Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection

We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, ge...

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Main Authors: Mukhopadhyay, Sabyasachi, Kurmi, Indrajit, Pradhan, Asima, Ghosh, Nirmalya, Panigrahi, Prasanta K., Das, Nandan Kumar
Other Authors: School of Chemical and Biomedical Engineering
Format: Article
Language:English
Published: 2018
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Online Access:https://hdl.handle.net/10356/88435
http://hdl.handle.net/10220/45748
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-884352023-12-29T06:48:24Z Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection Mukhopadhyay, Sabyasachi Kurmi, Indrajit Pradhan, Asima Ghosh, Nirmalya Panigrahi, Prasanta K. Das, Nandan Kumar School of Chemical and Biomedical Engineering Inverse Analysis On Light Scattering Tissue Characterization DRNTU::Engineering::Bioengineering We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model. Published version 2018-08-29T09:17:33Z 2019-12-06T17:03:17Z 2018-08-29T09:17:33Z 2019-12-06T17:03:17Z 2017 Journal Article Mukhopadhyay, S., Das, N. K., Kurmi, I., Pradhan, A., Ghosh, N., & Panigrahi, P. K. (2017). Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection. Journal of Biomedical Optics, 22(10), 105005-. doi:10.1117/1.JBO.22.10.105005 1083-3668 https://hdl.handle.net/10356/88435 http://hdl.handle.net/10220/45748 10.1117/1.JBO.22.10.105005 en Journal of Biomedical Optics © 2017 SPIE. This paper was published in Journal of Biomedical Optics and is made available as an electronic reprint (preprint) with permission of SPIE. The published version is available at: [http://dx.doi.org/10.1117/1.JBO.22.10.105005]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. 8 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Inverse Analysis On Light Scattering
Tissue Characterization
DRNTU::Engineering::Bioengineering
spellingShingle Inverse Analysis On Light Scattering
Tissue Characterization
DRNTU::Engineering::Bioengineering
Mukhopadhyay, Sabyasachi
Kurmi, Indrajit
Pradhan, Asima
Ghosh, Nirmalya
Panigrahi, Prasanta K.
Das, Nandan Kumar
Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection
description We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model.
author2 School of Chemical and Biomedical Engineering
author_facet School of Chemical and Biomedical Engineering
Mukhopadhyay, Sabyasachi
Kurmi, Indrajit
Pradhan, Asima
Ghosh, Nirmalya
Panigrahi, Prasanta K.
Das, Nandan Kumar
format Article
author Mukhopadhyay, Sabyasachi
Kurmi, Indrajit
Pradhan, Asima
Ghosh, Nirmalya
Panigrahi, Prasanta K.
Das, Nandan Kumar
author_sort Mukhopadhyay, Sabyasachi
title Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection
title_short Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection
title_full Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection
title_fullStr Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection
title_full_unstemmed Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection
title_sort tissue multifractality and hidden markov model based integrated framework for optimum precancer detection
publishDate 2018
url https://hdl.handle.net/10356/88435
http://hdl.handle.net/10220/45748
_version_ 1787136570342506496