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We filtered the US images for various texture responses and use the pixel intensity distribution within each ROI as feature parameterizations. Our craniosynostosis dataset consisted of 22 CT-confirmed cases and 22 age-matched controls. One physician manually measured the vectors from the center of the skull to the outer cortex at every 10\u00a0deg for each image and we used the principal directions as shape features for parameterization. These parameters and the known diagnosis were used to train classifiers. Testing with cross-validation, we obtained 72.74% accuracy and 0.71 area under receiver operating characteristics curve for steatosis (p&lt;0.0001), 77.27% and 0.77 for adenomyosis (p&lt;0.0001), and 88.63% and 0.89 for craniosynostosis (p=0.0006). Our framework is able to detect a variety of diseases with high accuracy. 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