Model Optimasi Segmentasi Tumor Otak Dalam Magnetic Resonance Imaging Berbasis Fuzzy C-Means Dan Simulated Annealing

Authors

  • Amran Yobioktabera Politeknik Negeri Semarang
  • Afandi Nur Azis Thohari
  • Nurseno Bayu Aji

DOI:

https://doi.org/10.32497/orbith.v21i2.6708

Keywords:

Tumor Otak, Segmentasi, Fuzzy C-Means, Simulated Annealing

Abstract

Tumor otak atau biasa disebut dengan Brain Tumour merupakan salah satu jenis penyakit otak yang terjadi akibat evolusi sel secara abnormal dan tidak dapat dikontrol (uncontrolled) di dalam otak. Untuk mendeteksi tumor otak, para dokter biasa menggunakan Magnetic Resonance Imaging (MRI). MR Images merupakan teknik pencitraan medis yang dapat menampilkan informasi tentang anatomi jaringan lunak manusia. MR Images mampu menghasilkan gambar dengan kualitas tinggi dari struktur anatomi otak manusia. Hal ini menjadikan deteksi tumor melalui MR Images dipandang sebagai sesuatu yang penting dan krusial. Untuk melakukan deteksi tumor otak, diperlukan rangkaian tahap penelitian yang pada hasil akhirnya berupa tahapan klasifikasi. Salah satu tahapan penting pada rangkaian tahapan tersebut adalah tahapan segmentasi. Segmentasi citra pada MR Images dapat memisahkan objek tumor dengan objek non-tumor. Salah satu metode yang digunakan untuk melakukan segmentasi adalah Fuzzy C-Means (FCM). Penggunaan FCM sebagai metode segmentasi memiliki kelemahan, yaitu cluster yang dihasilkan tidak 100% berkualitas (misclustred). Untuk mengatasi masalah tersebut, maka diusulkan metode Simulated Annealing (SA) sebagai metode optimasi. Dengan metode optimasi SA ini, kualitas cluster pada proses segmentasi dapat memperoleh hasil yang optimal. Hal ini terlihat dari menurunnya nilai Xie-Beni hingga 7 (tujuh) kali lipat dibandingkan dengan segmentasi menggunakan FCM saja.

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Published

2025-07-31

Issue

Vol. 21 No. 2 (2025): Juli 2025

Section

Engineering Articles

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