浏览全部资源
扫码关注微信
收稿：2018-01-15，
修回：2018-5-16，
纸质出版：2018-10-16
移动端阅览
目的
2
由于舌体与周围组织颜色相似，轮廓模糊，传统的分割方法难以精准分割舌体，为此提出一种基于两阶段卷积神经网络的舌体分割方法。
方法
2
首先，在粗分割阶段，将卷积层和全连接层相结合构建网络Rsnet，采用区域建议策略得到舌体候选框，从候选框中进一步确定舌体，从而实现对舌体的定位，去除大量的干扰信息；然后，在精分割阶段，将卷积层与反卷积层相结合构建网络Fsnet，对粗分割舌象中的每一个像素点进行分类进而实现精分割；最后，采用形态学相关算法对精分割后的舌体图像进行后续处理，进一步消除噪点和边缘粗糙点。
结果
2
本文构建了包含2 764张舌象的数据集，在该数据集上进行五折交叉实验。实验结果表明，本文算法能够取得较为理想的分割结果且具有较快的处理速度。选取了精确度、召回率及F值作为评价标准，与3种常用的传统分割方法相比，在综合指标F值上分别提高了0.58、0.34、0.12，效率上至少提高6倍，与同样基于深度学习思想的MNC（multi-task network cascades）算法相比，在F值上提高0.17，效率上提高1.9倍。
结论
2
将基于深度学习的方法应用到舌体分割中，有利于实现舌象的准确、鲁棒、快速分割。在分割之前，先对舌体进行定位，有助于进一步减少分割中的错分与漏分。实验结果表明，本文算法有效提升了舌体分割的准确性，能够为后续的舌象自动识别和分析奠定坚实的基础。
Objective
2
The tongue is difficult to segment accurately due to the blurred contours and the similar colors of the surrounding tissue.Current tongue segmentation methods
whether based on texture analysis
edge detection
or threshold segmentation
mostly extract the color features of the tongue image
i.e.
they are pixel-based segmentation methods.Although color features are easy to extract
the position information of the target is difficult to express.The color between the tongue and the background is similar
and the color features of the tongue and the background may overlap.Therefore
tongue information is difficult to express by using the color features of the tongue image.The deep semantic information of the image should be extracted and more complete features must be obtained to achieve an accurate segmentation of the tongue body.A tongue segmentation method based on two-stage convolutional neural network is proposed in this paper.The cascade method is used to combine the networks
and the output of the previous stage is taken as the input of the next stage.
Method
2
First
in the rough segmentation stage
the rough segmentation network (Rsnet) consists of the convolutional and fully connected layers.The problem of excessive interference information in the original tongue image needs to be solved.Thus
the region suggestion strategy is adopted to obtain tongue candidate boxes
and the regions of interest are extracted from the similar background
i.e.
the tongue is located and a large amount of interference information are removed.Therefore
the influence of the tissue around the tongue during the segmentation of the tongue is weakened.Second
in the fine segmentation phase
the fine segmentation network (Fsnet) consists of the convolutional and deconvolutional layers.The regions of interest obtained in the previous stage are taken as the input to the Fsnet.The Softmax classifier is automatically trained and learned without manual intervention.With the trained Softmax classifier
each pixel of the image is classified to achieve fine segmentation and obtain a more accurate tongue image.Finally
the designed algorithm performs post-processing on the finely divided tongue image.The morphology-related algorithm is used to deal with the fine-segmented tongue image
and can further eliminate noise and edge roughness.Therefore
the segmentation result is further optimized.In addition
the training of deep convolutional neural network depends on many samples.The collection and labeling of medical images are difficult.Consequently
large-scale tongue image datasets are difficult to obtain.When a small-scale dataset is used for direct training
the network is not easy to converge; moreover
overfitting can occur easily.The desired results are difficult to achieve.In the training process
three aspects are considered
namely
training strategy
network structure
and dataset
to avoid the overfitting of models.
Result
2
In this study
a database of 2 764 tongue images is constructed
and the five-cross experiment is performed on this database.Experimental results show that the proposed algorithm can achieve better segmentation results and faster processing.Accuracy
recall rate
and F-measure are selected as the evaluation criteria.As opposed to the three common traditional segmentation methods
the proposed method can increase the comprehensive F-measure by 0.58
0.34
and 0.12 and the efficiency by at least 6 times.Moreover
as opposed to the MNC algorithm based on deep learning
the F-measure can be increased by 0.17 while efficiency can be increased by 1.9 times.
Conclusion
2
The method based on deep learning is applied to tongue segmentation to help realize accurate
robust
and rapid tongue segmentation.The tongue is positioned before segmentation
which helps reduce the division of the misclassification and leakage points further.The models are combined in a cascading manner
which is flexible and easily combines the model of the tongue positioning stage with other methods to assist in segmentation.Experimental results show that the accuracy of tongue segmentation is effectively improved
and a solid foundation is established for follow-up tongue automatic identification and analysis with the proposed algorithm.
Li D X, Guan J, Li F.Development of tongue instrument and progress of its application in traditional Chinese medicine tongue characterization research[J].World Chinese Medicine, 2017, 12(2):456-460.
李丹溪, 关静, 李峰.舌诊仪的发展及其在舌诊客观化研究中的应用现状[J].世界中医药, 2017, 12(2):456-460. [DOI:10.3969/j.issn.1673-7202.2017.02.054]
Li W S, Zhou C L, Zhang Z F.The segmentation of the body of the tongue based on the improved snake algorithm in traditional Chinese medicine[C]//Proceedings of the 5th World Congress on Intelligent Control and Automation.Hangzhou: IEEE, 2004: 5502-5505.[ DOI: 10.1109/WCICA.2004.1343785 http://dx.doi.org/10.1109/WCICA.2004.1343785 ]
Shen L S, Wang A M, Wei B G, et al.Image analysis for tongue characterization[J].Acta Electronica Sinica, 2001, 29(12A):1762-1765.
沈兰荪, 王爱民, 卫保国, 等.图像分析技术在舌诊客观化中的应用[J].电子学报, 2001, 29(12A):1762-1765. [DOI:10.3321/j.issn:0372-2112.2001.z1.009]
Zhang L, Qin J.Tongue-image segmentation based on gray projection and threshold-adaptive method[J].Journal of Clinical Rehabilitative Tissue Engineering Research, 2010, 14(9):1638-1641.
张灵, 秦鉴.基于灰度投影和阈值自动选取的舌像分割方法[J].中国组织工程研究与临床康复, 2010, 14(9):1638-1641. [DOI:10.3969/j.issn.1673-8225.2010.09.027]
Huang Z P, Huang Y S, Yi F L, et al.An automatic tongue segmentation algorithm based on OTSU and region growing[J].Lishizhen Medicine and Materia Medica Research, 2017, 28(12):3062-3064.
黄展鹏, 黄益栓, 易法令, 等.基于最大类间方差法和区域生长的舌体自动分割[J].时珍国医国药, 2017, 28(12):3062-3064.
Zhang Z S, Xi J Q, Liu Y.Study of an effective tongue body extraction algorithm[J].Microelectronics&Computer, 2015, 32(4):116-119, 124.
张志顺, 奚建清, 刘勇.一种有效的舌体提取算法研究[J].微电子学与计算机, 2015, 32(4):116-119, 124. [DOI:10.19304/j.cnki.issn1000-7180.2015.04.026]
Wei Y K, Fan P, Zeng G.Application of improved GrabCut method in tongue diagnosis system[J].Transducer and Microsystem Technologies, 2014, 33(10):157-160.
韦玉科, 范鹏, 曾贵.改进的GrabCut方法在舌诊系统中的应用[J].传感器与微系统, 2014, 33(10):157-160. [DOI:10.13873/J.1000-9787(2014)10-0157-04]
Girshick R, Donahue J, Darrell T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus, OH, USA: IEEE, 2014: 580-587.[ DOI: 10.1109/CVPR.2014.81 http://dx.doi.org/10.1109/CVPR.2014.81 ]
Girshick R.Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Santiago, Chile: IEEE, 2015: 1440-1448.[ DOI: 10.1109/ICCV.2015.169 http://dx.doi.org/10.1109/ICCV.2015.169 ]
Ren S Q, He K M, Girshick R, et al.Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.Montreal, Canada: MIT Press, 2015: 91-99.
Shelhamer E, Long J, Darrell T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651.[DOI:10.1109/TPAMI.2016.2572683]
Li Z C, Tang J H.Weakly supervised deep metric learning for community-contributed image retrieval[J].IEEE Transactions on Multimedia, 2015, 17(11):1989-1999.[DOI:10.1109/TMM.2015.2477035]
Chen Y S, Jiang H L, Li C Y, et al.Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J].IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10):6232-6251.[DOI:10.1109/TGRS.2016.2584107]
Nasrollahi K, Escalera S, Rasti P, et al.Deep learning based super-resolution for improved action recognition[C]//Proceedings of 2015 International Conference on Image Processing Theory, Tools and Applications.Orleans, France: IEEE, 2015: 67-72.[ DOI: 10.1109/IPTA.2015.7367098 http://dx.doi.org/10.1109/IPTA.2015.7367098 ]
Esteva A, Kuprel B, Novoa R A, et al.Dermatologist-level classification of skin cancer with deep neural networks[J].Nature, 2017, 542(7639):115-118.[DOI:10.1038/nature21056]
Liu F, Zhang J R, Yang H.Research progress of medical image recognition based on deep learning[J].Chinese Journal of Biomedical Engineering, 2018, 37(1):86-94.
刘飞, 张俊然, 杨豪.基于深度学习的医学图像识别研究进展[J].中国生物医学工程学报, 2018, 37(1):86-94. [DOI:10.3969/j.issn.0258-8021.2018.01.012]
Noh H, Hong S, Han B.Learning Deconvolution network for semantic segmentation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Santiago, Chile: IEEE, 2015: 1520-1528.[ DOI: 10.1109/ICCV.2015.178 http://dx.doi.org/10.1109/ICCV.2015.178 ]
Simonyan K, Zisserman A.Very deep convolutional networks for large-scale image recognition[EB/OL].[2018-01-05]https: //arxiv.org/abs/1409.1556.
Shin H C, Roth H R, Gao M C, et al.Deep convolutional neural networks for computer-aided detection:CNN architectures, dataset characteristics and transfer learning[J].IEEE Transactions on Medical Imaging, 2016, 35(5):1285-1298.[DOI:10.1109/TMI.2016.2528162]
Deng J, Dong W, Socher R, et al.ImageNet: a large-scale hierarchicalimage database[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition.Miami, FL, USA: IEEE, 2009: 248-255.[ DOI: 10.1109/CVPR.2009.5206848 http://dx.doi.org/10.1109/CVPR.2009.5206848 ]
Yosinski J, Clune J, Bengio Y, et al.How transferable are features in deep neural networks?[C]//Advances in Neural Information Processing Systems 27.Lake Tahoe, Nevada: Curran Associates, Inc., 2014: 3320-3328.
Donahue J, Jia Y Q, Vinyals O, et al.DeCAF: a deep convolutional activation feature for generic visual recognition[C]//Proceedings of the 31st International Conference on International Conference on Machine Learning.Beijing, China: JMLR, 2014: I-647-I-655.
Razavian A S, Azizpour H, Sullivan J, et al.CNN features off-the-shelf: an astounding baseline for recognition[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Colombia, OH, USA: IEEE, 2014: 512-519.[ DOI: 10.1109/CVPRW.2014.131 http://dx.doi.org/10.1109/CVPRW.2014.131 ]
Dai J F, He K M, Sun J.Instance-aware semantic segmentation via multi-task network cascades[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV, USA: IEEE, 2016: 3150-3158.[ DOI: 10.1109/CVPR.2016.343 http://dx.doi.org/10.1109/CVPR.2016.343 ]
相关作者
相关机构
京公网安备11010802024621
