20 March 2015 Automatic detection of spiculation of pulmonary nodules in computed tomography images
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Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 941409 (2015); doi: 10.1117/12.2081426
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States
Abstract
We present a fully automatic method for the assessment of spiculation of pulmonary nodules in low-dose Computed Tomography (CT) images. Spiculation is considered as one of the indicators of nodule malignancy and an important feature to assess in order to decide on a patient-tailored follow-up procedure. For this reason, lung cancer screening scenario would benefit from the presence of a fully automatic system for the assessment of spiculation. The presented framework relies on the fact that spiculated nodules mainly differ from non-spiculated ones in their morphology. In order to discriminate the two categories, information on morphology is captured by sampling intensity profiles along circular patterns on spherical surfaces centered on the nodule, in a multi-scale fashion. Each intensity profile is interpreted as a periodic signal, where the Fourier transform is applied, obtaining a spectrum. A library of spectra is created by clustering data via unsupervised learning. The centroids of the clusters are used to label back each spectrum in the sampling pattern. A compact descriptor encoding the nodule morphology is obtained as the histogram of labels along all the spherical surfaces and used to classify spiculated nodules via supervised learning. We tested our approach on a set of nodules from the Danish Lung Cancer Screening Trial (DLCST) dataset. Our results show that the proposed method outperforms other 3-D descriptors of morphology in the automatic assessment of spiculation.
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F. Ciompi, C. Jacobs, E. Th. Scholten, S. J. van Riel, M. M. W. Wille, M. Prokop, B. van Ginneken, "Automatic detection of spiculation of pulmonary nodules in computed tomography images", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941409 (20 March 2015); doi: 10.1117/12.2081426; https://doi.org/10.1117/12.2081426
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KEYWORDS
Lung cancer

Computed tomography

Spherical lenses

Optical spheres

Computer aided design

Machine learning

CAD systems

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