3 March 2017 Computer-assisted quantification of the skull deformity for craniosynostosis from 3D head CT images using morphological descriptor and hierarchical classification
Author Affiliations +
Abstract
This paper proposes morphological descriptors representing the degree of skull deformity for craniosynostosis in head CT images and a hierarchical classifier model distinguishing among normal and different types of craniosynostosis. First, to compare deformity surface model with mean normal surface model, mean normal surface models are generated for each age range and the mean normal surface model is deformed to the deformity surface model via multi-level threestage registration. Second, four shape features including local distance and area ratio indices are extracted in each five cranial bone. Finally, hierarchical SVM classifier is proposed to distinguish between the normal and deformity. As a result, the proposed method showed improved classification results compared to traditional cranial index. Our method can be used for the early diagnosis, surgical planning and postsurgical assessment of craniosynostosis as well as quantitative analysis of skull deformity.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Min Jin Lee, Min Jin Lee, Helen Hong, Helen Hong, Kyu Won Shim, Kyu Won Shim, Yong Oock Kim, Yong Oock Kim, } "Computer-assisted quantification of the skull deformity for craniosynostosis from 3D head CT images using morphological descriptor and hierarchical classification", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343F (3 March 2017); doi: 10.1117/12.2254448; https://doi.org/10.1117/12.2254448
PROCEEDINGS
6 PAGES


SHARE
Back to Top