Suppressed Non-local Spatial Intuitionistic Fuzzy C-means Image Segmentation Algorithm
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摘要: 针对传统的模糊C-均值(FCM)算法没有考虑图像像素的空间邻域信息，对噪声敏感，算法收敛较慢等问题，该文提出一种抑制式非局部空间直觉模糊C-均值图像分割算法。首先，通过计算像素的非局部空间信息提高抗噪能力，克服传统的FCM算法只考虑图像单个像素的灰度特征信息的缺陷，提高分割精度。其次，根据直觉模糊集理论，通过“投票模型”自适应生成犹豫度作为抑制因子修正隶属度，提高算法的运行效率。实验结果表明，该算法对噪声鲁棒性较强并且有较好的分割性能。Abstract: In order to deal with these issues of the traditional Fuzzy C-Means (FCM) algorithm, such as without consideration of the spatial neighborhood information of pixels, noise sensitivity and low convergence speed, a suppressed non-local spatial intuitionistic fuzzy c-means image segmentation algorithm is proposed. Firstly, in order to improve the accuracy of segmentation image, the non-local spatial information of pixel is used to improve anti-noise ability, and to overcome the shortcomings of the traditional FCM algorithm, which only considers the gray characteristic information of single pixel. Secondly, by using the ‘voting model’ based on the intuitionistic fuzzy set theory, the hesitation degrees are adaptively generated as inhibitory factors to modify the membership degrees, and then the operating efficiency is increased. Experimental results show that the new algorithm is robust to noise and has better segmentation performance.
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表 1 4种算法对人工图像的分割结果指标
分割算法 Vpc Vpe FCM 0.8449 0.3065 FCM-IFS 0.8431 0.3109 FCM_NLS 0.9223 0.1777 本文算法 0.9229 0.1765 
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表 2 4种算法的分割结果指标
测试图像 分割算法 Vpc Vpe PSNR DC #15088 FCM 0.8484 0.2535 3.2419 0.4046 FCM-IFS 0.8581 0.2397 3.2490 0.4316 FCM_NLS 0.9229 0.1502 8.0774 0.6364 本文算法 0.9245 0.1477 8.0832 0.6400 #42049 FCM 0.8992 0.1790 2.2577 0.8517 FCM-IFS 0.9010 0.1765 2.2609 0.8541 FCM_NLS 0.9267 0.1371 10.5479 0.8947 本文算法 0.9270 0.1369 10.5636 0.8937 #24063 FCM 0.7928 0.3778 4.0303 0.9239 FCM-IFS 0.7956 0.3740 3.9493 0.9259 FCM_NLS 0.8331 0.3262 7.1709 0.9707 本文算法 0.8371 0.3208 7.9568 0.9741 #118035 FCM 0.8382 0.2927 3.0429 0.9438 FCM-IFS 0.8377 0.2948 3.0053 0.9433 FCM_NLS 0.8410 0.2936 11.0120 0.9519 本文算法 0.8594 0.2693 11.7764 0.9487
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表 3 强度0.02高斯噪声下两种算法运行时间对比
图像编号 FCM_NLS算法 本文算法 运行时间(s) 迭代次数 运行时间(s) 迭代次数 #15088 439.63 24 423.17 13 #42049 421.56 23 420.66 14 #24063 436.69 37 418.97 20 #118035 404.66 64 405.08 19 #3096 427.91 94 413.07 36 #135069 433.81 38 426.97 26 #8068 442.48 19 408.73 14 #113044 436.53 28 405.51 18 #27 420.28 37 409.27 23 #101027 427.13 27 408.06 25
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