An Exploration of the Image Processing Techniques for the Detection of Leukemia
Author : A. Premnath and V. S. MeenakshiVolume 7 No.2 July-December 2018 pp 96-99
Abstract
In the pathological diagnostic method, categorization of blood cell has more essential to detect and analyze the disease. The complications that are connected with blood can be distributed only after the blood cell classification. The illness that begins with the bone marrow is the Leukemia. Therefore, it must be handled at the beginning step and proceeds to death if continuing untreated. This present research elucidates an investigation of diagnosing leukemia from microscopic blood image exhausting various image processing algorithms.
Keywords
Image Processing, Leukemia, Blood Cell, Noise Removal, Feature Extraction, Segmentation, Classification
Full Text:
References
[1] “Understanding Leukemia”, Leukemia and Lymphoma Society Fighting Blood Cancers.
[2] Suryani, Esti, Wiharto Wiharto, and Nizomjon Polvonov, “Identification and counting White blood cells and red blood cells using image processing case study of leukemia”, International Journal of Computer Science & Network Solutions, Vol. 2, No. 6, 2015.
[3] Kasmin, Fauziah, Anton Satria Prabuwono, and Azizi Abdullah. “Detection of Leukemia in Human Blood Sample Based On Microscopic Images: A Study.” Journal of Theoretical & Applied Information Technology Vol. 46, No. 2, December 2012.
[4] Rege M.V, Dr.B.W. Gawli, “Detection of Leukemia in Human blood sample through Image Processing: A Review”, International Journal of Modern Trends in Engineering and Research, Vol. 2, No. 10, October 2015.
[5] Niranjan Chatap, Sini Shibu, “Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour”, (IOSR-JCE), Vol. 16, No. 5, Ver. III, pp. 79-87, 2014.
[6] A. Arputha Regina, “Detection of Leukemia with Blood Microscopic Images”, IJIRCCE, Vol.3, Special Issue. 3, April 2015.
[7] Tejashri G. Patil and V. B. Raskar., “Blood microscopic image segmentation & acute leukemia detection”, IJERMT, Vol. 4, No. 9, SEP 2015.
[8] C. Vidhya, P. Saravana Kumar, K. Keerthika, C. Nagalakshmi and B. Medona devi., “Classification of acute lymphoblastic leukemia in blood microscopic images using SVM”, ICETSH-2015.
[9] Mrs. Trupti A. Kulkarni-Joshi, Prof. Dilip S. Bhosale., “A Fast Segmentation Scheme for Acute Lymphoblastic Leukemia Detection”, Vol. 3, No. 2, February.
[10] Himali P. Vaghela, Hardik Modi, and Manoj Pandya., “Leukemia Detection using Digital Image Processing Techniques” (IJAIS) – ISSN: 2249-0868 Vol. 10 – No.1, November 2015.
[11] Sonal G. Deore and Prof. Neeta Nemade, “Image Analysis Framework for Automatic Extraction of the Progress of an Infection” (IJARCSSE), Vol. 3, No. 6, June 2013.
[12] Sos Agaian, Monica Madhukar and Anthony T. Chronopoulos, “Automated Screening System for Acute Myelogenous Leukemia Detection in Blood Microscopic Images,” IEEE Systems Journal.
[13] Emad A. Mohammed, Mostafa and M. A. Mohammed, Christopher Naugler, Behrouz H. Far, “Chronic Lymphocytic Leukemia Cell Segmentation From Microscopic Blood Images Using Watershed Algorithm and Optimal Thresholding,” 26th IEEE Canadian Conference Of Electrical And Computer Engineering (CCECE), 2013.
[14] Ms. Minal D. Joshi, Prof. Atul H. Karode and Prof. S. R. Suralkar “White Blood Cells Segmentation and Classification to Detect Acute Leukemia”, (IJETTCS), Vol. 2, No. 3, May-June 2013.
[15] S. T. Khot, Sneha Bhalekar, Divya Jaggi and Dolly Rani., “An innovative approach in myelogenous leukemia detection using attributes analysis”, (IJAEEE), Vol. 2, No. 5, 2013.
[16] Subhan, Ms. Parminder Kaur., “Significant Analysis of Leukemic Cells Extraction and Detection Using KNN and Hough Transform Algorithm” International Journal of Computer Science Trends and Technology (IJCST), Vol. 3, No. 1, Jan-Feb 2015.
[17] Monika Mogra, 2, Vivek Srivastava., “A Comprehensive Review of Analysis of Counting Blood Cells Using Different Image Processing Algorithms” International Journal of Engineering Science Invention, Vol. 3, No. 6, pp. 29-31, 2014.
[18] Ms. Sneha Dhakne, Ms. Kumudini K. Borkute and Ms. Priyanka Ikhar, “Detection of a cancer cell in blood samples using an Effective algorithm” (IJCESR), Vol. 2, No. 6, 2015.
[19] Jamali Firmat Banzi and Xue Zhaojun, “Detecting Morphological Nature of Cancerous Cell Using Image Processing Algorithms” International Journal of Scientific and Research Publications, Vol. 3, No. 12, December 2013,
[20] W. Qiang and Z. Zhongli, “Reinforcement Learning, Algorithms, and Its Application,” International Conference on Mechatronic Science, Electric Engineering and Computer, Jilin, China, August 19-22, 2011, pp. 143-1146.
[21] T. Markiewicz, S. Osowski, B. Marianska and L. Moszczynski, “Automatic Recognition of the Blood Cells of Myelogenous Leukemia Using SVM”, Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, pp. 2496-2501, July 31 – August 4, 2005.
[22] S. Jagadeesh, Dr. E. Nagabhooshanam and Dr.S.Venkatachalam., “Image processing based approach to cancer cell prediction in blood samples”, International Journal of Technology and Engineering Sciences, Vol. 1, No. 1.
[23] K. S. Abdul Nasir, N. Mustafa and N. F. Mohd. Nasir, “Application of Thresholding Technique in Determining Ratio of Blood Cells For leukemia Detection”, Proceedings of International Conference on methods (ICoMMS), 11th–13th Oct 2009, Batu Ferringhi, Penang, Malaysia, 2009.
[24] Aimi Abdul Nasir and Mohd Yusoff Mashor, Hassan,” of acute Leukaemia Cells Using multilayer perceptron and Simplified Fuzzy ARTMAP Neural Networks, IAJIT.
[25] N. H. Harun, M. Y. Mashor, A. S. Abdul Nasir and H. Neighbor, Automated Classification of Blasts in Acute leukemia Blood Samples Using HMLP Network, Proceedings of 3rd International Conference on Computing and Informatics (ICOCI), 8th–9th”, Bandung, Indonesia, 2011.