Segmentasi Citra CT-Scan Pada Stroke Menggunakan Metode Algoritma K-Means Clustering dan Thresholding di Rumah Sakit Samarinda Medika Citra

Fransiska Lipa Payon

Abstract


Strokes examination using CT-Scan is a technique that produces 3D images of the brain without having to do surgery. The purpose of this study was to determine the results of strokes image segmentation using the K-Means Clustering and the Thresholding algorithm method at Samarinda Medika Citra Hospital, and to determine the results of the evaluation conducted by the Radiologist on the results of strokes image segmentation. Data processing in this study was carried out with qualitative techniques, i.e., observing and also evaluating the results of strokes image segmentation. The evaluation results for K-Means Clustering segmentation using k=3 are 70% correct. The evaluation results of the Thresholding method are 100% correct using the threshold value is 100-180. Based on the results of this study, the results of the Thresholding segmentation are more recommended for evaluation because they get the best threshold value.Keywords: K-Means Clustering, Strokes, Segmentation, Thresholding

References


Daftar Pustaka

KEMENKES. (2019). Keputusan Menteri Kesehatan Republik Indonesia Nomor Hk.01.07/Menkes/394/2019 Tentang Pedoman Nasional Pelayanan Kedokteran Tata Laksana Stroke. Menteri Kesehatan Republik Indonesia:Jakarta.

Ray, S., Kumar, V., Ahuja, C., & Khandelwal, N. (2018). An automatic method for complete brain matter segmentation from multislice CT scan. arXiv preprint arXiv:1809.06215.

Rudiansyah, M. (2018). Pengukuran Volume Pendarahan Otak Pasien Stroke Hemoragik Intraserebral Hasil Multi Slice CT Scan (MSCT) Menggunakan Gradient Vector Flow (GVF)-Hazard And Operability Study (HAZOP) And Safety Instrumented System Evaluation Based On RAMS+ C Measurement At Oil Treating Plant PT. Saka Indonesia Pangkah Limited (Doctoral dissertation, Institut Teknologi Sepuluh Nopember).

Thylashri, S., Mahesh Yadav, U., & Danush Chowdary, T. (2018). Image Segmentation Using K-Means Clustering Method for Brain Tumour Detection. International Journal of Engineering & Technology, 7(2.19), 97-100.

Muwardi, F., & Fadlil, A. (2017). Sistem Pengenalan Bunga Berbasis Pengolahan Citra dan Pengklasifikasi Jarak. J. Ilm. Tek. Elektro Komput. dan Inform, 3(2), 124-131

Sugandi, B. (2018). Teknologi Citra untuk Peningkatan Kualitas Hidup yang Lebih Baik. Jurnal Integrasi, 10(1), 21-27.

Dwi Nurhayati, O. (2015). Analisis Citra Digital CT Scan Dengan Metode Ekualisasi Histogram dan Statistik Orde Pertama. Jurnal Sistem Komputer, 5(1), 1-4.

Ediyanto, M. N. M., & Satyahadewi, N. (2013). Pengklasifikasian Karakteristik Dengan Metode K-Means Cluster Analysis. Bimaster: Buletin Ilmiah Matematika, Statistika dan Terapannya, 2(02).

Wicaksono, F. T., Wulaningrum, R., & Sanjaya, A. (2021). Penerapan Metode K-Mean untuk Menentukan Sanksi Karyawan yang Datang Terlambat. Nusantara of Engineering, 4(1), 89-89.

Bhayyu, V., & Elvira, N. (2019, May). Perbandingan Antara Metode Otsu Thresholding dan Multilevel Thresholding untuk Segmentasi Pembuluh Darah Retina. In Annual Research Seminar (ARS) (Vol. 4, No. 1, pp. 47.




DOI: https://doi.org/10.24198/jiif.v8i2.54408

Refbacks

  • There are currently no refbacks.


Journal Indexed By:
Visit Statistics: