The authors review 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. They 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 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. It is concluded 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.
|Title of host publication||Proceedings of SPIE - The International Society for Optical Engineering|
|Place of Publication||Bellingham, WA, USA|
|Number of pages||8|
|State||Published - 1985|
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics