MEASURING IMAGE TEXTURE TO SEPARATE DIFFICULT FROM EASY MAMMOGRAMS

We are investigating computerized techniques for sorting mammograms ac cording to whether the breast tissue is fatty or dense. The hypothesis is that areas of dense tissue are a major factor in making certain ma mmograms harder for both radiologists and computers to interpret. Bein g able to identify dense mammograms automatically could permit better use of the time and skills of expert radiologists by allowing the diff icult mammograms to be examined by the most experienced readers. In ad dition, the scope for computer-aided detection of abnormalities might be increased by concentrating on the easier, fatty mammograms. The mam mograms used in the experiment were classified independently by two ra diologists, who agreed in almost all cases. A number of local statisti cal and texture measures were then computed for patches from digitizat ions of these mammograms. One of the measures (local skewness in tiles ) gives a good separation between fatty and dense patches. This measur e has been incorporated into an automated procedure that separates off approximately two thirds of the fatty mammograms. This finding has be en replicated on mammograms taken from a UK screening programme. The r elationship between the fatty/dense distinction and the classification proposed by Wolfe is discussed.