Morphological image processing techniques in thermographic imaging.

Abstract

Mathematical morphology is a set algebra that defines some important new techniques in image processing. Morphological filters are closely related to order statistic and other nonlinear filters, but they are uniquely sensitive to shape. A morphological filter will preserve shapes similar to its structuring element shape while modifying dissimilar shapes. Most morphological filters are effective at removing both linear and nonlinear noise processes. However, the standard morphological operators introduce a statistical and deterministic bias to images. Fortunately, these operators exist in complementary pairs that are equally and oppositely biased. One way to alleviate the bias is to average the two complementary operators. The filters formed by such averages are the midrange filter (basic operators), the pseudomedian filter (singly compound operators) and the LOCO filter (doubly compound operators). In thermographic imaging, one often wishes to find exact temperatures or accurate isothermal contours. Therefore, techniques used to remove sensor noise and scanning artifact should not introduce bias. The LOCO filter that we have devised provides the shape control and noise suppression of morphological techniques without biasing the image. We will demonstrate the effects of different structuring element shapes on thermographic images of tissue heated by laser irradiation and electrosurgery.