With the advancement in medical data acquisition and telemedicine systems, image compression has become an important tool for image handling, as the tremendous amount of data generated in medical field needs to be stored and transmitted effectively. Volumetric MRI and CT images comprise a set of image slices that are correlated to each other. The prediction of the pixels in a slice depends not only upon the spatial information of the slice, but also the inter‐slice information to achieve compression. This article proposes an inter‐slice correlation switched predictor (ICSP) with block adaptive arithmetic encoding (BAAE) technique for 3D medical image data. The proposed ICSP exploits both inter‐slice and intra‐slice redundancies from the volumetric images efficiently. Novelty of the proposed technique is in selecting the correlation coefficient threshold (Tϒ) for switching of ICSP. Resolution independent gradient edge detector (RIGED) at optimal prediction threshold value is proposed for intra‐slice prediction. Use of RIGED, which is modality and resolution independent, brings the novelty and improved performance for 3D prediction of volumetric images. BAAE is employed for encoding of prediction error image to resulting in higher compression efficiency. The proposed technique is also extended for higher bit depth volumetric medical images (16‐bit depth) presenting significant compression gain of 3D images. The performance of the proposed technique is compared with the state‐of‐the art techniques in terms of bits per pixel (BPP) for 8‐bit depth and was found to be 31.21%, 27.55%, 21.89%, and 2.39% better than the JPEG‐2000, CALIC, JPEG‐LS, M‐CALIC, and 3D‐CALIC respectively. The proposed technique is 11.86%, 8.56%, 7.97%, 6.80%, and 4.86% better than the M‐CALIC, 3D CALIC, JPEG‐2000, JPEG‐LS and CALIC respectively for 16‐bit depth image datasets. The average value of compression ratio for 8‐bit and 16‐bit image dataset is obtained as 3.70 and 3.11 respectively by the proposed technique.

中文翻译：

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使用切片间相关切换预测方法的体积医学图像压缩

随着医疗数据采集和远程医疗系统的进步，图像压缩已成为图像处理的重要工具，因为医疗领域产生的海量数据需要有效地存储和传输。体积 MRI 和 CT 图像包括一组彼此相关的图像切片。切片中像素的预测不仅依赖于切片的空间信息，还依赖于实现压缩的切片间信息。本文提出了一种用于 3D 医学图像数据的具有块自适应算术编码 (BAAE) 技术的切片间相关切换预测器 (ICSP)。所提出的 ICSP 有效地利用了体积图像中的切片间和切片内冗余。所提出技术的新颖之处在于选择相关系数阈值 (Tϒ) 以切换 ICSP。建议在最佳预测阈值下使用分辨率无关梯度边缘检测器 (RIGED) 进行切片内预测。使用与模态和分辨率无关的 RIGED，为体积图像的 3D 预测带来了新颖性和改进的性能。BAAE 被用于预测误差图像的编码以产生更高的压缩效率。所提出的技术还扩展到更高位深度的立体医学图像（16 位深度），呈现出 3D 图像的显着压缩增益。在 8 位深度的每像素位数 (BPP) 方面，将所提出的技术的性能与最先进的技术进行比较，发现分别为 31.21%、27.55%、21.89% 和 2。分别比 JPEG-2000、CALIC、JPEG-LS、M-CALIC 和 3D-CALIC 好 39%。对于 16 位深度图像数据集，所提出的技术分别比 M-CALIC、3D CALIC、JPEG-2000、JPEG-LS 和 CALIC 好 11.86%、8.56%、7.97%、6.80% 和 4.86%。8 位和 16 位图像数据集的压缩率平均值分别为 3.70 和 3.11。