Pulmonary nodule evaluation based on analyses of contrast-enhanced CT images becomes useful for differentiating
malignant and benign nodules. There are several types of nodule regarding inside density (such as solid, mixed GGO,
and pure GGO) and size. This paper presents relationships between contrast enhancement characteristics and nodule
types. Thin-section, contrast-enhanced CT (pre-contrast, and post-contrast series acquired at 2 and 4 minutes) was
performed on 86 patients with pulmonary nodules (42 benign and 44 malignant). Nodule regions were segmented from
an isotropic volume reconstructed from each image series. In this study, the contrast-enhancement characteristics of
nodules were quantified by using CT number histogram. The CT number histograms inside the segmented nodules were
computed on pre-contrast and post-contrast series. A feature characterizing variation between two histograms was
computed by subtracting the histogram of post-contrast series from that of pre-contrast series, and dividing the
summation of subtracted frequency of each bin by the volume of the segmented nodule on pre-contrast series. The
nodules were classified into five types (α, β, γ, δ, and ε) on the basis of internal features extracted from CT number
histogram on pre-contrast series. The nodule data set was categorized into subset through the nodule type and size and
the performance of the feature to classify malignant from benign nodules was evaluated for each subset.
Pulmonary nodules are classified into three types such as solid, mixed GGO, and pure GGO types on the basis of the
visual assessment of CT appearance. In our current study a quantitative classification algorithm has been developed by
using volumetric data sets obtained from thin-section CT images. The algorithm can classify the pulmonary nodules into
five types (&agr;, &bgr;, &ggr;, &dgr;, and ε; on the basis of internal features extracted from CT number histograms inside nodules. We
applied dynamic enhanced single slice and multi slice CT images to this classification algorithm and we analyzed it in
each type.
Pulmonary nodules are classified into three types such as solid, mixed GGO, and pure GGO types on the basis of the visual assessment of CT appearance. In our current study a quantitative classification algorithm has been developed by using volumetric data sets obtained from thin-section CT images. The algorithm can classify the pulmonary nodules into five types (α, β, γ, δ, and ε) on the basis of internal features extracted from CT number histograms inside nodules. We applied dynamic enhanced single slice and multi slice CT images to this classification algorithm and we analyzed it in each type.
This paper presents a computerized classification scheme of pulmonary nodules in contrast enhanced dynamic CT images. Conventionally, we extracted 3D nodule images by using a deformable surface model. However, there was a limit in segmenting the 3D nodule images contacted with vessels and bronchi. In order to improve the segmentation accuracy of the 3D nodule images, we developed a software tool to eliminate the leaked region of the 3D nodule image due to vessels and bronchi interactively. Using our data set including 62 cases (27 benign and 35 malignant cases), we demonstrate how the segmentation accuracy affects the classification accuracy of our scheme.
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