Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

2020 - 20242

Settings

Author: search.filters.author.Anoop, B.N.Has files: No

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A Novel Deep Learning Approach for the Removal of Speckle Noise from Optical Coherence Tomography Images Using Gated Convolution–Deconvolution Structure
    (Springer Science and Business Media Deutschland GmbH, 2020) Menon, S.N.; Vineeth Reddy, V.B.; Yeshwanth, A.; Anoop, B.N.; Rajan, J.
    Optical coherence tomography (OCT) is an imaging technique widely used to image retina. Speckle noise in OCT images generally degrades the quality of the OCT images and makes the clinical diagnosis tedious. This paper proposes a new deep neural network despeckling scheme called gated convolution–deconvolution structure (GCDS). The robustness of the proposed method is evaluated on the publicly available OPTIMA challenge dataset and Duke dataset. The quantitative analysis based on PSNR shows that the results of the proposed method are superior to other state-of-the-art methods. The application of the proposed method for segmenting retinal cyst from OPTIMA challenge dataset was also studied. © 2020, Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    Item
    Attention Assisted Patch-Wise CNN for the Segmentation of Fluids from the Retinal Optical Coherence Tomography Images
    (Springer Science and Business Media Deutschland GmbH, 2024) Anoop, B.N.; Parida, S.; Ajith, B.; Girish, G.N.; Kothari, A.R.; Kavitha, M.S.; Rajan, J.
    Optical Coherence Tomography (OCT) is an important imaging modality in ophthalmology to visualize the abnormalities present in the retina. One of the major reasons for blindness is the accumulation of fluids in the various layers of the retina called retinal cysts. Accurate estimation of the type of cyst and its volume is important for effective treatment planning. In this paper, we propose attention assisted convolutional neural network-based architecture to detect and quantify three types of retinal cysts namely the intra-retinal cyst, sub-retinal cyst and pigmented epithelial detachment from the OCT images of the human retina. The proposed architecture has an encoder-decoder structure with an attention and a multi-scale module. The qualitative and quantitative performance of the model is evaluated on the publicly available RETOUCH retinal OCT fluid detection challenge data set. The proposed model outperforms the state-of-the-art methods in terms of precision, recall, and dice coefficient. Furthermore, the proposed model is computationally efficient due to its less number of model parameters. © Springer Nature Switzerland AG 2024.