naa a22 4500 510808034 CHVBK 20180411083427.0 cr unu---uuuuu 180411e20130701xx s 000 0 eng 10.1007/s11548-013-0838-2 doi (NATIONALLICENCE)springer-10.1007/s11548-013-0838-2 An evaluation of image descriptors combined with clinical data for breast cancer diagnosis [Elektronische Daten] [Daniel Moura, Miguel Guevara López] Purpose: Breast cancer computer-aided diagnosis (CADx) may utilize image descriptors, demographics, clinical observations, or a combination. CADx performance was compared for several image features, clinical descriptors (e.g. age and radiologist's observations), and combinations of both kinds of data. A novel descriptor invariant to rotation, histograms of gradient divergence (HGD), was developed to deal with round-shaped objects, such as masses. HGD was compared with conventional CADx features. Method : HGD and 11 conventional image descriptors were evaluated using cases from two publicly available mammography data sets, the digital database for screening mammography (DDSM) and the breast cancer digital repository (BCDR), with 1,762 and 362 instances, respectively. Three experiments were done for each data set according to the type of lesion (i.e., all lesions, masses, and calcifications), resulting in six scenarios. For each scenario, 100 training and test sets were generated via resampling without replacement and five machine learning classifiers were used to assess the diagnostic performance of the descriptors. Results : Clinical descriptors outperformed image descriptors in the DDSM sample (three out of six scenarios), and combining the two kind of descriptors was advantageous in five out of six scenarios. HGD was the best descriptor (or comparable to best) in 8 out of 12 scenarios, demonstrating promising capabilities to describe masses. Conclusions : The combination of clinical data and image descriptors was advantageous in most mammography CADx scenarios. A new descriptor based on the divergence of the gradient (HGD) was demonstrated to be a feasible predictor of breast masses' diagnosis. CARS, 2013 Breast cancer nationallicence Image descriptors nationallicence Clinical data nationallicence Machine learning classifiers nationallicence Computer-aided diagnosis (CADx) nationallicence Histograms of gradient divergence (HGD) nationallicence Moura Daniel Instituto de Engenharia Mecânica e Gestão Industrial, Universidade do Porto, Rua Dr Roberto Frias, 400, 4200-465, Porto, Portugal aut Guevara López Miguel Instituto de Engenharia Mecânica e Gestão Industrial, Universidade do Porto, Rua Dr Roberto Frias, 400, 4200-465, Porto, Portugal aut International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/4(2013-07-01), 561-574 1861-6410 8:4<561 2013 8 11548 https://doi.org/10.1007/s11548-013-0838-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-013-0838-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Moura Daniel Instituto de Engenharia Mecânica e Gestão Industrial, Universidade do Porto, Rua Dr Roberto Frias, 400, 4200-465, Porto, Portugal aut NATIONALLICENCE 700 1- Guevara López Miguel Instituto de Engenharia Mecânica e Gestão Industrial, Universidade do Porto, Rua Dr Roberto Frias, 400, 4200-465, Porto, Portugal aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/4(2013-07-01), 561-574 1861-6410 8:4<561 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer