为解决复杂背景中准确地进行文字分割的问题,提出了一种应用stroke滤波器进行文本分割的新方法。首先进行stroke滤波器的合理设计,并应用所设计的stroke滤波器来判别文本的彩色极性,得到初次分割的二值图。然后进行基于区域生长的文字...为解决复杂背景中准确地进行文字分割的问题,提出了一种应用stroke滤波器进行文本分割的新方法。首先进行stroke滤波器的合理设计,并应用所设计的stroke滤波器来判别文本的彩色极性,得到初次分割的二值图。然后进行基于区域生长的文字分割。最后,应用OCR(optical character recognition)模块提高文本分割的整体性能。将提出的算法与其他算法进行了比较,结果表明,所提算法更为有效。展开更多
Accurate and automatic segmentation of hyper-acute ischemic infarct from magnetic resonance imaging is of great importance in clinical trials. Manual delineation is labor intensive, exhibits great variability due to u...Accurate and automatic segmentation of hyper-acute ischemic infarct from magnetic resonance imaging is of great importance in clinical trials. Manual delineation is labor intensive, exhibits great variability due to unclear infarct boundaries, and most importantly, is not practical due to urgent time requirement for prompt therapy. In this paper, segmentation of hyper-acute ischemic infarcts from diffusion weighted imaging based on Support Vector Machine (SVM) is explored. Experiments showed that SVM could provide good agreement with manual delineations by experienced experts to achieve an average Dice coefficient of 0.7630.121. The proposed method could achieve significantly higher segmentation accuracy and could be a potential tool to assist clinicians for quantifying hyper-acute infarction and decision making especially for thrombolytic therapy.展开更多
文摘为解决复杂背景中准确地进行文字分割的问题,提出了一种应用stroke滤波器进行文本分割的新方法。首先进行stroke滤波器的合理设计,并应用所设计的stroke滤波器来判别文本的彩色极性,得到初次分割的二值图。然后进行基于区域生长的文字分割。最后,应用OCR(optical character recognition)模块提高文本分割的整体性能。将提出的算法与其他算法进行了比较,结果表明,所提算法更为有效。
文摘Accurate and automatic segmentation of hyper-acute ischemic infarct from magnetic resonance imaging is of great importance in clinical trials. Manual delineation is labor intensive, exhibits great variability due to unclear infarct boundaries, and most importantly, is not practical due to urgent time requirement for prompt therapy. In this paper, segmentation of hyper-acute ischemic infarcts from diffusion weighted imaging based on Support Vector Machine (SVM) is explored. Experiments showed that SVM could provide good agreement with manual delineations by experienced experts to achieve an average Dice coefficient of 0.7630.121. The proposed method could achieve significantly higher segmentation accuracy and could be a potential tool to assist clinicians for quantifying hyper-acute infarction and decision making especially for thrombolytic therapy.
