Hybrid Compression Model for Sets of Similar Images and Its Application to Progressive Transmission
| Autor: | Rui-Feng Wu, 吳瑞峰 |
|---|---|
| Rok vydání: | 2000 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 88 Medical images are usually stored and retrieved to insure accurate diagnosis. With the widespread acceptance of teleradiology systems and picture archival and communication systems (PACS) in the near feature, the vast amount of data in this field will cause a problem. Data compression schemes for medical images can reduce the volume of data handled and play an important role in the coming use of teleradiology systems and PACS. Therefore, in this thesis, a new hybrid compression model for compressing similar medical images is proposed to reduce the set redundancy and employ the progressive transmission technique to operate on the communication network that has finite bandwidth. The hybrid compression model exploits region growing to segment the median image that created by a set of similar images; and further, it uses centroid method to predict the values of original image data. The difference between the predict values and original data is stored for the stage of progressive transmission. To evaluate the performance of proposed method, several measurements for the compression ratio are also developed. The success of the method is helpful to reduce the storage required for similar medical images and provide low bit rate image data for progressive transmission on the communication network. The experimental results show that the performance using this method can be improved excess 5.6％, sometimes reach to 134.9％, than the centroid method. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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