Lung Nodule Volume Measurement using Digital Chest Tomosynthesis

被引:0
作者
Hadhazi, D. [1 ]
Czetenyi, B. [1 ]
Horvath, A. [1 ,2 ]
Orban, G. [1 ]
Horvath, G. [1 ]
Horvath, A. [1 ,2 ]
机构
[1] Budapest Univ Technol & Econ, Dept Measurement & Informat Syst, Budapest, Hungary
[2] Innomed Med Co, Dept Xray Syst, Budapest, Hungary
来源
2015 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC) | 2015年
关键词
biomedical image processing; digital X-ray tomosynthesis; computer aided diagnosis; lung nodule detection; volume measurement; BENIGN;
D O I
暂无
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
Lung cancer detection is one of the most important goals of medical diagnosis. To detect lung nodules usually classical X-ray and/or computed tomography (CT) images are used. The progression of the disease can be monitored if the doubling time of the volume of a pulmonary nodule is determined and followed, which means that the volume of a nodule has to be measured/estimated. To measure the nodule volume using classical X-ray images is almost impossible, while a CT-based diagnosis is rather expensive. Recently a new radiological image-based modality, digital tomosynthesis (DTS) has been developed. DTS can be considered as a 2.5D modality, where coronal slice images of a chest can be computed. The spatial resolution of a DTS image is much higher than that of a CT image, while the thickness of a slice is larger compared to a CT image. Thus DTS can also be used to determine lung nodule volumes although -because of the 2.5D reconstruction - volume estimation is a rather hard task. This paper proposes a new way for estimating nodule volume. The method was developed using an experimental database, which contains reconstructed images of 16 simulated small, elliptical nodules, placed into an anthropomorphic chest phantom, and was evaluated by a few real DTS images and an image base made from simulated projections from a public CT database.
引用
收藏
页码:2026 / 2031
页数:6
相关论文
共 50 条
  • [1] Automated lung segmentation in digital chest tomosynthesis
    Wang, Jiahui
    Dobbins, James T., III
    Li, Qiang
    MEDICAL PHYSICS, 2012, 39 (02) : 732 - 741
  • [2] Comparison of digital tomosynthesis and computed tomography for lung nodule detection in SOS screening program
    Grosso, Maurizio
    Priotto, Roberto
    Ghirardo, Donatella
    Talenti, Alberto
    Roberto, Emanuele
    Bertolaccini, Luca
    Terzi, Alberto
    Chauvie, Stephane
    RADIOLOGIA MEDICA, 2017, 122 (08): : 568 - 574
  • [3] Digital Chest Tomosynthesis: the Main Steps to a Computer Assisted Lung Diagnostic System
    Hadhazi, D.
    Varga, R.
    Horvath, A.
    Czetenyi, B.
    Horvath, G.
    2015 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA) PROCEEDINGS, 2015, : 40 - 45
  • [4] Comparison of digital tomosynthesis and computed tomography for lung nodule detection in SOS screening program
    Maurizio Grosso
    Roberto Priotto
    Donatella Ghirardo
    Alberto Talenti
    Emanuele Roberto
    Luca Bertolaccini
    Alberto Terzi
    Stéphane Chauvie
    La radiologia medica, 2017, 122 : 568 - 574
  • [5] Modified Gaussian Models for Pulmonary Nodule Simulation in Chest Tomosynthesis
    Mao, Qi
    Zhao, Shuguang
    Zheng, Qianqian
    Su, Shengchao
    Li, Liming
    Zhang, Xiaoqing
    JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2018, 8 (08) : 1718 - 1725
  • [6] Usefulness of Computerized Method for Lung Nodule Detection in Digital Chest Radiographs Using Temporal Subtraction Images
    Aoki, Takatoshi
    Oda, Nobuhiro
    Yamashita, Yoshiko
    Yamamoto, Keiji
    Korogi, Yukunori
    ACADEMIC RADIOLOGY, 2011, 18 (08) : 1000 - 1005
  • [7] Digital Tomosynthesis for Evaluating Metastatic Lung Nodules: Nodule Visibility, Learning Curves, and Reading Times
    Lee, Kyung Hee
    Goo, Jin Mo
    Lee, Sang Min
    Park, Chang Min
    Bahn, Young Fun
    Kim, Hyungjin
    Song, Yong Sub
    Hwang, Eui Jin
    KOREAN JOURNAL OF RADIOLOGY, 2015, 16 (02) : 430 - 439
  • [8] Preliminary study on a chest digital tomosynthesis: development and evaluation
    Lee, Y.
    Lee, S.
    JOURNAL OF INSTRUMENTATION, 2015, 10
  • [9] Lung nodule classification using deep feature fusion in chest radiography
    Wang, Changmiao
    Elazab, Ahmed
    Wu, Jianhuang
    Hu, Qingmao
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2017, 57 : 10 - 18
  • [10] Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter
    Teramoto, Atsushi
    Fujita, Hiroshi
    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2013, 8 (02) : 193 - 205