Emanuele Trucco

Emanuele Trucco


Research Focus & Metrics

Academic Career Length

41Years (1986-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2000 in Machine Vision and Applications
Verified
Journal Article
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AFAndrea Fusiello
Andrea Fusiello
ETEmanuele Trucco
Emanuele Trucco
AVAlessandro Verri
Alessandro Verri

7 June 2010 in IEEE Transactions on Information Technology in Biomedicine
Verified
Journal Article
Closed access
CLC A Lupascu
C A Lupascu
DTDomenico Tegolo
Domenico Tegolo
ETEmanuele Trucco
Emanuele Trucco
This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as well as the additional manual segmentation provided by DRIVE. Training was conducted confined to the dedicated training set from the DRIVE database, and feature-based AdaBoost classifier (FABC) was tested on the 20 images from the test set. FABC achieved an area under the receiver operating characteristic (ROC) curve of 0.9561, in line with state-of-the-art approaches, but outperforming their accuracy (0.9597 versus 0.9473 for the nearest performer).

1 January 2018 in Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
Verified
Journal Article
Open access
EPEnrico Pellegrini
Enrico Pellegrini
LBLucia Ballerini
Lucia Ballerini
MHMaria del C. Valdes Hernandez
Maria del C. Valdes Hernandez
FCFrancesca M. Chappell
Francesca M. Chappell
VGVictor González‐Castro
Victor González‐Castro
JWJoanna M Wardlaw
Joanna M Wardlaw
Introduction: Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear.Methods: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries.Results: Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1‐weighted sequences. Accuracy was highest for differentiating Alzheimer's disease from healthy controls and poor for differentiating healthy controls versus mild cognitive impairment versus Alzheimer's disease or mild cognitive impairment converters versus nonconverters. Accuracy increased using combined data types, but not by data source, sample size, or machine learning method.Discussion: Machine learning does not differentiate clinically relevant disease categories yet. More diverse data sets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field.

22 November 2002
Verified
Conference Paper
Closed access
AFAndrea Fusiello
Andrea Fusiello
VRV. Roberto
V. Roberto
ETEmanuele Trucco
Emanuele Trucco

9 October 2006 in IEEE Journal of Oceanic Engineering
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Journal Article
Closed access
ETEmanuele Trucco
Emanuele Trucco
AOA.T. Olmos-Antillon
A.T. Olmos-Antillon
A self-tuning image restoration filter based on a simplified version of the Jaffe-McGlamery underwater image formation model is presented. Optimal values of the filter parameters are estimated automatically for each individual image by optimizing a quality criterion based on a global contrast measure. The simplified model is ideally suitable for diffuse-light imaging with limited backscatter, but qualitative tests show good performance in a variety of imaging conditions. In addition, quantitative tests with a large number of frames from six real mission videos indicate a substantial performance improvement when restoration is used as a preprocessor for a classifier detecting man-made objects in unconstrained subsea videos

1 August 2013 in Pattern Recognition
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Journal Article
Closed access
YWYangfan Wang
Yangfan Wang
GJGuangrong Ji
Guangrong Ji
PLPing Lin
Ping Lin
ETEmanuele Trucco
Emanuele Trucco