Histogram Equalization for Improving Quality of Low-Resolution Ultrasonography Images

Retno Supriyanti, Subkhi Adhi Priyono, Eko Murdyantoro, Haris Budi Widodo


The current development of digital image processing techniques have been very rapid. Application of digital image processing both hardware and software are available with a variety of features as a form of superiority. Medical ultrasonography is one of the results of digital image processing technology.  It is a kind of  diagnostic imaging technique with ultrasonic that is used to produce images of internal organs and muscles, size, structure, and wound pathology, which makes this technique is useful for checking organ. However the images produced by low resolution ultrasonography device is not fully produce clear information. In this research we use histogram equalization to improve image quality. In this paper we emphasize on the comparison of the two methods in the histogram equalization, namely Enhance Contrast Using Histogram Equalization (ECHE) and Contrast-Limited Adaptive Histogram Equalization (CLAHE). The results showed that CLAHE give the best results, with the parameter value Nbins 256 and Distribution Rayleigh with MSE value 9744.80 and PSNR value 8.284150.


Histogram Equalization, Ultrasonography image, ECHE, CLAHE, MSE, PNSR


Mayo Clinic, “Fetal Ultrasound,” Patient Care and Health Info, 2016.

V. Chan and A. Perlas, “Basics of Ultrasound Imaging,” in Atlas of Ultrasound-Guided Procedures in Interventional Pain Management, Toronto, Canada: Springer Science+Business Media, 2011, pp. 13–19.

N. Shet, J. Chen, and E. L. Siegel, “Continuing challenges in defining image quality,” Pediatr. Radiol., vol. 41, no. 5, pp. 582–587, 2011.

J. H. Cho, H. K. Lee, K. R. Dong, and W. K. Chung, “A study on image quality management in PACS used by Korean hospitals,” J. Digit. Imaging, vol. 25, no. 6, pp. 720–728, 2012.

N. Mohanapriya, “Comparative Study of Different Enhancement Techniques for Medical Images,” Int. J. Comput. Appl., vol. 61, no. 20, pp. 39–44, 2013.

K. Panetta, A. Samani, and S. Agaian, “Choosing the Optimal Spatial Domain Measure of Enhancement for Mammogram Images,” Int. J. Biomed. Imaging, vol. 2014, no. 937849, 2014.

K. Karthikeyan and C. Chandrasekar, “Wavelet-based Image Enhancement Techniques for Improving Visual Quality of Ultrasonic Images,” Int. J. Comput. Appl., vol. 39, no. 17, pp. 975–8887, 2012.

B. Lalotra, R. Vig, and S. Budhiraja, “Multimodal medical image fusion using Butterworth high pass filter and Cross bilateral filter,” in MATEC Web of Conferences, 2016, vol. 01021.

M. Vaezi, C. K. Chua, and S. M. Chou, “Improving the process of making rapid prototyping models from medical ultrasound images,” Rapid Prototyp. J., vol. 18, no. December 2010, pp. 287–298, 2012.

D. K. Kumar, D. A. Kumar, G. S. A. I. Pujitha, K. B. A. Sai, K. M. Kalyan, B. S. R. I. S. Ananta, M. N. Kishore, and C. Engineering, “EDGE AND TEXTURE PRESERVING HYBRID ALGORITHM FOR DENOISING INFIELD ULTRASOUND MEDICAL IMAGES,” J. Theor. Appl. Inf. Technol., vol. 86, no. 1, pp. 120–130, 2016.

K. Nagata, M. Fujiwara, H. Kanazawa, T. Mogi, N. Iida, T. Mitsushima, A. T. Lefor, and H. Sugimoto, “Evaluation of dose reduction and image quality in CT colonography: Comparison of low-dose CT with iterative reconstruction and routine-dose CT with filtered back projection,” Eur. Radiol., vol. 25, no. 1, pp. 221–229, 2014.

M. T. Gadallah, “Visual Improvement for Hepatic Abscess Sonogram by Segmentation after Curvelet Denoising,” I.J. Image, Graph. Signal Process., vol. 7, no. November 1999, pp. 9–17, 2013.

J. Kaur and A. Jindal, “Article: Comparison of Thyroid Segmentation Algorithms in Ultrasound and Scintigraphy Images,” Int. J. Comput. Appl., vol. 50, no. 23, pp. 24–27, 2012.

Y. Chen, X. M. Zhou, and D. C. Liu, “Localizing Region-Based Level-set Contouring for Common Carotid Artery in Ultrasonography,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 11, no. 4, pp. 791–796, 2013.

W.-G. Teng and P.-L. Chang, “Identifying regions of interest in medical images using self-organizing maps.,” J. Med. Syst., vol. 36, no. 5, pp. 2761–8, 2012.

B. Gutiérrez-Becker, F. Arámbula Cosío, M. E. Guzmán Huerta, J. A. Benavides-Serralde, L. Camargo-Marín, and V. Medina Bañuelos, “Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model,” Med. Biol. Eng. Comput., vol. 51, no. 9, pp. 1021–1030, 2013.

H. E. Kocer, K. K. Cevik, M. Sivri, and M. Koplay, “Measuring the Effect of Filters on Segmentation of Developmental Dysplasia of the Hip,” Iran. J. Radiol., vol. 13, no. 3, 2016.

R. Gupta, I. Elamvazuthi, S. C. Dass, I. Faye, P. Vasant, J. George, and F. Izza, “Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method.,” Biomed. Eng. Online, vol. 13, no. 1, p. 157, 2014.

J. Y. Huang, K. J. Lin, and Y. S. Chen, “Fully automated computer-aided volume estimation system for thyroid planar scintigraphy,” Comput. Biol. Med., vol. 43, no. 10, pp. 1341–1352, 2013.

C. P. Loizou, “A review of ultrasound common carotid artery image and video segmentation techniques,” Med. Biol. Eng. Comput., vol. 52, no. 12, pp. 1073–1093, 2014.

R. Supriyanti, A. S. Setiadi, Y. Ramadhani, and H. B. Widodo, “Point Processing Method for Improving Dental Radiology Image Quality,” Int. J. Electr. Conputer Eng., vol. 6, no. 4, pp. 1587–1594, 2016.

H. B. Widodo, A. Soelaiman, Y. Ramadhani, and R. Supriyanti, “Calculating Contrast Stretching Variables in Order to Improve Dental Radiology Image Quality,” Int. Conf. Eng. Technol. Sustain. Dev., vol. 012002, 2015.

R. Supriyanti, S. Suwitno, H. B. Widodo, and T. I. Rosanti, “Brightness and Contrast Modification in Ultrasonography Images Using Edge Detection Results,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 14, no. 3, pp. 1090–1098, 2016.

R. Supriyanti, E. Pranata, Y. Ramadhani, and T. I. Rosanti, “Separability Filter for Localizing Abnormal Pupil:Identification of Input Image,” Telkomnika, vol. 11, no. 4, pp. 783–790, 2013.

R. Supriyanti, D. . Putri, E. Murdyantoro, and H. B. Widodo, “Comparing edge detection methods to localize uterus area on ultrasound image,” in International conference of Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2013, pp. 152–155.

R. Supriyanti, H. Habe, M. Kidode, and S. Nagata, “Compact cataract screening system : Design and practical data acquisition,” in Proceeding of International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2009, pp. 1–6.

R. Supriyanti, H. Habe, M. Kidode, and S. Nagata, “Extracting Appearance Information Inside the Pupil for Cataract Screening,” in 11 th IAPR Conference on Machine Vision Applications, 2009, pp. 342–345.

R. Supriyanti, H. Habe, M. Kidode, and S. Nagata, “A simple and robust method to screen cataracts using specular reflection appearance,” in Proc. SPIE 6915 Medical Imaging, 2008.

R. C. Gonzales and R. E. Woods, Digital Image Processing, 3rd editio. New Jersey: Prentice Hall, 2008.

DOI: http://dx.doi.org/10.12928/telkomnika.v15i3.5537


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