首页|A Transfer Function Design for Medical Volume Data Using a Knowledge Database Based on Deep Image and Primitive Intensity Profile Features Retrieval

A Transfer Function Design for Medical Volume Data Using a Knowledge Database Based on Deep Image and Primitive Intensity Profile Features Retrieval

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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 automat-ically 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 man-ual TF navigation and fine tuning.For our CBR-TF approach,we introduce a novel volumetric image feature which in-cludes 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 im-age 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.

data retrievaldirect volume renderingray analysistransfer function

Younhyun Jung、Jim Kong、盛斌、Jinman Kim

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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

Korea Health Technology Research and Development Project through the Korea Health Industry Development InstituteNational Research Foundation of Korea(NRF)Culture,Sports and Tourism Research and Development Program through the Korea Creative Content Agency Grant funded by the Mi

HI22C16512021R1F1A1059554RS-2023-00227648

2024

10.1007/s11390-024-3419-7

计算机科学技术学报(英文版)
中国计算机学会

计算机科学技术学报(英文版)

CSTPCD
影响因子：0.432
ISSN：1000-9000
年,卷(期)：2024.39(2)