Quantitative analysis of internal texture for classification of pulmonary nodules in three-dimensional thoracic images

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

doi:10.1117/12.387750

6 June 2000 Quantitative analysis of internal texture for classification of pulmonary nodules in three-dimensional thoracic images

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

Author Affiliations +

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387750
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

We are developing computerized feature extraction and classification methods to analyze malignant and benign pulmonary nodules in three-dimensional (3-D) thoracic images. This paper focuses on an approach for characterizing the internal texture which is one of important clues for differentiating between malignant and benign nodules. In this approach, each voxel was described in terms of shape index derived from curvatures on the voxel. The voxels inside the nodule were aggregated via shape histogram to quantify how much shape category was present in the nodule. Topological features were introduced to characterize the morphology of the cluster constructed from a set of voxels with the same shape category. The properties such as curvedness and CT density were also built into the representation. We evaluated the effectiveness of the topological and histogram features extracted from 3-D pulmonary nodules for classification of malignant and benign internal structures. We also compared the performance of the computerized classification with the experienced physicians. The classification performance based on the combined feature space reached the performance of the experienced physicians. Our results demonstrate the feasibility of using topological and histogram features for analyzing internal texture to assist physicians in making diagnostic decisions.

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Yoshiki Kawata, Noboru Niki, Hironobu Omatsu, Masahiko Kusumoto, Ryutaro Kakinuma, Kiyoshi Mori, Hiroyuki Nishiyama, Kenji Eguchi, Masahiro Kaneko, and Noriyuki Moriyama "Quantitative analysis of internal texture for classification of pulmonary nodules in three-dimensional thoracic images", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387750

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