Fusion of CT and MR Liver Images by SURF-Based Registration

Authors

DOI:

https://doi.org/10.18201/ijisae.2019457233

Keywords:

Image Fusion, SURF, Wavelet Transform

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.

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Published

12.12.2019

How to Cite

ASLAN, M. F., Durdu, A., & Sabanci, K. (2019). Fusion of CT and MR Liver Images by SURF-Based Registration. International Journal of Intelligent Systems and Applications in Engineering, 7(4), 216–221. https://doi.org/10.18201/ijisae.2019457233

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Section

Research Article

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