Komparasi Metode EM-GMM (Expectation Maximization-Gaussian Mixture Model) dan FCM (Fuzzy C-Means) dalam Segmentasi Citra Otak MRI (Magnetic Resonance Imaging) di RSUD Soetomo dalam Menentukan Area Tumor Otak

Sianipar, Win Heber Goklas (2017) Komparasi Metode EM-GMM (Expectation Maximization-Gaussian Mixture Model) dan FCM (Fuzzy C-Means) dalam Segmentasi Citra Otak MRI (Magnetic Resonance Imaging) di RSUD Soetomo dalam Menentukan Area Tumor Otak. Undergraduate thesis, Intitut Teknologi Sepuluh Nopember.

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Abstract

Segmentasi citra sudah populer dilakukan, terutama untuk para peneliti di bidang Biomedis maupun Teknik Informatika. Segmentasi citra dalam bidang kesehatan, mempunyai tujuan utama untuk menentukan atau mendeteksi dini area tumor, salah satu citra medis yang popular saat ini adalah MRI. Penelitian ini, akan dilakukan studi data citra MRI di Rumah Sakit Umum Daerah Soetomo, Surabaya. Metode segmentasi yang dipakai adalah klastering menggunakan FCM dan EM-GMM. Dimana inisialisasi jumlah klaster ditentukan berdasarkan Silhouette Index untuk EMGMM dan Partition Coefficient Index Untuk FCM. Selain berdasarkan nilai index tersebut penentuan dilakukan berdasarkan pengamatan subjektif dari pihak medis. Dalam komparasinya berdasarkan nilai similaritynya didapatkan metode EM-GMM lebih robust terhadap Salt and Pepper Noise dibanding FCM dan FCM lebih robust terhadap Gaussian Noise dibanding EM-GMM.====================================================================================================================Image segmentation has been popular, especially for researchers in the field of Biomedical and Informatics Engineering. Image segmentation in the health field, has the primary goal of determining or detecting early tumor areas, one of the most popular medical images currently is MRI. This research, will be studying MRI image data at RSUD Soetomo, Surabaya. The segmentation method used is clustering using FCM and EM-GMM. Where initialisation of cluster number is determined based on Silhouette Index for EM-GMM and Partition Coefficient Index For FCM. In addition to the index value based on the determination is done based on subjective observations from the medical party. In comparation based on the value of its similarity obtained a more robust EM-GMM method against Salt and Pepper Noise than FCM and FCM more robust against Gaussian Noise than EM-GMM.

Item Type: Thesis (Undergraduate)
Additional Information: RSSt 519.22 Sia k
Uncontrolled Keywords: EM; FCM; GMM; Klastering; MRI; Optimasi; Partition Coefficient Index; RSUD Soetomo; Segmentasi; Silhouette Index; Similarity; Statistika Fitur Citra; Tumor
Subjects: Q Science > QA Mathematics > QA248_Fuzzy Sets
R Medicine > R Medicine (General)
Divisions: Faculty of Mathematics and Science > Statistics > (S1) Undergraduate Theses
Depositing User: Win Heber Goklas Sianipar
Date Deposited: 16 Jan 2018 08:16
Last Modified: 16 Jan 2018 08:16
URI: http://repository.its.ac.id/id/eprint/48535

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