The novel coronavirus 2019 (COVID-19) first appeared in Wuhan province of China and spread quickly around the globe and became a pandemic. The gold standard for confirming COVID-19 infection is through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) assay. The lack of sufficient RT-PCR testing capacity, false negative results of RT-PCR, time to get back the results and other logistical constraints enabled the epidemic to continue to spread albeit interventions like regional or complete country lockdowns. Therefore, chest radiographs such as CT and X-ray can be used to supplement PCR in combating the virus from spreading. In this work, we focus on proposing a deep learning tool that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images. The result of the experiments shows that the utilized models can provide accuracy up to 98% via pre-trained network and 94.1% accuracy by using the modified CNN.
Image inpainting is the process of filling in the missing region to preserve continuity of its overall content and semantic. In this paper, we present a novel approach to improve an existing scheme, called exemplar-based inpainting algorithm, using Topological Data Analysis (TDA). TDA is a mathematical approach concern studying shapes or objects to gain information about connectivity and closeness property of those objects. The challenge in using exemplar-based inpainting is that missing regions neighborhood area needs to have a relatively simple texture and structure. We studied the topological properties (e.g. number of connected components) of missing regions surrounding the missing area by building a sequence of simplicial complexes (known as persistent homology) based on a selected group of uniform Local binary Pattern LBP. Connected components of image regions generated by certain landmark pixels, at different thresholds, automatically quantify the texture nature of the missing regions surrounding areas. Such quantification help determine the appropriate size of patch propagation. We have modified the patch propagation priority function using geometrical properties of curvature of isophote and improved the matching criteria of patches by calculating the correlation coefficients from spatial, gradient and Laplacian domain. We use several image quality measures to illustrate the performance of our approach in comparison to similar inpainting algorithms. In particular, we shall illustrate that our proposed scheme outperforms the state-of-the-art exemplar-based inpainting algorithms.
Image Steganography is the technique of hiding sensitive data (secrete message) inside cover images in a way that no suspicion occurs to attackers, while steganalysis is the technique of detecting the embedded data by unauthorized persons. As a first step of detecting hidden data, distinguishing between original (Images without secrete message) and Stego (Images contain secrete message) is important. In this paper we design and propose a novel scheme based on the emerging field of Topological Data Analysis (TDA) concept of persistent homological (PH) invariants (e.g. No. of connected components), associated with certain image features. Selected group of Uniform Local Binary Pattern (LBP), which is a texture descriptor, codes representing the image features used to construct a sequence of simplicial complexes (SC) from an increasing sequence of distance thresholds (T). We calculate the corresponding non-increasing sequence of homological invariants which shows the speed at which the constructed sequence of SCs terminates. This approach is sensitive to differentiate original images from stego images. We test this approach on three different embedding techniques which are Traditional Least Significant Bits (TLSB) embedding technique, spatial Universal Wavelet Relative Distortion (S-UNIWARD) and LSB-Witness embedding technique together with a large number of images chosen randomly from large database of images. Preliminary results show that the PH sequence defines a discriminates criterion for steganalysis purpose with over 90% classification accuracy.
Image quality is a major factor influencing pattern recognition accuracy and help detect image tampering for forensics. We are concerned with investigating topological image texture analysis techniques to assess different type of degradation. We use Local Binary Pattern (LBP) as a texture feature descriptor. For any image construct simplicial complexes for selected groups of uniform LBP bins and calculate persistent homology invariants (e.g. number of connected components). We investigated image quality discriminating characteristics of these simplicial complexes by computing these models for a large dataset of face images that are affected by the presence of shadows as a result of variation in illumination conditions. Our tests demonstrate that for specific uniform LBP patterns, the number of connected component not only distinguish between different levels of shadow effects but also help detect the infected regions as well.
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