Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation

Farhan Akram, Vivek Kumar Singh, Hatem A. Rashwan, Mohamed Abdel-Nasser, Md Mostafa Kamal Sarker, Nidhi Pandey, Domenec Puig

Research output: Other contribution

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In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization. In the generator network of the adversarial framework, spatial pyramid pooling, kernel factorization and squeeze excitation block are employed to enhance the feature representation in spatial domain on different scales with reduced computational complexity. In turn, the discriminator network of the adversarial framework is formulated by combining convolutional layers with an additional squeeze excitation block to differentiate the generated segmentation mask from its respective ground truth. Before feeding the images to the network, we pre-processed them by using edge sharpening and Gaussian regularization to reach an optimized solution for vessel segmentation. The output of the trained model is post-processed using morphological operations to remove the small speckles of noise. The proposed method qualitatively and quantitatively outperforms state-of-the-art vessel segmentation methods using DRIVE and STARE datasets.
Original languageUndefined/Unknown
TypeOnline preprint
Media of outputArXiv preprint server
Publication statusPublished - 05 Jul 2019

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Bibliographical note

9 pages, 4 figures


  • eess.IV
  • cs.CV

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