Deep choroid layer segmentation using hybrid features extraction from OCT images

The choroid layer, situated between the retina and sclera, is a tissue layer that contains blood vessels. Optical coherence tomography (OCT) is a method that utilizes light for imaging purposes to capture detailed images of this specific part of the retina. Although there have been notable advancements, the automated choroid segmentation persists difficult due to the inherently low contrast of OCT images. Handcrafted features, which provide domain-specific knowledge, and convolutional neural network (CNN) methods, which handle large sets of general features, are both employed in addressing this challenge. There is a plea to merge these two different classes of feature-generation methods. The challenge is to form a combined set of features that can outperform either feature extraction method. We proposed a cascaded method for choroid layer segmentation that logically combines a CNN feature set with handcrafted features. Our method used handcrafted features, Gabor features, Haar features, and gray-level co-occurrence features due to the robustness to segment low-contrast images. A support vector machine was independently trained using the CNN feature set and handcrafted feature set, which were then linearly combined for the final choroid segmentation. The method under consideration was assessed using a dataset comprising 525 images. Furthermore, we introduced two metrics to quantitatively evaluate the thickness of the layer: (i) the pixel-wise error in the segmentation and (ii) the average error in the generated thickness map. Through experimentation, the results demonstrated that our proposed method successfully accomplished the intended objective, a remarkable accuracy of 97 percent, with a mean error rate of 2.84. Moreover, it outperformed existing state-of-the-art segmentation methods.