Curvature-based characterization of shape and internal intensity structure for classification of pulmonary nodules using thin-section CT images

Yoshiki Kawata; Noboru Niki; Hironobu Ohmatsu; Masahiko Kusumoto; Ryutaro Kakinuma; Kiyoshi Mori; Hiroyuki Nishiyama; Kenji Eguchi; Masahiro Kaneko; Noriyuki Moriyama

doi:10.1117/12.348610

21 May 1999 Curvature-based characterization of shape and internal intensity structure for classification of pulmonary nodules using thin-section CT images

Yoshiki Kawata, Noboru Niki, Hironobu Ohmatsu, Masahiko Kusumoto, Ryutaro Kakinuma, Kiyoshi Mori, Hiroyuki Nishiyama, Kenji Eguchi, Masahiro Kaneko, Noriyuki Moriyama

Author Affiliations +

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348610
Event: Medical Imaging '99, 1999, San Diego, CA, United States

Abstract

This paper presents a curvature based approach to characterize the internal intensity structure of pulmonary nodules in thin-section CT images. This approach makes use of shape index, curvedness, and CT value to represent locally each voxel in a 3D pulmonary nodule image. From the distribution of shape index, curvedness, and CT value over the 3D pulmonary nodule image a set of 3D moment features, histogram features, and 3D texture features is computed to classify benign and malignant pulmonary 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.

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Yoshiki Kawata, Noboru Niki, Hironobu Ohmatsu, Masahiko Kusumoto, Ryutaro Kakinuma, Kiyoshi Mori, Hiroyuki Nishiyama, Kenji Eguchi, Masahiro Kaneko, and Noriyuki Moriyama "Curvature-based characterization of shape and internal intensity structure for classification of pulmonary nodules using thin-section CT images", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348610

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