26 January 2017 Improvement of co-occurrence matrix calculation and collagen fibers orientation estimation
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Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 101601B (2017) https://doi.org/10.1117/12.2256721
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina
Abstract
Gray-level co-occurrence matrix (GLCM) is a statistical method widely used to characterize images and specifically, for Second Harmonic Generation (SHG) collagen images characterization. This method takes into account the spatial relationship between the image pixels, at specific angle. It is usually calculated for four orientations, at specific distances. Over these matrix, a textural feature function is calculated. Often, results of different orientations are compared or averaged to get a unique statistic parameter. In the present report, we will demonstrate the error that bring with this methodology, and following, we offer the correction formula. Preferred orientation of SHG images is proposed as structural property to characterize biological samples. For example, for determining the parallelism grade of collagen fibers regarding the ovarian epithelium. Here, we present a robust method to calculate this parameter, based on the two-dimensional Fourier transform. Finally, we show how these two elements help improve the discrimination between normal and pathological ovarian tissues.
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Angel A. Zeitoune, Angel A. Zeitoune, Luciana A. Erbes, Luciana A. Erbes, Victor H. Casco, Victor H. Casco, Javier F. Adur, Javier F. Adur, } "Improvement of co-occurrence matrix calculation and collagen fibers orientation estimation", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101601B (26 January 2017); doi: 10.1117/12.2256721; https://doi.org/10.1117/12.2256721
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