基于医学图像ROI形状估计的改进SPIHT算法

为提高医学图像的传输质量和编码效率，提出了一种新的位平面提升方法和基于感兴趣区域（ROI）形状估计的改进分层树集合分割排序（SPIHT）算法．位平面提升时，采用交错提升方法，在不需传输掩模的情况下实现感兴趣区域与背景的相对质量可调；对提升后的位平面，通过传输ROI外接规则形状的几何参数，根据估计的掩模信息超前判定零树，节省图像比特数．实验结果表明，在相同的截断码流下，相比SPIHT算法，改进的算法无论是ROI还是整幅图像都有更好的图像质量，且码率越低效果越明显．

Improved SPIHT Algorithm Based on ROI Shape Estimation of Medical Image

In order to improve the transmission quality and coding efficiency of medical images,a novel bitplane lifting algorithm and an improved SPIHT (Set Partitioning in Hierarchical Trees) algorithm based on ROI(Region of Interest) shape estimation is proposed.By implementing the interlaced lifting of bitplane,the relative quality of the ROI and the background becomes adjustable without transmitting any mask information.For the bitplane after the lifting,the estimated mask information can be obtained by transmitting the geometric parameter of the circum rule shape of ROI.According to the estimated mask information,zero trees are determined beforehand,so that the coding bits of the image can be saved.Experimental results show that,at the same truncation bit rate,both the ROI and the whole image obtained by the improved algorithm are of better image quality than those obtained by the traditional SPIHT algorithm,and that the lower the bit rate is,the more obvious the results are.