融合纹理信息的SLIC算法在医学图像中的研究
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摘要
随着超像素算法的发展, SLIC (Simple linear iterative clustering)由于时间复杂度低及良好的分割结果而被广泛关注.但是由于传统的SLIC算法并没有考虑到图像的纹理信息,故而对于纹理较复杂的图像分割效果略有不足. LBP (Local binary pattern)对于纹理的识别有着优秀的表现而且时间复杂度低,但是对于噪声的鲁棒性较差,并且会产生纹理偏移.因此,本文首先针对传统的LBP中存在的问题进行改进;然后将改进后的算法与SLIC结合,提出一种融合纹理信息的超像素算法—SLICT (Simple linear iterative clustering based on texture).为验证分割效果,本文选取纹理较多的医学图像进行实验,采用心脏MRI数据库进行验证并与其他超像素算法进行对比.实验表明, SLICT在边缘召回率、欠分割错误率以及覆盖率上的综合表现优于其他算法.从分割结果上来看, SLICT不但能够更好地贴合图像边缘,而且对于连续区域的分割效果也较好,更适合纹理较复杂的图像.
With the development of superpixel algorithm, SLIC(Simple linear iterative clustering) is widely noticed because of its low time-complexity and remarkable results. However, traditional SLIC does not take texture information into consideration, which leads to unsatisfactory results on texture-complex images. LBP(Local binary pattern) algorithm has good performance on texture analysis. But it is not robust enough to texture noise, and it may also lead to texture deviation. So, we propose simple linear iterative clustering based on texture(SLICT) by improving the traditional LBP algorithm and combining it with SLIC. To verify our method's effectiveness, we use medical images which contain complex texture from cardiac MRI dataset and compare our algorithm with other superpixel algorithms. The experiment results show that SLICT has better performance than the other algorithms in boundary recall, under-segmentation error and coverage rate. Besides, image segmentation results show that SLICT can adhere to boundary more tightly and have better results on consistent area, which proves SLICT is more suitable to texture-complex images.
With the development of superpixel algorithm, SLIC(Simple linear iterative clustering) is widely noticed because of its low time-complexity and remarkable results. However, traditional SLIC does not take texture information into consideration, which leads to unsatisfactory results on texture-complex images. LBP(Local binary pattern) algorithm has good performance on texture analysis. But it is not robust enough to texture noise, and it may also lead to texture deviation. So, we propose simple linear iterative clustering based on texture(SLICT) by improving the traditional LBP algorithm and combining it with SLIC. To verify our method's effectiveness, we use medical images which contain complex texture from cardiac MRI dataset and compare our algorithm with other superpixel algorithms. The experiment results show that SLICT has better performance than the other algorithms in boundary recall, under-segmentation error and coverage rate. Besides, image segmentation results show that SLICT can adhere to boundary more tightly and have better results on consistent area, which proves SLICT is more suitable to texture-complex images.
引文
1 Stutz D, Hermans A, Leibe B. Superpixels:an evaluation of the state-of-the-art. Computer Vision amd Image Understanding, 2018, 166(1):1-27
2 Chan Si-Xian, Zhou Xiao-Long, Zhang Zhuo, Chen ShengYong. Interactive multi-label image segmentation with multi-layer tumors automat. Acta Automatica Sinica, 2017,43(10):1829-1840(产思贤,周小龙,张卓,陈胜勇.一种基于超像素的肿瘤自动攻击交互式分割算法.自动化学报, 2017, 43(10):1829-1840)
3 Wang Yun-Fei, Feng Guo-Qiang, Liu Hua-Wei, Zhao BoXin. Superpixel-based mean and mean square deviation dark channel for single image fog removal. Acta Automatica Sinica, 2018, 44(3):481-489(汪云飞,冯国强,刘华伟,赵搏欣.基于超像素的均值–均方差暗通道单幅图像去雾方法.自动化学报, 2018, 44(3):481-489)
4 Lin Hua-Feng, Li Jing, Liu Guo-Dong, Liang Da-Chuan, Li Dong-Min. Saliency detection method using adaptive background template and spatial prior. Acta Automatica Sinica,2017, 43(10):1736-1748(林华锋,李静,刘国栋,梁大川,李东民.基于自适应背景模板与空间先验的显著性物体检测方法.自动化学报, 2017, 43(10):1736-1748)
5 Yang F, Lu H, Yang M H. Robust superpixel tracking. IEEE Transactions on Image Processing, 2014, 23(4):1639-1651
6 Liu Da-Qian, Liu Wan-Jun, Fei Bo-Wen, Qu Hai-Cheng.A new method of anti-interference matching under foreground constraint for target tracking. Acta Automatica Sinica, 2018, 44(6):1138-1152(刘大千,刘万军,费博雯,曲海成.前景约束下的抗干扰匹配目标跟踪方法.自动化学报, 2018, 44(6):1138-1152)
7 Wang Z L, Feng J S, Yan S C, Xi H S. Image classification via object-aware holistic superpixel selection. IEEE Transactions on Image Processing, 2013, 22(11):4341-4352.
8 Bodis-Szomoru A, Riemenschneider H, Gool L V. Superpixel meshes for fast edge-preserving surface reconstruction.In:Proceedings of the 2015 Computer Vision and Pattern Recognition. Boston, USA:IEEE, 2015. 2011-2020
9 Geiger A, Wang C. Joint 3D object and layout inference from a single RGB-D image. In:Proceedings of the 2015German Conference on Pattern Recognition. Aachen, Germany:Springer, 2015. 183-195
10 Gadde R, Jampani V, Kiefel M, Kappler D, Gehler P V. Superpixel convolutional networks using bilateral inceptions.In:Proceedings of the 2016 European Conference on Computer Vision. Amsterdam, The Netherlands:Springer, 2016.597-613
11 Shi J, Malik J. Normalized cuts and image segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8):888-905
12 Moore A P, Prince S J D, Warrell J, Mohammed U, Jones G.Superpixel lattices. In:Proceedings of the 2008 Computer Vision and Pattern Recognition. Anchorage, USA:IEEE,2008. 1-8
13 Veksler O, Boykov Y, Mehrani P. Superpixels and supervoxels in an energy optimization framework. In:Proceedings of the 2010 European Conference on Computer Vision. Heraklion, Greece:Springer, 2010. 211-224
14 Liu M Y, Tuzel O, Ramalingam S, Chellappa R. Entropy rate superpixel segmentation. In:Proceedings of the2011 Computer Vision and Pattern Recognition. Colorado Springs, USA:IEEE, 2011. 2097-2104
15 Bergh M V D, Boix X, Roig G, Capitani B D, Gool L V.SEEDS:superpixels extracted via energy-driven sampling.In:Proceedings of the 2012 European Conference on Computer Vision. Florence, Italy:Springer, 2012. 13-26
16 Shen J B, Du Y F, Wang W G, Li X L. Lazy random walks for superpixel segmentation. IEEE Transactions on Image Processing, 2014, 23(4):1451-1462
17 Yin S, Qian Y, Gong M. Unsupervised hierarchical image segmentation through fuzzy entropy maximization. Pattern Recognition, 2017, 68(1):245-259
18 Yin S, Zhao X, Wang W, Gong M. Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization. Pattern Recognition, 2014, 47(9):2894-2907
19 Wei X, Yang Q, Gong Y, Ahuja N, Yang M H. Superpixel hierarchy. IEEE Transactions on Image Processing, 2018,27(10):4838-4849
20 Levinshtein A, Stere A, Kutulakos K N, Fleet D J, Dickinson S J, Siddiqi K. TurboPixels:fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12):2290-2297
21 Wang J, Wang X. VCells:simple and efficient superpixels using edge-weighted centroidal voronoi tessellations. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012, 34(6):1241-1247
22 Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 31(11):2274-2282
23 Achanta R, Susstrunk S. Superpixels and polygons using simple non-iterative clustering. In:Proceedings of the 2017IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA:IEEE, 2017. 4895-4904
24 Shen J B, Hao X P, Liang Z Y, Liu Y, Wang W G, Shao L.Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Transactions on Image Processing, 2016,25(12):5933-5942
25 Chen J, Li Z, Bo H. Linear spectral clustering superpixel. IEEE Transactions on Image Processing, 2017, 26(7):3317-3330
26 Hu Z, Qin Z, Li Q. Watershed superpixel. In:Proceedings of the 2015 IEEE International Conference on Image Processing. Quebec City, Canada, 2015. 349-353
27 Boemer F, Ratner E, Lendasse A. Parameter-free image segmentation with SLIC. Neurocomputing, 2018, 277(1):228-236
28 Zhang Ya-Ya, Liu Xiao-Wei, Liu Fu-Tai, Zhang Jian-Ting.Color image segmentation based on improved SLIC method.Computer Engineering, 2015, 41(4):205-209(张亚亚,刘小伟,刘福太,张建廷.基于改进SLIC方法的彩色图像分割.计算机工程, 2015, 41(4):205-209)
29 Liu Y J, Yu C C, Yu M J, He Y. Manifold SLIC:a fast method to compute content-sensitive superpixels. In:Proceedings of the 2016 Computer Vision and Pattern Recognition. Las Vegas, USA:IEEE, 2016. 651-659
30 Liu Y J, Yu M, Li B J, He Y. Intrinsic manifold SLIC:a simple and efficient method for computing content-sensitive superpixels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3):653-666
31 Nan Bing-Fei, Mu Zhi-Chun. SLIC0-based superpixel segmentation method with texture fusion. Chinese Journal of Scientific Instrument, 2014, 35(3):527-534(南柄飞,穆志纯.基于SLIC0融合纹理信息的超像素分割方法.仪器仪表学报, 2014, 35(3):527-534)
32 Ojala T, Pietikainen M, Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 24(7):971-987
33 Hafiane A, Seetharaman G, Palaniappan K, Zavidovique B.Rotationally invariant hashing of median binary patterns for texture classification. In:Proceedings of the 2008 International Conference Image Analysis and Recognition. Póvoa de Varzim, Portugal:Springer, 2008. 619-629
34 Guo Z, Zhang L, Zhang D. Rotation invariant texture classification using LBP variance(LBPV)with global matching.Pattern Recognition, 2010, 43(3):706-719
35 Andreopoulos A, Tsotsos J K. Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Medical Image Analysis, 2008, 12(3):335-357
36 Ojala T, Harwood I. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 1996, 29(1):51-59
37 Ojala T, Maenpaa T, Pietikainen M, et al. Outex—new framework for empirical evaluation of texture analysis algorithms. In:Proceedings of the 2002 Pattern Recognition.Quebec City, Canada:IEEE, 2002. 701-706
38 Otsu N. A Thresholding selection method from gray-level histogram. IEEE Transactions on Systems Man and Cybernetics, 1979, 9(1):62-66
2 Chan Si-Xian, Zhou Xiao-Long, Zhang Zhuo, Chen ShengYong. Interactive multi-label image segmentation with multi-layer tumors automat. Acta Automatica Sinica, 2017,43(10):1829-1840(产思贤,周小龙,张卓,陈胜勇.一种基于超像素的肿瘤自动攻击交互式分割算法.自动化学报, 2017, 43(10):1829-1840)
3 Wang Yun-Fei, Feng Guo-Qiang, Liu Hua-Wei, Zhao BoXin. Superpixel-based mean and mean square deviation dark channel for single image fog removal. Acta Automatica Sinica, 2018, 44(3):481-489(汪云飞,冯国强,刘华伟,赵搏欣.基于超像素的均值–均方差暗通道单幅图像去雾方法.自动化学报, 2018, 44(3):481-489)
4 Lin Hua-Feng, Li Jing, Liu Guo-Dong, Liang Da-Chuan, Li Dong-Min. Saliency detection method using adaptive background template and spatial prior. Acta Automatica Sinica,2017, 43(10):1736-1748(林华锋,李静,刘国栋,梁大川,李东民.基于自适应背景模板与空间先验的显著性物体检测方法.自动化学报, 2017, 43(10):1736-1748)
5 Yang F, Lu H, Yang M H. Robust superpixel tracking. IEEE Transactions on Image Processing, 2014, 23(4):1639-1651
6 Liu Da-Qian, Liu Wan-Jun, Fei Bo-Wen, Qu Hai-Cheng.A new method of anti-interference matching under foreground constraint for target tracking. Acta Automatica Sinica, 2018, 44(6):1138-1152(刘大千,刘万军,费博雯,曲海成.前景约束下的抗干扰匹配目标跟踪方法.自动化学报, 2018, 44(6):1138-1152)
7 Wang Z L, Feng J S, Yan S C, Xi H S. Image classification via object-aware holistic superpixel selection. IEEE Transactions on Image Processing, 2013, 22(11):4341-4352.
8 Bodis-Szomoru A, Riemenschneider H, Gool L V. Superpixel meshes for fast edge-preserving surface reconstruction.In:Proceedings of the 2015 Computer Vision and Pattern Recognition. Boston, USA:IEEE, 2015. 2011-2020
9 Geiger A, Wang C. Joint 3D object and layout inference from a single RGB-D image. In:Proceedings of the 2015German Conference on Pattern Recognition. Aachen, Germany:Springer, 2015. 183-195
10 Gadde R, Jampani V, Kiefel M, Kappler D, Gehler P V. Superpixel convolutional networks using bilateral inceptions.In:Proceedings of the 2016 European Conference on Computer Vision. Amsterdam, The Netherlands:Springer, 2016.597-613
11 Shi J, Malik J. Normalized cuts and image segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8):888-905
12 Moore A P, Prince S J D, Warrell J, Mohammed U, Jones G.Superpixel lattices. In:Proceedings of the 2008 Computer Vision and Pattern Recognition. Anchorage, USA:IEEE,2008. 1-8
13 Veksler O, Boykov Y, Mehrani P. Superpixels and supervoxels in an energy optimization framework. In:Proceedings of the 2010 European Conference on Computer Vision. Heraklion, Greece:Springer, 2010. 211-224
14 Liu M Y, Tuzel O, Ramalingam S, Chellappa R. Entropy rate superpixel segmentation. In:Proceedings of the2011 Computer Vision and Pattern Recognition. Colorado Springs, USA:IEEE, 2011. 2097-2104
15 Bergh M V D, Boix X, Roig G, Capitani B D, Gool L V.SEEDS:superpixels extracted via energy-driven sampling.In:Proceedings of the 2012 European Conference on Computer Vision. Florence, Italy:Springer, 2012. 13-26
16 Shen J B, Du Y F, Wang W G, Li X L. Lazy random walks for superpixel segmentation. IEEE Transactions on Image Processing, 2014, 23(4):1451-1462
17 Yin S, Qian Y, Gong M. Unsupervised hierarchical image segmentation through fuzzy entropy maximization. Pattern Recognition, 2017, 68(1):245-259
18 Yin S, Zhao X, Wang W, Gong M. Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization. Pattern Recognition, 2014, 47(9):2894-2907
19 Wei X, Yang Q, Gong Y, Ahuja N, Yang M H. Superpixel hierarchy. IEEE Transactions on Image Processing, 2018,27(10):4838-4849
20 Levinshtein A, Stere A, Kutulakos K N, Fleet D J, Dickinson S J, Siddiqi K. TurboPixels:fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12):2290-2297
21 Wang J, Wang X. VCells:simple and efficient superpixels using edge-weighted centroidal voronoi tessellations. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012, 34(6):1241-1247
22 Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 31(11):2274-2282
23 Achanta R, Susstrunk S. Superpixels and polygons using simple non-iterative clustering. In:Proceedings of the 2017IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA:IEEE, 2017. 4895-4904
24 Shen J B, Hao X P, Liang Z Y, Liu Y, Wang W G, Shao L.Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Transactions on Image Processing, 2016,25(12):5933-5942
25 Chen J, Li Z, Bo H. Linear spectral clustering superpixel. IEEE Transactions on Image Processing, 2017, 26(7):3317-3330
26 Hu Z, Qin Z, Li Q. Watershed superpixel. In:Proceedings of the 2015 IEEE International Conference on Image Processing. Quebec City, Canada, 2015. 349-353
27 Boemer F, Ratner E, Lendasse A. Parameter-free image segmentation with SLIC. Neurocomputing, 2018, 277(1):228-236
28 Zhang Ya-Ya, Liu Xiao-Wei, Liu Fu-Tai, Zhang Jian-Ting.Color image segmentation based on improved SLIC method.Computer Engineering, 2015, 41(4):205-209(张亚亚,刘小伟,刘福太,张建廷.基于改进SLIC方法的彩色图像分割.计算机工程, 2015, 41(4):205-209)
29 Liu Y J, Yu C C, Yu M J, He Y. Manifold SLIC:a fast method to compute content-sensitive superpixels. In:Proceedings of the 2016 Computer Vision and Pattern Recognition. Las Vegas, USA:IEEE, 2016. 651-659
30 Liu Y J, Yu M, Li B J, He Y. Intrinsic manifold SLIC:a simple and efficient method for computing content-sensitive superpixels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3):653-666
31 Nan Bing-Fei, Mu Zhi-Chun. SLIC0-based superpixel segmentation method with texture fusion. Chinese Journal of Scientific Instrument, 2014, 35(3):527-534(南柄飞,穆志纯.基于SLIC0融合纹理信息的超像素分割方法.仪器仪表学报, 2014, 35(3):527-534)
32 Ojala T, Pietikainen M, Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 24(7):971-987
33 Hafiane A, Seetharaman G, Palaniappan K, Zavidovique B.Rotationally invariant hashing of median binary patterns for texture classification. In:Proceedings of the 2008 International Conference Image Analysis and Recognition. Póvoa de Varzim, Portugal:Springer, 2008. 619-629
34 Guo Z, Zhang L, Zhang D. Rotation invariant texture classification using LBP variance(LBPV)with global matching.Pattern Recognition, 2010, 43(3):706-719
35 Andreopoulos A, Tsotsos J K. Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Medical Image Analysis, 2008, 12(3):335-357
36 Ojala T, Harwood I. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 1996, 29(1):51-59
37 Ojala T, Maenpaa T, Pietikainen M, et al. Outex—new framework for empirical evaluation of texture analysis algorithms. In:Proceedings of the 2002 Pattern Recognition.Quebec City, Canada:IEEE, 2002. 701-706
38 Otsu N. A Thresholding selection method from gray-level histogram. IEEE Transactions on Systems Man and Cybernetics, 1979, 9(1):62-66