Entropy fractal analysis of medical images using ROSETA

Holger M. Jaenisch; Marvin P. Carroll; Jim Scoggins; James W. Handley

doi:10.1117/12.176574

26 May 1994 Entropy fractal analysis of medical images using ROSETA

Holger M. Jaenisch, Marvin P. Carroll, Jim Scoggins, James W. Handley

Author Affiliations +

Proceedings Volume 2132, Clinical Applications of Modern Imaging Technology II; (1994) https://doi.org/10.1117/12.176574
Event: OE/LASE '94, 1994, Los Angeles, CA, United States

Abstract

The use of fractal statistics for characterizing and synthesizing medical imagery has in recent time been demonstrated as feasible. Traditionally, global fractal dimensions based on morphological coverings were used to quantify the texture of sampled data sets. This texture could be used to describe the second order statistics in 2D Magnetic Resonance Imaging, or microscopic images. With the realization of the benefits of fractal analysis has come a need for faster and more efficient computational algorithms. ROSETA (Range Over Standard Deviation, Experimental Trend Analysis) is an algorithm which yields substantial computational performance improvements by calculating entropy-based fractal statistics instead of morphological geometric statistics. ROSETA may be used as a robust general purpose analytical tool and several examples of its implementation are described. A simple variation of this algorithm is also presented which facilitates manual calculation using a calculator. This simple fixed point calculation may be used to analyze blood pressure, temperature, and heart rates as select discrete time samples with very few total points.

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Holger M. Jaenisch, Marvin P. Carroll, Jim Scoggins, and James W. Handley "Entropy fractal analysis of medical images using ROSETA", Proc. SPIE 2132, Clinical Applications of Modern Imaging Technology II, (26 May 1994); https://doi.org/10.1117/12.176574

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