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In this preliminary study, a new computer-aided detection (CAD) scheme for pulmonary embolism (PE)
detection was developed and tested. The scheme applies multiple steps including lung segmentation, candidate
extraction using intensity mask and tobogganing method, feature extraction, false positive reduction using a multifeature
based artificial neural network (ANN) and a k-nearest neighbor (KNN) classifier to detect and classify suspicious
PE lesions. In particular, a new method to define the surrounding background regions of interest (ROI) depicting PE
candidates was proposed and tested in an attempt to reduce the detection of false positive regions. In this study, the
authors also investigated following methods to improve CAD performance, which include a grouping and scoring
method, feature selection using genetic algorithm, and limitation on allowed suspicious lesions to be cued in one
examination. To test the scheme performance, a set of 20 chest CT examinations were selected. Among them, 18 are
positive cases depicted 44 verified PE lesions and the remaining 2 were negative cases. The dataset was also divided into
a training subset (9 examinations) and a testing subset (11 examinations), respectively. The experimental results showed
when applying to the testing dataset CAD scheme using tobogganing method alone achieved 2D region-based sensitivity
of 72.1% (220/305) and 3D lesion-based sensitivity of 83.3% (20/24) with total 19,653 2D false-positive (FP) PE
regions (1,786.6 per case or approximately 6.3 per CT slice). Applying the proposed new method to improve lung region
segmentation and better define the surrounding background ROI, the scheme reduced the region-based sensitivity by
6.5% to 65.6% or lesion-based sensitivity by 4.1% to 79.2% while reducing the FP rate by 65.6% to 6,752 regions (or
613.8 per case). After applying the methods of grouping, the maximum scoring, a genetic algorithm (GA) to delete
"redundant" features, and limiting the maximum number of cued-lesions in one examination, CAD scheme further
reduced FP rate to 50 per case. Based on the FROC curve, an operating threshold was set up in which the CAD scheme
could ultimately achieve 63.2% detection sensitivity with 18.4 FP regions per case when applying to the testing dataset.
This study investigated the feasibility of several methods applying to the CAD scheme in detecting PE lesions and
demonstrated that CAD performance could depend on many factors including better defining candidate ROI and its
background, optimizing the 2D region grouping and scoring methods, selecting the optimal feature set, and limiting the
number of allowed cueing lesions per examination.
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