SUMMARYThe closest iterative point (ICP) algorithm is commonly used in medical image registration. However, due to its natural limitation, the processing time and registration accuracy need to be further advanced. In this paper, by computing the moments of the reference and floating images, the centroids are computed and thus the initial translation parameters are obtained. The rotation angles acquired respectively by the second-order central moments, inertia matrix, Karhunen-Loeve transformation (K-LT) and singular value decomposition (SVD) are referred to as the initial rotation parameters of the ICP algorithm for image registration. The edges of the reference and floating images are detected by Canny operator and then the binarization images involving the feature points are acquired. The experimental results show that this proposed method has a fairly simple implementation, a low computational load, a fast registration and good registration accuracy. It also can efficiently avoid trapping into the local optimum and is adapted for both mono-modality and multi-modality image registrations.

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The COVID-19 pandemic creates a worldwide threat to human health, medical practitioners, social structures, and finance sectors. The coronavirus epidemic has a significant impact on people's health, survival, employment, and financial crises; while also having noticeable harmful effects on our environment in a short span of time. In this context, the complexity of the Corona Virus transmission is estimated and analyzed by the measure of non-linearity called the Generalized Fractal Dimensions (GFD) on the chest X-Ray images. Grayscale image is considered as the most important suitable tool in the medical image processing. Particularly, COVID-19 affects the human lungs vigorously within a few days. It is a very challenging task to differentiate the COVID-19 infections from the various respiratory diseases represented in this study. The multifractal dimension measure is calculated for the original, noisy and denoised images to estimate the robustness of COVID-19 and other noticeable diseases. Also the comparison of COVID-19 X-Ray images is performed graphically with the images of healthy and other diseases to state the level of complexity of diseases in terms of GFD curves. In addition, the Mean Absolute Error (MAE) and the Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the denoising process involved in the proposed comparative analysis of the representative grayscale images.

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