Detection of Leukemia Using Image Processing

A. Sivasangari, G. Sasikumar

Abstract


Leukemia   disease   is one   of    the   leading   causes   of death   among   human. Its  cure  rate and  prognosis   depends   mainly   on  the  early  detection   and  diagnosis  of   the  disease. At  the  moment, identification  of  blood  disorders  is  through   visual  inspection  of  microscopic  images  by  examining  changes  like  texture, geometry, colour  and   statistical  analysis  of  images . This  project  aims  to  preliminary  of  developing  a  detection  of  leukemia  types  using   microscopic  blood  sample using MATLAB. Images  are  used  as  they  are  cheap  and  do  not  expensive  for testing  and  lab  equipment.

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References


FauziahKasmin, Anton SatriaPrabuwono, Azizi Abdullah, “Detection Of Leukemia in Human Blood Sample Based on Microscopis Images: A Study,” Journal Of Theoretical and Applied Information Technology, vol. 46 no 2, 2012.

High White Blood Cell Count [Online]. http://www.buzzle.com/articles/high-white-blood-cell-count.html

Ruggero DonidaLabati, Vincenzo Piuri, Fabio Scotti, “The Acute Lymphoblastic Leukemia Image Database for Image Processing,” UniversitaDegliStudi Di Milano, 10.1109/ICIP.2011.6115881, 2011.

RaymondH.chan,chung-waHo and Mila nikolova, “Salt and pepper noise reduction by mediantype noise detections and detail-preserving Regulation”, IEEE Transaction on image processing, Vol.14,No.10,October 2005

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, ISSN: 2320-8007, 2013

TheEncyclopedia of Surgery [Online]. : http://www.surgeryencyclopedia.com/Ce-Fi/Complete-Blood-Count.html

C.R., Valencio, M.N., Tronco, A.C.B.,Domingos, C.R.B., “Knowledge Extraction Using Visualization of Hemoglobin Parameters to Identify Thalassemia”,Proceedings of the 17th IEE Symposium onComputer Based Medical Systems, 2004, pp.1-6.

R., Adollah, M.Y., Mashor, N.F.M, Nasir, H.,Rosline, H., Mahsin, H., Adilah, “Blood CellImage Segmentation: A Review”, Biomed 2008, Proceedings 21, 2008, pp. 141-144.

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

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

M.C., Colunga, O.S., Siordia, S.J., Maybank, “Leukocyte Recognition Using EMAlgorithm”, MICAI 2009, LNAI 5845, Springer Verlag Berlin Heidelberg, 2009, pp. 545-555.

K.S., Srinivisan, D., Lakshmi, H., Ranganathan, N., Gunasekaran, “Non Invasive Estimation of Hemoglobin in Blood Using Color Analysis”, 1st International Conference on Industrial and Information System, ICIIS 2006, Sri Lanka, 8 – 11 August 2006, pp 547-549.

W., Shitong, W., Min, “A new Detection Algorithm (NDA) Based on Fuzzy Cellular Neural Networks for White Blood Cell Detection”, IEEE Transactions on Information Technology in Biomedicine, Vol. 10, No. 1, January 2006, pp. 5-10.

H., Shin, M.K., Markey, “A Machine Learning Perspective on the Development of Clinical Decision Support System Utilizing Mass Spectra of Blood Samples”, Journal of Biomedical Informatics 39. 2006, pp. 227-248.

M., Chitsaz, C., S., Woo, “Software Agent with Reinforcement Learning Approach for Medical Image Segmentation”.




DOI: https://doi.org/10.23956/ijarcsse.v8i4.632

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