A color image encryption scheme based on a 2D coupled chaotic system and diagonal scrambling algorithm

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.

MRI T_2 star mapping、T_1 images与3D DESS融合图在隐匿性膝关节软骨损伤中的应用

目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。

Infrared image segmentation method based on 2D histogram shape modification and optimal objective function

In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.

Defect detection method based on 2D entropy image segmentation

In order to improve the work efficiency of non-destructive testing(NDT)and the reliability of NDT results,an automatic method to detect defects in the ultrasonic image was researched.According to the characterization of ultrasonic D-scan image,clutter wave suppression and de-noising were presented firstly.Then,the image is processed by binaryzation using KSW 2 D entropy based on image segmentation method.The results showed that,the global threshold based segmentation method was somewhat ineffective for D-scan image because of under-segmentation.Especially,when the image is big in size,small targets which are composed by a small amount of pixels are often undetected.Whereas,local threshold based image segmentation method is effective in recognizing small defects because it takes local image character into account.

2-D mini mumfuzzy entropy method of image thresholding based on genetic algorithm

A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.

A new projection model based robust 2D-3D registration method on Fourier-Mellin space for image guided intervention

An automatic method is proposed to solve the registration problem,which aligns a single 2D fluoroscopic image to a 3D image volume without demanding any additional media like calibration plate or user interactions.First,a mathematic projection model is designed which can reduce the influence of projection distortion on parameter optimization and improve the registration accuracy.Then,a two stage optimization method is proposed,which enables a robust registration in a wide parameter space.Furthermore,an automatic registration framework is proposed based on the FourierMellin robust image comparison descriptor.Experimental results show that the registration method has a high accuracy with average rotation error of 0.6 degree and average translation error of 1.4mm.

Clinical application of improved 2D computer-assisted fluoroscopic navigation through simulating a 3D vertebrae image to guide pedicle screw internal fixation

Objective To study the effect of using improved 2D computer-assisted fluoroscopic navigation through simulating 3D vertebrae image to guide pedicle screw internal fixation.Methods Posterior pedicle screw internal fixation,distraction

A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.

Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images

AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.

3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model

Structure reconstruction of 3 D anatomy from biplanar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3 D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh.Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field.Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and1.2 mm, separately. The average reconstruction time is 3 minutes.

Imaging the crystal orientation of 2D transition metal dichalcogenides using polarization-resolved second-harmonic generation

We use laser-scanning nonlinear imaging microscopy in atomically thin transition metal dichalcogenides(TMDs)to reveal information on the crystalline orientation distribution,within the 2D lattice.In particular,we perform polarization-resolved second-harmonic generation(PSHG)imaging in a stationary,raster-scanned chemical vapor deposition(CVD)-grown WS2 flake,in order to obtain with high precision a spatially resolved map of the orientation of its main crystallographic axis(armchair orientation).By fitting the experimental PSHG images of sub-micron resolution into a generalized nonlinear model,we are able to determine the armchair orientation for every pixel of the image of the 2D material,with further improved resolution.This pixel-wise mapping of the armchair orientation of 2D WS2 allows us to distinguish between different domains,reveal fine structure,and estimate the crystal orientation variability,which can be used as a unique crystal quality marker over large areas.The necessity and superiority of a polarization-resolved analysis over intensity-only measurements is experimentally demonstrated,while the advantages of PSHG over other techniques are analysed and discussed.

Image-Based Feature Extraction Technique for Inclined Crack Quanti cation Using Pulsed Eddy Current

Existing eddy current non-destructive testing(NDT) techniques generally do not consider the inclination angle of inclined cracks, which potentially harms a larger region of a tested structure. This work proposes the use of 2 D scan images generated by using pulsed eddy current(PEC) non-destructive testing(NDT) technique in the quantification of the inclination and depth of inclined cracks. The image-based feature extraction technique e ectively identifies the crack axis, which consequently enables extraction of features from the extracted linear scans. The technique extracts linear scans from the images to allow the extraction of three novel image-based features, namely the length of extracted linear scans(LLS), the linear scan skewness(LSS), and the highest value on linear scan(LSmax). The correlation of the three features to surface crack inclination angles and depths were analysed and found to be highly dependent on the crack depths, while only LLS and LSS are correlated to the crack inclination angles.

Camera Pose Estimation Using Collaborative Databases and Single Building Image

Cities are in constant change and city managers aim to keep an updated digital model of the city for city governance. There are a lot of images uploaded daily on image sharing platforms (as “Flickr”, “Twitter”, etc.). These images feature a rough localization and no orientation information. Nevertheless, they can help to populate an active collaborative database of street images usable to maintain a city 3D model, but their localization and orientation need to be known. Based on these images, we propose the Data Gathering system for image Pose Estimation (DGPE) that helps to find the pose (position and orientation) of the camera used to shoot them with better accuracy than the sole GPS localization that may be embedded in the image header. DGPE uses both visual and semantic information, existing in a single image processed by a fully automatic chain composed of three main layers: Data retrieval and preprocessing layer, Features extraction layer, Decision Making layer. In this article, we present the whole system details and compare its detection results with a state of the art method. Finally, we show the obtained localization, and often orientation results, combining both semantic and visual information processing on 47 images. Our multilayer system succeeds in 26% of our test cases in finding a better localization and orientation of the original photo. This is achieved by using only the image content and associated metadata. The use of semantic information found on social media such as comments, hash tags, etc. has doubled the success rate to 59%. It has reduced the search area and thus made the visual search more accurate.

Crack Length Quantification Based on Planar Eddy-Current Sensor Array and Two-Dimensional Image

Eddy-current (EC) testing is an effective electromagnetic non-destructive testing (NDT) technique.Planar eddy-current sensor arrays have several advantages such as good coherence,fast response speed,and high sensitivity,which can be used for micro-damage inspection of crucial parts in mechanical equipments and aerospace aviation.The main purpose of this research is to detect the defect in a metallic material surface and identify the length of a crack using planar eddy-current sensor arrays in different directions.The principle and characteristics of planar eddy-current sensor arrays are introduced,and a crack length quantification algorithm in different directions is investigated.A damage quantitative detection system is established based on a field programmable gate array and ARM processor.The system is utilized to inspect the micro defect in a metallic material,which is carved to micro crack with size of 7mm(length)×0.1mm(width)×1mm(depth).The experimental data show that the sensor arrays can be used for the length measurement repeatedly,and that the uncertainty of the length measurement is below ±0.2mm.

Watermarking Images in the Frequency Domain by Exploiting Self-Inverting Permutations

In this work we propose efficient codec algorithms for watermarking images that are intended for uploading on the web under intellectual property protection. Headed to this direction, we recently suggested a way in which an integer number w which being transformed into a self-inverting permutation, can be represented in a two dimensional (2D) object and thus, since images are 2D structures, we have proposed a watermarking algorithm that embeds marks on them using the 2D representation of w in the spatial domain. Based on the idea behind this technique, we now expand the usage of this concept by marking the image in the frequency domain. In particular, we propose a watermarking technique that also uses the 2D representation of self-inverting permutations and utilizes marking at specific areas thanks to partial modifications of the image’s Discrete Fourier Transform (DFT). Those modifications are made on the magnitude of specific frequency bands and they are the least possible additive information ensuring robustness and imperceptiveness. We have experimentally evaluated our algorithms using various images of different characteristics under JPEG compression. The experimental results show an improvement in comparison to the previously obtained results and they also depict the validity of our proposed codec algorithms.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

An Effective Method of Threshold Selection for Small Object Image

The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.

Cosmetic Lifting Effect of a Formula Combining Tetrahydroxypropyl Ethylenediamine, Ononis Spinosa and Glycerin as Shown by a New 2-D Imaging Method

Facial sagging is a major aging change that affects the overall shape of the face. Sagging can negatively impact self-perception and consequently quality of life. In order to fight against this aging sign, we developed a combination of three active ingredients: Tetrahydroxypropyl Ethylenediamine (THPE), Ononis Spinosa extract and Glycerin. In a clinical study, we assessed the efficacy of this association on facial sagging and wrinkles, with a special focus on immediate and lasting effects. Thirty-nine women applied the tested product daily during an eight-week treatment period. Clinical grading by experts and a new, easy to operate, 2-D imaging method were performed at different time points. The 2-D imaging method measured the upper eyelid lifting effect. The results showed an immediate and significant improvement in sagging and wrinkle parameters;these benefits were still observed seven hours after the first application. Results also showed a significant long-term improvement. The lifting effect calculated from the 2-D pictures was 1.08 mm immediately after application;the same method showed a 1.80 mm lifting effect following the eight-week treatment period. These results suggest that a skin care product combining THPE, Ononis Spinosa and Glycerin properties may be used to reduce sagging as well as wrinkles with both immediate and long term benefits.

葡萄糖受体介导的肿瘤靶向2-DG修饰超顺磁性氧化铁的制备及体外实验

目的合成一种2-脱氧-D-葡萄糖(2-DG)包被γ-Fe2O3纳米探针,检测其性质特征及对前列腺癌PC3细胞葡萄糖受体的靶向性摄取。方法运用化学共沉淀法合成γ-Fe2O3@二巯基丁二酸(DMSA),再与2-DG共轭生成γ-Fe2O3@DMSA-DG,红外光谱和透射电镜检测粒子结构特征;噻唑蓝试验(MTT)法进行细胞毒性实验,分别加入含无糖DMEM的探针培养基γ-Fe2O3@DMSA、γ-Fe2O3@DMSA-DG、γ-Fe2O3@DMSA-DG+2-DG(4.5 mg/mL),将探针在不同时间(10 min、20 min、1 h、2 h)孵育PC3细胞,运用普鲁士兰染色法和铁三嗪法分析PC3细胞对探针的吸收情况,并以体外MRI观察T2WI信号强度变化。结果 2-DG以酰胺键连接γ-Fe2O3@DMSA,粒子平均直径10 mm;探针未见明显毒性;γ-Fe2O3@DMSA-DG可被PC3细胞靶向摄取,并可在早期被2-DG抑制。结论本研究成功合成γ-Fe2O3@DMSA-DG探针,其对PC3细胞葡萄糖受体有较好的靶向性,使用临床型MR仪可对其进行监测。