The automated detection of lung nodules in CT scans is an important problem in computer-aided diagnosis. In this paper an approach to nodule candidate detection is presented which utilises the local image features of shape index and curvedness. False-positive candidates are removed by means of a two-step approach using kNN classification. The kNN classifiers are trained using features of the image intensity gradients and grey-values in addition to further measures of shape index and curvedness profiles in the candidate regions. The training set consisted of data from 698 scans while the independent test set comprised a further 142 images. At 84% sensitivity an average of 8.2 false-positive detections per scan were observed.
To improve the detection of nodules in chest radiographs, large databases of chest radiographs with annotated, proven nodules are needed for training of both radiologists and computer-aided detection systems. The construction of such databases is a laborious and time-consuming task. This study presents a novel technique to produce large amounts of chest x-rays with annotated, simulated nodules. Realistic nodules in radiographs are generated using real nodules segmented from CT images. Results from an observer study indicate that the simulated nodules can not be distinguished from real nodules. This method has great potential to aid the development of automated detection systems and to generate teaching files for human observers.
Computed Tomography (CT) has become the new reference standard for quantification of emphysema. The most popular measure for emphysema derived from CT is the Pixel Index (PI), which expresses the fraction of the lung volume with abnormally low intensity values. As PI is calculated from a single, fixed threshold on intensity, this measure is strongly influenced by noise. This effect shows up clearly when comparing the PI score for a high-dose scan to the PI score for a low-dose (i.e. noisy) scan of the same subject. This paper presents a class of noise filters that make use of a local noise estimate to specify the filtering strength: Local Noise Variance Weighted Averaging (LNVWA). The performance of the filter is assessed by comparing high-dose and low-dose PI scores for 11 subjects. LNVWA improves the reproducibility of high-dose PI scores: For an emphysema threshold of -910 HU, the root-mean-square difference in PI score drops from 10% of the lung volume to 3.3% of the lung volume if LNVWA is used.