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Table Of Integrals Series And Products

Review: Digital image steganography: Survey and analysis of current methods

Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. Recently, deep learning based retinal vessel segmentation methods have reached the state-of-the-art performance. Due to the extreme variations in the morphology of the vessels against the noisy background, these methods still have issues of dealing with small thin vessels, low discriminative ability at the optic disk area, etc. In this paper, we proposed a U-Net-like model with the weighted attention mechanism and the skip connection scheme for addressing these issues. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed methods.

Weighted Res-UNet for High-Quality Retina Vessel Segmentation

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Introduction to Functional Magnetic Resonance Imaging, Principles and TechniquesIntroduction to Functional Magnetic Resonance Imaging, Principles and Techniques. By BuxtonR. B.. Cambridge University Press, Cambridge2002. ISBN 0-521-58113-3. 536 pages 134 line diagrams, 34 halftone figures. Price hardback: GBP 95.00.

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning

Objective: Diabetic retinopathy (DR) is characterized by the progressive deterioration of retina with the appearance of different types of lesions that include microaneurysms, hemorrhages, exudates, etc Detection of these lesions plays a significant role for early diagnosis of DR Methods: To this aim, this paper proposes a novel and automated lesion detection scheme, which consists of the four main steps: vessel extraction and optic disc removal, preprocessing, candidate lesion detection, and postprocessing The optic disc and the blood vessels are suppressed first to facilitate further processing Curvelet-based edge enhancement is done to separate out the dark lesions from the poorly illuminated retinal background, while the contrast between the bright lesions and the background is enhanced through an optimally designed wideband bandpass filter The mutual information of the maximum matched filter response and the maximum Laplacian of Gaussian response are then jointly maximized Differential evolution algorithm is used to determine the optimal values for the parameters of the fuzzy functions that determine the thresholds of segmenting the candidate regions Morphology-based postprocessing is finally applied to exclude the falsely detected candidate pixels Results and Conclusions: Extensive simulations on different publicly available databases highlight an improved performance over the existing methods with an average accuracy of $9771\%$ and robustness in detecting the various types of DR lesions irrespective of their intrinsic properties

Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy

Region based QIM digital watermarking scheme for image database in DCT domain

The paper proposes a blind spread spectrum watermarking scheme where watermark information is embedded redundantly in the multilevel wavelet coefficients of the cover image. It has been shown that for a given embedding distortion, data embedding in LL and HH bands offers higher resiliency through better spectrum spreading compared to LH and HL band embedding, although the security of the hidden data is better in the later case. High resiliency of the scheme is supported by the good visual quality of the watermark images extracted from the several distorted watermarked images. Data hiding capacity is increased using code division multiple access (CDMA) technique and the mutual information values for the different watermark images are able to detect the degree of distortion already occurred in the watermarked image.

http://www.isical.ac.in/~malay/Papers/Conf/ICIP'04_1595.pdf

A blind CDMA image watermarking scheme in wavelet domain

On optimal fuzzy c-means clustering for energy efficient cooperative spectrum sensing in cognitive radio networks

The paper proposes a digital image watermarking scheme that selects regions for data embedding based on information measure. Two valued kernels of Hadamard transformation cause smaller image information change during embedding compared to other transform domains such as DCT (discrete cosine transform), DFT (discrete Fourier transform), Fourier–Mellin and wavelet-based embedding. Moreover, the usage of Hadamard transform as signal decomposition tool offers advantages in terms of simpler implementation, low computation cost and high resiliency at low quality compression considering both JPEG and JPEG 2000 framework. Compression resiliency is further improved using adaptive negative modulation. These facts are validated comparing the performance with some other existing watermarking schemes as well as DCT domain implementation of the proposed scheme.

https://www.isical.ac.in/~malay/Papers/ijec_10.pdf

Santi P. Maity

Papers

Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy

Region based QIM digital watermarking scheme for image database in DCT domain

A blind CDMA image watermarking scheme in wavelet domain

On optimal fuzzy c-means clustering for energy efficient cooperative spectrum sensing in cognitive radio networks

DHT domain digital watermarking with low loss in image informations