| Paper: | MLSP-P2.10 | ||
| Session: | Bioinformatics and Biomedical Applications | ||
| Time: | Wednesday, May 19, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Image and Video Processing Applications | ||
| Title: | MEDICAL IMAGE COMPRESSION USING POST-SEGMENTATION APPROACH | ||
| Authors: | Sung Yoon; North Carolina A&T State University | ||
| Ji Lee; North Carolina A&T State University | |||
| Jung Kim; North Carolina A&T State University | |||
| Winser Alexander; North Carolina State University | |||
| Abstract: | This paper presents a medical image coding technique that is suitable for interactive telemedicine over networks. The new encoding scheme allows a server to progressively transmit only a part of a compressed image over a network as requested by a client. This technique is different from the region scalable coding scheme in JPEG 2000 since it does not require that a region of interest (ROI) be defined when encoding occurs. In our proposed method, a medical image is encoded at full resolution and stored in the server. A user can receive a basic image at low resolution and then specify a ROI. The server can then provide full resolution for the ROI. Our technique allows a user to select the ROI after the compression has been done. We employ integer wavelet lifting to support lossless coding for medical images that strictly require lossless compression. This paper shows the benefits of the proposed technique with examples and simulation results. | ||
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