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A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing

by Himali Vaghela, Hardik Modi, Manoj Pandya, M. B. Potdar
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 5
Year of Publication: 2016
Authors: Himali Vaghela, Hardik Modi, Manoj Pandya, M. B. Potdar
10.5120/ijais2016451607

Himali Vaghela, Hardik Modi, Manoj Pandya, M. B. Potdar . A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing. International Journal of Applied Information Systems. 11, 5 ( Oct 2016), 9-16. DOI=10.5120/ijais2016451607

@article{ 10.5120/ijais2016451607,
author = { Himali Vaghela, Hardik Modi, Manoj Pandya, M. B. Potdar },
title = { A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing },
journal = { International Journal of Applied Information Systems },
issue_date = { Oct 2016 },
volume = { 11 },
number = { 5 },
month = { Oct },
year = { 2016 },
issn = { 2249-0868 },
pages = { 9-16 },
numpages = {9},
url = { https://www.ijais.org/archives/volume11/number5/941-2016451607/ },
doi = { 10.5120/ijais2016451607 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:04:12.935394+05:30
%A Himali Vaghela
%A Hardik Modi
%A Manoj Pandya
%A M. B. Potdar
%T A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 5
%P 9-16
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, some shape based features like area, perimeter, roundness, standard deviation etc. are used to recognize different types of white blood cells like monocyte, lymphocytes, eosinophil, basophil, neutrophils etc. Using image processing techniques, result can be obtained within 3-4 minute. To perform shape base features operation, contrast of RGB image has to be increased for better detection of white cells. After recognition of each and every cell, classification is performed to detect either it is CML (Chronic Myelogenous Leukemia) or CLL (chronic Lymphocytic leukemia). This algorithm is performed on 30 images. Out of 30, it is successful on 28 images. So it gives accuracy of 93.33%.

References
  1. Tang, J. (2010, April). A color image segmentation algorithm based on region growing. In Computer engineering and technology (iccet), 2010 2nd international conference on (Vol. 6, pp. V6-634). IEEE.
  2. Shi, Z., Jinlong, Q., & Guangjie, Q. (2008, October). Urinary Sediment Overlapping Cells Image Segmentation Based on Combination Strategy. In Computational Intelligence and Design, 2008. ISCID'08. International Symposium on (Vol. 2, pp. 3-7). IEEE.
  3. Preetha, M. M. S. J., Suresh, L. P., & Bosco, M. J. (2012, March). Image segmentation using seeded region growing. In Computing, Electronics and Electrical Technologies (ICCEET), 2012 International Conference on (pp. 576-583). IEEE.
  4. Shu, J., Fu, H., Qiu, G., Kaye, P., & Ilyas, M. (2013, July). Segmenting overlapping cell nuclei in digital histopathology images. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE (pp. 5445-5448). IEEE.
  5. Yanyu, L., & Deliang, L. (2009, December). A novel algorithm of image edge detection based on the order morphology. In Computer Science-Technology and Applications, 2009. IFCSTA'09. International Forum on (Vol. 1, pp. 458-461). IEEE.
  6. Huang, H., Wang, H., Guo, F., & Zhang, J. (2011, January). A Gray-scale image edge detection algorithm based on mathematical morphology. In Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on (Vol. 1, pp. 62-65). IEEE.
  7. Haixia, Y., Xiaohui, Z., & Yanjun, L. (2009, May). Algorithm of image enhancement based on order morphology transformation. In Information Technology and Applications, 2009. IFITA'09. International Forum on (Vol. 2, pp. 251-253). IEEE
  8. You, Y., & Yu, H. (2004, August). A separating algorithm based on granulometry for overlapping circular cell images. In Intelligent Mechatronics and Automation, 2004. Proceedings. 2004 International Conference on (pp. 244-248). IEEE.
  9. Guan, P. P., & Yan, H. (2011, July). Blood cell image segmentation based on the Hough transform and fuzzy curve tracing. In Machine Learning and Cybernetics (ICMLC), 2011 International Conference on (Vol. 4, pp. 1696-1701). IEEE.
  10. Huang, J. (2010, October). An improved algorithm of overlapping cell division. In Intelligent Computing and Integrated Systems (ICISS), 2010 International Conference on (pp. 687-691). IEEE.
  11. Li, S., Wu, L., & Sun, Y. (2011, November). Design and Implementation of an Improved Overlapped Cell Image Segmentation Method. In Robot, Vision and Signal Processing (RVSP), 2011 First International Conference on (pp. 204-207). IEEE.
  12. Tulsani, H., Gupta, R., & Kapoor, R. (2013, November). An improved methodology for blood cell counting. In Multimedia, Signal Processing and Communication Technologies (IMPACT), 2013 International Conference on (pp. 88-92). IEEE.
  13. Na, S., & Heru, X. (2009, November). The segmentation of overlapping milk somatic cells based on improved watershed algorithm. In Artificial Intelligence and Computational Intelligence, 2009. AICI'09. International Conference on (Vol. 3, pp. 563-566). IEEE.
  14. Chan, H., Jiang, B., & Jiang, B. (2009, May). Wavelet transform and morphology image segmentation algorism for blood cell. In Industrial Electronics and Applications, 2009. ICIEA 2009. 4th IEEE Conference on (pp. 542-545). IEEE
  15. Gongwen, X., Zhijun, Z., Weihua, Y., & Li'Na, X. (2014, June). On medical image segmentation based on wavelet transform. In 2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA) (pp. 671-674). IEEE.
  16. Ping, X., & Tinglong, T. (2011, October). Watershed image segmentation based on nonlinear combination morphology filter. In Image and Signal Processing (CISP), 2011 4th International Congress on (Vol. 4, pp. 2026-2029). IEEE.
  17. Chen, W. B., & Zhang, X. (2010, April). A New Watershed Algorithm for Cellular Image Segmentation Based on Mathematical Morphology. In Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on (pp. 653-656). IEEE.
  18. Shu, J., Fu, H., Qiu, G., Kaye, P., & Ilyas, M. (2013, July). Segmenting overlapping cell nuclei in digital histopathology images. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE (pp. 5445-5448). IEEE.
  19. Xia, Y., & Feng, D. (2009, September). A General Image Segmentation Model and its Application. In Image and Graphics, 2009. ICIG'09. Fifth International Conference on (pp. 227-231). IEEE.
  20. Sulaiman, S. N., Isa, N. A. M., Yusoff, I. A., & Othman, N. H. (2010, November). Overlapping cells separation method for cervical cell images. In Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on (pp. 1218-1222). IEEE.
  21. Preetha, M. M. S. J., Suresh, L. P., & Bosco, M. J. (2012, March). Image segmentation using seeded region growing. In Computing, Electronics and Electrical Technologies (ICCEET), 2012 International Conference on (pp. 576-583). IEEE.
  22. Gonzalez-Hidalgo, M., Guerrero-Pena, F. A., Herold-Garcia, S., Jaume-i-Capo, A., & Marrero-Fernandez, P. D. (2015). Red Blood Cell Cluster Separation from Digital Images for use in Sickle Cell Disease. Biomedical and Health Informatics, IEEE Journal of, 19(4), 1514-1525.
  23. Wenzhong, Y. (2009, June). Mathematical Morphology Based Enhancement for Chromosome Images. In Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009. 3rd International Conference on (pp. 1-3). IEEE.
  24. Pathak, V., Dhyani, P., Dhyani, M., & Sharma, P. (2014, May). Circular morphological feature extraction for nuclear medicine facilitation. In Recent Advances and Innovations in Engineering (ICRAIE), 2014 (pp. 1-6). IEEE.
  25. Qi, X., Xing, F., Foran, D. J., & Yang, L. (2012). Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. Biomedical Engineering, IEEE Transactions on, 59(3), 754-765.
  26. Nguyen, N. T., Duong, A. D., & Vu, H. Q. (2011). Cell splitting with high degree of overlapping in peripheral blood smear. International Journal of Computer Theory and Engineering, 3(3), 473.
  27. Mohapatra, S., Samanta, S. S., Patra, D., & Satpathi, S. (2011, February). Fuzzy based blood image segmentation for automated leukemia detection. In Devices and Communications (ICDeCom), 2011 International Conference on (pp. 1-5). IEEE.
  28. Mohapatra, S., & Patra, D. (2010, December). Automated leukemia detection using hausdorff dimension in blood microscopic images. In Emerging Trends in Robotics and Communication Technologies (INTERACT), 2010 International Conference on (pp. 64-68). IEEE.
  29. Mohapatra, S., Patra, D., & Satpathi, S. (2010, December). Image analysis of blood microscopic images for acute leukemia detection. In Industrial Electronics, Control & Robotics (IECR), 2010 International Conference on (pp. 215-219). IEEE.
  30. Mohapatra, S., Patra, D., Kumar, S., & Satpathi, S. (2012, December). Kernel induced rough c-means clustering for lymphocyte image segmentation. In Intelligent Human Computer Interaction (IHCI), 2012 4th International Conference on (pp. 1-6). IEEE.
  31. Mohapatra, S., & Patra, D. (2010, December). Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images. In Systems in Medicine and Biology (ICSMB), 2010 International Conference on (pp. 49-54). IEEE.
  32. Mohammed, E. A., Mohamed, M. M., Naugler, C., & Far, B. H. (2013, May). Chronic lymphocytic leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding. In Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on (pp. 1-5). IEEE.
  33. Lim, H. N., Mashor, M. Y., & Hassan, R. (2012, February). White blood cell segmentation for acute leukemia bone marrow images. In Biomedical Engineering (ICoBE), 2012 International Conference on (pp. 357-361). IEEE.
  34. Fatma, M., & Sharma, J. (2014, November). Identification and classification of acute leukemia using neural network. In Medical Imaging, m-Health and Emerging Communication Systems (MedCom), 2014 International Conference on (pp. 142-145). IEEE.
  35. Akrimi, J. A., Suliman, A., George, L. E., & Ahmad, A. R. (2014, November). Classification red blood cells using support vector machine. In Information Technology and Multimedia (ICIMU), 2014 International Conference on (pp. 265-269). IEEE.
  36. Jabar, F. H., Ismail, W., Salam, R. A., & Hassan, R. (2013, December). Image Segmentation Using an Adaptive Clustering Technique for the Detection of Acute Leukemia Blood Cells Images. In Advanced Computer Science Applications and Technologies (ACSAT), 2013 International Conference on (pp. 373-378). IEEE.
  37. Abdul Nasir, A. S., Mashor, M. Y., & Rosline, H. (2011, May). Unsupervised colour segmentation of white blood cell for acute leukaemia images. In Imaging Systems and Techniques (IST), 2011 IEEE International Conference on (pp. 142-145). IEEE.
  38. Berge, H., Taylor, D., Krishnan, S., & Douglas, T. S. (2011, March). Improved red blood cell counting in thin blood smears. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on (pp. 204-207). IEEE.
  39. Ge, J., Gong, Z., Chen, J. J., Liu, J., Nguyen, J., Yang, Z. Y., ... & Sun, Y. (2014, May). A system for automated counting of fetal and maternal red blood cells in clinical KB test. In Robotics and Automation (ICRA), 2014 IEEE International Conference on (pp. 1706-1711). IEEE.
  40. Supardi, N. Z., Mashor, M. Y., Harun, N. H., Bakri, F. A., & Hassan, R. (2012, March). Classification of blasts in acute leukemia blood samples using k-nearest neighbour. In Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on (pp. 461-465). IEEE.
  41. Rawat, J., Singh, A., Bhadauria, H. S., & Kumar, I. (2014, December). Comparative analysis of segmentation algorithms for leukocyte extraction in the acute Lymphoblastic Leukemia images. In Parallel, Distributed and Grid Computing (PDGC), 2014 International Conference on (pp. 245-250). IEEE.
  42. Das, B. K., Jha, K. K., & Dutta, H. S. (2014, March). A New Approach for Segmentation and Identification of Disease Affected Blood Cells. In Intelligent Computing Applications (ICICA), 2014 International Conference on (pp. 208-212). IEEE.
  43. Madhloom, H. T., Kareem, S. A., & Ariffin, H. (2012, November). A Robust Feature Extraction and Selection Method for the Recognition of Lymphocytes versus Acute Lymphoblastic Leukemia. In Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on (pp. 330-335). IEEE.
  44. Putzu, L., & Di Ruberto, C. (2013, January). White blood cells identification and counting from microscopic blood image. In Proceedings of World Academy of Science, Engineering and Technology (No. 73, p. 363). World Academy of Science, Engineering and Technology (WASET).
  45. Himali P Vaghela, Hardik Modi, Manoj Pandya and M B Potdar. Article: Leukemia Detection using Digital Image Processing Techniques. International Journal of Applied Information Systems 10(1):43-51, November 2015. Published by Foundation of Computer Science (FCS), NY, USA
  46. Figure 1.(b) [Online]: Available at: http://medicineworld.org/images/blogs/4-2009/chronic-lymphoid-leukemia.jpg
Index Terms

Computer Science
Information Sciences

Keywords

Chronic leukemia detection shape based features extraction and identification image classification Medical Image Processing

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