Texture as a Diagnostic Signal in Mammograms

Yelda Semizer, Melchi Michel, Karla Evans, Jeremy Wolfe

Research output: Contribution to conferenceOtherpeer-review


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.
Original languageEnglish
Publication statusAccepted/In press - 2018

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