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Diamond: multi-dimensional indexing technique for medical images retrieval using vertical fragmentation approach

机译:钻石:使用垂直碎片方法检索的医学图像多维索引技术

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Over the last decade, a huge number of medical visual data are widely used for diagnose, treatment, and follow-up. Retrieving needed medical image(s) from a huge number of images is one of the most widely used features in medical information systems, especially in medical image search engines. Indexing as part of search engines (or information retrieval systems), increases the speed (efficiency) of search and the information retrieval process. In this paper, a multidimensional indexing technique for medical images is presented that can improve effectiveness and efficiency of medical image search engines. The structure of the proposed multi-dimensional indexing technique and its main operations (i.e., creation, insertion, deletion and search) is designed and evaluated. In order to create this multidimensional index, the "vertical fragmentation" approach (which is usually applied for distributed database design) is used to determine the each of dimensions; roughly speaking, dimensions are different aspects of the medical images for a/some information need (e.g., image type and format, type of disorder, etc.). Accordingly, data structure of the proposed multidimensional indexing technique (which is named "Diamond") is formed by using the vertical fragmentation of medical image attributes (to differentiate the dimensions), and then using agglomerative hierarchical clustering to build up the hierarchy in each dimension. The proposed multi-dimensional indexing technique is implemented using the open-source search engine Lucene and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier Platform (available for the benchmarking of information retrieval systems). In this evaluation, efficiency and effectiveness measures of the proposed multidimensional indexing technique (Diamond) are evaluated experimentally, beside the analysis of the designed data structure and its operations. For the experimental evaluation data set, images from Tabriz Behbood Hospital and a subset of TCIA images were used. Experimental evaluation results show that Diamond, the proposed multidimensional indexing technique improves both efficiency and effectiveness for a medical image search engine.
机译:在过去的十年中,一个庞大的医疗可视化数据的数量被广泛用于诊断,治疗和随访。从图像的庞大数量的检索所需的医用图像(一个或多个)是最广泛使用的功能在医疗信息系统中的一个,特别是在医学图像搜索引擎。索引搜索引擎(或信息检索系统)的一部分,增加搜索和信息检索过程的速度(效率)。在本文中,对医用图像的多维索引技术,提出能够提高效力和医用图像搜索引擎的效率。所提出的多维索引技术和它的主要操作(即,创建,插入,删除和搜索)的结构被设计和评估。为了创建该多维索引中,“纵向分割”的方法(其通常适用于分布式数据库设计)被用于确定每个维度;粗略地说,尺寸是一个医学图像的不同方面/一些信息的需求(例如,图像类型和格式,病症的类型,等等)。因此,所提出的多维索引技术的数据结构(被命名为“钻石”)是通过使用医用图像属性的垂直片段化(区分的尺寸),并且然后使用凝聚层次聚类建立的层级中的每个尺寸形成。所提出的多维索引技术是使用开源搜索引擎Lucene的实施,并与内置在Lucene搜索引擎提供的索引技术,同时还与梗平台(可用于信息检索系统的基准)。在该评价中,所提出的多维索引技术(钻石)的效率和效力措施实验评价,所设计的数据结构和它的操作的分析旁边。为实验评价数据集,使用来自大不里士Behbood医院和TCIA图像的子集的图像。实验评价结果表明,钻石,所提出的多维索引技术提高效率和效益的医疗图像搜索引擎。

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