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Modeling of intensity priors for knowledge-based level set algorithm in calvarial tumors segmentation.

Authors
Type
Published Article
Journal
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Publication Date
Volume
9
Issue
Pt 2
Pages
864–871
Identifiers
PMID: 17354854
Source
Medline
License
Unknown

Abstract

In this paper, an automatic knowledge-based framework for level set segmentation of 3D calvarial tumors from Computed Tomography images is presented. Calvarial tumors can be located in both soft and bone tissue, occupying wide range of image intensities, making automatic segmentation and computational modeling a challenging task. The objective of this study is to analyze and validate different approaches in intensity priors modeling with an attention to multiclass problems. One, two, and three class Gaussian mixture models and a discrete model are evaluated considering probability density modeling accuracy and segmentation outcome. Segmentation results were validated in comparison to manually segmented golden standards, using analysis in ROC (Receiver Operating Curve) space and Dice similarity coefficient.

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