Digital Image Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Leukemia Detection using Image Processing


Affiliations
1 Department of ECE, SNS College of Engineering, Coimbatore, India
     

   Subscribe/Renew Journal


Leukemia is a type of cancer which causes death among human. Only its detection and diagnosis helps to increase its cure rate. Presently, identification of cancer cells or blood disorders is by inspecting the microscopic images visually. This is done by analyzing the variations in texture, geometry, colour and statistical analysis of images. This paper describes various feature extraction techniques that can be used to detect leukemia using microscopic blood sample images. Image analysis plays an important in this method. Here first the cell biology basics are discussed and then the implementation of our proposed technique is carried out. Since our aim is to provide the cheapest method, only images are used. The tool we have used for the detection of cancer cells is MATLAB.

Keywords

Leukemia, Blood Cells, Edge Detection, GLCM, Gabor, Wavelet, MATLAB.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Adollah .R, Mashor .M.Y, Nasir .N.F.M, Rosline .H, Mashin .H, Adillah .H, “Blood Image Segmentation: A Review”, Biomed 2008, Proceedings 21, 2008, pp. 141-144.

  • Deepika N. Patil and Uday P. Khot. “Image processing Based Abnormal Blood Cells Detection”, International Journal of Technical Research and Applications e-ISSN: 2320-8163.

  • Fauziah Kasmin, Anton Satri Prabuwono, Azizi Abdullah, “Detection of Leukemia in Human Blood Sample Based on Microscopic Images: A Study”, Journal of Theoretical and Applied Information Technology, Volume 46 No.2, December 2012.

  • Himali P. Vaghela, Hardik Modi, Manoj Pandya, M.B. Potdar “Leukemia Detection using Image processing Techniques”, International Journal of Applied Information Systems – ISSN:2249-0868, Volume 10 – No.1, November 2015.

  • Hirimutugoda .Y.M, Wijayarathna .G. “Artificial Intelligence-Based Approach for Determination of Haematalogic Diseases”, IEEE, 2009.

  • Malhar Bhatt, Shashi Prabha, “Detection of Abnormal Blood Cells using Image Processing Technique”, International Journal of Electrical and Electronics Engineers, ISSN – 2321-2055 (E), IJEEE, Volume 07, Issue 01, 2015.

  • Mohamed Ali, David Clausi “Using the Canny Edge Detector for Feature Extraction and Enhancement of Remote Sensing Images”, 0-7803-7031-7/01/$10.00 (C) 2001 IEEE.

  • Mohapatra .S, Patra .D, Satpathi .S, “Image Analysis of Blood Microscopic Images for Leukemia Detection”, International Conference on Industrial Electronics, Control and Robotics, IEEE, 2010, pp. 215-219.

  • Navin D. Jambhekar, “Red Blood Cells Classification using Image Processing”, Science Research Reporter 1(3): 151-154, Nov. 2011, ISSN: 2279-7846 (online).

  • Nur Alom Taludkar, Daizy Deb, Sudipta Roy, “Automated Blood Cancer Detection Using Image Processing Based on Fuzzy system”, International Journal of advanced Research in Computer Science and Software Engineering, Volume 4, Isssue 8, August 2014.

  • Osowski .S, Siroic .R, Markiewicz .T, Siwek .K, “Application of Support Vector Machine and Genetic Algorithm for Improved Blood Cell Recognition”, IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 7, July 2009, pp. 2159-2168.

  • Piurri .V, Scotti .F, “Morphological Classification of Blood Leukocytes by Microscope Images”, IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Boston, MA, USA, 14 – 16 July 2004, pp. 103-108.

  • Ritter .N, Cooper .J, “Segmentation and Border Identification of cells in Images of Peripheral Blood Smear Slides”, 30th Australian Computer Science Conference, Conference in Research and Practice in Information Technology, Vol. 62, 2007, pp. 161-169.

  • Ruberto .C.D, Dempester .A, Khan .S, Jarra .B, “Analysis of Infected Blood cell Images using Morphological Operators, Image and Vision Computing”, IEEE Vol. 20, 2002, pp. 133-146.

  • S. Jagadeesh, Dr. E. Nagabhooshanam, Dr. S. Venkatachalam, “Image Processing Based approach to Cancer Cell Prediction in Blood Samples”, International Journal of Technology and Engineering Sciences, Vol.1(1), ISSN: 2320-8007.

  • Sabino .D.M.U, Costa .L.D.F, Rizzatti .E.G, Zago .M.A, “A Texture Approach to Leukocyte recognition, Real time Imaging”, IEEE Vol. 10, 2004, pp. 205-206.

  • Shailesh J. Mishra, Mrs. A.P.Deshmukh. “Detection of Leukemia Using MALAB”, International Journal of Advanced Research in Electronics and Communication Engineering, Volume 4, Issue 2, February 2015.

  • Tek .F.B, Dempster .A.G, Kale .I, “Parasite Detection and Identification for Automated Thin Blood Film Malaria Diagnosis, Computer Vision and Image Understanding”, IEEE Vol. 114, 2010, pp. 21-32.

  • Valencio .C.R, Tronco .M.N, Domingos .A.C.B, C.R.B. “Knowledge extraction using visualization of Heamoglobin parameters to identify thalassemia”, proceedings of the 17th IEEE symposium on Computer based medical systems, 2002, pp. 1-6.

  • Wongseree .W, Chaiyaratna .N, “Thalassaemic Patient Classification Using a Neural Network and Genetic Programming”, IEEE, 2003, pp. 2926-2931.


Abstract Views: 364

PDF Views: 3




  • Leukemia Detection using Image Processing

Abstract Views: 364  |  PDF Views: 3

Authors

V. Venmathi
Department of ECE, SNS College of Engineering, Coimbatore, India
K. N. Shobana
Department of ECE, SNS College of Engineering, Coimbatore, India
Akshaya Kumar
Department of ECE, SNS College of Engineering, Coimbatore, India
D. Gajeshwaran
Department of ECE, SNS College of Engineering, Coimbatore, India

Abstract


Leukemia is a type of cancer which causes death among human. Only its detection and diagnosis helps to increase its cure rate. Presently, identification of cancer cells or blood disorders is by inspecting the microscopic images visually. This is done by analyzing the variations in texture, geometry, colour and statistical analysis of images. This paper describes various feature extraction techniques that can be used to detect leukemia using microscopic blood sample images. Image analysis plays an important in this method. Here first the cell biology basics are discussed and then the implementation of our proposed technique is carried out. Since our aim is to provide the cheapest method, only images are used. The tool we have used for the detection of cancer cells is MATLAB.

Keywords


Leukemia, Blood Cells, Edge Detection, GLCM, Gabor, Wavelet, MATLAB.

References