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Abstract
Penelitian ini mengusulkan metode sederhana yaitu dengan mengekstraksi arean yang diduga memiliki ciri posterior dan ciri latar belakang. Dataset yang digunakan terdiri atas 121 citra USG payudara yang dikelompokan menjadi 84 kasus posterior enhancement, 16 kasus posterior shadow , dan 21 kasus no feature. Tahap awal, pra pengolahan citra nodul kanker payudara dimulai dengan mem-filter citra yang telah di-crop menggunakan adaptive median filter. Pra pengolahan citra USG payudara dilakukan untuk menghilangkan noise, marker dan label. Kemudian melakukan proses segmentasi. Citra hasil segmentasi di ekstraksi ciri dengan cara pengambilan nilai rata-rata intensitas dari area bawah nodul citra dengan metode block difference. Hasil klasifikasi citra nodul payudara menggunakan metode block difference mampu mencapai akurasi 86,78%.
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References
- American Cancer Society, “Facts & Figures 2019,†Am. Cancer Soc., pp. 1–76, 2019, [Online]. Available: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2019/cancer-facts-and-figures-2019.pdf.
- S. Ghosh, S. Mondal, and B. Ghosh, “A comparative study of breast cancer detection based on SVM and MLP BPN classifier,†in Automation, Control, Energy and Systems (ACES), 2014 First International Conference on, 2014, pp. 1–4.
- K. E. Maturen, A. P. Wasnik, J. E. Bailey, E. G. Higgins, and J. M. Rubin, “Posterior acoustic enhancement in hepatocellular carcinoma,†J. Ultrasound Med., vol. 30, no. 4, pp. 495–499, 2011.
- K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny, “Automatic segmentation of breast lesions on ultrasound,†Med. Phys., vol. 28, no. 8, pp. 1652–1659, 2001, doi: 10.1118/1.1386426.
- A. Madabhushi, Y. Peng, M. Rosen, and S. Weinstein, “Distinguishing lesions from posterior acoustic shadowing in breast ultrasound via non-linear dimensionality reduction,†Annu. Int. Conf. IEEE Eng. Med. Biol. - Proc., pp. 3070–3073, 2006, doi: 10.1109/IEMBS.2006.260189.
- Z. Zhou et al., “Classification of benign and malignant breast tumors in ultrasound images with posterior acoustic shadowing using half-contour features,†J. Med. Biol. Eng., vol. 35, no. 2, pp. 178–187, 2015, doi: 10.1007/s40846-015-0031-x.
- Y.-S. Chen, W.-R. Chen, and P.-H. Tsui, “Contour extraction for breast tumor in ultrasound image,†in Proceedings - International Conference on Machine Learning and Cybernetics, 2014, vol. 2, doi: 10.1109/ICMLC.2014.7009664.
- J. Cui et al., “Characterization of posterior acoustic features of breast masses on ultrasound images using artificial neural network,†in Medical Imaging, 2008, p. 691521.
- “ACR BI-RADS Atlas,†Igarss 2014, pp. 1–5, 2014.
- M. Rahmawaty, H. A. Nugroho, Y. Triyani, I. Ardiyanto, and I. Soesanti, “Classification of breast ultrasound images based on texture analysis,†in Proceedings of 2016 1st International Conference on Biomedical Engineering: Empowering Biomedical Technology for Better Future, IBIOMED 2016, 2017, pp. 1–6, doi: 10.1109/IBIOMED.2016.7869825.
- H. D. Cheng, J. Shan, W. Ju, Y. Guo, and L. Zhang, “Automated breast cancer detection and classification using ultrasound images: A survey,†Pattern Recognit., vol. 43, no. 1, pp. 299–317, Jan. 2010, doi: 10.1016/j.patcog.2009.05.012.
References
American Cancer Society, “Facts & Figures 2019,†Am. Cancer Soc., pp. 1–76, 2019, [Online]. Available: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2019/cancer-facts-and-figures-2019.pdf.
S. Ghosh, S. Mondal, and B. Ghosh, “A comparative study of breast cancer detection based on SVM and MLP BPN classifier,†in Automation, Control, Energy and Systems (ACES), 2014 First International Conference on, 2014, pp. 1–4.
K. E. Maturen, A. P. Wasnik, J. E. Bailey, E. G. Higgins, and J. M. Rubin, “Posterior acoustic enhancement in hepatocellular carcinoma,†J. Ultrasound Med., vol. 30, no. 4, pp. 495–499, 2011.
K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny, “Automatic segmentation of breast lesions on ultrasound,†Med. Phys., vol. 28, no. 8, pp. 1652–1659, 2001, doi: 10.1118/1.1386426.
A. Madabhushi, Y. Peng, M. Rosen, and S. Weinstein, “Distinguishing lesions from posterior acoustic shadowing in breast ultrasound via non-linear dimensionality reduction,†Annu. Int. Conf. IEEE Eng. Med. Biol. - Proc., pp. 3070–3073, 2006, doi: 10.1109/IEMBS.2006.260189.
Z. Zhou et al., “Classification of benign and malignant breast tumors in ultrasound images with posterior acoustic shadowing using half-contour features,†J. Med. Biol. Eng., vol. 35, no. 2, pp. 178–187, 2015, doi: 10.1007/s40846-015-0031-x.
Y.-S. Chen, W.-R. Chen, and P.-H. Tsui, “Contour extraction for breast tumor in ultrasound image,†in Proceedings - International Conference on Machine Learning and Cybernetics, 2014, vol. 2, doi: 10.1109/ICMLC.2014.7009664.
J. Cui et al., “Characterization of posterior acoustic features of breast masses on ultrasound images using artificial neural network,†in Medical Imaging, 2008, p. 691521.
“ACR BI-RADS Atlas,†Igarss 2014, pp. 1–5, 2014.
M. Rahmawaty, H. A. Nugroho, Y. Triyani, I. Ardiyanto, and I. Soesanti, “Classification of breast ultrasound images based on texture analysis,†in Proceedings of 2016 1st International Conference on Biomedical Engineering: Empowering Biomedical Technology for Better Future, IBIOMED 2016, 2017, pp. 1–6, doi: 10.1109/IBIOMED.2016.7869825.
H. D. Cheng, J. Shan, W. Ju, Y. Guo, and L. Zhang, “Automated breast cancer detection and classification using ultrasound images: A survey,†Pattern Recognit., vol. 43, no. 1, pp. 299–317, Jan. 2010, doi: 10.1016/j.patcog.2009.05.012.