Combining image texture and morphological features in low-resource perception models for signal detection tasks

Texture analysis holds significant importance in various imaging fields due to its ability to provide statistical, structural, and intrinsic spatial information from images. In this work, we examine several first and second-order texture features on simulated and clinical DBT images. We will present some essential characteristics of texture features that show higher discriminatory potential for mass detection in digital breast tomosynthesis. We will further examine the use of these texture features along with morphological features in a two-stage visual search (VS) model observer for mass detection in DBT. Our preliminary results show that incorporation of texture features reduced the number of suspicious locations in the first stage of VS observer model. Critically, this would also be the first attempt to extend the use of these perception models to clinical data.