Histogram Equalization with Filtering Techniques for Enhancement of Low Quality Microscopic Blood Smear Images

Laghouiter Oussama, Muhammad Mahadi Abdul Jamil, Wan Mahani Hafizah, Mohamad Nazib Adon


This paper presents image enhancement and filtering techniques for microscope blood smear image, in order to improve low image quality that have characteristics: blurred, the diminished true color of objects which are cells , unclear boundary and low contrast between the cells and background. Therefore in this paper proposed histogram equalization (HE) technique followed with filtering techniques such as median filter. HE utilizing to adjust the contrast which based on intensity pixels values, hence able to measure image quality through image histogram as shown in results, while removing noise from the images using filtering and gamma correction parameter in order to distinguish between background and foreground (cells) to get clear borders also. These techniques have been implemented on 46 blood samples. The proposed method successfully improve the readability of the cells in the low quality of blood smear images this mean that contain more information with a good effectiveness which lead for the correct sickness detection and data analysis.


Blood Samples; Histogram Equalization Image Filtering; Image Enhancement;

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Gupta, S. and Purkayastha, M.S.S., 2012. Image enhancement and analysis of microscopic images using various image processing techniques. Proceedings of the International Journal of Engineering Research and Applications, 2(3), pp.44-8.

Kolin, D.L. and Wiseman, P.W., 2007. Advances in image correlation spectroscopy: measuring number densities, aggregation states, and dynamics of fluorescently labeled macromolecules in cells. Cell biochemistry and biophysics, 49(3), pp.141-164.

Buttarello, M. and Plebani, M., 2008. Automated blood cell counts. American journal of clinical pathology, 130(1), pp.104-116.

Behrenbruch, C.P., Petroudi, S., Bond, S., Declerck, J.D., Leong, F.J. and Brady, J.M., 2014. Image filtering techniques for medical image post-processing: an overview. The British journal of radiology.

Laghouiter, O., Jamil, A., Mahadi, M., Mahmud, W., & Hafizah, W. M. (2015). Image segmentation techniques for red blood cell: on overview. Jurnal Teknologi, 77(6), 71-75.

Bedi, S.S. and Khandelwal, R., 2013. Various image enhancement techniques-a critical review. International Journal of Advanced Research in Computer and Communication Engineering, 2(3).

Tan, X. and Triggs, B., 2010. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE transactions on image processing, 19(6), pp.1635-1650.

Felzenszwalb, P.F. and Huttenlocher, D.P., 2004. Efficient graph-based image segmentation. International journal of computer vision, 59(2), pp.167-181.

Gonzalez, J.P. and Ozguner, U., 2000. Lane detection using histogrambased segmentation and decision trees. In Intelligent Transportation Systems, 2000. Proceedings. 2000 IEEE (pp. 346-351). IEEE.

Sharif, J. M., Miswan, M. F., Ngadi, M. A., Salam, M. S. H., & bin Abdul Jamil, M. M. (2012, February). Red blood cell segmentation using masking and watershed algorithm: A preliminary study. In Biomedical Engineering (ICoBE), 2012 International Conference on (pp. 258-262). IEEE.

Gonzalez, R.C. and Woods, R.E., 2007. Image processing. Digital image processing, 2.

Ibrahim, H. and Kong, N.S.P., 2007. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4).

Kim, J.Y., Kim, L.S. and Hwang, S.H., 2001. An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE transactions on circuits and systems for video technology, 11(4), pp.475-484.

Sun, T. and Neuvo, Y., 1994. Detail-preserving median based filters in image processing. Pattern Recognition Letters, 15(4), pp.341-347.

Gonzalez, Rafael C., and Richard E. Woods. "Image processing." Digital image processing 3 p 136 (2012).

Wu, W., Tan, O., Pappuru, R.R., Duan, H. and Huang, D., 2013. Assessment of frame-averaging algorithms in OCT image analysis. Ophthalmic Surgery, Lasers and Imaging Retina, 44(2), pp.168-175.


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ISSN: 2180-1843

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