Growth-rate estimation of pulmonary nodules in three-dimensional thoracic CT images based on CT density histogram analysis and its application to nodule classification

2006 ◽  
Author(s):  
Y. Kawata ◽  
M. Nakaoka ◽  
N. Niki ◽  
H. Ohmatsu ◽  
M. Kusumoto ◽  
...  
Keyword(s):  
Growth Rate ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Histogram Analysis ◽  
Rate Estimation ◽  
Thoracic Ct ◽  
Density Histogram ◽  
Ct Density ◽  
Nodule Classification

A computerized approach for estimating pulmonary nodule growth rates in three-dimensional thoracic CT images based on CT density histogram

2005 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Ohmatsu ◽  
Masahiko Kusumoto ◽  
Ryutaro Kakinuma ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Growth Rates ◽  
Three Dimensional ◽  
Ct Images ◽  
Thoracic Ct ◽  
Density Histogram ◽  
Nodule Growth ◽  
Ct Density

Pulmonary nodule classification based on CT density distribution using 3D thoracic CT images

2004 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Ohamatsu ◽  
Masahiko Kusumoto ◽  
Ryutaro Kakinuma ◽  
...  
Keyword(s):  
Density Distribution ◽  
Pulmonary Nodule ◽  
Ct Images ◽  
Thoracic Ct ◽  
Ct Density ◽  
Nodule Classification

Computer-aided classification of pulmonary nodules in surrounding and internal feature spaces using three-dimensional thoracic CT images

2002 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Ohamatsu ◽  
Masahiko Kusumoto ◽  
Ryutaro Kakinuma ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Thoracic Ct ◽  
Feature Spaces ◽  
Computer Aided ◽  
Internal Feature

A new model-based framework for lung tissue segmentation in three-dimensional thoracic CT images

2017 ◽  
Vol 12 (2) ◽  
pp. 339-346 ◽  
Author(s):  
Zeinab Naseri Samaghcheh ◽  
Fatemeh Abdoli ◽  
Hamid Abrishami Moghaddam ◽  
Mohammadreza Modaresi ◽  
Neda Pak
Keyword(s):  
Lung Tissue ◽  
Three Dimensional ◽  
Ct Images ◽  
Tissue Segmentation ◽  
New Model ◽  
Model Based ◽  
Thoracic Ct

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.

Image-guided decision support system for pulmonary nodule classification in 3D thoracic CT images

2004 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Ohmatsu ◽  
Masahiro Kusumoto ◽  
Ryutaro Kakinuma ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Pulmonary Nodule ◽  
Ct Images ◽  
Image Guided ◽  
Thoracic Ct ◽  
Nodule Classification
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