Computer Aided Differential Diagnosis of Pulmonary Nodules Using Curvature Based Analysis

Authors:
Y. Kawata;N. Niki;H. Ohmatsu;R. Kakinuma;K. Mori;K. Eguchi;M. Kaneko;N. Moriyama;M. Kusumoto;H. Nishiyama
Affiliations:
-;-;-;-;-;-;-;-;-;-
Venue:
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Year:
1999

Citing 0
Cited 2

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Abstract

This paper focuses on characterizing the internal intensity structure of pulmonary nodules in thin-section CT images for classification between benign and malignant nodules. This approach makes use of shape index, curvedness, and CT density to represent locally each voxel constructing the three-dimensional (3D) pulmonary nodule image. From the distribution of shape index, curvedness, and CT density over the 3D pulmonary nodule image a set of histogram features, and 3D texture features is computed to classify benign and malignant nodules. Linear discriminant analysis is used for classification and a receiver operating characteristic (ROC) analysis is used to evaluate the classification accuracy. The potential usefulness of the curvature based features in the computer-aided differential diagnosis is demonstrated by using ROC curves as the performance measure.