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3 January 2020 Feasibility of Nakagami parametric image for texture analysis
Michael Chang, Bino Varghese, Jamie Gunter, Kwang J. Lee, Darryl H. Hwang, Vinay Duddalwar
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Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 1133002 (2020) https://doi.org/10.1117/12.2540513
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia
Abstract
Purpose: Evaluate the feasibility of using a Nakagami model to create an accurate parametric image from ultrasound imaging data for the differentiation of homogenous and heterogeneous texture phantoms. Analysis was done on the raw data i.e., radiofrequency (RF) data collected before any post processing that can affect the images. Materials and methods: The Nakagami parametric image was constructed on demodulated RF data with the sliding window technique to create a map of local parameters. The Nakagami parameter (m) for the entire image was found by averaging all values. By design, when m is greater than 1, the distribution is post-Rayleigh. When m is equal to 1, the distribution is Rayleigh. To test the technique, two agar phantoms were constructed, using varying amounts of flour as the scatterer. The higher amount of flour scatterer was meant to mimic heterogeneous texture and the lesser amount meant to mimic homogeneous texture. Scans were done on each phantom and analyzed for differences in the Nakagami parameter. Results: Phantom 1 displayed a post-Rayleigh distribution (m = 36.1±7.0), while phantom 2 did so, to a lesser extent (m = 1.64±0.12). As the distribution transitions from Rayleigh to post Rayleigh, the scatterers in the sample go from being periodically located/randomly distributed to large numbers of randomly distributed scatterers. Conclusion: Our study suggests that Nakagami parametric based metrics may be used to increase robustness of texture analysis, considering the analysis is done on the raw data before any post processing that can affect the images is introduced.
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Michael Chang, Bino Varghese, Jamie Gunter, Kwang J. Lee, Darryl H. Hwang, and Vinay Duddalwar "Feasibility of Nakagami parametric image for texture analysis", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133002 (3 January 2020); https://doi.org/10.1117/12.2540513
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