Abstract:
Various types of medical images are generated for different clinical diagnostic needs. Such as magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). Single function medical imaging technology is difficult to accurately diagnose the disease. Therefore, multi-modal medical image fusion technology can obtain images with robust anatomical significance and high spectral information. First, combining the image enhancement technique based on pixel processing with the Laplace pyramid decomposition scheme, which can obtain the residual sub-band image (RSI) with clearer image texture features and the base sub-band image (BSI) with smoother. Then, the high-frequency coefficient fused image is obtained using the image fusion rule based on the interest information for the RSI image. And a smoother pyramid top-level fused image is obtained by using the fusion rule of the local energy maximum scheme for the BSI image. Finally, the inverse Laplace pyramid transformation is utilized to reconstruct the fusion image. The experimental results show that the fused image preserves image edge information, enhances image detail information for better visual effects and objective evaluation metrics have more advantages including
QMI,
QIFC,
QVIF,
QME,
QSD,
QUQI,
QSSIM.