Abstract
Abstract A fractal analysis procedure for the classification of ultrasonic images of liver tissue is proposed. Based on the two properties of fractals (self‐similarity and self‐affinity), it is possible to recognize not only normal and abnormal liver tissues but also different kinds of abnormalities. Two‐stage procedures are performed in the classification. In the first stage, the mass density feature vectors are estimated by the property of self‐similarity, classification of normal and abnormal ultrasonic liver tissues can be obtained from the differences between their mass density feature vectors. In the second stage, based on the property of self‐affinity, the fractal Brownian motion model is adopted to represent the imaging surface. By evaluating the fractal Brownian feature vectors in about 27 abnormal ultrasonic liver images, it is found that it is possible to classify different kinds of diseases using the model. Moreover, the measurement of lacunarities can be added to improve the method to classify tissues more accurately.
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