Abstract:In this work, a new and secure digital image scrambling algorithm for grayscale images is presented. The main goal of this algorithm is to change and reorder the positions of the pixel values of the grayscale images according to the rules of Conway's Game of Life. For this purpose, an image scrambling matrix is proposed. The proposed algorithm is mostly applied to known benchmark images of various sizes and histograms. Moreover, the proposed algorithm is applied to noisy and low-contrast images to evaluate the performance of the algorithm in consumer applications. The scrambling performance of the algorithm is examined through the evaluation of the correlation values between adjacent pixels in horizontal, vertical, and diagonal directions and the gray difference degree between a pixel value and its neighbor pixel values of the plain and scrambled images. Furthermore, the performance of our algorithm is compared to various state-of-the-art methods. Additionally, in order to measure the reliability of the algorithm, we perform attack analyses such as damage and occlusion. Results show that the proposed algorithm is robust and ensures high security with a powerful scrambling performance. Finally, the proposed algorithm can be used in applications providing security of information, such as image encryption and watermarking.
Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand-crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.