Logarithmic Fuzzy Entropy Function for Similarity Measurement in Multimodal Medical Images Registration. (12th February 2020)

• Record Type: Journal Article
• Authors: Miao, Yu; Gao, Jiaying; Zhang, Ke; Shi, Weili; Li, Yanfang; Zhao, Jiashi; Jiang, Zhengang; Yang, Huamin; He, Fei; He, Wei; Qin, Jun; Chen, Tao
• Other Names: Adamczyk Sebastian Guest Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2020(2020)
• Publication Date: 2020-02-12
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2020/5487168
• Languages: English
• ISSNs: 1748-670X

Abstract

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 ) + ∆( 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.