Multimodality image fusion using centre-based genetic algorithm and fuzzy logic
by Subbiah Parvathy Velmurugan; Pothiraj Sivakumar; Murugan Pallikonda Rajasekaran
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 28, No. 4, 2018

Abstract: The Fusion method is used to detect and treatment for the disease in a successful manner which integrates various modalities. Nowadays, medical image fusion system is a demanding task in healthcare applications such as tumour detection, analysis, research, and treatment. In this paper, we propose a multimodality medical image fusion using Centre-Based Genetic Algorithm (CBGA) and fuzzy logic which is examined by the use of the quantitative measure. Here, at first, we estimate the segmentation map from the source images (MRI and CT). After that, the source images are decomposed based on lifting wavelet transform. Then, a fuzzy-based approach is used to fuse high-frequency wavelet coefficients of the MRI and CT images. Mainly, the output of three various fusion rules incorporated by fuzzy logic (weighted averaging, selection using Pixel-Based Decision Map; PDM, and selection using Region-Based Decision Map; RDM), based on a dissimilarity measure of the source images. Then a CBGA is used to fuse the low-frequency wavelet coefficient of the MRI and CT images. At last, we combine low- and high-frequency wavelet coefficients of the source images to obtain the fused image.

Online publication date: Tue, 06-Nov-2018

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