Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Joylin Rini, R.
- Classification of Distinct Plasmodium Species in Thin Blood Smear Images using Kapur Segmentation Strategy
Abstract Views :165 |
PDF Views:0
Authors
T. Abimala
1,
R. Joylin Rini
1
Affiliations
1 ICE Department, St. Joseph’s College of Engineering, Chennai, IN
1 ICE Department, St. Joseph’s College of Engineering, Chennai, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 5 (2019), Pagination: 2309-2316Abstract
Malaria is a mosquito-borne irresistible chronic sickness of humans and other creatures brought about by parasitic protozoans which belong to Plasmodium type. Malaria causes side effects that incorporate fever, fatigue, vomiting and cerebral pains. If not properly treated, it can bring about yellow skin, unconsciousness and even death. Malaria is induced by five species of plasmodium- P. Falciparum, P. Vivax, P. Malariae, P. Ovale and P. Knowlesi. In this paper, a venture has been formulated to develop an automated diagnosis strategy for classifying the malarial parasites. The blood smear images obtained from CDC database were segmented by utilizing Fuzzy C Means (FCM) and kapur segmentation strategies. The segmented image has been further utilized to extract features and the extracted measurements have been utilized for classifying the plasmodium species using SVM (Support Vector Machine) classification technique.Keywords
Plasmodium, P. Falciparum, P. Vivax, P. Malariae, P. Ovale, P. Knowlesi, Support Vector Machine.
References
- N. Siva Balan et al, “Optimal Multilevel Image Thresholding to Improve the Visibility of Plasmodium sp. in Blood Smear Images”, Proceedings of the International Conference on Soft Computing Systems, Advances in Intelligent Systems and Computing , Springer (2016).
- K. Manickavasagam et al, “Development of Systems for Classification of Different Plasmodium Species in Thin Blood Smear Microscopic Images”, J. Adv. Microsc.Vol. 9, No.2Res. 2014.
- Feminna Sheeba et al, “Detection of Plasmodium Falciparum in Peripheral Blood Smear Images”, Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications, Springer 2013.
- Sri Widodo et al, “Texture Analysis To Detect Malaria Tropica In Blood Smears Image Using SUPPORT VECTOR MACHINE”, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163, Volume 1 Issue 8 (September 2014).
- A.S.Abdul Nasir et al, “Segmentation Based Approach for Detection of Malaria Parasites Using Moving K-Means Clustering”, 2012 IEEE EMBS International Conference on Biomedical Engineering and Sciences, Langkawi ,17th - 19th December 2012
- Zazilah May et al, “Automated Quantification and Classification of Malaria Parasites in Thin Blood Smears”, 2013 IEEE International Conference on Signal and Irnage Processing Applications (ICSIPA).
- J. Somasekar et al, “Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging”, Computers and Electrical Engineering xxx (2015).
- Abimala.T et al, “Optimal Multi-level Thresholding for RGB Images using Kapur’s Entropy and Firefly Algorithm”, International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.87 (2015).
- Pal, N. and Pal, S. A review on image segmentation techniques, Pattern Recognition, 26(9) : 1277-1294, 1993.
- Sathya, P.D. and Kayalvizhi, R. Optimal multilevel thresholding using Bacterial for aging algorithm, Expert Systems with Applications, 38:15549–15564, 2011.
- Sezgin, M and Sankar, B. Survey over Image Thresholding Techniques and Quantitative Performance Evaluation, Journal of Electronic Imaging. 13(1): 146 –165, 2004.
- Kapur, J. N., Sahoo, P. K., and Wong, A. K. C. A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision Graphics Image Processing, 29: 273–285, 1985.
- Yang, X.S. Nature-Inspired Metaheuristic Algorithms, Luniver Press, Frome, UK, 2nd edition, 2011.
- Fister, I., Yang, X. S., Fister, D., and FisterJr, I. (2014). Firefly algorithm: a brief review of the expanding literature. In Cuckoo Search and Firefly Algorithm (pp. 347-360). Springer International Publishing.
- Rajinikanth V, Sri Madhava Raja N, Latha K. Optimal Multilevel Image Thresholding: An Analysis with PSO and BFO Algorithms. Aust. J. Basic and Appl. Sci., 8(9): 443-454, 2014.
- Sarkar S, Das S. Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy – A Differential Evolution Approach. IEEE T. on Image Processing, 22(12): 4788-4797, 2013.
- Papamarkos, N., Strouthopoulos, C and Andreadis, I. Multithresholding of color and gray-level images through a neural network technique, Image and Vision Computing, 18(3): 213–222, 2000.
- Rougemont M, Van Saanen M, Sahli R, Hinrikson HP, Bille J et al. (2004) Detection of four plasmodium species in blood from humans by 18s rrnagenesubunit-based and species-specific realtime pcr assays. J ClinMicrobiol 42:5636-5643.
- Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI (2005) The global distribution of clinical episodes of plasmodium falcipar ummalaria, Nature 434: 214-217.
- H. Reyburn, (2010) New who guidelines for the treatment of malaria. BMJ 28:340.
- Toha S, Ngah U (2007) Computer aided medical diagnosis for the identification of malaria parasites, in: Signal Processing, Communications and Networking, ICSCN’07. International Conference on, IEEE.521-522.
- Frean J (2010) Microscopic determination of malaria parasite load: role of image analysis. Micrsocopy: Science, Technology, Applications, and Eductaion862-866.
- Somasekar J, Reddy B, Reddy E, Lai C (2011) Computer vision for malariaparasite classification in erythrocytes, International Journal on ComputerScience and Engineering 3: 2251-2256.
- Edison M, Jeeva J, Singh M (2011) Digital analysis of changes by Plasmodiumvivax malaria in erythrocytes. Indian Journal of Experimental Biology 49: 11-15.
- Anggraini D, Nugroho AS, Pratama C, Rozi IE, Iskandar A A, et al. (2011) Automated status identification of microscopic images obtained from malariathin blood smears. In: Electrical Engineering and Informatics (ICEEI), 2011International Conference on, IEEE. 1-6.
- Tek FB, Dempster AG, Kale I (2010) Parasite detection and identification for automated thin blood film malaria diagnosis, Computer Vision and Image Understanding 114: 21-32.
- Elter M, Hasslmeyer E, Zerfass T (2011) Detection of malaria parasites in thickblood films. Conf Proc IEEE Eng Med Biol Soc. 2011. 5140-5144.
- Mandal S, Kumar A, Chatterjee J, Manjunatha M, Ray A (2010) Segmentation of blood smear images using normalized cuts for detection of malarial parasites. in: India Conference (INDICON), 2010 Annual IEEE1-4.
- Makkapati VV, Rao RM (2011) Ontology-based malaria parasite stage and species identification from peripheral blood smear images. Conf. Proc. IEEEEng Med BiolSoc 2011 2011:6138-41.