Adaptive quantization via fuzzy classified priority mapping for liver ultrasound compression

Abstract

This paper proposes adaptive quantization based on fuzzy classified priority mapping in order to achieve higher encoding efficiency. The priority map serves as a quantization mask, which is adaptively adjusted according to the statistical characteristics in terms of histograms based on the results of Fuzzy C-mean clustering. With its soft clustering property, the results illustrate robustness to ambiguity of the data and thus retain much more information than hard clustering. The priority map represents levels of significance as the Most Significant Group (MSG), the Normal Significant Group (NSG), and the Lowest Significant Group (LSG). The significant candidates of irregular liver tissues requiring special doctor attention will be assigned with higher priority than those from the regular ones. The higher the priority, the greater the number of bits assigned for encoding. An analysis of suitable quantization step size has been conducted. With the selection of appropriate quantization parameters for each priority level, the blocking artifacts can be greatly reduced. This helps to reduce the encoding bit rate and enhance the compression efficiency for the transmission and storage while maintaining an acceptable diagnostic image quality.