Journal of Computer and Electrical Engineering. 2010 December; 2(6): p. 1048-1052.
  21. Wang W, Song H. Cell Cluster Image Segmentation on Form Analysis. In IEEE Third International Conference on Natural Computation, ICNC; 2007. p. 833-836.
  22. Hengen H, Spoor SL, Pandit MC. Analysis of Blood and Bone Marrow Smears Using Digital Image Processing Techniques. In International Society for Optics and Photonics; 2002. p. 624-635.
  23. Labati RD, Piuri V, Scotti F. The Acute Lymphoblastic Leukemia Image Database For Image Processing. In 18th IEEE international conference on Image processing (ICIP); 2011; Università degli Studi di Milano, Department of Information Technology, via Bramante65, 26013 Crema, Italy. p. 2089-2092.
  24. Kekre H, Archana B, Galiyal HR. Segmentation of Blast using Vector Quantization Technique. International journal of Computer Applications. 2013 June; 4(5).
  25. Soltanzadeh R, Rabbani H, Talebi A. Extraction of nucleolus candidate zone in white blood cells of peripheral blood smear images using curvelet transform. Computational and Mathematical Methods in Medicine. 2012 February; 2012(12,): p. 1-12.
  26. Negm AS, Hassana OA, kandil AH. A Decision Support System for Acute Leukemia Classification from Digital Microscopic Images. Journal of Advanced Research. 2015.
  27. Sharma D, Yadav U, Sharma P. The concept of sensitivity and specificity in relation to two types of errors and its application in medical research. Journal of Reliability and Statistical Studies. 2009; 2(2): p. 53-58.
  28. Schmitz A, Schäfer T, Schäfer H, Döring C, Ackermann J, Dichter N, et al. Automated Image Analysis of Hodgkin lymphoma. 2012 September.
  29. Aimi Salihah A, Mashor MY, Harun NH, Rosline H. Colour Image Enhancement Techniques for Acute Leukemia Blood Cell Morphological Features. In IEEE International Conference on Systems Man and Cybernetics (SMC); 2010. p. 3677-3682.
  30. Aimi Salihah AN, Mashor M, Abdullah AA. Improving Blast Segmentation of Acute Myelogenous Leukemia (AML) Images Using Bright Stretching Technique. In Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010); 2010.; Perlis, Malaysia. p. 16-17.
  31. Halim NA, Mashor M, Abdul Nasir A, Mokhtar N, Rosline H. Nucleus Segmentation Technique for Acute Leukemia. In IEEE 7th International Colloquium on Signal Processing and its Applications, (CSPA); 2011. p. 192-197.
Index Terms

Computer Science
Information Sciences

Keywords

Leukemia segmentation image enhancement K-means and watershed method.

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