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Mouse models have been widely used in cardiovascular research when investigating the progression or treatment of various diseases. It is always challenging to find non-invasive tools for early detection of diseases. This led us to the development of small animal models for diagnostic imaging techniques, such as high-frequency quantitative ultrasound imaging. This work describes the development of an ultrasound tissue classification technique that could be used to quantitatively assess atherosclerotic plaques in mouse heart. Signal and image processing of radiofrequency ultrasound data have been performed in time, frequency, and wavelet domains to quantitatively assess the severity of atherosclerosis in an APOE-KO mouse model fed with high fat diet. Multiple in vitro experiments were conducted, and ultrasound images of high contrast-resolution were obtained using parameters from various domains. For example, images reconstructed using the time variance (Tσ2), and frequency skewness (PDskew) parameters showed high-contrast resolutions of approximately 17.95±2.98 dB, and 9.67±0.24 dB (n=4) between normal tissue and atherosclerotic lesions. The technique is integrated with a custom-designed high-frequency ultrasound imaging system and is able to provide parametric images of high contrast and special resolution down to 24 microns.