Publications

@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}

}

@article{gonzalez-castro_pixel_2014,

title = {Pixel classification using general adaptive neighborhood-based features},

author = {Víctor González-Castro and Johan Debayle and Vladimir Curic},

url = {https://ieeexplore.ieee.org/abstract/document/6977356},

year  = {2014},

date = {2014-01-01},

journal = {2014 22nd International Conference on Pattern Recognition},

pages = {3750–3755},

abstract = {This paper presents a new descriptor using General Adaptive Neighborhoods (GANs) for classifying pixels in texture images. GANs define a spatial region around each pixel that fits its local structure, and pixel features are derived from region-based and intensity-based measurements. The method outperforms others, achieving 97.25% accuracy in five-class classification and high area under curve values in binary classifications using the VisTex database.},

note = {Publisher: IEEE},

keywords = {Adaptive Neighborhoods, mathematical morphology, pixel classification, texture classification},

pubstate = {published},

tppubtype = {article}

}