MULTIRESOLUTIONAL TEXTURE ANALYSIS FOR ULTRASOUND TISSUE CHARACTERIZATION

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Abstract
Multiresolutional texture analysis techniques using wavelet transforms have been used for predicting percentage intramuscular fat (IMFAT) of beef ribeye muscle. Ultrasound B-mode images have been collected from 207 live beef animals and digitized through frame grabber connected to a personal computer. Since fat has acoustic properties that are appreciably different from that of muscle, transmitted ultrasound is reflected from the interface between fat and muscle. As fat deposits increase, the speckle content of ultrasound B-mode images also increases. Since the speckle alters the texture of the image, IMFAT can be estimated using texture analysis methods. The Haar wavelet was used as a basis function for generating the fast wavelet transform and several features were calculated from the 2-D wavelet decomposed ultrasound images. Significant features included energy ratios, second and fourth order central moments. Correlation coefficients of the features with the actual IMFAT were calculated to select features for predicting IMFAT. Linear regression (LR) models were used as a tool for predicting IMFAT from the selected features.

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