1 May 1992 Effect of vector quantization on ultrasound tissue characterization
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Abstract
This paper presents a case study of the effects of compression error on computerized tissue characterization of normal and fatty ultrasound liver images. Two compression techniques were studied, pruned-tree structured vectored quantization (PTSVQ) and PTSVQ with splitting. Vector quantization is a technique for representing a block of image values, or vector, by the vector in a codebook that is closest to the original vector. Splitting is a technique for decomposing image pixel values into the high and low values. The high values are compressed reversibly while the low values are compressed via PTSVQ. Tissue characterization was accomplished by extracting features from a region of interest (ROI). These features included measuring fractal dimension and statistics concerning run length and co-occurrence probabilities of pixels separated by a given direction and distance. The results were: (1) PTSVQ with splitting produced less image distortion at moderate bit rates than PTSVQ as measured by mean square error; (2) PTSVQ with splitting produced more degradation of the tissue characterizer; and (3) Rotation of the ROIs greatly reduced the degradation of the tissue characterizer for both types of compression. This type of rotation uses interpolation to derive pixel values for rotated lattice points that fall between original lattice points. A possible explanation for these results is that PTSVQ caused irregular distortions at edges depending upon the amount of region information included in the design of the codebook. The interpolation during rotation reduces these irregularities.
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Brian Krasner, Brian Krasner, Shih-Chung Benedict Lo, Shih-Chung Benedict Lo, Brian S. Garra, Brian S. Garra, Seong Ki Mun, Seong Ki Mun, } "Effect of vector quantization on ultrasound tissue characterization", Proc. SPIE 1653, Medical Imaging VI: Image Capture, Formatting, and Display, (1 May 1992); doi: 10.1117/12.59499; https://doi.org/10.1117/12.59499
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