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Automatic detection and segmentation of focal liver lesions in contrast enhanced CT images

Militzer A, Hager T, Jäger F, Tietjen C, Hornegger J (2010)

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Publication Type: Conference contribution, Conference Contribution

Publication year: 2010

Journal

Original Authors: Militzer A., Hager T., Jäger F., Tietjen C., Hornegger J.

Book Volume: null

Pages Range: 2524-2527

Event location: Istanbul

Journal Issue: null

DOI: 10.1109/ICPR.2010.618

Abstract

In this paper a novel system for automatic detection and segmentation of focal liver lesions in CT images is presented. It utilizes a probabilistic boosting tree to classify points in the liver as either lesion or parenchyma, thus providing both detection and segmentation of the lesions at the same time and fully automatically. To make the segmentation more robust, an iterative classification scheme is integrated, that incorporates knowledge gained from earlier iterations into later decisions. Finally, a comprehensive evaluation of both the segmentation and the detection performance for the most common hypodense lesions is given. Detection rates of 77% could be achieved with a sensitivity of 0.95 and a specificity of 0.93 for lesion segmentation at the same settings. © 2010 IEEE.

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How to cite

APA:

Militzer, A., Hager, T., Jäger, F., Tietjen, C., & Hornegger, J. (2010). Automatic detection and segmentation of focal liver lesions in contrast enhanced CT images. In Proceedings of the 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 2524-2527). Istanbul.

MLA:

Militzer, Arne, et al. "Automatic detection and segmentation of focal liver lesions in contrast enhanced CT images." Proceedings of the 2010 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul 2010. 2524-2527.

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