這篇論文最主要的目的是要找出一個可以節省時間且增進壓縮率的醫學影像壓縮方法。離散小波轉換是一種常見的轉換方法，但是它所花費的時間複雜度很高。為了改善時間複雜度，我們使用低複雜度的離散餘弦轉換取代小波轉換，並且重新排列離散餘弦轉換後的係數來達到空間導向的樹狀結構。由於和小波轉換一樣擁有空間的對應關係，我們可以把它應用在以小波為基礎的壓縮演算法上，如SPIHT。我們使用不同大小的離散餘弦轉換區塊來找出最好的結果。實驗結果顯示，在相同的壓縮率下，本篇論文提出來的演算法在較大的區塊上有比較好的重建影像視覺效果。在時間方面，不管是何種醫學影像，離散餘弦轉換都比離散小波轉換來的快速。

Medical images are a special classification in their dark background, directional and smooth properties. For efficiency of compressing these images, we propose a compression algorithm based on discrete cosine transform (DCT) with SPIHT. The discrete wavelet transform (DWT) is a useful transform method, but the cost of computational complexity is very high. In order to decrease the computational complexity, we use the DCT instead of DWT. We reorder the DCT coefficients to follow the feature of spatial orientation tree. Therefore, we can use the DWT-based compression method such as SPIHT (Set partitioning in hierarchical trees) to obtain the same compression performance. Simulation results reveal that the proposed method employing a large-size of DCT blocks improves the quality of the reconstructed medical images in terms of PSNR over the conventional SPIHT at the same bit-rate. In the computational time, the DCT is faster than DWT in different types of medical images.

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