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Analysis of Medical Images Using Fractal Geometry
Abstract
Fractal dimension is an emerging research area in order to characterize the complex or irritated objects found in nature. These complex objects are failed to analyze by classical Euclidian geometry. The concept of FD has extensively applied in many areas of application in image processing. The thought of the FD will work based upon the theory of self-similarity because it holds structures that are nested with one another. Over the last years, fractal geometry was applied extensively in medical image analysis in order to detect cancer cells in human body because our vascular system, nervous system, bones, and breast tissue are so complex and irregular in pattern, and also successfully applied in ECG signal, brain imaging for tumor detection, trabeculation analysis, etc. In order to analyze these complex structures, most of the researchers are adopting the concept of fractal geometry by means of box counting technique. This chapter presents an overview of box counting and its improved algorithms and how they work and their application in the field of medical image processing.
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