Abstract
We apply ideas from algebraic topology to study distributions on object spaces. We present a framework for using persistence landscapes to vectorize persistence diagrams as in Bubenik(2015)[3] and Patrangenaru et al.(2018)[13]. We apply these methods to analyze data from The Cancer Imaging Archive (TCIA), using a technique developed earlier for regular types of digital images. The aim of this study is a comparison of brain images of CPTAC Glioblastoma patients with similar images from clinically normal individuals. Result shows persistence landscape may capture topological features distinguishing the two groups.
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Shen, C., Patrangenaru, V. (2020). Topological Object Data Analysis Methods with an Application to Medical Imaging. In: Aneiros, G., Horová, I., Hušková, M., Vieu, P. (eds) Functional and High-Dimensional Statistics and Related Fields. IWFOS 2020. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-47756-1_31
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DOI: https://doi.org/10.1007/978-3-030-47756-1_31
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