Anqi Qiu^1,2, Xianyi Chen^1,2, Xingming Sun^1,2,*, Shuai Wang³, Guo Wei⁴

Journal of Information Hiding and Privacy Protection, Vol.1, No.2, pp. 49-60, 2019, DOI:10.32604/jihpp.2019.05881

Abstract A new information hiding technology named coverless information hiding is proposed. It uses original natural images as stego images to represent secret information. The focus of coverless image steganography method is how to represent image features and establish a map relationship between image feature and the secret information. In this paper, we use three kinds of features which are Local Binary Pattern (LBP), the mean value of pixels and the variance value of pixels. On this basis, we realize the transmission of secret information. Firstly, the hash sequence of the original cover image is obtained according to the description of… More >

Yuxi Jia¹, Feng Li^1,2, Fei Wang^1,2,*, Yan Gui^1,2,3

Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 11-21, 2019, DOI:10.32604/jihpp.2019.05979

Abstract The Brain-Computer Interfaces (BCIs) had been proposed and used in therapeutics for decades. However, the need of time-consuming calibration phase and the lack of robustness, which are caused by little-labeled data, are restricting the advance and application of BCI, especially for the BCI based on motor imagery (MI). In this paper, we reviewed the recent development in the machine learning algorithm used in the MI-based BCI, which may provide potential solutions for addressing the issue. We classified these algorithms into two categories, namely, and enhancing the representation and expanding the training set. Specifically, these methods of enhancing the representation of… More >

Jingcheng Chen¹, Zhili Zhou^1,2,*, Zhaoqing Pan¹, Ching-nung Yang³

Journal of New Media, Vol.1, No.2, pp. 87-99, 2019, DOI:10.32604/jnm.2019.06582

Abstract Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs, so that… More >

Xiaoli Li^1,*, Chao Ye¹, Yujia Yan², Zhenlong Du¹

Journal of New Media, Vol.1, No.2, pp. 75-85, 2019, DOI:10.32604/jnm.2019.06259

Abstract In order to improve the quality of low-dose computational tomography (CT) images, the paper proposes an improved image denoising approach based on WGAN-gp with Wasserstein distance. For improving the training and the convergence efficiency, the given method introduces the gradient penalty term to WGAN network. The novel perceptual loss is introduced to make the texture information of the low-dose images sensitive to the diagnostician eye. The experimental results show that compared with the state-of-art methods, the time complexity is reduced, and the visual quality of low-dose CT images is significantly improved. More >

Wei Chen^{1, 2}, Tingzhu Sun^{1, 2}, Fangming Bi^{1, 2, *}, Tongfeng Sun^{1, 2}, Chaogang Tang^{1, 2}, Biruk Assefa^{1, 3}

Journal of New Media, Vol.1, No.1, pp. 35-44, 2019, DOI:10.32604/jnm.2019.05803

Abstract Image restoration is an image processing technology with great practical value in the field of computer vision. It is a computer technology that estimates the image information of the damaged area according to the residual image information of the damaged image and carries out automatic repair. This article firstly classify and summarize image restoration algorithms, and describe recent advances in the research respectively from three aspects including image restoration based on partial differential equation, based on the texture of image restoration and based on deep learning, then make the brief analysis of digital image restoration of subjective and objective evaluation… More >

Weijin Tan^1,*, Yunqing Wu¹, Peng Wu¹, Beijing Chen^1,2

Journal of New Media, Vol.1, No.1, pp. 11-25, 2019, DOI:10.32604/jnm.2019.06219

Abstract Digital images can be tampered easily with simple image editing software tools. Therefore, image forensic investigation on the authenticity of digital images’ content is increasingly important. Copy-move is one of the most common types of image forgeries. Thus, an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper. These methods are classified into three types: block-based methods, keypoint-based methods, and deep learning-based methods. In addition, the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost. Finally, further research directions are discussed. More >

Wenjia Wang^{1, 2}, Xiuqing Qian^{1, 2}, Qi Li^{1, 2}, Gong Zhang^{1, 2}, Huangxuan Zhao^{1, 2}, Tan Li^{1, 2}, Yang Yu^{1, 2}, Hongfang Song^{1, 2, *}, Zhicheng Liu^{1, 2, *}

Molecular & Cellular Biomechanics, Vol.16, No.1, pp. 59-74, 2019, DOI:10.32604/mcb.2019.06393

Abstract Pupillary block is considered as an important cause of primary angle-closure glaucoma (PACG). In order to investigate the effect of pupillary block on the hydrodynamics of aqueous humor (AH) in anterior chamber (AC) and potential risks, a 3D printed eye model was developed to mimic the AH flow driven by fluid generation, the differential pressure between AC and posterior chambers (PC) and pupillary block. Particle image velocimetry technology was applied to visualize flow distribution. The results demonstrated obvious differences in AH flow with and without pupillary block. Under the normal condition (without pupillary block), the flow filed of AH was… More >

Junhao Yang¹, Chunxiao Chen^1,*, Qingyang Zang¹, Jianfei Li¹

Molecular & Cellular Biomechanics, Vol.15, No.4, pp. 203-214, 2018, DOI:10.32604/mcb.2018.03824

Abstract Pathological slide is increasingly applied in the diagnosis of breast tumors despite the issues of large amount of data, slow viewing and high subjectivity. To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor. Combined with multi-channel threshold and watershed segmentation, a sample database including single cell, adhesive cell and invalid cell was established. Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation level with relative error of… More >

Ruihan Zhang^1,2, Junhao Yang¹, Chunxiao Chen^1,*

Molecular & Cellular Biomechanics, Vol.15, No.3, pp. 177-187, 2018, DOI: 10.3970/mcb.2018.04292

Abstract The proportion of cells staining for the nuclear antigen Ki-67 is an important predictive indicator for assessment of tumor cell proliferation and growth in routine pathological investigation. Instead of traditional scoring methods based on the experience of a trained laboratory scientist, deep learning approach can be automatically used to analyze the expression of Ki-67 as well. Deep learning based on convolutional neural networks (CNN) for image classification and single shot multibox detector (SSD) for object detection are used to investigate the expression of Ki-67 for assessment of biopsies from patients with breast cancer in this study. The results focus on… More >

Yuxiang Huang¹, Chuliu He¹, Jiaqiu Wang², Yuehong Miao¹, Tongjin Zhu¹, Ping Zhou¹, Zhiyong Li^1,2,*

Molecular & Cellular Biomechanics, Vol.15, No.2, pp. 117-125, 2018, DOI: 10.3970/mcb.2018.02478

Abstract Intravascular optical coherence tomography (IVOCT) is becoming more and more popular in clinical diagnosis of coronary atherosclerotic. However, reading IVOCT images is of large amount of work. This article describes a method based on image feature extraction and support vector machine (SVM) to achieve semi-automatic segmentation of IVOCT images. The image features utilized in this work including light attenuation coefficients and image textures based on gray level co-occurrence matrix. Different sets of hyper-parameters and image features were tested. This method achieved an accuracy of 83% on the test images. Single class accuracy of 89% for fibrous, 79.3% for calcification and… More >