PET-CT based automated lung nodule detection.
An automatic method is presented in order to detect lung nodules in PET-CT studies. Using the foreground and background mean ratio independently in every nodule, we can detect the region of the nodules properly. The size and intensity of the lesions do not affect the result of the algorithm, although size constraints are present in the final classification step. The CT image is also used to classify the found lesions built on lung segmentation. We also deal with those cases when nearby and similar nodules are merged into one by a split-up post-processing step. With our method the time of the localization can be decreased from more than one hour to maximum five minutes. The method had been implemented and validated on real clinical cases in Interview Fusion clinical evaluation software (Mediso). Results indicate that our approach is very effective in detecting lung nodules and can be a valuable aid for physicians working in the daily routine of oncology.
Mediso Medical Imaging Systems Ltd., Baross str. 91-95, Budapest, Hungary. norbert.zsoter@mediso.hu
Norbert
N
Zsoter
Peter
P
Bandi
Gergely
G
Szabo
Zoltan
Z
Toth
Ralph A
RA
Bundschuh
Julia
J
Dinges
Laszlo
L
Papp
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000175:diagnosis*; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis*; D014057:Tomography, X-Ray Computed