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Morphological shape analysis refers to the ability to understand how anatomical structures tend to vary due to various reasons such as: development of certain pathology, aging, and surgery. The medical imaging research community is constantly concerned with enhancing the ability to distinguish between anatomy of healthy individuals and those with pathology. This paper focuses on analysis of human vertebral structures.
Medical experts often examine hundreds of spine x-rays to determine existence of various vertebral pathologies. In this paper, we present a novel framework for analysis of vertebral structures. We propose to perform shape-based morphological analysis of vertebral structures using parameters of a multi-scale shape model. The framework uses wavelet-ICA shape representation in combination with regression analysis and hypothesis testing methods. The new framework incorporates the following: rich multi-scale representation of vertebral shapes using wavele t-ICA, feature selection approaches, and statistical analysis techniques.
We aim at the following: (a) Discriminate between two groups of normal and pathological vertebrae. (b) Establish relationship between localized modes of variations and clinical outcome variables. We demonstrate the ability of the proposed framework to localize shape changes in vertebral structures related to various pathologies. Experiments have demonstrated the ability of the proposed framework to discriminate between classes of normal and pathological vertebrae. Within a regression analysis framework, we were able to establish a statistically relevant relationship between the wavelet-ICA modes of deformations and clinical information.
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