Paper Info
Title
Evaluation of fast 2d and 3d medical image retrieval approaches based on image miniatures
Abstract
The present work evaluates four medical image retrieval approaches based on features derived from image miniatures. We argue that due to the restricted domain of medical image data, the standardized acquisition protocols and the absence of a potentially cluttered background a holistic image description is sufficient to capture high-level image similarities. We compare four different miniature 2D and 3D descriptors and corresponding metrics, in terms of their retrieval performance: (A) plain miniatures together with euclidean distances in a k Nearest Neighbor based retrieval backed by kD-trees; (B) correlations of rigidly aligned miniatures, initialized using the kD-tree; (C) distribution fields together with the l1 -norm; (D) SIFT-like histogram of gradients using the χ2-distance. We evaluate the approaches on two data sets: the ImageClef 2009 benchmark of 2D radiographs with the aim to categorize the images and a large set of 3D-CTs representing a realistic sample in a hospital PACS with the objective to estimate the location of the query volume.
Year
DOI
Venue
2011
10.1007/978-3-642-28460-1_12
MCBR-CDS
Keywords
Field
DocType
image miniature,high-level image similarity,medical image data,corresponding metrics,sift-like histogram,retrieval performance,holistic image description,cluttered background,medical image retrieval
k-nearest neighbors algorithm,Computer vision,Data set,Automatic image annotation,Pattern recognition,Computer science,Image texture,Image retrieval,Histogram of oriented gradients,Artificial intelligence,Euclidean geometry,Visual Word
Conference
Citations 
PageRank 
References 
3
0.45
11
Authors
6
Name
Order
Citations
PageRank
René Donner115211.92
Sebastian Haas2472.66
Andreas Burner3221.96
Markus Holzer4272.48
Horst Bischof58751541.43
Georg Langs664857.73