最大稳定极值区域与角点检测结合的医疗图像文本区域检测算法

医疗图像的文本区域检测在网络协同诊疗和医疗云的迅速发展中起着至关重要的作用。鉴于医疗图像相对复杂、文本字体太小而难以提取这一特殊问题，本文有针对性地提出了一种基于最大稳定极值区域算法与改进的角点检测算法相结合的医疗图像文本区域检测算法。该算法首先使用成分特征分析对最大稳定极值区域进行滤除，其次使用改进的SUSAN（Smallest Univalue Segment Assimilating Nucleus，最小核值相似区）角点检测算子对剩余最大稳定极值区域进行检测并滤除，最后将检测出的所有文本区域使用膨胀处理进行合并即可得到医疗图像的文本区域。实验结果表明，使用该算法提取出的医疗图像文本区域的准确率、召回率和综合性能分别为0.9、0.92和0.91，达到了理想的检测效果。

Research on Multi-featured Medical Image Text Area Detection Algorithm

The text area detection of medical images plays a crucial role in the rapid development of network collaborative diagnosis and medical cloud. In view of the relatively complicated medical image and the small font size, it is difficult to extract this special problem. This paper proposes a medical image text region detection algorithm based on the combination of the maximally stable extremal regions algorithm and the improved corner detection algorithm. The algorithm first uses the component feature analysis to filter the maximally stable extremal regions, and then uses the improved SUSAN corner detection operator to detect and filter the remaining maximally stable extremal regions, and finally use all the detected text regions to expand. Processing merges to get the text area of the medical image. The experimental results show that the accuracy, recall and overall performance of the medical image text area extracted by the algorithm are 0.9, 0.92 and 0.91, respectively, which achieves the ideal detection effect.