PROGRESS IN SIGNAL AND TEXTURE DISCRIMINATION IN MEDICAL IMAGING.

We begin with a review of the concepts of first, second, and higher order statistics and the ability of human observers to extract textural information of these orders from images. This ability has been found to be very high for first order statistics and very low for third and higher order statistics. We next explore some classes of second order statistics where the human observer is greatly outperformed by machine analysis and explain this within the "texton" theory of Julesz. Example images from phase-sensitive detection systems such as medical ultrasound are then presented. The signal detection theory used previously to study the detectability of first order changes in images is generalized to analyze the detectability and classification of textural changes within an image. We conclude that second order statistical properties contain a wealth of unused information that can be easily extracted both for system performance evaluation and for classification of tissue textural changes.