SEGMENTASI CITRA MRI MENGGUNAKAN DETEKSI TEPI UNTUK IDENTIFIKASI KANKER PAYUDARA
DOI:
https://doi.org/10.30812/matrik.v15i2.38Keywords:
segmentation, MRI, edge detection, breast cancerAbstract
One type of cancer that is capable identifed using MRI technology is breast cancer. Breast cancer is still the leading cause of death world. therefore early detection of this disease is needed. In identifying breast cancer, a doctor or radiologist analyzing the results of magnetic resonance image that is stored in the format of the Digital Imaging Communication In Medicine (DICOM). It takes skill and experience suffcient for diagnosis is appropriate, and accurate, so it is necessary to create a digital image processing applications by utilizing the process of object segmentation and edge detection to assist the physician or radiologist in identifying breast cancer. MRI image segmentation using edge detection to identifcation of breast cancer using a method stages gryascale change the image format, then the binary image thresholding and edge detection process using the latest Robert operator. Of the 20 tested the input image to produce images with the appearance of the boundary line of each region or object that is visible and there are no edges are cut off, with the average computation time less than one minute.
Downloads
References
[2]. Kadir, Abdul [2013], Dasar Pengolahan Citra dengan DELPHI, 1st.edition, CV.ANDI OFFSET., Yogyakarta.
[3]. Nurhasanah and Ihwan, Andi [2013]. Deteksi Tepi Citra Kanker Payudara dengan Menggunakan Laplacian of Gaussian (LOG). Procedings Semirata FMIPA Universitas Lampung.
[4]. Nurhasanah [2011]. Segmentasi Jaringan Ota Putih, Jaringan Otak Abu-Abu dan cairan Otak dari Citra MRI Menggunakan Teknik K-Means Clustering. Jurnal Aplikasi Fisika Vol.7, No.2. FMIPA Universsitas Tanjungpura.
[5]. Gonzalez, Rafael, Woods, Richard E [2007]. Digilat Image Processing, Third Edition. Pearson
Downloads
Published
Issue
Section
How to Cite
Similar Articles
- Nella Rosa Sudianjaya, Chastine Fatichah, Segmentation and Classification of Breast Cancer Histopathological Image Utilizing U-Net and Transfer Learning ResNet50 , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 1 (2024)
- Didih Rizki Chandranegara, Faras Haidar Pratama, Sidiq Fajrianur, Moch Rizky Eka Putra, Zamah Sari, Automated Detection of Breast Cancer Histopathology Image Using Convolutional Neural Network and Transfer Learning , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 3 (2023)
- Rizky Hafizh Jatmiko, Yoga Pristyanto, Investigating The Effectiveness of Various Convolutional Neural Network Model Architectures for Skin Cancer Melanoma Classification , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 1 (2023)
- Bob Subhan Riza, Jufriadif Na'am, Sumijan Sumijan, Tuberculosis Extra Pulmonary Bacilli Detection System Based on Ziehl Neelsen Images with Segmentation , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 1 (2022)
- Suhirman Suhirman, Shoffan Saifullah, Ahmad Tri Hidayat, Rr Hajar Puji Sejati, Otsu Method for Chicken Egg Embryo Detection based-on Increase Image Quality , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 2 (2022)
- Syafri Arlis, Muhammad Reza Putra, Musli Yanto, Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 3 (2024)
- Bambang Krismono Triwijoyo, SEGMENTASI CITRA PEMBULUH DARAH RETINA MENGGUNAKAN METODE DETEKSI GARIS MULTI SKALA , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 15 No. 1 (2015)
- Tb Ai Munandar, Ajif Yunizar Yusuf Pratama, Regional Clustering Based on Types of Non-Communicable Diseases Using k-Means Algorithm , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 2 (2024)
- Irma Binti Sya'idah, Sugiyarto Surono, Goh Khang Wen, DynamicWeighted Particle Swarm Optimization - Support Vector Machine Optimization in Recursive Feature Elimination Feature Selection , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 3 (2024)
- Ardi Mardiana, Ade Bastian, Ano Tarsono, Dony Susandi, Safari Yonasi, Optimized YOLOv8 Model for Accurate Detection and Quantificationof Mango Flowers , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 3 (2025)
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Ahmat Adil, Bambang Krismono Triwijoyo, Sistem Informasi Geografis Pemetaan Jaringan Irigasi dan Embung di Lombok Tengah , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 20 No. 2 (2021)
- Bambang Krismono Triwijoyo, Ahmat Adil, Anthony Anggrawan, Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 1 (2021)
- Bambang Krismono Triwijoyo, Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 18 No. 2 (2019)
- Dadang Priyanto, Bambang Krismono Triwijoyo, Deny Jollyta, Hairani Hairani, Ni Gusti Ayu Dasriani, Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 3 (2023)
- Anthony Anggrawan, Raisul Azhar, Bambang Krismono Triwijoyo, Mayadi Mayadi, Developing Application in Anticipating DDoS Attacks on Server Computer Machines , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 20 No. 2 (2021)
- Bambang Krismono Triwijoyo, SEGMENTASI CITRA PEMBULUH DARAH RETINA MENGGUNAKAN METODE DETEKSI GARIS MULTI SKALA , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 15 No. 1 (2015)