Searching digital biomedical images is a challenges problem. Prevalent retrieval techniques involve human-supplied text annotations to describe image contents. Biomedical images, such as pathology slides, usually have higher resolution than general-purpose pictures, making it additionally difficut to index. Precise object segmentation is also extremely difficult and is still an open problem. We are developing a multiresolution region-based retreival system for high-resolution biomedical image databases. The system, based on wavelets, the IRM (Integrated Region Matching) distance, and image classification, is highly robust to inacurate image segmentation and various visual alterations. Tested on a database of more than 70,000 pathology image fragments, the system has demonstrated high accuracy and fast speed.
搜索数字生物医学图像是一个难题。普遍的检索技术涉及人类提供的文本注释来描述图像内容。生物医学图像(例如病理切片)通常比通用图片具有更高的分辨率,因此很难索引。精确的对象分割也非常困难,并且仍然是一个未解决的问题。我们正在开发用于高分辨率生物医学图像数据库的基于多分辨率区域的返修系统。该系统基于小波,IRM(集成区域匹配)距离和图像分类,对于不精确的图像分割和各种视觉变化具有很高的鲁棒性。该系统在超过70,000个病理图像片段的数据库上进行了测试,证明了其较高的准确性和速度。 P>
机译:生物医学图像检索系统的性能评估ImageCLEF 2004-2013年医学图像检索任务概述
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机译:SIMPLlcity:图片库和生物医学图像数据库的基于区域的图像检索系统
机译:使用多种功能的基于区域的图像检索。
机译:评估生物医学图像检索系统的性能– ImageCLEF 2004–2013医学图像检索任务概述
机译:基于区域的生物医学图像检索