Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

Kaioqiong Sun; Jayaram K. Udupa; Dewey Odhner; Yubing Tong; Drew A. Torigian

doi:10.1117/12.2044315

12 March 2014 Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

Kaioqiong Sun, Jayaram K. Udupa, Dewey Odhner, Yubing Tong, Drew A. Torigian

Author Affiliations +

Proceedings Volume 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling; 90361P (2014) https://doi.org/10.1117/12.2044315
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

Citation Download Citation

Kaioqiong Sun, Jayaram K. Udupa, Dewey Odhner, Yubing Tong, and Drew A. Torigian "Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90361P (12 March 2014); https://doi.org/10.1117/12.2044315

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