Ridgelet-based Texture Classification of Tissues in Computed Tomography

L. Semler, L. Dettori, and B. Kerr (USA)


Texture Classification, Multi-resolution Analysis, Ridgelet, Computed Tomography


The research presented in this article is aimed at the development of an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The article focuses on using ridgelet-based multi-resolution texture analysis. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The classification step is implemented through a decision tree classifier based on the cross-validation Classification and Regression Tree approach. The discriminating power of several ridgelet based texture descriptors are investigated. Preliminary results indicate that Entropy signatures are the most effective descriptors for ridgelets. Generally, multiple resolutions have a higher discriminating power than a single resolution level, and in this application, combining two resolutions instead of three increases performance.

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