A study of three-dimensional curvatures and curvatures of four-dimensional hypersurface for analyzing pulmonary nodules on high-resolution CT images

2005 ◽  
Vol 36 (10) ◽  
pp. 16-29 ◽  
Author(s):  
Yoshiki Kawata ◽  
Hiroaki Kubo ◽  
Noboru Niki ◽  
Hironobu Ohmatsu ◽  
Noriyuki Moriyama
Keyword(s):  
High Resolution ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
High Resolution Ct

3D segmentation of non-isolated pulmonary nodules in high resolution CT images

2005 ◽  
Author(s):  
Xiangwei Zhang ◽  
Geoffrey McLennan ◽  
Eric A. Hoffman ◽  
Milan Sonka
Keyword(s):  
High Resolution ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
3D Segmentation ◽  
High Resolution Ct

Classification experiments of pulmonary nodules using high-resolution CT images

2002 ◽  
Author(s):  
Wakako Ichikawa ◽  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Ohmatsu ◽  
Masahiko Kusumoto ◽  
...  
Keyword(s):  
High Resolution ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
High Resolution Ct

scholarly journals Cardiac fiber tracking on super high-resolution CT images: a comparative study

2020 ◽  
Vol 7 (02) ◽  
pp. 1
Author(s):  
Hirohisa Oda ◽  
Holger R. Roth ◽  
Takaaki Sugino ◽  
Naoki Sunaguchi ◽  
Noriko Usami ◽  
...  
Keyword(s):  
High Resolution ◽  
Comparative Study ◽  
Fiber Tracking ◽  
Ct Images ◽  
High Resolution Ct ◽  
Cardiac Fiber

scholarly journals AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

2017 ◽  
Vol 36 (2) ◽  
pp. 65 ◽  
Author(s):  
Elaheh Aghabalaei Khordehchi ◽  
Ahmad Ayatollahi ◽  
Mohammad Reza Daliri
Keyword(s):  
Lung Nodule ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Region Merging ◽  
Three Dimensional Objects ◽  
Ct Data ◽  
Statistical Region Merging

Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

FRACTAL DIMENSIONALITY OF PORE AND GRAIN VOLUME OF A SILICICLASTIC MARINE SAND

2000 ◽  
Vol 11 (08) ◽  
pp. 1555-1559 ◽  
Author(s):  
A. H. REED ◽  
R. B. PANDEY ◽  
D. L. LAVOIE
Keyword(s):  
Weight Loss ◽  
High Resolution ◽  
Pore Volume ◽  
Three Dimensional ◽  
Fractal Dimensionality ◽  
Ct Images ◽  
Spatial Distributions ◽  
Two Dimensional ◽  
Linear Gradient ◽  
Marine Sand

Three-dimensional (3D) spatial distributions of pore and grain volumes were determined from high-resolution computer tomography (CT) images of resin-impregnated marine sands. Using a linear gradient extrapolation method, cubic three-dimensional samples were constructed from two-dimensional CT images. Image porosity (0.37) was found to be consistent with the estimate of porosity by water weight loss technique (0.36). Scaling of the pore volume (Vp) with the linear size (L), V ~ LD provides the fractal dimensionalities of the pore volume (D = 2.74 ± 0.02) and grain volume (D = 2.90 ± 0.02) typical for sedimentary materials.

Multiple Focal Pure Ground-Glass Opacities on High-Resolution CT Images: Clinical Significance in Patients With Lung Cancer

2010 ◽  
Vol 195 (2) ◽  
pp. W131-W138 ◽  
Author(s):  
Shin Tsutsui ◽  
Kazuto Ashizawa ◽  
Kazunori Minami ◽  
Tsutomu Tagawa ◽  
Takeshi Nagayasu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Resolution ◽  
Clinical Significance ◽  
Ct Images ◽  
Ground Glass ◽  
High Resolution Ct

Toward reliable multigenerational analysis of anatomical trees in 3D high-resolution CT images

2003 ◽  
Author(s):  
Kun-Chang Yu ◽  
Erik L. Ritman ◽  
Atilla P. Kiraly ◽  
Shu-Yen Wan ◽  
Mair Zamir ◽  
...  
Keyword(s):  
High Resolution ◽  
Ct Images ◽  
High Resolution Ct

Automatic identification of colonic polyp in high-resolution CT images

2004 ◽  
Author(s):  
Jamshid Dehmeshki ◽  
Hamdan Amin ◽  
Wing Wong ◽  
Mandana Ebadian Dehkordi ◽  
Nahid Kamangari ◽  
...  
Keyword(s):  
High Resolution ◽  
Colonic Polyp ◽  
Ct Images ◽  
Automatic Identification ◽  
High Resolution Ct
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