Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

Copyright protection of medical images is a vital goal in the era of smart healthcare systems. In recent telemedicine applications, medical images are sensed using medical imaging devices and transmitted to remote places for screening by physicians and specialists. During their transmission, the medical images could be tampered with by intruders. Traditional watermarking methods embed the information in the host images to protect the copyright of medical images. The embedding destroys the original image and cannot be applied efficiently to images used in medicine that require high integrity. Robust zero-watermarking methods are preferable over other watermarking algorithms in medical image security due to their outstanding performance. Most existing methods are presented based on moments and moment invariants, which have become a prominent method for zero-watermarking due to their favorable image description capabilities and geometric invariance. Although moment-based zero-watermarking can be an effective approach to image copyright protection, several present approaches cannot effectively resist geometric attacks, and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms rely on traditional moment computation, which suffers from numerical error accumulation, leading to numerical instabilities, and time consumption and affecting the performance of these moment-based zero-watermarking techniques. In this paper, we derived multi-channel Gaussian-Hermite moments of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve lossless copyright protection of the color medical images. We performed experiments where their outcomes ensure the robustness of the proposed zero-watermarking algorithms against various attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior robustness, security, and time computation.

Novel encryption for color images using fractional-order hyperchaotic system

The fractional-order functions show better performance than their corresponding integer-order functions in various image processing applications. In this paper, the authors propose a novel utilization of fractional-order chaotic systems in color image encryption. The 4D hyperchaotic Chen system of fractional-order combined with the Fibonacci Q-matrix. The proposed encryption algorithm consists of three steps: in step#1, the input image decomposed into the primary color channels, R, G, & B. The confusion and diffusion operations are performed for each channel independently. In step#2, the 4D hyperchaotic Chen system of fractional orders generates random numbers to permit pixel positions. In step#3, we split the permitted image into 2 × 2 blocks where the Fibonacci Q-matrix diffused each of them. Experiments performed where the obtained results ensure the efficiency of the proposed encryption algorithm and its ability to resist attacks.

Prognostic implication of PD-L1 expression and associated tumor infiltrating lymphocytes in metastatic breast cancer

Introduction: One of the most important regulators of immune response is the programmed death receptor 1 (PD-1) and its interaction with its ligand (PD-L1), which negatively influences the immune response. Objectives: This study aims to clarify PD-L1 expression levels and the associated tumor infiltrating lymphocytes (TILs) in patients with metastatic breast cancer, and to assess their influence on the prognosis of these patients and the association with clinico-pathologic criteria. Patients and Methods: PD-L1 expression was analyzed using immunohistochemistry (IHC) while TILs count was assessed by histopathological examination of the hematoxylin and eosin (H & E) stained full tumor sections from 50 patients diagnosed with stage IV breast cancer at Ain Shams University hospital, Cairo, Egypt. Results: PD-L1 expression was demonstrated on TILs in 21 of 50 specimens, and on tumor cells in 13 of 50 specimens. Triple negative breast cancer (TNBC) and ER-/Her2+ subtypes were significantly associated with TIL infiltration and PD-L1 expression (on TILs and tumor cells). High TIL infiltration was significantly associated with worse overall survival (OS) and progression free survival (PFS) (P=0.0238 [HR 4.7, 95% CI: 1.22-18.11] and P=0.0262 [HR 3.1, 95% CI: 1.14-8.59] respectively). No correlation was found between PD-L1 expression (on tumor or TILs) and the survival of the patients (OS nor PFS). Conclusion: High TIL count infiltrating the breast tumor is associated with worse OS and PFS in patients with metastatic breast cancer. High PD-L1 expression correlated with high counts of TIL levels around the tumor. These findings have major clinical implications in using immune-checkpoint inhibitors in treating breast cancer patients.

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi

The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.

New fractional-order shifted Gegenbauer moments for image analysis and recognition

Orthogonal moments are used to represent digital images with minimum redundancy. Orthogonal moments with fractional-orders show better capabilities in digital image analysis than integer-order moments. In this work, the authors present new fractional-order shifted Gegenbauer polynomials. These new polynomials are used to define a novel set of orthogonal fractional-order shifted Gegenbauer moments (FrSGMs). The proposed method is applied in gray-scale image analysis and recognition. The invariances to rotation, scaling and translation (RST), are achieved using invariant fractional-order geometric moments. Experiments are conducted to evaluate the proposed FrSGMs and compare with the classical orthogonal integer-order Gegenbauer moments (GMs) and the existing orthogonal fractional-order moments. The new FrSGMs outperformed GMs and the existing orthogonal fractional-order moments in terms of image recognition and reconstruction, RST invariance, and robustness to noise.

New machine learning method for image-based diagnosis of COVID-19

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.

Evaluation of zinc level in skin of patients with necrolytic acral erythema

BACKGROUND

Necrolytic acral erythema (NAE) is considered a cutaneous sign of hepatitis C virus infection. Its exact pathogenesis is still not fully understood, with some reports about decreased serum zinc levels but none about its level in the skin.

OBJECTIVES

To assess skin (lesional and perilesional) and serum zinc levels in patients with NAE and compare them with levels in control subjects.

METHODS

Fifteen patients with NAE and 10 healthy controls were included in this study. Assessment of zinc level, in serum by graphite furnace atomic absorption spectrophotometry and in lesional and perilesional skin biopsies by flame atomic absorption spectrometry, was done in all subjects. Re-evaluation of serum and lesional skin zinc level was done after oral zinc treatment.

RESULTS

Mean±SD zinc levels were significantly lower in patients (serum 0·44 ± 0·13 mg L(-1) ; lesional skin 42·6 ± 18·9 mg L(-1) ; perilesional skin 32·5 ± 17·2 mg L(-1) ) than controls (serum 1·17 ± 0·29 mg L(-1) ; skin 100·1 ± 2·77 mg L(-1) ), with a positive correlation between lesional and perilesional skin zinc (r = 0·91, P < 0·01). Oral zinc supplementation significantly increased serum and skin zinc levels (by 159% and 4%, respectively; P < 0·05).

CONCLUSIONS

NAE is associated with decreased serum and skin zinc levels. Oral zinc supplementation corrects decreased levels of plasma and skin zinc much earlier than the desired clinical benefits appear.