Abstract
Texture is present in a large number of medical images. Its structure codes selected properties of visualized organ and tissues so texture can be rich source of information regarding their condition. Quantitative texture analysis plays significant role in imaging diagnosis support systems, enabling segmentation of analyzed organs, detection of lesions, and assessment of the degree of their pathological change. Unfortunately, medical images are often corrupted by noise which affect texture based image features. One of the steps of texture feature extraction is reduction of gray levels number which is performed after a normalization of pixel intensities inside a region of interest. This reduces the noise effect on texture feature values. We demonstrated, based on analysis of natural and MR images, that such reduction improves classification accuracy while reducing the computational costs.
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Strzelecki, M., Kociołek, M., Materka, A. (2019). On the Influence of Image Features Wordlength Reduction on Texture Classification. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2018. Advances in Intelligent Systems and Computing, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-91211-0_2
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