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Combining wavelets transform and Hu moments with self-organizing maps for medical image categorization

[+] Author Affiliations
Leandro A. Silva

University of São Paulo, Department of Electronic Systems Engineering, São Paulo, Brazil and Mackenzie Presbyterian University, School of Computing and Informatics, São Paulo, Brazil

Emilio Del-Moral-Hernandez

University of São Paulo, Department of Electronic Systems Engineering, São Paulo, Brazil

Ramon A. Moreno

University of São Paulo Medical School, Heart Institute (InCor), São Paulo, Brazil

Sérgio S. Furuie

University of São Paulo, Department of Electronic Systems Engineering, São Paulo, Brazil

J. Electron. Imaging. 20(4), 043002 (October 21, 2011). doi:10.1117/1.3645598
History: Received April 24, 2010; Revised August 24, 2011; Accepted September 13, 2011; Published October 21, 2011; Online October 21, 2011
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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.

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Citation

Leandro A. Silva ; Emilio Del-Moral-Hernandez ; Ramon A. Moreno and Sérgio S. Furuie
"Combining wavelets transform and Hu moments with self-organizing maps for medical image categorization", J. Electron. Imaging. 20(4), 043002 (October 21, 2011). ; http://dx.doi.org/10.1117/1.3645598


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