Medical images rather than any other types of images need high storage space. The lack of storage facilities, especially in developing countries, encourages researchers to find solutions for this problem. Compression of medical images is a priority, although it leads to some loss in the stored images. This paper introduces a framework for medical image storage and retrieval for the purpose of diagnosis. This framework adopts decimation as a tool for image compression, while interpolation is used as a tool for further image reconstruction. The quality of the reconstructed images is evaluated with a scale-invariant feature transform (SIFT)-based technique. Another task involved in this paper is the automatic diagnosis from the reconstructed images based on deep learning. Different types of interpolation algorithms are investigated and compared in this framework for the process of medical image reconstruction.

Abstract This paper presents an enhancement method to deal with the medical images which have low resolution as corneal images that have hexagonal nature and contain edges which must be preserved. So its resolution should be increased to help ophthalmologists to accurately diagnose and monitor diseases. Image interpolation is employed for resolution enhancement. In this paper, we consider interpolation techniques such as: polynomial interpolation, adaptive polynomial interpolation, inverse interpolation, and super-resolution (SR) reconstruction in order to enhance corneal image interpolation-based and learning-based techniques. The polynomial interpolation includes: bilinear, bicubic, cubic spline and cubic O-MOMS as well as their adaptive techniques. While the inverse interpolation techniques comprises linear minimum mean square error (LMMSE), maximum entropy, and a regularized image interpolation. Although polynomial based techniques are the most popular due to their simplicity, they don’t take into account the local activity levels of the image to be interpolated and cause a blurring effect in data. Interpolation is applied to each pixel to adapt for changing local activity levels, this adaptation reduces the blur effect and provides a better visual quality. Polynomial interpolation and its adaptive techniques are called signal synthesis techniques because they are based on the use of known neighborhoods to synthesize unknown pixel values. However, they did not meet the prerequisites of modern sampling theory in the interpolation process, so a pre-filtering step is required in the reconstruction process; a correction filter is needed before the interpolation process to compensate for the non-ideality of the image acquisition model. This correction filter is obtained as the inverse of the cross-correlation sequence between the acquisition model filter and the reconstruction filter, hence the inverse interpolation technique is applied which is superior where low image degradation model is taken into consideration. Finally the SR technique is proposed and compared to other previous techniques. Simulations are conducted to investigate the performance of the considered proposed techniques. It is shown that the adaptive polynomial image interpolation methods and inverse interpolation techniques give satisfactory results in terms of peak signal-to-noise ratio (PSNR), while the proposed SR technique is the most superior.

Abstract In the Artificial Intelligence (AI) field, the deep learning is considered a method falls in the wide machine learning algorithms family based on the learning principle. The known traditional and Conventional Neural Networks (CNNs) have been utilized in the pattern recognition techniques based on the deep learning concepts from different images. Due to the importance of the Anomaly Detection (AD) in automatic diagnosis, it is essential and vital point in the image and medical signal processing. In this paper, the AD has been tested and evaluated using the signals of the medical EEG employing the spectrogram and medical corneal images. The deep learning based on the CNN models are employed in the processes of training and testing, each image input passes through a convolution layers series and Kernels filters. For the classification, the pooling and Fully Connected (FC) layers have been utilized for this purpose. In this research paper, the experiments computer simulation have been presented, its results reveal that the success and superiority of the presented proposed techniques in the automated medical diagnosis.

Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems. In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully-connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems.

In this paper, we present hybrid watermarking and error control techniques for reliable cornea and infrared frame communication through wireless networks in Internet of Things (IoT) applications. In the proposed watermarking technique, two stages of Singular Value Decomposition (SVD) watermarking are used. In the embedding stage, two watermark images are embedded in either the cornea or infrared frames using Block-based SVD (B-SVD) and SVD schemes, respectively. Then, the resulting watermarked cornea or infrared frames are transmitted through an erroneous wireless channel. At the receiver, the received corrupted cornea or infrared frames are recovered using a proposed hybrid post-processing error control technique. This technique comprises a Circular Spatial-scan Order Interpolation Algorithm (CSOIA) and a temporal Partitioning Motion Compensation Algorithm (PMCA) to reconstruct the erroneous cornea or infrared frames. Then, the Bayesian Kalman Filter (BKF) is utilized in the amelioration process due to its efficiency to smooth the remanent inherent corruptions in the formerly reconstructed frames to obtain high cornea or infrared video quality. After that, the watermark extraction stage is implemented to extract the watermark images from the watermarked frames. Simulation results on several cornea and infrared frames show that the suggested hybrid watermarking, and error control techniques reveal adequate subjective and objective video quality compared to those obtained with the traditional methods. In addition, the watermark detectability, robustness, and security are enhanced. Moreover, the experimental results show that the proposed watermarking technique is superior and more secure than the other previous techniques for embedding and extraction of watermarks, efficiently, in the presence of attacks.

In the field of Artificial Intelligence (AI), deep learning is a method falls in the wider family of machine learning algorithms that works on the principle of learning. Convolutional Neural Networks (CNNs) can be used for pattern recognition from different images based on deep learning. Anomaly detection is a very vital area in medical signal and image processing due to its importance in automatic diagnosis. Anomaly detection from medical EEG signals based on spectrogram and medical corneal images are tested and evaluated in this paper. Technically, deep learning CNN models are used in the train and test processes, each input image will pass through a series of convolution layers with filters (Kernels), pooling, and fully connected layers (FC) for the classification purposes. The presented simulation results reveal the success of the proposed techniques towards automated medical diagnosis.

A new digital image interpolation method that is performed in the wavelet domain with a least squares algorithm is presented in this paper. This method estimates wavelet coefficients in the high frequency sub-images of the estimated High-Resolution (HR) image from the Low-Resolution (LR) image using a least squares algorithm. An inverse wavelet transform is then performed for the synthesis of the HR image. Experimental results show that the proposed method outperforms other commonly used methods such as the bilinear, bicubic, and traditional least squares methods, objectively and subjectively.

In this paper we propose an modified model for the E0 stream cipher, which is the Encryption system used in the Bluetooth specification. The modified model is manipulated by adding a fifth linear feed back shift register (LFSR) to the main key stream generator of the E0 cipher. The contribution of this modification is indicated in increasing the computational complexity of Ophir Levy and Avishai Wool attack from 285 to 2125.25. Increasing the quality of encryption is also illustrated through different images encryption with the implementation of several measuring factors such as the correlation coefficient, the maximum deviation, and the irregular deviation.