Special Issue "Entropy Based Image Registration"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: 31 March 2021.
Interests: Medical image analysis;Artificial Intelligence;Image registration
Interests: image processing; AI; healthcare
Special Issues and Collections in MDPI journals
Interests: medical image reconstruction/analysis; computational methods in medical imaging; medical image processing and segmentation; computer aided detection/diagnosis; medical image construction techniques and imaging in diagnostic radiology/echography, Artificial Intelligence
The concept of entropy indicates the degree of irregularity or uncertainty in a system. Usually, there are two concepts: entropy and information. Entropy represents the uncertainty, which means one is unconfident about the occurrence of a process. An increase in the uncertainty of a system will reduce the entropy of that system. Information represents the difference between the maximum and the actual value of entropy of a system. The analysis of medical images requires, among many others, statistical methods to achieve certain relationship between two or more images. The analysis of this relationship usually becomes manageable once a correspondence is set up between the images by means of image registration. A universal image registration solution is not possible but various techniques can be tailored for particular applications such as images acquired by MRI, US, CT, PET or retinal images. Both multi-modal image registration and multisubject image registration are difficult, but different stochastic models of the registration problem yield different entropic measures/entropy estimators to quantify the quality of image registration.
This Special Issue collects recent results drawn from research areas of medical imaging and image processing, such as parametric and nonparametric entropy estimation problem from the perspective of image registration, Rényi entropy-based image registration, level set entropy for nonrigid registration, and entropy-based registration algorithm. Contributions addressing any of these issues are very welcome.
Aim: to bring together the latest research into entropy based image registration.
Scope: parametric entropy estimation problem for image registration, non-parametric entropy estimation problem for image registration, Rényi entropy-based image registration, level set entropy for non-rigid registration, entropy-based registration algorithm, entropic graphs for registration.
Prof. Luminita Moraru
Dr. Nilanjan Dey
Dr. Simona Moldovanu
Manuscript Submission Information
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- Rényi entropy-based image registration
- Non-rigid registration
- Parametric and non-parametric entropy estimation problem for image registration
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Authors: Sayan Chakraborty , Ratika Pradhan, Amira S. Ashour, Luminita Moraru , *Nilanjan Dey
Dept. of CA, Sikkim Manipal Institute of Technology；
Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta Univ.,
Department of Chemistry, Physics & Environment, Faculty of Sciences and Environment, Dunarea de Jos
University of Galati, 47 Domneasca Str., 800008 Galati, Romania；
Department of Information Technology, Techno India College of Technology, West Bengal, 740000, India；
Abstract: Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer
(GWO) based non-rigid demons registration is proposed to support the retinal image registration process.
A comparative study of the proposed GWO-based demons registration framework with cuckoo search,
firefly algorithm, and particle swarm optimization- based demons registration is conducted. In addition, a
comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons,
and Thirion’s demons which are optimized using the proposed GWO is carried out. The results
established the superiority of the GWO-based framework which achieved 0.9977correlation and fast
processing compared to the use of the other optimization algorithms. Morover, the GWO-based Wang’s
demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework in
terms of less registration error (8.36×10 -5 ).