摘要
Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure concerning its neighbors.In DVR,transfer function(TF)plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of them.TF generation typically involves non-intuitive trial-and-error optimization of rendering parameters,which is time-consuming and inefficient.Attempts at mitigating this manual process 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,CBR-TF,where we introduce a new content-based retrieval(CBR)method to automatically navigate the knowledge database.Instead of pre-designed TFs,our knowledge database contains volumes with SOI labels.Given an input volume,our CBR-TF approach retrieves relevant volumes(with SOI labels)from the knowledge database;the retrieved labels are then used to generate and optimize TFs of the input.This approach largely reduces manual TF navigation and fine tuning.For our CBR-TF approach,we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the profile.For the regional spatial semantics,we adopt a convolutional neural network to obtain high-level image feature representations.For the intensity profile,we extend the dynamic time warping technique to address subtle alignment differences between similar profiles(SOIs).Finally,we propose a two-stage CBR scheme to enable the use of these two different feature representations in a complementary manner,thereby improving SOI retrieval performance.We demonstrate the capabilities of our CBR-TF approach with comparison with a conventional approach in visualization,where an intensity profile matching algorithm is used,and also with potential use-cases in medical volume visualization.
作者
Younhyun Jung
Jim Kong
盛斌
Jinman Kim
Younhyun Jung;Jim Kong;Bin Sheng;Jinman Kim(School of Computing,Gachon University,Seongnam 13120,Korea;School of Computer Science,The University of Sydney,Sydney 2006,Australia;Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
基金
supported by the Korea Health Technology Research and Development Project through the Korea Health Industry Development Institute under Grant No.HI22C1651
the National Research Foundation of Korea(NRF)under Grant No.2021R1F1A1059554
the Culture,Sports and Tourism Research and Development Program through the Korea Creative Content Agency Grant funded by the Ministry of Culture,Sports and Tourism of Korea under Grant No.RS-2023-00227648.