Paper Info
Title
Texture bags: anomaly retrieval in medical images based on local 3d-texture similarity
Abstract
Providing efficient access to the huge amounts of existing medical imaging data is a highly relevant but challenging problem. In this paper, we present an effective method for content-based image retrieval (CBIR) of anomalies in medical imaging data, based on similarity of local 3D texture. During learning, a texture vocabulary is obtained from training data in an unsupervised fashion by extracting the dominant structure of texture descriptors. It is based on a 3D extension of the Local Binary Pattern operator (LBP), and captures texture properties via descriptor histograms of supervoxels, or texture bags. For retrieval, our method computes a texture histogram of a query region marked by a physician, and searches for similar bags via diffusion distance. The retrieval result is a ranked list of cases based on the occurrence of regions with similar local texture structure. Experiments show that the proposed local texture retrieval approach outperforms analogous global similarity measures.
Year
DOI
Venue
2011
10.1007/978-3-642-28460-1_11
MCBR-CDS
Keywords
Field
DocType
texture bag,similar local texture structure,anomaly retrieval,texture descriptors,retrieval result,texture histogram,medical imaging data,content-based image retrieval,medical image,proposed local texture retrieval,captures texture property,texture vocabulary
Histogram,Computer vision,Texture compression,Pattern recognition,Ranking,Image texture,Medical imaging,Local binary patterns,Image retrieval,Feature extraction,Artificial intelligence,Geography
Conference
Citations 
PageRank 
References 
10
0.66
20
Authors
6
Name
Order
Citations
PageRank
Andreas Burner1221.96
René Donner215211.92
Marius Mayerhoefer3100.66
Markus Holzer4272.48
Franz Kainberger5335.97
Georg Langs664857.73