TY - GEN
T1 - Fusion of 3D Medical ROIs based on Transfer Functions
AU - Fu, Jingfei
AU - Zhang, Wenyao
AU - Cao, Yuanzhao
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/5/22
Y1 - 2020/5/22
N2 - Volume rendering is a key visualization technology in the medical field. It is often used to observe medical volumes such as CTs and MRIs, but the rendering results may not present multiple regions of interest (ROIs) well. In order to solve this problem, a method of fusing ROIs based on transfer functions is proposed in this paper. The entire process is divided into three steps. Firstly, two pairs of one-dimensional color transfer function (1D-CTF) and one-dimensional opacity transfer function (1D-OTF) are designed to separate different target structures. Secondly, a fusion method is taken to combine the 1D-CTFs and 1D-OTFs to get a new pair of transfer functions. Finally, the original volume is rendered again with the new transfer functions to get the fusion image of ROIs. Experiments show that the resulting images not only integrate the ROIs appearing in the two different cases, but also present their spatial relationships clearly. Besides, both visual perception and information entropy evaluation confirm that this kind of fusion is better than image-level fusion and accumulation-level fusion. On the whole, this method facilitates the analysis of medical volumes and the diagnosis of diseases.
AB - Volume rendering is a key visualization technology in the medical field. It is often used to observe medical volumes such as CTs and MRIs, but the rendering results may not present multiple regions of interest (ROIs) well. In order to solve this problem, a method of fusing ROIs based on transfer functions is proposed in this paper. The entire process is divided into three steps. Firstly, two pairs of one-dimensional color transfer function (1D-CTF) and one-dimensional opacity transfer function (1D-OTF) are designed to separate different target structures. Secondly, a fusion method is taken to combine the 1D-CTFs and 1D-OTFs to get a new pair of transfer functions. Finally, the original volume is rendered again with the new transfer functions to get the fusion image of ROIs. Experiments show that the resulting images not only integrate the ROIs appearing in the two different cases, but also present their spatial relationships clearly. Besides, both visual perception and information entropy evaluation confirm that this kind of fusion is better than image-level fusion and accumulation-level fusion. On the whole, this method facilitates the analysis of medical volumes and the diagnosis of diseases.
KW - Fusion
KW - Medical visualization
KW - Transfer function
KW - Volume rendering
UR - http://www.scopus.com/inward/record.url?scp=85092638380&partnerID=8YFLogxK
U2 - 10.1145/3405758.3405765
DO - 10.1145/3405758.3405765
M3 - Conference contribution
AN - SCOPUS:85092638380
T3 - ACM International Conference Proceeding Series
SP - 67
EP - 73
BT - Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2020
PB - Association for Computing Machinery
T2 - 12th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2020
Y2 - 22 May 2020 through 24 May 2020
ER -