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CircWaveDL: Modeling of Optical Coherence Tomography Images Based on a new Supervised Tensor-based Dictionary Learning for Classification of Macular abnormalities
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  • Roya Arian,
  • Aliraza Vard,
  • Rahele Kafieh,
  • Gerlind Plonka,
  • Hossein Rabbani
Roya Arian

Corresponding Author:[email protected]

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Aliraza Vard
Rahele Kafieh
Gerlind Plonka
Hossein Rabbani


Modeling optical coherence tomography (OCT) images is highly beneficial for various image processing applications as well as assisting ophthalmologists in the early detection of macular abnormalities. Sparse representation-based models, particularly dictionary learning (DL), play an important role in image modeling. Traditionally, DL transforms higher-order tensors into vectors and aggregates them into matrices, disregarding the multi-dimensional inherent structure of data. To overcome this problem, tensor-based DL approaches have been developed. In this study, we propose a tensor-based DL algorithm named CircWaveDL for OCT classification where both the training data and the dictionary are higher-order tensors. Instead of random initialization of the dictionary, we suggested initializing it with CircWave atoms, which has previously demonstrated its effectiveness in OCT classification. This algorithm employs CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into lower dimensions. Subsequently, we learn a sub-dictionary for each class using the training tensor of that class. A test tensor is reconstructed using each sub-dictionary individually and every test B-scan is assigned to the class with the minimal residual error. To assess the generalizability of the model, we have tested it on three different databases. Furthermore, we introduce a new heatmap generation approach based on averaging the most significant atoms of the learned sub-dictionaries, demonstrating that selecting an appropriate sub-dictionary for test B-scan restoration can lead to better reconstructions, emphasizing distinctive features of different classes. CircWaveDL demonstrates a high level of generalizability according to external validation conducted on three different databases and it outperforms previous classification methods designed for similar datasets.
11 Dec 2023Submitted to TechRxiv
14 Dec 2023Published in TechRxiv