Abstract
The development of techniques for image denoising is a very challenging issue because noise must be removed without destroying image features that are important for medical diagnosis. This paper shows how validation of denoising algorithms can be performed by means of a vector measure of the image quality that takes into account noise cancellation and detail preservation by resorting to a fuzzy segmentation of the image data. Results of computer simulations show that the method overcomes the limitations of current techniques based on scalar image quality measurements. Furthermore it is conceptually simple and easy to implement.
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Russo, F. (2010). Validation of Denoising Algorithms for Medical Imaging. In: Mukhopadhyay, S.C., Lay-Ekuakille, A. (eds) Advances in Biomedical Sensing, Measurements, Instrumentation and Systems. Lecture Notes in Electrical Engineering, vol 55. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05167-8_7
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DOI: https://doi.org/10.1007/978-3-642-05167-8_7
Publisher Name: Springer, Berlin, Heidelberg
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