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Title: Fusion of CT and MR Liver Images by SURF-Based Registration
Authors: Aslan, Muhammet Fatih
Durdu, Akif
Sabancı, Kadir
Keywords: Bilgisayar Bilimleri, Yapay Zeka
Issue Date: 2019
Abstract: Medical imaging plays an important role in the diagnosis and treatment of different diseases. Images with more details are obtained by image fusion for more accurate analysis of medical images. In this study, Computed Tomography (CT) and Magnetic Resonance (MR) images of the liver from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) are fused using different combinations of different wavelet types such as daubechies, coiflet and symlet. To accomplish this task, first the preprocessing steps are completed, and then registration is performed using Speed up Robust Features (SURF). As a result, to measure the quality of the obtained fusion image Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measurement (SSIM), Mean Structural Similarity (MSSIM) and Feature Similarity Index (FSIM) metrics are used.
ISSN: 2147-6799
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections

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