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Journal of Electronic Imaging

Combining wavelets transform and Hu moments with self-organizing maps for medical image categorization
Author(s): Leandro A. Silva; Emilio del-Moral-Hernandez; Ramon A. Moreno; Sérgio S. Furuie
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Paper Abstract

Images are fundamental sources of information in modern medicine. The images stored in a database and divided in categories are an important step for image retrieval. For an automatic categorization process, detailed analysis is done regarding image representation and generalization method. The baseline method for this process, in the medical image context, is using thumbnails and K-nearest neighbor (KNN), which is easily implemented and has had satisfactory results in literature. This work addresses an alternative method for automatic categorization, which jointly uses discrete wavelet transform with Hu's moments for image representation and self-organizing maps (SOM) neural networks combined with the KNN classifier (SOM-KNN), for medical image categorization. Furthermore, extensive experiments are conducted, to define the best wavelet family and to select the best coefficients set, to consider the remaining wavelet coefficients set (not selected as the best ones) through their Hu's moments, and to carry out a contrastive study with other successful approaches for categorization. The categorization result from a database with 10,000 images in 116 categories yielded 81.8% of correct rate, which is much better than the 67.9% obtained by the baseline method; and the time consumed in classification processing with SOM-KNN is 100 times shorter than KNN.

Paper Details

Date Published: 1 October 2011
PDF: 9 pages
J. Electron. Imaging. 20(4) 043002 doi: 10.1117/1.3645598
Published in: Journal of Electronic Imaging Volume 20, Issue 4
Show Author Affiliations
Leandro A. Silva, Univ. de São Paulo (Brazil)
Emilio del-Moral-Hernandez, Univ. de São Paulo (Brazil)
Ramon A. Moreno, Univ. de São Paulo (Brazil)
Sérgio S. Furuie, Univ. de São Paulo (Brazil)

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