Publications

@article{gonzalez-castro_automatic_2015,

title = {Automatic classification of skin lesions using color mathematical morphology-based texture descriptors},

author = {Víctor González-Castro and Johan Debayle and Yanal Wazaefi and Mehdi Rahim and Caroline Gaudy-Marqueste and Jean-Jacques Grob and Bernard Fertil},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9534/953409/Automatic-classification-of-skin-lesions-using-color-mathematical-morphology-based/10.1117/12.2182592.short},

year  = {2015},

date = {2015-01-01},

journal = {Twelfth International Conference on Quality Control by Artificial Vision 2015},

volume = {9534},

pages = {53–59},

abstract = {This paper presents an automatic method for classifying skin lesions in dermoscopic images using color texture analysis. It combines mathematical morphology for local pixel descriptors with Kohonen Self-Organizing Maps (SOM) for clustering and global texture description, eliminating the need for segmentation. Two approaches—classical and adaptive morphology—achieve similar AUC scores (0.854 and 0.859), surpassing dermatologist predictions (0.792).},

note = {Publisher: SPIE},

keywords = {dermoscopic imaging, machine learning, skin lesion classification, texture analysis},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_texture_2015,

title = {Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions},

author = {Víctor González-Castro and Johan Debayle and Yanal Wazaefi and Mehdi Rahim and Caroline Gaudy-Marqueste and Jean-Jacques Grob and Bernard Fertil},

url = {https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-24/issue-6/061104/Texture-descriptors-based-on-adaptive-neighborhoods-for-classification-of-pigmented/10.1117/1.JEI.24.6.061104.short},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Electronic Imaging},

volume = {24},

number = {6},

pages = {061104–061104},

abstract = {This paper proposes two texture descriptors for the automatic classification of skin lesions from dermoscopic images, focusing on color texture analysis. The first descriptor uses adaptive mathematical morphology (MM) and Kohonen self-organizing maps (SOM), while the second uses local binary patterns (LBP) with adaptive neighborhoods. Neither approach requires prior segmentation. The results show that the adaptive neighborhood-based LBP approach outperforms both nonadaptive versions of the proposed descriptors and dermatologists' visual predictions, as confirmed by receiver operating characteristic analysis.},

note = {Publisher: Society of Photo-Optical Instrumentation Engineers},

keywords = {Adaptive Neighborhoods, Dermoscopic Images, LBP, Local Binary Patterns, skin lesion classification, texture descriptors},

pubstate = {published},

tppubtype = {article}

}