Title
Logarithmic Fuzzy Entropy Function for Similarity Measurement in Multimodal Medical Images Registration.
Abstract
Multimodal medical images are useful for observing tissue structure clearly in clinical practice. To integrate multimodal information, multimodal registration is significant. The entropy-based registration applies a structure descriptor set to replace the original multimodal image and compute similarity to express the correlation of images. The accuracy and converging rate of the registration depend on this set. We propose a new method, logarithmic fuzzy entropy function, to compute the descriptor set. It is obvious that the proposed method can increase the upper bound value from log(r) to log(r) + increment (r) so that a more representative structural descriptor set is formed. The experiment results show that our method has faster converging rate and wider quantified range in multimodal medical images registration.
Year
DOI
Venue
2020
10.1155/2020/5487168
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Field
DocType
Volume
Computer vision,Pattern recognition,Computer science,Upper and lower bounds,Clinical Practice,Fuzzy entropy,Correlation,Artificial intelligence,Logarithm
Journal
2020.0
ISSN
Citations 
PageRank 
1748-670X
0
0.34
References 
Authors
0
12
Name
Order
Citations
PageRank
yu miao147.18
Jiaying Gao200.34
Ke Zhang300.34
WeiLi Shi415.10
Yanfang Li500.34
Jiashi Zhao600.34
Zhengang Jiang7226.42
Huamin Yang81917.29
Fei He900.34
Wei He1000.34
Jun Qin1101.35
Tao Chen1200.34