Publications

@article{aprovitolaKneeBoneSegmentation2016,

title = {Knee Bone Segmentation from MRI: A Classification and Literature Review},

author = { Andrea Aprovitola and Luigi Gallo},

doi = {10.1016/j.bbe.2015.12.007},

issn = {02085216},

year  = {2016},

date = {2016-01-01},

urldate = {2016-12-06},

journal = {Biocybernetics and Biomedical Engineering},

volume = {36},

number = {2},

pages = {437--449},

abstract = {Segmentation of cartilage from Magnetic Resonance (MR) images has evolved as a tool for the diagnosis of knee joint pathologies. However, accuracy and reproducibility of automated methods of cartilage segmentation may require the prior extraction of bone surfaces from MR imaging sequences specifically designed to evidence the cartilage and not the bone. Thus a priori knowledge of knee joint structures and fully automated segmentation methods are adopted to provide reliable detection of bone surfaces. In this paper, we review knee bone segmentation methods from MR images. We classified the methods proposed in literature according to the level of a priori knowledge, the level of automation and the level of manual user interaction. Furthermore we discuss the segmentation results in literature in relation to the MR sequences used to image the bone.},

keywords = {Healthcare, Knee bone, MRI, Segmentation},

pubstate = {published},

tppubtype = {article}

}

@article{aprovitola_knee_2016,

title = {Knee bone segmentation from MRI: A classification and literature review},

author = {Andrea Aprovitola and Luigi Gallo},

url = {http://linkinghub.elsevier.com/retrieve/pii/S020852161630002X},

doi = {10.1016/j.bbe.2015.12.007},

issn = {02085216},

year  = {2016},

date = {2016-01-01},

urldate = {2016-12-06},

journal = {Biocybernetics and Biomedical Engineering},

volume = {36},

number = {2},

pages = {437–449},

abstract = {Segmentation of cartilage from Magnetic Resonance (MR) images has evolved as a tool for the diagnosis of knee joint pathologies. However, accuracy and reproducibility of automated methods of cartilage segmentation may require the prior extraction of bone surfaces from MR imaging sequences specifically designed to evidence the cartilage and not the bone. Thus a priori knowledge of knee joint structures and fully automated segmentation methods are adopted to provide reliable detection of bone surfaces. In this paper, we review knee bone segmentation methods from MR images. We classified the methods proposed in literature according to the level of a priori knowledge, the level of automation and the level of manual user interaction. Furthermore we discuss the segmentation results in literature in relation to the MR sequences used to image the bone.},

keywords = {Healthcare, Knee bone, MRI, Segmentation},

pubstate = {published},

tppubtype = {article}

}