Automated diagnosis of brain tumours astrocytomas using Probabilistic Neural Network clustering and Support Vector Machines
Automated diagnosis of brain tumours astrocytomas using Probabilistic Neural Network clustering and Support Vector Machines
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DOI：
10.1142/s0129065705000013
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发表时间：
2005-02-01
影响因子：
8
通讯作者：
Nikiforidis, G
中科院分区：
文献类型：
--
作者：
Glotsos, D;Tohka, J;Nikiforidis, G
建立了一个计算机辅助诊断系统，用于辅助脑星形细胞瘤恶性程度分级。将来自140个星形胶质细胞活检的显微镜图像数字化，并使用概率神经网络基于像素的聚类算法自动分割细胞核。一个决策树分类方案，通过分析从分割提取的核特征来区分低，中，高级别的肿瘤。核与支持向量机分类器。核被分割，平均准确率为86.5%。低、中和高级别肿瘤的识别准确率分别为95%、88.3%和91%。所提出的算法可以作为组织病理学家的第二意见工具。
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.