Adaptive geodesic transform for segmentation of vertebrae on CT images

Bilwaj Gaonkar; Liao Shu; Gerardo Hermosillo; Yiqiang Zhan

doi:10.1117/12.2043527

18 March 2014 Adaptive geodesic transform for segmentation of vertebrae on CT images

Bilwaj Gaonkar, Liao Shu, Gerardo Hermosillo, Yiqiang Zhan

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 903516 (2014) https://doi.org/10.1117/12.2043527
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in¹ to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm² to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.

Citation Download Citation

Bilwaj Gaonkar, Liao Shu, Gerardo Hermosillo, and Yiqiang Zhan "Adaptive geodesic transform for segmentation of vertebrae on CT images", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903516 (18 March 2014); https://doi.org/10.1117/12.2043527

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