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High performance lung nodule detection schemes in CT using local and global information
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10.1118/1.4737109
/content/aapm/journal/medphys/39/8/10.1118/1.4737109
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/39/8/10.1118/1.4737109

Figures

Image of FIG. 1.
FIG. 1.

Histogram of the nodule size.

Image of FIG. 2.
FIG. 2.

Overall scheme of the computerized detection technique using 2D and 3D information.

Image of FIG. 3.
FIG. 3.

Uniformly distributed viewpoints on a sphere generated by spiral-scanning technique.

Image of FIG. 4.
FIG. 4.

Illustration of the coordinate system showing a nodule O and a viewpoint P. The reformatted 2D image generated from the viewpoint passes the center of the nodule and is perpendicular to the line connecting the center of the nodule and the viewpoint.

Image of FIG. 5.
FIG. 5.

A nodule (left column) and a blood vessel (right column) in the consecutive CT slices [(a) and (b)], the 2D reformatted images [(c) and (d)], and the segmented images [(e) and (f)]. As expected, the nodule appears circular in all the consecutive CT slices and 2D reformatted images. Although the blood vessel appears as nodule-like circular objects in all the consecutive CT slices, it appears clearly as noncircular linear structures in some “effective” 2D reformatted images. These “effective” 2D reformatted images enable us to well distinguish nodules from blood vessels.

Image of FIG. 6.
FIG. 6.

Relationship between two 2D features for nodules (circle) and false positives (dot).

Image of FIG. 7.
FIG. 7.

Two rules for removing many false positives. The lines, circles, and dots indicate the rules, nodules, and false positives, respectively.

Image of FIG. 8.
FIG. 8.

FROC curves of the 2D + 3D scheme obtained with different numbers of viewpoints. The performance levels using 24 and 42 viewpoints are considerably higher than that using 11 viewpoints.

Image of FIG. 9.
FIG. 9.

FROC curves of the 2D + 3D scheme obtained with different percentage threshold (T). The performance levels using different percentage thresholds are close to one another.

Image of FIG. 10.
FIG. 10.

FROC curves of 2D + 3D scheme obtained with no rule, one rule, and two rules. The performance levels using no rule, one rule, and two rules are close to one another.

Image of FIG. 11.
FIG. 11.

Time required to classify all 2D nodule candidates at different levels of sensitivity for the 2D + 3D scheme with no rule, one rule, and two rules (initial sensitivity = 91%). When the sensitivity is set at 80%, the needed time with two rules was about a third of that with no rule.

Image of FIG. 12.
FIG. 12.

FROC curves of the 2D + 3D scheme obtained with nodules confirmed by two, three, or four radiologists. If the nodules were confirmed by more radiologists, the performance of our CAD schemes for these nodules were higher.

Image of FIG. 13.
FIG. 13.

FROC curves for the 2D scheme, 3D scheme, 2D + 3D scheme, 2D − 3D scheme, and 3D − 2D scheme. The performance levels of nodule detection using local 2D information and global 3D information (2D + 3D scheme, 2D − 3D scheme, and 3D − 2D scheme) are higher than that using 2D information alone (2D scheme). The performance of nodule detection using 3D information alone (3D scheme) is the lowest.

Tables

Generic image for table
TABLE I.

Features employed in this study.

Generic image for table
TABLE II.

The sensitivity and number of false positives of the five CAD schemes.

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/content/aapm/journal/medphys/39/8/10.1118/1.4737109
2012-07-31
2014-04-17
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: High performance lung nodule detection schemes in CT using local and global information
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/39/8/10.1118/1.4737109
10.1118/1.4737109
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