Research Article
A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks
Table 2
Summary of the performance measures for the proposed compression method at
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| Category | This study () | Ayoobkhan et al. [7] | ROI | NROI | Entire image | PSNR (dB) | PSNR (dB) | CR | PSNR (dB) | CR | PSNR (dB) |
| Neck | 50.84 | 47.41 | 10.57 | 48.32 | 6.28 | 45.54 | Chest | 50.81 | 47.1 | 8.25 | 47.91 | 6.86 | 46.42 | Front skull view | 50.24 | 47.77 | 9.98 | 49.45 | 8.02 | 41.64 | Right skull view | 48.44 | 47.07 | 8.4 | 48.16 | 8.11 | 40.29 | Left skull view | 48.46 | 46.28 | 9.64 | 46.64 | 8.19 | 41.43 | Pelvic girdle | 51.2 | 48.4 | 9.96 | 50.65 | 8.55 | 42.81 | Radius and ulna bones | 49.57 | 46.54 | 9.59 | 47.46 | 8.62 | 42.04 | Pelvic girdle and back bone | 47.15 | 45.52 | 8.94 | 46.85 | 9.07 | 39.5 | Hand x-ray | 47.48 | 45.57 | 10.31 | 45.99 | 9.18 | 39.25 | Leg | 48.77 | 45.81 | 9.92 | 47.2 | 9.35 | 38.4 | Left mammogram | 48.19 | 46.7 | 9.84 | 47.36 | 14 | 36.16 | Right mammogram | 47.7 | 46.73 | 10.77 | 47.53 | 14.1 | 35.64 | Average | 49.07 | 46.74 | 9.68 | 47.79 | 9.19 | 40.76 |
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