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Entropy
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Entropy Based Image Registration
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Entropy Based Image Registration

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 7663

Special Issue Editors

Prof. Dr. Luminita Moraru

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Guest Editor
Department of Physics, Faculty of Sciences, University Dunarea de Jos of Galati, 800201 Galati, Romania
Interests: medical image analysis;artificial intelligence;image registration
Dr. Nilanjan Dey

E-Mail Website
Guest Editor
Techno India College of Technology, Kolkata, West Bengal 700156, India
Interests: image processing; AI; healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Simona Moldovanu

E-Mail Website
Guest Editor
Department of Computer Science and Information Technology, Faculty of Automation, Computers Sciences, Electronics and Electrical Engineering, University Dunarea de Jos of Galati, 800201 Galati, Romania
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Rényi entropy-based image registration
  • Non-rigid registration
  • Parametric and non-parametric entropy estimation problem for image registration

Published Papers (3 papers)

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Research

21 pages, 7495 KiB  
Article
Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution
by Qingyu Deng, Zeyi Shi and Congjie Ou
Entropy 2022, 24(3), 319; https://doi.org/10.3390/e24030319 - 23 Feb 2022
Cited by 8 | Viewed by 1623
Abstract
In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high [...] Read more.
In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high efficiency. The entropy-based and variance-based algorithms are two main kinds of image thresholding methods, and have been independently developed for different kinds of images over the years. In this paper, their advantages are combined and a new algorithm is proposed to deal with a more general scope of images, including the long-range correlations among the pixels that can be determined by a nonextensive parameter. In comparison with the other famous entropy-based and variance-based image thresholding algorithms, the new algorithm performs better in terms of correctness and robustness, as quantitatively demonstrated by four quality indices, ME, RAE, MHD, and PSNR. Furthermore, the whole process of the new algorithm has potential application in self-adaptive object recognition. Full article
(This article belongs to the Special Issue Entropy Based Image Registration)
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16 pages, 4847 KiB  
Article
Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images
by Simona Moldovanu, Lenuta Pană Toporaș, Anjan Biswas and Luminita Moraru
Entropy 2020, 22(11), 1299; https://doi.org/10.3390/e22111299 - 14 Nov 2020
Cited by 10 | Viewed by 2486
Abstract
A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented [...] Read more.
A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods. Full article
(This article belongs to the Special Issue Entropy Based Image Registration)
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22 pages, 5848 KiB  
Article
Grey-Wolf-Based Wang’s Demons for Retinal Image Registration
by Sayan Chakraborty, Ratika Pradhan, Amira S. Ashour, Luminita Moraru and Nilanjan Dey
Entropy 2020, 22(6), 659; https://doi.org/10.3390/e22060659 - 15 Jun 2020
Cited by 7 | Viewed by 2588
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, [...] Read more.
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 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36 × 10−5. Full article
(This article belongs to the Special Issue Entropy Based Image Registration)
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