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All Journal Journal of Food and Pharmaceutical Science Transmisi: Jurnal Ilmiah Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknik Elektro Sinergi Jurnal Hukum Novelty International Journal of Artificial Intelligence Research Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Journal of Robotics and Control (JRC) Jurnal Riset Rekayasa Elektro Indonesian Journal of Electrical Engineering and Computer Science Journal of Food and Pharmaceutical Sciences Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Control Systems and Optimization Letters
Son Ali Akbar
Universitas Ahmad Dahlan
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Neuroscience

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Analysis of the Influence of Number of Segments on Similarity Level in Wound Image Segmentation Using K-Means Clustering Algorithm Furizal, Furizal; Mawarni, Syifa’ah Setya; Akbar, Son Ali; Yudhana, Anton; Kusno, Murinto
Control Systems and Optimization Letters Vol 1, No 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v1i3.33

Abstract

This study underscores the importance of wound image segmentation in the medical world to speed up first aid for victims and increase the efficiency of medical personnel in providing appropriate treatment. Although the body has a protective function from external threats, the skin can be easily damaged and cause injuries that require rapid detection and treatment. This study used the K-Means clustering algorithm to segment the external wound image dataset consisting of three types of wounds, namely abrasion, puncture, and laceration. The results showed that K-Means clustering is an effective method for segmenting wound images. The greater the number of segments used, the better the quality of the resulting segmentation. However, it is necessary to take into account the specific characteristics of each type of wound and the number of segments used in order to choose the most suitable segmentation method. Evaluation using various metrics, such as VOI, GCE, MSE, and PSNR, provides an objective assessment of the quality of segmentation. The results showed that abrasion wounds were easier to segment compared to puncture wounds and lacerations. In addition, the size of the image file also affects the speed of program execution, although it is not always absolute depending on the characteristics of the image.
Co-Authors Adhi Prahara, Adhi Agung Dwi Nugroho, Agung Dwi Agus Aktawan, Agus Ahmad Azhari Ahmad Raditya Cahya Baswara Aimee Cepee Morana Anton Yudhana Anton Yudhana Anton Yudhana Arief Setyo Nugroho Bin Ghazali, Kamarul Hawari Dian Nova Kusuma Hardani Eko Aribowo Fatma Nuraisyah, Fatma Fiftin Noviyanto Furizal, Furizal Habsah Hasan Hasan, Fadlur Rahman T Hasan, Geri Asbi Ikhsan, Ismawan Noor Kalbuadi, Dimas Baskoro Kawarul Hawari Ghazali Khusnul Nur Vaizi Luh Putu Ratna Sundari Mawarni, Syifa’ah Setya Murinto Murinto Nisfa Sri Ayu Kaliky Novitasari, Putri Rachma Nuril Anwar, Nuril Pramudya Wisnujati Prihatmadi, Farhan Adyaqsa Puriyanto, Riky Dwi Retno Damarina Ruly Erwin AfanDika Sakti Oktasari Sunardi, Sunardi Suryanto, Imam Wahyu Sapto Aji Wita Setyaningrum Zeehaida Mohamed