Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

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Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

In the face of an adversary, cryptography is a matter of coordination. It addresses a variety of topics such as confidentiality, authentication, and several vital distributions. Modern cryptography offers the framework for knowing precisely what these concerns are, how to test protocols intended to address them, and how to create protocols that you can trust in their protection. Advanced computer technology can access multimedia easily. Multimedia technologies have recently become popular in operation, and multimedia data protection has become the key concern. In this correspondence, the fundamental problems related to the problem of cryptography were addressed, and surveys of imaging strategies focused on disorderly schemes were also discussed. The chaotic cryptography of images can be accomplished with chaos properties, including deterministic dynamics, unpredictable action, and nonlinear transformation. This definition contributes to approaches that can simultaneously provide protection functionality and an overall visual check that might be acceptable for such applications. In different implementations, including military, legal, and medical processes, digital photographs are commonly used. These applications must monitor the access to images and include ways of checking the accuracy of images. In this paper, a detailed review of chaotic Scheme-based image encryption techniques is presented.

A Review on Chaotic Scheme-Based Image Encryption Techniques

In the new era, multimedia technology is used extensively. Multimedia data transfers over the Internet are not adequately stable. Data protection is needed for digital images transfer to avoid unauthorized entities. Many encryption strategies have been established for multimedia data over a medium that is not stable. This paper aims to compare the techniques used in encoding multimedia content. This paper explains the process for assessment for chaos image encryption algorithm. The chaos-based image encryption algorithm is used mainly because of its high protection and efficiency. Parameters such as MSC, encryption quality and avalanche effect check the image’s quality. This paper presents a comparative review of non-chaotic and chaotic image encryption techniques.

A Comparative Review on Non-chaotic and Chaotic Image Encryption Techniques

The brain tumor is the 22nd most common cancer worldwide, with 1.8% of new cancers. It is likely the most severe ailment that necessitates early discovery and treatment, and it requires the competence of neurosubject-matter experts and radiologists. Because of their enormous increases in data search and extraction speed and accuracy, as well as individualized treatment suggestions, machine and deep learning techniques are being increasingly commonly applied throughout healthcare industries. The current study depicts the methodologies and procedures used to detect a tumor inside the brain utilizing machine and deep learning techniques. Initially, data were preprocessed using contrast limited adaptive histogram equalization. Then, features were extracted using principal component analysis and independent component analysis (ICA). Next, the image was smoothed using multiple optimization techniques such as firefly and cuckoo search, lion, and bat optimization. Finally, Naïve Bayes and recurrent neural networks were utilized to classify the improved results. According to the findings, the ICA with cuckoo search and Naïve Bayes has the best mean square error rate of 1.02. With 64.81% peak signal-to-noise and 98.61% accuracy, ICA with hybrid optimization and a recurrent neural network (RNN) proved to better than the other algorithms. Furthermore, a Smartphone application is designed to perform quick and decisive actions. It helps neurologists and patients identify the tumor from a brain image in the early stages.

/pdf/computational-intelligence-and-metaheuristic-techniques-for-3otxw8g6.pdf

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

Information security has become a focal topic in the information and digital age. How to realize secure transmission and the secure storage of image data is a major research focus of information security. Aiming at this hot topic, in order to improve the security of image data transmission, this paper proposes an image encryption algorithm based on improved Arnold transform and a chaotic pulse-coupled neural network. Firstly, the oscillatory reset voltage is introduced into the uncoupled impulse neural network, which makes the uncoupled impulse neural network exhibit chaotic characteristics. The chaotic sequence is generated by multiple iterations of the chaotic pulse-coupled neural network, and then the image is pre-encrypted by XOR operation with the generated chaotic sequence. Secondly, using the improved Arnold transform, the pre-encrypted image is scrambled to further improve the scrambling degree and encryption effect of the pre-encrypted image so as to obtain the final ciphertext image. Finally, the security analysis and experimental simulation of the encrypted image are carried out. The results of quantitative evaluation show that the proposed algorithm has a better encryption effect than the partial encryption algorithm. The algorithm is highly sensitive to keys and plaintexts, has a large key space, and can effectively resist differential attacks and attacks such as noise and clipping.

https://www.mdpi.com/1099-4300/24/8/1103/pdf?version=1660729470

A Novel Image Encryption Algorithm Based on Improved Arnold Transform and Chaotic Pulse-Coupled Neural Network

https://robots.iopscience.iop.org/article/10.1088/1757-899X/993/1/012062/pdf

Secure Surveillance System Using Chaotic Image Encryption Technique

Due to Internet of Things (IoT), it has become easy to surveil the critical regions. Images are important parts of Surveillance Systems, and it is required to protect the images during transmission and storage. These secure surveillance frameworks are required in IoT systems, because any kind of information leakage can thwart the legal system as well as personal privacy. In this paper, a secure surveillance framework for IoT systems is proposed using image encryption. A hyperchaotic map is used to generate the pseudorandom sequences. The initial parameters of the hyperchaotic map are obtained using partial-regeneration-based non-dominated optimization (PRNDO). The permutation and diffusion processes are applied to generate the encrypted images, and the convolution neural network (CNN) can play an essential role in this part. The performance of the proposed framework is assessed by drawing comparisons with competitive techniques based on security parameters. It shows that the proposed framework provides promising results as compared to the existing techniques.

Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map

The principle point of this paper is to talk about the Video surveillance system plays major role in the security applications now a days. The need for surveillance systems are increasing drastically. Internet of things makes Video surveillance more effective. Internet of Things is an interrelated communication network for several systems and having the ability to transfer the information through the network without human or computer interaction. This paper reviews the network traffic due to Video surveillance and its management with Software Defined Networking. This paper also reviews the applications of Internet of things in Video surveillance system.

Gopal Ghosh

Papers

Secure Surveillance System Using Chaotic Image Encryption Technique

A Review on Chaotic Scheme-Based Image Encryption Techniques

A Comparative Review on Non-chaotic and Chaotic Image Encryption Techniques

Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map

Internet of Things Based Video Surveillance Systems for Security Applications