Search

In this paper, the authors describe a new automatic extraction algorithm of the pulmonary major and minor fissures from three dimensional (3-D) chest thin-section images of helical computed tomography (CT). These fissures are useful for the analysis of pulmonary conformation and the diagnosis of lung cancer. This algorithm consists mainly of the correction and the emphasis of a 2-D linear shadow. The authors applied the proposed algorithm to 25 sets of CT examinations of 12 patients. The results showed that major and minor fissures can be extracted by the proposed algorithm, without reference to linear artifacts on axial CT images by the beam hardening effect, and the motion artifacts by the cardiac beat.

Characterization of pulmonary nodules plays a significant role in the differential diagnosis of lung cancer. This paper presents a method to quantify surface characteristics of small pulmonary nodules with well-defined surfaces based on thin-section CT images. The segmentation of the three-dimensional (3-D) nodule images are obtained by a 3-D deformable surfaces approach. The feature extraction algorithms are designed to quantify the surface characteristic parameters from 3-D nodule images by using surface curvatures and ridge lines. Experimental results of the authors' method applied to the patients 3-D nodule images demonstrate its performance.

This paper present an automatic extraction algorithm of the pulmonary major and minor fissures from three-dimensional (3-D) chest thin-section computed tomography (CT) images of helical CT. These fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. The proposed algorithm improves on the previous extraction method using the surface-curvatures calculation for density profile and morphological filters. The proposed method can extract the major and minor fissures in contact with the nodule and the chest walls. We apply the proposed algorithm to 12 patients. The results of our method are more accuracy to extract fissures around pulmonary lesions than by the previous method. The warped fissures extracted by our method show that lesions near fissures are malignant. Extracted fissures will aid in the diagnosis of lung cancer and in the analysis of automatic pulmonary conformation by using a computer.

Multi-scale curvature indexes are introduced to characterize the internal intensity structure of pulmonary nodules in thin-section CT images. This approach makes use of shape index, curvedness, and CT density to represent locally each voxel constructing the three-dimensional (3D) pulmonary nodule image. Using features extracted from the histogram of the multi-scale curvature indexes and CT density, the pulmonary nodules are discriminated between benign and malignant cases by the linear discriminant classifier. In this study a data set of 128 pulmonary nodules is analyzed to investigate which scale provides high classification accuracy between malignant and benign nodules. Additionally, the extracted features are evaluated for four different regions: (i) entire 3D pulmonary nodule; (ii) core region in the 3D pulmonary nodule; (iii) complement of the-core region in the 3D pulmonary nodule; (iv) neighborhood region surrounding the 3D pulmonary nodule. The effectiveness of the multi-scale curvature indexes in a computer-aided differential diagnosis is demonstrated by receiver operating characteristic (ROC) analysis.