Analysis of evolving processes in pulmonary nodules using a sequence of three-dimensional thoracic images

被引:8
|
作者
Kawata, Y [1 ]
Niki, N [1 ]
Ohmatsu, H [1 ]
Kusumoto, M [1 ]
Kakinuma, R [1 ]
Mori, K [1 ]
Nishiyama, H [1 ]
Eguchi, K [1 ]
Kaneko, A [1 ]
Moriyama, N [1 ]
机构
[1] Univ Tokushima, Tokushima 770, Japan
来源
MEDICAL IMAGING: 2001: IMAGE PROCESSING, PTS 1-3 | 2001年 / 4322卷
关键词
computer-aided diagnosis; evolution; pulmonary nodule; registration;
D O I
10.1117/12.431081
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a method to analyze volume evolutions of pulmonary nodules for discrimination between malignant and benign nodules. Our method consists of four steps; (1) The 3-D rigid registration of the two successive 3-D thoracic CT images, (2) the 3-D affine registration of the two successive region-of-interest (ROI) images, (3) non-rigid registration between local volumetric ROIs, and (4) analysis of the local displacement field between successive temporal images In preliminary study, the method was applied to die successive 3-D thoracic images of two pulmonary lesions including a metastasis malignant case and a inflammatory benign to quantify the, evolving process in the pulmonary nodules and surrounding structure. The time intervals between successive 3-D thoracic images for the benign and malignant cases were 120 and 30 days, respectively. From the display of the displacement fields and the contrasted image by the vector field operator based on the Jacobian, it was observed that the benign case reduced in the volume and the surrounding structure was involved into the nodule in the evolution process. It was also observed that the malignant case expanded in the volume. These experimental results indicate that our method is a promising tool to quantify how the lesions evolve their volume and surrounding structures.
引用
收藏
页码:1890 / 1901
页数:4
相关论文
共 50 条
  • [41] Image-guided decision support system for pulmonary nodules classirication in 3-D thoracic CT images
    Kawata, Y
    Niki, N
    Ohmatsu, H
    Kusumoto, M
    Kakinuma, R
    Mori, K
    Yamada, K
    Nishiyama, H
    Eguchi, K
    Kaneko, M
    Moriyama, N
    MEDICAL IMAGING 2004: IMAGE PROCESSING, PTS 1-3, 2004, 5370 : 1008 - 1017
  • [42] Eigen image recognition of pulmonary nodules from thoracic CT images by use of subspace method
    Fukano, G
    Nakantura, Y
    Takizawa, H
    Mizuno, S
    Yamamoto, S
    Doi, K
    Katsuragawa, S
    Matsumoto, T
    Tateno, Y
    Iinuma, T
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2005, E88D (06) : 1273 - 1283
  • [43] Registration of two-dimensional cardiac images to preprocedural three-dimensional images for interventional applications
    Smolíková-Wachowiak, R
    Wachowiak, MP
    Fenster, A
    Drangova, M
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2005, 22 (02) : 219 - 228
  • [44] Automated Detection of Lung Nodules with Three-dimensional Convolutional Neural Networks
    Perez, Gustavo
    Arbelaez, Pablo
    13TH INTERNATIONAL CONFERENCE ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, 2017, 10572
  • [45] False-positive reduction using hessian features in computer-aided detection of pulmonary nodules on thoracic CT images
    Sahiner, B
    Ge, ZY
    Chan, HP
    Hadjiiski, LM
    Bogot, N
    Cascade, PN
    Kazerooni, EA
    Medical Imaging 2005: Image Processing, Pt 1-3, 2005, 5747 : 790 - 795
  • [46] AN EXPERIMENTAL STUDY ON REGISTRATION THREE-DIMENSIONAL RANGE IMAGES USING RANGE AND INTENSITY DATA
    Altuntas, Cihan
    PIA11: PHOTOGRAMMETRIC IMAGE ANALYSIS, 2011, 2011, 38-3 (W22): : 115 - 118
  • [47] Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram
    Minami, Kazuhiro
    Kawata, Yoshiki
    Niki, Nooru
    Ohmatsu, Hironobu
    Mori, Kiyoshi
    Yamada, Kouzou
    Eguchi, Kenji
    Kaneko, Masahiro
    Moriyama, Noriyuki
    MEDICAL IMAGING 2008: COMPUTER-AIDED DIAGNOSIS, PTS 1 AND 2, 2008, 6915
  • [48] Three-Dimensional Images for Thoracoscopic Segmentectomy: An Alternative to Preoperative Localization
    Zhu, Yining
    Luo, Ming
    Wang, Jian
    Shan, Limei
    Ge, Lingxia
    Yao, Fei
    JOURNAL OF SURGICAL RESEARCH, 2025, 305 : 237 - 245
  • [49] Evolving Cellular Automata to Segment Hyperspectral Images Using Low Dimensional Images for Training
    Priego, B.
    Bellas, Francisco
    Duro, Richard J.
    BIOINSPIRED COMPUTATION IN ARTIFICIAL SYSTEMS, PT II, 2015, 9108 : 117 - 126
  • [50] MODELING THREE-DIMENSIONAL MORPHOLOGICAL STRUCTURES USING SPHERICAL HARMONICS
    Shen, Li
    Farid, Hany
    McPeek, Mark A.
    EVOLUTION, 2009, 63 (04) : 1003 - 1016