Statistical modeling and classification of reflectance confocal microscopy images

Abdelghafour Halimi; Hadj Batatia; Jimmy Le Digabel; Gwendal Josse; Jean-Yves Tourneret

doi:10.1109/CAMSAP.2017.8313069

Statistical modeling and classification of reflectance confocal microscopy images

Abdelghafour Halimi
, Hadj Batatia
, Jimmy Le Digabel
, Gwendal Josse
, Jean-Yves Tourneret

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper deals with the characterization and classification of reflectance confocal microscopy images of human skin. A special attention will be given to the identification and characterization of the lentigo, a phenomenon that originates at the dermo-epidermic junction of the skin. Confocal images are acquired at different skin depths with a high resolution. For each depth, the histograms of pixel intensities are determined, and well statistically modelled with a generalized gamma distribution (GGD). The scale, shape and translation parameters associated with the GGD are estimated using a new natural gradient descent algorithm showing fast convergence properties when compared to state-of-the-art estimation methods. Results show that the estimated parameters can be used to classify clinical images of lentigo and healthy patients. They also show that the scale and shape parameters are good features to identify and characterize the presence of lentigo in skin tissues.

Original language	English
Title of host publication	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Publisher	IEEE
ISBN (Electronic)	9781538612514
DOIs	https://doi.org/10.1109/CAMSAP.2017.8313069
Publication status	Published - 12 Mar 2018
Event	7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing 2017 - Curacao, Curaçao Duration: 10 Dec 2017 → 13 Dec 2017 http://www.cs.huji.ac.il/conferences/CAMSAP17/

Conference

Conference	7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing 2017
Abbreviated title	CAMSAP 2017
Country/Territory	Curaçao
City	Curacao
Period	10/12/17 → 13/12/17
Internet address	http://www.cs.huji.ac.il/conferences/CAMSAP17/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

lentigo characterization
maximum likelihood estimation
natural gradient
Reflectance confocal microscopy

ASJC Scopus subject areas

Signal Processing
Control and Optimization
Instrumentation

Access to Document

10.1109/CAMSAP.2017.8313069

Cite this

@inproceedings{b753b069bf4e4aae81d51d75b6507b84,

title = "Statistical modeling and classification of reflectance confocal microscopy images",

abstract = "This paper deals with the characterization and classification of reflectance confocal microscopy images of human skin. A special attention will be given to the identification and characterization of the lentigo, a phenomenon that originates at the dermo-epidermic junction of the skin. Confocal images are acquired at different skin depths with a high resolution. For each depth, the histograms of pixel intensities are determined, and well statistically modelled with a generalized gamma distribution (GGD). The scale, shape and translation parameters associated with the GGD are estimated using a new natural gradient descent algorithm showing fast convergence properties when compared to state-of-the-art estimation methods. Results show that the estimated parameters can be used to classify clinical images of lentigo and healthy patients. They also show that the scale and shape parameters are good features to identify and characterize the presence of lentigo in skin tissues.",

keywords = "lentigo characterization, maximum likelihood estimation, natural gradient, Reflectance confocal microscopy",

author = "Abdelghafour Halimi and Hadj Batatia and \{Le Digabel\}, Jimmy and Gwendal Josse and Jean-Yves Tourneret",

year = "2018",

month = mar,

day = "12",

doi = "10.1109/CAMSAP.2017.8313069",

language = "English",

booktitle = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)",

publisher = "IEEE",

address = "United States",

note = "7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing 2017, CAMSAP 2017 ; Conference date: 10-12-2017 Through 13-12-2017",

url = "http://www.cs.huji.ac.il/conferences/CAMSAP17/",

}

Halimi, A, Batatia, H, Le Digabel, J, Josse, G & Tourneret, J-Y 2018, Statistical modeling and classification of reflectance confocal microscopy images. in 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)., 8313069, IEEE, 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing 2017, Curacao, Curaçao, 10/12/17. https://doi.org/10.1109/CAMSAP.2017.8313069

TY - GEN

T1 - Statistical modeling and classification of reflectance confocal microscopy images

AU - Halimi, Abdelghafour

AU - Batatia, Hadj

AU - Le Digabel, Jimmy

AU - Josse, Gwendal

AU - Tourneret, Jean-Yves

PY - 2018/3/12

Y1 - 2018/3/12

N2 - This paper deals with the characterization and classification of reflectance confocal microscopy images of human skin. A special attention will be given to the identification and characterization of the lentigo, a phenomenon that originates at the dermo-epidermic junction of the skin. Confocal images are acquired at different skin depths with a high resolution. For each depth, the histograms of pixel intensities are determined, and well statistically modelled with a generalized gamma distribution (GGD). The scale, shape and translation parameters associated with the GGD are estimated using a new natural gradient descent algorithm showing fast convergence properties when compared to state-of-the-art estimation methods. Results show that the estimated parameters can be used to classify clinical images of lentigo and healthy patients. They also show that the scale and shape parameters are good features to identify and characterize the presence of lentigo in skin tissues.

AB - This paper deals with the characterization and classification of reflectance confocal microscopy images of human skin. A special attention will be given to the identification and characterization of the lentigo, a phenomenon that originates at the dermo-epidermic junction of the skin. Confocal images are acquired at different skin depths with a high resolution. For each depth, the histograms of pixel intensities are determined, and well statistically modelled with a generalized gamma distribution (GGD). The scale, shape and translation parameters associated with the GGD are estimated using a new natural gradient descent algorithm showing fast convergence properties when compared to state-of-the-art estimation methods. Results show that the estimated parameters can be used to classify clinical images of lentigo and healthy patients. They also show that the scale and shape parameters are good features to identify and characterize the presence of lentigo in skin tissues.

KW - lentigo characterization

KW - maximum likelihood estimation

KW - natural gradient

KW - Reflectance confocal microscopy

UR - https://www.scopus.com/pages/publications/85051142171

U2 - 10.1109/CAMSAP.2017.8313069

DO - 10.1109/CAMSAP.2017.8313069

M3 - Conference contribution

BT - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

PB - IEEE

T2 - 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing 2017

Y2 - 10 December 2017 through 13 December 2017

ER -

Statistical modeling and classification of reflectance confocal microscopy images

Abstract

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this