PENINGKATAN KUALITAS CITRA MALARIA MENGGUNAKAN METODE CONTRAST ENHANCEMENT BERBASIS HISTOGRAM

Doni Setyawan, Aryati Wuryandari, Rahmat Ari Wibowo

Abstract


Akusisi citra digital malaria dapat menghasilkan citra dengan kontras rendah. Hal ini dapat disebabkan karena pengaturan pencahayaan pada mikroskop yang rendah dan penyesuaian parameter pada image capturing software yang tidak tepat. Citra malaria dengan kontras rendah dapat menjadikan segmentasi eritrosit dan plasmodium dari background menjadi tidak akurat. Segmentasi yang tidak akurat juga berdampak terhadap proses ekstraksi fitur dan klasifikasi pada sistem diagnosis malaria otomatis. Oleh karena itu, diperlukan contrast enhancement pada tahap pra pemrosesan untuk menghasilkan citra dengan kualitas yang baik. Pada penelitian ini, kinerja metode contrast enhancement berbasis histogram, yaitu Histogram Equalization (HE), Adapative Histogram Equalization (AHE), dan Contrast Limited Adaptive Histogram Equalization (CLAHE) diuji pada citra malaria untuk mengetahui metode yang menghasilkan citra dengan kualitas terbaik.  Berdasarkan nilai MSE dan PSNR, metode CLAHE memberikan kinerja yang lebih baik dibandingkan HE dan AHE. Perbaikan kontras menggunakan CLAHE dapat menghasilkan citra dengan visual eritrosit dan plasmodium yang lebih jelas dan noise yang minimal.

Full Text:

PDF

References


J. Hawadak, R. R. Dongang Nana, and V. Singh, “Global trend of Plasmodium malariae and Plasmodium ovale spp. malaria infections in the last two decades (2000–2020): a systematic review and meta-analysis,†Parasit Vectors, vol. 14, no. 1, pp. 1–14, Dec. 2021, doi: 10.1186/S13071-021-04797-0/TABLES/2.

A. Loddo, C. di Ruberto, and M. Kocher, “Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology,†Sensors, vol. 18, no. 2, p. 513, Feb. 2018, doi: 10.3390/s18020513.

A. Monroe, N. A. Williams, S. Ogoma, C. Karema, and F. Okumu, “Reflections on the 2021 World Malaria Report and the future of malaria control,†Malar J, vol. 21, no. 1, pp. 1–6, May 2022, doi: 10.1186/S12936-022-04178-7.

N. Tangpukdee, C. Duangdee, P. Wilairatana, and S. Krudsood, “Malaria Diagnosis: A Brief Review,†Korean J Parasitol, vol. 47, no. 2, pp. 93–102, 2009, doi: 10.3347/KJP.2009.47.2.93.

M. Poostchi, K. Silamut, R. J. Maude, S. Jaeger, and G. Thoma, “Image analysis and machine learning for detecting malaria,†Translational Research, vol. 194, pp. 36–55, Apr. 2018, doi: 10.1016/J.TRSL.2017.12.004.

A. Rahman, H. Zunair, T. R. Reme, M. S. Rahman, and M. R. C. Mahdy, “A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset,†Tissue Cell, vol. 69, p. 101473, Apr. 2021, doi: 10.1016/j.tice.2020.101473.

Z. Jan, A. Khan, M. Sajjad, K. Muhammad, S. Rho, and I. Mehmood, “A review on automated diagnosis of malaria parasite in microscopic blood smears images,†Multimed Tools Appl, vol. 77, no. 8, pp. 9801–9826, 2018, doi: 10.1007/s11042-017-4495-2.

S. E. V. Haryanto, M. Y. Mashor, A. S. A. Nasir, and H. Jaafar, “Malaria parasite detection with histogram color space method in Giemsa-stained blood cell images,†in 2017 5th International Conference on Cyber and IT Service Management (CITSM), Aug. 2017, pp. 1–4. doi: 10.1109/CITSM.2017.8089291.

P. Aggarwal, A. Khatter, and G. Vyas, “An Intensity Threshold based Image Segmentation of Malaria Infected Cells,†in 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), Feb. 2018, pp. 549–553. doi: 10.1109/ICCMC.2018.8487494.

Y. Purwar, S. L. Shah, G. Clarke, A. Almugairi, and A. Muehlenbachs, “Automated and unsupervised detection of malarial parasites in microscopic images,†Malar J, vol. 10, no. 1, p. 364, Dec. 2011, doi: 10.1186/1475-2875-10-364.

J. E. Arco, J. M. Górriz, J. Ramírez, I. Ãlvarez, and C. G. Puntonet, “Digital image analysis for automatic enumeration of malaria parasites using morphological operations,†Expert Syst Appl, vol. 42, no. 6, pp. 3041–3047, Apr. 2015, doi: 10.1016/j.eswa.2014.11.037.

M. Harris, Bonhwa Ku, Chaeseung Lim, and Hansoek Ko, “Automated malaria cell counter using Hough transform based method,†in 2017 IEEE International Conference on Consumer Electronics (ICCE), 2017, pp. 404–405. doi: 10.1109/ICCE.2017.7889372.

S. v. Militante, “Malaria Disease Recognition through Adaptive Deep Learning Models of Convolutional Neural Network,†in 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Dec. 2019, pp. 1–6. doi: 10.1109/ICETAS48360.2019.9117446.

W. A. Mustafa and H. Yazid, “Image Enhancement Technique on Contrast Variation: A Comprehensive Review,†Journal of Telecommunication, Electronic and Computer Engineering, vol. 9, no. 3, pp. 199–204, 2017, Accessed: Jul. 16, 2022. [Online]. Available: https://www.researchgate.net/publication/320322102

B. Gupta and M. Tiwari, “Minimum mean brightness error contrast enhancement of color images using adaptive gamma correction with color preserving framework,†Optik (Stuttg), vol. 127, no. 4, pp. 1671–1676, Feb. 2016, doi: 10.1016/j.ijleo.2015.10.068.

D. Asamoah, E. Ofori, S. Opoku, and J. Danso, “Measuring the Performance of Image Contrast Enhancement Technique,†Int J Comput Appl, vol. 181, no. 22, pp. 6–13, Oct. 2018, doi: 10.5120/IJCA2018917899.

A. Loddo, C. di Ruberto, M. Kocher, and G. Prod’hom, “MP-IDB: The Malaria Parasite Image Database for Image Processing and Analysis,†in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11379, Springer, Cham, 2019, pp. 57–65. doi: 10.1007/978-3-030-13835-6_7.

M. Hamid, alfa nugrah A. H. Usman, S. Lutif, A. Fuad, and A. Mubarak, “Penerapan Metode Peningkatan Kualitas Citra Contrast Stretching Dan Histogram Equalization Untuk Identifikasi Keaslian Citra Sertipikat Hak Atas Tanah,†Jurnal Informatika dan Komputer, vol. 5, no. 2, pp. 92–98, Aug. 2022, doi: 10.33387/JIKO.V5I2.4635.

R. K. Hapsari, M. I. Utoyo, R. Rulaningtyas, and H. Suprajitno, “Comparison of Histogram Based Image Enhancement Methods on Iris Images,†J Phys Conf Ser, vol. 1569, no. 2, p. 022002, Jul. 2020, doi: 10.1088/1742-6596/1569/2/022002.

S. Gupta and Y. Kaur, “Review of Different Local and Global Contrast Enhancement Techniques for a Digital Image,†Int J Comput Appl, vol. 100, no. 18, pp. 975–8887, 2014.

Y. Chang, C. Jung, P. Ke, H. Song, and J. Hwang, “Automatic Contrast-Limited Adaptive Histogram Equalization with Dual Gamma Correction,†IEEE Access, vol. 6, pp. 11782–11792, Jan. 2018, doi: 10.1109/ACCESS.2018.2797872.

J. V.L and R. Gopikakumari, “IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements,†Int J Comput Appl, vol. 79, no. 9, pp. 1–9, Oct. 2013, doi: 10.5120/13766-1620.




DOI: https://doi.org/10.33387/jiko.v5i3.5237

Refbacks

  • There are currently no refbacks.