Blood Smear Image Based Malaria Parasite and Infected-Erythrocyte Detection and Segmentation
- 272 Downloads
In this study, an automatic malaria parasite detector is proposed to perceive the malaria-infected erythrocytes in a blood smear image and to separate parasites from the infected erythrocytes. The detector hence can verify whether a patient is infected with malaria. It could more objectively and efficiently help a doctor in diagnosing malaria. The experimental results show that the proposed method can provide impressive performance in segmenting the malaria-infected erythrocytes and the parasites from a blood smear image taken under a microscope. This paper also presents a weighted Sobel operation to compute the image gradient. The experimental results demonstrates that the weighted Sobel operation can provide more clear-cut and thinner object contours in object segmentation.
KeywordsMalaria parasite Blood smear image Edge detection Image segmentation
MHT and YKC conceived the study. SSY designed the approach and performed the computational analysis with CCJ. MHT and YKC supervised the work and tested the program. MHT, SSY, YKC and CCJ wrote the manuscript. MHT prepared the samples and collected the data. MHT and YKC contributed analyzing experimental studies. All authors read and approved the final manuscript. YKC and SSY contributed equally and are the correspondents as well as listed in alphabetical order.
- 3.Chan, Y. K., Tsai, M. H., Huang, D. C., and Zheng, Z. H., Leukocyte nucleus segmentation and nucleus lobe counting. BMC Bioinf 11(558):1–18, 2010.Google Scholar
- 4.Chiodini, P. L., Moody, A. H., and Manser, D. W., Atlas of medical helminthology and protozoology, 4th edition. Churchill Livingstone, Edinburgh, New York, 2001.Google Scholar
- 5.Cross, C., Malaria Control Measure. (http://malaria.wellcome.ac.uk/doc_WTD023987.html), November 2004.
- 6.Gelasca, E. D., Byun, J. B., Obara, and Manjunath, B. S., Evaluation and benchmark for biological image segmentation. 15th IEEE International Conference on Image Processing (ICIP 2008), San Diego, CA, USA, pp. 1816–1819, 2008.Google Scholar
- 7.Gonzalez, R. C., and Woods, R. E., Digital image processing, 3rd edition. Prentice Hall, Upper Saddle River, N.J, 2008.Google Scholar
- 8.Haupt, R. L., and Haupt, S. E., Practical genetic algorithms, vol. 2. John Wiley, Hoboken, N.J., 2004.Google Scholar
- 10.Le, M. T., Bretschneider, T. R., Kuss, C., and Preiser P. R., A novel semi-automatic image processing approach to determine plasmodium falciparum parasitemia in giemsa-stained thin blood smears. BMC Cell Biol. 9(15), 2008.Google Scholar
- 12.Nasir, A. S. A., Mashor, M. Y., and Mohamed, Z., Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering. WSEAS Trans. Biol. Biomed. 1(10):41–55, 2013.Google Scholar
- 13.Nixon, M. S., and Aguado, A. S., Feature extraction and image processing, 1st edition. Newnes, Oxford, 2002.Google Scholar
- 16.Perez-Jorge, E. V., and Herchline, T., Malaria: eMedicine Infectious Diseases. (http://emedicine.medscape.com/article/221134-overview).
- 20.Shi, Y. Q., and Sun, H., Image and video compression for multimedia engineering fundamentals, algorithms, standards. 2nd ed. CRC Press, Taylor & Francis Group, 2008.Google Scholar
- 23.Tsutsumi, Y., Case 206, Malaria Falciparum. (http://www.fujita-hu.ac.jp/~tsutsumi/case/case206.htm).
- 24.Vallejo, A. F., Chaparro, P. E., Benavides, Y., Álvarez, Á., Quintero, J. P., Padilla, J., Arévalo-Herrera, M., and Herrera, S., High prevalence of sub-microscopic infections in Colombia. Malar. J. 14(201), May 2015, doi: 10.1186/s12936-015-0711-6.
- 25.World Health Organization, World Malaria Report 2014, Geneva: World Health Organization, 2014.Google Scholar
- 26.Zuiderveld, K., Contrast limited adaptive histogram equalization. In: Heckbert, P. (Ed.) Graphics gems IV, Academic Press, pp. 474–485, 1994.Google Scholar
- 27.Zhou, Z., Wu, S., Chang, K. J., Chen, W. R., Chen, Y. S., Kuo, W. H., Lin, C. C., and Tsui, P. H., Classification of benign and malignant breast tumors in ultrasound images with posterior acoustic shadowing using half-contour features. J. Med. Biol. Eng. 35:178–187, 2015.PubMedCentralCrossRefPubMedGoogle Scholar