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Proceedings Paper

Automatic detection of spiculation of pulmonary nodules in computed tomography images
Author(s): F. Ciompi; C. Jacobs; E. Th. Scholten; S. J. van Riel; M. M. W. Wille; M. Prokop; B. van Ginneken
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Paper 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.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941409 (20 March 2015); doi: 10.1117/12.2081426
Show Author Affiliations
F. Ciompi, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
C. Jacobs, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Fraunhofer MEVIS (Germany)
E. Th. Scholten, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
S. J. van Riel, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
M. M. W. Wille, Gentofte Hospital (Denmark)
M. Prokop, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
B. van Ginneken, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Fraunhofer MEVIS (Germany)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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