@article {534,
	title = {A local fuzzy thresholding methodology for multiregion image segmentation},
	journal = {Knowledge-Based Systems},
	volume = {83},
	year = {2015},
	month = {07/2015},
	pages = {1-12},
	abstract = {<p>Abstract Thresholding is a direct and simple approach to extract different regions from an image. In its basic formulation, thresholding searches for a global value that maximizes the separation between output classes. The use of a single hard threshold value is precisely the source of important segmentation errors in many scenarios like noisy images or uneven illumination. If no connectivity or closed objects are considered, the method is prone to produce isolated pixels. In this paper a new multiregion thresholding methodology is presented to overcome the common drawbacks of thresholding methods when images are corrupted with artifacts and noise. It is based on relating each pixel in the image to different output centroids via a fuzzy membership function, avoiding any initial hard decision. The starting point of the technique is the definition of the output centroids using a clustering method compatible with most thresholding techniques in the literature. The method makes use of the spatial information through a local aggregation step where the membership degree of each pixel is modified by local information that takes into account the memberships of the surrounding pixels. This makes the method robust to noise and artifacts. The general formulation of the proposed methodology allows the design of spatial aggregations for multiple applications, including the possibility of including heuristic information via a fuzzy inference rule base.</p>
},
	issn = {0950-7051},
	doi = {http://dx.doi.org/10.1016/j.knosys.2015.02.029},
	url = {http://www.sciencedirect.com/science/article/pii/S095070511500129X},
	author = {Santiago Aja-Fern{\'a}ndez and Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero}
}
@article {tristan2012least,
	title = {Least squares for diffusion tensor estimation revisited: Propagation of uncertainty with Rician and non-Rician signals},
	journal = {NeuroImage},
	volume = {59},
	number = {4},
	year = {2012},
	pages = {4032{\textendash}4043},
	publisher = {Academic Press},
	author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin}
}
@conference {tristan2008local,
	title = {Local similarity measures for demons-like registration algorithms},
	booktitle = {Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on},
	year = {2008},
	pages = {1087{\textendash}1090},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez}
}
