使用现代图像处理方法的医疗信息学

R. Kashyap, Surendra Rahamatkar
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引用次数: 2

摘要

医学图像分割是异常状态图像分析的第一步,大大减少了图像物质调查的多面性。基于局部区域的活动等值线可能会有一些负担。分割非常依赖于底层的形状选择,这是一个非常有能力的差事。在一些情况下,手工协作是不可行的。为了克服这些不足,提出了一种基于阴影增强的Harris查找器和中心显著性图的医学图像观看者考虑对象的无监督分割方法。研究了不同的技术来考虑图像数据,并提出了一种以前使用的基于能量的活动轮廓法,该方法依赖于高确定性预测的选择来分配假名,从而减少了人工解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Healthcare Informatics Using Modern Image Processing Approaches
Medical image segmentation is the first venture for abnormal state image analysis, significantly lessening the multifaceted nature of substance investigation of pictures. The local region-based active contour may have a few burdens. Segmentation comes about to intensely rely on the underlying shape choice which is an exceptionally capable errand. In a few circumstances, manual collaborations are infeasible. To defeat these deficiencies, the proposed method for unsupervised segmentation of viewer's consideration object of medical images given the technique with the help of the shading boosting Harris finder and the center saliency map. Investigated distinctive techniques to consider the image data and present a formerly utilized energy-based active contour method dependent on the choice of high certainty forecasts to allocate pseudo-names consequently with the point of diminishing the manual explanations.
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