Analytical Cellular Pathology

Analytical Cellular Pathology / 2012 / Article
Special Issue

1st Congress of the International Academy of Digital Pathology Quebec City, Canada, August 3–5, 2011. Part II

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Open Access

Volume 35 |Article ID 912956 | 4 pages | https://doi.org/10.3233/ACP-2011-0045

Multifractal Feature Descriptor for Histopathology

Abstract

Background: Histologic image analysis plays an important role in cancer diagnosis. It describes the structure of the body tissues and abnormal structure gives the suspicion of the cancer or some other diseases. Observing the structural changes of these chaotic textures from the human eye is challenging process. However, the challenge can be defeat by forming mathematical descriptor to represent the histologic texture and classify the structural changes via a sophisticated computational method.Objective: In this paper, we propose a texture descriptor to observe the histologic texture into highly discriminative feature space.Methods: Fractal dimension describes the self-similar structures in different and more accurate manner than topological dimension. Further, the fractal phenomenon has been extended to natural structures (images) as multifractal dimension. We exploited the multifractal analysis to represent the histologic texture, which derive more discriminative feature space for classification.Results: We utilized a set of histologic images (belongs to liver and prostate specimens) to assess the discriminative power of the multifractal features. The experiment was organized to classify the given histologic texture as cancer and non-cancer. The results show the discrimination capability of multifractal features by achieving approximately 95% of correct classification rate.Conclusion: Multifractal features are more effective to describe the histologic texture. The proposed feature descriptor showed high classification rate for both liver and prostate data sample datasets.

Copyright © 2012 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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