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15 March 2019 Advanced statistical analysis to classify high dimensionality textural probability-distribution matrices
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Temporomandibular Joint (TMJ) Osteoarthritis (OA) is associated with significant pain and disability. It is really hard to diagnose TMJ OA during early stages of the disease. Subchondral bone texture has been observed to change in the TMJ early during TMJ OA progression. We believe that raw probability-distribution matrices describing image texture encode important information that might aid diagnosing TMJ OA. In this paper we present novel statistical methods for High Dimensionality Low Sample Size Data (HDLSSD) to test the discriminatory power of probability-distribution matrices in computed from TMJ OA medical scans. Our results, and comparison with previous results obtained from the summary features obtained from them indicate that probability-distribution matrices are an important piece of information provided by texture analysis methods and should not be down sampled for analysis.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jack Prothero, Jean-Baptiste Vimort, Antonio Ruellas, J. S. Marron, Matthew McCormick, Pablo Hernandez-Cerdan, Lucia Cevidanes, Erika Benavides, and Beatriz Paniagua "Advanced statistical analysis to classify high dimensionality textural probability-distribution matrices", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1095318 (15 March 2019);

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