Segmentasi Citra Kanker Serviks Menggunakan Markov Random Field dan Algoritma K-Means
Cervical Cancer Image Segmentation Using Markov Random Field and K-Means Algorithm
Abstract
Cervical cancer is a dangerous disease caused by malignant tumors that grow on the cervix and has globally attacked many women. Pap smear test is one of the early prevention efforts for cervical cancer. Medical personnel often have difficulty identifying images of cervical cancer cells. Several studies have used the K-Means clustering method to identify cervical cancer cell images from Herlev dataset. This study uses the Herlev dataset with the K-Means clustering algorithm and also used the Markov Random Field parameter as a feature for the process of identifying cervical cancer cell images. This study compared the results of the proposed method with some differences in the preprocessing process. The experimental results show an accuracy of 74,51% for RGB channels without low pass filter. Accuracy of 75,11% is obtained from the segmentation process using RGB channels with low pass filter. A further increase in accuracy was obtained by 75,76% when the segmentation process used the grayscale channel with low pass Filter. Based on the segmentation experiment with the highest segmentation accuracy results, the classification process using K-Nearest Neighbor (KNN) gives an accuracy of 89,29%.
Downloads
References
Syaiful, F. L. Tarigan, and F. Zuska, “Skrining Kanker Serviks dengan Pemeriksaan PAP Smear pada Profesi Bidan di Rumah Sakit TK II Putri Hijau Medan Tahun 2017,” Jurnal Riset Hesti Medan, vol. 3, no. 2, pp. 1–15, 2018.
S. Rio and E. S. T. Suci, “Persepsi tentang Kanker Serviks dan Upaya Prevensinya pada Perempuan yang Memiliki Keluarga dengan Riwayat Kanker,” Jurnal Kesehatan Reproduksi, vol. 4, no. 3, pp. 159–169, 2017.
A.n., “Cervical Cancer,” WHO, 2018. [Online]. Available: https://www.who.int/health-topics/cervical-cancer#tab=tab_1. [Accessed: 08-Oct-2020].
H. Latifah, E. Nurachmah, and Hiryadi, “Menjalani Pemeriksaan PAP Smear Pasien Kanker Serviks di Poli Kandungan,” Jurnal Keperawatan Suaka Insan, vol. 5, no. 1, pp. 90–99, 2020.
Y. Kusumawati, R. W. Nugrahaningtyas, and E. N. Rahmawati, “Pengetahuan, Deteksi Dini dan Vaksinasi HPV sebagai Faktor Pencegah Kanker Serviks di Kabupaten Sukoharjo,” Jurnal Kesehatan Masyarakat, vol. 11, no. 2, pp. 204–213, 2016.
E. Martin, “Pap-Smear Classification,” p. 101, 2003.
Rahmadwati, “Sistem Diagnosis Kanker Servik Berdasarkan Karakteristik Morfologi,” Jurnal EECCIS, vol. 7, no. 2, pp. 191–6, 2014.
S. Gautam, H. K. K., N. Jith, A. K. Sao, A. Bhavsar, and A. Natarajan, “Considerations for a PAP Smear Image Analysis System with CNN Features,” pp. 1–8, 2018.
N. P. Husain and C. Fatichah, “Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Radiating Component Normalized Generalized GVFS,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), vol. 6, no. 1, pp. 107–114, 2017.
W. D. Tanti, E. Purwanti, and A. Supardi, “Identifikasi Kanker Serviks Dari Citra Papsmear Berbasis Kecerdasan Buatan Winda,” Jurnal Fisika dan Terapannya, vol. 3, no. 3, pp. 98–111, 2015.
M. Sholik and C. Fatichah, “Klasifikasi Sel Serviks Pada Citra Pap Smear berdasarkan Fitur Bentuk Deskriptor Regional dan Fitur Tekstur Uniform Rotated Local Binary Pattern,” JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 15, no. 2, p. 214, 2017.
Kurnianingsih et al., “Segmentation and classification of cervical cells using deep learning,” IEEE Access, vol. 7, no. August, pp. 116925–116941, 2019.
N. Putu, A. Oka, I. K. Gede, D. Putra, and K. S. Wibawa, “Klasifikasi Sel Nukleus Pap Smear Menggunakan Metode Backpropagation Neural Network,” Jurnal Ilmiah Merpati, vol. 7, no. 3, pp. 224–232, 2019.
S. D. Jadhav and H. P. Channe, “Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques,” International Journal of Science and Research (IJSR), vol. 5, no. 1, pp. 1842–1845, 2016.
S. H. Wibowo and F. Susanto, “Penerapan Metode Gaussian Smoothing Untuk,” Jurnal Media Infotama, vol. 12, no. 2, pp. 129–135, 2016.
F. Riandari, “Implementasi Metode Geometric Mean Filter Untuk Perbaikan Dengan Reduksi Noise Pada Citra Digital,” Jurnal Mantik Penusa, vol. 2, no. 2, pp. 175–179, 2018.
I. A. Kesuma, Herman, and Munawir, “Penerapan Metode Klaster K-Means Pada Segmentasi Warna Citra,” Seminar Nasional Inovasi dan Teknologi Informasi, vol. 1, no. 3, pp. 427–430, 2016.
H. Pangaribuan, “Optimalisasi Kualitas Citra Digital Dengan Metode Ketetanggaan Piksel,” Jurnal Ilmiah Informatika, vol. 7, no. 01, p. 18, 2019.
M. Nugraheni, “Aplikasi Transformasi Watershed Untuk Segmentasi Citra Dengan Spatial Filter Sebagai Pemroses Awal,” Seminar Nasional Informatika 2010, vol. 1, no. 1, pp. 76–81, 2010.
P. A. Cahyan, M. Aswin, and A. Mustofa, “Segmentasi Citra Digital dengan Menggunakan Algoritma Watershed dan Lowpass Filter sebagai Proses Awal,” Jurnal Mahasiswa TEUB, vol. 1, no. 1, pp. 403–494, 2013.
Y. Antawiryawan, S. Violina, and A. Romadhony, “Analisis Segmentasi Citra Tuberculosis Menggunakan Markov Random Field,” pp. 1–6, 2011.
A. B. Suksmono and A. Hirose, “Adaptive noise reduction of InSAR images based on a complex-valued MRF model and its application to phase unwrapping problem,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 3, pp. 699–709, 2002.
A. Kusworo and A. B. Suksmono, “Pembangkitan Dan Pemulihan Citra Biner Markov Random Field (MRF) Secara Stokastik Dengan Algoritma Markov Chain Monte Carlo (MCMC),” Berkala Fisika, vol. 12, no. 4, pp. 145–152, 2009.
F. G. Febrinanto, C. Dewi, and A. T. Wiratno, “Implementasi Algoritme K-Means Sebagai Metode Segmentasi Citra Dalam Identifikasi Penyakit Daun Jeruk,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, vol. 2, no. 11, pp. 5375–5383, 2018.
H. Rezatofighi, N. Tsoi, J. Y. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized intersection over union: A metric and a loss for bounding box regression,” arXiv, pp. 658–666, 2019.
Copyright (c) 2021 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;