Web-based Application for Cancerous Object Segmentation in Ultrasound Images Using Active Contour Method
Keywords:
active contour, cancer, ultrasonography, segmentation, website
Abstract
Segmentation, or the process of separating clinical objects from surrounding tissue in medical images, is an important step in the Computer-Aided Diagnosis (CAD) system. The CAD system is developed to assist radiologists in diagnosing cancer malignancy, which in this research is found in ultrasound (US) medical imaging. The manual segmentation process, which cannot be accessed remotely, is a limitation of the CAD system because cancer objects are screened frequently, continuously, and at all times. Therefore, this research aims to build a user-friendly web application called COSION (Cancerous Object Segmentation) that provides easy access for radiologists to segment cancer objects in US images by adopting an active contour method called HERBAC (Hybrid Edge & Region-Based Active Contour). The waterfall method was used to develop the web application with Django as the web framework. The successfully built web application is named Cosion. Cosion was tested on 114 radiology breast and thyroid US images. Functional, portability, efficiency, reliability, expert validation, and usability testing concluded that Cosion runs well and is suitable for use with a functionality value of 0.9375, an average GTmetrix score of 96.43±0.66%, 100% stress testing percentage, 77.5% expert validation, and 75.8% usability. These quantitative performances indicate that the COSION web application is suitable for implementation in the CAD system for US medical imaging.
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References
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Nugroho, A., Hidayat, R., Nugroho, H. A., and Debayle, J. 2020. “Combinatorial Active Contour Bilateral Filter for Ultrasound Image Segmentation,” Journal of Medical Imaging (7:05), pp. 1–13. (https://doi.org/10.1117/1.jmi.7.5.057003).
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Nugroho, A., Hidayat, R., Nugroho, H. A., and Debayle, J. 2021b. “Development of Active Contour Model For Radiological Ultrasound Image Segmentation,” Universitas Gadjah Mada.
Nugroho, A., Nugroho, H. A., and Choridah, L. 2015. Active Contour Bilateral Filtering for Breast Lesions Segmentation on Ultrasound Images, pp. 36–40.
P2PTM Kemenkes RI. 2019. “Apa Itu Kanker?,” 05 Februari 2019. (http://p2ptm.kemkes.go.id/infographic-p2ptm/penyakit-kanker-dan-kelainan-darah/page/14/apa-itu-kanker).
Pathan, R. K., Alam, F. I., Yasmin, S., Hamd, Z. Y., Aljuaid, H., Khandaker, M. U., and Lau, S. L. 2022. “Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling,” Healthcare (10:12). (https://doi.org/10.3390/healthcare10122367).
de Paula, T. H., Lobosco, M., and dos Santos, R. W. 2010. “A Web-Based Tool for the Automatic Segmentation of Cardiac MRI,” in XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010, P. D. Bamidis and N. Pallikarakis (eds.), Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 667–670.
Rodríguez-Cristerna, A., Gómez-Flores, W., and de Albuquerque Pereira, W. C. 2017. “A Computer-Aided Diagnosis System for Breast Ultrasound Based on Weighted BI-RADS Classes,” Computer Methods and Programs in Biomedicine (153), pp. 33–40. (https://doi.org/10.1016/j.cmpb.2017.10.004).
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Samsung, M. I. 2016. “Ultrasound: S Detect for Thyroid RS80.” (https://www.youtube.com/watch?v=Syu4NjD2qwA).
Setiawan, A., and Widyanto, R. A. 2018. “Evaluasi Website Perguruan Tinggi Menggunakan Metode Usability Testing,” Jurnal Informatika: Jurnal Pengembangan IT (JPIT) (3:03).
Syaputri, S. 2019. “SEGMENTASI CITRA THORAX PARU-PARU MANUSIA DARI SINAR-X,” JOURNAL ONLINE OF PHYSICS (4:2), pp. 8–10.
Vallat, R. 2018. “Pingouin: Statistics in Python,” Journal of Open Source Software (3:31), p. 1026. (https://doi.org/10.21105/joss.01026).
Ximenes Vasconcelos, F. F., Medeiros, A. G., Peixoto, S. A., and Rebouças Filho, P. P. 2019. “Automatic Skin Lesions Segmentation Based on a New Morphological Approach via Geodesic Active Contour,” Cognitive Systems Research (55), Elsevier, pp. 44–59. (https://doi.org/10.1016/J.COGSYS.2018.12.008).
Almajalid, R., Shan, J., Du, Y., and Zhang, M. 2018. “Development of a Deep-Learning-Based Method for Breast Ultrasound Image Segmentation,” in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1103–1108. (https://doi.org/10.1109/ICMLA.2018.00179).
Arslan Tuncer, S. 2019. “Optic Disc Segmentation Based on Template Matching and Active Contour Method,” Balkan Journal of Electrical and Computer Engineering (7:1), pp. 56–63. (https://doi.org/10.17694/bajece.470796).
Asthana, A., and Olivieri, J. 2009. “Quantifying Software Reliability and Readiness,” in 2009 IEEE International Workshop Technical Committee on Communications Quality and Reliability, pp. 1–6. (https://doi.org/10.1109/CQR.2009.5137352).
Babu, Kr., Ksony, Indira, Nd., Prasad, K. V., and Shameem, S. 2021. “An Effective Brain Tumor Detection from T1w MR Images Using Active Contour Segmentation Techniques,” Journal of Physics: Conference Series (1804:1), IOP Publishing, p. 12174. (https://doi.org/10.1088/1742-6596/1804/1/012174).
Balaji, S., and Murugaiyan, M. S. 2012. “Waterfall vs V-Model vs Agile : A Comparative Study on SDLC,” WATEERFALL Vs V-MODEL Vs AGILE : A COMPARATIVE STUDY ON SDLC (2:1), pp. 26–30.
Caselles, V., Kimmel, R., and Sapiro, G. 1997. “Geodesic Active Contours,” International Journal of Computer Vision (22:1), pp. 61–79. (https://doi.org/10.1023/A:1007979827043).
Chan, T. F., and Vese, L. A. 2001. “Active Contours without Edges,” IEEE Transactions on Image Processing (10:2), pp. 266–277. (https://doi.org/10.1109/83.902291).
Chen, L., Zhou, Y., Wang, Y., and Yang, J. 2006. “GACV: Geodesic-Aided C-V Method,” Pattern Recognition (39:7), pp. 1391–1395. (https://doi.org/10.1016/j.patcog.2006.01.017).
Chen, X., Williams, B. M., Vallabhaneni, S. R., Czanner, G., and Williams, R. 2019. Learning Active Contour Models for Medical Image Segmentation, pp. 11632–11640.
Globocan. 2020. “The Global Cancer Observatory.” (https://gco.iarc.fr/).
GTmetrix. 2020. “Everything You Need to Know about the New GTmetrix Report (Powered by Lighthouse).” (https://gtmetrix.com/blog/everything-you-need-to-know-about-the-new-gtmetrix-report-powered-by-lighthouse/, accessed November 30, 2022).
Ji, X., Xiao, W., Ye, H., Chen, R., Wu, J., Mao, Y., and Yang, H. 2021. “Ultrasonographic Measurement of the Optic Nerve Sheath Diameter in Dysthyroid Optic Neuropathy,” Eye (Basingstoke) (35:2), Springer US, pp. 568–574. (https://doi.org/10.1038/s41433-020-0904-2).
Kass, M., Witkin, A., and Terzopoulos, D. 1988. “Snakes: Active Contour Models,” International Journal of Computer Vision (1:4), pp. 321–331. (https://doi.org/10.1007/BF00133570).
Ma’arief, A., Tjandi, Y., Imran, A., and Vitalocca, D. 2019. “Aplikasi Pelaporan Penyuluhan/Bimbingan Pegawai Penyuluh Agama Islam Kementerian Agama Kabupaten Kepulauan Selayar,” Jurnal MediaTIK (1), pp. 1–6. (https://ojs.unm.ac.id/mediaTIK/article/view/9788).
Munarto, R., Wiryadinata, R., and Yogiyansyah, D. 2018. “Segmentasi Citra USG (Ultrasonography) Kanker Payudara Menggunakan Fuzzy C-Means Clustering,” Setrum : Sistem Kendali-Tenaga-Elektronika-Telekomunikasi-Komputer (6:2), p. 238. (https://doi.org/10.36055/setrum.v6i2.2770).
Nalarita, Y., and Listiawan, T. 2018. “Pengembangan E-Modul Kontekstual Interaktif Berbasis Web Pada Mata Pelajaran Kimia Senyawa Hidrokarbon,” Multitek Indonesia (12:2), p. 85. (https://doi.org/10.24269/mtkind.v12i2.1125).
Noble, A., and Boukerroui, D. 2006. “Ultrasound Image Segmentation : A Survey,” IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers (25:8), pp. 987–1010.
Noble, J. A. 2010. “Ultrasound Image Segmentation and Tissue Characterization,” Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine (224:2), pp. 307–316. (https://doi.org/10.1243/09544119JEIM604).
Noviana, D., Aliambar, S. H., Ulum, M. F., Siswandi, R., Widyananta, B. J., Soehartono, R. H., Soesatyoratih, R., and Zaenab, S. 2018. Diagnosis Ultrasonografi Pada Hewan Kecil Edisi Kedua, PT Penerbit IPB Press. (https://books.google.co.id/books?id=ko3rDwAAQBAJ).
Nugroho, A., Hidayat, R., Adi Nugroho, H., and Debayle, J. 2020. “Cancerous Object Detection Using Morphological Region-Based Active Contour in Ultrasound Images,” Journal of Physics: Conference Series (1444:1). (https://doi.org/10.1088/1742-6596/1444/1/012011).
Nugroho, A., Hidayat, R., and Nugroho, H. A. 2019. “Artifact Removal in Radiological Ultrasound Images Using Selective and Adaptive Median Filter,” ACM International Conference Proceeding Series, pp. 237–241. (https://doi.org/10.1145/3309074.3309119).
Nugroho, A., Hidayat, R., Nugroho, H. A., and Debayle, J. 2020. “Combinatorial Active Contour Bilateral Filter for Ultrasound Image Segmentation,” Journal of Medical Imaging (7:05), pp. 1–13. (https://doi.org/10.1117/1.jmi.7.5.057003).
Nugroho, A., Hidayat, R., Nugroho, H. A., and Debayle, J. 2021a. “Ultrasound Object Detection Using Morphological Region-Based Active Contour: An Application System,” International Journal of Innovation and Learning (29:4), Inderscience, pp. 412–430. (https://doi.org/10.1504/IJIL.2021.115497).
Nugroho, A., Hidayat, R., Nugroho, H. A., and Debayle, J. 2021b. “Development of Active Contour Model For Radiological Ultrasound Image Segmentation,” Universitas Gadjah Mada.
Nugroho, A., Nugroho, H. A., and Choridah, L. 2015. Active Contour Bilateral Filtering for Breast Lesions Segmentation on Ultrasound Images, pp. 36–40.
P2PTM Kemenkes RI. 2019. “Apa Itu Kanker?,” 05 Februari 2019. (http://p2ptm.kemkes.go.id/infographic-p2ptm/penyakit-kanker-dan-kelainan-darah/page/14/apa-itu-kanker).
Pathan, R. K., Alam, F. I., Yasmin, S., Hamd, Z. Y., Aljuaid, H., Khandaker, M. U., and Lau, S. L. 2022. “Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling,” Healthcare (10:12). (https://doi.org/10.3390/healthcare10122367).
de Paula, T. H., Lobosco, M., and dos Santos, R. W. 2010. “A Web-Based Tool for the Automatic Segmentation of Cardiac MRI,” in XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010, P. D. Bamidis and N. Pallikarakis (eds.), Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 667–670.
Rodríguez-Cristerna, A., Gómez-Flores, W., and de Albuquerque Pereira, W. C. 2017. “A Computer-Aided Diagnosis System for Breast Ultrasound Based on Weighted BI-RADS Classes,” Computer Methods and Programs in Biomedicine (153), pp. 33–40. (https://doi.org/10.1016/j.cmpb.2017.10.004).
SA, E. Y. M. 2015. “RS80A S Detect Categorization for Breast.” (https://www.youtube.com/watch?v=gfw5Qex6Ln8).
Samsung, M. I. 2016. “Ultrasound: S Detect for Thyroid RS80.” (https://www.youtube.com/watch?v=Syu4NjD2qwA).
Setiawan, A., and Widyanto, R. A. 2018. “Evaluasi Website Perguruan Tinggi Menggunakan Metode Usability Testing,” Jurnal Informatika: Jurnal Pengembangan IT (JPIT) (3:03).
Syaputri, S. 2019. “SEGMENTASI CITRA THORAX PARU-PARU MANUSIA DARI SINAR-X,” JOURNAL ONLINE OF PHYSICS (4:2), pp. 8–10.
Vallat, R. 2018. “Pingouin: Statistics in Python,” Journal of Open Source Software (3:31), p. 1026. (https://doi.org/10.21105/joss.01026).
Ximenes Vasconcelos, F. F., Medeiros, A. G., Peixoto, S. A., and Rebouças Filho, P. P. 2019. “Automatic Skin Lesions Segmentation Based on a New Morphological Approach via Geodesic Active Contour,” Cognitive Systems Research (55), Elsevier, pp. 44–59. (https://doi.org/10.1016/J.COGSYS.2018.12.008).
Published
2023-10-11
How to Cite
Oktaviyanti, D., Nugroho, A., Wibawanto, H., & Subiyanto. (2023). Web-based Application for Cancerous Object Segmentation in Ultrasound Images Using Active Contour Method. Jurnal Sistem Informasi, 19(2), 1-16. https://doi.org/10.21609/jsi.v19i2.1280
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Articles
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