Publications

@article{Hobson15,
title = {Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset},
author = {Peter Hobson and Brian C. Lovell and Gennaro Percannella and Mario Vento and Arnold Wiliem},
doi = {10.1016/j.artmed.2015.08.001},
year = {2015},
date = {2015-08-13},
journal = {Artificial Intelligence in Medicine},
keywords = {Medical image analysis},
pubstate = {published},
tppubtype = {article}
}

@article{Azzopardi2014,
title = {Trainable COSFIRE filters for vessel delineation with application to retinal images},
author = {George Azzopardi and Nicola Strisciuglio and Mario Vento and Nicolai Petkov},
url = {http://www.sciencedirect.com/science/article/pii/S1361841514001364},
issn = {1361-8415},
year = {2015},
date = {2015-01-14},
journal = {Medical Image Analysis},
volume = {19},
number = {1},
pages = {46–57},
abstract = {Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis.

We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding.

The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.},
keywords = {Image analysis and recognition, Medical image analysis},
pubstate = {published},
tppubtype = {article}
}

@article{si_pr_2015,
title = {Special issue on the analysis and recognition of indirect immuno-fluorescence images},
author = {Pasquale Foggia and Gennaro Percannella and Paolo Soda and Mario Vento},
year = {2014},
date = {2014-01-15},
journal = {Pattern Recognition},
pages = {2303-2304},
keywords = {Medical image analysis},
pubstate = {published},
tppubtype = {article}
}