Template matching is used for many applications in image processing. Cross Correlation is the basic statistical approach to image registration. It is used for template matching or pattern recognition. Template can be considered a sub-image from the reference image, and the image can be considered as a sensed image. The objective is to establish the correspondence between the reference image and sensed image. It gives the measure of the degree of similarity between an image and template. This paper describes medical image registration by template matching based on Normalized Cross-Correlation (NCC) using Cauchy-Schwartz inequality. The algorithm for template matching using NCC is implemented in MATLAB. The algorithm does the template matching and uses the Cauchy-Schwartz's inequality to simplify the procedure. The developed algorithm is robust for similarity measure. An experimental result with medical images registration with noise and without noise is shown in the results section.

No abstract

No abstract

Registration is the process of finding transformations that makes correspondence between related image pairs so that pixels in the two images precisely coincide to the same points in the scene. Once registered, the image can be combined or fused in a way that improves useful information extraction. The log-polar transform (LPT) is a well known space variant image registration scheme used for medical images. However though LPT is invariant to rotation and scale changes, it does not support translation faithfully. In this paper, a hybrid algorithm for image registration that produces faithful mapping under affine geometrical distortion using LPT, Fourier transform (FT), and phase correlation has been presented. The resultant algorithm is invariant to rotation, scale and translation. The proposed algorithm is verified on medical images with affine shifting, partial data, and in the presence of noise.

No abstract