  Transfer function (TF) plays a key role for the generation of direct volume
rendering (DVR), by enabling accurate identification of structures of interest
(SOIs) interactively as well as ensuring appropriate visibility of them.
Attempts at mitigating the repetitive manual process of TF design have led to
approaches that make use of a knowledge database consisting of pre-designed TFs
by domain experts. In these approaches, a user navigates the knowledge database
to find the most suitable pre-designed TF for their input volume to visualize
the SOIs. Although these approaches potentially reduce the workload to generate
the TFs, they, however, require manual TF navigation of the knowledge database,
as well as the likely fine tuning of the selected TF to suit the input. In this
work, we propose a TF design approach where we introduce a new content-based
retrieval (CBR) to automatically navigate the knowledge database. Instead of
pre-designed TFs, our knowledge database contains image volumes with SOI
labels. Given an input image volume, our CBR approach retrieves relevant image
volumes (with SOI labels) from the knowledge database; the retrieved labels are
then used to generate and optimize TFs of the input. This approach does not
need any manual TF navigation and fine tuning. For improving SOI retrieval
performance, we propose a two-stage CBR scheme to enable the use of local
intensity and regional deep image feature representations in a complementary
manner. We demonstrate the capabilities of our approach with comparison to a
conventional CBR approach in visualization, where an intensity profile matching
algorithm is used, and also with potential use-cases in medical image volume
visualization where DVR plays an indispensable role for different clinical
usages.
