| Περίληψη: | Use of multi-detector CT in lung cancer screening has the potential to detect smaller
lung nodules with improved sensitivity. In this study the development of a Computer
Aided Detection (CAD) system for lung nodules is reported. A combination of two
segmentation approaches is used, to segment lung regions. Following segmentation, a
selective enhancement filter is applied for ''initial'' identification of nodule seed points in
lung regions. Candidate lung nodule regions were delineated with the use of a region
growing algorithm, with thresholds provided by minimum error thresholding. False
positive regions were subsequently removed using two Support Vector Machines (SVM)
classifiers in cascade, utilizing a set of 6 morphological features extracted from
corresponding nodule candidate regions of the enhanced and the original images. The
proposed automated scheme was tested on a reference dataset of 21 cases provided by the
Lung Imaging Database Consortium. System performance on a case and slice basis
provided sensitivities of 91% and 81% respectively, both with an average of 5 FPs per
slice. Further analysis of the slice dataset with respect to size, contrast and location of
nodules provided sensitivities of 81%, 83% and 85% for nodules of small size, low
contrast and near pleura. This CAD scheme may be a useful tool in assisting radiologists
in lung nodule detection.
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