Special Issue "Entropy in Image Analysis II"

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

Deadline for manuscript submissions: 20 December 2019.

Special Issue Editor

Dr. Amelia Carolina Sparavigna
E-Mail Website
Guest Editor
Department of Applied Science and Technology, Polytechnic University of Turin, Turin, Italy
Interests: general physics and mathematics; optics; software; image processing applied to microscopy and satellite imagery
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. This analysis often needs numerical and analytical methods that are highly sophisticated, particularly for those applications in medicine, security, and remote sensing where the results of the processing consist of data of vital importance.

Since it is involved in numerous applications requiring reliable quantitative results, image analysis has produced a large number of approaches and algorithms, sometimes limited to specific functions in a small range of tasks, sometimes generic enough to be applied to a wide range of tasks. In this framework, a key role can be played by entropy, in the form of Shannon entropy or generalized entropy, used directly in processing methods or in the evaluation of results, to maximize the success of a final decision support system.

Since active research in image processing is still engaged in the search for methods that are truly comparable to the abilities of human vision capabilities, I solicit your contribution to this Special Issue of this journal, which is devoted to the use of entropy in extracting information from images and to the decision processes related to image analyses.

Dr. Amelia Carolina Sparavigna
Guest Editor

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 papers will be 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 1600 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

  • Image entropy
  • Shannon entropy
  • Tsallis entropy
  • Generalized entropies
  • Image processing
  • Image segmentation
  • Retinex methods
  • Medical imaging
  • Remote sensing
  • Security

Related Special Issue

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Investigating Detectability of Infrared Radiation Based on Image Evaluation for Engine Flame
Entropy 2019, 21(10), 946; https://doi.org/10.3390/e21100946 - 27 Sep 2019
Abstract
Aiming at the application requirements of infrared detection, the influence of earth background interference on plume radiation detection is investigated and discussed in this article. The infrared image of the earth’s atmospheric background radiation is simulated by the spectral correlation based on the [...] Read more.
Aiming at the application requirements of infrared detection, the influence of earth background interference on plume radiation detection is investigated and discussed in this article. The infrared image of the earth’s atmospheric background radiation is simulated by the spectral correlation based on the conversion model of the surface radiation with different bands. The infrared radiation image of the jet flame and the background is generated by overlapping the infrared radiation of the engine flame and the background radiation according to the detection angle of view. Through the image quality evaluation model, the detectability of the flame is analyzed. The simulating results show that the comprehensive statistical features such as image information entropy, variance and signal-to-clutter ratio can be used to evaluate the detectability of the engine flame. Full article
(This article belongs to the Special Issue Entropy in Image Analysis II)
Show Figures

Figure 1

Open AccessArticle
On the Security of a Latin-Bit Cube-Based Image Chaotic Encryption Algorithm
Entropy 2019, 21(9), 888; https://doi.org/10.3390/e21090888 - 12 Sep 2019
Abstract
In this paper, the security analysis of an image chaotic encryption algorithm based on Latin cubes and bit cubes is given. The proposed algorithm adopts a first-scrambling-diffusion- second-scrambling three-stage encryption scheme. First, a finite field is constructed using chaotic sequences. Then, the Latin [...] Read more.
In this paper, the security analysis of an image chaotic encryption algorithm based on Latin cubes and bit cubes is given. The proposed algorithm adopts a first-scrambling-diffusion- second-scrambling three-stage encryption scheme. First, a finite field is constructed using chaotic sequences. Then, the Latin cubes are generated from finite field operation and used for image chaotic encryption. In addition, according to the statistical characteristics of the diffusion image in the diffusion stage, the algorithm also uses different Latin cube combinations to scramble the diffusion image for the second time. However, the generation of Latin cubes in this algorithm is independent of plain image, while, in the diffusion stage, when any one bit in the plain image changes, the corresponding number of bits in the cipher image follows the change with obvious regularity. Thus, the equivalent secret keys can be obtained by chosen plaintext attack. Theoretical analysis and experimental results indicate that only a maximum of 2.5 × w × h 3 + 6 plain images are needed to crack the cipher image with w × h resolution. The size of equivalent keys deciphered by the method proposed in this paper are much smaller than other general methods of cryptanalysis for similar encryption schemes. Full article
(This article belongs to the Special Issue Entropy in Image Analysis II)
Show Figures

Figure 1

Open AccessArticle
A Secure and Fast Image Encryption Scheme Based on Double Chaotic S-Boxes
Entropy 2019, 21(8), 790; https://doi.org/10.3390/e21080790 - 13 Aug 2019
Abstract
In order to improve the security and efficiency of image encryption systems comprehensively, a novel chaotic S-box based image encryption scheme is proposed. Firstly, a new compound chaotic system, Sine-Tent map, is proposed to widen the chaotic range and improve the chaotic performance [...] Read more.
In order to improve the security and efficiency of image encryption systems comprehensively, a novel chaotic S-box based image encryption scheme is proposed. Firstly, a new compound chaotic system, Sine-Tent map, is proposed to widen the chaotic range and improve the chaotic performance of 1D discrete chaotic maps. As a result, the new compound chaotic system is more suitable for cryptosystem. Secondly, an efficient and simple method for generating S-boxes is proposed, which can greatly improve the efficiency of S-box production. Thirdly, a novel double S-box based image encryption algorithm is proposed. By introducing equivalent key sequences {r, t} related with image ciphertext, the proposed cryptosystem can resist the four classical types of attacks, which is an advantage over other S-box based encryption schemes. Furthermore, it enhanced the resistance of the system to differential analysis attack by two rounds of forward and backward confusion-diffusion operation with double S-boxes. The simulation results and security analysis verify the effectiveness of the proposed scheme. The new scheme has obvious efficiency advantages, which means that it has better application potential in real-time image encryption. Full article
(This article belongs to the Special Issue Entropy in Image Analysis II)
Show Figures

Figure 1

Open AccessArticle
Efficacy of Quantitative Muscle Ultrasound Using Texture-Feature Parametric Imaging in Detecting Pompe Disease in Children
Entropy 2019, 21(7), 714; https://doi.org/10.3390/e21070714 - 22 Jul 2019
Abstract
Pompe disease is a hereditary neuromuscular disorder attributed to acid α-glucosidase deficiency, and accurately identifying this disease is essential. Our aim was to discriminate normal muscles from neuropathic muscles in children affected by Pompe disease using a texture-feature parametric imaging method that simultaneously [...] Read more.
Pompe disease is a hereditary neuromuscular disorder attributed to acid α-glucosidase deficiency, and accurately identifying this disease is essential. Our aim was to discriminate normal muscles from neuropathic muscles in children affected by Pompe disease using a texture-feature parametric imaging method that simultaneously considers microstructure and macrostructure. The study included 22 children aged 0.02–54 months with Pompe disease and six healthy children aged 2–12 months with normal muscles. For each subject, transverse ultrasound images of the bilateral rectus femoris and sartorius muscles were obtained. Gray-level co-occurrence matrix-based Haralick’s features were used for constructing parametric images and identifying neuropathic muscles: autocorrelation (AUT), contrast, energy (ENE), entropy (ENT), maximum probability (MAXP), variance (VAR), and cluster prominence (CPR). Stepwise regression was used in feature selection. The Fisher linear discriminant analysis was used for combination of the selected features to distinguish between normal and pathological muscles. The VAR and CPR were the optimal feature set for classifying normal and pathological rectus femoris muscles, whereas the ENE, VAR, and CPR were the optimal feature set for distinguishing between normal and pathological sartorius muscles. The two feature sets were combined to discriminate between children with and without neuropathic muscles affected by Pompe disease, achieving an accuracy of 94.6%, a specificity of 100%, a sensitivity of 93.2%, and an area under the receiver operating characteristic curve of 0.98 ± 0.02. The CPR for the rectus femoris muscles and the AUT, ENT, MAXP, and VAR for the sartorius muscles exhibited statistically significant differences in distinguishing between the infantile-onset Pompe disease and late-onset Pompe disease groups (p < 0.05). Texture-feature parametric imaging can be used to quantify and map tissue structures in skeletal muscles and distinguish between pathological and normal muscles in children or newborns. Full article
(This article belongs to the Special Issue Entropy in Image Analysis II)
Show Figures

Figure 1

Open AccessArticle
Detection of Salient Crowd Motion Based on Repulsive Force Network and Direction Entropy
Entropy 2019, 21(6), 608; https://doi.org/10.3390/e21060608 - 20 Jun 2019
Abstract
This paper proposes a method for salient crowd motion detection based on direction entropy and a repulsive force network. This work focuses on how to effectively detect salient regions in crowd movement through calculating the crowd vector field and constructing the weighted network [...] Read more.
This paper proposes a method for salient crowd motion detection based on direction entropy and a repulsive force network. This work focuses on how to effectively detect salient regions in crowd movement through calculating the crowd vector field and constructing the weighted network using the repulsive force. The interaction force between two particles calculated by the repulsive force formula is used to determine the relationship between these two particles. The network node strength is used as a feature parameter to construct a two-dimensional feature matrix. Furthermore, the entropy of the velocity vector direction is calculated to describe the instability of the crowd movement. Finally, the feature matrix of the repulsive force network and direction entropy are integrated together to detect the salient crowd motion. Experimental results and comparison show that the proposed method can efficiently detect the salient crowd motion. Full article
(This article belongs to the Special Issue Entropy in Image Analysis II)
Show Figures

Figure 1

Open AccessArticle
A New Algorithm for Medical Color Images Encryption Using Chaotic Systems
Entropy 2019, 21(6), 577; https://doi.org/10.3390/e21060577 - 10 Jun 2019
Abstract
In this paper, we present a new algorithm based on chaotic systems to protect medical images against attacks. The proposed algorithm has two main parts: A high-speed permutation process and adaptive diffusion. After the implementation of the algorithm in the MATLAB software, it [...] Read more.
In this paper, we present a new algorithm based on chaotic systems to protect medical images against attacks. The proposed algorithm has two main parts: A high-speed permutation process and adaptive diffusion. After the implementation of the algorithm in the MATLAB software, it is observed that the algorithm is effective and appropriate. Also, to quantitatively evaluate the uniformity of the histogram, the chi-square test is done. Key sensitivity analysis demonstrates that images cannot be decrypted whenever a small change happens in the key, which indicates that the algorithm is suitable. Clearly, part of special images is selected to test the selected plain-text, like an all-white image and an all-black image. Entropy results obtained from the implementation of the algorithm on this type of images show that the proposed method is suitable for this particular type of images. In addition, the obtained results from noise and occlusion attacks analysis show that the proposed algorithm can withstand against these types of attacks. Moreover, it can be seen that the images after encryption and decryption are of good quality; the measures such as the correlation coefficients, the entropy, the number of pixel change rate (NPCR), and the uniform average change intensity (UACI) have suitable values; and the method is better than previous methods. Full article
(This article belongs to the Special Issue Entropy in Image Analysis II)
Show Figures

Figure 1

Open AccessArticle
Improvement of Image Binarization Methods Using Image Preprocessing with Local Entropy Filtering for Alphanumerical Character Recognition Purposes
Entropy 2019, 21(6), 562; https://doi.org/10.3390/e21060562 - 04 Jun 2019
Abstract
Automatic text recognition from the natural images acquired in uncontrolled lighting conditions is a challenging task due to the presence of shadows hindering the shape analysis and classification of individual characters. Since the optical character recognition methods require prior image binarization, the application [...] Read more.
Automatic text recognition from the natural images acquired in uncontrolled lighting conditions is a challenging task due to the presence of shadows hindering the shape analysis and classification of individual characters. Since the optical character recognition methods require prior image binarization, the application of classical global thresholding methods in such case makes it impossible to preserve the visibility of all characters. Nevertheless, the use of adaptive binarization does not always lead to satisfactory results for heavily unevenly illuminated document images. In this paper, the image preprocessing methodology with the use of local image entropy filtering is proposed, allowing for the improvement of various commonly used image thresholding methods, which can be useful also for text recognition purposes. The proposed approach was verified using a dataset of 140 differently illuminated document images subjected to further text recognition. Experimental results, expressed as Levenshtein distances and F-Measure values for obtained text strings, are promising and confirm the usefulness of the proposed approach. Full article
(This article belongs to the Special Issue Entropy in Image Analysis II)
Show Figures

Figure 1

Planned Papers

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.

Title: Pattern Classification Based Lossless Compression of Red Blood Cell Images of Malaria Infection Using Deep Learning
Authors: Y. Dong, W. D. Pan and D. Wu
Affiliation: University of Alabama in Huntsville, AL 35899, USA
Abstract: Malaria occurs in nearly 100 countries worldwide and imposes a huge toll on human health and heavy socioeconomic burdens. There is a notable challenge in telemedicine on efficient storage and rapid transfer of massive malaria infection image datasets for malaria infection. To this end, we propose a novel lossless compression method for red blood cell images by integrating compression with pattern classification based on stacked autoencoders. Our previous study on using autoencoders as a deep learning method to losslessly compress image datasets showed the advantage of exploiting in-class correlations by training the autoencoders using the images known to belong to the same class. However, the compression performance suffers if the images used for training come from different classes. Therefore, in this work, we introduce a more realistic framework where the input images are first classified before being compressed using autoencoders. We study how the accuracy of the classifiers would affect the overall compression ratios for two-class image dataset compression. We conduct information- theoretic analysis based on probabilistic distributions of the prediction residues, and derive formulas for compressed bit rates as a function of classification accuracies. We then use synthesized data based on the models to verify the theoretical results. Next, we use real malaria infection image datasets to evaluate the relations between classification accuracies and compressed bit rates. Simulation results show that the joint classification/compression method can achieve more efficient compression than other traditional lossless compression methods, such as JPEG2000, JPEG-LS, CALIC, and WebP.

Back to TopTop