ABSTRACT
We have developed a multiscale algorithm for elastic (or molded) alignment of images. There is a wide array of medical applications of elastic (as opposed to strictly rigid) alignment: Subtraction of previous images from current ones to identify changes is perhaps the most obvious. We present preliminary results of this molding technique on a variety of images, and conclude with some closing remarks about this and future directions and goals of this work.
Subject(s)
Algorithms , Image Processing, Computer-Assisted/statistics & numerical data , Biophysical Phenomena , Biophysics , Elasticity , Female , Humans , Magnetic Resonance Imaging/statistics & numerical data , Mammography/statistics & numerical data , Models, Theoretical , Radiographic Image EnhancementABSTRACT
We have developed a multiscale algorithm for elastic registration of images. Rigid registration has many applications but it is often limited by distortions in the images. For example, different views of the same object produce distortions. Common examples of slightly different views producing a distortion can be found in medical imaging, such as matching a current mammogram or chest radiograph with one from a previous year, and in remote sensing, such as matching images taken from different satellite positions. We have developed two methods of elastic registration. Both are multiscale but one used an iterative minimization of the local error and the other uses a windowed correlation. We present preliminary results of the elastic registration method used on windowed correlations.
