Content-Based Tissue Image Mining
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Image analysis using multifractals
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
A Short Review of Methods for Face Detection and Multifractal Analysis
CW '09 Proceedings of the 2009 International Conference on CyberWorlds
Multifractal Measures for Tissue Image Classification and Retrieval
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
Biomedical image classification with random subwindows and decision trees
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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Tissue image classification is a challenging problem due to the fact that the images contain highly irregular shapes in complex spatial arrangement. The multi-fractal formalism has been found useful in characterizing the intensity distribution present in such images, as it can effectively resolve local densities and also represent various structures present in the image. This paper presents a detailed study of feature vectors derived from the distribution of Holder exponents and the geometrical characteristics of the multi-fractal spectra that can be used in applications requiring image classification and retrieval. The paper also gives the results of experimental analysis performed using a tissue image database and demonstrates the effectiveness of the proposed multi-fractal-based descriptors in tissue image classification and retrieval. Implementation aspects that need to be considered for improving classification accuracy and the feature representation capability of the proposed descriptors are also outlined.