DocumentCode :
1818926
Title :
A web-accessible framework for the automated storage and texture analysis of biomedical images
Author :
Barnathan, Michael ; Zhang, Jingjing ; Megalooikonomou, Vasileios
Author_Institution :
Dept. of Comput. & Inf. Sci., Temple Univ., Philadelphia, PA
fYear :
2008
fDate :
14-17 May 2008
Firstpage :
257
Lastpage :
259
Abstract :
We present a framework for automated image texture analysis that utilizes vector quantization (VQ), an image compression technique, to perform common data mining operations, such as classification, clustering, and similarity searches, on 2D and 3D image datasets. We additionally demonstrate the effectiveness of this framework in a medical imaging context through MIDMS (Medical Image Data Mining System), a web-based system written in Perl and Matlab. MIDMS is capable of automating submission, normalization, compression, and real-time querying of general user-submitted medical image data. Our framework processes submissions by generating a locally optimal codebook for submitted datasets using the Generalized Lloyd Algorithm (GLA). After generating the codebook, our framework uses the codeword usage frequency of each image as the image´s feature vector. Users of the framework may then perform highly accurate classification and similarity search experiments on these vectors using either the histogram model (HM) or summed Euclidean distance (SED) metrics, as confirmed by previous experiments utilizing these techniques. Our framework has the potential to assist researchers and clinicians in the sharing, mining, and analysis of large quantities of medical imaging data.
Keywords :
Internet; data mining; image classification; image texture; medical image processing; pattern clustering; vector quantisation; Web-accessible framework; automated image texture analysis; codeword usage frequency; data mining operations; generalized Lloyd algorithm; histogram model; image classification; image clustering; image compression technique; medical image data mining system; medical imaging; similarity searches; summed Euclidean distance metrics; vector quantization; Biomedical imaging; Data mining; Frequency; Histograms; Image analysis; Image coding; Image storage; Image texture analysis; Storage automation; Vector quantization; Classification; Pattern analysis; Similarity Searches; Texture descriptors; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-2002-5
Electronic_ISBN :
978-1-4244-2003-2
Type :
conf
DOI :
10.1109/ISBI.2008.4540981
Filename :
4540981
Link To Document :
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