This study explores detection performance trends of human observers with respect to two parameters: task
complexity determined by the frequency content of background and signal, and image viewing mode: singleslice
(ss) versus multi-slice (ms) stack-browsing image presentation. The images are 3D correlated Gaussian
noise with a 3D Gaussian signal centered in the image volume. To vary task complexity, we consider three
different noise kernels while keeping the signal spread constant across all images. In ss mode, only the
central slice of the volume is presented to the observer, while in ms mode all slices are available. All human
readings are conducted in a controlled viewing environment on a 5MP digital mammography medical display.
Overall, in line with the literature, we find that human performance increases in ms relative to ss image
presentation mode. Furthermore, our experiments indicate that the extent of difference between ms and
ss performance is influenced by the properties of image data (level of task complexity): the difference in
performance increases (from ΔAUC= 0.14 to ΔAUC= 0.30) as the difference in the frequency content of the
signal and the background increases. In other words, the benefit of having additional slices available in ms
mode is larger for lower-complexity tasks. Future studies shall focus on comparing the results of the present
study to the existing model observers for volumetric images, ultimately aiming to design an anthropomorphic
model observer for volumetric detection tasks.