A new robust adaptive fusion method for double-modality medical image PET/CT is proposed according to the Piella framework The algorithm consists of the following three steps Firstly, the registered PET and CT images are decomposed using the nonsubsampled contourlet transform (NSCT) Secondly, in order to highlight the lesions of the low-frequency image, low-frequency components are fused by pulse-coupled neural network (PCNN) that has a higher sensitivity to featured area with low intensities With regard to high-frequency subbands, the Gauss random matrix is used for compression measurements, histogram distance between the every two corresponding subblocks of high coefficient is employed as match measure, and regional energy is used as activity measure The fusion factor is then calculated by using the match measure and the activity measure The high-frequency measurement value is fused according to the fusion factor, and high-frequency fusion image is reconstructed by using the orthogonal matching pursuit algorithm of the high-frequency measurement after fusion Thirdly, the final image is acquired through the NSCT inverse transformation of the low-frequency fusion image and the reconstructed high-frequency fusion image To validate the proposed algorithm, four comparative experiments were performed: comparative experiment with other image fusion algorithms, comparison of different activity measures, different match measures, and PET/CT fusion results of lung cancer (20 groups) The experimental results showed that the proposed algorithm could better retain and show the lesion information, and is superior to other fusion algorithms based on both the subjective and objective evaluations

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A New Robust Adaptive Fusion Method for Double-Modality Medical Image PET/CT

Considering the influence of the projection matrix on Compressed Censing (CS), a novel method is proposed to optimize the projection matrix. In order to improve the signal's reconstruction precise and the stability of the optimization algorithm of the projection matrix, the proposed method adopts a differentiable threshold function to shrink the off-diagonal items of a Gram matrix corresponding to the mutual coherence between the projection matrix and sparse dictionary, and introduces a gradient descent approach based on the Wolf's-conditions to solve the optimization projection matrix. The Basis-Pursuit (BP) algorithm and the Orthogonal Matching Pursuit (OMP) algorithm are applied to find the solution of the minimum 0 l -norm optimization issue and the compressed sensing are utilized to sense and reconstruct the random vectors, wavelet's noise test signals and pictures. The results of the simulation show the proposed method based on the projection matrix optimization is able to improve the quality of the reconstruction performance.

Novel Optimization Method for Projection Matrix in Compress Sensing Theory

This paper proposes a novel Sensing Dictionary-based Iterative Hard Thresholding(SDIHT) algorithm,which can collaboratively construct the measurement matrix and the reconstruction algorithm in compressive sensingPairs of measurement matrix and sensing dictionary are used for compressive projection and designing reconstruction algorithm respectivelyThe original sparse signal can be recovered exactly until the residual is reduced to zero as iteration proceedsA sufficient condition for SDIHT algorithm is given and provedThe benefit of SDIHT is its high reconstruction accuracy and low computational complexityComputer simulation indicates that when the signal sparsity or the measurement number is fixed,SDHIT algorithm can reconstruct 0-1 sparse signal and two dimensional images with better performance and higher efficiency than IHT,OMP and BIHT algorithm can

Collaborative Construction of Measurement Matrix and Reconstruction Algorithm in Compressive Sensing

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Collaborative Construction of Measurement Matrix and Reconstruction Algorithm in Compressive Sensing