An automated CT based lung nodule detection scheme using geometric analysis of signed distance field

Abstract

The authors present a new computerized scheme to automatically detect lung nodules depicted on computed tomography (CT) images. The procedure is performed in the signed distance field of the CT images. To obtain an accurate signed distance field, CT images are first interpolated linearly along the axial direction to form an isotropic data set. Then a lung segmentation strategy is applied to smooth the lung border aiming to include as many juxtapleural nodules as possible while minimizing over segmentations of the lung regions. Potential nodule regions are then detected by locating local maximas of signed distances in each subvolume with values and the sizes larger than the smallest nodule of interest in the three-dimensional space. Finally, all detected candidates are scored by computing the similarity distance of their medial axis-like shapes obtained through a progressive clustering strategy combined with a marching cube algorithm from a sphere based shape. A free-response receiver operating characteristics curve is computed to assess the scheme performance. A performance test on 52 low-dose CT screening examinations that depict 184 verified lung nodules showed that during the initial stage the scheme achieved an asymptotic maximum sensitivity of 95.1% (175/184) with an average of 1200 suspicious voxels per CT examination. The nine missed nodules included two small solid nodules (with a diameter < or =3.1 mm) and seven nonsolid nodules. The final performance level after the similarity scoring stage was an absolute sensitivity level, namely, including the nine missed during the initial stage, of 81.5% (150/184) with 6.5 false-positive identifications per CT examination. This preliminary study demonstrates the feasibility of applying a simple and robust geometric model using the signed distance field to identify suspicious lung nodules. In the authors' data set the sensitivity of this scheme is not affected by nodule size. In addition to potentially being a stand alone approach, the signed distance field based method can be easily implemented as an initial filtering step in other computer-aided detection schemes.

Figure 1

Schematic diagram of the lung nodule detection algorithm.

Figure 2

Flowchart of the lung segmentation algorithm: (a) an original 2D CT image; (b) initial thresholded results; (c) nonlung region flooding; (d) enlargement of (c); (e) inner lung border tracing; and (f) lung border smoothing.

Figure 3

Examples of four POI locations.

Figure 4

Surface-based representations of the clustered volumes representing the POIs in Fig. 3.

Figure 5

Shape distributions of a sphere, a tube, and a plane, each in the form of a surface mesh.

Figure 6

Shape distributions of the objects displayed in Fig. 4.

Figure 7

The relationship between the selected threshold for signed distance field and the number of POIs (top) and the relationship between the selected threshold for signed distance field and the sensitivity (bottom).

Figure 8

Four performance (FROC) curves for nodule detection representing different sizes of interest (≥3 and ≤6 mm) and different types (solid and nonsolid).