Biomedical Imaging GroupSTI
English only   BIG > Publications > Texture Model

 Home Page
 News & Events
 Tutorials and Reviews
 Download Algorithms

 All BibTeX References

A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT

P. Cirujeda, Y.D. Cid, H. Müller, D. Rubin, T.A. Aguilera, B.W. Loo Jr., M. Diehn, X. Binefa, A. Depeursinge

IEEE Transactions on Medical Imaging, vol. 35, no. 12, pp. 2620-2630, December 2016.

This paper proposes a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter-variations of the feature space dimensions. When compared to the classical use of the average for feature aggregation, feature covariances preserve spatial co-variations between features. The obtained Riesz-covariance descriptors lie on a manifold governed by Riemannian geometry allowing geodesic measurements and differentiations. The latter property is incorporated both into a kernel for support vector machines (SVM) and a manifold-aware sparse regularized classifier. The effectiveness of the presented models is evaluated on a dataset of 110 patients with non-small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy of 81.3-82.7%. The anatomical location of recurrence could be discriminated between local, regional and distant failure with an accuracy of 78.3-93.3%. The obtained results open novel research perspectives by revealing the importance of the nodular regions used to build the predictive models.

AUTHOR="Cirujeda, P. and Cid, Y.D. and M{\"{u}}ller, H. and Rubin, D.
        and Aguilera, T.A. and Loo Jr., B.W. and Diehn, M. and Binefa, X.
        and Depeursinge, A.",
TITLE="A \mbox{3-D} {R}iesz-Covariance Texture Model for Prediction of
        Nodule Recurrence in Lung {CT}",
JOURNAL="{IEEE} Transactions on Medical Imaging",

© 2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.