Tissue Image Classification Using Multi-Fractal Spectra

Ramakrishnan Mukundan and Anna Hemsley
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Ramakrishnan Mukundan: University of Canterbury, New Zealand
Anna Hemsley: University of Canterbury, New Zealand

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2010, vol. 1, issue 2, 62-75

Abstract: 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.

Date: 2010
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