Purpose -The purpose of this paper is to show an efficient method for the detection of signs of early lung cancer. Various image processing algorithms are presented for different types of lesions, and a scheme is proposed for the combination of results. Design/methodology/approach -A computer aided detection (CAD) scheme was developed for detection of lung cancer. It enables different lesion enhancer algorithms, sensitive to specific lesion subtypes, to be used simultaneously. Three image processing algorithms are presented for the detection of small nodules, large ones, and infiltrated areas. The outputs are merged, the false detection rate is reduced with four separated support vector machine (SVM) classifiers. The classifier input comes from a feature selection algorithm selecting from various textural and geometric features. A total of 761 images were used for testing, including the database of the Japanese Society of Radiological Technology (JSRT). Findings -The fusion of algorithms reduced false positives on average by 0.6 per image, while the sensitivity remained 80%. On the JSRT database the system managed to find 60.2% of lesions at an average of 2.0 false positives per image. The effect of using different result evaluation criteria was tested and a difference as high as 4 percentage points in sensitivity was measured. The system was compared to other published methods.Originality/value -This study proves the usefulness of lesion enhancement decomposition, while proposing a scheme for the fusion of algorithms. Furthermore, a new algorithm is introduced for the detection of infiltrated areas, possible signs of lung cancer, neglected by previous solutions. * Marczibányi tér 5.,
In this paper, we address the problem of small pulmonary nodule detection on digital tomosynthesis (DT) scans. We propose efficient, domain-specific filters for the enhancement and classification of bright, rounded structures in three-dimensional volumes.First, 61 DT slices per scan are reconstructed from the DT projections by filtered backprojection (FBP). Next, nodule candidates are searched slice-wise calculating the determinant of the Hessian (DoH). Then a large number of false candidates are removed by a supervised classifier. The features for classification include coordinates, image correlation and overlap with vessels. For the segmentation of the vascular tree, a modification of the Frangi filter is employed.The system is evaluated on simulated DT scans generated from a computed tomography database. A subset of the LIDC/IDRI database of 37 scans was used. 42% of nodules could be detected while producing on average 100 false positives per scan. Sensitivity increased to 77% when restricting the search to nodules marked as visible.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.