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摘要: 在眼科疾病检测中，为了对被检测者进行快速、准确、自动化的瞳孔定位，提出一种改进径向对称变换的瞳孔中心点定位算法。首先利用灰度积分投影法结合最大类间方差法，完成对人眼图像的粗分割，并根据多团块筛选条件提取出只包含瞳孔的感兴趣区域(Region Of Interest，ROI)。然后对ROI采用最小外接矩形结合灰度级形态学线性滤波方法，完成搜索半径范围的设置。最后，利用改进的径向对称变换算法进行瞳孔中心点定位。实验结果表明：本文算法的定位误差在8 pixel以内，平均定位时间为0.366 s，能够适应人眼图像中噪声干扰、采集不完整等大量非理性状态，满足多种红外眼科疾病检测设备对瞳孔定位算法的要求。Abstract: In order to quickly, accurately and automatically locate a pupil in ophthalmic disease detection, a location algorithm for a pupil’s center based on radial symmetry transformation was proposed. Firstly, the gray integral projection method combined with the maximum class square error method was used to complete the rough segmentation of human eye image, and a Region Of Interest (ROI) solely containing the pupil was extracted according to multi-lump screening conditions. Then the search radius range was set by using a minimum circumscribed rectangle on the ROI combined with gray-level morphological linear filtering. Finally, the pupil center was located using an improved radial symmetry transformation algorithm. The experimental results show that the location error of this algorithm is less than 8 pixels and the average processing time is 0.366 s. It can adapt to a large number of irrational states such as noise interference and an incomplete collection of human eye images and meets the pupil location performance requirements for many kinds of infrared ophthalmology disease detection equipment.
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图 2 人眼图像ROI分割示意图。（a）竖直投影曲线对照图；(b)水平投影曲线对照图；(c)粗分割图像；(d)二值化图像
Figure 2. Schematic diagram of ROI segmentation of a human eye image. (a) Vertical projection curve contrast diagram; (b) horizontal projection curve contrast diagram; (c) coarse segmentation image; (d) binary image
图 3 受睫毛遮挡的ROI提取示意图。(a)样本1图像；(b)样本1二值化图像；(c)样本1的ROI图像；(d)样本2图像；(e)样本2的二值化图像；(f)样本2的ROI图像,图像源于数据库CASIA-IrisV4
Figure 3. Schematic diagram of ROI extraction when pupil was obscured by eyelashes. (a) Image of sample 1; (b) binarization image of sample 1; (c) ROI image of the sample 1; (d) image of sample 2; (e) binarization image of sample 2; (f) ROI image of sample 2, images are derived from the CASIA-IrisV4 database
图 4 结合灰度级形态学滤波的ROI提取示意图。（a）线性结构元素；（b）样本1的ROI图像；（c）样本2的ROI图像
Figure 4. Schematic diagram of ROI extraction combined with grayscale morphological filtering. (a) Linear structural element; (b) ROI image of sample 1; (c) ROI image of sample 2
图 5 ROI的最小外接矩形示意图
Figure 5. Schematic diagrams of minimum circumscribed rectangles of the ROI
图 6 (a)~(d)为瞳孔定位结果图；(e)~(h)为人眼图像定位结果
Figure 6. (a)~(d) Pupil positioning; (e)~(h) localization of human eye
图 7 不完整瞳孔区域图像的定位结果图。(a)~(d)人眼图像； (e)~(h)定位结果
Figure 7. Incomplete pupil area image positioning. (a)~(d) Images of human eye; (e)~(h) localization of human eye
图 8 3种定位算法结果对比
Figure 8. Comparison of localization results by three different algorithms
图 9 3种算法在CASIA-IrisV4数据库的定位结果
Figure 9. Comparison of localization results by three different algorithms in CASIA-IrisV4 database
表 1 3种算法的精确度和实时性比较
Table 1. Comparison of accuracy and real-time performance of three algorithms
定位方法 定位误差/pixel 定位时间/s 本文算法 6.318 0.366 传统径向对称变换算法 103.681 4.610 基于梯度均值的定位算法 7.242 10.923 
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表 2 3种算法适用性比较
Table 2. Comparison of applicability of three algorithms
定位方法 定位准确率/% 定位时间/s 本文方法 98 0.053 传统径向对称变换算法 81 1.093 基于梯度均值的定位算法 93 8.583
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