In the last two decades, incredible progress in various medical imaging modalities and sensing techniques have been made, leading to the proliferation of three-dimensional (3D) imagery. Byproduct of such great progress is the production of huge volume of medical images and this big data place a burden on automatic image processing methods for diagnostic assistance processes. Moreover, large amount of medical imaging data needs to be transmitted with no loss of information for the purpose of telemedicine, remote diagnosis etc. In this work, we consider a hybrid lossless compression technique with object-based features for three-dimensional (3D) medical images. Our approach utilizes two phases as follows: first we determine the volume of interest (VOI) for a given 3D medical imagery using selective bounding volume (SBV) method, and second the obtained VOI is encoded using a hybrid lossless algorithm using Lembel-Ziv-Welch Coding (LZW) followed by arithmetic coding (L to A). Experimental results show that our proposed 3D medical image compression method is comparable with other existing standard lossless encoding methods such as Huffman Coding, Run Length Coding, LZW, and Arithmetic Coding and obtains superior results overall.

中文翻译：

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一种用于三维医学图像的混合无损压缩技术

在过去的二十年中，各种医学成像方式和传感技术取得了令人难以置信的进步，导致了 3D (3D) 图像的激增。如此巨大进步的副产品是产生大量医学图像，而这种大数据给诊断辅助过程的自动图像处理方法带来了负担。此外，为了远程医疗、远程诊断等目的，需要在不丢失信息的情况下传输大量医学成像数据。在这项工作中，我们考虑了一种具有基于对象的三维（3D）特征的混合无损压缩技术医学图像。我们的方法利用如下两个阶段：首先，我们使用选择性边界体积 (SBV) 方法确定给定 3D 医学图像的感兴趣体积 (VOI)，其次，使用Lembel-Ziv-Welch编码（LZW）和算术编码（L到A）使用混合无损算法对获得的VOI进行编码。实验结果表明，我们提出的 3D 医学图像压缩方法与其他现有的标准无损编码方法如 Huffman Coding、Run Length Coding、LZW 和 Arithmetic Coding 相当，并且总体上获得了优异的结果。