Pengolahan Citra untuk Meningkatkan Visualisasi Lesi Jinak Citra USG Payudara
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Abstract
Salah satu modalitas diagnosis abnormalitas pada payudara adalah Ultrasonografi (USG). Pemeriksaan USG menggunakan gelombang suara frekuensi tinggi yang tidak bersifat pengion, sehingga aman bagi jaringan tubuh manusia. Namun, citra USG memiliki kelemahan, salah satunya adalah visualisasi yang kurang akibat tingginya tingkat noise pada citra. Kelemahan tersebut dapat dibantu dengan pengolahan citra. Penelitian ini bertujuan mengurangi noise dan meningkatkan visualisasi abnormalitas lesi jinak dan mikrokalsifikasi pada citra USG payudara. Program peningkatan kualitas citra dibuat dengan melibatkan enam kombinasi teknik filtering dan contrast enhancement, seperti seperti median filter, gaussian filter, wiener filter, CLAHE (Contrast Limited Adaptive Histogram Equalitation) dan intensity adjustment. Citra selanjutkan akan mengalami segmentasi dengan thresholding. Sementara itu, evaluasi kualitas citra dilakukan dengan pengukuran sinyal pada citra hasil, noise yang berhasil dibuang dan kuesioner ke dokter klinisi. Kombinasi gaussian filter dan intensity adjustment menghasilkan nilai sinyal tertinggi yaitu sebesar 408,57. Kombinasi median filter dan CLAHE merupakan kombinasi yang paling baik dalam mengurangi noise pada citra USG payudara lesi jinak. Secara umum, kombinasi median filter dan CLAHE merupakan kombinasi yang mampu menampilkan visualisasi lesi jinak yang paling baik.
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References
Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. “Global cancer statistics, 2012,” CA Cancer J Clin 65, 87–108, 2015
Brahma, B, “Deteksi Dini Kanker Payudara” Rumah Sakit Kanker Dharmais, Jakarta, 2012.
Huang, Q., Luo, Y. & Zhang, Q, “Breast ultrasound image segmentation: a survey”. Int. J CARS 12, 493–507, 2017.
Costantini M, Belli P, Lombardi R, Franceschini G, Mulè A, Bonomo L, “Characterization of solid breast masses use of the sonographic breast imaging reporting and data system lexicon,” J Ultrasound Med 25(5): 649–659, 2006.
Anderson BO, Shyyan R, Eniu A, Smith RA, Yip CH, Bese NS, Carlson RW, “Breast cancer in limited-resource countries: an overview of the breast health global initiative 2005 guidelines,” Breast J 12(s1): S3–S15, 2006.
Sudarsih, K., Budi, W. S., & Suryono, S, “Analisis Keseragaman Citra pada Pesawat Ultrasonografi (USG),” Berkala Fisika, 17(1), 33-38, 2014.
C. P. Loizou and CS Pattichis, Despeckle Filtering for Ultrasound Imaging and Video. Volume I: Algorithms and Software, Second. Cyprus: Morgan & Claypool Publishers, 2015.
Xiao G, Brady M, Noble JA, Zhang Y, “Segmentation of ultrasound B-mode images with intensity inhomogeneity correction,” IEEE Trans Med Imaging 21(1):48–57, 2002.
Globocan, “Breast Cancer Estimated Incidence, Mortality and Prevalence Worldwide in 2012,” IARC. s.l.: Globocan.
Mansel RE, Webster DJT, Sweetland HM, “Benign Disorders and Bisease of the Breast. 3rd,” Saunders Elsevier. 41-3, 57- 8, 81-3, 157-8, 213-6, 257-67, 308-10, 2010.
Merih Guray and Aysegul A. Sahin, “Benign, Breast Diseases: Classification, Diagnosis, and Management.University of Texas,” M. D. Anderson Cancer Center, Houston, Texas, USA, 2006.
Morrow Monica, “Physical Examination of the Breast. In Harris JR, Morrow Monica, Lippman ME, Osborn CK. Disease of the Breast. 5th edition,” Philadelphia. Wolthers Kluwers Health. 25-37, 2014.
Smriti, S, “Comparative Evaluation of Filters for Liver Ultrasound Image Enhancement,” Durg, C. G. India, 2013
Suman Shrestha, “Image Denoising Using New Adaptive Based Median Filter,” Signal & Image Processing: An International Journal (SIPIJ) Volume 5, No.4, 2014.
C. Rubini, N. Pavithra, “Contrast Enhancement of MRI Images using AHE and CLAHE Techniques,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, vol. 9 Issue 2, 2019.
P. Deepa and M. Suganthi, “Performance Evaluation of Various Denoising Filters for Medical Image,” (IJCSIT) International Journal of Computer Science and Information Technologies, vol. 5 (3) 4205-4209, 2014
Sana'a khudayer Jadwa, “Wiener Filter based Medical Image De-Noising,” Journal of Science and Engineering Applications vol. 7–Issue 09, 318-323, 2018, ISSN 2319–7560, 2018.
Iza Sazanita Isa, Siti Noraini Sulaiman, Muzaimi Mustapha, Sailudin Darus, “Evaluating Denoising Performances of Fundamental Filters for T2- Weighted MRI Images,” Procedia Computer Science 60 (2015) 760 – 768, 2015.
Suhas.S, C R Venugopal, “MRI Image preprocessing and Noise removal technique using linear and nonlinear filters,” International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 2017.
Brij Bhan Singh and Shailendra Patel, “Efficient Medical Image Enhancement using CLAHE Enhancement and Wavelet Fusion,” International Journal of Computer Applications (0975 – 8887) vol. 167, 2017.