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Proceedings Paper

Analysis of texture patterns in medical images with an application to breast imaging
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Paper Abstract

We propose a methodological framework for texture analysis in medical images that is based on Vector Quantization (VQ), a method traditionally used for image compression. In this framework, the codeword usage histogram is used as a texture descriptor of the image. This descriptor can be used effectively for similarity searches, clustering, classification and other retrieval operations. We present an application of this approach to the analysis of x-ray galactograms; we analyze the texture in retroareolar regions of interests (ROIs) in order to distinguish between patients with reported galactographic findings and normal subjects. We decompose these ROIs into equi-size blocks and use VQ to represent each block with the closest codeword from a codebook. Each image is represented as a vector of frequencies of codeword appearance. We perform k-nearest neighbor classification of the texture patterns employing the histogram model as a similarity measure. The classification accuracy reached up to 96% for certain experimental settings; these results demonstrate that the proposed approach can be effective in performing similarity analysis of texture patterns in breast imaging. The proposed texture analysis framework has a potential to assist the interpretation of clinical images in general and facilitate the investigation of relationships among structure, texture and function or pathology.

Paper Details

Date Published: 30 March 2007
PDF: 8 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 651421 (30 March 2007); doi: 10.1117/12.713605
Show Author Affiliations
Vasileios Megalooikonomou, Temple Univ. (United States)
Jingjing Zhang, Temple Univ. (United States)
Despina Kontos, Hospital of the Univ. of Pennsylvania (United States)
Predrag R. Bakic, Hospital of the Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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