A Transfer Function Design Using A Knowledge Database based on Deep Image and Primitive Intensity Profile Features Retrieval

TL;DR

This paper introduces an automated, content-based retrieval method for transfer function design in volume rendering, utilizing deep image features and a knowledge database to improve visualization without manual fine-tuning.

Contribution

The work presents a novel content-based retrieval approach using deep features for automatic transfer function generation from a knowledge database, reducing manual effort.

Findings

Two-stage CBR improves retrieval accuracy.

Deep image features outperform intensity profile matching.

Method enhances medical volume visualization efficiency.

Abstract

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.