Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method

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Abstract
We are developing a computer-aided detection system to aid radiologists in diagnosing lung cancer in thoracic computed tomographic (CT) images. The purpose of this study was to improve the false-positive (FP) reduction stage of our algorithm by developing and incorporating a gradient field technique. This technique extracts 3D shape information from the gray-scale values within a volume of interest. The gradient field feature values are higher for spherical objects, and lower for elongated and irregularly-shaped objects. A data set of 55 thin CT scans from 40 patients was used to evaluate the usefulness of the gradient field technique. After initial nodule candidate detection and rule-based first stage FP reduction, there were 3487 FP and 65 true positive (TP) objects in our data set. Linear discriminant classifiers with and without the gradient field feature were designed for the second stage FP reduction. The accuracy of these classifiers was evaluated using the area Az under the receiver operating characteristic (ROC) curve. The Az values were 0.93 and 0.91 with and without the gradient field feature, respectively. The improvement with the gradient field feature was statistically significant (p=0.01).

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