结合PCANet与线性判别分析的视网膜光学相干断层扫描图像分类
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摘要
目的主成分分析网络(PCANet)能提取图像的纹理特征,线性判别分析(LDA)提取的特征有类别区分性。本文结合这两种方法的优点,提出一种带线性判别分析的主成分分析网络(PCANet-LDA),用于视网膜光学相干断层扫描(OCT)图像中的老年性黄斑变性(AMD)、糖尿病性黄斑水肿(DME)及正常(NOR)这3类的全自动分类。方法 PCANet-LDA算法是在PCANet的基础上添加了LDA监督层,该层加入了类标签对特征进行监督投影。首先,对OCT视网膜图像进行去噪、二值化及对齐裁剪等一系列预处理,获得感兴趣的视网膜区域;然后,将预处理图像送入一个两层的PCA卷积层,训练PCA滤波器组并提取图像的PCA特征;接着,将PCA特征送入一个非线性输出层,通过二值散列和块直方图等处理,得到图像的特征;之后,将带有类标签的图像特征送入一个LDA监督层,学习LDA矩阵并用其对图像特征进行投影,使特征具有类别区分性;最后,将投影的特征送入线性支持向量机(SVM)中对分类器进行训练和分类。结果实验分别在医院临床数据集和杜克数据集上进行,先对OCT图像预处理进行前后对比实验,然后对PCANet特征提取的有效性进行分析,最后对PCANet算法、Sc SPM算法以及提出的PCANet-LDA3种分类算法的分类效果进行对比实验。在临床数据集上,PCANet-LDA算法的总体分类正确率为97. 20%,高出PCANet算法3. 77%,且略优于Sc SPM算法;在杜克数据集上,PCANet-LDA算法的总体分类正确率为99. 52%,高出PCANet算法1. 64%,略优于Sc SPM算法。结论 PCANet-LDA算法的分类正确率明显高于PCANet,且优于目前用于2D视网膜OCT图像分类的先进的Sc SPM算法。因此,提出的PCANet-LDA算法在视网膜OCT图像的分类上是有效且先进的,可作为视网膜OCT图像分类的基准算法。
Objective Optical coherence tomography( OCT) is a 3D scanning imaging technology that has been widely used in ophthalmology as a clinical auxiliary to identify various eye lesions. Therefore,the classification technique of retinal OCT images is greatly important for the detection and treatment of retinopathy. Many effective OCT classification algorithms have been recently developed,and almost all these have artificial design features; however,retinal OCT images acquired from clinic usually contains a complex pathological structure. Therefore,the features from OCT images must be directly learned.Principal component analysis network( PCANet) is a simple version of convolutional neural network,which can directly extract the texture features of images,whereas features extracted by linear discriminant analysis( LDA) are more distinguishable for image classification. Combining the advantages of these two methods,this paper presents a PCANet with LDA(PCANet-LDA) for the automatic classification of three types of retinal OCT images,including age-related macular degeneration( AMD),diabetic macular edema( DME),and normal( NOR). Method The proposed PCANet-LDA algorithm adds an LDA supervisory layer based on the PCANet to allow the supervision of extracted image features by class labels.This algorithm can be implemented in three steps. The first step is the OCT image preprocessing,which involves a series of preprocessing including perceiving,fitting,and normalizing stages on retinal OCT images to obtain an interested retinal region for image classification. The second step is the PCANet feature extraction,where the preprocessed OCT images are sent into a PCA convolution layer with two stages and a nonlinear output layer. In the PCA convolution layer,PCA filter banks are learned,and the PCA features of retinal OCT images can be extracted. In the nonlinear output layer,the extracted PCA features are translated to PCANet features of the input images by some basic data-processing components,including binary hashing and blockwise histograms. The third step is the LDA supervisory layer,which uses the LDA idea to learn an LDA matrix from the PCANet features with class labels of AMD,DME,and NOR. Then,the LDA matrix is used to project PCANet features into a low-dimensional space to make the projected features more distinguishable for classification. Finally,the projected features are used to train a linear support vector machine and classify the retinal OCT images.Result Both experiments are done on two retinal OCT dataset,including the clinic dataset obtained from a hospital and Duke dataset. First,the comparative examples of AMD,DME and NOR retinal OCT images before and after preprocessing shows that the image preprocessing cuts out the non-retinal regions in the OCT image,leaving the meaningful retinal areas.Moreover,the remaining retina is rotated to a unified horizontal state to reduce the impact of inconsistent direction of retina on classification. Then,the sample PCANet feature maps extracted from AMD and DME retinal OCT images show that the PCA filter trained by PCANet tends to capture meaningful pathological structure information,which contributes to the classification of retinal OCT images. Finally,the correct classification rates of the PCANet algorithm,the Sc SPM algorithm,and the PCANet-LDA algorithm proposed in this paper are compared. On the clinic dataset,the overall correct classification rate of the PCANet-LDA algorithm is 97. 20%,which is 3. 77% higher than that of the PCANet algorithm and slightly higher than that of the Sc SPM algorithm. On the Duke dataset,the overall correct classification rate of the PCANet-LDA algorithm is 99. 52%,which is 1. 64% higher than that of the PCANet algorithm and a slightly higher than that of the Sc SPM algorithm. Conclusion The PCANet algorithm can extract effective features. Accordingly,the PCANet-LDA algorithm obtains more distinguishing features by LDA method,to yield a higher correct classification rate than that of the PCANet and Sc SPM algorithms; the latter is a state-of-the-art two-dimensional OCT image classification of the retina. Therefore,the proposed PCANet-LDA algorithm is effective,advanced in the classification of retinal OCT images,and can be a baseline algorithm for retinal OCT image classification.
Objective Optical coherence tomography( OCT) is a 3D scanning imaging technology that has been widely used in ophthalmology as a clinical auxiliary to identify various eye lesions. Therefore,the classification technique of retinal OCT images is greatly important for the detection and treatment of retinopathy. Many effective OCT classification algorithms have been recently developed,and almost all these have artificial design features; however,retinal OCT images acquired from clinic usually contains a complex pathological structure. Therefore,the features from OCT images must be directly learned.Principal component analysis network( PCANet) is a simple version of convolutional neural network,which can directly extract the texture features of images,whereas features extracted by linear discriminant analysis( LDA) are more distinguishable for image classification. Combining the advantages of these two methods,this paper presents a PCANet with LDA(PCANet-LDA) for the automatic classification of three types of retinal OCT images,including age-related macular degeneration( AMD),diabetic macular edema( DME),and normal( NOR). Method The proposed PCANet-LDA algorithm adds an LDA supervisory layer based on the PCANet to allow the supervision of extracted image features by class labels.This algorithm can be implemented in three steps. The first step is the OCT image preprocessing,which involves a series of preprocessing including perceiving,fitting,and normalizing stages on retinal OCT images to obtain an interested retinal region for image classification. The second step is the PCANet feature extraction,where the preprocessed OCT images are sent into a PCA convolution layer with two stages and a nonlinear output layer. In the PCA convolution layer,PCA filter banks are learned,and the PCA features of retinal OCT images can be extracted. In the nonlinear output layer,the extracted PCA features are translated to PCANet features of the input images by some basic data-processing components,including binary hashing and blockwise histograms. The third step is the LDA supervisory layer,which uses the LDA idea to learn an LDA matrix from the PCANet features with class labels of AMD,DME,and NOR. Then,the LDA matrix is used to project PCANet features into a low-dimensional space to make the projected features more distinguishable for classification. Finally,the projected features are used to train a linear support vector machine and classify the retinal OCT images.Result Both experiments are done on two retinal OCT dataset,including the clinic dataset obtained from a hospital and Duke dataset. First,the comparative examples of AMD,DME and NOR retinal OCT images before and after preprocessing shows that the image preprocessing cuts out the non-retinal regions in the OCT image,leaving the meaningful retinal areas.Moreover,the remaining retina is rotated to a unified horizontal state to reduce the impact of inconsistent direction of retina on classification. Then,the sample PCANet feature maps extracted from AMD and DME retinal OCT images show that the PCA filter trained by PCANet tends to capture meaningful pathological structure information,which contributes to the classification of retinal OCT images. Finally,the correct classification rates of the PCANet algorithm,the Sc SPM algorithm,and the PCANet-LDA algorithm proposed in this paper are compared. On the clinic dataset,the overall correct classification rate of the PCANet-LDA algorithm is 97. 20%,which is 3. 77% higher than that of the PCANet algorithm and slightly higher than that of the Sc SPM algorithm. On the Duke dataset,the overall correct classification rate of the PCANet-LDA algorithm is 99. 52%,which is 1. 64% higher than that of the PCANet algorithm and a slightly higher than that of the Sc SPM algorithm. Conclusion The PCANet algorithm can extract effective features. Accordingly,the PCANet-LDA algorithm obtains more distinguishing features by LDA method,to yield a higher correct classification rate than that of the PCANet and Sc SPM algorithms; the latter is a state-of-the-art two-dimensional OCT image classification of the retina. Therefore,the proposed PCANet-LDA algorithm is effective,advanced in the classification of retinal OCT images,and can be a baseline algorithm for retinal OCT image classification.
引文
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[15]Yang J C,Yu K,Gong Y H,et al.Linear Spatial Pyramid matching using sparse coding for image classification[EB/OL].[EB/OL].[2018-02-21].http://www.ifp.illinois.edu/~jyang29/Sc SPM.htm.
[16]Srinivasan P P,Kim L A,Mettu P S,et al.Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images[EB/OL].[2018-02-21].http://www.duke.edu/~sf59/Srinivasan_BOE_2014_dataset.htm.
[2]Fan L J,Sun Y K,Zhang T,et al.Three dimensional segmentation to detect retinal boundary surfaces from OCT volume data[J].Journal of Image and Graphics,2013,18(3):330-335.[樊鲁杰,孙延奎,张田,等.光学相干层析视网膜体数据的三维分割[J].中国图象图形学报,2013,18(3):330-335.][DOI:10.11834/jig.20130313]
[3]Roy A G,Conjeti S,Karri S P K,et al.Re Lay Net:retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks[J].Biomedical Optics Express,2017,8(8):3627-3642.[DOI:10.1364/BOE.8.003627]
[4]Gao S S,Liu L,Bailey S T,et al.Quantification of choroidal neovascularization vessel length using optical coherence tomography angiography[J].Journal of Biomedical Optics,2016,21(7):#076010.[DOI:10.1117/1.JBO.21.7.076010]
[5]Liu Y Y,Chen M,Ishikawa H,et al.Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding[J].Medical Image Analysis,2011,15(5):748-759.[DOI:10.1016/j.media.2011.06.005]
[6]Srinivasan P P,Kim L A,Mettu P S,et al.Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images[J].Biomedical Optics Express,2014,5(10):3568-3577.[DOI:10.1364/BOE.5.003568]
[7]Sun Y K,Li S,Sun Z Y.Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning[J].Journal of Biomedical Optics,2017,22(1):#16012.[DOI:10.1117/1.JBO.22.1.016012]
[8]Karri S P K,Chakraborty D,Chatterjee J.Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration[J].Biomedical Optics Express,2017,8(2):579-592.[DOI:10.1364/BOE.8.000579]
[9]Krizhevsky A,Sutskever I,Hinton G E.Imagenet classification with deep convolutional neural networks[C]//Proceedings of the25th International Conference on Neural Information Processing Systems.Lake Tahoe,Nevada:Curran Associates Inc,2012:1097-1105.
[10]Le Cun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444.[DOI:10.1038/nature14539]
[11]Prenta2ic'P,Heisler M,Mammo Z,et al.Segmentation of the foveal microvasculature using deep learning networks[J].Journal of Biomedical Optics,2016,21(7):#075008.[DOI:10.1117/1.JBO.21.7.075008]
[12]Chan T H,Jia K,Gao S H,et al.PCANet:A simple deep learning baseline for image classification?[J].IEEE Transactions on Image Processing,2015,24(12):5017-5032.[DOI:10.1109/TIP.2015.2475625]
[13]Fang L Y,Wang C,Li S T,et al.Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels[J].Journal of Biomedical Optics,2017,22(11):116011.[DOI:10.1117/1.JBO.22.11.116011]
[14]Chan T H,Jia K,Gao S H,et al.PCANet:A simple deep learning baseline for image classification?[EB/OL].[2018-02-21].http://mx.nthu.edu.tw/~tsunghan/download/PCANet_demo_pyramid.rar.
[15]Yang J C,Yu K,Gong Y H,et al.Linear Spatial Pyramid matching using sparse coding for image classification[EB/OL].[EB/OL].[2018-02-21].http://www.ifp.illinois.edu/~jyang29/Sc SPM.htm.
[16]Srinivasan P P,Kim L A,Mettu P S,et al.Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images[EB/OL].[2018-02-21].http://www.duke.edu/~sf59/Srinivasan_BOE_2014_dataset.htm.