Our previous work on DBT image texture indicates that certain texture features may impact human observer performance for the task of low-contrast mass detection. Despite this, little is yet known about these texture statistics in the context of medical imaging. In this study, we investigate the factors that influence texture features in simulated DBT images. Specifically, we explore whether or not changes in quantum noise and anatomical variations are reflected in image texture curves. Our findings concerning the effects of Wiener filtration and changes in DBT system parameters indicate that texture statistics are affected by both anatomical variations and quantum noise.
William H. Nisbett, Amar Kavuri, and Mini Das, "Investigating the contributions of anatomical variations and quantum noise to image texture in digital breast tomosynthesis," Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105730H (Presented at SPIE Medical Imaging: February 12, 2018; Published: 9 March 2018); https://doi.org/10.1117/12.2294981.
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Study of self-shadowing effect as a simple means to realize nanostructured thin films and layers with special attentions to birefringent obliquely deposited thin films and photo-luminescent porous silicon