software & computing: Software helps see the unseen in ophthalmology

摘要

Looking for differences between two highly similar drawings may be a pleasant childhood task, but looking for differences between many highly complex images can be tedious, not to mention strenuous. A common way to bring out differences between images is to subtract one image from the next or to sequentially alternate images to trigger the recognition of movement--a task the human eye is good at. This works for images taken in a lab environment, where the object and the camera have fixed positions, commonly seen, for example, in the familiar movies of growing plants and flowers. However, when the camera is not fixed or if the object moves, this technique fails. In the case of medical photos that follow the progression of disease or the effect of treatment, one image with respect to the next usually suffers severe motion artifacts that prohibit image subtraction or image-alternating approaches. To correct these motion errors, one typical approach is the use of a so-called registration algorithm. Such an algorithm compares a sequence of "floating" images to a reference image and calculates the image differences for different positions of the floating image. The floating image is said to match with the reference image when a position has been found with the lowest image difference. Then the program presents the result to the user for verification or rejection of that match. If the match fails, the user must adjust parameters of the matching algorithm and rerun the algorithm.