Automatic segmentation of ground-glass opacity nodule on chest CT images by histogram modeling and local contrast

Julip Jung; Helen Hong; Jin Mo Goo

doi:10.1117/12.911769

23 February 2012 Automatic segmentation of ground-glass opacity nodule on chest CT images by histogram modeling and local contrast

Julip Jung, Helen Hong, Jin Mo Goo

Author Affiliations +

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83152T (2012) https://doi.org/10.1117/12.911769
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

We propose an automatic segmentation of Ground Glass Opacity (GGO) nodules on chest CT images by histogram modeling and local contrast. First, optimal volume circumscribing a nodule is calculated by clicking inside of GGO nodule. To remove noises while preserving a nodule boundary, anisotropic diffusion filtering is applied to the optimal volume. Second, for deciding an appropriate threshold value of GGO nodule, histogram modeling is performed by Gaussian Mixture Modeling (GMM) with three components such as lung parenchyma, nodule, and chest wall or vessels. Third, the attached chest wall and vessels are separated from the GGO nodules by maximum curvature points linking and morphological erosion with adaptive circular mask. Fourth, initial boundary of GGO nodule is refined using local contrast information. Experimental results show that attached neighbor structures are well separated from GGO nodules while missed GGO region is refined. The proposed segmentation method can be used for measurement of the growth rate of nodule and the proportion of solid portion inside nodule.

Citation Download Citation

Julip Jung, Helen Hong, and Jin Mo Goo "Automatic segmentation of ground-glass opacity nodule on chest CT images by histogram modeling and local contrast", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83152T (23 February 2012); https://doi.org/10.1117/12.911769

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