Optical Coherence Tomography (OCT) has become an important auxiliary diagnostic technology in the field of fundus disease detection due to its advantages of high resolution and high penetration depth. However, speckle noise exists in retinal images acquired by OCT. It is a difficult problem for OCT image processing technology to keep the middle layer structure information in the process of OCT image denoising. A novel OCT retinal image denoising method based on Gaussian mixture model and variational image decomposition is proposed. Firstly, the proposed BL-G-BM3D variational image decomposition model is used to initially denoise OCT images, and then the Gaussian mixture model is used to cluster the initial denoising results to obtain binary masks that can distinguish background and structure. Finally, the final denoising results are obtained by multiplying the mask image with the initial denoising results. An OCT retinal image with high noise and low contrast was tested and compared with five commonly used denoising methods. The results show that the proposed method can achieve both de-noising effect and laminar structure preservation for high-noise OCT retinal images.
Automatic segmentation of retinal blood vessels is a nontrivial task due to the complexity of retinal fundus image. In this paper, a new network named MFCTrans-net is proposed for retinal blood vessel segmentation. The MFCTrans-net is an improvement over original U-Net, which can be summarized as (1) to better fuse the encoder and decoder features and reduce the semantic gap, we replace the skip connection in the original U-net by the Channel-wise Cross Fusion Transformer(CCT); (2) two side paths are added to the U-Net which allow the network to capture features at multiple scales; (3) a novel loss function is also proposed which focuses on the topology integrity of vessel meanwhile maintaining pixel segmentation accuracy. The proposed network has been developed and evaluated in the DRIVE, CHASE-DB1 and IOSTAR datasets, which offer a manual segmentation of the vascular tree by each of its images. The performance of our method is evaluated in terms of visual effects and quantitative evaluation metrics on these four publicly available datasets with comparison to several representative methods. Furthermore, we use the proposed method to segment a collection of the experimentally obtained retinal blood vessel images with poor quality. The experimental results demonstrate the performance of our proposed method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.