arrow
返回

A Clustering Based Transfer Function for Volume Rendering Using Gray-Gradient Mode Histogram

delete2019-01-01
delete4
delete
OA
AI
Y
Yisha Lan
Y
Yimin Ding
X
Xin Luo
Y
Yanzhao Zhang
C
Chenxi Huang
E
E. Y. K. Ng
W
Weihong Huang
周
周雪忠 (Xuezhong Zhou)
J
Jie Su
Y
Yonghong Peng
Z
Zhicheng Wang *
Y
Yongqiang Cheng *
车文良 封面图
车文良 (Wenliang Che) *
DOI:10.1109/ACCESS.2019.2923080delete
delete原文链接
delete分享
delete收藏
查看原文
摘要

摘要

En 中文
Volume rendering is an emerging technique widely used in the medical field to visualize human organs using tomography image slices. In volume rendering, sliced medical images are transformed into attributes, such as color and opacity through transfer function. Thus, the design of the transfer function directly affects the result of medical images visualization. A well-designed transfer function can improve both the image quality and visualization speed. In one of our previous paper, we designed a multi-dimensional transfer function based on region growth to determine the transparency of a voxel, where both gray threshold and gray change threshold are used to calculate the transparency. In this paper, a new approach of the transfer function is proposed based on clustering analysis of gray-gradient mode histogram, where volume data is represented in a two-dimensional histogram. Clustering analysis is carried out based on the spatial information of volume data in the histogram, and the transfer function is automatically generated by means of clustering analysis of the spatial information. The dataset of human thoracic is used in our experiment to evaluate the performance of volume rendering using the proposed transfer function. By comparing with the original transfer function implemented in two popularly used volume rendering systems, visualization toolkit (VTK) and RadiAnt DICOM Viewer, the effectiveness and performance of the proposed transfer function are demonstrated in terms of the rendering efficiency and image quality, where more accurate and clearer features are presented rather than a blur red area. Furthermore, the complex operations on the two-dimensional histogram are avoided in our proposed approach and more detailed information can be seen from our final visualized image.
Keyword:
Gray-gradient mode histogram
clustering analysis
transfer function
volume rendering
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

IEEE Access 封面图
IEEE Access
IF:
3.6
论文数:
9.8W
被引数:
29.4W

机构

B
Beijing Jiaotong University
学者数:
2.2W
论文数: 1.7W
被引数: 1.2W
C
Central South University
学者数:
10.0W
论文数: 7.2W
被引数: 10.9W
U
University of Hull
学者数:
7.3K
论文数: 7.0K
被引数: 6.8K
N
Nanyang Technological University
学者数:
4.9W
论文数: 4.8W
被引数: 8.1W
T
tongji university
学者数:
7.9W
论文数: 6.0W
被引数: 98
U
University of Sunderland
学者数:
675
论文数: 623
被引数: 662
学者 查看更多机构
引用论文

引用论文

A Hybrid Active Contour Segmentation Method for Mvocardial D-S ⋮CT Images
err2018-01-01
err15
errOAAI
errHuang, Chenxi; Shan, Xiaoying; Lan, Yisha; Liu, Lu; Cai, Haidong; Che, Wenliang; Hao, Yongtao; Cheng, Yongqiang; Peng, Yonghong
err分享
err收藏
Visualization of color anatomy and molecular fluorescence in whole-mouse cryo-imaging
err2011-04-01
err23
errOAAI
errGargesha, Madhusudhana; Qutaish, Mohammed Q.; Roy, Debashish; Steyer, Grant J.; Watanabe, Michiko; Wilson, David L.
err分享
err收藏
A New Framework for the Integrative Analytics of Intravascular Ultrasound and Optical Coherence Tomography Images
err2018-01-01
err16
errOAAI
errHuang, Chenxi; Xie, Yuan; Lan, Yisha; Hao, Yongtao; Chen, Fei; Cheng, Yongqiang; Peng, Yonghong
err分享
err收藏
err
IF0
err
err0
PREAI
err
err分享
err收藏
A New Pulse Coupled Neural Network (PCNN) for Brain Medical Image Fusion Empowered by Shuffled Frog Leaping Algorithm
err2019-03-20
err44
errOAAI
errHuang, Chenxi; Tian, Ganxun; Lan, Yisha; Peng, Yonghong; Ng, E. Y. K.; Hao, Yongtao; Cheng, Yongqiang; Che, Wenliang
err分享
err收藏
没有更多内容