Improvement of co-occurrence matrix calculation and collagen fibers orientation estimation
| Autor: | Victor Hugo Casco, Angel A. Zeitoune, Luciana Ariadna Erbes, Javier Adur |
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| Rok vydání: | 2017 |
| Předmět: |
Pixel
business.industry Orientation (computer vision) Quantitative Biology::Tissues and Organs Statistical parameter Texture (geology) symbols.namesake Matrix (mathematics) Co-occurrence matrix Fourier transform Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition symbols Computer vision Artificial intelligence business Biological system Mathematics |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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