Lung cancer detection from thoracic CT scans using 3-D deformable models based on statistical anatomical analysis

Authors:
Hotaka Takizawa;Shigeyuki Ishii
Affiliations:
University of Tsukuba, Japan;University of Tsukuba, Japan
Venue:
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Year:
2011

Citing 5
Cited 0

Image Analysis Using Mathematical Morphology

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Recognition of Lung Abnormalities Using Deformable Templates

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
Quoit Filter - A New Filter Based on Mathematical Morphology to Extract the Isolated Shadow, and Its Application to Automatic Detection of Lung Cancer in X-ray CT

ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the present paper, we propose a novel recognition method of pulmonary nodules (possible lung cancers) in thoracic CT scans. Pulmonary nodules and blood vessels are represented by 3-D deformable spherical and cylindrical models. The validity of these object models are evaluated by the probability distributions that reflect the results of the statistical anatomical analysis of blood vessel trees in human lungs. The fidelity of the object models to CT scans are evaluated by five similarity measurements based on the differences in intensity distributions between the CT scans and templates produced from the object models. Through these evaluations, the posterior probabilities of hypotheses that the object models appear in the CT scans are calculated by use of the Bayes theorem. The nodule recognition is performed by the maximum a posterior estimation. Experimental results obtained by applying the proposed method to actual CT scans are shown.