Content-Based Image Retrieval Systems -Using 3D Shape Retrieval Methods with Medical Application
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Abstract
The lack of evaluations of the retrieval quality of systems becomes apparent along with the unavailability of large image databases free of charge with defined query topics and gold standards. However, some databases are available, from the NIH (National Institutes of Health), for example. Ideas for creating such image databases and evaluation methods are proposed. The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation. We also discuss some of the key challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. Recent developments in techniques for modeling, digitizing and visualizing 3D shapes has led to an explosion in the number of available 3D models on the Internet and in domain-specific databases. This has led to the development of 3D shape retrieval systems that, given a query object, retrieve similar 3D objects. For visualization, 3D shapes are often represented as a surface, in particular polygonal meshes, for example in VRML format. Often these models contain holes, intersecting polygons, are not manifold, and do not enclose a volume unambiguously. On the contrary, 3D volume models, such as solid models produced by CAD systems, or voxels models, enclose a volume properly. This paper surveys the literature on methods for content based 3D retrieval, taking into account the applicability to surface models as well as to volume models. The methods are evaluated with respect to several requirements of content based 3D shape retrieval, such as: (1) shape re-presentation requirements, (2) properties of dissimilarity measures, (3) efficiency, (4) discrimination abilities, (5) robustness.
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Key words: 3D shape & Medical image retrieval, visual information retrieval, PCA, DICOM, ADL’s
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