Remote sensing image fusion via compressive sensing

In this paper, we propose a compressive sensing-based method to pan-sharpen the low-resolution multispectral (LRM) data, with the help of high-resolution panchromatic (HRP) data. In order to successfully implement the compressive sensing theory in pan-sharpening, two requirements should be satisfied: (i) forming a comprehensive dictionary in which the estimated coefficient vectors are sparse; and (ii) there is no correlation between the constructed dictionary and the measurement matrix. To fulfill these, we propose two novel strategies. The first is to construct a dictionary that is trained with patches across different image scales. Patches at different scales or equivalently multiscale patches provide texture atoms without requiring any external database or any prior atoms. The redundancy of the dictionary is removed through K-singular value decomposition (K-SVD). Second, we design an iterative l1-l2 minimization algorithm based on alternating direction method of multipliers (ADMM) to seek the sparse coefficient vectors. The proposed algorithm stacks missing high-resolution multispectral (HRM) data with the captured LRM data, so that the latter is used as a constraint for the estimation of the former during the process of seeking the representation coefficients. Three datasets are used to test the performance of the proposed method. A comparative study between the proposed method and several state-of-the-art ones shows its effectiveness in dealing with complex structures of remote sensing imagery

Novel techniques for registration of multimodal medical images

Medical image registration is a critical image processing task in many applications such as image-guided surgery (IGS) and image-guided radiotherapy. Herein, a novel automatic inter-modal affine registration technique is proposed based on the correlation ratio (CR) similarity metric firstly. The technique is demonstrated through registering intra-operative ultrasound (US) scans with magnetic resonance (MR) images of 22 patients from a publicly available database. By using landmark-based mean target registration errors (mTRE) for evaluation, the technique has achieved a result of 2.79$\pm$1.13 mm from an initial value of 5.40$\pm$4.31 mm. A nonparametric statistical analysis performed using the Wilcoxon rank sum test shows that there is a significant difference between pre- and post-registration mTREs with a $p$-value of $0.0058$. To achieve this result, the MRI was deemed as the fix image ($I_f$) and the US as the moving image ($I_m$) and then $I_m$ was transformed to align with $I_f$. Covariance matrix adaptation evolutionary strategy (CMA-ES) was utilized to find the optimal affine transformation in registration of $I_m$ to $I_f$. In addition to quantitative validation using mTRE, the results were validated qualitatively by overlaying pre- and post-registration US and MRI to allow visual assessment of the alignment. The proposed fully automatic registration method significantly improved the alignment of MRI and US images and can therefore be used to aid neurosurgeons in resection of brain tumors. In addition to proposing new methods for registration of US and MRI, three different datasets of corresponding CT and US images of vertebrae were collected and presented. In the first dataset, two human patients’ lumbar vertebrae are presented and the US images are simulated from the CT images. The second dataset includes corresponding CT and US images of a phantom, made of post-mortem canine cervical and thoracic vertebrae. The third dataset includes the CT and US images of a lamb’s lumbar vertebrae. For the two latter datasets, 15 corresponding landmarks were provided and fiducial registration of the corresponding images was performed to acquire a silver standard ground truth of the registration. This dataset will be released online to allow validation of US-CT registration techniques