2015 |
Nicola Strisciuglio; George Azzopardi; Mario Vento; Nicolai Petkov Unsupervised delineation of the vessel tree in retinal fundus images Conference Computational Vision and Medical Image Processing VIPIMAGE 2015, 2015, (Best Paper Award). BibTeX | Tag: Medical image analysis @conference{StrisciuglioRetina15, title = {Unsupervised delineation of the vessel tree in retinal fundus images}, author = {Nicola Strisciuglio and George Azzopardi and Mario Vento and Nicolai Petkov}, editor = {J. Tavares and R.M. Natal Jorge}, year = {2015}, date = {2015-10-19}, booktitle = {Computational Vision and Medical Image Processing VIPIMAGE 2015}, pages = {149-155}, note = {Best Paper Award}, keywords = {Medical image analysis}, pubstate = {published}, tppubtype = {conference} } |
Nicola Strisciuglio; George Azzopardi; Mario Vento; Nicolai Petkov Multiscale Blood Vessel Delineation Using B-COSFIRE Filters Book Chapter Azzopardi, George; Petkov, Nicolai (Ed.): Computer Analysis of Images and Patterns, 9257 , pp. 300-312, Springer International Publishing, 2015, ISBN: 978-3-319-23117-4. BibTeX | Tag: Image analysis and recognition, Medical image analysis | Links: @inbook{Strisciuglio15, title = {Multiscale Blood Vessel Delineation Using B-COSFIRE Filters}, author = {Nicola Strisciuglio and George Azzopardi and Mario Vento and Nicolai Petkov}, editor = {George Azzopardi and Nicolai Petkov}, url = {http://link.springer.com/chapter/10.1007%2F978-3-319-23117-4_26}, doi = {10.1007/978-3-319-23117-4_26}, isbn = {978-3-319-23117-4}, year = {2015}, date = {2015-08-26}, booktitle = {Computer Analysis of Images and Patterns}, volume = {9257}, pages = {300-312}, publisher = {Springer International Publishing}, series = {9257}, keywords = {Image analysis and recognition, Medical image analysis}, pubstate = {published}, tppubtype = {inbook} } |
Peter Hobson; Brian C. Lovell; Gennaro Percannella; Mario Vento; Arnold Wiliem Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset Journal Article Artificial Intelligence in Medicine, 2015. BibTeX | Tag: Medical image analysis | Links: @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} } |
George Azzopardi; Nicola Strisciuglio; Mario Vento; Nicolai Petkov Trainable COSFIRE filters for vessel delineation with application to retinal images Journal Article Medical Image Analysis, 19 (1), pp. 46–57, 2015, ISSN: 1361-8415. Abstract | BibTeX | Tag: Image analysis and recognition, Medical image analysis | Links: @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} } 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. |
2014 |
Pasquale Foggia; Gennaro Percannella; Paolo Soda; Mario Vento Special issue on the analysis and recognition of indirect immuno-fluorescence images Journal Article Pattern Recognition, pp. 2303-2304, 2014. BibTeX | Tag: Medical image analysis @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} } |
Publications
2015 |
Unsupervised delineation of the vessel tree in retinal fundus images Conference Computational Vision and Medical Image Processing VIPIMAGE 2015, 2015, (Best Paper Award). |
Multiscale Blood Vessel Delineation Using B-COSFIRE Filters Book Chapter Azzopardi, George; Petkov, Nicolai (Ed.): Computer Analysis of Images and Patterns, 9257 , pp. 300-312, Springer International Publishing, 2015, ISBN: 978-3-319-23117-4. |
Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset Journal Article Artificial Intelligence in Medicine, 2015. |
Trainable COSFIRE filters for vessel delineation with application to retinal images Journal Article Medical Image Analysis, 19 (1), pp. 46–57, 2015, ISSN: 1361-8415. |
2014 |
Special issue on the analysis and recognition of indirect immuno-fluorescence images Journal Article Pattern Recognition, pp. 2303-2304, 2014. |