Goudas Theodosios, Maglogiannis Ilias
Department of Digital Systems, University of Piraeus, Grigoriou Lampraki 126, PC 18532, Piraeus, Greece,
J Med Syst. 2015 Mar;39(3):31. doi: 10.1007/s10916-015-0225-3. Epub 2015 Feb 14.
The paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images. The proposed tool utilizes adaptive thresholding and a Support Vector Machines classifier. The segmentation results are enhanced through a Majority Voting and a Watershed technique, while an object labeling algorithm has been developed for the fast and accurate validation of the recognized cells. Expert pathologists evaluated the tool and the reported results are satisfying and reproducible.
本文提出了一种先进的图像分析工具,用于在显微镜图像中对癌细胞和凋亡细胞进行准确、快速的表征和定量分析。所提出的工具利用了自适应阈值处理和支持向量机分类器。通过多数投票和分水岭技术增强分割结果,同时开发了一种目标标记算法,用于对识别出的细胞进行快速、准确的验证。专家病理学家对该工具进行了评估,报告的结果令人满意且具有可重复性。