Texture as a Diagnostic Signal in Mammograms

Abstract

Radiologists can discriminate between normal and abnormal breast tissue at a glance, suggesting that radiologists might be using some “global signal” of abnormality. Our study investigated whether texture descriptions can be used to characterize the global signal of abnormality and whether radiologists use this information during interpretation. Synthetic images were generated using a texture synthesis algorithm trained on texture descriptions extracted from sections of mammograms. Radiologists completed a task that required rating the abnormality of briefly presented tissue sections. When the abnormal tissue had no visible lesion, radiologists seemed to use texture descriptions; performance was similar across real and synthesized tissue sections. However, when the abnormal tissue had a visible lesion, radiologists seemed to rely on additional mechanisms beyond the texture descriptions; performance increased for the real tissue sections. These findings suggest that radiologists can use texture descriptions as global signals of abnormality in interpretation of breast tissue