Image registration algorithm for a pc-based system

Image registration algorithms are essential for subtractive analysis of sequential images. Discrepancies in lighting, image orientation, and scale must be minimized before effective subtraction of two images can occur. We have successfully implemented computationally intensive algorithms for registration, which include illuminance normalization and magnification correction, in a PC-based image processing system. A homomorphic filter in the spatial domain is used to reduce the illumination variations in the images. A modified sequential similarity detection technique is used to derive the minimum error factor associated with each combination of translation, magnification, and rotation variations. Each variation of the test image is masked with one of three masks, and the squares of the pixel intensity differences are summed for every test image. An adaptive threshold is used to decrease the time required for a misfit by aborting the test image under consideration when its summation exceeds the value of the previous best fit summation. After the best fit parameters are obtained, they are used to register the images so that the images can be subtracted. The difference image is subjected to further image enhancement operations. The execution time of the image registration algorithm has been reduced through use of a hybrid program written in C and Assembly languages. Applications of the registration algorithms in analysis of fundus images will be presented.