Multiresolution-fractal feature extraction and tumor detection: analytical model and implementation

Khan M. Iftekharuddin; Carlos Parra

doi:10.1117/12.504091

13 November 2003 Multiresolution-fractal feature extraction and tumor detection: analytical modeling and implementation

Khan M. Iftekharuddin, Carlos Parra

Proceedings Volume 5207, Wavelets: Applications in Signal and Image Processing X; (2003) https://doi.org/10.1117/12.504091
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

We propose formal analytical models for identification of tumors in medical images based on the hypothesis that the tumors have a fractal (self-similar) growth behavior. Therefore, the images of these tumors may be characterized as Fractional Brownian motion (fBm) processes with a fractal dimension (D) that is distinctly different than that of the image of the surrounding tissue. In order to extract the desired features that delineate different tissues in a MR image, we study multiresolution signal decomposition and its relation to fBm. The fBm has proven successful to modeling a variety of physical phenomena and non-stationary processes, such as medical images, that share essential properties such as self-similarity, scale invariance and fractal dimension (D). We have developed the theoretical framework that combines wavelet analysis with multiresolution fBm to compute D.

Citation Download Citation

Khan M. Iftekharuddin and Carlos Parra "Multiresolution-fractal feature extraction and tumor detection: analytical modeling and implementation", Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); https://doi.org/10.1117/12.504091

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