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Evaluation of Gabor elementary function-based medical image compression.

机译:基于Gabor基本功能的医学图像压缩评估。

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Due to the high volume of medical imaging studies performed nationwide, and the desire to get away from the maintenance of long-term film archives, storage of digitized medical images is being promoted as an alternative. Computer-based storage would be more practicable if compressed rather than original images could be stored. In this study, compression methods based on Gabor functions were implemented for simulated nuclear medicine liver images, and their performance was assessed objectively. A large number of nuclear medicine liver images with and without space-occupying lesions were simulated. Then various compression schemes based on transformation of the images into the "information space" proposed by Gabor were implemented. Two tasks were examined: (1) determination of the presence or absence of the lesion in a given location, and (2) determination of the presence or absence of the lesion in one of several locations. Gabor-based compression has not previously been implemented on medical images, nor has any rigorous task-based measure of quality been used to assess the compression.; Task-based performance using the compressed/reconstructed images was compared to that using the original images according to the following measures: (1) mean square error, (2) Hotelling trace criterion, an index shown by Fiete and others to correlate with performance for nuclear medicine images, (3) Bayesian maximum likelihood ideal observer signal to noise ratio, and (4) area under the receiver operating characteristic curve. For compression based on thresholding of the complex Gabor coefficients, a better than 2:1 compression of the simulated nuclear medicine liver images was obtained without appreciable reduction in image quality, which when combined with gains expected from bit-reduction schemes, corresponds to an overall approximate 8:1 compression.
机译:由于在全国范围内进行了大量的医学成像研究,并且希望摆脱长期胶片档案的保存,因此,人们正在寻求替代方法来存储数字化医学图像。如果可以存储压缩图像而不是原始图像,则基于计算机的存储将更加实用。在这项研究中,基于Gabor函数的压缩方法被用于模拟核医学肝图像,并对其性能进行了客观评估。模拟了大量有和没有占位性病变的核医学肝脏图像。然后,基于Gabor提出的基于将图像转换为“信息空间”的各种压缩方案被实现。检查了两个任务:(1)在给定位置确定病变的存在或不存在,以及(2)在多个位置之一确定病变的存在或不存在。基于Gabor的压缩以前尚未在医学图像上实现,也没有使用任何基于任务的严格质量度量来评估压缩。根据以下度量,将使用压缩/重构图像的任务性能与使用原始图像的任务性能进行了比较:(1)均方差;(2)Hotelling跟踪标准,Fiete显示的索引以及与该性能相关的其他指标核医学图像;(3)贝叶斯最大似然理想观察者信噪比;(4)接收器工作特性曲线下的面积。对于基于复杂Gabor系数阈值的压缩,在不明显降低图像质量的情况下,获得了模拟核医学肝脏图像的2:1压缩更好,当与比特缩减方案预期的增益相结合时,则相当于总体大约8:1压缩。

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