Conference Paper

ICube: A similarity-based data cube for medical images

DOI: 10.1109/CBMS.2010.6042663 Conference: IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS 2010), Perth, Australia, October 12-15, 2010
Source: DBLP


Issuing analytical (OLAP) similarity queries based on images over data warehouses is a core problem in the medical field, as these queries can allow for the investigation and analysis of health data in decisionmaking processes. In this paper, we propose iCube, a similarity-based data cube for medical images. iCube is an extended data warehouse that encompasses a dimension table specifically designed to store intrinsic features of images, therefore allowing OLAP similarity queries over images. We also show how to build iCube and how to perform OLAP query processing over images. Comparisons of iCube with the current data warehousing technology aided by a metric access method showed that iCube provided an impre ssive performance improvement to process OLAP similarity queries over images. iCube performance gain ranged from 43% up to 76.7%.

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    • "The authors proposed a clustering structure measure to explore images, dynamic aggregation selection to improve computations and a new type of OLAP operation to support overlapping. iCube [3] is a similarity-based data cube for medical images. It added a special dimension to store content-based features providing capabilities for OLAP similarity queries over images. "
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