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Efficient Lossless Compression of 4D Medical Images with Advanced Video Coding

This research paper presents an effective lossless compression method for 4D medical images using the advanced video coding scheme. The method utilizes spatial and temporal redundancies to reduce the size of the images. Experimental results demonstrate significant improvements in compression ratios compared to existing techniques.

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Efficient Lossless Compression of 4D Medical Images with Advanced Video Coding

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  1. Efficient Lossless Compression of 4D Medical Images Based on the Advanced Video Coding SchemeVictor Sanchez, Panos Nasiopoulos,Rafeef Abugharbieh IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2008 指導老師:萬書言 副教授 姓名:廖志浩

  2. Outline Introduction Method Performance and results Conclusion

  3. Introduction(1/2) 過去相關的技術都是針對2D或3D影像,針對4D影像相對來講是一個新的技術 近來也有提出幾個方法利用4D影像的redundancies 來壓縮 4-D wavelet transforms 3-D motion compensation algorithms

  4. Introduction(2/2) 提出一個有效的無失真壓縮技術,以高階的影像編碼技術(H.264/AVC)來處裡4D的影像。 此方法以重複使用multiframemotion compensation來有效的縮小4D影像的redundancies。

  5. Method 4-D denote as I(x, y, z, t) x, y denote the slices(2-D) z denote the volume(3-D) t denote the time

  6. Method • Steps: • Spatial redundancies • Temporal redundancies • The data generated in 1) encoded use motion vector coding algorithm and compress by CABAC • The data generated in 2) and 3) comprise the final compressed bit stream

  7. Method (Spatial redundancies) • First slice of each volume is encoded as an I-frame while the remaining s − 1 slices are encoded as P-frames • Slices are encoded using non-overlapping macroblocks of 16 × 16 pixels

  8. Method (Temporal redundancies) • The residual slices generated in step 1) are first arranged into s sets, first residual slice set is encoded as an I-frame while the remaining n − 1 are encoded as P-frames.

  9. Differential Coding of Motion Vectors(1/3) • The algorithm calculates the difference between two consecutive sets of motion vectors and the difference is then entropy encoded using CABAC.

  10. Differential Coding of Motion Vectors(2/3)

  11. Differential Coding of Motion Vectors(3/3)

  12. Method • Comprise the final compressed bit stream

  13. Performance and results Test the proposed compression method on 20 fMRI sequences, 5 4-D-MRI sequences, and 5 PET sequences

  14. Performance and results • Computed the mean square error (MSE) • U*V = monochrome image • O( i, j ) original sequence • R( i, j) decode sequence

  15. Performance and results • The best improvement in compression ratio was achieved on the fMRI sequences. • fMRI typically feature high correlation amongslices along the t-dimension making them suitable for our proposed compression method.

  16. Performance and results • 4-D-MRI sequences is lower number of volumes (i.e., have lower temporal resolution), which results in a lower correlation between slices in the temporal dimension. • the compression ratios still up to 50% better than those of 3D-JPEG2000.

  17. Performance and results • The lowest improvement was the PET sequences • PET data have little well-defined structures and low spatial resolution that makes it more difficult to estimate motion • still have about 7% over 3D-JPEG2000 on these sequences.

  18. conclusion • This method finds the optimal slices, and thus, reduces the energy contained in each volume. • Our quantitative experimental results show significant improvements in compression ratio of up to three times that of current 2-D and 3-D state-of-the-art compression techniques.

  19. Thanks For Your Attention

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