基于Otsu算法与数学形态学的图像分割算法研究
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摘要
图像分割是计算机视觉领域的一个重要而基本的问题；是图像处理、分析和理解等领域中的一项关键技术。图像分割的研究一直都是图像处理技术研究中的热点和焦点之一。图像分割的算法层出不穷,在计算机视觉、模式识别和医学图像处理等实际中得到了广泛的应用。
阈值图像分割方法是最常用的图像分割方法。它通过选取阈值将图像分为目标和背景两部分,其关键技术是阈值的选取。最大类间方差(Otsu)法是常用的阈值图像分割方法之一,基于直方图,通过考虑图像的灰度信息选取阈值。但是在图像信噪比低,灰度差异不明显,目标面积小等情况下,该算法效率会随之下降,甚至会产生错误的分割。
针对Otsu分割算法的不足,根据不同军事目标红外图像的不同特点采用了不同的分割方法与之结合,提出了改进Otsu算法。新算法不仅考虑图像的灰度信息,而且还考虑领域空间的相关信息的特点来保证图像分割精度,然后针对分割结果的空洞利用了数学形态学进行了处理,以达到最好的效果。
文中对改进算法的有效性进行了实验,实验结果表明,新算法可行、可靠,达到了理想的分割效果,实现了比传统方法更快速、稳定的图像分割。
Image segmentation is one of the basic and important techniques in computer vision, and it is a pivotal technique in image process, analysis and image engineering. The study on the image segmentation is always one of the key points in the image segmentation technique study, many image segmentation algorithms are advanced, and it is widely used in computer vision pattern recognition and medicine image processing.
Threshold image segmentation method is in common use in the image segmentation. It distingusihes the image into objects and background by picking threshold, so the threshold picking is the key technique. The Otsu method is often used as one of the threshold image segmentation methods, which is based on histogram, chooses threshold by analysing image gray level information. But this method is ineffective when facing complex image, such as image with low SNR or gray level discrepancy and tiny area, the algorithms effect decrease, even occur errors.
Aiming at the shortage of Otsu segmentation algorithm, this paper studies different algorithms, focuses on different military objects'infrared image segmentation methods that combine with the Otsu method, and advances a improved method. The new method not only considers the gray information of the image, but the related information of domain space to ensure the accuracy of image segmentation. And then aiming at the hole in the segmentation result, this paper fills the hole with Mathematic Morphology to get the best results.
This paper does the experiment with the improved method for the validity, as the result shows, this algorithm is feasible and credible,and gives an ideal image segmentation outcome, and it is more fast and steady than conventional method.
阈值图像分割方法是最常用的图像分割方法。它通过选取阈值将图像分为目标和背景两部分,其关键技术是阈值的选取。最大类间方差(Otsu)法是常用的阈值图像分割方法之一,基于直方图,通过考虑图像的灰度信息选取阈值。但是在图像信噪比低,灰度差异不明显,目标面积小等情况下,该算法效率会随之下降,甚至会产生错误的分割。
针对Otsu分割算法的不足,根据不同军事目标红外图像的不同特点采用了不同的分割方法与之结合,提出了改进Otsu算法。新算法不仅考虑图像的灰度信息,而且还考虑领域空间的相关信息的特点来保证图像分割精度,然后针对分割结果的空洞利用了数学形态学进行了处理,以达到最好的效果。
文中对改进算法的有效性进行了实验,实验结果表明,新算法可行、可靠,达到了理想的分割效果,实现了比传统方法更快速、稳定的图像分割。
Image segmentation is one of the basic and important techniques in computer vision, and it is a pivotal technique in image process, analysis and image engineering. The study on the image segmentation is always one of the key points in the image segmentation technique study, many image segmentation algorithms are advanced, and it is widely used in computer vision pattern recognition and medicine image processing.
Threshold image segmentation method is in common use in the image segmentation. It distingusihes the image into objects and background by picking threshold, so the threshold picking is the key technique. The Otsu method is often used as one of the threshold image segmentation methods, which is based on histogram, chooses threshold by analysing image gray level information. But this method is ineffective when facing complex image, such as image with low SNR or gray level discrepancy and tiny area, the algorithms effect decrease, even occur errors.
Aiming at the shortage of Otsu segmentation algorithm, this paper studies different algorithms, focuses on different military objects'infrared image segmentation methods that combine with the Otsu method, and advances a improved method. The new method not only considers the gray information of the image, but the related information of domain space to ensure the accuracy of image segmentation. And then aiming at the hole in the segmentation result, this paper fills the hole with Mathematic Morphology to get the best results.
This paper does the experiment with the improved method for the validity, as the result shows, this algorithm is feasible and credible,and gives an ideal image segmentation outcome, and it is more fast and steady than conventional method.
引文
1.章毓晋.图像分割[M],北京：科学出版社,2001,2-6.
2.崔屹.数字图像处理技术及应用[M],北京：电子工业出版社,1997,6-17.
3. Zhang Yujin. A Review of Recent Evaluation Methods for Image Segmentation[C]. International Symposium on Signal Processing and Its Application,2001,34(8):130-165.
4. Pal NR, Pal SK. A Review of image segmentation techniques[J]. Pattern Recognition,993, 26(9):1277-1294.
5.罗希平,田捷诸,葛婴等.图像分割方法综述[J],模式识别与人工智能,1999,12(3)：300-312.
6. Zhang YJ. A Survey on Evaluation Methods for Image Segmentation[J]. Pattern Recogniti--on,1996,29(8):1335-1346.
7.王爱民,沈兰荪.图像分割研究综述[J],测控技术,2000,19(5)：1-5.
8. SAHOO P K, SOLTARNI S, WANG A K. A Survey of Thresholding Techniques Computer Vision[J]. Graphics and Image Processing,1998,32(41):233-377.
9.魏弘博,吕振肃,蒋田仔等.图像分割技术纵览[J],甘肃科学学报,2004,16(2)：19-24.
10.史飞.基于PCNN的图像处理技术研究[D],兰州大学,2003.
11. JIAN-PING FAN, DAVID K Y. Automatic image segmentation by integrating color-edge extraction and seeded region growing[J]. IEEE Transon Image Processing,2001,10(10):1454-1466.
12.柏子游,张勇,虞烈.一种彩色图像的色彩分割方法[J],模式识别与人工智能,1999,12(2)：241-244.
13.钱志柏.基于模糊聚类和PCNN的图像分割新算法研究[D],兰州大学,2004.
14. XIAO-PING ZHANG, MITA D. DESAI. Segmentation of bright targets using wavelets and adaptive thresholding[C]. IEEE Transactions on Image Processing,2001,10(7):1020-1030.
15. JOHN M.GAUCH. Image segmentation and analysis via multi-scale gradient watershed hierarchies[J]. IEEE transactions on Image Processing,1999,8(1):69-79.
16.姬光荣,时鹏,秦勃等.基于内禀性结构模式的图像分割[J],电子学报,2002,30(10)：1428-1430.
17.乐宋进,武和雷,胡泳芬.图像分割方法的研究现状与展望[J],南昌水专学报,2004,23(2)：15-20.
18. ZI-KUAN CHEN, TAO YANG, XIN CHEN, et al. Wavelet based adaptive thresholding method for image segmentation[J]. Optical Engineering,2001,40(5):868-874.
19.魏志成,周激流.一种新的图像分割自适应算法的研究[J],中国图像图形学报,2000,5(3)：216-220.
20.王月兰,曾迎生.信息融合技术在彩色图像分割方法中的应用[J],计算机学报,2003,23(7)：763-767.
21.单志广,魏涛等.一种基于视觉熵的图像分割压缩方法[J],北京科技大学学报,2000,22(2)：185-189.
22. Y J ZHANG A survey on evaluation methods for image segmentation[J]. Pattern Recognition,1996,29(8):1335-1346.
23.刘勃.基于PCNN的图像滤波与图像分割[D],兰州大学,2005.
24.郝智泉,吕汉兴.牌照生产中图像分割技术的应用[J],机械与电子,2003,21(2)：74-76.
25.瞿继双,王超,王正志.一种基于多阈值的形态学提取遥感图像海岸线特征方法[J],中国图像图形学报,2003,52(3)：805-809.
26. Otsu NA. Threshold Selection Method from Gray-level Histogram[J]. IEEE Traps,1979, SMC-9:62-66.
27. SU Lee, et al. A comparative Performance Study of Several Global Thresholding Techniques for Segmentation. CVGIP,1990,52(21):171-190.
28.刘京南,陈从颜,余玲玲等.一种快速二维熵阈值分割方法[J],计算机应用研究,2002,19(1)：67-68.
29. Chen WT, Wen CH, Yang CW. A Fast Two-dimension Entropic Thresholding Algorithm[J]. Pattern Recognition,1994,27(9):885-893.
30.张毅军,吴雪芊,夏良正.二维熵图像阈值分割的快速递推算法[J],模式识别与人工智能,1997,10(3)：259-264.
31.梁光明,刘东华,李波,唐朝京.用于显微细胞图像的二维自适应阈值分割算法的优化[J],中国图像图形学,2003,8(7)：764-768.
32. Reddi SS, Rudin SF, Keshavan HR. An Optimal Multiple Threshold Scheme for Image Segmentation[J]. IEEE Trans Systems, Man, Cybernetics,1984,14(4):661-665.
33. Jian Gong, Liyuan Li, Wsina Chen. Fast Recursive Algorithms for Two-dimensional Thresholding[J]. Pattern Recognition,1998,31(3):295-300.
34.景晓军,蔡安妮,孙景鳌.一种基于二维最大类间方差的图像分割算法[J],通信学报,2001,22(4)：71-76.
35.郝颖明,朱枫.二维Otsu自适应阈值的快速算法[J],中国图像图形学报,2005,10(4)：484-488.
36.饶海涛,翁桂荣.基于数学形态学的图像边缘检测[J],苏州大学学报(自然科学版), 2004,20(2)：42-45.
37.周赞,李久贤,夏良正.基于区域增长的红外图像分割[J],南京理工大学学报,Dec. 2002,26(Supplement):25-28.
38.付忠良.图像阈值选取方法-Otsu方法的推广[J],计算机应用,2000,20(5)：37-39.
39. Lee SU, Chung SY. A comparative performance study of several global thresholding techniques for segmentation [J]. Computer Vision Graphics and Image Processing,1990,52 (3):171-190.
40.刘莞尔,严高师,戴霞.飞机的红外图像分割算法[J],电子科技大学学报,2008,29(3)：2-3.
41.蒋一明,王克勇,郑链.坦克目标红外图像分割算法研究[J],北京理工大学学报,2007,36(Supplement):2-3.
42.张波,张焕春.经亚枝.一种采用数学形态学方法的姿态指示器填充算法[J],南京航空航天大学学报,2003,35(2)：152-156.
43. WANG Hongbo, ZHUANG Zhihong, ZHANG Qingtai, et al. Infrared image segmentation algorithm in image guidance[J]. Infrared and Laser Engineering,2003,32(3):234-238.
2.崔屹.数字图像处理技术及应用[M],北京：电子工业出版社,1997,6-17.
3. Zhang Yujin. A Review of Recent Evaluation Methods for Image Segmentation[C]. International Symposium on Signal Processing and Its Application,2001,34(8):130-165.
4. Pal NR, Pal SK. A Review of image segmentation techniques[J]. Pattern Recognition,993, 26(9):1277-1294.
5.罗希平,田捷诸,葛婴等.图像分割方法综述[J],模式识别与人工智能,1999,12(3)：300-312.
6. Zhang YJ. A Survey on Evaluation Methods for Image Segmentation[J]. Pattern Recogniti--on,1996,29(8):1335-1346.
7.王爱民,沈兰荪.图像分割研究综述[J],测控技术,2000,19(5)：1-5.
8. SAHOO P K, SOLTARNI S, WANG A K. A Survey of Thresholding Techniques Computer Vision[J]. Graphics and Image Processing,1998,32(41):233-377.
9.魏弘博,吕振肃,蒋田仔等.图像分割技术纵览[J],甘肃科学学报,2004,16(2)：19-24.
10.史飞.基于PCNN的图像处理技术研究[D],兰州大学,2003.
11. JIAN-PING FAN, DAVID K Y. Automatic image segmentation by integrating color-edge extraction and seeded region growing[J]. IEEE Transon Image Processing,2001,10(10):1454-1466.
12.柏子游,张勇,虞烈.一种彩色图像的色彩分割方法[J],模式识别与人工智能,1999,12(2)：241-244.
13.钱志柏.基于模糊聚类和PCNN的图像分割新算法研究[D],兰州大学,2004.
14. XIAO-PING ZHANG, MITA D. DESAI. Segmentation of bright targets using wavelets and adaptive thresholding[C]. IEEE Transactions on Image Processing,2001,10(7):1020-1030.
15. JOHN M.GAUCH. Image segmentation and analysis via multi-scale gradient watershed hierarchies[J]. IEEE transactions on Image Processing,1999,8(1):69-79.
16.姬光荣,时鹏,秦勃等.基于内禀性结构模式的图像分割[J],电子学报,2002,30(10)：1428-1430.
17.乐宋进,武和雷,胡泳芬.图像分割方法的研究现状与展望[J],南昌水专学报,2004,23(2)：15-20.
18. ZI-KUAN CHEN, TAO YANG, XIN CHEN, et al. Wavelet based adaptive thresholding method for image segmentation[J]. Optical Engineering,2001,40(5):868-874.
19.魏志成,周激流.一种新的图像分割自适应算法的研究[J],中国图像图形学报,2000,5(3)：216-220.
20.王月兰,曾迎生.信息融合技术在彩色图像分割方法中的应用[J],计算机学报,2003,23(7)：763-767.
21.单志广,魏涛等.一种基于视觉熵的图像分割压缩方法[J],北京科技大学学报,2000,22(2)：185-189.
22. Y J ZHANG A survey on evaluation methods for image segmentation[J]. Pattern Recognition,1996,29(8):1335-1346.
23.刘勃.基于PCNN的图像滤波与图像分割[D],兰州大学,2005.
24.郝智泉,吕汉兴.牌照生产中图像分割技术的应用[J],机械与电子,2003,21(2)：74-76.
25.瞿继双,王超,王正志.一种基于多阈值的形态学提取遥感图像海岸线特征方法[J],中国图像图形学报,2003,52(3)：805-809.
26. Otsu NA. Threshold Selection Method from Gray-level Histogram[J]. IEEE Traps,1979, SMC-9:62-66.
27. SU Lee, et al. A comparative Performance Study of Several Global Thresholding Techniques for Segmentation. CVGIP,1990,52(21):171-190.
28.刘京南,陈从颜,余玲玲等.一种快速二维熵阈值分割方法[J],计算机应用研究,2002,19(1)：67-68.
29. Chen WT, Wen CH, Yang CW. A Fast Two-dimension Entropic Thresholding Algorithm[J]. Pattern Recognition,1994,27(9):885-893.
30.张毅军,吴雪芊,夏良正.二维熵图像阈值分割的快速递推算法[J],模式识别与人工智能,1997,10(3)：259-264.
31.梁光明,刘东华,李波,唐朝京.用于显微细胞图像的二维自适应阈值分割算法的优化[J],中国图像图形学,2003,8(7)：764-768.
32. Reddi SS, Rudin SF, Keshavan HR. An Optimal Multiple Threshold Scheme for Image Segmentation[J]. IEEE Trans Systems, Man, Cybernetics,1984,14(4):661-665.
33. Jian Gong, Liyuan Li, Wsina Chen. Fast Recursive Algorithms for Two-dimensional Thresholding[J]. Pattern Recognition,1998,31(3):295-300.
34.景晓军,蔡安妮,孙景鳌.一种基于二维最大类间方差的图像分割算法[J],通信学报,2001,22(4)：71-76.
35.郝颖明,朱枫.二维Otsu自适应阈值的快速算法[J],中国图像图形学报,2005,10(4)：484-488.
36.饶海涛,翁桂荣.基于数学形态学的图像边缘检测[J],苏州大学学报(自然科学版), 2004,20(2)：42-45.
37.周赞,李久贤,夏良正.基于区域增长的红外图像分割[J],南京理工大学学报,Dec. 2002,26(Supplement):25-28.
38.付忠良.图像阈值选取方法-Otsu方法的推广[J],计算机应用,2000,20(5)：37-39.
39. Lee SU, Chung SY. A comparative performance study of several global thresholding techniques for segmentation [J]. Computer Vision Graphics and Image Processing,1990,52 (3):171-190.
40.刘莞尔,严高师,戴霞.飞机的红外图像分割算法[J],电子科技大学学报,2008,29(3)：2-3.
41.蒋一明,王克勇,郑链.坦克目标红外图像分割算法研究[J],北京理工大学学报,2007,36(Supplement):2-3.
42.张波,张焕春.经亚枝.一种采用数学形态学方法的姿态指示器填充算法[J],南京航空航天大学学报,2003,35(2)：152-156.
43. WANG Hongbo, ZHUANG Zhihong, ZHANG Qingtai, et al. Infrared image segmentation algorithm in image guidance[J]. Infrared and Laser Engineering,2003,32(3):234-238.
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