Estimation of an Entropy-based Functional

Maurizi, Brian

doi:10.3390/e12030338

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…It is important to mention that these estimators have been studied in different contexts. Maurizi [23] studied the works by Vasicek [17] and van Es [18] to estimate the entropy H(Z) when the random variable has support [0, 1]. Noughabi and Park [24] considered them to propose goodness of fit tests for the Laplace distribution.…”

Section: Introductionmentioning

confidence: 99%

Entropy Estimators in SAR Image Classification

Cassetti

Delgadino

Rey

et al. 2022

Entropy

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Remotely sensed data are essential for understanding environmental dynamics, for their forecasting, and for early detection of disasters. Microwave remote sensing sensors complement the information provided by observations in the optical spectrum, with the advantage of being less sensitive to adverse atmospherical conditions and of carrying their own source of illumination. On the one hand, new generations and constellations of Synthetic Aperture Radar (SAR) sensors provide images with high spatial and temporal resolution and excellent coverage. On the other hand, SAR images suffer from speckle noise and need specific models and information extraction techniques. In this sense, the G0 family of distributions is a suitable model for SAR intensity data because it describes well areas with different degrees of texture. Information theory has gained a place in signal and image processing for parameter estimation and feature extraction. Entropy stands out as one of the most expressive features in this realm. We evaluate the performance of several parametric and non-parametric Shannon entropy estimators as input for supervised and unsupervised classification algorithms. We also propose a methodology for fine-tuning non-parametric entropy estimators. Finally, we apply these techniques to actual data.

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Section: Introductionmentioning

confidence: 99%

Entropy Estimators in SAR Image Classification

Cassetti

Delgadino

Rey

et al. 2022

Entropy

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“…The technique we investigate in this study utilizes an approach formulated with a significantly different underlying mathematical structure [9]. It is based on the use of various forms of entropy analysis for the detection of perfluorocarbon nanoparticles targeted to tumor neovasculature [10]–[13].…”

Section: Introductionmentioning

confidence: 99%

Application of a real-time, calculable limiting form of the Renyi entropy for molecular imaging of tumors

Marsh

Wallace

McCarthy

et al. 2010

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Previously, we reported new methods for ultrasound signal characterization using entropy, Hf; a generalized entropy, the Renyi entropy, If(r); and a limiting form of Renyi entropy suitable for real-time calculation, If,∞. All of these quantities demonstrated significantly more sensitivity to subtle changes in scattering architecture than energy-based methods in certain settings. In this study, the real-time calculable limit of the Renyi entropy, If,∞, is applied for the imaging of angiogenic murine neovasculature in a breast cancer xenograft using a targeted contrast agent. It is shown that this approach may be used to detect reliably the accumulation of targeted nanoparticles at five minutes post-injection in this in vivo model.

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Estimation of an Entropy-based Functional

Cited by 2 publications

References 22 publications

Entropy Estimators in SAR Image Classification

Entropy Estimators in SAR Image Classification

Application of a real-time, calculable limiting form of the Renyi entropy for molecular imaging of tumors

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