A Simple Image Encryption Based on Binary Image Affine Transformation and Zigzag Process

In this paper, we propose a new and simple method for image encryption. It uses an external secret key of 128 bits long and an internal secret key. The novelties of the proposed encryption process are the methods used to extract an internal key to apply the zigzag process, affine transformation, and substitution-diffusion process. Initially, an original gray-scale image is converted into binary images. An internal secret key is extracted from binary images. The two keys are combined to compute the substitution-diffusion keys. The zigzag process is firstly applied on each binary image. Using an external key, every zigzag binary image is reflected or rotated and a new gray-scale image is reconstructed. The new image is divided into many nonoverlapping subblocks, and each subblock uses its own key to take out a substitution-diffusion process. We tested our algorithms on many biomedical and nonmedical images. It is seen from evaluation metrics that the proposed image encryption scheme provides good statistical and diffusion properties and can resist many kinds of attacks. It is an efficient and secure scheme for real-time encryption and transmission of biomedical images in telemedicine.

New Model of Heteroasociative min Memory Robust to Acquisition Noise

Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be very efficient, but they manifest some weakness with mixed noise. If an appropriate kernel is not used, that is, a subset of the pattern to be recalled that is not affected by noise, memories fail noticeably. A possible problem for building kernels with sufficient conditions, using binary and gray-scale images, is not knowing how the noise is registered in these images. A solution to this problem is presented by analyzing the behavior of the acquisition noise. What is new about this analysis is that, noise can be mapped to a distance obtained by a distance transform. Furthermore, this analysis provides the basis for a new model of min heteroassociative memory that is robust to the acquisition/mixed noise. The proposed model is novel because min associative memories are typically inoperative to mixed noise. The new model of heteroassocitative memory obtains very interesting results with this type of noise.

Degradation state identification for hydraulic pumps using modified hierarchical decomposition and image processing

Monitoring the degradation state of hydraulic pumps is of great significance to the safe and stable operation of equipment. As an important step, feature extraction has always been challenging. The non-stationary and nonlinear characteristics of vibration signals are likely to weaken the performance of traditional features. The two-dimensional image representation of vibration signals can provide more information for feature extraction, but it is challenging to obtain sufficient information based on small-size images. To solve these problems, a method for feature extraction based on modified hierarchical decomposition (MHD) and image processing is proposed in this paper. First, a set of signals decomposed by MHD are converted into gray-scale images. Second, features from accelerated segment test (FAST) algorithm are applied to detecting the feature points of the gray-scale image. Third, the real part of Gabor filter bank is used to convolve the images, and the responses of feature points are used to calculate histograms that are regarded as feature vectors. The method for feature extraction fully acquires the multi-layered texture information of small-size images and removes the redundant information. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the proposed method are validated using experimental data, and the results show that the highest recognition rate of our proposed method can reach 100%. The results of the comparison among the proposed method, local binary pattern (LBP), and one-dimensional ternary patterns (1D-TPs) certify the superiorities of the proposed method. It obtains the highest classification accuracy (99.7%–98%) and the lowest feature set dimension (13–10).

Analysis the digital images by using morphology operators

In this paper, we deal with morphology images that try to improve the use of images. On the one hand, the process is used to obtain the histogram of the image then converted it into a non-color image (gray scale). The next step is to perform the erosion, dilation, open and close operations on the images, how these methods have important effects, and how can be used on a variable number of images, and found the differences between them. These operations were applied on four different images, check images, four basic operations (dilation, erosion, open and close) for each image were performed. Then, retrieving process to the original state of the image (the colored copy) was applied. The results found that retrieving the original images is difficult, and there is the occurrence of some noises on the image when it was retrieved. Finally, conclusions of the work are presented.

Role of Advanced MRI Techniques in the Quantitative Assessment of Liver Fat—A Multimodality-Based Comparative Study of Diagnostic Performance in a Tertiary Care Institute

Objective To evaluate the accuracy of noninvasive imaging methods including gray-scale ultrasound, ultrasound shear wave elastography, unenhanced computed tomography (CT), and proton density fat fraction (PDFF) on magnetic resonance imaging (MRI) using three-dimensional (3D) multiecho multipoint chemical shift–encoded spoiled gradient echo (q-DIXON) sequence in the quantification of hepatic steatosis, with proton MR spectroscopy (H1-MRS) as the reference standard in Indian population. Methods Our study included 100 consecutive adult patients referred to the department of radiology in our hospital for imaging of liver. Fat content of liver was recorded using MRI (H1-MRS and q-DIXON), unenhanced CT (average liver attenuation [ALI] and liver attenuation index [LAI]) and ultrasonography (USG) (gray-scale grading and shear wave elastography [SWE]). Data were analyzed by linear regression and Bland–Altman analysis for each technique compared with H1-MRS. The diagnostic performances of all the methods were compared using DeLong test, for detection of mild and moderate-to-severe hepatic steatosis, separately. Results MRI q-DIXON PDFF showed excellent correlation (r = 0.917, r2 = 0.840) and strong agreement (1.48 ±3.01) with H1-MRS-derived PDFF measurements. Unenhanced CT-based methods showed moderate correlation with modest agreement (r = −0.826, r2 = 0.681, −40.18 ± 16.05 for ALI and r = −0.858, r2 = 0.735, 13.4 ± 15.3 for LAI) whereas USG gray-scale assessment showed low correlation (weighted Kappa value 0.366) with H1-MRS PDFF. No correlation was found between USG-SWE results and PDFF measured with H1-MRS. Comparison of areas under curve (AUCs) using DeLong test revealed that MRI q-DIXON method performed the best for diagnosis of hepatic steatosis compared with rest. For moderate to severe steatosis, MRI q-DIXON and unenhanced CT-based methods had comparable diagnostic performance with AUCs not showing statistically significant differences. Conclusion MRI q-DIXON shows strongest correlation with MRS and should be preferred for estimation of hepatic fat, especially when MRS is not available. Unenhanced CT shows limited diagnostic performance in detecting mild steatosis; however, it certainly has a role in diagnosing moderate-to-severe hepatic steatosis, such as evaluating donor candidates for living donor liver transplantation. USG, using both the traditional four-grade visual assessment and elastography in the present form, appears to have limited role in liver fat quantification.

Can spectral doppler high specificity and gray scale nail assessment suggest inflammation in psoriatic arthritis patients and control groups?

Introduction: The use of high resolution ultrasonography (US) has become a very important tool in nail assessment. This study evaluated nails clinical and ultrasound measurements in psoriatic arthritis (PsA) patients. Methods: A cross sectional study was performed with a total of 60 patients, PSA patients, 10 healthy individuals and 6 hand osteoarthritis patients (OA). Only PsA patients meeting the CASPAR criteria were included in the study. Results: Spectral Doppler (sD) was used to analyze 208 nail beds. Nail plates on the gray scale (GS) showed loss of the normal three-layered pattern in 89 nails (45.9%). Power Doppler (PD) signal was detected in 189 nail beds (92.2%), showing inflammatory activity in most of the patients and nails assessed. Resistance index (RI) was significantly lower in PsA patients as compared to control groups in both the longitudinal and transverse planes (p<0.001).Nail enthesitis was observed when RI values were below 0.4, characterizing 100% sensitivity and 96% specificity (p<0.01). Conclusions: Nail US scanning presented statistical significance in PsA patients. Future studies could show many inflammatory situations, requiring treatment assessment.