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Medical Imaging Modeling

  2. Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the...

    Authors: Weiguo Cao, Marc J. Pomeroy, Yongfeng Gao, Matthew A. Barish, Almas F. Abbasi, Perry J. Pickhardt and Zhengrong Liang
    Citation: Visual Computing for Industry, Biomedicine, and Art 2019 2:25
    Content type: Review Published on:

  3. 4-Dimensional cone-beam computed tomography (4D-CBCT) offers several key advantages over conventional 3D-CBCT in moving target localization/delineation, structure de-blurring, target motion tracking, treatment...

    Authors: You Zhang, Xiaokun Huang and Jing Wang
    Citation: Visual Computing for Industry, Biomedicine, and Art 2019 2:23
    Content type: Original Article Published on:

  4. In a positron emission tomography (PET) scanner, the time-of-flight (TOF) information gives us rough event position along the line-of-response (LOR). Using the TOF information for PET image reconstruction is a...

    Authors: Gengsheng L. Zeng, Ya Li and Qiu Huang
    Citation: Visual Computing for Industry, Biomedicine, and Art 2019 2:22
    Content type: Original Article Published on:

  5. Computer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulati...

    Authors: Yongfeng Gao, Jiaxing Tan, Zhengrong Liang, Lihong Li and Yumei Huo
    Citation: Visual Computing for Industry, Biomedicine, and Art 2019 2:15
    Content type: Original Article Published on:

  6. It can be challenging to detect tumor margins during surgery for complete resection. The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissu...

    Authors: Ling Ma, Guolan Lu, Dongsheng Wang, Xulei Qin, Zhuo Georgia Chen and Baowei Fei
    Citation: Visual Computing for Industry, Biomedicine, and Art 2019 2:18
    Content type: Original Article Published on:

  7. Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single image. These imaging biomarkers can aid in the generation of prediction models aimed to further...

    Authors: Renee Cattell, Shenglan Chen and Chuan Huang
    Citation: Visual Computing for Industry, Biomedicine, and Art 2019 2:19
    Content type: Original Article Published on:

  8. In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research...

    Authors: Bin Zheng, Yuchen Qiu, Faranak Aghaei, Seyedehnafiseh Mirniaharikandehei, Morteza Heidari and Gopichandh Danala
    Citation: Visual Computing for Industry, Biomedicine, and Art 2019 2:17
    Content type: Original Article Published on:

  9. Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels, i.e., the tissue heterogeneity, and has been recognized as important biomarkers in various clinical tasks. Spectral com...

    Authors: Yongfeng Gao, Yongyi Shi, Weiguo Cao, Shu Zhang and Zhengrong Liang
    Citation: Visual Computing for Industry, Biomedicine, and Art 2019 2:16
    Content type: Original Article Published on:

  11. Sparse-view tomography has many applications such as in low-dose computed tomography (CT). Using under-sampled data, a perfect image is not expected. The goal of this paper is to obtain a tomographic image tha...

    Authors: Gengsheng L. Zeng
    Citation: Visual Computing for Industry, Biomedicine, and Art 2019 2:13
    Content type: Original Article Published on: