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Model for the detection of signals in images with multiple suspicious locations
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10.1118/1.3002413
/content/aapm/journal/medphys/35/12/10.1118/1.3002413
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/35/12/10.1118/1.3002413

Figures

Image of FIG. 1.
FIG. 1.

The signal distribution , the noise nodule distribution , and the distribution of the maximum noise nodule distribution used in Example I (top). In the bottom graphic is plotted the signal specificity function .

Image of FIG. 2.
FIG. 2.

Comparison of the ROC curves obtained for Example I by five different methods of assigning the image global abnormality score. The first method is based on the likelihood ratio statistic , given by Eq. (19). The second method uses the maximum individual likelihood ratio as discussed in Sec. ???. The third method uses the value of the most likely signal and noise nodule combination, as discussed in Sec. ???. The fourth method uses the maximum contrast value found, and the fifth method uses the maximum signal specificity score found .

Image of FIG. 3.
FIG. 3.

Comparison of the FROC curves obtained with four different methods for Example I. In the first method, the statistic is used, as discussed in Sec. ???. In the second method, also the statistic is used, but limited to the set of features of the most likely signal and noise nodule combination , as discussed in Sec. ???. The third method uses the contrast values, and the fourth method uses the signal specificity score .

Image of FIG. 4.
FIG. 4.

Comparison between the real signal specificity function and the specificity function used for image evaluation in Example II.

Image of FIG. 5.
FIG. 5.

Comparison of the ROC curves for Example II. The significance of the curves is the same as in Fig. 2.

Image of FIG. 6.
FIG. 6.

Comparison of the FROC curves for Example II. The significance of the curves is the same as in Fig. 3.

Image of FIG. 7.
FIG. 7.

Comparisons between the ROC curves in Examples I and II for the tests using the global likelihood ratio statistic and the maximum specificity score .

Image of FIG. 8.
FIG. 8.

Comparisons between the FROC curves in Examples I and II for the free response tests using the likelihood ratio statistic for individual suspicious locations and for the test using the specificity score .

Image of FIG. 9.
FIG. 9.

Signal distribution , noise nodule distribution , and distribution of the maximum noise nodule distribution used in Example III (top). In the bottom graph, the signal specificity function is plotted.

Image of FIG. 10.
FIG. 10.

Comparison of the ROC curves for Example III. The significance of the curves is the same as in Fig. 2.

Image of FIG. 11.
FIG. 11.

Comparison of the FROC curves for Example III. The significance of the curves is the same as in Fig. 3.

Image of FIG. 12.
FIG. 12.

Comparison of the ROC curves obtained in Example III with the results obtained for a modified distribution of the number of signals per signal-present image , in which almost all signal-present images contain three signals.

Image of FIG. 13.
FIG. 13.

Comparison of the FROC curves obtained in Example III with the results obtained for a modified distribution of the number of signals per signal-present image , in which almost all signal-present images contain three signals.

Tables

Generic image for table
TABLE I.

Model parameters used in the example presented. In the cases where different values have been used for a parameter for data generation and evaluation, the values are presented in the form .

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/content/aapm/journal/medphys/35/12/10.1118/1.3002413
2008-11-14
2014-04-24
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Model for the detection of signals in images with multiple suspicious locations
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/35/12/10.1118/1.3002413
10.1118/1.3002413
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