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Fractal analysis of high-resolution CT images as a tool for quantification of lung diseases
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1995
Year
Computed TomographyEngineeringShape AnalysisDiagnostic ImagingImage AnalysisPattern RecognitionLung DiseasesCt ScanBiostatisticsCt Lung ImagesRadiologyGeometric ModelingMachine VisionMedical ImagingGrey Scale ImageMedical Image ComputingHigh-resolution CtFractal GeometryComputer VisionNatural SciencesBiomedical ImagingComputer-aided DiagnosisMedical Image AnalysisFractal Analysis
Fractal geometry is increasingly being used to model complex naturally occurring phenomena. There are two types of fractals in nature-geometric fractals and stochastic fractals. The pulmonary branching structure is a geometric fractal and the intensity of its grey scale image is a stochastic fractal. In this paper, we attempt to quantify the texture of CT lung images using properties of both types of fractals. A simple algorithm for detection of abnormality in human lungs, based on 2D and 3D fractal dimensions, is presented. This method involves calculating the local fractal dimensions, based on intensities, in the 2D slice to aid enhancement. Following this, grey level thresholding is performed and a global fractal dimension, based on structure, for the entire data is estimated in 2D and 3D. High resolution CT images of normal and abnormal lungs were analyzed. Preliminary results showed that classification of normal and abnormal images could be obtained based on the differences between their global fractal dimensions.