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  • Open Access

    ARTICLE

    DeepFake Videos Detection Based on Texture Features

    Bozhi Xu1, Jiarui Liu1, Jifan Liang1, Wei Lu1,*, Yue Zhang2

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1375-1388, 2021, DOI:10.32604/cmc.2021.016760

    Abstract In recent years, with the rapid development of deep learning technologies, some neural network models have been applied to generate fake media. DeepFakes, a deep learning based forgery technology, can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes. The spread of face manipulation videos is very easy to bring fake information. Therefore, it is important to develop effective detection methods to verify the authenticity of the videos. Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in… More >

  • Open Access

    ARTICLE

    Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm

    Yuxiang Huang1, Chuliu He1, Jiaqiu Wang2, Yuehong Miao1, Tongjin Zhu1, Ping Zhou1, Zhiyong Li1,2,*

    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 >

  • Open Access

    ARTICLE

    The Correlation Between Texture Features and Fibrous Cap Thickness of Lipid-Rich Atheroma Based on Optical Coherence Tomography Imaging

    Chunliu He1, Jiaqiu Wang2, Yuxiang Huang1, Tongjing Zhu1, Yuehong Miao1, Zhiyong Li1,2*

    Molecular & Cellular Biomechanics, Vol.13, No.1, pp. 23-36, 2016, DOI:10.3970/mcb.2016.013.027

    Abstract Fibrous cap thickness (FCT) is seen as critical to plaque vulnerability. Therefore, the development of automatic algorithms for the quantification of FCT is for estimating cardiovascular risk of patients. Intravascular optical coherence tomography (IVOCT) is currently the only in vivo imaging modality with which FCT, the critical component of plaque vulnerability, can be assessed accurately. This study was aimed to discussion the correlation between the texture features of OCT images and the FCT in lipid-rich atheroma. Methods: Firstly, a full automatic segmentation algorithm based on unsupervised fuzzy c means (FCM) clustering with geometric constrains was developed to segment the ROIs… More >

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