Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC. 

https://link.springer.com/content/pdf/10.1007/s11042-022-11956-7.pdf

Security of medical images for telemedicine: a systematic review

Image segmentation is an essential step involved in most computer vision systems used in several areas such as engineering, communications, transportation, business, and social sciences. Besides, several techniques are employed to accomplish the task of segmentation for example (Threshold Based, Region Based, Edge Based, Clustering Based, Neural Network, and Partial Differential Equation). However, thresholding based techniques are the most frequently used techniques for segmentation due to its simplicity, easy to understand and implement. Thresholding in simple words can be defined as the process of dividing an image into multiple homogeneous classes. Mainly, thresholding techniques can be classified into two main categories namely Bi-level Thresholding (BT) and Multi-level Thresholding (MT) based on the number of thresholds selected. BT methods aim to find a single optimal threshold value that separates an image based on each pixel intensity into two regions namely foreground and background. While the MT methods are used to divide an image into two or more classes using two or more thresholds. However, to accomplish the selection task of the optimal thresholds traditional thresholding methods are exhaustive and time-consuming. On the other hand, optimization algorithms proved its efficiency in solving several real-world issues, which make the use of optimization algorithms a promising way of solving thresholding problems. This study presents a literature review of nature inspired optimization algorithms (NIOAs) and its applications in thresholding to optimize the selection of optimal thresholds process based image segmentation domain, also covers a large-scale of the researches published in this area of image segmentation based NIOAs over the last ten years.

Multi-level Thresholding Image Segmentation Based on Nature-Inspired Optimization Algorithms: A Comprehensive Review

In this paper, image enhancement is realized by using the Incomplete Beta Function (IBF) as the gray transformation curve. The main idea is to employ Artificial Bee Colony Algorithm (ABCA) to select the optimal parameters of IBF, which corresponds to the best curve of grayscale transformation. Designing specific fitness function constrains the evolutionary direction of the bees and then better images can be obtained. By comparing among the results of histogram equalization, unsharp masking, and Genetic Algorithm based methods, we come to the conclusion that ABCA is an effective method in image enhancement which is superior to the other three methods, and not only has the better optimizing ability than Genetic algorithm but also it converges quickly.

/pdf/adaptive-image-enhancement-based-on-artificial-bee-colony-33h4f0xvx4.pdf

Adaptive Image Enhancement Based on Artificial Bee Colony Algorithm

Diabetic Retinopathy is a vision impairment caused by blood vessel degeneration in the retina. It is becoming more widespread as it is linked to diabetes. Diabetic retinopathy can lead to blindness. Early detection of diabetic retinopathy by an ophthalmologist can help avoid vision loss and other complications. Diabetic retinopathy is currently diagnosed by visually recognizing irregularities on fundus pictures. This procedure, however, necessitates the use of ophthalmic imaging technologies to acquire fundus images as well as a detailed visual analysis of the stored photos, resulting in a costly and time-consuming diagnosis. The fundamental goal of this project is to create an easy-to-use machine learning model tool that can accurately predict diabetic retinopathy using pre-recorded digital fundus images. To create the suggested classifier model, we gathered annotated fundus images from publicly accessible data repositories and used two machine learning methods, support vector machine (SVM) and deep neural network (DNN). On test data, the proposed SVM model had a mean area under the receiver operating characteristic curve (AUC) of 97.11%, whereas the DNN model had a mean AUC of 99.15%.

/pdf/automatic-screening-of-diabetic-retinopathy-using-fundus-2tpd13u8.pdf

Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms

In dark or poorly lit environments, it is often difficult for the naked eye to distinguish low-light-level images because of low brightness, low contrast and noise, and it is difficult to perform subsequent image processing (such as video surveillance and target detection). To solve these problems, this paper proposes a low-light-level image enhancement algorithm based on deep learning. First, the low-light-level image is segmented into several super-pixels, and the noise level of each super-pixel is estimated by the ratio of the local standard deviation to the local gradient. Then, the image is inverted and smoothed by a BM3D filter, and the structural filter adaptive method is used to obtain complete images without noise but with the correct texture. Finally, the noise-free image and texture-complete images are applied to the integrated network, which can not only enhance the contrast but also effectively prevent the over-enhancement of the contrast. The experimental results show that this method is superior to traditional methods in terms of both subjective and objective evaluation, and the peak signal–noise ratio and improved structural similarity are 31.64 dB and 91.2%, respectively.

Lichuan Ning

Papers

Image Enhancement Based on Histogram Equalization