Publications

@article{pellegrini_machine_2018,

title = {Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review},

author = {Enrico Pellegrini and Lucía Ballerini and María del Carmen Valdés-Hernández and Francesca M Chappell and Victor González-Castro and Devasuda Anblagan and Samuel Danso and Susana Muñoz-Maniega and Dominic Job and Cyril Pernet},

url = {https://www.sciencedirect.com/science/article/pii/S2352872918300447},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring},

volume = {10},

pages = {519–535},

abstract = {Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear.},

note = {Publisher: No longer published by Elsevier},

keywords = {Cerebrovascular Disease, Classification, Dementia, machine learning, MRI, Pathological Aging, segmentation, small vessel disease},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_combined_2009,

title = {A combined and intelligent new segmentation method for boar semen based on thresholding and watershed transform},

author = {Víctor González-Castro and Enrique Alegre and Patricia Morala-Argüello and S Suárez},

url = {https://www.research.ed.ac.uk/en/publications/a-combined-and-intelligent-new-segmentation-method-for-boar-semen},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {International Journal of Imaging},

volume = {2},

number = {9 S},

pages = {70–80},

abstract = {This work presents a new method to segment images of alive and dead spermatozoa in ositive phase contrast. This method improves previous segmentation methods applying an intelligent threshold combined with watershed segmentation. First, it applies an intelligent thresholding segmentation that changes the value of threshold when the binary image obtained is not fulfill the surface and eccentricity factors. Then, using the same automatic criteria, the bad segmented images are processed by means of the watershed transform. Using this new method a 90.96% of the spermatozoa have been correctly segmented. This approach could be useful to commercial Computer Assisted Semen Analysis systems that need new and more accurate segmentation processes.},

note = {Publisher: Indian Society for Development and Environment Research},

keywords = {images, segmentation, segmentation method, semen, threshold, watershed},

pubstate = {published},

tppubtype = {article}

}

@article{martinez_san_martin_segmentation_nodate,

title = {A segmentation approach for evaluating wear of inserts in milling machines with computer vision techniques},

author = {Guillermo Martínez San Martín and Laura Fernández-Robles and Enrique Alegre and Óscar García-Olalla Olivera},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=4jZgNVkAAAAJ&sortby=title&citation_for_view=4jZgNVkAAAAJ:Se3iqnhoufwC},

abstract = {Measuring tool wear in milling machines is an important task to evaluate the lifetime of the cutting parts (inserts) and deciding whether we should replace them. In our research, we propose to use computer vision algorithms to perform this task. Part of the research is to evaluate the accuracy of different segmentation algorithms that segment the area of wear. We have used two methods: k-Means and Mean Shift. To evaluate the segmentation results the Dice coefficient was used, obtaining with Mean Shift a QS= 0.5923 for all the edges and a QS= 0.6831 just for edges with high wear.},

keywords = {Computer vision, milling machines, segmentation, Tool wear},

pubstate = {published},

tppubtype = {article}

}