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Proceedings Paper

Computer-assisted quantification of the skull deformity for craniosynostosis from 3D head CT images using morphological descriptor and hierarchical classification
Author(s): Min Jin Lee; Helen Hong; Kyu Won Shim; Yong Oock Kim
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Paper 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.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343F (3 March 2017); doi: 10.1117/12.2254448
Show Author Affiliations
Min Jin Lee, Seoul Women's Univ. (Korea, Republic of)
Helen Hong, Seoul Women's Univ. (Korea, Republic of)
Kyu Won Shim, Yonsei Univ. College of Medicine, Severance Children's Hospital (Korea, Republic of)
Yong Oock Kim, Yonsei Univ. College of Medicine, Severance Children's Hospital (Korea, Republic of)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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