Image Processing Approach for Malarial Parasite Identification
K M Khatri, V R Ratnaparkhe, S S Agrawal and A S Bhalchandra. Article: Image Processing Approach for Malarial Parasite Identification. IJCA Proceedings on National Conference on Growth of Technologies in Electronics, Telecom and Computers - India's Perception GTETC-IP:5-7, May 2014. Full text available. BibTeX
@article{key:article,
author = {K. M. Khatri and V. R. Ratnaparkhe and S. S. Agrawal and A. S. Bhalchandra},
title = {Article: Image Processing Approach for Malarial Parasite Identification},
journal = {IJCA Proceedings on National Conference on Growth of Technologies in Electronics, Telecom and Computers - India's Perception},
year = {2014},
volume = {GTETC-IP},
pages = {5-7},
month = {May},
note = {Full text available}
}
Abstract
Malaria is one of the serious infectious disease which is because of mosquito bites. Diagnosis of malaria is done by microscopic examination of blood. But this diagnosis method is time consuming and requires pathologists. This paper aims to introducing fast and accurate method based on image processing for malaria parasite identification. The database was generated by taking the microscopic images of blood of 30 malarial patients. Based on morphological operations total number of cells are counted. Infected cells are analyzed based on intensity profiles within the cells. The result is validated by comparing with manual analysis. This approach can be used in rural areas where less experts are available and the delayed diagnosis may lead to complications in patients health.
References
- WHO: World Malaria Report 2012 World Health Organization.
- Selena W. S. Sio, Weiling Sun, Saravana Kumar, Wong Zeng Bin, Soon Shan Tan, Sim Heng Ong, Haruhisa Kikuchi, Yoshiteru Oshima, Kevin S. W. Tan, 2006. "MalariaCount: An image analysis-based program for the accurate determination of parasitemia", Science Direct.
- Xianfeng Yang, Ping Xue, Qi Tian , "Invariant Salient Region Selection and Scale Normalization of Image" 'School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
- Siwei Lyu Eero P. Simoncelli, "Nonlinear Image Representation Using Divisive Normalization", Howard Hughes Medical Institute, and Center for Neuroscience New York University.
- Rafael C. Gonzalez, Richard E. Woods,"Digital Image Processing" Third Edition (2009).
- Young-Won Song and Satish S. Udpa, "A New Morphological Approach For Reducing Speckle Noise In Ultrasonic Images", Iowa State University Materials Characterization Research Group, Department of Electrical and Computer Engineering, Ames, IA 5001 1
- Sumi Yoshino ', Toshiyuki Tanaka I, Masaru Tanaka ', Hiroshi Oka2 "Application of Morphology for Detection of Dots in Tumor", Department of Applied Physics and Physico-Informatics, Keio University, 3-14-1 Hiyoshi Kohoku-ku, Yokohama-shi, 223-8522, Japan
- Renata Kalicka, Anna Pietrenko-D?browska, Seweryn Lipi?ski, "Efficiency of New Method of Removing The Noisy Background From The Sequence of MRI Scans Depending On Structuring Elements Used To Morphological Processing", Gda?sk University of Technology, Faculty of Electronics, Telecommunications and Informatics
- Marek Piotrowski, Piotr S. Szczepaniak, "Active Contour Based Segmentation Of Low-Contrast Medical Images", Institute of Computer Science, Technical University of tbdi, Lodz, Poland Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland.
- A. Nabout, HA. Now Eldin, "The topological contour closure requirement for object extraction from 2D-digital images. ", Group of Automatic Control and Technical Cybernetics University of Wuppertal, 42097 Wuppertal, Germany.