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Abstract

An ultrasonic imaging and signal analysis system, combining a 3.5 MHz linear array B-scanner and an array processor, has been realized for a clinical liver tissue classification study. Based on the complete unprocessed rf data set of a real time B-image, local envelope power spectra averaged over interactively defined regions are computed. These spectra are partially corrected for several tissue type independent effects that affect comparability. A library containing the in vivo data of 65 patients with known liver tissue states in three groups (normal, cirrhotic, fat) was built up. An algorithm based on numerical optimization of parametrized spectrum characteristics combined with a k-nearest-neighbors classifier, applied to the library, leads to correct classification rates of 85.8 to 87.5 percent in discriminating pathological from normal tissue states. The method can be used for k-weighted color coded tissue type imaging with acceptable spatial resolution.

Keywords

Diffuse liver disease kNN-classifier numerical optimization pattern recognition power spectrum analysis tissue classification tissue type imaging ultrasound

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In Vivo Study of Online Liver Tissue Classification Based on Envelope Power Spectrum Analysis

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