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Two fast algorithms for interpolation of ultrasonic sector-scans were developed. Both algorithms are based on line-drawing algorithms and are free from multiplications in the innermost loops. The algorithms were compared to the following conventional interpolators: 2-D windowed sinc, bicubic spline, 4 × 4 point bicubic spline, bilinear, and nearest neighbor. The most accurate of the two new algorithms is about eight times faster than nearest neighbor interpolation. The quantitative errors are of the same order as the errors of the nearest neighbor interpolator. The subjective image quality is between nearest neighbor and bilinear interpolation.

Computer texture analysis methods use texture features that are traditionally chosen from a large set of fixed features known in literature. These fixed features are often not specifically designed to the problem at hand, and as a result they may have low discriminative power, and/or may be correlated. Increasing the number of selected fixed features is statistically not a good solution in limited data environments such as medical imaging. For that reason, we developed an adaptive texture feature extraction method (ATFE) that extracts a small number of features that are tuned to the problem at hand. By using a feed-forward neural network, we ensure that even nonlinear relations are captured from the data. Using extensive, repeated synthetic ultrasonic images, we compared the performance of ATFE with the optimal feature set. We show that the ATFE method is capable of robust operation on small data sets with a performance close to that of the optimal feature set. Another experiment confirms that our ATFE is capable of capturing nonlinear relations from the dataset. We conclude that our method can improve performance in practical, limited dataset situations where an optimal fixed feature set can be hard to find.

Two fast algorithms for interpolation of ultrasonic sector-scans were developed. Both algorithms are based on line-drawing algorithms and are free from multiplications in the innermost loops. The algorithms were compared to the following conventional interpolators: 2-D windowed sinc, bicubic spline, 4 × 4 point bicubic spline, bilinear, and nearest neighbor. The most accurate of the two new algorithms is about eight times faster than nearest neighbor interpolation. The quantitative errors are of the same order as the errors of the nearest neighbor interpolator. The subjective image quality is between nearest neighbor and bilinear interpolation.

The performance of five features of ultrasonic tissue characterization (UTC) of metastases in vivoin liver was investigated. We acquired serial radiofrequency data sets of 12 patients with metastases in the liver from adenocarcinoma of the colon. Parenchyma and metastases UTC features were estimated in semiautomatically segmented regions. Over 200 metastases were measured in patients and 43 dummy metastases in healthy volunteers. Two attenuation features could be estimated in only 15% of the metastases, and these were not different from those in parenchyma. The texture features signal-to-noise ratio (SNR) could not discriminate real from dummy metastases. Average backscatter intensity, b0, is an established discriminative echographic image feature. However, the metastases that were hypoechoic relative to surrounding parenchyma appeared to be isoechoic relative to normal liver parenchyma. They were visible because of an increased b0 in the surrounding liver parenchyma. Finally, we found an increased backscatter coefficient slope vs. frequency in hypoechoic metastases that may predict a deterioration of lesion contrast at higher transducer frequencies. We conclude that the backscatter coefficient slope can improve detection of metastases, and that b0 measured relative to normal liver parenchyma should be used to correctly correlate metastasis echography with histology.