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No Reference Medical Image Quality Measurement based on Spread Spectrum and Discrete Wavelet Transform using ROI Processing

机译:基于ROI处理的基于扩频和离散小波变换的无参考医学图像质量测量

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In this paper a new No-Reference (NR) objective quality measurement method based on spread spectrum technique and discrete wavelet transform using ROI processing is proposed. In this method we divide the original image into two separate sub-images called ROI and non-ROI. Region of interest (ROI) is the decision area in a medical image which is very important .This area may indicate a disease and must resulted in a right diagnosis. We use the spread spectrum embedding algorithm to embed a binary mark into DCT transform of non-ROI part of image. This method is useful when guaranteeing a certain level of quality is an important and vital concern. At the receiver side we extract ROI part with least degradation. Then the mark is extracted from non-ROI part and a measure of its degradation is used to estimate the quality of the original image. The performance of our proposed method is evaluated by calculating MSE and PSNR of extracted mark and measuring their correlation with degradation of the whole image. The applications of this work could be compression and storage of images with an acceptable quality level, or compression and transmission over a network for telemedicine applications while preserving an appropriate quality level.
机译:提出了一种基于扩频技术和利用ROI处理的离散小波变换的无参考(NR)客观质量测量方法。在这种方法中,我们将原始图像分为两个单独的子图像,称为ROI和non-ROI。感兴趣区域(ROI)是医学图像中非常重要的决策区域。该区域可能表示疾病,必须做出正确的诊断。我们使用扩展频谱嵌入算法将二进制标记嵌入到图像非ROI部分的DCT变换中。当保证一定水平的质量是重要且至关重要的问题时,此方法很有用。在接收器端,我们提取的ROI部分降级最小。然后,从非ROI部分提取标记,并使用其退化程度的度量来估计原始图像的质量。通过计算提取标记的MSE和PSNR并测量它们与整个图像退化之间的相关性,可以评估我们提出的方法的性能。这项工作的应用可能是压缩和存储具有可接受质量级别的图像,或者是通过网络进行压缩和传输以用于远程医疗应用程序,同时保持适当的质量级别。

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