Abstract
This paper presents a study on microscopic images to classify cervical precancers by a multifractal analysis. Since internal structure of tissue is non-deterministic, multifractal spectrum is required to characterize such structure. The periodic structure of collagen present in the stromal region of cervical tissue gets disordered with progress in grade of dysplasia. This disorder is investigated through the multifractal study, enabling us to discriminate between normal and abnormal human cervical tissue sections. Holder exponent classifies normal from abnormal dysplasia by capturing local irregularities present in the image. While mean of Hausdorff–Besicovich dimension which describes global regularity are used to classify various grades of dysplasia. The box-counting method is used to estimate the fractal dimension. The results show, remarkably, the classification feature of multifractal analysis.
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Singh, P., Jagtap, J., Pantola, C., Agarwal, A., Pradhan, A. (2014). A Multifractal-Based Image Analysis for Cervical Dysplasia Classification. In: Bandt, C., Barnsley, M., Devaney, R., Falconer, K., Kannan, V., Kumar P.B., V. (eds) Fractals, Wavelets, and their Applications. Springer Proceedings in Mathematics & Statistics, vol 92. Springer, Cham. https://doi.org/10.1007/978-3-319-08105-2_26
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DOI: https://doi.org/10.1007/978-3-319-08105-2_26
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