基于多特征发现的生理切片骨小梁识别技术研究  

Study on Physiological Slice Trabecular Recognition Based on Multi-feature-found

作　　者：叶含笑[1] 金红婷[1] 袁昕[2] 应航[2] 

机构地区：[1]浙江中医药大学,浙江杭州310053 [2]浙江大学,浙江杭州310013

出　　处：《计量学报》2017年第1期78-81,共4页Acta Metrologica Sinica

基　　金：国家自然科学基金(81373669)

摘　　要：目标区域计算机自动识别是骨组织形态计量学的发展趋势。研究图像多特征信息,识别苏木素尹红染色病理切片中的骨小梁。分析骨组织病理切片HSL颜色空间通道的直方图特征,进行基于颜色通道的阈值分割,采用Lazy Snapping算法获得骨小梁深层次特征信息,改进扫描种子填充算法,使其融合目标区域多特征信息。实验结果表明改进的扫描种子填充算法将复杂图像骨小梁提取时间从常规软件的10多分钟提速到257ms。改进的扫描线算法,能快速获得稳健的目标区域。Target recognition is a development trend of bone histomorphometry. Study on the characteristics of image information, it is necessary to discriminate trabecular bone of bone tissue sliced by yin hung-hematoxylin. Analysis bone tissue image histogram channel characteristics of HSL color space, based on the image segmentation threshold of color channels, by Lazy Snapping method, it will obtain the image features of trabecular bone in-depth information, and it will improve scanning seed filling algorithm to extract multi feature information by fusion target area. The results show that extract trabecular bone by analysis image multi-feature let repetitive operation improve, improved scanning seed filling algorithm let complex image such as trabeeular extraction speed from ten minutes to 257 ms. Improved scan line algorithm can get the robust target area more quickly.

关 键 词：计量学 骨组织形态测量 病理切片 图像多特征发现 骨小梁识别 

分 类 号：TB96[机械工程—光学工程]