Accuracy improvement of lung cancer detection based on spatial statistical analysis of thoracic CT scans

Authors:
Hotaka Takizawa;Shinji Yamamoto;Tsuyoshi Shiina
Affiliations:
University of Tsukuba, Japan;Chukyo University, Japan;University of Tsukuba, Japan
Venue:
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Year:
2007

Citing 5
Cited 0

Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of lung nodule shadows from chest X-ray CT images using 3D Markov random field models

Systems and Computers in Japan
Eigen Image Recognition of Pulmonary Nodules from Thoracic CT Images by Use of Subspace Method

IEICE - Transactions on Information and Systems
Construction Method of Three-Dimensional Deformable Template Models for Tree-Shaped Organs

IEICE - Transactions on Information and Systems
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	0.01

Visualization

Abstract

This paper describes a novel discrimination method of lung cancers based on statistical analysis of thoracic computed tomography (CT) scans. Our previous Computer-Aided Diagnosis (CAD) system can detect lung cancers from CT scans, but, at the same time, yields many false positives. In order to reduce the false positives, the method proposed in the present paper uses a relationship between lung cancers, false positives and image information on CT scans. The trend of variation of the relationships is acquired through statistical analysis of a set of CT scans prepared for training. In testing, by use of the trend, the method predicts the appearance of lung cancers and false positives in a CT scan, and improves the accuracy of the previous CAD system by modifying the system's output based on the prediction. The method is applied to 218 actual thoracic CT scans with 386 actual lung cancers. Receiver operating characteristic (ROC) analysis is used to evaluate the results. The area under the ROC curve (Az) is statistically significantly improved from 0.918 to 0.931.