Automatic Quality Control for Wavelet-Based Compression of Volumetric Medical Images Using Distortion-Constrained Adaptive Vector Quantization

Abstract

The enormous data of volumetric medical images (VMI) bring a transmission and storage problem that can be solved by using a compression technique. For the lossy compression of a very long VMI sequence, automatically maintaining the diagnosis features in reconstructed images is essential. The proposed wavelet-based adaptive vector quantizer incorporates a distortion-constrained codebook replenishment (DCCR) mechanism to meet a user-defined quality demand in peak signal-to-noise ratio. Combining a codebook updating strategy and the well-known set partitioning in hierarchical trees (SPIHT) technique, the DCCR mechanism provides an excellent coding gain. Experimental results show that the proposed approach is superior to the pure SPIHT and the JPEG2000 algorithms in terms of coding performance. We also propose an iterative fast searching algorithm to find the desired signal quality along an energy-quality curve instead of a traditional rate-distortion curve. The algorithm performs the quality control quickly, smoothly, and reliably.