Statistical modeling and classification of reflectance confocal microscopy images

Abdelghafour Halimi (1) , Hadj Batatia (1) , Jimmy Le Digabel (2) , Gwendal Josse (2) , Jean-Yves Tourneret (3, 4)

1 IRIT-TCI - Traitement et Compréhension d’Images
2 Laboratoires Pierre Fabre
3 IRIT-MINDS - CoMputational imagINg anD viSion
4 Toulouse INP - Institut National Polytechnique (Toulouse)

This paper deals with the characterization and the classification of reflectance confocal microscopy images of human skin. The aim is to identify and characterize the skin lentigo, a phenomenon that originates at the dermo-epidermic junction. High resolution images are acquired at different depths of the skin. In this paper, an analysis of confocal images is performed for each depth and the histogram of pixel intensities in the image is determined. It is modeled by a generalized gamma distribution parameterized by a translation, scale and shape parameters ( , and). These parameters are estimated using the natural gradient descent algorithm and used to achieve the classification between healthy and lentigo patients of clinical images. The obtained results show that the scale and shape parameters (beta and rho) are good features to identify and characterize the presence of lentigo in skin tissues.

Mots clés: Texture analysis; Lentigo characterization; Natural gradient descent; Reflectance confocal microscopy; Statistical modeling; Support vector machine; Maximum Likelihood estimation; Classification

Domaines: Traitement du signal et de l'image [eess.SP]

Abdelghafour Halimi, Hadj Batatia, Jimmy Le Digabel, Gwendal Josse, Jean-Yves Tourneret. Statistical modeling and classification of reflectance confocal microscopy images. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2017), Dec 2017, Curaçao, Netherlands Antilles. pp.1-5. ⟨hal-02617310⟩