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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor JPM17: Jurnal Pengabdian Masyarakat Jurnal Teknologi Elektro INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Eksplora Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat Konvergensi KOMPUTIKA - Jurnal Sistem Komputer Civitas Ministerium: Jurnal Pengabdian Kepada Masyarakat Jurnal Kreativitas dan Inovasi (Jurnal Kreanova)
Fajar Astuti Hermawati
Universitas 17 Agustus 1945 Surabaya
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Segmentasi Kepala Janin pada Citra Ultrasound Menggunakan Arsitektur Jaringan U-Net Hermawati, Fajar Astuti; Jaya, Viko Adi
Komputika : Jurnal Sistem Komputer Vol 13 No 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12158

Abstract

Digital image processing has been utilized in various fields, including the medical field. One example is used to detect the location of vital organs in the human body. This research aims to produce a cut shape of the fetal head area on an ultrasound image (USG) by applying a deep-learning segmentation method. This research stage was carried out with ultrasound image acquisition, followed by an image preprocessing process to improve image quality so that the results of the segmentation process were better, followed by applying the segmentation method. This research focuses on using the U-Net method to segment the fetal head in ultrasound images. Using 995 ultrasound images of the fetal head in the training process, the best accuracy was obtained at 90.55%. The performance of the segmentation results for 335 ultrasound images of the fetal head at the testing stage using the Jaccard coefficient measurement was obtained on average at 87%. The results of this segmentation can be used for further purposes, such as fetal biometric measurements or 3D visualization. Keywords – Image Segmentation; USG Image; Fetal Head; U-Net Architecture; Image Preprocessing
Co-Authors ., Tigor Aidil Primasetya Armin Balok Hariadi, Balok Dilarida Alrizeki Elsen Ronando Evi Triandini Fathonah, Nenden Siti Fatonah, Nenden Siti Handayani Tjandrasa Herry Widhiarto I Ketut Putu Suniantara Indasyah, Enny Jalu Prasetya Mulya Jamila, Siti Nur Jaya, Viko Adi Kurnianingtyas, Yuniar Luvia Friska Narulita Mangambali, Hermawan Ali Nanik Suciati Nina Aulia Ramadhani Prasetyo, Cahyo Tri Pratama, Achmad Yogie Pratama, Bima Agung Putra, Ressa Ardiansya Reza Sarwo Widagdo Roenadi Koesdijarto Sidqon, Mohammad Suanto, Edy Tanjung, Christian Adikusuma Valantino Ateng Pamolango Windari, Diana Ayu