Shape-Based Classification of 3D Head Data
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Classification and feature selection for craniosynostosis
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Efficient symbolic signatures for classifying craniosynostosis skull deformities
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Craniosynostosis is the premature fusion of the bones of the calvaria resulting in abnormal skull shapes that can be associated with increased intracranial pressure. While craniosynostoses of multiple different types can be easily diagnosed, quantifying the severity of the abnormality is much more subjective and not a standard part of clinical practice. For this purpose we have developed a severity-based retrieval system that uses a logistic regression approach to quantify the severity of the abnormality of each of three types of craniosynostoses. We compare several different sparse feature selection techniques: L1 regularized logistic regression, fused lasso, and clustering lasso (cLasso). We evaluate our methodology in three ways: 1) for classification of normal vs. abnormal skulls, 2) for comparing pre-operative to post-operative skulls, and 3) for retrieving skulls in order of abnormality severity as compared with the ordering of a craniofacial expert.