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In order to protect the ROI (region of interest) characteristics while greatly improving medical imaging compression ratio, we are proposing an algorithm for medical imagining compression that is oriented to ROI-characteristics protection. Firstly, an improved ROI segmentation algorithm is put forward based on the analysis of the ROI segmentation. Then, after the ROI segmented, the ROI edge is extracted and encoded with Freeman chain coding. Finally, the ROI is compressed by lossless compression with shearlet; the ROB (region of background) is compressed by the method of high ratio lossy compression combining with Wavelet and Fractal. Simulation results show that the ROI is segmented precisely. It holds edge integrity and has high quality reconstruction processed by the presented method, helping protect ROI characteristics while greatly improving the compression ratio.

The new health care reform in China is causing an increase in the demand for PACS (picture archiving and communication systems). Medical equipment of digital imaging has been producing massive medical imaging in hospitals. This poses a serious challenge to the limited transmission bandwidth and storage capacity of PACS. So there is an urgent need for efficient medical imaging compression algorithm.

JEPG2000 is the basic method in current medical imaging compression for PACS. The most important thing it did was to support the ROI compression. For example, [

To solve the above problem, first of all, ROI should be accurately segmented. For this purpose, an improved ROI segmentation algorithm is proposed based on ITTI visual attention model and wavelet analysis. Secondly, to protect the edge of the ROI, freeman chain coding is used to encode ROI edge obtained at the last step of ROI segmentation. Thirdly, in order to improve the compression ratio while maintaining imaging reconstruction quality, ROI and ROB are compressed with different processes; ROI is compressed lossless with shearlet to maintain the integrity of the ROI; and ROB is compressed combining wavelet and fractal method to improve compression ratio. Finally, edge-coded information and ROI and ROB coded information are reconstructed to gain the whole recovery image.

There are many ways to segment ROI. The main ways include feature point segmentation, human interaction segmentation, and segmentation based on visual attention mechanism. Among them, feature point segmentation method is applied only to the imaging having a certain type of characteristic. Although human interaction segmentation method has a better result, it is strongly influenced by subjective factors. Besides, its efficiency is low, not good for PACS as a large library.

ITTI, proposed by Laurent ITTI on the basis of feature integration theory, is currently the most widely used visual attention model. Firstly, ITTI model structures multi-scale image pyramid by Gaussian filtering method. Then finding the significant point through a series of treatment containing the Center-Surround algorithm, normalization, iterative portfolio, RSM (returns suppression mechanism) and WTA (Winner take all) network. Finally it gets ROI by salient points grown [

But the multi-scale image pyramid structured by Gaussian filter method is not consistent with the image processing model constructed by the mechanism of human visual attention. Moreover, there is a huge amount of calculation to be done to structure the multi-scale image pyramid with Gaussian filtering method, which is time- consuming.

Wavelet transform is a tool for time-frequency analysis. Multy-resolution feature is its most impeccable property, and wavelet multi-resolution feature is consistent with the human visual system. Therefore it matches the human visual attention mechanism. Comparison of Wavelet and Gaussian image pyramid is shown in figure [

As can be seen from

From

80.8%. It has more desired result.

ROI edge feature has a very high value of health diagnostic, but the edge can be severely damaged in the case of a high compression ratio. The damage interferes with the physician’s follow-up mission diagnosis and treatment. In order to protect the ROI edge, the article suggests firstly obtaining ROI edge, then encoding it. This coded information is transmitted as a component of the compressed information. Finally the code stream has two parts including edge information and transform compressed information of ROI and ROB. Decoder integrated the two parts to get imaging with well protected edge.

Traditional way of obtaining edge often uses operator of edge detection, but the detector operators are too sensitive to noise (e.g. Prewitt operator and Sobel operator) although they have certain smoothing effect and they have removed part of the pseudo-edge to leave out the real edge. At the same time, the positioning accuracy is not high. Robert operator is not ideal for the imaging with familiar Gaussian noise. The processed edge will be disconnected. In this paper, we get ROI through significant point growing. So obtaining processing can be finished by just preserving the pixels that does not meet region growing criteria. It is convenient and advanced. In this paper, we discuss growing with the 8 neighborhoods, setting the threshold value T as 80, and preserving the pixels t in the storage E [

Freeman chain coding is a lossless compression algorithm of image edge. It could use only a small amount of data to store much information. But its criterion is difficult to meet because the criterion is sensitive to noise. However, because of the adequate application of wavelet and its filter characteristic, this paper can use Freeman chain coding to encode edge [

Shearlet was born as a new tool to overcome the limitations of wavelet. Although it has not distributed three high frequency detail sub-image and a low frequency profile sub-image, its image information can be concentrated on the sub-picture containing the larger coefficient. According to this good sparse features, ROI was compressed based on shearlet in this paper [

Step 1. Shearlet transform.

Four levels of shearlet are transformed on ROI. Each level generates 10 directional sub-image.

Step 2. Quantization.

Threshold of 10 direction transform coefficients, calculates the average value, using the coefficients sum of maximum average value to approximate source image, which is the initial step of compression and de-noising.

Step 3. Entropy coding.

Huffman entropy coding of the selected coefficient [

Aiming at these problems that the wavelet compression ratio is not high and the Fractal needs long encoding time. Wavelet and Fractal were combined to compress ROB with high compression ratio and within only a little time. So we just encode low frequency sub-image while eliminating the high-frequency sub-images directly with fractal because imaging energy is always concentrated on those areas after Wavelet transform. The following are the algorithm steps of the ROB specific compression:

Step 1. Four levels of wavelet are transformed on ROB to produce 12 high-frequency sub-image and a low- frequency sub-image. For the wavelet base in this step, a D9/7 wavelet is chosen on the basis of the confirmed experiment [

Step 2. High frequency sub-image information is filtered out. The low-frequency sub-image is reconstructed. Fractal encodes the reconstructed image [

The overall framework of the algorithm is shown in

In the formula,

In the formula,

where

K is the number of sub-image.

ROI and the whole image were carried out on the compression simulation. As for ROI, comparing classic wavelet with the shealet is used in this article. For the whole image, this paper compares classical JEPG2000 algorithm of PACS with proposed algorithm [

It can be seen in

Ratio | PSNR/db | MSSIN | ||
---|---|---|---|---|

Wavelet shearlet | Wavelet shearlet | |||

20 | 34 | 87 | 0.99 | 1.00 |

30 | 31 | 83 | 0.98 | 1.00 |

40 | 30 | 81 | 0.95 | 1.00 |

50 | 30 | 80 | 0.95 | 0.99 |

60 | 27 | 76 | 0.91 | 0.99 |

Ratio | PSNR/db | MSSIN | |||
---|---|---|---|---|---|

JEPG2000 improved | JEPG2000 improved | ||||

20 | 33 | 41 | 0.991 | 0.998 | |

30 | 32 | 38 | 0.978 | 0.984 | |

40 | 31 | 34 | 0.973 | 0.982 | |

50 | 31 | 34 | 0.968 | 0.979 | |

60 | 30 | 31 | 0.949 | 0.973 | |

Ratio | JEPG2000 | Improved |
---|---|---|

20 | 10.29 | 11.24 |

30 | 10.79 | 11.57 |

40 | 6.25 | 7.09 |

50 | 6.23 | 7.18 |

60 | 4.14 | 5.17 |

the reconstruct quality is high. When the whole image compression under the same compression ratio, in the proposed algorithm, the values of PSNR and MSSIM are higher than those of JEPG2000 classic algorithm.

Because the

This paper introduced wavelet to ITTI model, whose segmentation accuracy reached to 80.8%. We used the Freeman chain coding to encode the edge of ROI. This method significantly protected the edge, because of the good spare characteristic of shearlet. We also used shearlet to compress ROI. The PSNR value of shearlet was higher than average wavelet’s 51 dB, and the MSSIM value of shearlet was as high as 1.0. In general, the characteristics of ROI were completely protected. Because of the application of the wavelet and its filtering properties, the algorithm in this paper has the noise robustness suitable to mass storage of medical image compression for PACS.

RenjunShuai,YangShen,JingPan, (2015) An Algorithm for Medical Imagining Compression That Is Oriented to ROI-Characteristics Protection. Journal of Applied Mathematics and Physics,03,854-861. doi: 10.4236/jamp.2015.37106