Objective

2

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

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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

2

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 ScSPM 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 ScSPM 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 ScSPM algorithm.

Conclusion

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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 ScSPM 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.