QBISM: extending a DBMS to support 3D medical images

Arya, Manish; Cody, William F.; Faloutsos, Christos; Richardson, Joel E.; Toga, Arthur W.

doi:10.1109/icde.1994.283046

Cited by 22 publications

(16 citation statements)

References 27 publications

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“…Since the value of x 1 is arbitrary, independent of the specific value of s 1 we have that x 1 + s 1 is divisible by two with probability 1/2, by four with probability 1/4, and so on. Therefore, the number of additional blocks required is C 1 with probability 1/2, C 2 with probability 1/2 2 , and so C j with probability 1/2 j , until C m with probability 1/2 m and C m+1 with probability 1/2 m . Thus, all cases are taken in consideration and their respective probabilities sum to unity.…”

Section: Proof Of Linearitymentioning

confidence: 99%

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Analysis of the n-dimensional quadtree decomposition for arbitrary hyperrectangles

Faloutsos

Jagadish

Manolopoulos

1997

IEEE Trans. Knowl. Data Eng.

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Abstract-We give a closed-form expression for the average number of n-dimensional quadtree nodes ("pieces" or "blocks") required by an n-dimensional hyperrectangle aligned with the axes. Our formula includes as special cases the formulae of previous efforts for two-dimensional spaces [8]. It also agrees with theoretical and empirical results that the number of blocks depends on the hypersurface of the hyperrectangle and not on its hypervolume. The practical use of the derived formula is that it allows the estimation of the space requirements of the n-dimensional quadtree decomposition. Quadtrees are used extensively in twodimensional spaces (geographic information systems and spatial databases in general), as well in higher dimensionality spaces (as oct-trees for three-dimensional spaces, e.g., in graphics, robotics, and three-dimensional medical images [2]). Our formula permits the estimation of the space requirements for data hyperrectangles when stored in an index structure like a (n-dimensional) quadtree, as well as the estimation of the search time for query hyperrectangles, for the so-called linear quadtrees [17]. A theoretical contribution of the paper is the observation that the number of blocks is a piece-wise linear function of the sides of the hyperrectangle.

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Section: Proof Of Linearitymentioning

confidence: 99%

“…• In image databases, e.g., [2], where three-dimensional brain scans have to be stored. Regions in these brain scans can be encoded using oct-trees, to save space and to achieve faster response on range queries.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the n-dimensional quadtree decomposition for arbitrary hyperrectangles

Faloutsos

Jagadish

Manolopoulos

1997

IEEE Trans. Knowl. Data Eng.

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“…For example, in [3] we stored the three-dimensional MRI-scans of human brains using an octtree decomposition; we observed that the number of octants required to cover the surface of human brains increased exponentially with the resolution, with an exponent of 2.6 (close to the fractal dimension 2.7 of mammal brains [17, p. 113]). Thus, knowledge of the fractal dimension of a surface (or set of points, in general) is useful in the prediction of the storage requirements for the resulting quadtreesÂocttrees [9].…”

Section: Discussion Conclusionmentioning

confidence: 99%

Relaxing the Uniformity and Independence Assumptions Using the Concept of Fractal Dimension

Faloutsos

Kamel

1997

Journal of Computer and System Sciences

125

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We propose the concept of fractal dimension of a set of points, in order to quantify the deviation from the uniformity distribution. Using measurements on real data sets (road intersections of U.S. counties, population versus area of different nations, etc.) we provide evidence that real data indeed are skewed, and, moreover, we show that for several scales of interest they behave as mathematical fractals with a measurable noninteger fractal dimension. Armed with this tool, we then show its practical use in predicting the performance of spatial access methods and, specifically, of R-trees. We provide the first analysis of R-trees for skewed distributions of points; we develop a formula that estimates the number of disk accesses for range queries, given only the fractal dimension of the point set and its count. Experiments on real data sets show that the formula is very accurate; the relative error is usually below 50, and it rarely exceeds 100. We believe that the fractal dimension will help replace the uniformity and independence assumptions, allowing more accurate analysis for any spatial access method, as well as better estimates for query optimization on multiattribute queries. ] 1997 Academic Press

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“…Many special-purpose systems have been built, such as QSISM [23] and Visible Human interfaces [19]. They differ from RasDaMan and rView in several respects: they are streamlined to particular data structures (such as 3D integer images) and applications [23] and frequently even particular data sets [ 191. Furthermore, they sometimes operate on single MDD items only as opposed to RasDaMan where retrieval allows to state ad hoc selection predicates on MDD sets. Object-relational database technology attempts to extend the relational model with user-defined attribute types and corresponding operations.…”

Section: Related Workmentioning

confidence: 99%

Visualizing multidimensional raster data with rView

Dehmel¹,

Baumann²

Proceedings 11th International Workshop on Database and Expert Systems Applications

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rView is a visual frontend to the RasDaMan DBMS, providing raster data visualization functionality and a graphical user interface to the database system. RasDaMan, a commerciul array DBMS whose prototype has been developed by FORWISS, is designed for raster data of arbitrary dimensionality and base type, which makes it a powerful storage system for all types of rastered data. This generaliq calls for a similarily Jlexible visualization tool for data stored in the database. rView has been designed as a generic visualization tool with very few restrictions regarding dimensionality and base (i.e., array cell) type; among other things, it provides several visualization techniques like texture mapping, voxel-and heightjield rendering, which may be combined with user-conjigurable mappings to the RGB colour space.

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QBISM: extending a DBMS to support 3D medical images

Cited by 22 publications

References 27 publications

Analysis of the n-dimensional quadtree decomposition for arbitrary hyperrectangles

Analysis of the n-dimensional quadtree decomposition for arbitrary hyperrectangles

Relaxing the Uniformity and Independence Assumptions Using the Concept of Fractal Dimension

Visualizing multidimensional raster data with rView

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