Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan.
J Med Syst. 2012 Oct;36(5):2761-8. doi: 10.1007/s10916-011-9752-8. Epub 2011 Jul 5.
Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.
近年来，数据采集、处理和可视化技术的进步对医学成像产生了巨大的影响。然而，医学图像的解释仍然几乎总是由放射科医生来完成。人工智能和图像处理的发展已经显示出计算机辅助诊断（CAD）越来越大的潜力。尽管如此，开发一种通用的方法来处理各种常用类型的医学图像（例如 X 射线、MRI 和超声图像）仍然具有挑战性。为了便于诊断，我们建议使用图像分割来使用自组织映射（SOM）发现感兴趣区域（ROI）。我们设计了一种两阶段 SOM 方法，可用于精确识别医学图像的主要颜色，然后将其分割成几个小区域。此外，通过适当进行递归合并步骤，将较小的区域合并为较大的区域，放射科医生通常可以在医学图像中识别一个或多个 ROI。
