中药光谱成像图像自适应区域增长分割方法

Self-adaptive region growing algorithm to segment images of spectral imaging for TCM assessment

为了消除背景噪声对药材光谱图像检测结果的干扰,根据中药材光谱图像的特点,设计一种能够自适应对中药材光谱图像进行有效区域(ROI)分割的区域增长算法。该区域增长算法根据药材光谱图像的灰度直方图分布来自动选取种子点和分割阈值,在生长的同时进行连通性分析,生长结束后通过区域填充技术来消除图像中出现的孔洞。实验表明:该方法能够自动、准确地进行ROI分割,分割偏差小于8%,并且能较好地消除噪声的干扰,没有产生无意义的生长区域。

The spectral imaging method for assessing the traditional Chinese medicine（TCM） can evaluate the quality of the medicines and identify their authenticity by using their spectral images.It can also make the assessing procedure fast and non-destructive.In order to eliminate the influence of the background noise on the assessing results of TCM spectral images,extraction of ROI（region of interest） of the TCM spectral images is needed.Since the difference inherent in different kinds of TCM spectral images and the available fixed-threshold segmentation method can not meet the demand of self-adaptive segment,a self-adaptive region growing algorithm to extract ROI of the images is proposed based on the characteristic of TCM.This algorithm can automatically choose the seeds and threshold of the region growing process according to the gray-level histograms of the TCM spectral images,the connectivity among the pixels is taken into the consideration during the growing period,and the regionfill technique is applied to modify the cavity in ROI after the growing.It proves that this algorithm can extract ROI of the images automatically and precisely,the deviation of segmentation is less than 8%,its antinoise ability is good,and the nonsensical growing result is not appear.