Zia Work: June Liu

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Multi-modal medical image fusion plays a critical role in clinical diagnosis by combining complementary information from different imaging modalities, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). This paper proposes an efficient image fusion method based on Principal Component Analysis (PCA) and Adaptive Histogram Equalization (AHE). Unlike traditional wavelet-based methods that suffer from spatial discontinuities, the proposed approach utilizes PCA to determine the optimal weighting coefficients for pixel intensity integration, preserving the spectral integrity of the source images. Subsequently, AHE is applied to enhance the contrast of the fused output. Experimental results demonstrate that the proposed method outperforms existing fusion algorithms in terms of entropy, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), providing radiologists with high-quality diagnostic imagery. june liu zia work

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June Liu is a USC alumnus. She completed a Master of Public Policy in 2015 from the USC Price School of Public Policy. METRANS - Transportation Consortium This paper proposes an efficient image fusion method

Let $I_1(x,y)$ and $I_2(x,y)$ represent the two registered source images. The column vectors of the images are organized into a matrix $S$.

: Specializes in research on and metabolic diseases like obesity and diabetes at the Mayo Clinic. Zia et al.