Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines.
A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.
Glotsos
Dimitris
D
Department of Medical Physics, University of Patras, Rio-Patras 26500, Greece. dimglo@med.upatras.gr
Tohka
Jussi
J
Ravazoula
Panagiota
P
Cavouras
Dionisis
D
Nikiforidis
George
G
D000465:Algorithms; D001254:Astrocytoma / Q000175:diagnosis*; D001932:Brain Neoplasms / Q000175:diagnosis*; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity