Identifikasi Kerusakan Jaringan Histologi Pada Ginjal Dengan Fitur Tekstur Menggunakan Model Fitur Gray Level Coocurrence Matrix (GLCM)

  • Zainul Arifin
  • Izzati Muhimmah
  • Ika Firdianingsih

Abstract

Congestion is one type of damage that occurs in the histology of the kidney tissue, where there is excessive blood within the blood vessels of a particular area. In this research, we will design a system to detect congestion in the histology network of the kidneys using texture feature approach with gray level cooccurence matrix (GLCM) method, as well as using vector machine support method (SVM). The subject of data used is histology image of mouse kidney tissue using H & E staining obtained at medical faculty of Islamic university of indonesia. image data used as many as 50 congestion training images and 50 normal training images with the size 256 x 256 pixels. while the test image uses an average size of 3000 x 3000 pixels. the results of the experiments performed with cluster parameters k = 2 to k = 5 show good results, with an average class accuracy rate of 80%, and the accuracy of the segment is 96%.

Published
2017-10-17
How to Cite
ARIFIN, Zainul; MUHIMMAH, Izzati; FIRDIANINGSIH, Ika. Identifikasi Kerusakan Jaringan Histologi Pada Ginjal Dengan Fitur Tekstur Menggunakan Model Fitur Gray Level Coocurrence Matrix (GLCM). INFORMAL: Informatics Journal, [S.l.], v. 2, n. 2, p. 101-113, oct. 2017. ISSN 2503-250X. Available at: <https://jurnal.unej.ac.id/index.php/INFORMAL/article/view/5567>. Date accessed: 26 may 2024.