Publications
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Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines.
Source: International Journal of Neural Systems 2005 Feb;15(1-2):1-11.
Author: Glotsos D, Tohka J, Ravazoula P, Cavouras D, Nikiforidis G.
PubMed ID: 15912578
Abstract:
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.
Source: International Journal of Neural Systems 2005 Feb;15(1-2):1-11.
Author: Glotsos D, Tohka J, Ravazoula P, Cavouras D, Nikiforidis G.
PubMed ID: 15912578
Abstract:
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.