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ICIAR
2011
Springer

A Texture-Based Probabilistic Approach for Lung Nodule Segmentation

10 years 3 months ago
A Texture-Based Probabilistic Approach for Lung Nodule Segmentation
Producing consistent segmentations of lung nodules in CT scans is a persistent problem of image processing algorithms. Many hard-segmentation approaches are proposed in the literature, but soft segmentation of lung nodules remains largely unexplored. In this paper, we propose a classification-based approach based on pixel-level texture features that produces soft (probabilistic) segmentations. We tested this classifier on the publicly available Lung Image Database Consortium (LIDC) dataset. We further refined the classification results with a post-processing algorithm based on the variability index. The algorithm performed well on nodules not adjacent to the chest wall, producing a soft overlap between radiologists’ based segmentation and computer-based segmentation of 0.52. In the long term, these soft segmentations will be useful for representing the uncertainty in nodule boundaries that is manifest in radiological image segmentations.
Olga Zinoveva, Dmitry Zinovev, Stephen A. Siena, D
Added 29 Aug 2011
Updated 29 Aug 2011
Type Journal
Year 2011
Where ICIAR
Authors Olga Zinoveva, Dmitry Zinovev, Stephen A. Siena, Daniela Stan Raicu, Jacob D. Furst, Samuel G. Armato III
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